WorldWideScience

Sample records for spatial resolution remote

  1. Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning

    Science.gov (United States)

    Beck, J. M.; Bridges, S.; Collins, C.; Rushing, J.; Graves, S. J.

    2017-12-01

    Researchers at the Information Technology and Systems Center at the University of Alabama in Huntsville are using Deep Learning with Convolutional Neural Networks (CNNs) to develop a method for enhancing the spatial resolutions of moderate resolution (10-60m) multispectral satellite imagery. This enhancement will effectively match the resolutions of imagery from multiple sensors to provide increased global temporal-spatial coverage for a variety of Earth science products. Our research is centered on using Deep Learning for automatically generating transformations for increasing the spatial resolution of remotely sensed images with different spatial, spectral, and temporal resolutions. One of the most important steps in using images from multiple sensors is to transform the different image layers into the same spatial resolution, preferably the highest spatial resolution, without compromising the spectral information. Recent advances in Deep Learning have shown that CNNs can be used to effectively and efficiently upscale or enhance the spatial resolution of multispectral images with the use of an auxiliary data source such as a high spatial resolution panchromatic image. In contrast, we are using both the spatial and spectral details inherent in low spatial resolution multispectral images for image enhancement without the use of a panchromatic image. This presentation will discuss how this technology will benefit many Earth Science applications that use remotely sensed images with moderate spatial resolutions.

  2. Automated Verification of Spatial Resolution in Remotely Sensed Imagery

    Science.gov (United States)

    Davis, Bruce; Ryan, Robert; Holekamp, Kara; Vaughn, Ronald

    2011-01-01

    Image spatial resolution characteristics can vary widely among sources. In the case of aerial-based imaging systems, the image spatial resolution characteristics can even vary between acquisitions. In these systems, aircraft altitude, speed, and sensor look angle all affect image spatial resolution. Image spatial resolution needs to be verified with estimators that include the ground sample distance (GSD), the modulation transfer function (MTF), and the relative edge response (RER), all of which are key components of image quality, along with signal-to-noise ratio (SNR) and dynamic range. Knowledge of spatial resolution parameters is important to determine if features of interest are distinguishable in imagery or associated products, and to develop image restoration algorithms. An automated Spatial Resolution Verification Tool (SRVT) was developed to rapidly determine the spatial resolution characteristics of remotely sensed aerial and satellite imagery. Most current methods for assessing spatial resolution characteristics of imagery rely on pre-deployed engineered targets and are performed only at selected times within preselected scenes. The SRVT addresses these insufficiencies by finding uniform, high-contrast edges from urban scenes and then using these edges to determine standard estimators of spatial resolution, such as the MTF and the RER. The SRVT was developed using the MATLAB programming language and environment. This automated software algorithm assesses every image in an acquired data set, using edges found within each image, and in many cases eliminating the need for dedicated edge targets. The SRVT automatically identifies high-contrast, uniform edges and calculates the MTF and RER of each image, and when possible, within sections of an image, so that the variation of spatial resolution characteristics across the image can be analyzed. The automated algorithm is capable of quickly verifying the spatial resolution quality of all images within a data

  3. A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

    Directory of Open Access Journals (Sweden)

    Guizhou Wang

    2013-01-01

    Full Text Available This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine. Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy.

  4. Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings.

    Science.gov (United States)

    Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P

    2017-09-15

    Major end users of Digital Soil Mapping (DSM) such as policy makers and agricultural extension workers are faced with choosing the appropriate remote sensing data. The objective of this research is to analyze the spatial resolution effects of different remote sensing images on soil prediction models in two smallholder farms in Southern India called Kothapally (Telangana State), and Masuti (Karnataka State), and provide empirical guidelines to choose the appropriate remote sensing images in DSM. Bayesian kriging (BK) was utilized to characterize the spatial pattern of exchangeable potassium (K ex ) in the topsoil (0-15 cm) at different spatial resolutions by incorporating spectral indices from Landsat 8 (30 m), RapidEye (5 m), and WorldView-2/GeoEye-1/Pleiades-1A images (2 m). Some spectral indices such as band reflectances, band ratios, Crust Index and Atmospherically Resistant Vegetation Index from multiple images showed relatively strong correlations with soil K ex in two study areas. The research also suggested that fine spatial resolution WorldView-2/GeoEye-1/Pleiades-1A-based and RapidEye-based soil prediction models would not necessarily have higher prediction performance than coarse spatial resolution Landsat 8-based soil prediction models. The end users of DSM in smallholder farm settings need select the appropriate spectral indices and consider different factors such as the spatial resolution, band width, spectral resolution, temporal frequency, cost, and processing time of different remote sensing images. Overall, remote sensing-based Digital Soil Mapping has potential to be promoted to smallholder farm settings all over the world and help smallholder farmers implement sustainable and field-specific soil nutrient management scheme. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

    Science.gov (United States)

    Eken, S.; Aydın, E.; Sayar, A.

    2017-11-01

    In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

  6. Predicting spatial variations of tree species richness in tropical forests from high-resolution remote sensing.

    Science.gov (United States)

    Fricker, Geoffrey A; Wolf, Jeffrey A; Saatchi, Sassan S; Gillespie, Thomas W

    2015-10-01

    There is an increasing interest in identifying theories, empirical data sets, and remote-sensing metrics that can quantify tropical forest alpha diversity at a landscape scale. Quantifying patterns of tree species richness in the field is time consuming, especially in regions with over 100 tree species/ha. We examine species richness in a 50-ha plot in Barro Colorado Island in Panama and test if biophysical measurements of canopy reflectance from high-resolution satellite imagery and detailed vertical forest structure and topography from light detection and ranging (lidar) are associated with species richness across four tree size classes (>1, 1-10, >10, and >20 cm dbh) and three spatial scales (1, 0.25, and 0.04 ha). We use the 2010 tree inventory, including 204,757 individuals belonging to 301 species of freestanding woody plants or 166 ± 1.5 species/ha (mean ± SE), to compare with remote-sensing data. All remote-sensing metrics became less correlated with species richness as spatial resolution decreased from 1.0 ha to 0.04 ha and tree size increased from 1 cm to 20 cm dbh. When all stems with dbh > 1 cm in 1-ha plots were compared to remote-sensing metrics, standard deviation in canopy reflectance explained 13% of the variance in species richness. The standard deviations of canopy height and the topographic wetness index (TWI) derived from lidar were the best metrics to explain the spatial variance in species richness (15% and 24%, respectively). Using multiple regression models, we made predictions of species richness across Barro Colorado Island (BCI) at the 1-ha spatial scale for different tree size classes. We predicted variation in tree species richness among all plants (adjusted r² = 0.35) and trees with dbh > 10 cm (adjusted r² = 0.25). However, the best model results were for understory trees and shrubs (dbh 1-10 cm) (adjusted r² = 0.52) that comprise the majority of species richness in tropical forests. Our results indicate that high-resolution

  7. DIFET: DISTRIBUTED FEATURE EXTRACTION TOOL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    S. Eken

    2017-11-01

    Full Text Available In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

  8. Spatial and temporal remote sensing data fusion for vegetation monitoring

    Science.gov (United States)

    The suite of available remote sensing instruments varies widely in terms of sensor characteristics, spatial resolution and acquisition frequency. For example, the Moderate-resolution Imaging Spectroradiometer (MODIS) provides daily global observations at 250m to 1km spatial resolution. While imagery...

  9. A novel airport extraction model based on saliency region detection for high spatial resolution remote sensing images

    Science.gov (United States)

    Lv, Wen; Zhang, Libao; Zhu, Yongchun

    2017-06-01

    The airport is one of the most crucial traffic facilities in military and civil fields. Automatic airport extraction in high spatial resolution remote sensing images has many applications such as regional planning and military reconnaissance. Traditional airport extraction strategies usually base on prior knowledge and locate the airport target by template matching and classification, which will cause high computation complexity and large costs of computing resources for high spatial resolution remote sensing images. In this paper, we propose a novel automatic airport extraction model based on saliency region detection, airport runway extraction and adaptive threshold segmentation. In saliency region detection, we choose frequency-tuned (FT) model for computing airport saliency using low level features of color and luminance that is easy and fast to implement and can provide full-resolution saliency maps. In airport runway extraction, Hough transform is adopted to count the number of parallel line segments. In adaptive threshold segmentation, the Otsu threshold segmentation algorithm is proposed to obtain more accurate airport regions. The experimental results demonstrate that the proposed model outperforms existing saliency analysis models and shows good performance in the extraction of the airport.

  10. Research on Horizontal Accuracy Method of High Spatial Resolution Remotely Sensed Orthophoto Image

    Science.gov (United States)

    Xu, Y. M.; Zhang, J. X.; Yu, F.; Dong, S.

    2018-04-01

    At present, in the inspection and acceptance of high spatial resolution remotly sensed orthophoto image, the horizontal accuracy detection is testing and evaluating the accuracy of images, which mostly based on a set of testing points with the same accuracy and reliability. However, it is difficult to get a set of testing points with the same accuracy and reliability in the areas where the field measurement is difficult and the reference data with high accuracy is not enough. So it is difficult to test and evaluate the horizontal accuracy of the orthophoto image. The uncertainty of the horizontal accuracy has become a bottleneck for the application of satellite borne high-resolution remote sensing image and the scope of service expansion. Therefore, this paper proposes a new method to test the horizontal accuracy of orthophoto image. This method using the testing points with different accuracy and reliability. These points' source is high accuracy reference data and field measurement. The new method solves the horizontal accuracy detection of the orthophoto image in the difficult areas and provides the basis for providing reliable orthophoto images to the users.

  11. Edge Detection from High Resolution Remote Sensing Images using Two-Dimensional log Gabor Filter in Frequency Domain

    International Nuclear Information System (INIS)

    Wang, K; Yu, T; Meng, Q Y; Wang, G K; Li, S P; Liu, S H

    2014-01-01

    Edges are vital features to describe the structural information of images, especially high spatial resolution remote sensing images. Edge features can be used to define the boundaries between different ground objects in high spatial resolution remote sensing images. Thus edge detection is important in the remote sensing image processing. Even though many different edge detection algorithms have been proposed, it is difficult to extract the edge features from high spatial resolution remote sensing image including complex ground objects. This paper introduces a novel method to detect edges from the high spatial resolution remote sensing image based on frequency domain. Firstly, the high spatial resolution remote sensing images are Fourier transformed to obtain the magnitude spectrum image (frequency image) by FFT. Then, the frequency spectrum is analyzed by using the radius and angle sampling. Finally, two-dimensional log Gabor filter with optimal parameters is designed according to the result of spectrum analysis. Finally, dot product between the result of Fourier transform and the log Gabor filter is inverse Fourier transformed to obtain the detections. The experimental result shows that the proposed algorithm can detect edge features from the high resolution remote sensing image commendably

  12. Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

    Directory of Open Access Journals (Sweden)

    Linyi Li

    2017-01-01

    Full Text Available In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

  13. Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA (Final)

    Science.gov (United States)

    EPA announced the availability of the final report,Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA . This report and associated land use/land cover (LULC) coverage is the result o...

  14. Super-Resolution Reconstruction of Remote Sensing Images Using Multifractal Analysis

    Directory of Open Access Journals (Sweden)

    Mao-Gui Hu

    2009-10-01

    Full Text Available Satellite remote sensing (RS is an important contributor to Earth observation, providing various kinds of imagery every day, but low spatial resolution remains a critical bottleneck in a lot of applications, restricting higher spatial resolution analysis (e.g., intraurban. In this study, a multifractal-based super-resolution reconstruction method is proposed to alleviate this problem. The multifractal characteristic is common in Nature. The self-similarity or self-affinity presented in the image is useful to estimate details at larger and smaller scales than the original. We first look for the presence of multifractal characteristics in the images. Then we estimate parameters of the information transfer function and noise of the low resolution image. Finally, a noise-free, spatial resolutionenhanced image is generated by a fractal coding-based denoising and downscaling method. The empirical case shows that the reconstructed super-resolution image performs well indetail enhancement. This method is not only useful for remote sensing in investigating Earth, but also for other images with multifractal characteristics.

  15. Estimating Gross Primary Production in Cropland with High Spatial and Temporal Scale Remote Sensing Data

    Science.gov (United States)

    Lin, S.; Li, J.; Liu, Q.

    2018-04-01

    Satellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scale, for example, higher spatial resolution data as Landsat data have 30-m spatial resolution but 16 days revisit period, while high temporal scale data such as geostationary data have 30-minute imaging period, which has lower spatial resolution (> 1 km). The objective of this study is to investigate whether combining high spatial and temporal resolution remote sensing data can improve the gross primary production (GPP) estimation accuracy in cropland. For this analysis we used three years (from 2010 to 2012) Landsat based NDVI data, MOD13 vegetation index product and Geostationary Operational Environmental Satellite (GOES) geostationary data as input parameters to estimate GPP in a small region cropland of Nebraska, US. Then we validated the remote sensing based GPP with the in-situ measurement carbon flux data. Results showed that: 1) the overall correlation between GOES visible band and in-situ measurement photosynthesis active radiation (PAR) is about 50 % (R2 = 0.52) and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64 % of PAR variance (R2 = 0.64); 2) estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R2 = 0.85, RMSE MODIS 1-km NDVI/EVI product import; 3) using daily meteorology data as input for GPP estimation in high spatial resolution data would have higher relevance than 8-day and 16-day input. Generally speaking, using the high spatial resolution and high frequency satellite based remote sensing data can improve GPP estimation accuracy in cropland.

  16. SAGA GIS based processing of spatial high resolution temperature data

    International Nuclear Information System (INIS)

    Gerlitz, Lars; Bechtel, Benjamin; Kawohl, Tobias; Boehner, Juergen; Zaksek, Klemen

    2013-01-01

    Many climate change impact studies require surface and near surface temperature data with high spatial and temporal resolution. The resolution of state of the art climate models and remote sensing data is often by far to coarse to represent the meso- and microscale distinctions of temperatures. This is particularly the case for regions with a huge variability of topoclimates, such as mountainous or urban areas. Statistical downscaling techniques are promising methods to refine gridded temperature data with limited spatial resolution, particularly due to their low demand for computer capacity. This paper presents two downscaling approaches - one for climate model output and one for remote sensing data. Both are methodically based on the FOSS-GIS platform SAGA. (orig.)

  17. ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATA

    Directory of Open Access Journals (Sweden)

    S. Lin

    2018-04-01

    Full Text Available Satellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scale, for example, higher spatial resolution data as Landsat data have 30-m spatial resolution but 16 days revisit period, while high temporal scale data such as geostationary data have 30-minute imaging period, which has lower spatial resolution (> 1 km. The objective of this study is to investigate whether combining high spatial and temporal resolution remote sensing data can improve the gross primary production (GPP estimation accuracy in cropland. For this analysis we used three years (from 2010 to 2012 Landsat based NDVI data, MOD13 vegetation index product and Geostationary Operational Environmental Satellite (GOES geostationary data as input parameters to estimate GPP in a small region cropland of Nebraska, US. Then we validated the remote sensing based GPP with the in-situ measurement carbon flux data. Results showed that: 1 the overall correlation between GOES visible band and in-situ measurement photosynthesis active radiation (PAR is about 50 % (R2 = 0.52 and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64 % of PAR variance (R2 = 0.64; 2 estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R2 = 0.85, RMSE < 3 gC/m2/day, which has better performance than using MODIS 1-km NDVI/EVI product import; 3 using daily meteorology data as input for GPP estimation in high spatial resolution data would have higher relevance than 8-day and 16-day input. Generally speaking, using the high spatial resolution and high frequency satellite based remote sensing data can improve GPP estimation accuracy in cropland.

  18. Prevalence of pure versus mixed snow cover pixels across spatial resolutions in alpine environments: implications for binary and fractional remote sensing approaches

    Science.gov (United States)

    Selkowitz, David J.; Forster, Richard; Caldwell, Megan K.

    2014-01-01

    Remote sensing of snow-covered area (SCA) can be binary (indicating the presence/absence of snow cover at each pixel) or fractional (indicating the fraction of each pixel covered by snow). Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed) pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days and averaged 37

  19. Remote state preparation of spatial qubits

    Energy Technology Data Exchange (ETDEWEB)

    Solis-Prosser, M. A.; Neves, L. [Center for Optics and Photonics, Universidad de Concepcion, Casilla 4016, Concepcion (Chile) and Departamento de Fisica, Universidad de Concepcion, Casilla 160-C, Concepcion (Chile)

    2011-07-15

    We study the quantum communication protocol of remote state preparation (RSP) for pure states of qubits encoded in single photons transmitted through a double slit, the so-called spatial qubits. Two measurement strategies that one can adopt to remotely prepare the states are discussed. The first strategy is the well-known spatial postselection, where a single-pixel detector measures the transverse position of the photon between the focal and the image plane of a lens. The second strategy, proposed by ourselves, is a generalized measurement divided into two steps: the implementation of a two-outcome positive operator-valued measurement (POVM) followed by the spatial postselection at the focal plane of the lens by a two-pixel detector in each output of the POVM. In both cases we analyze the effects of the finite spatial resolution of the detectors over three figures of merit of the protocol, namely, the probability of preparation, the fidelity, and purity of the remotely prepared states. It is shown that our strategy improves these figures compared with spatial postselection, at the expense of increasing the classical communication cost as well as the required experimental resources. In addition, we present a modified version of our strategy for RSP of spatial qudits which is able to prepare arbitrary pure states, unlike spatial postselection alone. We expect that our study may also be extended for RSP of the angular spectrum of a single-photon field as an alternative for quantum teleportation which requires very inefficient nonlinear interactions.

  20. Remote state preparation of spatial qubits

    International Nuclear Information System (INIS)

    Solis-Prosser, M. A.; Neves, L.

    2011-01-01

    We study the quantum communication protocol of remote state preparation (RSP) for pure states of qubits encoded in single photons transmitted through a double slit, the so-called spatial qubits. Two measurement strategies that one can adopt to remotely prepare the states are discussed. The first strategy is the well-known spatial postselection, where a single-pixel detector measures the transverse position of the photon between the focal and the image plane of a lens. The second strategy, proposed by ourselves, is a generalized measurement divided into two steps: the implementation of a two-outcome positive operator-valued measurement (POVM) followed by the spatial postselection at the focal plane of the lens by a two-pixel detector in each output of the POVM. In both cases we analyze the effects of the finite spatial resolution of the detectors over three figures of merit of the protocol, namely, the probability of preparation, the fidelity, and purity of the remotely prepared states. It is shown that our strategy improves these figures compared with spatial postselection, at the expense of increasing the classical communication cost as well as the required experimental resources. In addition, we present a modified version of our strategy for RSP of spatial qudits which is able to prepare arbitrary pure states, unlike spatial postselection alone. We expect that our study may also be extended for RSP of the angular spectrum of a single-photon field as an alternative for quantum teleportation which requires very inefficient nonlinear interactions.

  1. Object-based vegetation classification with high resolution remote sensing imagery

    Science.gov (United States)

    Yu, Qian

    Vegetation species are valuable indicators to understand the earth system. Information from mapping of vegetation species and community distribution at large scales provides important insight for studying the phenological (growth) cycles of vegetation and plant physiology. Such information plays an important role in land process modeling including climate, ecosystem and hydrological models. The rapidly growing remote sensing technology has increased its potential in vegetation species mapping. However, extracting information at a species level is still a challenging research topic. I proposed an effective method for extracting vegetation species distribution from remotely sensed data and investigated some ways for accuracy improvement. The study consists of three phases. Firstly, a statistical analysis was conducted to explore the spatial variation and class separability of vegetation as a function of image scale. This analysis aimed to confirm that high resolution imagery contains the information on spatial vegetation variation and these species classes can be potentially separable. The second phase was a major effort in advancing classification by proposing a method for extracting vegetation species from high spatial resolution remote sensing data. The proposed classification employs an object-based approach that integrates GIS and remote sensing data and explores the usefulness of ancillary information. The whole process includes image segmentation, feature generation and selection, and nearest neighbor classification. The third phase introduces a spatial regression model for evaluating the mapping quality from the above vegetation classification results. The effects of six categories of sample characteristics on the classification uncertainty are examined: topography, sample membership, sample density, spatial composition characteristics, training reliability and sample object features. This evaluation analysis answered several interesting scientific questions

  2. Spatial Resolution Assessment of the Telops Airborne TIR Imagery

    Science.gov (United States)

    Mousakhani, S.; Eslami, M.; Saadatseresht, M.

    2017-09-01

    Having a high spatial resolution of Thermal InfraRed (TIR) Sensors is a challenge in remote sensing applications. Airborne high spatial resolution TIR is a novel source of data that became available lately. Recent developments in spatial resolution of the TIR sensors have been an interesting topic for scientists. TIR sensors are very sensitive to the energies emitted from objects. Past researches have been shown that increasing the spatial resolution of an airborne image will decrease the spectral content of the data and will reduce the Signal to Noise Ratio (SNR). Therefore, in this paper a comprehensive assessment is adapted to estimate an appropriate spatial resolution of the TIR data (TELOPS TIR data), in consideration of the SNR. So, firstly, a low-pass filter is applied on TIR data and the achieved products fed to a classification method for analysing of the accuracy improvement. The obtained results show that, there is no significant change in classification accuracy by applying low-pass filter. Furthermore, estimation of the appropriate spatial resolution of the TIR data is evaluated for obtaining higher spectral content and SNR. For this purpose, different resolutions of the TIR data are created and fed to the maximum likelihood classification method separately. The results illustrated in the case of using images with ground pixel size four times greater than the original image, the classification accuracy is not reduced. Also, SNR and spectral contents are improved. But the corners sharpening is declined.

  3. Multi-granularity synthesis segmentation for high spatial resolution Remote sensing images

    International Nuclear Information System (INIS)

    Yi, Lina; Liu, Pengfei; Qiao, Xiaojun; Zhang, Xiaoning; Gao, Yuan; Feng, Boyan

    2014-01-01

    Traditional segmentation method can only partition an image in a single granularity space, with segmentation accuracy limited to the single granularity space. This paper proposes a multi-granularity synthesis segmentation method for high spatial resolution remote sensing images based on a quotient space model. Firstly, we divide the whole image area into multiple granules (regions), each region is consisted of ground objects that have similar optimal segmentation scale, and then select and synthesize the sub-optimal segmentations of each region to get the final segmentation result. To validate this method, the land cover category map is used to guide the scale synthesis of multi-scale image segmentations for Quickbird image land use classification. Firstly, the image is coarsely divided into multiple regions, each region belongs to a certain land cover category. Then multi-scale segmentation results are generated by the Mumford-Shah function based region merging method. For each land cover category, the optimal segmentation scale is selected by the supervised segmentation accuracy assessment method. Finally, the optimal scales of segmentation results are synthesized under the guide of land cover category. Experiments show that the multi-granularity synthesis segmentation can produce more accurate segmentation than that of a single granularity space and benefit the classification

  4. Combining Remote Sensing imagery of both fine and coarse spatial resolution to Estimate Crop Evapotranspiration and quantifying its Influence on Crop Growth Monitoring.

    Science.gov (United States)

    Sepulcre-Cantó, Guadalupe; Gellens-Meulenberghs, Françoise; Arboleda, Alirio; Duveiller, Gregory; Piccard, Isabelle; de Wit, Allard; Tychon, Bernard; Bakary, Djaby; Defourny, Pierre

    2010-05-01

    This study has been carried out in the framework of the GLOBAM -Global Agricultural Monitoring system by integration of earth observation and modeling techniques- project whose objective is to fill the methodological gap between the state of the art of local crop monitoring and the operational requirements of the global monitoring system programs. To achieve this goal, the research aims to develop an integrated approach using remote sensing and crop growth modeling. Evapotranspiration (ET) is a valuable parameter in the crop monitoring context since it provides information on the plant water stress status, which strongly influences crop development and, by extension, crop yield. To assess crop evapotranspiration over the GLOBAM study areas (300x300 km sites in Northern Europe and Central Ethiopia), a Soil-Vegetation-Atmosphere Transfer (SVAT) model forced with remote sensing and numerical weather prediction data has been used. This model runs at pre-operational level in the framework of the EUMETSAT LSA-SAF (Land Surface Analysis Satellite Application Facility) using SEVIRI and ECMWF data, as well as the ECOCLIMAP database to characterize the vegetation. The model generates ET images at the Meteosat Second Generation (MSG) spatial resolution (3 km at subsatellite point),with a temporal resolution of 30 min and monitors the entire MSG disk which covers Europe, Africa and part of Sud America . The SVAT model was run for 2007 using two approaches. The first approach is at the standard pre-operational mode. The second incorporates remote sensing information at various spatial resolutions going from LANDSAT (30m) to SEVIRI (3-5 km) passing by AWIFS (56m) and MODIS (250m). Fine spatial resolution data consists of crop type classification which enable to identify areas where pure crop specific MODIS time series can be compiled and used to derive Leaf Area Index estimations for the most important crops (wheat and maize). The use of this information allowed to characterize

  5. Constraining the dynamics of the water budget at high spatial resolution in the world's water towers using models and remote sensing data; Snake River Basin, USA

    Science.gov (United States)

    Watson, K. A.; Masarik, M. T.; Flores, A. N.

    2016-12-01

    Mountainous, snow-dominated basins are often referred to as the water towers of the world because they store precipitation in seasonal snowpacks, which gradually melt and provide water supplies to downstream communities. Yet significant uncertainties remain in terms of quantifying the stores and fluxes of water in these regions as well as the associated energy exchanges. Constraining these stores and fluxes is crucial for advancing process understanding and managing these water resources in a changing climate. Remote sensing data are particularly important to these efforts due to the remoteness of these landscapes and high spatial variability in water budget components. We have developed a high resolution regional climate dataset extending from 1986 to the present for the Snake River Basin in the northwestern USA. The Snake River Basin is the largest tributary of the Columbia River by volume and a critically important basin for regional economies and communities. The core of the dataset was developed using a regional climate model, forced by reanalysis data. Specifically the Weather Research and Forecasting (WRF) model was used to dynamically downscale the North American Regional Reanalysis (NARR) over the region at 3 km horizontal resolution for the period of interest. A suite of satellite remote sensing products provide independent, albeit uncertain, constraint on a number of components of the water and energy budgets for the region across a range of spatial and temporal scales. For example, GRACE data are used to constrain basinwide terrestrial water storage and MODIS products are used to constrain the spatial and temporal evolution of evapotranspiration and snow cover. The joint use of both models and remote sensing products allows for both better understanding of water cycle dynamics and associated hydrometeorologic processes, and identification of limitations in both the remote sensing products and regional climate simulations.

  6. Analysis of the impact of spatial resolution on land/water classifications using high-resolution aerial imagery

    Science.gov (United States)

    Enwright, Nicholas M.; Jones, William R.; Garber, Adrienne L.; Keller, Matthew J.

    2014-01-01

    Long-term monitoring efforts often use remote sensing to track trends in habitat or landscape conditions over time. To most appropriately compare observations over time, long-term monitoring efforts strive for consistency in methods. Thus, advances and changes in technology over time can present a challenge. For instance, modern camera technology has led to an increasing availability of very high-resolution imagery (i.e. submetre and metre) and a shift from analogue to digital photography. While numerous studies have shown that image resolution can impact the accuracy of classifications, most of these studies have focused on the impacts of comparing spatial resolution changes greater than 2 m. Thus, a knowledge gap exists on the impacts of minor changes in spatial resolution (i.e. submetre to about 1.5 m) in very high-resolution aerial imagery (i.e. 2 m resolution or less). This study compared the impact of spatial resolution on land/water classifications of an area dominated by coastal marsh vegetation in Louisiana, USA, using 1:12,000 scale colour-infrared analogue aerial photography (AAP) scanned at four different dot-per-inch resolutions simulating ground sample distances (GSDs) of 0.33, 0.54, 1, and 2 m. Analysis of the impact of spatial resolution on land/water classifications was conducted by exploring various spatial aspects of the classifications including density of waterbodies and frequency distributions in waterbody sizes. This study found that a small-magnitude change (1–1.5 m) in spatial resolution had little to no impact on the amount of water classified (i.e. percentage mapped was less than 1.5%), but had a significant impact on the mapping of very small waterbodies (i.e. waterbodies ≤ 250 m2). These findings should interest those using temporal image classifications derived from very high-resolution aerial photography as a component of long-term monitoring programs.

  7. Spatial-Spectral Approaches to Edge Detection in Hyperspectral Remote Sensing

    Science.gov (United States)

    Cox, Cary M.

    This dissertation advances geoinformation science at the intersection of hyperspectral remote sensing and edge detection methods. A relatively new phenomenology among its remote sensing peers, hyperspectral imagery (HSI) comprises only about 7% of all remote sensing research - there are five times as many radar-focused peer reviewed journal articles than hyperspectral-focused peer reviewed journal articles. Similarly, edge detection studies comprise only about 8% of image processing research, most of which is dedicated to image processing techniques most closely associated with end results, such as image classification and feature extraction. Given the centrality of edge detection to mapping, that most important of geographic functions, improving the collective understanding of hyperspectral imagery edge detection methods constitutes a research objective aligned to the heart of geoinformation sciences. Consequently, this dissertation endeavors to narrow the HSI edge detection research gap by advancing three HSI edge detection methods designed to leverage HSI's unique chemical identification capabilities in pursuit of generating accurate, high-quality edge planes. The Di Zenzo-based gradient edge detection algorithm, an innovative version of the Resmini HySPADE edge detection algorithm and a level set-based edge detection algorithm are tested against 15 traditional and non-traditional HSI datasets spanning a range of HSI data configurations, spectral resolutions, spatial resolutions, bandpasses and applications. This study empirically measures algorithm performance against Dr. John Canny's six criteria for a good edge operator: false positives, false negatives, localization, single-point response, robustness to noise and unbroken edges. The end state is a suite of spatial-spectral edge detection algorithms that produce satisfactory edge results against a range of hyperspectral data types applicable to a diverse set of earth remote sensing applications. This work

  8. INTEGRATION OF SPATIAL INFORMATION WITH COLOR FOR CONTENT RETRIEVAL OF REMOTE SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    Bikesh Kumar Singh

    2010-08-01

    Full Text Available There is rapid increase in image databases of remote sensing images due to image satellites with high resolution, commercial applications of remote sensing & high available bandwidth in last few years. The problem of content-based image retrieval (CBIR of remotely sensed images presents a major challenge not only because of the surprisingly increasing volume of images acquired from a wide range of sensors but also because of the complexity of images themselves. In this paper, a software system for content-based retrieval of remote sensing images using RGB and HSV color spaces is presented. Further, we also compare our results with spatiogram based content retrieval which integrates spatial information along with color histogram. Experimental results show that the integration of spatial information in color improves the image analysis of remote sensing data. In general, retrievals in HSV color space showed better performance than in RGB color space.

  9. Classification of high resolution remote sensing image based on geo-ontology and conditional random fields

    Science.gov (United States)

    Hong, Liang

    2013-10-01

    The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remote sensing analysis methodology is widely accepted and applied in high resolution remote sensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remote sensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remote sensing image.

  10. High Spatial Resolution Airborne Multispectral Thermal Infrared Remote Sensing Data for Analysis of Urban Landscape Characteristics

    Science.gov (United States)

    Quattrochi, Dale A.; Luvall, Jeffrey C.; Estes, Maurice G., Jr.; Arnold, James E. (Technical Monitor)

    2000-01-01

    We have used airborne multispectral thermal infrared (TIR) remote sensing data collected at a high spatial resolution (i.e., 10m) over several cities in the United States to study thermal energy characteristics of the urban landscape. These TIR data provide a unique opportunity to quantify thermal responses from discrete surfaces typical of the urban landscape and to identify both the spatial arrangement and patterns of thermal processes across the city. The information obtained from these data is critical to understanding how urban surfaces drive or force development of the Urban Heat Island (UHI) effect, which exists as a dome of elevated air temperatures that presides over cities in contrast to surrounding non-urbanized areas. The UHI is most pronounced in the summertime where urban surfaces, such as rooftops and pavement, store solar radiation throughout the day, and release this stored energy slowly after sunset creating air temperatures over the city that are in excess of 2-4'C warmer in contrast with non-urban or rural air temperatures. The UHI can also exist as a daytime phenomenon with surface temperatures in downtown areas of cities exceeding 38'C. The implications of the UHI are significant, particularly as an additive source of thermal energy input that exacerbates the overall production of ground level ozone over cities. We have used the Airborne Thermal and Land Applications Sensor (ATLAS), flown onboard a Lear 23 jet aircraft from the NASA Stennis Space Center, to acquire high spatial resolution multispectral TIR data (i.e., 6 bandwidths between 8.2-12.2 (um) over Huntsville, Alabama, Atlanta, Georgia, Baton Rouge, Louisiana, Salt Lake City, Utah, and Sacramento, California. These TIR data have been used to produce maps and other products, showing the spatial distribution of heating and cooling patterns over these cities to better understand how the morphology of the urban landscape affects development of the UHI. In turn, these data have been used

  11. Land cover mapping and change detection in urban watersheds using QuickBird high spatial resolution satellite imagery

    Science.gov (United States)

    Hester, David Barry

    The objective of this research was to develop methods for urban land cover analysis using QuickBird high spatial resolution satellite imagery. Such imagery has emerged as a rich commercially available remote sensing data source and has enjoyed high-profile broadcast news media and Internet applications, but methods of quantitative analysis have not been thoroughly explored. The research described here consists of three studies focused on the use of pan-sharpened 61-cm spatial resolution QuickBird imagery, the spatial resolution of which is the highest of any commercial satellite. In the first study, a per-pixel land cover classification method is developed for use with this imagery. This method utilizes a per-pixel classification approach to generate an accurate six-category high spatial resolution land cover map of a developing suburban area. The primary objective of the second study was to develop an accurate land cover change detection method for use with QuickBird land cover products. This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and meaningful high spatial resolution land cover change analysis. The third study described here is an urban planning application of the high spatial resolution QuickBird-based land cover product developed in the first study. This work both meaningfully connects this exciting new data source to urban watershed management and makes an important empirical contribution to the study of suburban watersheds. Its analysis of residential roads and driveways as well as retail parking lots sheds valuable light on the impact of transportation-related land use on the suburban landscape. Broadly, these studies provide new methods for using state-of-the-art remote sensing data to inform land cover analysis and urban planning. These methods are widely adaptable and produce land cover products that are both meaningful and accurate. As additional high spatial resolution satellites are launched and the

  12. Spatially resolved remote measurement of temperature by neutron resonance absorption

    Energy Technology Data Exchange (ETDEWEB)

    Tremsin, A.S., E-mail: ast@ssl.berkeley.edu [Space Sciences Laboratory, University of California at Berkeley, 7 Gauss Way, Berkeley, CA 94720 (United States); Kockelmann, W.; Pooley, D.E. [STFC, Rutherford Appleton Laboratory, ISIS Facility, Didcot OX11 0QX (United Kingdom); Feller, W.B. [NOVA Scientific, Inc., 10 Picker Road, Sturbridge, MA 01566 (United States)

    2015-12-11

    Deep penetration of neutrons into most engineering materials enables non-destructive studies of their bulk properties. The existence of sharp resonances in neutron absorption spectra enables isotopically-resolved imaging of elements present in a sample, as demonstrated by previous studies. At the same time the Doppler broadening of resonance peaks provides a method of remote measurement of temperature distributions within the same sample. This technique can be implemented at a pulsed neutron source with a short initial pulse allowing for the measurement of the energy of each registered neutron by the time of flight technique. A neutron counting detector with relatively high timing and spatial resolution is used to demonstrate the possibility to obtain temperature distributions across a 100 µm Ta foil with ~millimeter spatial resolution. Moreover, a neutron transmission measurement over a wide energy range can provide spatially resolved sample information such as temperature, elemental composition and microstructure properties simultaneously.

  13. Towards breaking the spatial resolution barriers: An optical flow and super-resolution approach for sea ice motion estimation

    Science.gov (United States)

    Petrou, Zisis I.; Xian, Yang; Tian, YingLi

    2018-04-01

    Estimation of sea ice motion at fine scales is important for a number of regional and local level applications, including modeling of sea ice distribution, ocean-atmosphere and climate dynamics, as well as safe navigation and sea operations. In this study, we propose an optical flow and super-resolution approach to accurately estimate motion from remote sensing images at a higher spatial resolution than the original data. First, an external example learning-based super-resolution method is applied on the original images to generate higher resolution versions. Then, an optical flow approach is applied on the higher resolution images, identifying sparse correspondences and interpolating them to extract a dense motion vector field with continuous values and subpixel accuracies. Our proposed approach is successfully evaluated on passive microwave, optical, and Synthetic Aperture Radar data, proving appropriate for multi-sensor applications and different spatial resolutions. The approach estimates motion with similar or higher accuracy than the original data, while increasing the spatial resolution of up to eight times. In addition, the adopted optical flow component outperforms a state-of-the-art pattern matching method. Overall, the proposed approach results in accurate motion vectors with unprecedented spatial resolutions of up to 1.5 km for passive microwave data covering the entire Arctic and 20 m for radar data, and proves promising for numerous scientific and operational applications.

  14. Deriving temporally continuous soil moisture estimations at fine resolution by downscaling remotely sensed product

    Science.gov (United States)

    Jin, Yan; Ge, Yong; Wang, Jianghao; Heuvelink, Gerard B. M.

    2018-06-01

    Land surface soil moisture (SSM) has important roles in the energy balance of the land surface and in the water cycle. Downscaling of coarse-resolution SSM remote sensing products is an efficient way for producing fine-resolution data. However, the downscaling methods used most widely require full-coverage visible/infrared satellite data as ancillary information. These methods are restricted to cloud-free days, making them unsuitable for continuous monitoring. The purpose of this study is to overcome this limitation to obtain temporally continuous fine-resolution SSM estimations. The local spatial heterogeneities of SSM and multiscale ancillary variables were considered in the downscaling process both to solve the problem of the strong variability of SSM and to benefit from the fusion of ancillary information. The generation of continuous downscaled remote sensing data was achieved via two principal steps. For cloud-free days, a stepwise hybrid geostatistical downscaling approach, based on geographically weighted area-to-area regression kriging (GWATARK), was employed by combining multiscale ancillary variables with passive microwave remote sensing data. Then, the GWATARK-estimated SSM and China Soil Moisture Dataset from Microwave Data Assimilation SSM data were combined to estimate fine-resolution data for cloudy days. The developed methodology was validated by application to the 25-km resolution daily AMSR-E SSM product to produce continuous SSM estimations at 1-km resolution over the Tibetan Plateau. In comparison with ground-based observations, the downscaled estimations showed correlation (R ≥ 0.7) for both ascending and descending overpasses. The analysis indicated the high potential of the proposed approach for producing a temporally continuous SSM product at fine spatial resolution.

  15. Classification of High Spatial Resolution, Hyperspectral ...

    Science.gov (United States)

    EPA announced the availability of the final report,Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA . This report and associated land use/land cover (LULC) coverage is the result of a collaborative effort among an interdisciplinary team of scientists with the U.S. Environmental Protection Agency's (U.S. EPA's) Office of Research and Development in Cincinnati, Ohio. A primary goal of this project is to enhance the use of geography and spatial analytic tools in risk assessment, and to improve the scientific basis for risk management decisions affecting drinking water and water quality. The land use/land cover classification is derived from 82 flight lines of Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery acquired from July 24 through August 9, 2002 via fixed-wing aircraft.

  16. Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

    Science.gov (United States)

    Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

    2018-04-01

    Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure

  17. Identifying Spatial Units of Human Occupation in the Brazilian Amazon Using Landsat and CBERS Multi-Resolution Imagery

    OpenAIRE

    Dal’Asta, Ana Paula; Brigatti, Newton; Amaral, Silvana; Escada, Maria Isabel Sobral; Monteiro, Antonio Miguel Vieira

    2012-01-01

    Every spatial unit of human occupation is part of a network structuring an extensive process of urbanization in the Amazon territory. Multi-resolution remote sensing data were used to identify and map human presence and activities in the Sustainable Forest District of Cuiabá-Santarém highway (BR-163), west of Pará, Brazil. The limits of spatial units of human occupation were mapped based on digital classification of Landsat-TM5 (Thematic Mapper 5) image (30m spatial resolution). High-spatial-...

  18. Wind turbine extraction from high spatial resolution remote sensing images based on saliency detection

    Science.gov (United States)

    Chen, Jingbo; Yue, Anzhi; Wang, Chengyi; Huang, Qingqing; Chen, Jiansheng; Meng, Yu; He, Dongxu

    2018-01-01

    The wind turbine is a device that converts the wind's kinetic energy into electrical power. Accurate and automatic extraction of wind turbine is instructive for government departments to plan wind power plant projects. A hybrid and practical framework based on saliency detection for wind turbine extraction, using Google Earth image at spatial resolution of 1 m, is proposed. It can be viewed as a two-phase procedure: coarsely detection and fine extraction. In the first stage, we introduced a frequency-tuned saliency detection approach for initially detecting the area of interest of the wind turbines. This method exploited features of color and luminance, was simple to implement, and was computationally efficient. Taking into account the complexity of remote sensing images, in the second stage, we proposed a fast method for fine-tuning results in frequency domain and then extracted wind turbines from these salient objects by removing the irrelevant salient areas according to the special properties of the wind turbines. Experiments demonstrated that our approach consistently obtains higher precision and better recall rates. Our method was also compared with other techniques from the literature and proves that it is more applicable and robust.

  19. Low-Cost Ultra-High Spatial and Temporal Resolution Mapping of Intertidal Rock Platforms

    Science.gov (United States)

    Bryson, M.; Johnson-Roberson, M.; Murphy, R.

    2012-07-01

    Intertidal ecosystems have primarily been studied using field-based sampling; remote sensing offers the ability to collect data over large areas in a snapshot of time which could compliment field-based sampling methods by extrapolating them into the wider spatial and temporal context. Conventional remote sensing tools (such as satellite and aircraft imaging) provide data at relatively course, sub-meter resolutions or with limited temporal resolutions and relatively high costs for small-scale environmental science and ecology studies. In this paper, we describe a low-cost, kite-based imaging system and photogrammetric pipeline that was developed for constructing highresolution, 3D, photo-realistic terrain models of intertidal rocky shores. The processing pipeline uses automatic image feature detection and matching, structure-from-motion and photo-textured terrain surface reconstruction algorithms that require minimal human input and only a small number of ground control points and allow the use of cheap, consumer-grade digital cameras. The resulting maps combine colour and topographic information at sub-centimeter resolutions over an area of approximately 100m, thus enabling spatial properties of the intertidal environment to be determined across a hierarchy of spatial scales. Results of the system are presented for an intertidal rock platform at Cape Banks, Sydney, Australia. Potential uses of this technique include mapping of plant (micro- and macro-algae) and animal (e.g. gastropods) assemblages at multiple spatial and temporal scales.

  20. Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks

    Science.gov (United States)

    Audebert, Nicolas; Le Saux, Bertrand; Lefèvre, Sébastien

    2018-06-01

    In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data. Especially, we study how deep fully convolutional networks can be adapted to deal with multi-modal and multi-scale remote sensing data for semantic labeling. Our contributions are threefold: (a) we present an efficient multi-scale approach to leverage both a large spatial context and the high resolution data, (b) we investigate early and late fusion of Lidar and multispectral data, (c) we validate our methods on two public datasets with state-of-the-art results. Our results indicate that late fusion make it possible to recover errors steaming from ambiguous data, while early fusion allows for better joint-feature learning but at the cost of higher sensitivity to missing data.

  1. A temporal and spatial scaling method for quantifying daily photosynthesis using remote sensing data

    Energy Technology Data Exchange (ETDEWEB)

    Liu, J.; Chen, W.; Sarich, M. [Intermap Technologies Ltd., Nepean, ON (Canada); Cihlar, J. [Canada Centre for Remote Sensing, Ottawa, ON (Canada); Goulden, M. [California Univ., Irvine, CA (United States)

    1998-06-01

    Remote sensing to monitor the behaviour of terrestrial ecosystems over large areas was discussed. For this type of application the boreal ecosystem productivity simulator (BEPS) was developed, with the subsequent incorporation of the more advanced photosynthetic model. The new model improves the methodology through analytical spatial and temporal integration of canopy photosynthesis processes, and is suitable for regional remote sensing applications at moderate resolutions of 250 to 1000 m. 10 refs., 1 tab., 3 figs.

  2. Advancements in Modelling of Land Surface Energy Fluxes with Remote Sensing at Different Spatial Scales

    DEFF Research Database (Denmark)

    Guzinski, Radoslaw

    uxes, such as sensible heat ux, ground heat ux and net radiation, are also necessary. While it is possible to measure those uxes with ground-based instruments at local scales, at region scales they usually need to be modelled or estimated with the help of satellite remote sensing data. Even though...... to increase the spatial resolution of the reliable DTD-modelled fluxes from 1 km to 30 m. Furthermore, synergies between remote sensing based models and distributed hydrological models were studied with the aim of improving spatial performance of the hydrological models through incorporation of remote sensing...... of this study was to look at, and improve, various approaches for modelling the land-surface energy uxes at different spatial scales. The work was done using physically-based Two-Source Energy Balance (TSEB) approach as well as semi-empirical \\Triangle" approach. The TSEB-based approach was the main focus...

  3. Prevalence of Pure Versus Mixed Snow Cover Pixels across Spatial Resolutions in Alpine Environments

    Directory of Open Access Journals (Sweden)

    David J. Selkowitz

    2014-12-01

    Full Text Available Remote sensing of snow-covered area (SCA can be binary (indicating the presence/absence of snow cover at each pixel or fractional (indicating the fraction of each pixel covered by snow. Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days

  4. Spatial dynamics of thermokarst and thermo-erosion at lakes and ponds in North Siberia and Northwest Alaska using high-resolution remote sensing

    Science.gov (United States)

    Grosse, G.; Tillapaugh, M.; Romanovsky, V. E.; Walter, K. M.; Plug, L. J.

    2008-12-01

    Formation, growth, and drainage of thermokarst lakes in ice-rich permafrost deposits are important factors of landscape dynamics in extent Arctic lowlands. Monitoring of spatial and temporal dynamics of such lakes will allow an assessment of permafrost stability and enhance the capabilities for modelling and quantifying biogeochemical processes related to permafrost degradation in a warming Arctic. In this study we use high-resolution remote sensing and GIS to analyze the development of thermokarst lakes and ponds in two study regions in North Siberia and Northwest Alaska. The sites are 1) the Cherskii region in the Kolyma lowland (Siberia) and 2) the Kitluk River area on the northern Seward Peninsula (Alaska). Both regions are characterized by continuous permafrost, a highly dissected and dynamic thermokarst landscape, uplands of Late Pleistocene permafrost deposits with high excess ice contents, and a large total volume of permafrost-stored carbon. These ice-rich Yedoma or Yedoma-like deposits are highly vulnerable to permafrost degradation forced by climate warming or other surface disturbance. Time series of high- resolution imagery (aerial, Corona, Ikonos, Alos Prism) covering more than 50 years of lake dynamics allow detailed assessments of processes and spatial patterns of thermokarst lake expansion and drainage in continuous permafrost. Time series of high-resolution imagery (aerial, Corona, Ikonos, Alos Prism) covering more than 50 years of lake dynamics allow detailed assessments of processes and spatial patterns of thermokarst lake expansion and drainage in continuous permafrost. Processes identified include thaw slumping, wave undercutting of frozen sediments or peat blocks and subsequent mass wasting, thaw collapse of near-shore zones, sinkhole formation and ice-wedge tunnelling, and gully formation by thermo-erosion. We use GIS-based tools to relate the remote sensing results to field data (ground ice content, topography, lithology, and relative age

  5. Integrating remote sensing and spatially explicit epidemiological modeling

    Science.gov (United States)

    Finger, Flavio; Knox, Allyn; Bertuzzo, Enrico; Mari, Lorenzo; Bompangue, Didier; Gatto, Marino; Rinaldo, Andrea

    2015-04-01

    Spatially explicit epidemiological models are a crucial tool for the prediction of epidemiological patterns in time and space as well as for the allocation of health care resources. In addition they can provide valuable information about epidemiological processes and allow for the identification of environmental drivers of the disease spread. Most epidemiological models rely on environmental data as inputs. They can either be measured in the field by the means of conventional instruments or using remote sensing techniques to measure suitable proxies of the variables of interest. The later benefit from several advantages over conventional methods, including data availability, which can be an issue especially in developing, and spatial as well as temporal resolution of the data, which is particularly crucial for spatially explicit models. Here we present the case study of a spatially explicit, semi-mechanistic model applied to recurring cholera outbreaks in the Lake Kivu area (Democratic Republic of the Congo). The model describes the cholera incidence in eight health zones on the shore of the lake. Remotely sensed datasets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers. Human mobility and its effect on the disease spread is also taken into account. Several model configurations are tested on a data set of reported cases. The best models, accounting for different environmental drivers, and selected using the Akaike information criterion, are formally compared via cross validation. The best performing model accounts for seasonality, El Niño Southern Oscillation, precipitation and human mobility.

  6. Spatial Variability in Column CO2 Inferred from High Resolution GEOS-5 Global Model Simulations: Implications for Remote Sensing and Inversions

    Science.gov (United States)

    Ott, L.; Putman, B.; Collatz, J.; Gregg, W.

    2012-01-01

    Column CO2 observations from current and future remote sensing missions represent a major advancement in our understanding of the carbon cycle and are expected to help constrain source and sink distributions. However, data assimilation and inversion methods are challenged by the difference in scale of models and observations. OCO-2 footprints represent an area of several square kilometers while NASA s future ASCENDS lidar mission is likely to have an even smaller footprint. In contrast, the resolution of models used in global inversions are typically hundreds of kilometers wide and often cover areas that include combinations of land, ocean and coastal areas and areas of significant topographic, land cover, and population density variations. To improve understanding of scales of atmospheric CO2 variability and representativeness of satellite observations, we will present results from a global, 10-km simulation of meteorology and atmospheric CO2 distributions performed using NASA s GEOS-5 general circulation model. This resolution, typical of mesoscale atmospheric models, represents an order of magnitude increase in resolution over typical global simulations of atmospheric composition allowing new insight into small scale CO2 variations across a wide range of surface flux and meteorological conditions. The simulation includes high resolution flux datasets provided by NASA s Carbon Monitoring System Flux Pilot Project at half degree resolution that have been down-scaled to 10-km using remote sensing datasets. Probability distribution functions are calculated over larger areas more typical of global models (100-400 km) to characterize subgrid-scale variability in these models. Particular emphasis is placed on coastal regions and regions containing megacities and fires to evaluate the ability of coarse resolution models to represent these small scale features. Additionally, model output are sampled using averaging kernels characteristic of OCO-2 and ASCENDS measurement

  7. Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

    Directory of Open Access Journals (Sweden)

    Daniel A. Griffith

    2016-06-01

    Full Text Available Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pixels in realistically-sized remotely-sensed images, a situation that more recently has changed. Recent advances in spatial statistical estimation theory support the extraction of information and the distilling of knowledge from remotely-sensed images in a way that accounts for latent spatial autocorrelation. This paper summarizes an effective methodological approach to achieve this end, illustrating results with a 2002 remotely sensed-image of the Florida Everglades, and simulation experiments. Specifically, uncertainty of spatial autocorrelation parameter in a spatial autoregressive model is modeled with a beta-beta mixture approach and is further investigated with three different sampling strategies: coterminous sampling, random sub-region sampling, and increasing domain sub-regions. The results suggest that uncertainty associated with remotely-sensed data should be cast in consideration of spatial autocorrelation. It emphasizes that one remaining challenge is to better quantify the spatial variability of spatial autocorrelation estimates across geographic landscapes.

  8. Ontology-Guided Image Interpretation for GEOBIA of High Spatial Resolution Remote Sense Imagery: A Coastal Area Case Study

    Directory of Open Access Journals (Sweden)

    Helingjie Huang

    2017-03-01

    Full Text Available Image interpretation is a major topic in the remote sensing community. With the increasing acquisition of high spatial resolution (HSR remotely sensed images, incorporating geographic object-based image analysis (GEOBIA is becoming an important sub-discipline for improving remote sensing applications. The idea of integrating the human ability to understand images inspires research related to introducing expert knowledge into image object–based interpretation. The relevant work involved three parts: (1 identification and formalization of domain knowledge; (2 image segmentation and feature extraction; and (3 matching image objects with geographic concepts. This paper presents a novel way that combines multi-scaled segmented image objects with geographic concepts to express context in an ontology-guided image interpretation. Spectral features and geometric features of a single object are extracted after segmentation and topological relationships are also used in the interpretation. Web ontology language–query language (OWL-QL formalize domain knowledge. Then the interpretation matching procedure is implemented by the OWL-QL query-answering. Compared with a supervised classification, which does not consider context, the proposed method validates two HSR images of coastal areas in China. Both the number of interpreted classes increased (19 classes over 10 classes in Case 1 and 12 classes over seven in Case 2, and the overall accuracy improved (0.77 over 0.55 in Case 1 and 0.86 over 0.65 in Case 2. The additional context of the image objects improved accuracy during image classification. The proposed approach shows the pivotal role of ontology for knowledge-guided interpretation.

  9. A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

    Science.gov (United States)

    Sedano, Fernando; Kempeneers, Pieter; Strobl, Peter; Kucera, Jan; Vogt, Peter; Seebach, Lucia; San-Miguel-Ayanz, Jesús

    2011-09-01

    This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.

  10. The fusion of satellite and UAV data: simulation of high spatial resolution band

    Science.gov (United States)

    Jenerowicz, Agnieszka; Siok, Katarzyna; Woroszkiewicz, Malgorzata; Orych, Agata

    2017-10-01

    Remote sensing techniques used in the precision agriculture and farming that apply imagery data obtained with sensors mounted on UAV platforms became more popular in the last few years due to the availability of low- cost UAV platforms and low- cost sensors. Data obtained from low altitudes with low- cost sensors can be characterised by high spatial and radiometric resolution but quite low spectral resolution, therefore the application of imagery data obtained with such technology is quite limited and can be used only for the basic land cover classification. To enrich the spectral resolution of imagery data acquired with low- cost sensors from low altitudes, the authors proposed the fusion of RGB data obtained with UAV platform with multispectral satellite imagery. The fusion is based on the pansharpening process, that aims to integrate the spatial details of the high-resolution panchromatic image with the spectral information of lower resolution multispectral or hyperspectral imagery to obtain multispectral or hyperspectral images with high spatial resolution. The key of pansharpening is to properly estimate the missing spatial details of multispectral images while preserving their spectral properties. In the research, the authors presented the fusion of RGB images (with high spatial resolution) obtained with sensors mounted on low- cost UAV platforms and multispectral satellite imagery with satellite sensors, i.e. Landsat 8 OLI. To perform the fusion of UAV data with satellite imagery, the simulation of the panchromatic bands from RGB data based on the spectral channels linear combination, was conducted. Next, for simulated bands and multispectral satellite images, the Gram-Schmidt pansharpening method was applied. As a result of the fusion, the authors obtained several multispectral images with very high spatial resolution and then analysed the spatial and spectral accuracies of processed images.

  11. Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005-2015

    Science.gov (United States)

    Thompson, David R.; Kahn, Brian H.; Green, Robert O.; Chien, Steve A.; Middleton, Elizabeth M.; Tran, Daniel Q.

    2018-02-01

    The distribution of ice, liquid, and mixed phase clouds is important for Earth's planetary radiation budget, impacting cloud optical properties, evolution, and solar reflectivity. Most remote orbital thermodynamic phase measurements observe kilometer scales and are insensitive to mixed phases. This under-constrains important processes with outsize radiative forcing impact, such as spatial partitioning in mixed phase clouds. To date, the fine spatial structure of cloud phase has not been measured at global scales. Imaging spectroscopy of reflected solar energy from 1.4 to 1.8 µm can address this gap: it directly measures ice and water absorption, a robust indicator of cloud top thermodynamic phase, with spatial resolution of tens to hundreds of meters. We report the first such global high spatial resolution survey based on data from 2005 to 2015 acquired by the Hyperion imaging spectrometer onboard NASA's Earth Observer 1 (EO-1) spacecraft. Seasonal and latitudinal distributions corroborate observations by the Atmospheric Infrared Sounder (AIRS). For extratropical cloud systems, just 25 % of variance observed at GCM grid scales of 100 km was related to irreducible measurement error, while 75 % was explained by spatial correlations possible at finer resolutions.

  12. Remote Raman microimaging using an AOTF and a spatially coherent microfiber optical probe

    International Nuclear Information System (INIS)

    Trey Skinner, H.; Cooney, T.F.; Sharma, S.K.; Angel, S.M.

    1996-01-01

    A fiber-optic Raman microimaging probe is described that is suitable for acquiring high-spatial-resolution Raman images in sampling situations with no clear line of sight. A high-power near-infrared diode laser combined with an acousto-optic tunable filter and a spatially coherent optical fiber bundle allow fluorescence-free Raman images of remotely located samples to be acquired at distances up to several meters. The feasibility of this technique is demonstrated with Raman images of (1) a pellet containing a mixture of a highly scattering sample, bis-methylstyrylbenzene (BMSB), KCl, and graphite, and (2) a partially graphitized diamond. These images clearly show phase boundaries over an area of approximately 0.1 mm 2 with ∼4-μm resolution. copyright 1996 Society for Applied Spectroscopy

  13. Challenges of Remote Sensing and Spatial Information Education and Technology Transfer in a Fast Developing Industry

    Science.gov (United States)

    Tsai, F.; Chen, L.-C.

    2014-04-01

    During the past decade, Taiwan has experienced an unusual and fast growing in the industry of mapping, remote sensing, spatial information and related markets. A successful space program and dozens of advanced airborne and ground-based remote sensing instruments as well as mobile mapping systems have been implemented and put into operation to support the vast demands of geospatial data acquisition. Moreover, in addition to the government agencies and research institutes, there are also tens of companies in the private sector providing geo-spatial data and services. However, the fast developing industry is also posing a great challenge to the education sector in Taiwan, especially the higher education for geo-spatial information. Facing this fast developing industry, the demands of skilled professionals and new technologies in order to address diversified needs are indubitably high. Consequently, while delighting in the expanding and prospering benefitted from the fast growing industry, how to fulfill these demands has become a challenge for the remote sensing and spatial information disciplines in the higher education institutes in Taiwan. This paper provides a brief insight into the status of the remote sensing and spatial information industry in Taiwan as well as the challenges of the education and technology transfer to support the increasing demands and to ensure the continuous development of the industry. In addition to the report of the current status of the remote sensing and spatial information related courses and programs in the colleges and universities, current and potential threatening issues and possible resolutions are also discussed in different points of view.

  14. An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions.

    Science.gov (United States)

    Xie, Dengfeng; Zhang, Jinshui; Zhu, Xiufang; Pan, Yaozhong; Liu, Hongli; Yuan, Zhoumiqi; Yun, Ya

    2016-02-05

    Remote sensing technology plays an important role in monitoring rapid changes of the Earth's surface. However, sensors that can simultaneously provide satellite images with both high temporal and spatial resolution haven't been designed yet. This paper proposes an improved spatial and temporal adaptive reflectance fusion model (STARFM) with the help of an Unmixing-based method (USTARFM) to generate the high spatial and temporal data needed for the study of heterogeneous areas. The results showed that the USTARFM had higher accuracy than STARFM methods in two aspects of analysis: individual bands and of heterogeneity analysis. Taking the predicted NIR band as an example, the correlation coefficients (r) for the USTARFM, STARFM and unmixing methods were 0.96, 0.95, 0.90, respectively (p-value data fusion problems faced when using STARFM. Additionally, the USTARFM method could help researchers achieve better performance than STARFM at a smaller window size from its heterogeneous land surface quantitative representation.

  15. Kite aerial photography for low-cost, ultra-high spatial resolution multi-spectral mapping of intertidal landscapes.

    Directory of Open Access Journals (Sweden)

    Mitch Bryson

    Full Text Available Intertidal ecosystems have primarily been studied using field-based sampling; remote sensing offers the ability to collect data over large areas in a snapshot of time that could complement field-based sampling methods by extrapolating them into the wider spatial and temporal context. Conventional remote sensing tools (such as satellite and aircraft imaging provide data at limited spatial and temporal resolutions and relatively high costs for small-scale environmental science and ecologically-focussed studies. In this paper, we describe a low-cost, kite-based imaging system and photogrammetric/mapping procedure that was developed for constructing high-resolution, three-dimensional, multi-spectral terrain models of intertidal rocky shores. The processing procedure uses automatic image feature detection and matching, structure-from-motion and photo-textured terrain surface reconstruction algorithms that require minimal human input and only a small number of ground control points and allow the use of cheap, consumer-grade digital cameras. The resulting maps combine imagery at visible and near-infrared wavelengths and topographic information at sub-centimeter resolutions over an intertidal shoreline 200 m long, thus enabling spatial properties of the intertidal environment to be determined across a hierarchy of spatial scales. Results of the system are presented for an intertidal rocky shore at Jervis Bay, New South Wales, Australia. Potential uses of this technique include mapping of plant (micro- and macro-algae and animal (e.g. gastropods assemblages at multiple spatial and temporal scales.

  16. Kite aerial photography for low-cost, ultra-high spatial resolution multi-spectral mapping of intertidal landscapes.

    Science.gov (United States)

    Bryson, Mitch; Johnson-Roberson, Matthew; Murphy, Richard J; Bongiorno, Daniel

    2013-01-01

    Intertidal ecosystems have primarily been studied using field-based sampling; remote sensing offers the ability to collect data over large areas in a snapshot of time that could complement field-based sampling methods by extrapolating them into the wider spatial and temporal context. Conventional remote sensing tools (such as satellite and aircraft imaging) provide data at limited spatial and temporal resolutions and relatively high costs for small-scale environmental science and ecologically-focussed studies. In this paper, we describe a low-cost, kite-based imaging system and photogrammetric/mapping procedure that was developed for constructing high-resolution, three-dimensional, multi-spectral terrain models of intertidal rocky shores. The processing procedure uses automatic image feature detection and matching, structure-from-motion and photo-textured terrain surface reconstruction algorithms that require minimal human input and only a small number of ground control points and allow the use of cheap, consumer-grade digital cameras. The resulting maps combine imagery at visible and near-infrared wavelengths and topographic information at sub-centimeter resolutions over an intertidal shoreline 200 m long, thus enabling spatial properties of the intertidal environment to be determined across a hierarchy of spatial scales. Results of the system are presented for an intertidal rocky shore at Jervis Bay, New South Wales, Australia. Potential uses of this technique include mapping of plant (micro- and macro-algae) and animal (e.g. gastropods) assemblages at multiple spatial and temporal scales.

  17. Using high-resolution soil moisture modelling to assess the uncertainty of microwave remotely sensed soil moisture products at the correct spatial and temporal support

    Science.gov (United States)

    Wanders, N.; Karssenberg, D.; Bierkens, M. F. P.; Van Dam, J. C.; De Jong, S. M.

    2012-04-01

    Soil moisture is a key variable in the hydrological cycle and important in hydrological modelling. When assimilating soil moisture into flood forecasting models, the improvement of forecasting skills depends on the ability to accurately estimate the spatial and temporal patterns of soil moisture content throughout the river basin. Space-borne remote sensing may provide this information with a high temporal and spatial resolution and with a global coverage. Currently three microwave soil moisture products are available: AMSR-E, ASCAT and SMOS. The quality of these satellite-based products is often assessed by comparing them with in-situ observations of soil moisture. This comparison is however hampered by the difference in spatial and temporal support (i.e., resolution, scale), because the spatial resolution of microwave satellites is rather low compared to in-situ field measurements. Thus, the aim of this study is to derive a method to assess the uncertainty of microwave satellite soil moisture products at the correct spatial support. To overcome the difference in support size between in-situ soil moisture observations and remote sensed soil moisture, we used a stochastic, distributed unsaturated zone model (SWAP, van Dam (2000)) that is upscaled to the support of different satellite products. A detailed assessment of the SWAP model uncertainty is included to ensure that the uncertainty in satellite soil moisture is not overestimated due to an underestimation of the model uncertainty. We simulated unsaturated water flow up to a depth of 1.5m with a vertical resolution of 1 to 10 cm and on a horizontal grid of 1 km2 for the period Jan 2010 - Jun 2011. The SWAP model was first calibrated and validated on in-situ data of the REMEDHUS soil moisture network (Spain). Next, to evaluate the satellite products, the model was run for areas in the proximity of 79 meteorological stations in Spain, where model results were aggregated to the correct support of the satellite

  18. Monitoring of Vegetation Impact Due to Trampling on Cadillac Mountain Summit Using High Spatial Resolution Remote Sensing Data Sets

    Science.gov (United States)

    Kim, Min-Kook; Daigle, John J.

    2012-11-01

    Cadillac Mountain—the highest peak along the eastern seaboard of the United States—is a major tourist destination in Acadia National Park, Maine. Managing vegetation impact due to trampling on the Cadillac Mountain summit is extremely challenging because of the large number of visitors and the general open nature of landscape in this fragile subalpine environmental setting. Since 2000, more intensive management strategies—based on placing physical barriers and educational messages for visitors—have been employed to protect threatened vegetation, decrease vegetation impact, and enhance vegetation recovery in the vicinity of the summit loop trail. The primary purpose of this study was to evaluate the effect of the management strategies employed. For this purpose, vegetation cover changes between 2001 and 2007 were detected using multispectral high spatial resolution remote sensing data sets. A normalized difference vegetation index was employed to identify the rates of increase and decrease in the vegetation areas. Three buffering distances (30, 60, and 90 m) from the edges of the trail were used to define multiple spatial extents of the site, and the same spatial extents were employed at a nearby control site that had no visitors. No significant differences were detected between the mean rates of vegetation increase and decrease at the experimental site compared with a nearby control site in the case of a small spatial scale (≤30 m) comparison (in all cases P > 0.05). However, in the medium (≤60 m) and large (≤90 m) spatial scales, the rates of increased vegetation were significantly greater and rates of decreased vegetation significantly lower at the experimental site compared with the control site (in all cases P Management implications are explored in terms of the spatial strategies used to decrease the impact of trampling on vegetation.

  19. Remotely Sensed Data for High Resolution Agro-Environmental Policy Analysis

    Science.gov (United States)

    Welle, Paul

    Policy analyses of agricultural and environmental systems are often limited due to data constraints. Measurement campaigns can be costly, especially when the area of interest includes oceans, forests, agricultural regions or other dispersed spatial domains. Satellite based remote sensing offers a way to increase the spatial and temporal resolution of policy analysis concerning these systems. However, there are key limitations to the implementation of satellite data. Uncertainty in data derived from remote-sensing can be significant, and traditional methods of policy analysis for managing uncertainty on large datasets can be computationally expensive. Moreover, while satellite data can increasingly offer estimates of some parameters such as weather or crop use, other information regarding demographic or economic data is unlikely to be estimated using these techniques. Managing these challenges in practical policy analysis remains a challenge. In this dissertation, I conduct five case studies which rely heavily on data sourced from orbital sensors. First, I assess the magnitude of climate and anthropogenic stress on coral reef ecosystems. Second, I conduct an impact assessment of soil salinity on California agriculture. Third, I measure the propensity of growers to adapt their cropping practices to soil salinization in agriculture. Fourth, I analyze whether small-scale desalination units could be applied on farms in California in order mitigate the effects of drought and salinization as well as prevent agricultural drainage from entering vulnerable ecosystems. And fifth, I assess the feasibility of satellite-based remote sensing for salinity measurement at global scale. Through these case studies, I confront both the challenges and benefits associated with implementing satellite based-remote sensing for improved policy analysis.

  20. Towards high temporal and moderate spatial resolutions in the remote sensing retrieval of evapotranspiration by combining geostationary and polar orbit satellite data

    Science.gov (United States)

    Barrios, José Miguel; Ghilain, Nicolas; Arboleda, Alirio; Gellens-Meulenberghs, Françoise

    2014-05-01

    Evapotranspiration (ET) is the water flux going from the surface into the atmosphere as result of soil and surface water evaporation and plant transpiration. It constitutes a key component of the water cycle and its quantification is of crucial importance for a number of applications like water management, climatic modelling, agriculture monitoring and planning, etc. Estimating ET is not an easy task; specially if large areas are envisaged and various spatio-temporal patterns of ET are present as result of heterogeneity in land cover, land use and climatic conditions. In this respect, spaceborne remote sensing (RS) provides the only alternative to continuously measure surface parameters related to ET over large areas. The Royal Meteorological Institute (RMI) of Belgium, in the framework of EUMETSAT's "Land Surface Analysis-Satellite Application Facility" (LSA-SAF), has developed a model for the estimation of ET. The model is forced by RS data, numerical weather predictions and land cover information. The RS forcing is derived from measurements by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. This ET model is operational and delivers ET estimations over the whole field of view of the MSG satellite (Europe, Africa and Eastern South America) (http://landsaf.meteo.pt) every 30 minutes. The spatial resolution of MSG is 3 x 3 km at subsatellite point and about 4 x 5 km in continental Europe. The spatial resolution of this product may constrain its full exploitation as the interest of potential users (farmers and natural resources scientists) may lie on smaller spatial units. This study aimed at testing methodological alternatives to combine RS imagery (geostationary and polar orbit satellites) for the estimation of ET such that the spatial resolution of the final product is improved. In particular, the study consisted in the implementation of two approaches for combining the current ET estimations with

  1. Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

    Science.gov (United States)

    Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

    2016-01-01

    The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial

  2. Urban Spatial Ecological Performance Based on the Data of Remote Sensing of Guyuan

    Science.gov (United States)

    Ren, X.-J.; Chen, X.-J.; Ma, Q.

    2018-04-01

    The evolution analysis of urban landuse and spatial ecological performance are necessary and useful to recognizing the stage of urban development and revealing the regularity and connotation of urban spatial expansion. Moreover, it lies in the core that should be exmined in the urban sustainable development. In this paper, detailed information has been acquired from the high-resolution satellite imageries of Guyuan, China case study. With the support of GIS, the land-use mapping information and the land cover changes are analyzed, and the process of urban spatial ecological performance evolution by the hierarchical methodology is explored. Results demonstrate that in the past 11 years, the urban spatial ecological performance show an improved process with the dramatic landcover change in Guyuan. Firstly, the landuse structure of Guyuan changes significantly and shows an obvious stage characteristic. Secondly, the urban ecological performance of Guyuan continues to be optimized over the 11 years. Thirdly, the findings suggest that a dynamic monitoring mechanism of urban land use based on high-resolution remote sensing data should be established in urban development, and the rational development of urban land use should be guided by the spatial ecological performance as the basic value orientation.

  3. Spatial resolution influence on the identification of land cover classes in the Amazon environment

    Directory of Open Access Journals (Sweden)

    PONZONI FLÁVIO J.

    2002-01-01

    Full Text Available To evaluate the role played by the spatial resolution in distinguishing land cover classes in the Amazon region, different levels of spatial resolution (60, 100, 120, 200 and 250 meters were simulated from a Landsat_5 Thematic Mapper (TM image. Thematic maps were produced by visual interpretation from the original (30 x 30 meters and simulated set of images. The map legend included primary forest, old and young woody secondary succession, and non-forest. The results indicated that for the discrimination between primary forest and non-forest, spatial resolution did not have great influence for pixel size equal or lower than 200 meters. The contrary was verified for the identification of old and young woody secondary vegetation due to their occurrence in small polygons. To avoid significant changes in the calculated area of these land cover types, a spatial resolution better than 100 meters is required. This result is an indication that the use of the future Brazilian remote sensing satellite (SSR-1 for secondary succession identification may be unreliable, especially for latitudes between S10degrees and S15degrees where critical areas of deforestation are located and pixel size is expected to vary within the same scene from 100 meters (S10degrees to 200 meters (S15degrees.

  4. High resolution remote sensing of densely urbanised regions: a case study of Hong Kong.

    Science.gov (United States)

    Nichol, Janet E; Wong, Man Sing

    2009-01-01

    Data on the urban environment such as climate or air quality is usually collected at a few point monitoring stations distributed over a city. However, the synoptic viewpoint of satellites where a whole city is visible on a single image permits the collection of spatially comprehensive data at city-wide scale. In spite of rapid developments in remote sensing systems, deficiencies in image resolution and algorithm development still exist for applications such as air quality monitoring and urban heat island analysis. This paper describes state-of-the-art techniques for enhancing and maximising the spatial detail available from satellite images, and demonstrates their applications to the densely urbanised environment of Hong Kong. An Emissivity Modulation technique for spatial enhancement of thermal satellite images permits modelling of urban microclimate in combination with other urban structural parameters at local scale. For air quality monitoring, a Minimum Reflectance Technique (MRT) has been developed for MODIS 500 m images. The techniques described can promote the routine utilization of remotely sensed images for environmental monitoring in cities of the 21(st) century.

  5. A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites.

    Science.gov (United States)

    Maynard, Jonathan J; Karl, Jason W

    2017-01-01

    Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral 'fingerprint' of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites

  6. The spatial resolution of epidemic peaks.

    Directory of Open Access Journals (Sweden)

    Harriet L Mills

    2014-04-01

    Full Text Available The emergence of novel respiratory pathogens can challenge the capacity of key health care resources, such as intensive care units, that are constrained to serve only specific geographical populations. An ability to predict the magnitude and timing of peak incidence at the scale of a single large population would help to accurately assess the value of interventions designed to reduce that peak. However, current disease-dynamic theory does not provide a clear understanding of the relationship between: epidemic trajectories at the scale of interest (e.g. city; population mobility; and higher resolution spatial effects (e.g. transmission within small neighbourhoods. Here, we used a spatially-explicit stochastic meta-population model of arbitrary spatial resolution to determine the effect of resolution on model-derived epidemic trajectories. We simulated an influenza-like pathogen spreading across theoretical and actual population densities and varied our assumptions about mobility using Latin-Hypercube sampling. Even though, by design, cumulative attack rates were the same for all resolutions and mobilities, peak incidences were different. Clear thresholds existed for all tested populations, such that models with resolutions lower than the threshold substantially overestimated population-wide peak incidence. The effect of resolution was most important in populations which were of lower density and lower mobility. With the expectation of accurate spatial incidence datasets in the near future, our objective was to provide a framework for how to use these data correctly in a spatial meta-population model. Our results suggest that there is a fundamental spatial resolution for any pathogen-population pair. If underlying interactions between pathogens and spatially heterogeneous populations are represented at this resolution or higher, accurate predictions of peak incidence for city-scale epidemics are feasible.

  7. Identifying Spatial Units of Human Occupation in the Brazilian Amazon Using Landsat and CBERS Multi-Resolution Imagery

    Directory of Open Access Journals (Sweden)

    Maria Isabel Sobral Escada

    2012-01-01

    Full Text Available Every spatial unit of human occupation is part of a network structuring an extensive process of urbanization in the Amazon territory. Multi-resolution remote sensing data were used to identify and map human presence and activities in the Sustainable Forest District of Cuiabá-Santarém highway (BR-163, west of Pará, Brazil. The limits of spatial units of human occupation were mapped based on digital classification of Landsat-TM5 (Thematic Mapper 5 image (30m spatial resolution. High-spatial-resolution CBERS-HRC (China-Brazil Earth Resources Satellite-High-Resolution Camera images (5 m merged with CBERS-CCD (Charge Coupled Device images (20 m were used to map spatial arrangements inside each populated unit, describing intra-urban characteristics. Fieldwork data validated and refined the classification maps that supported the categorization of the units. A total of 133 spatial units were individualized, comprising population centers as municipal seats, villages and communities, and units of human activities, such as sawmills, farmhouses, landing strips, etc. From the high-resolution analysis, 32 population centers were grouped in four categories, described according to their level of urbanization and spatial organization as: structured, recent, established and dependent on connectivity. This multi-resolution approach provided spatial information about the urbanization process and organization of the territory. It may be extended into other areas or be further used to devise a monitoring system, contributing to the discussion of public policy priorities for sustainable development in the Amazon.

  8. High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertão

    Directory of Open Access Journals (Sweden)

    Ran Goldblatt

    2017-12-01

    Full Text Available Semi-arid ecosystems play a key role in global agricultural production, seasonal carbon cycle dynamics, and longer-run climate change. Because semi-arid landscapes are heterogeneous and often sparsely vegetated, repeated and large-scale ecosystem assessments of these regions have to date been impossible. Here, we assess the potential of high-spatial resolution visible band imagery for semi-arid ecosystem mapping. We use WorldView satellite imagery at 0.3–0.5 m resolution to develop a reference data set of nearly 10,000 labeled examples of three classes—trees, shrubs/grasses, and bare land—across 1000 km 2 of the semi-arid Sertão region of northeast Brazil. Using Google Earth Engine, we show that classification with low-spectral but high-spatial resolution input (WorldView outperforms classification with the full spectral information available from Landsat 30 m resolution imagery as input. Classification with high spatial resolution input improves detection of sparse vegetation and distinction between trees and seasonal shrubs and grasses, two features which are lost at coarser spatial (but higher spectral resolution input. Our total tree cover estimates for the study area disagree with recent estimates using other methods that may underestimate treecover because they confuse trees with seasonal vegetation (shrubs and grasses. This distinction is important for monitoring seasonal and long-run carbon cycle and ecosystem health. Our results suggest that newer remote sensing products that promise high frequency global coverage at high spatial but lower spectral resolution may offer new possibilities for direct monitoring of the world’s semi-arid ecosystems, and we provide methods that could be scaled to do so.

  9. A ROUGH SET DECISION TREE BASED MLP-CNN FOR VERY HIGH RESOLUTION REMOTELY SENSED IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    C. Zhang

    2017-09-01

    Full Text Available Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP, which are unable to exploit abundant spatial details within VHR images. This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

  10. High resolution remote sensing of water surface patterns

    Science.gov (United States)

    Woodget, A.; Visser, F.; Maddock, I.; Carbonneau, P.

    2012-12-01

    The assessment of in-stream habitat availability within fluvial environments in the UK traditionally includes the mapping of patterns which appear on the surface of the water, known as 'surface flow types' (SFTs). The UK's River Habitat Survey identifies ten key SFTs, including categories such as rippled flow, upwelling, broken standing waves and smooth flow. SFTs result from the interaction between the underlying channel morphology, water depth and velocity and reflect the local flow hydraulics. It has been shown that SFTs can be both biologically and hydraulically distinct. SFT mapping is usually conducted from the river banks where estimates of spatial coverage are made by eye. This approach is affected by user subjectivity and inaccuracies in the spatial extent of mapped units. Remote sensing and specifically the recent developments in unmanned aerial systems (UAS) may now offer an alternative approach for SFT mapping, with the capability for rapid and repeatable collection of very high resolution imagery from low altitudes, under bespoke flight conditions. This PhD research is aimed at investigating the mapping of SFTs using high resolution optical imagery (less than 10cm) collected from a helicopter-based UAS flown at low altitudes (less than 100m). This paper presents the initial findings from a series of structured experiments on the River Arrow, a small lowland river in Warwickshire, UK. These experiments investigate the potential for mapping SFTs from still and video imagery of different spatial resolutions collected at different flying altitudes and from different viewing angles (i.e. vertical and oblique). Imagery is processed using 3D mosaicking software to create orthophotos and digital elevation models (DEM). The types of image analysis which are tested include a simple, manual visual assessment undertaken in a GIS environment, based on the high resolution optical imagery. In addition, an object-based image analysis approach which makes use of the

  11. Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Rui Guo

    2018-03-01

    Full Text Available Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification.

  12. An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

    OpenAIRE

    Chandi Witharana; Heather J. Lynch

    2016-01-01

    The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR) satellite imagery and closely examined the transferability of knowle...

  13. An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies

    Directory of Open Access Journals (Sweden)

    Xiaohuan Yang

    2009-02-01

    Full Text Available The spatial distribution of population is closely related to land use and land cover (LULC patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B data integrated with a Pattern Decomposition Method (PDM and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM. The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable.

  14. An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies

    Science.gov (United States)

    Yang, Xiaohuan; Huang, Yaohuan; Dong, Pinliang; Jiang, Dong; Liu, Honghui

    2009-01-01

    The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS) have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS) is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B) data integrated with a Pattern Decomposition Method (PDM) and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM). The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable. PMID:22399959

  15. Estimating spatially distributed soil texture using time series of thermal remote sensing - a case study in central Europe

    Science.gov (United States)

    Müller, Benjamin; Bernhardt, Matthias; Jackisch, Conrad; Schulz, Karsten

    2016-09-01

    For understanding water and solute transport processes, knowledge about the respective hydraulic properties is necessary. Commonly, hydraulic parameters are estimated via pedo-transfer functions using soil texture data to avoid cost-intensive measurements of hydraulic parameters in the laboratory. Therefore, current soil texture information is only available at a coarse spatial resolution of 250 to 1000 m. Here, a method is presented to derive high-resolution (15 m) spatial topsoil texture patterns for the meso-scale Attert catchment (Luxembourg, 288 km2) from 28 images of ASTER (advanced spaceborne thermal emission and reflection radiometer) thermal remote sensing. A principle component analysis of the images reveals the most dominant thermal patterns (principle components, PCs) that are related to 212 fractional soil texture samples. Within a multiple linear regression framework, distributed soil texture information is estimated and related uncertainties are assessed. An overall root mean squared error (RMSE) of 12.7 percentage points (pp) lies well within and even below the range of recent studies on soil texture estimation, while requiring sparser sample setups and a less diverse set of basic spatial input. This approach will improve the generation of spatially distributed topsoil maps, particularly for hydrologic modeling purposes, and will expand the usage of thermal remote sensing products.

  16. A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents

    Science.gov (United States)

    McShane, Ryan R.; Driscoll, Katelyn P.; Sando, Roy

    2017-09-27

    Many approaches have been developed for measuring or estimating actual evapotranspiration (ETa), and research over many years has led to the development of remote sensing methods that are reliably reproducible and effective in estimating ETa. Several remote sensing methods can be used to estimate ETa at the high spatial resolution of agricultural fields and the large extent of river basins. More complex remote sensing methods apply an analytical approach to ETa estimation using physically based models of varied complexity that require a combination of ground-based and remote sensing data, and are grounded in the theory behind the surface energy balance model. This report, funded through cooperation with the International Joint Commission, provides an overview of selected remote sensing methods used for estimating water consumed through ETa and focuses on Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Operational Simplified Surface Energy Balance (SSEBop), two energy balance models for estimating ETa that are currently applied successfully in the United States. The METRIC model can produce maps of ETa at high spatial resolution (30 meters using Landsat data) for specific areas smaller than several hundred square kilometers in extent, an improvement in practice over methods used more generally at larger scales. Many studies validating METRIC estimates of ETa against measurements from lysimeters have shown model accuracies on daily to seasonal time scales ranging from 85 to 95 percent. The METRIC model is accurate, but the greater complexity of METRIC results in greater data requirements, and the internalized calibration of METRIC leads to greater skill required for implementation. In contrast, SSEBop is a simpler model, having reduced data requirements and greater ease of implementation without a substantial loss of accuracy in estimating ETa. The SSEBop model has been used to produce maps of ETa over very large extents (the

  17. Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Zhiyong Lv

    2018-03-01

    Full Text Available In recent decades, land cover change detection (LCCD using very high-spatial resolution (VHR remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution, respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors’ knowledge, this type

  18. Selecting Appropriate Spatial Scale for Mapping Plastic-Mulched Farmland with Satellite Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Hasituya

    2017-03-01

    Full Text Available In recent years, the area of plastic-mulched farmland (PMF has undergone rapid growth and raised remarkable environmental problems. Therefore, mapping the PMF plays a crucial role in agricultural production, environmental protection and resource management. However, appropriate data selection criteria are currently lacking. Thus, this study was carried out in two main plastic-mulching practice regions, Jizhou and Guyuan, to look for an appropriate spatial scale for mapping PMF with remote sensing. The average local variance (ALV function was used to obtain the appropriate spatial scale for mapping PMF based on the GaoFen-1 (GF-1 satellite imagery. Afterwards, in order to validate the effectiveness of the selected method and to interpret the relationship between the appropriate spatial scale derived from the ALV and the spatial scale with the highest classification accuracy, we classified the imagery with varying spatial resolution by the Support Vector Machine (SVM algorithm using the spectral features, textural features and the combined spectral and textural features respectively. The results indicated that the appropriate spatial scales from the ALV lie between 8 m and 20 m for mapping the PMF both in Jizhou and Guyuan. However, there is a proportional relation: the spatial scale with the highest classification accuracy is at the 1/2 location of the appropriate spatial scale generated from the ALV in Jizhou and at the 2/3 location of the appropriate spatial scale generated from the ALV in Guyuan. Therefore, the ALV method for quantitatively selecting the appropriate spatial scale for mapping PMF with remote sensing imagery has theoretical and practical significance.

  19. Scanning SQUID susceptometers with sub-micron spatial resolution

    Energy Technology Data Exchange (ETDEWEB)

    Kirtley, John R., E-mail: jkirtley@stanford.edu; Rosenberg, Aaron J.; Palmstrom, Johanna C.; Holland, Connor M.; Moler, Kathryn A. [Department of Applied Physics, Stanford University, Stanford, California 94305-4045 (United States); Paulius, Lisa [Department of Physics, Western Michigan University, Kalamazoo, Michigan 49008-5252 (United States); Spanton, Eric M. [Department of Physics, Stanford University, Stanford, California 94305-4045 (United States); Schiessl, Daniel [Attocube Systems AG, Königinstraße 11A, 80539 Munich (Germany); Jermain, Colin L.; Gibbons, Jonathan [Department of Physics, Cornell University, Cornell, Ithaca, New York 14853 (United States); Fung, Y.-K.K.; Gibson, Gerald W. [IBM Research Division, T. J. Watson Research Center, Yorktown Heights, New York 10598 (United States); Huber, Martin E. [Department of Physics, University of Colorado Denver, Denver, Colorado 80217-3364 (United States); Ralph, Daniel C. [Department of Physics, Cornell University, Cornell, Ithaca, New York 14853 (United States); Kavli Institute at Cornell, Ithaca, New York 14853 (United States); Ketchen, Mark B. [OcteVue, Hadley, Massachusetts 01035 (United States)

    2016-09-15

    Superconducting QUantum Interference Device (SQUID) microscopy has excellent magnetic field sensitivity, but suffers from modest spatial resolution when compared with other scanning probes. This spatial resolution is determined by both the size of the field sensitive area and the spacing between this area and the sample surface. In this paper we describe scanning SQUID susceptometers that achieve sub-micron spatial resolution while retaining a white noise floor flux sensitivity of ≈2μΦ{sub 0}/Hz{sup 1/2}. This high spatial resolution is accomplished by deep sub-micron feature sizes, well shielded pickup loops fabricated using a planarized process, and a deep etch step that minimizes the spacing between the sample surface and the SQUID pickup loop. We describe the design, modeling, fabrication, and testing of these sensors. Although sub-micron spatial resolution has been achieved previously in scanning SQUID sensors, our sensors not only achieve high spatial resolution but also have integrated modulation coils for flux feedback, integrated field coils for susceptibility measurements, and batch processing. They are therefore a generally applicable tool for imaging sample magnetization, currents, and susceptibilities with higher spatial resolution than previous susceptometers.

  20. Scanning SQUID susceptometers with sub-micron spatial resolution

    International Nuclear Information System (INIS)

    Kirtley, John R.; Rosenberg, Aaron J.; Palmstrom, Johanna C.; Holland, Connor M.; Moler, Kathryn A.; Paulius, Lisa; Spanton, Eric M.; Schiessl, Daniel; Jermain, Colin L.; Gibbons, Jonathan; Fung, Y.-K.K.; Gibson, Gerald W.; Huber, Martin E.; Ralph, Daniel C.; Ketchen, Mark B.

    2016-01-01

    Superconducting QUantum Interference Device (SQUID) microscopy has excellent magnetic field sensitivity, but suffers from modest spatial resolution when compared with other scanning probes. This spatial resolution is determined by both the size of the field sensitive area and the spacing between this area and the sample surface. In this paper we describe scanning SQUID susceptometers that achieve sub-micron spatial resolution while retaining a white noise floor flux sensitivity of ≈2μΦ_0/Hz"1"/"2. This high spatial resolution is accomplished by deep sub-micron feature sizes, well shielded pickup loops fabricated using a planarized process, and a deep etch step that minimizes the spacing between the sample surface and the SQUID pickup loop. We describe the design, modeling, fabrication, and testing of these sensors. Although sub-micron spatial resolution has been achieved previously in scanning SQUID sensors, our sensors not only achieve high spatial resolution but also have integrated modulation coils for flux feedback, integrated field coils for susceptibility measurements, and batch processing. They are therefore a generally applicable tool for imaging sample magnetization, currents, and susceptibilities with higher spatial resolution than previous susceptometers.

  1. [Estimation of desert vegetation coverage based on multi-source remote sensing data].

    Science.gov (United States)

    Wan, Hong-Mei; Li, Xia; Dong, Dao-Rui

    2012-12-01

    Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study areaAbstract: Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study area and based on the ground investigation and the multi-source remote sensing data of different resolutions, the estimation models for desert vegetation coverage were built, with the precisions of different estimation methods and models compared. The results showed that with the increasing spatial resolution of remote sensing data, the precisions of the estimation models increased. The estimation precision of the models based on the high, middle-high, and middle-low resolution remote sensing data was 89.5%, 87.0%, and 84.56%, respectively, and the precisions of the remote sensing models were higher than that of vegetation index method. This study revealed the change patterns of the estimation precision of desert vegetation coverage based on different spatial resolution remote sensing data, and realized the quantitative conversion of the parameters and scales among the high, middle, and low spatial resolution remote sensing data of desert vegetation coverage, which would provide direct evidence for establishing and implementing comprehensive remote sensing monitoring scheme for the ecological restoration in the study area.

  2. Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification

    OpenAIRE

    Xueke Li; Taixia Wu; Kai Liu; Yao Li; Lifu Zhang

    2016-01-01

    The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, ...

  3. The Analysis of Burrows Recognition Accuracy in XINJIANG'S Pasture Area Based on Uav Visible Images with Different Spatial Resolution

    Science.gov (United States)

    Sun, D.; Zheng, J. H.; Ma, T.; Chen, J. J.; Li, X.

    2018-04-01

    The rodent disaster is one of the main biological disasters in grassland in northern Xinjiang. The eating and digging behaviors will cause the destruction of ground vegetation, which seriously affected the development of animal husbandry and grassland ecological security. UAV low altitude remote sensing, as an emerging technique with high spatial resolution, can effectively recognize the burrows. However, how to select the appropriate spatial resolution to monitor the calamity of the rodent disaster is the first problem we need to pay attention to. The purpose of this study is to explore the optimal spatial scale on identification of the burrows by evaluating the impact of different spatial resolution for the burrows identification accuracy. In this study, we shoot burrows from different flight heights to obtain visible images of different spatial resolution. Then an object-oriented method is used to identify the caves, and we also evaluate the accuracy of the classification. We found that the highest classification accuracy of holes, the average has reached more than 80 %. At the altitude of 24 m and the spatial resolution of 1cm, the accuracy of the classification is the highest We have created a unique and effective way to identify burrows by using UAVs visible images. We draw the following conclusion: the best spatial resolution of burrows recognition is 1 cm using DJI PHANTOM-3 UAV, and the improvement of spatial resolution does not necessarily lead to the improvement of classification accuracy. This study lays the foundation for future research and can be extended to similar studies elsewhere.

  4. Object-based methods for individual tree identification and tree species classification from high-spatial resolution imagery

    Science.gov (United States)

    Wang, Le

    2003-10-01

    Modern forest management poses an increasing need for detailed knowledge of forest information at different spatial scales. At the forest level, the information for tree species assemblage is desired whereas at or below the stand level, individual tree related information is preferred. Remote Sensing provides an effective tool to extract the above information at multiple spatial scales in the continuous time domain. To date, the increasing volume and readily availability of high-spatial-resolution data have lead to a much wider application of remotely sensed products. Nevertheless, to make effective use of the improving spatial resolution, conventional pixel-based classification methods are far from satisfactory. Correspondingly, developing object-based methods becomes a central challenge for researchers in the field of Remote Sensing. This thesis focuses on the development of methods for accurate individual tree identification and tree species classification. We develop a method in which individual tree crown boundaries and treetop locations are derived under a unified framework. We apply a two-stage approach with edge detection followed by marker-controlled watershed segmentation. Treetops are modeled from radiometry and geometry aspects. Specifically, treetops are assumed to be represented by local radiation maxima and to be located near the center of the tree-crown. As a result, a marker image was created from the derived treetop to guide a watershed segmentation to further differentiate overlapping trees and to produce a segmented image comprised of individual tree crowns. The image segmentation method developed achieves a promising result for a 256 x 256 CASI image. Then further effort is made to extend our methods to the multiscales which are constructed from a wavelet decomposition. A scale consistency and geometric consistency are designed to examine the gradients along the scale-space for the purpose of separating true crown boundary from unwanted

  5. Autonomous agricultural remote sensing systems with high spatial and temporal resolutions

    Science.gov (United States)

    Xiang, Haitao

    In this research, two novel agricultural remote sensing (RS) systems, a Stand-alone Infield Crop Monitor RS System (SICMRS) and an autonomous Unmanned Aerial Vehicles (UAV) based RS system have been studied. A high-resolution digital color and multi-spectral camera was used as the image sensor for the SICMRS system. An artificially intelligent (AI) controller based on artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) was developed. Morrow Plots corn field RS images in the 2004 and 2006 growing seasons were collected by the SICMRS system. The field site contained 8 subplots (9.14 m x 9.14 m) that were planted with corn and three different fertilizer treatments were used among those subplots. The raw RS images were geometrically corrected, resampled to 10cm resolution, removed soil background and calibrated to real reflectance. The RS images from two growing seasons were studied and 10 different vegetation indices were derived from each day's image. The result from the image processing demonstrated that the vegetation indices have temporal effects. To achieve high quality RS data, one has to utilize the right indices and capture the images at the right time in the growing season. Maximum variations among the image data set are within the V6-V10 stages, which indicated that these stages are the best period to identify the spatial variability caused by the nutrient stress in the corn field. The derived vegetation indices were also used to build yield prediction models via the linear regression method. At that point, all of the yield prediction models were evaluated by comparing the R2-value and the best index model from each day's image was picked based on the highest R 2-value. It was shown that the green normalized difference vegetation (GNDVI) based model is more sensitive to yield prediction than other indices-based models. During the VT-R4 stages, the GNDVI based models were able to explain more than 95% potential corn yield

  6. High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

    Science.gov (United States)

    Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

    2018-04-01

    With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

  7. A research of road centerline extraction algorithm from high resolution remote sensing images

    Science.gov (United States)

    Zhang, Yushan; Xu, Tingfa

    2017-09-01

    Satellite remote sensing technology has become one of the most effective methods for land surface monitoring in recent years, due to its advantages such as short period, large scale and rich information. Meanwhile, road extraction is an important field in the applications of high resolution remote sensing images. An intelligent and automatic road extraction algorithm with high precision has great significance for transportation, road network updating and urban planning. The fuzzy c-means (FCM) clustering segmentation algorithms have been used in road extraction, but the traditional algorithms did not consider spatial information. An improved fuzzy C-means clustering algorithm combined with spatial information (SFCM) is proposed in this paper, which is proved to be effective for noisy image segmentation. Firstly, the image is segmented using the SFCM. Secondly, the segmentation result is processed by mathematical morphology to remover the joint region. Thirdly, the road centerlines are extracted by morphology thinning and burr trimming. The average integrity of the centerline extraction algorithm is 97.98%, the average accuracy is 95.36% and the average quality is 93.59%. Experimental results show that the proposed method in this paper is effective for road centerline extraction.

  8. A study of spatial resolution in pollution exposure modelling

    Directory of Open Access Journals (Sweden)

    Gustafsson Susanna

    2007-06-01

    Full Text Available Abstract Background This study is part of several ongoing projects concerning epidemiological research into the effects on health of exposure to air pollutants in the region of Scania, southern Sweden. The aim is to investigate the optimal spatial resolution, with respect to temporal resolution, for a pollutant database of NOx-values which will be used mainly for epidemiological studies with durations of days, weeks or longer periods. The fact that a pollutant database has a fixed spatial resolution makes the choice critical for the future use of the database. Results The results from the study showed that the accuracy between the modelled concentrations of the reference grid with high spatial resolution (100 m, denoted the fine grid, and the coarser grids (200, 400, 800 and 1600 meters improved with increasing spatial resolution. When the pollutant values were aggregated in time (from hours to days and weeks the disagreement between the fine grid and the coarser grids were significantly reduced. The results also illustrate a considerable difference in optimal spatial resolution depending on the characteristic of the study area (rural or urban areas. To estimate the accuracy of the modelled values comparison were made with measured NOx values. The mean difference between the modelled and the measured value were 0.6 μg/m3 and the standard deviation 5.9 μg/m3 for the daily difference. Conclusion The choice of spatial resolution should not considerably deteriorate the accuracy of the modelled NOx values. Considering the comparison between modelled and measured values we estimate that an error due to coarse resolution greater than 1 μg/m3 is inadvisable if a time resolution of one day is used. Based on the study of different spatial resolutions we conclude that for urban areas a spatial resolution of 200–400 m is suitable; and for rural areas the spatial resolution could be coarser (about 1600 m. This implies that we should develop a pollutant

  9. Remote sensing, geographical information systems, and spatial modeling for analyzing public transit services

    Science.gov (United States)

    Wu, Changshan

    Public transit service is a promising transportation mode because of its potential to address urban sustainability. Current ridership of public transit, however, is very low in most urban regions, particularly those in the United States. This woeful transit ridership can be attributed to many factors, among which poor service quality is key. Given this, there is a need for transit planning and analysis to improve service quality. Traditionally, spatially aggregate data are utilized in transit analysis and planning. Examples include data associated with the census, zip codes, states, etc. Few studies, however, address the influences of spatially aggregate data on transit planning results. In this research, previous studies in transit planning that use spatially aggregate data are reviewed. Next, problems associated with the utilization of aggregate data, the so-called modifiable areal unit problem (MAUP), are detailed and the need for fine resolution data to support public transit planning is argued. Fine resolution data is generated using intelligent interpolation techniques with the help of remote sensing imagery. In particular, impervious surface fraction, an important socio-economic indicator, is estimated through a fully constrained linear spectral mixture model using Landsat Enhanced Thematic Mapper Plus (ETM+) data within the metropolitan area of Columbus, Ohio in the United States. Four endmembers, low albedo, high albedo, vegetation, and soil are selected to model heterogeneous urban land cover. Impervious surface fraction is estimated by analyzing low and high albedo endmembers. With the derived impervious surface fraction, three spatial interpolation methods, spatial regression, dasymetric mapping, and cokriging, are developed to interpolate detailed population density. Results suggest that cokriging applied to impervious surface is a better alternative for estimating fine resolution population density. With the derived fine resolution data, a multiple

  10. Single Photon Counting Large Format Imaging Sensors with High Spatial and Temporal Resolution

    Science.gov (United States)

    Siegmund, O. H. W.; Ertley, C.; Vallerga, J. V.; Cremer, T.; Craven, C. A.; Lyashenko, A.; Minot, M. J.

    High time resolution astronomical and remote sensing applications have been addressed with microchannel plate based imaging, photon time tagging detector sealed tube schemes. These are being realized with the advent of cross strip readout techniques with high performance encoding electronics and atomic layer deposited (ALD) microchannel plate technologies. Sealed tube devices up to 20 cm square have now been successfully implemented with sub nanosecond timing and imaging. The objective is to provide sensors with large areas (25 cm2 to 400 cm2) with spatial resolutions of 5 MHz and event timing accuracy of 100 ps. High-performance ASIC versions of these electronics are in development with better event rate, power and mass suitable for spaceflight instruments.

  11. Spatial resolution requirements for digital radiology

    International Nuclear Information System (INIS)

    Seeley, G.W.; Dallas, W.J.; Guillian, J.; Ovitt, T.; Standen, J.

    1990-01-01

    This paper describes research to define the needed spatial resolution for maintaining diagnostic accuracy in digital systems. Posteroanterior images from 30 normal and 30 abnormal studies of patients with various stages of interstitial disease were digitized at 51 p/mm with 12 bits of gray level and then processed in a computer to reduce spatial resolution from 5.0 to 2.5, 1.875, and in 1.25 Ip/mm. A Kodak laser writer using a LUT devised to ensure the copies had equal densities to those measured from the original images was used to write the images back to film. These film images were then shown to radiologists (one resolution level per radiologist). They were asked to give their diagnosis and certainty for each image (receiver operating characteristic [ROC] paradigm) and also to rate each image on overall spatial and contrast resolution as well as the visibility of seven diagnostically important structures

  12. Resolution Enhancement of Multilook Imagery

    Energy Technology Data Exchange (ETDEWEB)

    Galbraith, Amy E. [Univ. of Arizona, Tucson, AZ (United States)

    2004-07-01

    This dissertation studies the feasibility of enhancing the spatial resolution of multi-look remotely-sensed imagery using an iterative resolution enhancement algorithm known as Projection Onto Convex Sets (POCS). A multi-angle satellite image modeling tool is implemented, and simulated multi-look imagery is formed to test the resolution enhancement algorithm. Experiments are done to determine the optimal con guration and number of multi-angle low-resolution images needed for a quantitative improvement in the spatial resolution of the high-resolution estimate. The important topic of aliasing is examined in the context of the POCS resolution enhancement algorithm performance. In addition, the extension of the method to multispectral sensor images is discussed and an example is shown using multispectral confocal fluorescence imaging microscope data. Finally, the remote sensing issues of atmospheric path radiance and directional reflectance variations are explored to determine their effect on the resolution enhancement performance.

  13. Remote Sensing and Reflectance Profiling in Entomology.

    Science.gov (United States)

    Nansen, Christian; Elliott, Norman

    2016-01-01

    Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

  14. High-resolution space-time characterization of convective rain cells: implications on spatial aggregation and temporal sampling operated by coarser resolution instruments

    Science.gov (United States)

    Marra, Francesco; Morin, Efrat

    2017-04-01

    Forecasting the occurrence of flash floods and debris flows is fundamental to save lives and protect infrastructures and properties. These natural hazards are generated by high-intensity convective storms, on space-time scales that cannot be properly monitored by conventional instrumentation. Consequently, a number of early-warning systems are nowadays based on remote sensing precipitation observations, e.g. from weather radars or satellites, that proved effective in a wide range of situations. However, the uncertainty affecting rainfall estimates represents an important issue undermining the operational use of early-warning systems. The uncertainty related to remote sensing estimates results from (a) an instrumental component, intrinsic of the measurement operation, and (b) a discretization component, caused by the discretization of the continuous rainfall process. Improved understanding on these sources of uncertainty will provide crucial information to modelers and decision makers. This study aims at advancing knowledge on the (b) discretization component. To do so, we take advantage of an extremely-high resolution X-Band weather radar (60 m, 1 min) recently installed in the Eastern Mediterranean. The instrument monitors a semiarid to arid transition area also covered by an accurate C-Band weather radar and by a relatively sparse rain gauge network ( 1 gauge/ 450 km2). Radar quantitative precipitation estimation includes corrections reducing the errors due to ground echoes, orographic beam blockage and attenuation of the signal in heavy rain. Intense, convection-rich, flooding events recently occurred in the area serve as study cases. We (i) describe with very high detail the spatiotemporal characteristics of the convective cores, and (ii) quantify the uncertainty due to spatial aggregation (spatial discretization) and temporal sampling (temporal discretization) operated by coarser resolution remote sensing instruments. We show that instantaneous rain intensity

  15. Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.

    Directory of Open Access Journals (Sweden)

    Adam M Wilson

    2016-03-01

    Full Text Available Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties.

  16. Particle detector spatial resolution

    International Nuclear Information System (INIS)

    Perez-Mendez, V.

    1992-01-01

    Method and apparatus for producing separated columns of scintillation layer material, for use in detection of X-rays and high energy charged particles with improved spatial resolution is disclosed. A pattern of ridges or projections is formed on one surface of a substrate layer or in a thin polyimide layer, and the scintillation layer is grown at controlled temperature and growth rate on the ridge-containing material. The scintillation material preferentially forms cylinders or columns, separated by gaps conforming to the pattern of ridges, and these columns direct most of the light produced in the scintillation layer along individual columns for subsequent detection in a photodiode layer. The gaps may be filled with a light-absorbing material to further enhance the spatial resolution of the particle detector. 12 figs

  17. Classification of Volcanic Eruptions on Io and Earth Using Low-Resolution Remote Sensing Data

    Science.gov (United States)

    Davies, A. G.; Keszthelyi, L. P.

    2005-01-01

    Two bodies in the Solar System exhibit high-temperature active volcanism: Earth and Io. While there are important differences in the eruptions on Earth and Io, in low-spatial-resolution data (corresponding to the bulk of available and foreseeable data of Io), similar styles of effusive and explosive volcanism yield similar thermal flux densities. For example, a square metre of an active pahoehoe flow on Io looks very similar to a square metre of an active pahoehoe flow on Earth. If, from observed thermal emission as a function of wavelength and change in thermal emission with time, the eruption style of an ionian volcano can be constrained, estimates of volumetric fluxes can be made and compared with terrestrial volcanoes using techniques derived for analysing terrestrial remotely-sensed data. In this way we find that ionian volcanoes fundamentally differ from their terrestrial counterparts only in areal extent, with Io volcanoes covering larger areas, with higher volumetric flux. Io outbursts eruptions have enormous implied volumetric fluxes, and may scale with terrestrial flood basalt eruptions. Even with the low-spatial resolution data available it is possible to sometimes constrain and classify eruption style both on Io and Earth from the integrated thermal emission spectrum. Plotting 2 and 5 m fluxes reveals the evolution of individual eruptions of different styles, as well as the relative intensity of eruptions, allowing comparison to be made from individual eruptions on both planets. Analyses like this can be used for interpretation of low-resolution data until the next mission to the jovian system. For a number of Io volcanoes (including Pele, Prometheus, Amirani, Zamama, Culann, Tohil and Tvashtar) we do have high/moderate resolution imagery to aid determination of eruption mode from analyses based only on low spatial-resolution data.

  18. A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

    Science.gov (United States)

    Zhang, Ce; Pan, Xin; Li, Huapeng; Gardiner, Andy; Sargent, Isabel; Hare, Jonathon; Atkinson, Peter M.

    2018-06-01

    The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

  19. Monitoring of Antarctic moss ecosystems using a high spatial resolution imaging spectroscopy

    Science.gov (United States)

    Malenovsky, Zbynek; Lucieer, Arko; Robinson, Sharon; Harwin, Stephen; Turner, Darren; Veness, Tony

    2013-04-01

    The most abundant photosynthetically active plants growing along the rocky Antarctic shore are mosses of three species: Schistidium antarctici, Ceratodon purpureus, and Bryum pseudotriquetrum. Even though mosses are well adapted to the extreme climate conditions, their existence in Antarctica depends strongly on availability of liquid water from snowmelt during the short summer season. Recent changes in temperature, wind speed and stratospheric ozone are stimulating faster evaporation, which in turn influences moss growing rate, health state and abundance. This makes them an ideal bio-indicator of the Antarctic climate change. Very short growing season, lasting only about three months, requires a time efficient, easily deployable and spatially resolved method for monitoring the Antarctic moss beds. Ground and/or low-altitude airborne imaging spectroscopy (called also hyperspectral remote sensing) offers a fast and spatially explicit approach to investigate an actual spatial extent and physiological state of moss turfs. A dataset of ground-based spectral images was acquired with a mini-Hyperspec imaging spectrometer (Headwall Inc., the USA) during the Antarctic summer 2012 in the surroundings of the Australian Antarctic station Casey (Windmill Islands). The collection of high spatial resolution spectral images, with pixels about 2 cm in size containing from 162 up to 324 narrow spectral bands of wavelengths between 399 and 998 nm, was accompanied with point moss reflectance measurements recorded with the ASD HandHeld-2 spectroradiometer (Analytical Spectral Devices Inc., the USA). The first spectral analysis indicates significant differences in red-edge and near-infrared reflectance of differently watered moss patches. Contrary to high plants, where the Normalized Difference Vegetation Index (NDVI) represents an estimate of green biomass, NDVI of mosses indicates mainly the actual water content. Similarly to high plants, reflectance of visible wavelengths is

  20. Impaired temporal, not just spatial, resolution in amblyopia.

    Science.gov (United States)

    Spang, Karoline; Fahle, Manfred

    2009-11-01

    In amblyopia, neuronal deficits deteriorate spatial vision including visual acuity, possibly because of a lack of use-dependent fine-tuning of afferents to the visual cortex during infancy; but temporal processing may deteriorate as well. Temporal, rather than spatial, resolution was investigated in patients with amblyopia by means of a task based on time-defined figure-ground segregation. Patients had to indicate the quadrant of the visual field where a purely time-defined square appeared. The results showed a clear decrease in temporal resolution of patients' amblyopic eyes compared with the dominant eyes in this task. The extent of this decrease in figure-ground segregation based on time of motion onset only loosely correlated with the decrease in spatial resolution and spanned a smaller range than did the spatial loss. Control experiments with artificially induced blur in normal observers confirmed that the decrease in temporal resolution was not simply due to the acuity loss. Amblyopia not only decreases spatial resolution, but also temporal factors such as time-based figure-ground segregation, even at high stimulus contrasts. This finding suggests that the realm of neuronal processes that may be disturbed in amblyopia is larger than originally thought.

  1. Optical remote sensing

    CERN Document Server

    Prasad, Saurabh; Chanussot, Jocelyn

    2011-01-01

    Optical remote sensing relies on exploiting multispectral and hyper spectral imagery possessing high spatial and spectral resolutions respectively. These modalities, although useful for most remote sensing tasks, often present challenges that must be addressed for their effective exploitation. This book presents current state-of-the-art algorithms that address the following key challenges encountered in representation and analysis of such optical remotely sensed data: challenges in pre-processing images, storing and representing high dimensional data, fusing different sensor modalities, patter

  2. Remote sensing and modeling. A tool to provide the spatial information for biomass production potential

    Energy Technology Data Exchange (ETDEWEB)

    Guenther, K.P.; Wisskirchen, K.; Schroedter-Homscheidt, M. [DLR, Wessling (Germany). German Remote Sensing Data Center; Borg, E.; Fichtelmann, B. [DLR, Neustrelitz (Germany). German Remote Sensing Data Center

    2006-07-01

    Earth observation from space has been successfully demonstrated over a wide range of monitoring activities, mostly with the aim of measuring the spatial and temporal distribution of biophysical and geophysical parameters as e.g. the Normalized Difference Vegetation Index (NDVI), the land surface temperature (LST) or the land use classification (LCC). With the growing need for more reliable information of global biomass activity in the frame of climate change, the identification and quantification of carbon sinks and sources got of importance. The goal of our activities is to use time series of remote sensing data and carbon modeling to assess the biomass of large regions. Future activities will be discussed as reprocessing of archived time series (e.g. 30 years) of remote sensing data, which will be used as input to biomass modeling, improving the spatial resolution of local, historic land use maps by processing archived Landsat data (30m), using an innovative classification processor for deriving actual multi-temporal land use maps based MERIS data (300m) and delivering a biomass equivalent indicator as productivity indicator. (orig.)

  3. Optimization of Decision-Making for Spatial Sampling in the North China Plain, Based on Remote-Sensing a Priori Knowledge

    Science.gov (United States)

    Feng, J.; Bai, L.; Liu, S.; Su, X.; Hu, H.

    2012-07-01

    In this paper, the MODIS remote sensing data, featured with low-cost, high-timely and moderate/low spatial resolutions, in the North China Plain (NCP) as a study region were firstly used to carry out mixed-pixel spectral decomposition to extract an useful regionalized indicator parameter (RIP) (i.e., an available ratio, that is, fraction/percentage, of winter wheat planting area in each pixel as a regionalized indicator variable (RIV) of spatial sampling) from the initial selected indicators. Then, the RIV values were spatially analyzed, and the spatial structure characteristics (i.e., spatial correlation and variation) of the NCP were achieved, which were further processed to obtain the scalefitting, valid a priori knowledge or information of spatial sampling. Subsequently, founded upon an idea of rationally integrating probability-based and model-based sampling techniques and effectively utilizing the obtained a priori knowledge or information, the spatial sampling models and design schemes and their optimization and optimal selection were developed, as is a scientific basis of improving and optimizing the existing spatial sampling schemes of large-scale cropland remote sensing monitoring. Additionally, by the adaptive analysis and decision strategy the optimal local spatial prediction and gridded system of extrapolation results were able to excellently implement an adaptive report pattern of spatial sampling in accordance with report-covering units in order to satisfy the actual needs of sampling surveys.

  4. The impact of spatial resolution on resolving spatial precipitation patterns in the Himalayas

    OpenAIRE

    Bonekamp, P.N.J.; Collier, S.E.; Immerzeel, W.W.

    2017-01-01

    Frequently used gridded meteorological datasets poorly represent precipitation in the Himalaya due to their relatively low spatial resolution and the associated coarse representation of the complex topography. Dynamical downscaling using high-resolution atmospheric models may improve the accuracy and quality of the precipitation fields, as simulations at higher spatial resolution are more capable of resolving the interaction between the topography and the atmosphere. However, most physics par...

  5. Semantic risk estimation of suspected minefields based on spatial relationships analysis of minefield indicators from multi-level remote sensing imagery

    Science.gov (United States)

    Chan, Jonathan Cheung-Wai; Sahli, Hichem; Wang, Yuhang

    2005-06-01

    This paper presents semantic risk estimation of suspected minefields using spatial relationships of minefield indicators extracted from multi-level remote sensing. Both satellite image and pyramidal airborne acquisitions from 900m to 30m flying heights with resolutions from 1m to 2cm resolutions are used for identification of minefield indicators. R-Histogram [1] is a quantitative representation of spatial relationship between two objects in an image. Eight spatial relationships can be generated: 1) LEFT OF, 2) RIGHT OF, 3) ABOVE, 4) BELOW, 5) NEAR, 6) FAR, 7) INSIDE, 8) OUTSIDE. R-Histogram semantics are first generated from selected indicators and metrics such as topological proximity and directional relationships are trained for soft classification of risk index (normalized as 0-1). We presented a framework of how semantic metadata generated from remote sensing images are used in risk estimation. The resultant risk index identified seven out of twelve mine accidents occurred at high risk region. More importantly, comparison with ground truth obtained after mine clearance show that three out of the four identified pattern minefields falls into the area estimated at very high risk. A parcel-based per-field risk estimation can also be easily generated to show the usefulness of the risk index.

  6. Added-values of high spatiotemporal remote sensing data in crop yield estimation

    Science.gov (United States)

    Gao, F.; Anderson, M. C.

    2017-12-01

    Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing derived parameters have been used for estimating crop yield by using either empirical or crop growth models. The uses of remote sensing vegetation index (VI) in crop yield modeling have been typically evaluated at regional and country scales using coarse spatial resolution (a few hundred to kilo-meters) data or assessed over a small region at field level using moderate resolution spatial resolution data (10-100m). Both data sources have shown great potential in capturing spatial and temporal variability in crop yield. However, the added value of data with both high spatial and temporal resolution data has not been evaluated due to the lack of such data source with routine, global coverage. In recent years, more moderate resolution data have become freely available and data fusion approaches that combine data acquired from different spatial and temporal resolutions have been developed. These make the monitoring crop condition and estimating crop yield at field scale become possible. Here we investigate the added value of the high spatial and temporal VI for describing variability of crop yield. The explanatory ability of crop yield based on high spatial and temporal resolution remote sensing data was evaluated in a rain-fed agricultural area in the U.S. Corn Belt. Results show that the fused Landsat-MODIS (high spatial and temporal) VI explains yield variability better than single data source (Landsat or MODIS alone), with EVI2 performing slightly better than NDVI. The maximum VI describes yield variability better than cumulative VI. Even though VI is effective in explaining yield variability within season, the inter-annual variability is more complex and need additional information (e.g. weather, water use and management). Our findings augment the importance of high spatiotemporal remote sensing data and supports new moderate

  7. Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification

    Directory of Open Access Journals (Sweden)

    Xueke Li

    2016-05-01

    Full Text Available The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1 opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, China, by comparing the TG-1 (with a spatial resolution of 10 m to EO-1 Hyperion (with a spatial resolution of 30 m. The spectral feature of TG-1 was first analyzed and, thus, finding out optimal hyperspectral wavebands useful for the discrimination of urban areas. Based on this, the pixel-based maximum likelihood classifier (PMLC, pixel-based support vector machine (PSVM, hybrid maximum likelihood classifier (HMLC, and hybrid support vector machine (HSVM were implemented, as well as compared in the application of mapping urban land cover types. The hybrid classifier approach, which integrates the pixel-based classifier and the object-based segmentation approach, was demonstrated as an effective alternative to the conventional pixel-based classifiers for processing the satellite hyperspectral data, especially the fine spatial resolution data. For TG-1 imagery, the pixel-based urban classification was obtained with an average overall accuracy of 89.1%, whereas the hybrid urban classification was obtained with an average overall accuracy of 91.8%. For Hyperion imagery, the pixel-based urban classification was obtained with an average overall accuracy of 85.9%, whereas the hybrid urban classification was obtained with an average overall accuracy of 86.7%. Overall, it can be concluded that the fine spatial resolution satellite hyperspectral data TG-1 is promising in delineating complex urban scenes, especially when using an appropriate classifier, such as the

  8. Spatial scales of pollution from variable resolution satellite imaging

    International Nuclear Information System (INIS)

    Chudnovsky, Alexandra A.; Kostinski, Alex; Lyapustin, Alexei; Koutrakis, Petros

    2013-01-01

    The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not adequate for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM 2.5 as measured by the EPA ground monitoring stations was investigated at varying spatial scales. Our analysis suggested that the correlation between PM 2.5 and AOD decreased significantly as AOD resolution was degraded. This is so despite the intrinsic mismatch between PM 2.5 ground level measurements and AOD vertically integrated measurements. Furthermore, the fine resolution results indicated spatial variability in particle concentration at a sub-10 km scale. Finally, this spatial variability of AOD within the urban domain was shown to depend on PM 2.5 levels and wind speed. - Highlights: ► The correlation between PM 2.5 and AOD decreases as AOD resolution is degraded. ► High resolution MAIAC AOD 1 km retrieval can be used to investigate within-city PM 2.5 variability. ► Low pollution days exhibit higher spatial variability of AOD and PM 2.5 then moderate pollution days. ► AOD spatial variability within urban area is higher during the lower wind speed conditions. - The correlation between PM 2.5 and AOD decreases as AOD resolution is degraded. The new high-resolution MAIAC AOD retrieval has the potential to capture PM 2.5 variability at the intra-urban scale.

  9. Agro-hydrology and multi temporal high resolution remote sensing: toward an explicit spatial processes calibration

    Science.gov (United States)

    Ferrant, S.; Gascoin, S.; Veloso, A.; Salmon-Monviola, J.; Claverie, M.; Rivalland, V.; Dedieu, G.; Demarez, V.; Ceschia, E.; Probst, J.-L.; Durand, P.; Bustillo, V.

    2014-07-01

    The recent and forthcoming availability of high resolution satellite image series offers new opportunities in agro-hydrological research and modeling. We investigated the perspective offered by improving the crop growth dynamic simulation using the distributed agro-hydrological model, Topography based Nitrogen transfer and Transformation (TNT2), using LAI map series derived from 105 Formosat-2 (F2) images during the period 2006-2010. The TNT2 model (Beaujouan et al., 2002), calibrated with discharge and in-stream nitrate fluxes for the period 1985-2001, was tested on the 2006-2010 dataset (climate, land use, agricultural practices, discharge and nitrate fluxes at the outlet). A priori agricultural practices obtained from an extensive field survey such as seeding date, crop cultivar, and fertilizer amount were used as input variables. Continuous values of LAI as a function of cumulative daily temperature were obtained at the crop field level by fitting a double logistic equation against discrete satellite-derived LAI. Model predictions of LAI dynamics with a priori input parameters showed an temporal shift with observed LAI profiles irregularly distributed in space (between field crops) and time (between years). By re-setting seeding date at the crop field level, we proposed an optimization method to minimize efficiently this temporal shift and better fit the crop growth against the spatial observations as well as crop production. This optimization of simulated LAI has a negligible impact on water budget at the catchment scale (1 mm yr-1 in average) but a noticeable impact on in-stream nitrogen fluxes (around 12%) which is of interest considering nitrate stream contamination issues and TNT2 model objectives. This study demonstrates the contribution of forthcoming high spatial and temporal resolution products of Sentinel-2 satellite mission in improving agro-hydrological modeling by constraining the spatial representation of crop productivity.

  10. Effective spatial database support for acquiring spatial information from remote sensing images

    Science.gov (United States)

    Jin, Peiquan; Wan, Shouhong; Yue, Lihua

    2009-12-01

    In this paper, a new approach to maintain spatial information acquiring from remote-sensing images is presented, which is based on Object-Relational DBMS. According to this approach, the detected and recognized results of targets are stored and able to be further accessed in an ORDBMS-based spatial database system, and users can access the spatial information using the standard SQL interface. This approach is different from the traditional ArcSDE-based method, because the spatial information management module is totally integrated into the DBMS and becomes one of the core modules in the DBMS. We focus on three issues, namely the general framework for the ORDBMS-based spatial database system, the definitions of the add-in spatial data types and operators, and the process to develop a spatial Datablade on Informix. The results show that the ORDBMS-based spatial database support for image-based target detecting and recognition is easy and practical to be implemented.

  11. Application of High Resolution Air-Borne Remote Sensing Observations for Monitoring NOx Emissions

    Science.gov (United States)

    Souri, A.; Choi, Y.; Pan, S.; Curci, G.; Janz, S. J.; Kowalewski, M. G.; Liu, J.; Herman, J. R.; Weinheimer, A. J.

    2017-12-01

    Nitrogen oxides (NOx=NO+NO2) are one of the air pollutants, responsible for the formation of tropospheric ozone, acid rain and particulate nitrate. The anthropogenic NOx emissions are commonly estimated based on bottom-up inventories which are complicated by many potential sources of error. One way to improve the emission inventories is to use relevant observations to constrain them. Fortunately, Nitrogen dioxide (NO2) is one of the most successful detected species from remote sensing. Although many studies have shown the capability of using space-borne remote sensing observations for monitoring emissions, the insufficient sample number and footprint of current measurements have introduced a burden to constrain emissions at fine scales. Promisingly, there are several air-borne sensors collected for NASA's campaigns providing high spatial resolution of NO2 columns. Here, we use the well-characterized NO2 columns from the Airborne Compact Atmospheric Mapper (ACAM) onboard NASA's B200 aircraft into a 1×1 km regional model to constrain anthropogenic NOx emissions in the Houston-Galveston-Brazoria area. Firstly, in order to incorporate the data, we convert the NO2 slant column densities to vertical ones using a joint of a radiative transfer model and the 1x1 km regional model constrained by P3-B aircraft measurements. After conducting an inverse modeling method using the Kalman filter, we find the ACAM observations are resourceful at mitigating the overprediction of model in reproducing NO2 on regular days. Moreover, the ACAM provides a unique opportunity to detect an anomaly in emissions leading to strong air quality degradation that is lacking in previous works. Our study provides convincing evidence that future geostationary satellites with high spatial and temporal resolutions will give us insights into uncertainties associated with the emissions at regional scales.

  12. SPMK AND GRABCUT BASED TARGET EXTRACTION FROM HIGH RESOLUTION REMOTE SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    W. Cui

    2016-06-01

    Full Text Available Target detection and extraction from high resolution remote sensing images is a basic and wide needed application. In this paper, to improve the efficiency of image interpretation, we propose a detection and segmentation combined method to realize semi-automatic target extraction. We introduce the dense transform color scale invariant feature transform (TC-SIFT descriptor and the histogram of oriented gradients (HOG & HSV descriptor to characterize the spatial structure and color information of the targets. With the k-means cluster method, we get the bag of visual words, and then, we adopt three levels’ spatial pyramid (SP to represent the target patch. After gathering lots of different kinds of target image patches from many high resolution UAV images, and using the TC-SIFT-SP and the multi-scale HOG & HSV feature, we constructed the SVM classifier to detect the target. In this paper, we take buildings as the targets. Experiment results show that the target detection accuracy of buildings can reach to above 90%. Based on the detection results which are a series of rectangle regions of the targets. We select the rectangle regions as candidates for foreground and adopt the GrabCut based and boundary regularized semi-auto interactive segmentation algorithm to get the accurate boundary of the target. Experiment results show its accuracy and efficiency. It can be an effective way for some special targets extraction.

  13. Spmk and Grabcut Based Target Extraction from High Resolution Remote Sensing Images

    Science.gov (United States)

    Cui, Weihong; Wang, Guofeng; Feng, Chenyi; Zheng, Yiwei; Li, Jonathan; Zhang, Yi

    2016-06-01

    Target detection and extraction from high resolution remote sensing images is a basic and wide needed application. In this paper, to improve the efficiency of image interpretation, we propose a detection and segmentation combined method to realize semi-automatic target extraction. We introduce the dense transform color scale invariant feature transform (TC-SIFT) descriptor and the histogram of oriented gradients (HOG) & HSV descriptor to characterize the spatial structure and color information of the targets. With the k-means cluster method, we get the bag of visual words, and then, we adopt three levels' spatial pyramid (SP) to represent the target patch. After gathering lots of different kinds of target image patches from many high resolution UAV images, and using the TC-SIFT-SP and the multi-scale HOG & HSV feature, we constructed the SVM classifier to detect the target. In this paper, we take buildings as the targets. Experiment results show that the target detection accuracy of buildings can reach to above 90%. Based on the detection results which are a series of rectangle regions of the targets. We select the rectangle regions as candidates for foreground and adopt the GrabCut based and boundary regularized semi-auto interactive segmentation algorithm to get the accurate boundary of the target. Experiment results show its accuracy and efficiency. It can be an effective way for some special targets extraction.

  14. APPLICATION OF CONVOLUTIONAL NEURAL NETWORK IN CLASSIFICATION OF HIGH RESOLUTION AGRICULTURAL REMOTE SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    C. Yao

    2017-09-01

    Full Text Available With the rapid development of Precision Agriculture (PA promoted by high-resolution remote sensing, it makes significant sense in management and estimation of agriculture through crop classification of high-resolution remote sensing image. Due to the complex and fragmentation of the features and the surroundings in the circumstance of high-resolution, the accuracy of the traditional classification methods has not been able to meet the standard of agricultural problems. In this case, this paper proposed a classification method for high-resolution agricultural remote sensing images based on convolution neural networks(CNN. For training, a large number of training samples were produced by panchromatic images of GF-1 high-resolution satellite of China. In the experiment, through training and testing on the CNN under the toolbox of deep learning by MATLAB, the crop classification finally got the correct rate of 99.66 % after the gradual optimization of adjusting parameter during training. Through improving the accuracy of image classification and image recognition, the applications of CNN provide a reference value for the field of remote sensing in PA.

  15. Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2002-07

    Science.gov (United States)

    Pearson, D.K.; Gary, R.H.; Wilson, Z.D.

    2007-01-01

    Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is particularly useful when analyzing a wide variety of spatial data such as with remote sensing and spatial analysis. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This document presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup from 2002 through 2007.

  16. Spatial resolution in visual memory.

    Science.gov (United States)

    Ben-Shalom, Asaf; Ganel, Tzvi

    2015-04-01

    Representations in visual short-term memory are considered to contain relatively elaborated information on object structure. Conversely, representations in earlier stages of the visual hierarchy are thought to be dominated by a sensory-based, feed-forward buildup of information. In four experiments, we compared the spatial resolution of different object properties between two points in time along the processing hierarchy in visual short-term memory. Subjects were asked either to estimate the distance between objects or to estimate the size of one of the objects' features under two experimental conditions, of either a short or a long delay period between the presentation of the target stimulus and the probe. When different objects were referred to, similar spatial resolution was found for the two delay periods, suggesting that initial processing stages are sensitive to object-based properties. Conversely, superior resolution was found for the short, as compared with the long, delay when features were referred to. These findings suggest that initial representations in visual memory are hybrid in that they allow fine-grained resolution for object features alongside normal visual sensitivity to the segregation between objects. The findings are also discussed in reference to the distinction made in earlier studies between visual short-term memory and iconic memory.

  17. New device based on the super spatial resolution (SSR) method

    International Nuclear Information System (INIS)

    Soluri, A.; Atzeni, G.; Ucci, A.; Bellone, T.; Cusanno, F.; Rodilossi, G.; Massari, R.

    2013-01-01

    Recently it have been described that innovative methods, namely Super Spatial Resolution (SSR), can be used to improve the scintigraphic imaging. The aim of SSR techniques is the enhancement of the resolution of an imaging system, using information from several images. In this paper we describe a new experimental apparatus that could be used for molecular imaging and small animal imaging. In fact we present a new device, completely automated, that uses the SSR method and provides images with better spatial resolution in comparison to the original resolution. Preliminary small animal imaging studies confirm the feasibility of a very high resolution system in scintigraphic imaging and the possibility to have gamma cameras using the SSR method, to perform the applications on functional imaging. -- Highlights: • Super spatial resolution brings a high resolution image from scintigraphic images. • Resolution improvement depends on the signal to noise ratio of the original images. • The SSR shows significant improvement on spatial resolution in scintigraphic images. • The SSR method is potentially utilizable for all scintigraphic devices

  18. A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery.

    Science.gov (United States)

    Meng, Fan; Yang, Xiaomei; Zhou, Chenghu; Li, Zhi

    2017-09-15

    Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation. To maintain the coherence of structures, structure sparsity was brought in to encourage first filling-in of missing patches on image structures. The optimization model of patch inpainting was formulated under the adaptive neighborhood-consistency constraint, which was solved by a modified orthogonal matching pursuit (OMP) algorithm. In light of these ideas, the thick-cloud removal scheme was designed and applied to images with simulated and true clouds. Comparisons and experiments show that our method can not only keep structures and textures consistent with the surrounding ground information, but also yield rare smoothing effect and block effect, which is more suitable for the removal of clouds from high-spatial resolution RS imagery with salient structures and abundant textured features.

  19. High Resolution Satellite Remote Sensing of the 2013-2014 Eruption of Sinabung Volcano, Sumatra, Indonesia

    Science.gov (United States)

    Wessels, R. L.; Griswold, J. P.

    2014-12-01

    Satellite remote sensing provided timely observations of the volcanic unrest and several months-long eruption at Sinabung Volcano, Indonesia. Visible to thermal optical and synthetic aperture radar (SAR) systems provided frequent observations of Sinabung. High resolution image data with spatial resolutions from 0.5 to 1.5m offered detailed measurements of early summit deformation and subsequent lava dome and lava flow extrusion. The high resolution data were captured by commercial satellites such as WorldView-1 and -2 visible to near-infrared (VNIR) sensors and by CosmoSkyMed, Radarsat-2, and TerraSar-X SAR systems. Less frequent 90 to 100m spatial resolution night time thermal infrared (TIR) observations were provided by ASTER and Landsat-8. The combination of data from multiple sensors allowed us to construct a more complete timeline of volcanic activity than was available via only ground-based observations. This satellite observation timeline documents estimates of lava volume and effusion rates and major explosive and lava collapse events. Frequent, repeat volume estimates suggest at least three high effusion rate pulses of up to 20 m3/s occurred during the first three months of lava effusion with an average effusion rate of 6m3/s from January 2014 to August 2014. Many of these rates and events show some correlation to variations in the Real-time Seismic-Amplitude Measurement (RSAM) documented by the Indonesian Center for Volcanology and Geologic Hazard Mitigation (CVGHM).

  20. Detector Motion Method to Increase Spatial Resolution in Photon-Counting Detectors

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Daehee; Park, Kyeongjin; Lim, Kyung Taek; Cho, Gyuseong [Korea Advanced Institute of Science and Technology, Daejon (Korea, Republic of)

    2017-03-15

    Medical imaging requires high spatial resolution of an image to identify fine lesions. Photoncounting detectors in medical imaging have recently been rapidly replacing energy-integrating detectors due to the former's high spatial resolution, high efficiency and low noise. Spatial resolution in a photon counting image is determined by the pixel size. Therefore, the smaller the pixel size, the higher the spatial resolution that can be obtained in an image. However, detector redesigning is required to reduce pixel size, and an expensive fine process is required to integrate a signal processing unit with reduced pixel size. Furthermore, as the pixel size decreases, charge sharing severely deteriorates spatial resolution. To increase spatial resolution, we propose a detector motion method using a large pixel detector that is less affected by charge sharing. To verify the proposed method, we utilized a UNO-XRI photon-counting detector (1-mm CdTe, Timepix chip) at the maximum X-ray tube voltage of 80 kVp. A similar spatial resolution of a 55-μm-pixel image was achieved by application of the proposed method to a 110-μm-pixel detector with a higher signal-to-noise ratio. The proposed method could be a way to increase spatial resolution without a pixel redesign when pixels severely suffer from charge sharing as pixel size is reduced.

  1. Monitoring crop leaf area index time variation from higher resolution remotely sensed data

    International Nuclear Information System (INIS)

    Jiao, Sihong

    2014-01-01

    The leaf area index (LAI) is significant for research on global climate change and ecological environment. China HJ-1 satellite has a revisit cycle of four days, providing CCD data (HJ-1 CCD) with a resolution of 30 m. However, the HJ-1 CCD is incapable of obtaining observations at multiple angles. This is problematic because single angle observations provide insufficient data for determining the LAI. This article proposes a new method for determining LAI using HJ-1 CCD data. The proposed method uses background knowledge of dynamic land surface processes that are extracted from MODerate resolution Imaging Spectroradiometer (MODIS) LAI 1-km resolution data. To process the uncertainties that arise from using two data sources with different spatial resolutions, the proposed method is implemented in a dynamitic Bayesian network scheme by integrating a LAI dynamic process model and a canopy reflectance model with remotely sensed data. Validation results showed that the determination coefficient between estimated and measured LAI was 0.791, and the RMSE was 0.61. This method can enhance the accuracy of the retrieval results while retaining the time series variation characteristics of the vegetation LAI. The results suggest that this algorithm can be widely applied to determining high-resolution leaf area indices using data from China HJ-1 satellite even if information from single angle observations are insufficient for quantitative application

  2. Spatial scales of pollution from variable resolution satellite imaging.

    Science.gov (United States)

    Chudnovsky, Alexandra A; Kostinski, Alex; Lyapustin, Alexei; Koutrakis, Petros

    2013-01-01

    The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not adequate for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM(2.5) as measured by the EPA ground monitoring stations was investigated at varying spatial scales. Our analysis suggested that the correlation between PM(2.5) and AOD decreased significantly as AOD resolution was degraded. This is so despite the intrinsic mismatch between PM(2.5) ground level measurements and AOD vertically integrated measurements. Furthermore, the fine resolution results indicated spatial variability in particle concentration at a sub-10 km scale. Finally, this spatial variability of AOD within the urban domain was shown to depend on PM(2.5) levels and wind speed. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. GIS and remote sensing components of a spatial decision system in support of sustainable forest management

    Energy Technology Data Exchange (ETDEWEB)

    St-Onge, B.A.; Waaub, J.P.; Alleaume, S.; Couture, M. [Quebec Univ., Dept. of Geography, Montreal, PQ (Canada)

    1999-05-01

    The development of a tool used in decision making processes in forest management was discussed with special focus on two critical aspects of the decision aid system which pertain to spatial information. These are: (1) the use of up-to-date precise information, and (2) the accessibility of this information. This paper focuses on two approaches designed to provide accessible high quality information to the users of the decision aid system: i.e. the development of high resolution remote sensing methods, and a GIS data warehouse accessible over the Internet. 4 refs., 2 figs.

  4. Fusion of shallow and deep features for classification of high-resolution remote sensing images

    Science.gov (United States)

    Gao, Lang; Tian, Tian; Sun, Xiao; Li, Hang

    2018-02-01

    Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

  5. Using High Resolution Commercial Satellite Imagery to Quantify Spatial Features of Urban Areas and their Relationship to Quality of Life Indicators in Accra, Ghana

    Science.gov (United States)

    Sandborn, A.; Engstrom, R.; Yu, Q.

    2014-12-01

    Mapping urban areas via satellite imagery is an important task for detecting and anticipating land cover and land use change at multiple scales. As developing countries experience substantial urban growth and expansion, remotely sensed based estimates of population and quality of life indicators can provide timely and spatially explicit information to researchers and planners working to determine how cities are changing. In this study, we use commercial high spatial resolution satellite imagery in combination with fine resolution census data to determine the ability of using remotely sensed data to reveal the spatial patterns of quality of life in Accra, Ghana. Traditionally, spectral characteristics are used on a per-pixel basis to determine land cover; however, in this study, we test a new methodology that quantifies spatial characteristics using a variety of spatial features observed in the imagery to determine the properties of an urban area. The spatial characteristics used in this study include histograms of oriented gradients, PanTex, Fourier transform, and line support regions. These spatial features focus on extracting structural and textural patterns of built-up areas, such as homogeneous building orientations and straight line indices. Information derived from aggregating the descriptive statistics of the spatial features at both the fine-resolution census unit and the larger neighborhood level are then compared to census derived quality of life indicators including information about housing, education, and population estimates. Preliminary results indicate that there are correlations between straight line indices and census data including available electricity and literacy rates. Results from this study will be used to determine if this methodology provides a new and improved way to measure a city structure in developing cities and differentiate between residential and commercial land use zones, as well as formal versus informal housing areas.

  6. Agro-hydrology and multi-temporal high-resolution remote sensing: toward an explicit spatial processes calibration

    Science.gov (United States)

    Ferrant, S.; Gascoin, S.; Veloso, A.; Salmon-Monviola, J.; Claverie, M.; Rivalland, V.; Dedieu, G.; Demarez, V.; Ceschia, E.; Probst, J.-L.; Durand, P.; Bustillo, V.

    2014-12-01

    The growing availability of high-resolution satellite image series offers new opportunities in agro-hydrological research and modeling. We investigated the possibilities offered for improving crop-growth dynamic simulation with the distributed agro-hydrological model: topography-based nitrogen transfer and transformation (TNT2). We used a leaf area index (LAI) map series derived from 105 Formosat-2 (F2) images covering the period 2006-2010. The TNT2 model (Beaujouan et al., 2002), calibrated against discharge and in-stream nitrate fluxes for the period 1985-2001, was tested on the 2005-2010 data set (climate, land use, agricultural practices, and discharge and nitrate fluxes at the outlet). Data from the first year (2005) were used to initialize the hydrological model. A priori agricultural practices obtained from an extensive field survey, such as seeding date, crop cultivar, and amount of fertilizer, were used as input variables. Continuous values of LAI as a function of cumulative daily temperature were obtained at the crop-field level by fitting a double logistic equation against discrete satellite-derived LAI. Model predictions of LAI dynamics using the a priori input parameters displayed temporal shifts from those observed LAI profiles that are irregularly distributed in space (between field crops) and time (between years). By resetting the seeding date at the crop-field level, we have developed an optimization method designed to efficiently minimize this temporal shift and better fit the crop growth against both the spatial observations and crop production. This optimization of simulated LAI has a negligible impact on water budgets at the catchment scale (1 mm yr-1 on average) but a noticeable impact on in-stream nitrogen fluxes (around 12%), which is of interest when considering nitrate stream contamination issues and the objectives of TNT2 modeling. This study demonstrates the potential contribution of the forthcoming high spatial and temporal resolution

  7. Towards understanding temporal and spatial dynamics of seagrass landscapes using time-series remote sensing

    Science.gov (United States)

    Lyons, Mitchell B.; Roelfsema, Chris M.; Phinn, Stuart R.

    2013-03-01

    The spatial and temporal dynamics of seagrasses have been well studied at the leaf to patch scales, however, the link to large spatial extent landscape and population dynamics is still unresolved in seagrass ecology. Traditional remote sensing approaches have lacked the temporal resolution and consistency to appropriately address this issue. This study uses two high temporal resolution time-series of thematic seagrass cover maps to examine the spatial and temporal dynamics of seagrass at both an inter- and intra-annual time scales, one of the first globally to do so at this scale. Previous work by the authors developed an object-based approach to map seagrass cover level distribution from a long term archive of Landsat TM and ETM+ images on the Eastern Banks (≈200 km2), Moreton Bay, Australia. In this work a range of trend and time-series analysis methods are demonstrated for a time-series of 23 annual maps from 1988 to 2010 and a time-series of 16 monthly maps during 2008-2010. Significant new insight was presented regarding the inter- and intra-annual dynamics of seagrass persistence over time, seagrass cover level variability, seagrass cover level trajectory, and change in area of seagrass and cover levels over time. Overall we found that there was no significant decline in total seagrass area on the Eastern Banks, but there was a significant decline in seagrass cover level condition. A case study of two smaller communities within the Eastern Banks that experienced a decline in both overall seagrass area and condition are examined in detail, highlighting possible differences in environmental and process drivers. We demonstrate how trend and time-series analysis enabled seagrass distribution to be appropriately assessed in context of its spatial and temporal history and provides the ability to not only quantify change, but also describe the type of change. We also demonstrate the potential use of time-series analysis products to investigate seagrass growth and

  8. Study of the spatial resolution for binary readout detectors

    Energy Technology Data Exchange (ETDEWEB)

    Yonamine, R., E-mail: ryo.yonamine@ulb.ac.be; Maerschalk, T.; Lentdecker, G. De

    2016-07-11

    Often the binary readout is proposed for high granularity detectors to reduce the generated data volume to be readout at the price of a somewhat reduced spatial resolution compared to an analogue readout. We have been studying single hit resolutions obtained with a binary readout using simulations as well as analytical approaches. In this note we show that the detector geometry could be optimized to offer an equivalent spatial resolution than with an analogue readout.

  9. Thematic and spatial resolutions affect model-based predictions of tree species distribution.

    Science.gov (United States)

    Liang, Yu; He, Hong S; Fraser, Jacob S; Wu, ZhiWei

    2013-01-01

    Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution.

  10. MULTI-SCALE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES BY INTEGRATING MULTIPLE FEATURES

    Directory of Open Access Journals (Sweden)

    Y. Di

    2017-05-01

    Full Text Available Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA on the accuracy and slightly inferior to FNEA on the efficiency.

  11. Optimization of the resolution of remotely sensed digital elevation model to facilitate the simulation and spatial propagation of flood events in flat areas

    Science.gov (United States)

    Karapetsas, Nikolaos; Skoulikaris, Charalampos; Katsogiannos, Fotis; Zalidis, George; Alexandridis, Thomas

    2013-04-01

    The use of satellite remote sensing products, such as Digital Elevation Models (DEMs), under specific computational interfaces of Geographic Information Systems (GIS) has fostered and facilitated the acquisition of data on specific hydrologic features, such as slope, flow direction and flow accumulation, which are crucial inputs to hydrology or hydraulic models at the river basin scale. However, even though DEMs of different resolution varying from a few km up to 20m are freely available for the European continent, these remotely sensed elevation data are rather coarse in cases where large flat areas are dominant inside a watershed, resulting in an unsatisfactory representation of the terrain characteristics. This scientific work aims at implementing a combing interpolation technique for the amelioration of the analysis of a DEM in order to be used as the input ground model to a hydraulic model for the assessment of potential flood events propagation in plains. More specifically, the second version of the ASTER Global Digital Elevation Model (GDEM2), which has an overall accuracy of around 20 meters, was interpolated with a vast number of aerial control points available from the Hellenic Mapping and Cadastral Organization (HMCO). The uncertainty that was inherent in both the available datasets (ASTER & HMCO) and the appearance of uncorrelated errors and artifacts was minimized by incorporating geostatistical filtering. The resolution of the produced DEM was approximately 10 meters and its validation was conducted with the use of an external dataset of 220 geodetic survey points. The derived DEM was then used as an input to the hydraulic model InfoWorks RS, whose operation is based on the relief characteristics contained in the ground model, for defining, in an automated way, the cross section parameters and simulating the flood spatial distribution. The plain of Serres, which is located in the downstream part of the Struma/Strymon transboundary river basin shared

  12. The spatial resolution of silicon-based electron detectors in beta-autoradiography.

    Science.gov (United States)

    Cabello, Jorge; Wells, Kevin

    2010-03-21

    Thin tissue autoradiography is an imaging modality where ex-vivo tissue sections are placed in direct contact with autoradiographic film. These tissue sections contain a radiolabelled ligand bound to a specific biomolecule under study. This radioligand emits beta - or beta+ particles ionizing silver halide crystals in the film. High spatial resolution autoradiograms are obtained using low energy radioisotopes, such as (3)H where an intrinsic 0.1-1 microm spatial resolution can be achieved. Several digital alternatives have been presented over the past few years to replace conventional film but their spatial resolution has yet to equal film, although silicon-based imaging technologies have demonstrated higher sensitivity compared to conventional film. It will be shown in this work how pixel size is a critical parameter for achieving high spatial resolution for low energy uncollimated beta imaging. In this work we also examine the confounding factors impeding silicon-based technologies with respect to spatial resolution. The study considers charge diffusion in silicon and detector noise, and this is applied to a range of radioisotopes typically used in autoradiography. Finally an optimal detector geometry to obtain the best possible spatial resolution for a specific technology and a specific radioisotope is suggested.

  13. Spatial pattern evaluation of a calibrated national hydrological model - a remote-sensing-based diagnostic approach

    Science.gov (United States)

    Mendiguren, Gorka; Koch, Julian; Stisen, Simon

    2017-11-01

    Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds

  14. Rural and remote dental services shortages: filling the gaps through geo-spatial analysis evidence-based targeting.

    Science.gov (United States)

    Shiika, Yulia; Kruger, Estie; Tennant, Marc

    Australia has a significant mal-distribution of its limited dental workforce. Outside the major capital cities, the distribution of accessible dental care is at best patchy. This study applied geo-spatial analysis technology to locate gaps in dental service accessibility for rural and remote dwelling Australians, in order to test the hypothesis that there are a few key location points in Australia where further dental services could make a significant contribution to ameliorating the immediate shortage crisis. A total of 2,086 dental practices were located in country areas, covering a combined catchment area of 1.84 million square kilometers, based on 50 km catchment zones around each clinic. Geo-spatial analysis technology was used to identify gaps in the accessibility of dental services for rural and remote dwelling Australians. An extraction of data was obtained to analyse the integrated geographically-aligned database. Results: Resolution of the lack of dental practices for 74 townships (of greater than 500 residents) across Australia could potentially address access for 104,000 people. An examination of the socio-economic mix found that the majority of the dental practices (84%) are located in areas classified as less disadvantaged. Output from the study provided a cohesive national map that has identified locations that could have health improvement via the targeting of dental services to that location. The study identified potential location sites for dental clinics, to address the current inequity in accessing dental services in rural and remote Australia.

  15. Spatial Co-Registration of Ultra-High Resolution Visible, Multispectral and Thermal Images Acquired with a Micro-UAV over Antarctic Moss Beds

    Directory of Open Access Journals (Sweden)

    Darren Turner

    2014-05-01

    Full Text Available In recent times, the use of Unmanned Aerial Vehicles (UAVs as tools for environmental remote sensing has become more commonplace. Compared to traditional airborne remote sensing, UAVs can provide finer spatial resolution data (up to 1 cm/pixel and higher temporal resolution data. For the purposes of vegetation monitoring, the use of multiple sensors such as near infrared and thermal infrared cameras are of benefit. Collecting data with multiple sensors, however, requires an accurate spatial co-registration of the various UAV image datasets. In this study, we used an Oktokopter UAV to investigate the physiological state of Antarctic moss ecosystems using three sensors: (i a visible camera (1 cm/pixel, (ii a 6 band multispectral camera (3 cm/pixel, and (iii a thermal infrared camera (10 cm/pixel. Imagery from each sensor was geo-referenced and mosaicked with a combination of commercially available software and our own algorithms based on the Scale Invariant Feature Transform (SIFT. The validation of the mosaic’s spatial co-registration revealed a mean root mean squared error (RMSE of 1.78 pixels. A thematic map of moss health, derived from the multispectral mosaic using a Modified Triangular Vegetation Index (MTVI2, and an indicative map of moss surface temperature were then combined to demonstrate sufficient accuracy of our co-registration methodology for UAV-based monitoring of Antarctic moss beds.

  16. Comparison of alternative spatial resolutions in the application of a spatially distributed biogeochemical model over complex terrain

    Science.gov (United States)

    Turner, D.P.; Dodson, R.; Marks, D.

    1996-01-01

    Spatially distributed biogeochemical models may be applied over grids at a range of spatial resolutions, however, evaluation of potential errors and loss of information at relatively coarse resolutions is rare. In this study, a georeferenced database at the 1-km spatial resolution was developed to initialize and drive a process-based model (Forest-BGC) of water and carbon balance over a gridded 54976 km2 area covering two river basins in mountainous western Oregon. Corresponding data sets were also prepared at 10-km and 50-km spatial resolutions using commonly employed aggregation schemes. Estimates were made at each grid cell for climate variables including daily solar radiation, air temperature, humidity, and precipitation. The topographic structure, water holding capacity, vegetation type and leaf area index were likewise estimated for initial conditions. The daily time series for the climatic drivers was developed from interpolations of meteorological station data for the water year 1990 (1 October 1989-30 September 1990). Model outputs at the 1-km resolution showed good agreement with observed patterns in runoff and productivity. The ranges for model inputs at the 10-km and 50-km resolutions tended to contract because of the smoothed topography. Estimates for mean evapotranspiration and runoff were relatively insensitive to changing the spatial resolution of the grid whereas estimates of mean annual net primary production varied by 11%. The designation of a vegetation type and leaf area at the 50-km resolution often subsumed significant heterogeneity in vegetation, and this factor accounted for much of the difference in the mean values for the carbon flux variables. Although area wide means for model outputs were generally similar across resolutions, difference maps often revealed large areas of disagreement. Relatively high spatial resolution analyses of biogeochemical cycling are desirable from several perspectives and may be particularly important in the

  17. High-Resolution Remotely Sensed Small Target Detection by Imitating Fly Visual Perception Mechanism

    Directory of Open Access Journals (Sweden)

    Fengchen Huang

    2012-01-01

    Full Text Available The difficulty and limitation of small target detection methods for high-resolution remote sensing data have been a recent research hot spot. Inspired by the information capture and processing theory of fly visual system, this paper endeavors to construct a characterized model of information perception and make use of the advantages of fast and accurate small target detection under complex varied nature environment. The proposed model forms a theoretical basis of small target detection for high-resolution remote sensing data. After the comparison of prevailing simulation mechanism behind fly visual systems, we propose a fly-imitated visual system method of information processing for high-resolution remote sensing data. A small target detector and corresponding detection algorithm are designed by simulating the mechanism of information acquisition, compression, and fusion of fly visual system and the function of pool cell and the character of nonlinear self-adaption. Experiments verify the feasibility and rationality of the proposed small target detection model and fly-imitated visual perception method.

  18. High-resolution remotely sensed small target detection by imitating fly visual perception mechanism.

    Science.gov (United States)

    Huang, Fengchen; Xu, Lizhong; Li, Min; Tang, Min

    2012-01-01

    The difficulty and limitation of small target detection methods for high-resolution remote sensing data have been a recent research hot spot. Inspired by the information capture and processing theory of fly visual system, this paper endeavors to construct a characterized model of information perception and make use of the advantages of fast and accurate small target detection under complex varied nature environment. The proposed model forms a theoretical basis of small target detection for high-resolution remote sensing data. After the comparison of prevailing simulation mechanism behind fly visual systems, we propose a fly-imitated visual system method of information processing for high-resolution remote sensing data. A small target detector and corresponding detection algorithm are designed by simulating the mechanism of information acquisition, compression, and fusion of fly visual system and the function of pool cell and the character of nonlinear self-adaption. Experiments verify the feasibility and rationality of the proposed small target detection model and fly-imitated visual perception method.

  19. Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Chong Fan

    2017-02-01

    Full Text Available To solve the problem on inaccuracy when estimating the point spread function (PSF of the ideal original image in traditional projection onto convex set (POCS super-resolution (SR reconstruction, this paper presents an improved POCS SR algorithm based on PSF estimation of low-resolution (LR remote sensing images. The proposed algorithm can improve the spatial resolution of the image and benefit agricultural crop visual interpolation. The PSF of the highresolution (HR image is unknown in reality. Therefore, analysis of the relationship between the PSF of the HR image and the PSF of the LR image is important to estimate the PSF of the HR image by using multiple LR images. In this study, the linear relationship between the PSFs of the HR and LR images can be proven. In addition, the novel slant knife-edge method is employed, which can improve the accuracy of the PSF estimation of LR images. Finally, the proposed method is applied to reconstruct airborne digital sensor 40 (ADS40 three-line array images and the overlapped areas of two adjacent GF-2 images by embedding the estimated PSF of the HR image to the original POCS SR algorithm. Experimental results show that the proposed method yields higher quality of reconstructed images than that produced by the blind SR method and the bicubic interpolation method.

  20. Analysing and Correcting the Differences between Multi-Source and Multi-Scale Spatial Remote Sensing Observations

    Science.gov (United States)

    Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

    2014-01-01

    Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding

  1. Remote classification from an airborne camera using image super-resolution.

    Science.gov (United States)

    Woods, Matthew; Katsaggelos, Aggelos

    2017-02-01

    The image processing technique known as super-resolution (SR), which attempts to increase the effective pixel sampling density of a digital imager, has gained rapid popularity over the last decade. The majority of literature focuses on its ability to provide results that are visually pleasing to a human observer. In this paper, we instead examine the ability of SR to improve the resolution-critical capability of an imaging system to perform a classification task from a remote location, specifically from an airborne camera. In order to focus the scope of the study, we address and quantify results for the narrow case of text classification. However, we expect the results generalize to a large set of related, remote classification tasks. We generate theoretical results through simulation, which are corroborated by experiments with a camera mounted on a DJI Phantom 3 quadcopter.

  2. The impact of spatial resolution on resolving spatial precipitation patterns in the Himalayas

    NARCIS (Netherlands)

    Bonekamp, P.N.J.; Collier, S.E.; Immerzeel, W.W.

    2017-01-01

    Frequently used gridded meteorological datasets poorly represent precipitation in the Himalaya due to their relatively low spatial resolution and the associated coarse representation of the complex topography. Dynamical downscaling using high-resolution atmospheric models may improve the accuracy

  3. Neutron resonance transmission spectroscopy with high spatial and energy resolution at the J-PARC pulsed neutron source

    Energy Technology Data Exchange (ETDEWEB)

    Tremsin, A.S., E-mail: ast@ssl.berkeley.edu [University of California at Berkeley, 7 Gauss Way, Berkeley, CA 94720 (United States); Shinohara, T.; Kai, T.; Ooi, M. [Japan Atomic Energy Agency, 2–4 Shirakata-shirane, Tokai-mura, Naka-gun, Ibaraki 319-1195 (Japan); Kamiyama, T.; Kiyanagi, Y.; Shiota, Y. [Hokkaido University, Kita 13 Nishi 8 Kita-ku, Sapporo-shi, Hokkaido 060-8628 (Japan); McPhate, J.B.; Vallerga, J.V.; Siegmund, O.H.W. [University of California at Berkeley, 7 Gauss Way, Berkeley, CA 94720 (United States); Feller, W.B. [NOVA Scientific, Inc., 10 Picker Rd., Sturbridge, MA 01566 (United States)

    2014-05-11

    The sharp variation of neutron attenuation at certain energies specific to particular nuclides (the lower range being from ∼1 eV up to ∼1 keV), can be exploited for the remote mapping of element and/or isotope distributions, as well as temperature probing, within relatively thick samples. Intense pulsed neutron beam-lines at spallation sources combined with a high spatial, high-timing resolution neutron counting detector, provide a unique opportunity to measure neutron transmission spectra through the time-of-flight technique. We present the results of experiments where spatially resolved neutron resonances were measured, at energies up to 50 keV. These experiments were performed with the intense flux low background NOBORU neutron beamline at the J-PARC neutron source and the high timing resolution (∼20 ns at epithermal neutron energies) and spatial resolution (∼55 µm) neutron counting detector using microchannel plates coupled to a Timepix electronic readout. Simultaneous element-specific imaging was carried out for several materials, at a spatial resolution of ∼150 µm. The high timing resolution of our detector combined with the low background beamline, also enabled characterization of the neutron pulse itself – specifically its pulse width, which varies with neutron energy. The results of our measurements are in good agreement with the predicted results for the double pulse structure of the J-PARC facility, which provides two 100 ns-wide proton pulses separated by 600 ns, broadened by the neutron energy moderation process. Thermal neutron radiography can be conducted simultaneously with resonance transmission spectroscopy, and can reveal the internal structure of the samples. The transmission spectra measured in our experiments demonstrate the feasibility of mapping elemental distributions using this non-destructive technique, for those elements (and in certain cases, specific isotopes), which have resonance energies below a few keV, and with lower

  4. Study of spatial resolution in three-dimensional rotational angiography

    International Nuclear Information System (INIS)

    Enoki, Takuya; Nasada, Toshiya; Matsumoto, Kazuma; Umehara, Takayoshi

    2006-01-01

    In interventional radiology (IVR) of cerebral aneurysms, it is important to understand the form and physical relationships between the cerebral aneurysm and the surrounding vessels. However, because the vessels in the head area are highly complex, it can be difficult to comprehend the structure using conventional angiography. Therefore, three-dimensional rotational angiography (3D-RA) has been used in recent years. This article discusses studies of the spatial resolution of 3D-RA. We reconstructed 3D-RA of an acrylic slit phantom (slit widths: 0.5, 0.75, 1.0, 1.5 mm) and examined spatial resolution by visual evaluation and profile curves. When the slit phantom was arranged to avoid the effect of beam hardening, the spatial resolution of 3D-RA was found to be as high as 0.75 mm. When the slit phantom was placed orthogonal to the rotational axis of the C-arm, the spatial resolution of 3D-RA was decreased because of the cone angle effect of X-rays. However, it was considered within the allowable range for clinical study. Consequently, 3D-RA is valuable in IVR. (author)

  5. High Data Rate Satellite Communications for Environmental Remote Sensing

    Science.gov (United States)

    Jackson, J. M.; Munger, J.; Emch, P. G.; Sen, B.; Gu, D.

    2014-12-01

    Satellite to ground communication bandwidth limitations place constraints on current earth remote sensing instruments which limit the spatial and spectral resolution of data transmitted to the ground for processing. Instruments such as VIIRS, CrIS and OMPS on the Soumi-NPP spacecraft must aggregate data both spatially and spectrally in order to fit inside current data rate constraints limiting the optimal use of the as-built sensors. Future planned missions such as HyspIRI, SLI, PACE, and NISAR will have to trade spatial and spectral resolution if increased communication band width is not made available. A number of high-impact, environmental remote sensing disciplines such as hurricane observation, mega-city air quality, wild fire detection and monitoring, and monitoring of coastal oceans would benefit dramatically from enabling the downlinking of sensor data at higher spatial and spectral resolutions. The enabling technologies of multi-Gbps Ka-Band communication, flexible high speed on-board processing, and multi-Terabit SSRs are currently available with high technological maturity enabling high data volume mission requirements to be met with minimal mission constraints while utilizing a limited set of ground sites from NASA's Near Earth Network (NEN) or TDRSS. These enabling technologies will be described in detail with emphasis on benefits to future remote sensing missions currently under consideration by government agencies.

  6. Remote spatial memory in aging: all is not lost

    Directory of Open Access Journals (Sweden)

    R Shayna eRosenbaum

    2012-09-01

    Full Text Available The ability to acquire and retain spatial memories in order to navigate in new environments is known to decline with age, but little is known about the effect of aging on representations of environments learned long ago, in the remote past. To investigate the status of remote spatial memory in old age, we tested healthy young and older adults on a variety of mental navigation tests based on a large-scale city environment that was very familiar to participants but rarely visited by the older adults in recent years. We show that whereas performance on a route learning test of new spatial learning was significantly worse in older than younger adults, performance was comparable or better in the older adults on mental navigation tests based on a well-known environment learned long ago. An exception was in the older adults’ ability to vividly re-experience the well-known environment, and recognize and represent the visual details contained within it. The results are seen as analogous to the pattern of better semantic than episodic memory that has been found to accompany healthy aging.

  7. A Very High Spatial Resolution Detector for Small Animal PET

    International Nuclear Information System (INIS)

    Kanai Shah, M.S.

    2007-01-01

    Positron Emission Tomography (PET) is an in vivo analog of autoradiography and has the potential to become a powerful new tool in imaging biological processes in small laboratory animals. PET imaging of small animals can provide unique information that can help in advancement of human disease models as well as drug development. Clinical PET scanners used for human imaging are bulky, expensive and do not have adequate spatial resolution for small animal studies. Hence, dedicated, low cost instruments are required for conducting small animal studies with higher spatial resolution than what is currently achieved with clinical as well as dedicated small animal PET scanners. The goal of the proposed project is to investigate a new all solid-state detector design for small animal PET imaging. Exceptionally high spatial resolution, good timing resolution, and excellent energy resolution are expected from the proposed detector design. The Phase I project was aimed at demonstrating the feasibility of producing high performance solid-state detectors that provide high sensitivity, spatial resolution, and timing characteristics. Energy resolution characteristics of the new detector were also investigated. The goal of the Phase II project is to advance the promising solid-state detector technology for small animal PET and determine its full potential. Detectors modules will be built and characterized and finally, a bench-top small animal PET system will be assembled and evaluated

  8. Accelerator-based single-shot ultrafast transmission electron microscope with picosecond temporal resolution and nanometer spatial resolution

    Science.gov (United States)

    Xiang, D.; Fu, F.; Zhang, J.; Huang, X.; Wang, L.; Wang, X.; Wan, W.

    2014-09-01

    We present feasibility study of an accelerator-based ultrafast transmission electron microscope (u-TEM) capable of producing a full field image in a single-shot with simultaneous picosecond temporal resolution and nanometer spatial resolution. We study key physics related to performance of u-TEMs and discuss major challenges as well as possible solutions for practical realization of u-TEMs. The feasibility of u-TEMs is confirmed through simulations using realistic electron beam parameters. We anticipate that u-TEMs with a product of temporal and spatial resolution beyond 10-19 ms will open up new opportunities in probing matter at ultrafast temporal and ultrasmall spatial scales.

  9. CEST ANALYSIS: AUTOMATED CHANGE DETECTION FROM VERY-HIGH-RESOLUTION REMOTE SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    M. Ehlers

    2012-08-01

    Full Text Available A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST of the change algorithms is applied to calculate the probability of change for a particular location. CEST

  10. Spatial resolution enhancement of satellite image data using fusion approach

    Science.gov (United States)

    Lestiana, H.; Sukristiyanti

    2018-02-01

    Object identification using remote sensing data has a problem when the spatial resolution is not in accordance with the object. The fusion approach is one of methods to solve the problem, to improve the object recognition and to increase the objects information by combining data from multiple sensors. The application of fusion image can be used to estimate the environmental component that is needed to monitor in multiple views, such as evapotranspiration estimation, 3D ground-based characterisation, smart city application, urban environments, terrestrial mapping, and water vegetation. Based on fusion application method, the visible object in land area has been easily recognized using the method. The variety of object information in land area has increased the variation of environmental component estimation. The difficulties in recognizing the invisible object like Submarine Groundwater Discharge (SGD), especially in tropical area, might be decreased by the fusion method. The less variation of the object in the sea surface temperature is a challenge to be solved.

  11. Remote Sensing of Spatial Distributions of Greenhouse Gases in the Los Angeles Basin

    Science.gov (United States)

    Fu, Dejian; Sander, Stanley P.; Pongetti, Thomas J.; Cheung, Ross; Stutz, Jochen

    2010-01-01

    The Los Angeles air basin is a significant anthropogenic source of greenhouse gasses and pollutants including CO2, CH4, N2O, and CO, contributing significantly to regional and global climate change. Recent legislation in California, the California Global Warning Solutions Act (AB32), established a statewide cap for greenhouse gas emissions for 2020 based on 1990 emissions. Verifying the effectiveness of regional greenhouse gas emissions controls requires high-precision, regional-scale measurement methods combined with models that capture the principal anthropogenic and biogenic sources and sinks. We present a novel approach for monitoring the spatial distribution of greenhouse gases in the Los Angeles basin using high resolution remote sensing spectroscopy. We participated in the CalNex 2010 campaign to provide greenhouse gas distributions for comparison between top-down and bottom-up emission estimates.

  12. Emotional cues enhance the attentional effects on spatial and temporal resolution.

    Science.gov (United States)

    Bocanegra, Bruno R; Zeelenberg, René

    2011-12-01

    In the present study, we demonstrated that the emotional significance of a spatial cue enhances the effect of covert attention on spatial and temporal resolution (i.e., our ability to discriminate small spatial details and fast temporal flicker). Our results indicated that fearful face cues, as compared with neutral face cues, enhanced the attentional benefits in spatial resolution but also enhanced the attentional deficits in temporal resolution. Furthermore, we observed that the overall magnitudes of individuals' attentional effects correlated strongly with the magnitude of the emotion × attention interaction effect. Combined, these findings provide strong support for the idea that emotion enhances the strength of a cue's attentional response.

  13. Chromatic and Achromatic Spatial Resolution of Local Field Potentials in Awake Cortex.

    Science.gov (United States)

    Jansen, Michael; Li, Xiaobing; Lashgari, Reza; Kremkow, Jens; Bereshpolova, Yulia; Swadlow, Harvey A; Zaidi, Qasim; Alonso, Jose-Manuel

    2015-10-01

    Local field potentials (LFPs) have become an important measure of neuronal population activity in the brain and could provide robust signals to guide the implant of visual cortical prosthesis in the future. However, it remains unclear whether LFPs can detect weak cortical responses (e.g., cortical responses to equiluminant color) and whether they have enough visual spatial resolution to distinguish different chromatic and achromatic stimulus patterns. By recording from awake behaving macaques in primary visual cortex, here we demonstrate that LFPs respond robustly to pure chromatic stimuli and exhibit ∼2.5 times lower spatial resolution for chromatic than achromatic stimulus patterns, a value that resembles the ratio of achromatic/chromatic resolution measured with psychophysical experiments in humans. We also show that, although the spatial resolution of LFP decays with visual eccentricity as is also the case for single neurons, LFPs have higher spatial resolution and show weaker response suppression to low spatial frequencies than spiking multiunit activity. These results indicate that LFP recordings are an excellent approach to measure spatial resolution from local populations of neurons in visual cortex including those responsive to color. © The Author 2014. Published by Oxford University Press.

  14. High resolution remote sensing for reducing uncertainties in urban forest carbon offset life cycle assessments.

    Science.gov (United States)

    Tigges, Jan; Lakes, Tobia

    2017-10-04

    Urban forests reduce greenhouse gas emissions by storing and sequestering considerable amounts of carbon. However, few studies have considered the local scale of urban forests to effectively evaluate their potential long-term carbon offset. The lack of precise, consistent and up-to-date forest details is challenging for long-term prognoses. Therefore, this review aims to identify uncertainties in urban forest carbon offset assessment and discuss the extent to which such uncertainties can be reduced by recent progress in high resolution remote sensing. We do this by performing an extensive literature review and a case study combining remote sensing and life cycle assessment of urban forest carbon offset in Berlin, Germany. Recent progress in high resolution remote sensing and methods is adequate for delivering more precise details on the urban tree canopy, individual tree metrics, species, and age structures compared to conventional land use/cover class approaches. These area-wide consistent details can update life cycle inventories for more precise future prognoses. Additional improvements in classification accuracy can be achieved by a higher number of features derived from remote sensing data of increasing resolution, but first studies on this subject indicated that a smart selection of features already provides sufficient data that avoids redundancies and enables more efficient data processing. Our case study from Berlin could use remotely sensed individual tree species as consistent inventory of a life cycle assessment. However, a lack of growth, mortality and planting data forced us to make assumptions, therefore creating uncertainty in the long-term prognoses. Regarding temporal changes and reliable long-term estimates, more attention is required to detect changes of gradual growth, pruning and abrupt changes in tree planting and mortality. As such, precise long-term urban ecological monitoring using high resolution remote sensing should be intensified

  15. Charge-coupled devices for particle detection with high spatial resolution

    International Nuclear Information System (INIS)

    Farley, F.J.; Damerell, C.J.S.; Gillman, A.R.; Wickens, F.J.

    1980-10-01

    The results of a study of the possible application of a thin microelectronic device (the charge-coupled device) to high energy physics as particle detectors with good spatial resolution which can distinguish between tracks emerging from the primary vertex and those from secondary vertices due to the decay of short lived particles with higher flavours, are reported. Performance characteristics indicating the spatial resolution, particle discrimination, time resolution, readout time and lifetime of such detectors have been obtained. (U.K.)

  16. Extending remote sensing estimates of Greenland ice sheet melting

    Science.gov (United States)

    Heavner, M.; Loveland, R.

    2010-12-01

    The Melt Area Detection Index (MADI), a remote sensing algorithm to discriminate between dry and wet snow, has been previously developed and applied to the western portion of the Greenland ice sheet for the years 2000-2006, using Moderate Resolution Imaging Radiospectrometer (MODIS) data (Chylek et al, 2007). We extend that work both spatially and temporally by taking advantage of newly available data, and developing algorithms that facilitate the sensing of cloud cover and the automated inference of wet snow regions. The automated methods allow the development of a composite melt area data product with 0.25 km^2 spatial resolution and approximately two week temporal resolution. We discuss melt area dynamics that are inferred from this high resolution composite melt area. Chylek, P., M. McCabe, M. K. Dubey, and J. Dozier (2007), Remote sensing of Greenland ice sheet using multispectral near-infrared and visible radiances, J. Geophys. Res., 112, D24S20, doi:10.1029/2007JD008742.

  17. Investigation of spatial resolution characteristics of an in vivo microcomputed tomography system

    Energy Technology Data Exchange (ETDEWEB)

    Ghani, Muhammad U. [Center for Biomedical engineering and School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019 (United States); Zhou, Zhongxing [Center for Biomedical engineering and School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019 (United States); School of Precision and Optoelectronics Engineering, Tianjin University, Tianjin 300072 (China); Ren, Liqiang; Wong, Molly; Li, Yuhua; Zheng, Bin [Center for Biomedical engineering and School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019 (United States); Yang, Kai [Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114 (United States); Liu, Hong, E-mail: liu@ou.edu [Center for Biomedical engineering and School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019 (United States)

    2016-01-21

    The spatial resolution characteristics of an in vivo microcomputed tomography (CT) system was investigated in the in-plane (x–y), cross plane (z) and projection imaging modes. The microCT system utilized in this study employs a flat panel detector with a 127 µm pixel pitch, a microfocus x-ray tube with a focal spot size ranging from 5–30 µm, and accommodates three geometric magnifications (M) of 1.72, 2.54 and 5.10. The in-plane modulation transfer function (MTF) curves were measured as a function of the number of projections, geometric magnification (M), detector binning and reconstruction magnification (M{sub Recon}). The in plane cutoff frequency (10% MTF) ranged from 2.31 lp/mm (M=1.72, 2×2 binning) to 12.56 lp/mm (M=5.10, 1×1 binning) and a bar pattern phantom validated those measurements. A slight degradation in the spatial resolution was observed when comparing the image reconstruction with 511 and 918 projections, whose effect was visible at the lower frequencies. Small value of M{sub Recon} has little or no impact on the in-plane spatial resolution owning to a stable system. Large value of M{sub Recon} has implications on the spatial resolution and it was evident when comparing the bar pattern images reconstructed with M{sub Recon}=1.25 and 2.5. The cross plane MTF curves showed that the spatial resolution increased as the slice thickness decreased. The cutoff frequencies in the projection imaging mode yielded slightly higher values as compared to the in-plane and cross plane modes at all the geometric magnifications (M). At M=5.10, the cutoff resolution of the projection and cross plane on an ultra-high contrast resolution bar chip phantom were 14.9 lp/mm and 13–13.5 lp/mm. Due to the finite focal spot size of the x-ray tube, the detector blur and the reconstruction kernel functions, the system's spatial resolution does not reach the limiting spatial resolution as defined by the Nyquist's detector criteria with an ideal point source

  18. The edge-preservation multi-classifier relearning framework for the classification of high-resolution remotely sensed imagery

    Science.gov (United States)

    Han, Xiaopeng; Huang, Xin; Li, Jiayi; Li, Yansheng; Yang, Michael Ying; Gong, Jianya

    2018-04-01

    In recent years, the availability of high-resolution imagery has enabled more detailed observation of the Earth. However, it is imperative to simultaneously achieve accurate interpretation and preserve the spatial details for the classification of such high-resolution data. To this aim, we propose the edge-preservation multi-classifier relearning framework (EMRF). This multi-classifier framework is made up of support vector machine (SVM), random forest (RF), and sparse multinomial logistic regression via variable splitting and augmented Lagrangian (LORSAL) classifiers, considering their complementary characteristics. To better characterize complex scenes of remote sensing images, relearning based on landscape metrics is proposed, which iteratively quantizes both the landscape composition and spatial configuration by the use of the initial classification results. In addition, a novel tri-training strategy is proposed to solve the over-smoothing effect of relearning by means of automatic selection of training samples with low classification certainties, which always distribute in or near the edge areas. Finally, EMRF flexibly combines the strengths of relearning and tri-training via the classification certainties calculated by the probabilistic output of the respective classifiers. It should be noted that, in order to achieve an unbiased evaluation, we assessed the classification accuracy of the proposed framework using both edge and non-edge test samples. The experimental results obtained with four multispectral high-resolution images confirm the efficacy of the proposed framework, in terms of both edge and non-edge accuracy.

  19. Examining fire-induced forest changes using novel remote sensing technique: a case study in a mixed pine-oak forest

    Science.gov (United States)

    Meng, R.; Wu, J.; Zhao, F. R.; Cook, B.; Hanavan, R. P.; Serbin, S.

    2017-12-01

    Fire-induced forest changes has long been a central focus for forest ecology and global carbon cycling studies, and is becoming a pressing issue for global change biologists particularly with the projected increases in the frequency and intensity of fire with a warmer and drier climate. Compared with time-consuming and labor intensive field-based approaches, remote sensing offers a promising way to efficiently assess fire effects and monitor post-fire forest responses across a range of spatial and temporal scales. However, traditional remote sensing studies relying on simple optical spectral indices or coarse resolution imagery still face a number of technical challenges, including confusion or contamination of the signal by understory dynamics and mixed pixels with moderate to coarse resolution data (>= 30 m). As such, traditional remote sensing may not meet the increasing demand for more ecologically-meaningful monitoring and quantitation of fire-induced forest changes. Here we examined the use of novel remote sensing technique (i.e. airborne imaging spectroscopy and LiDAR measurement, very high spatial resolution (VHR) space-borne multi-spectral measurement, and high temporal-spatial resolution UAS-based (Unmanned Aerial System) imagery), in combination with field and phenocam measurements to map forest burn severity across spatial scales, quantify crown-scale post-fire forest recovery rate, and track fire-induced phenology changes in the burned areas. We focused on a mixed pine-oak forest undergoing multiple fire disturbances for the past several years in Long Island, NY as a case study. We demonstrate that (1) forest burn severity mapping from VHR remote sensing measurement can capture crown-scale heterogeneous fire patterns over large-scale; (2) the combination of VHR optical and structural measurements provides an efficient means to remotely sense species-level post-fire forest responses; (3) the UAS-based remote sensing enables monitoring of fire

  20. Mapping Entomological Dengue Risk Levels in Martinique Using High-Resolution Remote-Sensing Environmental Data

    Directory of Open Access Journals (Sweden)

    Vanessa Machault

    2014-12-01

    Full Text Available Controlling dengue virus transmission mainly involves integrated vector management. Risk maps at appropriate scales can provide valuable information for assessing entomological risk levels. Here, results from a spatio-temporal model of dwellings potentially harboring Aedes aegypti larvae from 2009 to 2011 in Tartane (Martinique, French Antilles using high spatial resolution remote-sensing environmental data and field entomological and meteorological information are presented. This tele-epidemiology methodology allows monitoring the dynamics of diseases closely related to weather/climate and environment variability. A Geoeye-1 image was processed to extract landscape elements that could surrogate societal or biological information related to the life cycle of Aedes vectors. These elements were subsequently included into statistical models with random effect. Various environmental and meteorological conditions have indeed been identified as risk/protective factors for the presence of Aedes aegypti immature stages in dwellings at a given date. These conditions were used to produce dynamic high spatio-temporal resolution maps from the presence of most containers harboring larvae. The produced risk maps are examples of modeled entomological maps at the housing level with daily temporal resolution. This finding is an important contribution to the development of targeted operational control systems for dengue and other vector-borne diseases, such as chikungunya, which is also present in Martinique.

  1. Ultra high spatial and temporal resolution breast imaging at 7T.

    Science.gov (United States)

    van de Bank, B L; Voogt, I J; Italiaander, M; Stehouwer, B L; Boer, V O; Luijten, P R; Klomp, D W J

    2013-04-01

    There is a need to obtain higher specificity in the detection of breast lesions using MRI. To address this need, Dynamic Contrast-Enhanced (DCE) MRI has been combined with other structural and functional MRI techniques. Unfortunately, owing to time constraints structural images at ultra-high spatial resolution can generally not be obtained during contrast uptake, whereas the relatively low spatial resolution of functional imaging (e.g. diffusion and perfusion) limits the detection of small lesions. To be able to increase spatial as well as temporal resolution simultaneously, the sensitivity of MR detection needs to increase as well as the ability to effectively accelerate the acquisition. The required gain in signal-to-noise ratio (SNR) can be obtained at 7T, whereas acceleration can be obtained with high-density receiver coil arrays. In this case, morphological imaging can be merged with DCE-MRI, and other functional techniques can be obtained at higher spatial resolution, and with less distortion [e.g. Diffusion Weighted Imaging (DWI)]. To test the feasibility of this concept, we developed a unilateral breast coil for 7T. It comprises a volume optimized dual-channel transmit coil combined with a 30-channel receive array coil. The high density of small coil elements enabled efficient acceleration in any direction to acquire ultra high spatial resolution MRI of close to 0.6 mm isotropic detail within a temporal resolution of 69 s, high spatial resolution MRI of 1.5 mm isotropic within an ultra high temporal resolution of 6.7 s and low distortion DWI at 7T, all validated in phantoms, healthy volunteers and a patient with a lesion in the right breast classified as Breast Imaging Reporting and Data System (BI-RADS) IV. Copyright © 2012 John Wiley & Sons, Ltd.

  2. Remote sensing of the Canadian Arctic: Modelling biophysical variables

    Science.gov (United States)

    Liu, Nanfeng

    It is anticipated that Arctic vegetation will respond in a variety of ways to altered temperature and precipitation patterns expected with climate change, including changes in phenology, productivity, biomass, cover and net ecosystem exchange. Remote sensing provides data and data processing methodologies for monitoring and assessing Arctic vegetation over large areas. The goal of this research was to explore the potential of hyperspectral and high spatial resolution multispectral remote sensing data for modelling two important Arctic biophysical variables: Percent Vegetation Cover (PVC) and the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). A series of field experiments were conducted to collect PVC and fAPAR at three Canadian Arctic sites: (1) Sabine Peninsula, Melville Island, NU; (2) Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU; and (3) Apex River Watershed (ARW), Baffin Island, NU. Linear relationships between biophysical variables and Vegetation Indices (VIs) were examined at different spatial scales using field spectra (for the Sabine Peninsula site) and high spatial resolution satellite data (for the CBAWO and ARW sites). At the Sabine Peninsula site, hyperspectral VIs exhibited a better performance for modelling PVC than multispectral VIs due to their capacity for sampling fine spectral features. The optimal hyperspectral bands were located at important spectral features observed in Arctic vegetation spectra, including leaf pigment absorption in the red wavelengths and at the red-edge, leaf water absorption in the near infrared, and leaf cellulose and lignin absorption in the shortwave infrared. At the CBAWO and ARW sites, field PVC and fAPAR exhibited strong correlations (R2 > 0.70) with the NDVI (Normalized Difference Vegetation Index) derived from high-resolution WorldView-2 data. Similarly, high spatial resolution satellite-derived fAPAR was correlated to MODIS fAPAR (R2 = 0.68), with a systematic

  3. Remote sensing and GIS integration: Towards intelligent imagery within a spatial data infrastructure

    Science.gov (United States)

    Abdelrahim, Mohamed Mahmoud Hosny

    2001-11-01

    times as much as the IKONOS GEOCARTERRA(TM) products. The developed IISP is a step closer towards the direct and active involvement of high-resolution remote sensing imagery in querying the real world and performing exploratory types of spatial analysis. (Abstract shortened by UMI.)

  4. Trade-off between angular and spatial resolutions in in vivo fiber tractography

    OpenAIRE

    Vos, Sjoerd B.; Aksoy, Murat; Han, Zhaoying; Holdsworth, Samantha J.; Maclaren, Julian; Viergever, Max A.; Leemans, Alexander; Bammer, Roland

    2016-01-01

    Tractography is becoming an increasingly popular method to reconstruct white matter connections in vivo. The diffusion MRI data that tractography is based on requires a high angular resolution to resolve crossing fibers whereas high spatial resolution is required to distinguish kissing from crossing fibers. However, scan time increases with increasing spatial and angular resolutions, which can become infeasible in clinical settings. Here we investigated the trade-off between spatial and angul...

  5. Anthropogenic heat flux: advisable spatial resolutions when input data are scarce

    Science.gov (United States)

    Gabey, A. M.; Grimmond, C. S. B.; Capel-Timms, I.

    2018-02-01

    Anthropogenic heat flux (QF) may be significant in cities, especially under low solar irradiance and at night. It is of interest to many practitioners including meteorologists, city planners and climatologists. QF estimates at fine temporal and spatial resolution can be derived from models that use varying amounts of empirical data. This study compares simple and detailed models in a European megacity (London) at 500 m spatial resolution. The simple model (LQF) uses spatially resolved population data and national energy statistics. The detailed model (GQF) additionally uses local energy, road network and workday population data. The Fractions Skill Score (FSS) and bias are used to rate the skill with which the simple model reproduces the spatial patterns and magnitudes of QF, and its sub-components, from the detailed model. LQF skill was consistently good across 90% of the city, away from the centre and major roads. The remaining 10% contained elevated emissions and "hot spots" representing 30-40% of the total city-wide energy. This structure was lost because it requires workday population, spatially resolved building energy consumption and/or road network data. Daily total building and traffic energy consumption estimates from national data were within ± 40% of local values. Progressively coarser spatial resolutions to 5 km improved skill for total QF, but important features (hot spots, transport network) were lost at all resolutions when residential population controlled spatial variations. The results demonstrate that simple QF models should be applied with conservative spatial resolution in cities that, like London, exhibit time-varying energy use patterns.

  6. Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

    Science.gov (United States)

    Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

    2016-01-01

    Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

  7. High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia.

    Science.gov (United States)

    Wang, Bin; Waters, Cathy; Orgill, Susan; Gray, Jonathan; Cowie, Annette; Clark, Anthony; Liu, De Li

    2018-07-15

    Efficient and effective modelling methods to assess soil organic carbon (SOC) stock are central in understanding the global carbon cycle and informing related land management decisions. However, mapping SOC stocks in semi-arid rangelands is challenging due to the lack of data and poor spatial coverage. The use of remote sensing data to provide an indirect measurement of SOC to inform digital soil mapping has the potential to provide more reliable and cost-effective estimates of SOC compared with field-based, direct measurement. Despite this potential, the role of remote sensing data in improving the knowledge of soil information in semi-arid rangelands has not been fully explored. This study firstly investigated the use of high spatial resolution satellite data (seasonal fractional cover data; SFC) together with elevation, lithology, climatic data and observed soil data to map the spatial distribution of SOC at two soil depths (0-5cm and 0-30cm) in semi-arid rangelands of eastern Australia. Overall, model performance statistics showed that random forest (RF) and boosted regression trees (BRT) models performed better than support vector machine (SVM). The models obtained moderate results with R 2 of 0.32 for SOC stock at 0-5cm and 0.44 at 0-30cm, RMSE of 3.51MgCha -1 at 0-5cm and 9.16MgCha -1 at 0-30cm without considering SFC covariates. In contrast, by including SFC, the model accuracy for predicting SOC stock improved by 7.4-12.7% at 0-5cm, and by 2.8-5.9% at 0-30cm, highlighting the importance of including SFC to enhance the performance of the three modelling techniques. Furthermore, our models produced a more accurate and higher resolution digital SOC stock map compared with other available mapping products for the region. The data and high-resolution maps from this study can be used for future soil carbon assessment and monitoring. Copyright © 2018 Elsevier B.V. All rights reserved.

  8. Spatial resolution in Micromegas detectors

    CERN Document Server

    Bayb, A; Giomataris, Ioanis; Zaccone, Henri; Bay, A; Perroud, Jean-Pierre; Ronga, F

    2001-01-01

    The performance of a telescope of Micromegas detectors has been studied in a pion beam at the CERN PS. With a gas filling of CF/sub 4 / and 20% isobutane and with a strip pitch of 100 mu m an accuracy of 14+or-3 mu m on the spatial resolution has been measured at normal incidence. A simulation demonstrates that the resolution is limited by the size of the holes of the mesh of the detector and could be reduced to 11 mu m in the same conditions with smaller holes. Even further improvement down to 8.5 mu m is feasible for the same gas with an optimized 75 mu m strip pitch. (5 refs).

  9. Estimating Vegetation Rainfall Interception Using Remote Sensing Observations at Very High Resolution

    Science.gov (United States)

    Cui, Y.; Zhao, P.; Hong, Y.; Fan, W.; Yan, B.; Xie, H.

    2017-12-01

    Abstract: As an important compont of evapotranspiration, vegetation rainfall interception is the proportion of gross rainfall that is intercepted, stored and subsequently evaporated from all parts of vegetation during or following rainfall. Accurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash model (RS-Gash model) is developed based on a modified Gash model for interception loss estimation using remote sensing observations at the regional scale, and has been applied and validated in the upper reach of the Heihe River Basin of China for different types of vegetation. To eliminate the scale error and the effect of mixed pixels, the RS-Gash model is applied at a fine scale of 30 m with the high resolution vegetation area index retrieved by using the unified model of bidirectional reflectance distribution function (BRDF-U) for the vegetation canopy. Field validation shows that the RMSE and R2 of the interception ratio are 3.7% and 0.9, respectively, indicating the model's strong stability and reliability at fine scale. The temporal variation of vegetation rainfall interception loss and its relationship with precipitation are further investigated. In summary, the RS-Gash model has demonstrated its effectiveness and reliability in estimating vegetation rainfall interception. When compared to the coarse resolution results, the application of this model at 30-m fine resolution is necessary to resolve the scaling issues as shown in this study. Keywords: rainfall interception; remote sensing; RS-Gash analytical model; high resolution

  10. Lenses and effective spatial resolution in macroscopic optical mapping

    International Nuclear Information System (INIS)

    Bien, Harold; Parikh, Puja; Entcheva, Emilia

    2007-01-01

    Optical mapping of excitation dynamically tracks electrical waves travelling through cardiac or brain tissue by the use of fluorescent dyes. There are several characteristics that set optical mapping apart from other imaging modalities: dynamically changing signals requiring short exposure times, dim fluorescence demanding sensitive sensors and wide fields of view (low magnification) resulting in poor optical performance. These conditions necessitate the use of optics with good light gathering ability, i.e. lenses having high numerical aperture. Previous optical mapping studies often used sensor resolution to estimate the minimum spatial feature resolvable, assuming perfect optics and infinite contrast. We examine here the influence of finite contrast and real optics on the effective spatial resolution in optical mapping under broad-field illumination for both lateral (in-plane) resolution and axial (depth) resolution of collected fluorescence signals

  11. Hyperspectral remote sensing of wild oyster reefs

    Science.gov (United States)

    Le Bris, Anthony; Rosa, Philippe; Lerouxel, Astrid; Cognie, Bruno; Gernez, Pierre; Launeau, Patrick; Robin, Marc; Barillé, Laurent

    2016-04-01

    The invasion of the wild oyster Crassostrea gigas along the western European Atlantic coast has generated changes in the structure and functioning of intertidal ecosystems. Considered as an invasive species and a trophic competitor of the cultivated conspecific oyster, it is now seen as a resource by oyster farmers following recurrent mass summer mortalities of oyster spat since 2008. Spatial distribution maps of wild oyster reefs are required by local authorities to help define management strategies. In this work, visible-near infrared (VNIR) hyperspectral and multispectral remote sensing was investigated to map two contrasted intertidal reef structures: clusters of vertical oysters building three-dimensional dense reefs in muddy areas and oysters growing horizontally creating large flat reefs in rocky areas. A spectral library, collected in situ for various conditions with an ASD spectroradiometer, was used to run Spectral Angle Mapper classifications on airborne data obtained with an HySpex sensor (160 spectral bands) and SPOT satellite HRG multispectral data (3 spectral bands). With HySpex spectral/spatial resolution, horizontal oysters in the rocky area were correctly classified but the detection was less efficient for vertical oysters in muddy areas. Poor results were obtained with the multispectral image and from spatially or spectrally degraded HySpex data, it was clear that the spectral resolution was more important than the spatial resolution. In fact, there was a systematic mud deposition on shells of vertical oyster reefs explaining the misclassification of 30% of pixels recognized as mud or microphytobenthos. Spatial distribution maps of oyster reefs were coupled with in situ biomass measurements to illustrate the interest of a remote sensing product to provide stock estimations of wild oyster reefs to be exploited by oyster producers. This work highlights the interest of developing remote sensing techniques for aquaculture applications in coastal

  12. Remote parallel rendering for high-resolution tiled display walls

    KAUST Repository

    Nachbaur, Daniel; Dumusc, Raphael; Bilgili, Ahmet; Hernando, Juan; Eilemann, Stefan

    2014-01-01

    © 2014 IEEE. We present a complete, robust and simple to use hardware and software stack delivering remote parallel rendering of complex geometrical and volumetric models to high resolution tiled display walls in a production environment. We describe the setup and configuration, present preliminary benchmarks showing interactive framerates, and describe our contributions for a seamless integration of all the software components.

  13. Remote parallel rendering for high-resolution tiled display walls

    KAUST Repository

    Nachbaur, Daniel

    2014-11-01

    © 2014 IEEE. We present a complete, robust and simple to use hardware and software stack delivering remote parallel rendering of complex geometrical and volumetric models to high resolution tiled display walls in a production environment. We describe the setup and configuration, present preliminary benchmarks showing interactive framerates, and describe our contributions for a seamless integration of all the software components.

  14. An evaluation for spatial resolution, using a single target on a medical image

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Kyung Sung [Dept. of Radiotechnology, Cheju Halla University, Cheju (Korea, Republic of)

    2016-12-15

    Hitherto, spatial resolution has commonly been evaluated by test patterns or phantoms built on some specific distances (from close to far) between two objects (or double targets). This evaluation method's shortcoming is that resolution is restricted to target distances of phantoms made for test. Therefore, in order to solve the problem, this study proposes and verifies a new method to efficiently test spatial resolution with a single target. For the research I used PSF and JND to propose an idea to measure spatial resolution. After that, I made experiments by commonly used phantoms to verify my new evaluation hypothesis inferred from the above method. To analyse the hypothesis, I used LabVIEW program and got a line pixel from digital image. The result was identical to my spatial-resolution hypothesis inferred from a single target. The findings of the experiment proves only a single target can be enough to relatively evaluate spatial resolution on a digital image. In other words, the limit of the traditional spatial-resolution evaluation method, based on double targets, can be overcome by my new evaluation one using a single target.

  15. Palm Swamp Wetland Ecosystems of the Upper Amazon: Characterizing their Distribution and Inundation State Using Multiple Resolution Microwave Remote Sensing

    Science.gov (United States)

    Podest, E.; McDonald, K. C.; Schröder, R.; Pinto, N.; Zimmermann, R.; Horna, V.

    2011-12-01

    Palm swamp wetlands are prevalent in the Amazon basin, including extensive regions in northern Peru. These ecosystems are characterized by constant surface inundation and moderate seasonal water level variation. The combination of constantly saturated soils, giving rise to low oxygen conditions, and warm temperatures year-round can lead to considerable methane release to the atmosphere. Because of the widespread occurrence and expected sensitivity of these ecosystems to climate change, knowledge of their spatial extent and inundation state is crucial for assessing the associated land-atmosphere carbon exchange. Precise spatio-temporal information on palm swamps is difficult to gather because of their remoteness and difficult accessibility. Spaceborne microwave remote sensing is an effective tool for characterizing these ecosystems since it is sensitive to surface water and vegetation structure and allows monitoring large inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination. We are developing a remote sensing methodology using multiple resolution microwave remote sensing data to determine palm swamp distribution and inundation state over focus regions in the Amazon basin in northern Peru. For this purpose, two types of multi-temporal microwave data are used: 1) high-resolution (100 m) data from the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR) to derive maps of palm swamp extent and inundation from dual-polarization fine-beam and multi-temporal HH-polarized ScanSAR, and 2) coarse resolution (25 km) combined active and passive microwave data from QuikSCAT and AMSR-E to derive inundated area fraction on a weekly basis. We compare information content and accuracy of the coarse resolution products to the PALSAR-based datasets to ensure information harmonization. The synergistic combination of high and low resolution datasets will allow for characterization of palm swamps and

  16. Portable remote sensing image processing system; Kahangata remote sensing gazo shori system

    Energy Technology Data Exchange (ETDEWEB)

    Fujikawa, S; Uchida, K; Tanaka, S; Jingo, H [Dowa Engineering Co. Ltd., Tokyo (Japan); Hato, M [Earth Remote Sensing Data Analysis Center, Tokyo (Japan)

    1997-10-22

    Recently, geological analysis using remote sensing data has been put into practice due to data with high spectral resolution and high spatial resolution. There has been a remarkable increase in both software and hardware of personal computer. Software is independent of hardware due to Windows. It has become easy to develop softwares. Under such situation, a portable remote sensing image processing system coping with Window 95 has been developed. Using this system, basic image processing can be conducted, and present location can be displayed on the image in real time by linking with GPS. Accordingly, it is not required to bring printed images for the field works of image processing. This system can be used instead of topographic maps for overseas surveys. Microsoft Visual C++ ver. 2.0 is used for the software. 1 fig.

  17. A high time and spatial resolution MRPC designed for muon tomography

    Science.gov (United States)

    Shi, L.; Wang, Y.; Huang, X.; Wang, X.; Zhu, W.; Li, Y.; Cheng, J.

    2014-12-01

    A prototype of cosmic muon scattering tomography system has been set up in Tsinghua University in Beijing. Multi-gap Resistive Plate Chamber (MRPC) is used in the system to get the muon tracks. Compared with other detectors, MRPC can not only provide the track but also the Time of Flight (ToF) between two detectors which can estimate the energy of particles. To get a more accurate track and higher efficiency of the tomography system, a new type of high time and two-dimensional spatial resolution MRPC has been developed. A series of experiments have been done to measure the efficiency, time resolution and spatial resolution. The results show that the efficiency can reach 95% and its time resolution is around 65 ps. The cluster size is around 4 and the spatial resolution can reach 200 μ m.

  18. CYBERNETIC BASIS AND SYSTEM PRACTICE OF REMOTE SENSING AND SPATIAL INFORMATION SCIENCE

    Directory of Open Access Journals (Sweden)

    X. Tan

    2017-09-01

    Full Text Available Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level.

  19. Cybernetic Basis and System Practice of Remote Sensing and Spatial Information Science

    Science.gov (United States)

    Tan, X.; Jing, X.; Chen, R.; Ming, Z.; He, L.; Sun, Y.; Sun, X.; Yan, L.

    2017-09-01

    Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level.

  20. Vegetation Coverage and Impervious Surface Area Estimated Based on the Estarfm Model and Remote Sensing Monitoring

    Science.gov (United States)

    Hu, Rongming; Wang, Shu; Guo, Jiao; Guo, Liankun

    2018-04-01

    Impervious surface area and vegetation coverage are important biophysical indicators of urban surface features which can be derived from medium-resolution images. However, remote sensing data obtained by a single sensor are easily affected by many factors such as weather conditions, and the spatial and temporal resolution can not meet the needs for soil erosion estimation. Therefore, the integrated multi-source remote sensing data are needed to carry out high spatio-temporal resolution vegetation coverage estimation. Two spatial and temporal vegetation coverage data and impervious data were obtained from MODIS and Landsat 8 remote sensing images. Based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the vegetation coverage data of two scales were fused and the data of vegetation coverage fusion (ESTARFM FVC) and impervious layer with high spatiotemporal resolution (30 m, 8 day) were obtained. On this basis, the spatial variability of the seepage-free surface and the vegetation cover landscape in the study area was measured by means of statistics and spatial autocorrelation analysis. The results showed that: 1) ESTARFM FVC and impermeable surface have higher accuracy and can characterize the characteristics of the biophysical components covered by the earth's surface; 2) The average impervious surface proportion and the spatial configuration of each area are different, which are affected by natural conditions and urbanization. In the urban area of Xi'an, which has typical characteristics of spontaneous urbanization, landscapes are fragmented and have less spatial dependence.

  1. VEGETATION COVERAGE AND IMPERVIOUS SURFACE AREA ESTIMATED BASED ON THE ESTARFM MODEL AND REMOTE SENSING MONITORING

    Directory of Open Access Journals (Sweden)

    R. Hu

    2018-04-01

    Full Text Available Impervious surface area and vegetation coverage are important biophysical indicators of urban surface features which can be derived from medium-resolution images. However, remote sensing data obtained by a single sensor are easily affected by many factors such as weather conditions, and the spatial and temporal resolution can not meet the needs for soil erosion estimation. Therefore, the integrated multi-source remote sensing data are needed to carry out high spatio-temporal resolution vegetation coverage estimation. Two spatial and temporal vegetation coverage data and impervious data were obtained from MODIS and Landsat 8 remote sensing images. Based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM, the vegetation coverage data of two scales were fused and the data of vegetation coverage fusion (ESTARFM FVC and impervious layer with high spatiotemporal resolution (30 m, 8 day were obtained. On this basis, the spatial variability of the seepage-free surface and the vegetation cover landscape in the study area was measured by means of statistics and spatial autocorrelation analysis. The results showed that: 1 ESTARFM FVC and impermeable surface have higher accuracy and can characterize the characteristics of the biophysical components covered by the earth's surface; 2 The average impervious surface proportion and the spatial configuration of each area are different, which are affected by natural conditions and urbanization. In the urban area of Xi'an, which has typical characteristics of spontaneous urbanization, landscapes are fragmented and have less spatial dependence.

  2. Spatial, Temporal and Spectral Satellite Image Fusion via Sparse Representation

    Science.gov (United States)

    Song, Huihui

    Remote sensing provides good measurements for monitoring and further analyzing the climate change, dynamics of ecosystem, and human activities in global or regional scales. Over the past two decades, the number of launched satellite sensors has been increasing with the development of aerospace technologies and the growing requirements on remote sensing data in a vast amount of application fields. However, a key technological challenge confronting these sensors is that they tradeoff between spatial resolution and other properties, including temporal resolution, spectral resolution, swath width, etc., due to the limitations of hardware technology and budget constraints. To increase the spatial resolution of data with other good properties, one possible cost-effective solution is to explore data integration methods that can fuse multi-resolution data from multiple sensors, thereby enhancing the application capabilities of available remote sensing data. In this thesis, we propose to fuse the spatial resolution with temporal resolution and spectral resolution, respectively, based on sparse representation theory. Taking the study case of Landsat ETM+ (with spatial resolution of 30m and temporal resolution of 16 days) and MODIS (with spatial resolution of 250m ~ 1km and daily temporal resolution) reflectance, we propose two spatial-temporal fusion methods to combine the fine spatial information of Landsat image and the daily temporal resolution of MODIS image. Motivated by that the images from these two sensors are comparable on corresponding bands, we propose to link their spatial information on available Landsat- MODIS image pair (captured on prior date) and then predict the Landsat image from the MODIS counterpart on prediction date. To well-learn the spatial details from the prior images, we use a redundant dictionary to extract the basic representation atoms for both Landsat and MODIS images based on sparse representation. Under the scenario of two prior Landsat

  3. Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

    Science.gov (United States)

    Wang, Min; Cui, Qi; Wang, Jie; Ming, Dongping; Lv, Guonian

    2017-01-01

    In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance.

  4. Hippocampal activation during the recall of remote spatial memories in radial maze tasks.

    Science.gov (United States)

    Schlesiger, Magdalene I; Cressey, John C; Boublil, Brittney; Koenig, Julie; Melvin, Neal R; Leutgeb, Jill K; Leutgeb, Stefan

    2013-11-01

    Temporally graded retrograde amnesia is observed in human patients with medial temporal lobe lesions as well as in animal models of medial temporal lobe lesions. A time-limited role for these structures in memory recall has also been suggested by the observation that the rodent hippocampus and entorhinal cortex are activated during the retrieval of recent but not of remote memories. One notable exception is the recall of remote memories for platform locations in the water maze, which requires an intact hippocampus and results in hippocampal activation irrespective of the age of the memory. These findings raise the question whether the hippocampus is always involved in the recall of spatial memories or, alternatively, whether it might be required for procedural computations in the water maze task, such as for calculating a path to a hidden platform. We performed spatial memory testing in radial maze tasks to distinguish between these possibilities. Radial maze tasks require a choice between spatial locations on a center platform and thus have a lesser requirement for navigation than the water maze. However, we used a behavioral design in the radial maze that retained other aspects of the standard water maze task, such as the use of multiple start locations and retention testing in a single trial. Using the immediate early gene c-fos as a marker for neuronal activation, we found that all hippocampal subregions were more activated during the recall of remote compared to recent spatial memories. In areas CA3 and CA1, activation during remote memory testing was higher than in rats that were merely reexposed to the testing environment after the same time interval. Conversely, Fos levels in the dentate gyrus were increased after retention testing to the extent that was also observed in the corresponding exposure control group. This pattern of hippocampal activation was also obtained in a second version of the task that only used a single start arm instead of multiple

  5. INTERACTIVE CHANGE DETECTION USING HIGH RESOLUTION REMOTE SENSING IMAGES BASED ON ACTIVE LEARNING WITH GAUSSIAN PROCESSES

    Directory of Open Access Journals (Sweden)

    H. Ru

    2016-06-01

    Full Text Available Although there have been many studies for change detection, the effective and efficient use of high resolution remote sensing images is still a problem. Conventional supervised methods need lots of annotations to classify the land cover categories and detect their changes. Besides, the training set in supervised methods often has lots of redundant samples without any essential information. In this study, we present a method for interactive change detection using high resolution remote sensing images with active learning to overcome the shortages of existing remote sensing image change detection techniques. In our method, there is no annotation of actual land cover category at the beginning. First, we find a certain number of the most representative objects in unsupervised way. Then, we can detect the change areas from multi-temporal high resolution remote sensing images by active learning with Gaussian processes in an interactive way gradually until the detection results do not change notably. The artificial labelling can be reduced substantially, and a desirable detection result can be obtained in a few iterations. The experiments on Geo-Eye1 and WorldView2 remote sensing images demonstrate the effectiveness and efficiency of our proposed method.

  6. High resolution or optimum resolution? Spatial analysis of the Federmesser site at Andernach, Germany

    NARCIS (Netherlands)

    Stapert, D; Street, M

    1997-01-01

    This paper discusses spatial analysis at site level. It is suggested that spatial analysis has to proceed in several levels, from global to more detailed questions, and that optimum resolution should be established when applying any quantitative methods in this field. As an example, the ring and

  7. A Global Survey of Cloud Thermodynamic Phase using High Spatial Resolution VSWIR Spectroscopy, 2005-2015

    Science.gov (United States)

    Thompson, D. R.; Kahn, B. H.; Green, R. O.; Chien, S.; Middleton, E.; Tran, D. Q.

    2017-12-01

    Clouds' variable ice and liquid content significantly influences their optical properties, evolution, and radiative forcing potential (Tan and Storelvmo, J. Atmos. Sci, 73, 2016). However, most remote measurements of thermodynamic phase have spatial resolutions of 1 km or more and are insensitive to mixed phases. This under-constrains important processes, such as spatial partitioning within mixed phase clouds, that carry outsize radiative forcing impacts. These uncertainties could shift Global Climate Model (GCM) predictions of future warming by over 1 degree Celsius (Tan et al., Science 352:6282, 2016). Imaging spectroscopy of reflected solar energy from the 1.4 - 1.8 μm shortwave infrared (SWIR) spectral range can address this observational gap. These observations can distinguish ice and water absorption, providing a robust and sensitive measurement of cloud top thermodynamic phase including mixed phases. Imaging spectrometers can resolve variations at scales of tens to hundreds of meters (Thompson et al., JGR-Atmospheres 121, 2016). We report the first such global high spatial resolution (30 m) survey, based on data from 2005-2015 acquired by the Hyperion imaging spectrometer onboard NASA's EO-1 spacecraft (Pearlman et al., Proc. SPIE 4135, 2001). Estimated seasonal and latitudinal distributions of cloud thermodynamic phase generally agree with observations made by other satellites such as the Atmospheric Infrared Sounder (AIRS). Variogram analyses reveal variability at different spatial scales. Our results corroborate previously observed zonal distributions, while adding insight into the spatial scales of processes governing cloud top thermodynamic phase. Figure: Thermodynamic phase retrievals. Top: Example of a cloud top thermodynamic phase map from the EO-1/Hyperion. Bottom: Latitudinal distributions of pure and mixed phase clouds, 2005-2015, showing Liquid Thickness Fraction (LTF). LTF=0 corresponds to pure ice absorption, while LTF=1 is pure liquid. The

  8. Advances on application of remote sensing technology to uranium prospecting in northwest of China

    International Nuclear Information System (INIS)

    Ye Fawang; Liu Dechang; Zhao Yingjun; Zhang Jielin; Fang Maolong

    2012-01-01

    Some advances on application of remote sensing technology to uranium prospecting in northwest of China since 21st century are presented in this paper. They included: (1) application of ETM multi-spectral remote sensing technology to identify the sandstone-type uranium ore-controlling structure in north of Ordos Basin and investigate the uranium metallogenetic geological conditions in Qiangtang Basin, Tibet, (2) application of ASTER multi-spectral and QuickBird high spatial resolution remote sensing technology to extract and analyze the oil-gas reduced alteration in Bashibulake uranium ore district, Xinjiang, (3) discovery of Salamubulake uranium metallogenetic belt in Keping, Xinjiang, using ASTER multi-spectral, QuickBird high spatial resolution, and CASI/SASI airborne hyper-spectral remote sensing comprehensively, and (4) application of CASI/SASI airborne hyper-spectral remote sensing technology to extract volcanicrock type uranium mineralization alteration in Baiyanghe area, Xinjiang. These application advances show the good application effects of remote sensing technology to uranium exploration in northwest of China, which provides important references for making further uranium prospecting using remote sensing technology. (authors)

  9. Spatial Analysis of Political Capital Citation Using Remote Sensing ...

    African Journals Online (AJOL)

    Spatial Analysis of Political Capital Citation Using Remote Sensing and GIS; A Case Study of Lokoja. ... The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader). If you would like more information about how to print, save, ...

  10. Spatial Ensemble Postprocessing of Precipitation Forecasts Using High Resolution Analyses

    Science.gov (United States)

    Lang, Moritz N.; Schicker, Irene; Kann, Alexander; Wang, Yong

    2017-04-01

    Ensemble prediction systems are designed to account for errors or uncertainties in the initial and boundary conditions, imperfect parameterizations, etc. However, due to sampling errors and underestimation of the model errors, these ensemble forecasts tend to be underdispersive, and to lack both reliability and sharpness. To overcome such limitations, statistical postprocessing methods are commonly applied to these forecasts. In this study, a full-distributional spatial post-processing method is applied to short-range precipitation forecasts over Austria using Standardized Anomaly Model Output Statistics (SAMOS). Following Stauffer et al. (2016), observation and forecast fields are transformed into standardized anomalies by subtracting a site-specific climatological mean and dividing by the climatological standard deviation. Due to the need of fitting only a single regression model for the whole domain, the SAMOS framework provides a computationally inexpensive method to create operationally calibrated probabilistic forecasts for any arbitrary location or for all grid points in the domain simultaneously. Taking advantage of the INCA system (Integrated Nowcasting through Comprehensive Analysis), high resolution analyses are used for the computation of the observed climatology and for model training. The INCA system operationally combines station measurements and remote sensing data into real-time objective analysis fields at 1 km-horizontal resolution and 1 h-temporal resolution. The precipitation forecast used in this study is obtained from a limited area model ensemble prediction system also operated by ZAMG. The so called ALADIN-LAEF provides, by applying a multi-physics approach, a 17-member forecast at a horizontal resolution of 10.9 km and a temporal resolution of 1 hour. The performed SAMOS approach statistically combines the in-house developed high resolution analysis and ensemble prediction system. The station-based validation of 6 hour precipitation sums

  11. Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning

    Directory of Open Access Journals (Sweden)

    Yang Chen

    2018-05-01

    Full Text Available Accurate information on urban surface water is important for assessing the role it plays in urban ecosystem services in the context of human survival and climate change. The precise extraction of urban water bodies from images is of great significance for urban planning and socioeconomic development. In this paper, a novel deep-learning architecture is proposed for the extraction of urban water bodies from high-resolution remote sensing (HRRS imagery. First, an adaptive simple linear iterative clustering algorithm is applied for segmentation of the remote-sensing image into high-quality superpixels. Then, a new convolutional neural network (CNN architecture is designed that can extract useful high-level features of water bodies from input data in a complex urban background and mark the superpixel as one of two classes: an including water or no-water pixel. Finally, a high-resolution image of water-extracted superpixels is generated. Experimental results show that the proposed method achieved higher accuracy for water extraction from the high-resolution remote-sensing images than traditional approaches, and the average overall accuracy is 99.14%.

  12. Positron flight in human tissues and its influence on PET image spatial resolution

    Energy Technology Data Exchange (ETDEWEB)

    Sanchez-Crespo, Alejandro; Larsson, Stig A. [Section of Nuclear Medicine, Department of Hospital Physics, Karolinska Hospital, 176 76, Stockholm (Sweden); Medical Radiation Physics, Department of Oncology-Pathology, Stockholm University and Karolinska Institute, Stockholm (Sweden); Andreo, Pedro [Medical Radiation Physics, Department of Oncology-Pathology, Stockholm University and Karolinska Institute, Stockholm (Sweden)

    2004-01-01

    The influence of the positron distance of flight in various human tissues on the spatial resolution in positron emission tomography (PET) was assessed for positrons from carbon-11, nitrogen-13, oxygen-15, fluorine-18, gallium-68 and rubidium-82. The investigation was performed using the Monte Carlo code PENELOPE to simulate the transport of positrons within human compact bone, adipose, soft and lung tissue. The simulations yielded 3D distributions of annihilation origins that were projected on the image plane in order to assess their impact on PET spatial resolution. The distributions obtained were cusp-shaped with long tails rather than Gaussian shaped, thus making conventional full width at half maximum (FWHM) measures uncertain. The full width at 20% of the maximum amplitude (FW20M) of the annihilation distributions yielded more appropriate values for root mean square addition of spatial resolution loss components. Large differences in spatial resolution losses due to the positron flight in various human tissues were found for the selected radionuclides. The contribution to image blur was found to be up to three times larger in lung tissue than in soft tissue or fat and five times larger than in bone tissue. For {sup 18}F, the spatial resolution losses were 0.54 mm in soft tissue and 1.52 mm in lung tissue, compared with 4.10 and 10.5 mm, respectively, for {sup 82}Rb. With lung tissue as a possible exception, the image blur due to the positron flight in all human tissues has a minor impact as long as PET cameras with a spatial resolution of 5-7 mm are used in combination with {sup 18}F-labelled radiopharmaceuticals. However, when ultra-high spatial resolution PET cameras, with 3-4 mm spatial resolution, are applied, especially in combination with other radionuclides, the positron flight may enter as a limiting factor for the total PET spatial resolution - particularly in lung tissue. (orig.)

  13. Positron flight in human tissues and its influence on PET image spatial resolution

    International Nuclear Information System (INIS)

    Sanchez-Crespo, Alejandro; Larsson, Stig A.; Andreo, Pedro

    2004-01-01

    The influence of the positron distance of flight in various human tissues on the spatial resolution in positron emission tomography (PET) was assessed for positrons from carbon-11, nitrogen-13, oxygen-15, fluorine-18, gallium-68 and rubidium-82. The investigation was performed using the Monte Carlo code PENELOPE to simulate the transport of positrons within human compact bone, adipose, soft and lung tissue. The simulations yielded 3D distributions of annihilation origins that were projected on the image plane in order to assess their impact on PET spatial resolution. The distributions obtained were cusp-shaped with long tails rather than Gaussian shaped, thus making conventional full width at half maximum (FWHM) measures uncertain. The full width at 20% of the maximum amplitude (FW20M) of the annihilation distributions yielded more appropriate values for root mean square addition of spatial resolution loss components. Large differences in spatial resolution losses due to the positron flight in various human tissues were found for the selected radionuclides. The contribution to image blur was found to be up to three times larger in lung tissue than in soft tissue or fat and five times larger than in bone tissue. For 18 F, the spatial resolution losses were 0.54 mm in soft tissue and 1.52 mm in lung tissue, compared with 4.10 and 10.5 mm, respectively, for 82 Rb. With lung tissue as a possible exception, the image blur due to the positron flight in all human tissues has a minor impact as long as PET cameras with a spatial resolution of 5-7 mm are used in combination with 18 F-labelled radiopharmaceuticals. However, when ultra-high spatial resolution PET cameras, with 3-4 mm spatial resolution, are applied, especially in combination with other radionuclides, the positron flight may enter as a limiting factor for the total PET spatial resolution - particularly in lung tissue. (orig.)

  14. Scale Issues Related to the Accuracy Assessment of Land Use/Land Cover Maps Produced Using Multi-Resolution Data: Comments on “The Improvement of Land Cover Classification by Thermal Remote Sensing”. Remote Sens. 2015, 7(7, 8368–8390

    Directory of Open Access Journals (Sweden)

    Brian A. Johnson

    2015-10-01

    Full Text Available Much remote sensing (RS research focuses on fusing, i.e., combining, multi-resolution/multi-sensor imagery for land use/land cover (LULC classification. In relation to this topic, Sun and Schulz [1] recently found that a combination of visible-to-near infrared (VNIR; 30 m spatial resolution and thermal infrared (TIR; 100–120 m spatial resolution Landsat data led to more accurate LULC classification. They also found that using multi-temporal TIR data alone for classification resulted in comparable (and in some cases higher classification accuracies to the use of multi-temporal VNIR data, which contrasts with the findings of other recent research [2]. This discrepancy, and the generally very high LULC accuracies achieved by Sun and Schulz (up to 99.2% overall accuracy for a combined VNIR/TIR classification result, can likely be explained by their use of an accuracy assessment procedure which does not take into account the multi-resolution nature of the data. Sun and Schulz used 10-fold cross-validation for accuracy assessment, which is not necessarily inappropriate for RS accuracy assessment in general. However, here it is shown that the typical pixel-based cross-validation approach results in non-independent training and validation data sets when the lower spatial resolution TIR images are used for classification, which causes classification accuracy to be overestimated.

  15. Reconstructed Image Spatial Resolution of Multiple Coincidences Compton Imager

    Science.gov (United States)

    Andreyev, Andriy; Sitek, Arkadiusz; Celler, Anna

    2010-02-01

    We study the multiple coincidences Compton imager (MCCI) which is based on a simultaneous acquisition of several photons emitted in cascade from a single nuclear decay. Theoretically, this technique should provide a major improvement in localization of a single radioactive source as compared to a standard Compton camera. In this work, we investigated the performance and limitations of MCCI using Monte Carlo computer simulations. Spatial resolutions of the reconstructed point source have been studied as a function of the MCCI parameters, including geometrical dimensions and detector characteristics such as materials, energy and spatial resolutions.

  16. High spatial resolution CT image reconstruction using parallel computing

    International Nuclear Information System (INIS)

    Yin Yin; Liu Li; Sun Gongxing

    2003-01-01

    Using the PC cluster system with 16 dual CPU nodes, we accelerate the FBP and OR-OSEM reconstruction of high spatial resolution image (2048 x 2048). Based on the number of projections, we rewrite the reconstruction algorithms into parallel format and dispatch the tasks to each CPU. By parallel computing, the speedup factor is roughly equal to the number of CPUs, which can be up to about 25 times when 25 CPUs used. This technique is very suitable for real-time high spatial resolution CT image reconstruction. (authors)

  17. Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging: RESOLUTION ADAPTABILITY OF ZM SCHEME

    Energy Technology Data Exchange (ETDEWEB)

    Yun, Yuxing [Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland WA USA; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing China; Fan, Jiwen [Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland WA USA; Xiao, Heng [Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland WA USA; Zhang, Guang J. [Scripps Institution of Oceanography, University of California, San Diego CA USA; Ghan, Steven J. [Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland WA USA; Xu, Kuan-Man [NASA Langley Research Center, Hampton VA USA; Ma, Po-Lun [Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland WA USA; Gustafson, William I. [Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland WA USA

    2017-11-01

    Realistic modeling of cumulus convection at fine model resolutions (a few to a few tens of km) is problematic since it requires the cumulus scheme to adapt to higher resolution than they were originally designed for (~100 km). To solve this problem, we implement the spatial averaging method proposed in Xiao et al. (2015) and also propose a temporal averaging method for the large-scale convective available potential energy (CAPE) tendency in the Zhang-McFarlane (ZM) cumulus parameterization. The resolution adaptability of the original ZM scheme, the scheme with spatial averaging, and the scheme with both spatial and temporal averaging at 4-32 km resolution is assessed using the Weather Research and Forecasting (WRF) model, by comparing with Cloud Resolving Model (CRM) results. We find that the original ZM scheme has very poor resolution adaptability, with sub-grid convective transport and precipitation increasing significantly as the resolution increases. The spatial averaging method improves the resolution adaptability of the ZM scheme and better conserves the total transport of moist static energy and total precipitation. With the temporal averaging method, the resolution adaptability of the scheme is further improved, with sub-grid convective precipitation becoming smaller than resolved precipitation for resolution higher than 8 km, which is consistent with the results from the CRM simulation. Both the spatial distribution and time series of precipitation are improved with the spatial and temporal averaging methods. The results may be helpful for developing resolution adaptability for other cumulus parameterizations that are based on quasi-equilibrium assumption.

  18. AN ACTIVE-PASSIVE COMBINED ALGORITHM FOR HIGH SPATIAL RESOLUTION RETRIEVAL OF SOIL MOISTURE FROM SATELLITE SENSORS (Invited)

    Science.gov (United States)

    Lakshmi, V.; Mladenova, I. E.; Narayan, U.

    2009-12-01

    Soil moisture is known to be an essential factor in controlling the partitioning of rainfall into surface runoff and infiltration and solar energy into latent and sensible heat fluxes. Remote sensing has long proven its capability to obtain soil moisture in near real-time. However, at the present time we have the Advanced Scanning Microwave Radiometer (AMSR-E) on board NASA’s AQUA platform is the only satellite sensor that supplies a soil moisture product. AMSR-E coarse spatial resolution (~ 50 km at 6.9 GHz) strongly limits its applicability for small scale studies. A very promising technique for spatial disaggregation by combining radar and radiometer observations has been demonstrated by the authors using a methodology is based on the assumption that any change in measured brightness temperature and backscatter from one to the next time step is due primarily to change in soil wetness. The approach uses radiometric estimates of soil moisture at a lower resolution to compute the sensitivity of radar to soil moisture at the lower resolution. This estimate of sensitivity is then disaggregated using vegetation water content, vegetation type and soil texture information, which are the variables on which determine the radar sensitivity to soil moisture and are generally available at a scale of radar observation. This change detection algorithm is applied to several locations. We have used aircraft observed active and passive data over Walnut Creek watershed in Central Iowa in 2002; the Little Washita Watershed in Oklahoma in 2003 and the Murrumbidgee Catchment in southeastern Australia for 2006. All of these locations have different soils and land cover conditions which leads to a rigorous test of the disaggregation algorithm. Furthermore, we compare the derived high spatial resolution soil moisture to in-situ sampling and ground observation networks

  19. Resolution enhancement of tri-stereo remote sensing images by super resolution methods

    Science.gov (United States)

    Tuna, Caglayan; Akoguz, Alper; Unal, Gozde; Sertel, Elif

    2016-10-01

    Super resolution (SR) refers to generation of a High Resolution (HR) image from a decimated, blurred, low-resolution (LR) image set, which can be either a single frame or multi-frame that contains a collection of several images acquired from slightly different views of the same observation area. In this study, we propose a novel application of tri-stereo Remote Sensing (RS) satellite images to the super resolution problem. Since the tri-stereo RS images of the same observation area are acquired from three different viewing angles along the flight path of the satellite, these RS images are properly suited to a SR application. We first estimate registration between the chosen reference LR image and other LR images to calculate the sub pixel shifts among the LR images. Then, the warping, blurring and down sampling matrix operators are created as sparse matrices to avoid high memory and computational requirements, which would otherwise make the RS-SR solution impractical. Finally, the overall system matrix, which is constructed based on the obtained operator matrices is used to obtain the estimate HR image in one step in each iteration of the SR algorithm. Both the Laplacian and total variation regularizers are incorporated separately into our algorithm and the results are presented to demonstrate an improved quantitative performance against the standard interpolation method as well as improved qualitative results due expert evaluations.

  20. Landform classification using a sub-pixel spatial attraction model to increase spatial resolution of digital elevation model (DEM

    Directory of Open Access Journals (Sweden)

    Marzieh Mokarrama

    2018-04-01

    Full Text Available The purpose of the present study is preparing a landform classification by using digital elevation model (DEM which has a high spatial resolution. To reach the mentioned aim, a sub-pixel spatial attraction model was used as a novel method for preparing DEM with a high spatial resolution in the north of Darab, Fars province, Iran. The sub-pixel attraction models convert the pixel into sub-pixels based on the neighboring pixels fraction values, which can only be attracted by a central pixel. Based on this approach, a mere maximum of eight neighboring pixels can be selected for calculating of the attraction value. In the mentioned model, other pixels are supposed to be far from the central pixel to receive any attraction. In the present study by using a sub-pixel attraction model, the spatial resolution of a DEM was increased. The design of the algorithm is accomplished by using a DEM with a spatial resolution of 30 m (the Advanced Space borne Thermal Emission and Reflection Radiometer; (ASTER and a 90 m (the Shuttle Radar Topography Mission; (SRTM. In the attraction model, scale factors of (S = 2, S = 3, and S = 4 with two neighboring methods of touching (T = 1 and quadrant (T = 2 are applied to the DEMs by using MATLAB software. The algorithm is evaluated by taking the best advantages of 487 sample points, which are measured by surveyors. The spatial attraction model with scale factor of (S = 2 gives better results compared to those scale factors which are greater than 2. Besides, the touching neighborhood method is turned to be more accurate than the quadrant method. In fact, dividing each pixel into more than two sub-pixels decreases the accuracy of the resulted DEM. On the other hand, in these cases DEM, is itself in charge of increasing the value of root-mean-square error (RMSE and shows that attraction models could not be used for S which is greater than 2. Thus considering results, the proposed model is highly capable of

  1. Analyzing Snowpack Metrics Over Large Spatial Extents Using Calibrated, Enhanced-Resolution Brightness Temperature Data and Long Short Term Memory Artificial Neural Networks

    Science.gov (United States)

    Norris, W.; J Q Farmer, C.

    2017-12-01

    Snow water equivalence (SWE) is a difficult metric to measure accurately over large spatial extents; snow-tell sites are too localized, and traditional remotely sensed brightness temperature data is at too coarse of a resolution to capture variation. The new Calibrated Enhanced-Resolution Brightness Temperature (CETB) data from the National Snow and Ice Data Center (NSIDC) offers remotely sensed brightness temperature data at an enhanced resolution of 3.125 km versus the original 25 km, which allows for large spatial extents to be analyzed with reduced uncertainty compared to the 25km product. While the 25km brightness temperature data has proved useful in past research — one group found decreasing trends in SWE outweighed increasing trends three to one in North America; other researchers used the data to incorporate winter conditions, like snow cover, into ecological zoning criterion — with the new 3.125 km data, it is possible to derive more accurate metrics for SWE, since we have far more spatial variability in measurements. Even with higher resolution data, using the 37 - 19 GHz frequencies to estimate SWE distorts the data during times of melt onset and accumulation onset. Past researchers employed statistical splines, while other successful attempts utilized non-parametric curve fitting to smooth out spikes distorting metrics. In this work, rather than using legacy curve fitting techniques, a Long Short Term Memory (LSTM) Artificial Neural Network (ANN) was trained to perform curve fitting on the data. LSTM ANN have shown great promise in modeling time series data, and with almost 40 years of data available — 14,235 days — there is plenty of training data for the ANN. LSTM's are ideal for this type of time series analysis because they allow important trends to persist for long periods of time, but ignore short term fluctuations; since LSTM's have poor mid- to short-term memory, they are ideal for smoothing out the large spikes generated in the melt

  2. Spatio-Temporal Series Remote Sensing Image Prediction Based on Multi-Dictionary Bayesian Fusion

    Directory of Open Access Journals (Sweden)

    Chu He

    2017-11-01

    Full Text Available Contradictions in spatial resolution and temporal coverage emerge from earth observation remote sensing images due to limitations in technology and cost. Therefore, how to combine remote sensing images with low spatial yet high temporal resolution as well as those with high spatial yet low temporal resolution to construct images with both high spatial resolution and high temporal coverage has become an important problem called spatio-temporal fusion problem in both research and practice. A Multi-Dictionary Bayesian Spatio-Temporal Reflectance Fusion Model (MDBFM has been proposed in this paper. First, multiple dictionaries from regions of different classes are trained. Second, a Bayesian framework is constructed to solve the dictionary selection problem. A pixel-dictionary likehood function and a dictionary-dictionary prior function are constructed under the Bayesian framework. Third, remote sensing images before and after the middle moment are combined to predict images at the middle moment. Diverse shapes and textures information is learned from different landscapes in multi-dictionary learning to help dictionaries capture the distinctions between regions. The Bayesian framework makes full use of the priori information while the input image is classified. The experiments with one simulated dataset and two satellite datasets validate that the MDBFM is highly effective in both subjective and objective evaluation indexes. The results of MDBFM show more precise details and have a higher similarity with real images when dealing with both type changes and phenology changes.

  3. Calculation of the spatial resolution in two-photon absorption spectroscopy applied to plasma diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Garcia-Lechuga, M. [Departamento de Física Teórica, Atómica y Óptica, Universidad de Valladolid, 47011-Valladolid (Spain); Laser Processing Group, Instituto de Óptica “Daza de Valdés,” CSIC, 28006-Madrid (Spain); Fuentes, L. M. [Departamento de Física Aplicada, Universidad de Valladolid, 47011-Valladolid (Spain); Grützmacher, K.; Pérez, C., E-mail: concha@opt.uva.es; Rosa, M. I. de la [Departamento de Física Teórica, Atómica y Óptica, Universidad de Valladolid, 47011-Valladolid (Spain)

    2014-10-07

    We report a detailed characterization of the spatial resolution provided by two-photon absorption spectroscopy suited for plasma diagnosis via the 1S-2S transition of atomic hydrogen for optogalvanic detection and laser induced fluorescence (LIF). A precise knowledge of the spatial resolution is crucial for a correct interpretation of measurements, if the plasma parameters to be analysed undergo strong spatial variations. The present study is based on a novel approach which provides a reliable and realistic determination of the spatial resolution. Measured irradiance distribution of laser beam waists in the overlap volume, provided by a high resolution UV camera, are employed to resolve coupled rate equations accounting for two-photon excitation, fluorescence decay and ionization. The resulting three-dimensional yield distributions reveal in detail the spatial resolution for optogalvanic and LIF detection and related saturation due to depletion. Two-photon absorption profiles broader than the Fourier transform-limited laser bandwidth are also incorporated in the calculations. The approach allows an accurate analysis of the spatial resolution present in recent and future measurements.

  4. Calculation of the spatial resolution in two-photon absorption spectroscopy applied to plasma diagnosis

    International Nuclear Information System (INIS)

    Garcia-Lechuga, M.; Fuentes, L. M.; Grützmacher, K.; Pérez, C.; Rosa, M. I. de la

    2014-01-01

    We report a detailed characterization of the spatial resolution provided by two-photon absorption spectroscopy suited for plasma diagnosis via the 1S-2S transition of atomic hydrogen for optogalvanic detection and laser induced fluorescence (LIF). A precise knowledge of the spatial resolution is crucial for a correct interpretation of measurements, if the plasma parameters to be analysed undergo strong spatial variations. The present study is based on a novel approach which provides a reliable and realistic determination of the spatial resolution. Measured irradiance distribution of laser beam waists in the overlap volume, provided by a high resolution UV camera, are employed to resolve coupled rate equations accounting for two-photon excitation, fluorescence decay and ionization. The resulting three-dimensional yield distributions reveal in detail the spatial resolution for optogalvanic and LIF detection and related saturation due to depletion. Two-photon absorption profiles broader than the Fourier transform-limited laser bandwidth are also incorporated in the calculations. The approach allows an accurate analysis of the spatial resolution present in recent and future measurements.

  5. Comparative study between ultrahigh spatial frequency algorithm and high spatial frequency algorithm in high-resolution CT of the lungs

    International Nuclear Information System (INIS)

    Oh, Yu Whan; Kim, Jung Kyuk; Suh, Won Hyuck

    1994-01-01

    To date, the high spatial frequency algorithm (HSFA) which reduces image smoothing and increases spatial resolution has been used for the evaluation of parenchymal lung diseases in thin-section high-resolution CT. In this study, we compared the ultrahigh spatial frequency algorithm (UHSFA) with the high spatial frequency algorithm in the assessment of thin section images of the lung parenchyma. Three radiologists compared the UHSFA and HSFA on identical CT images in a line-pair resolution phantom, one lung specimen, 2 patients with normal lung and 18 patients with abnormal lung parenchyma. Scanning of a line-pair resolution phantom demonstrated no difference in resolution between two techniques but it showed that outer lines of the line pairs with maximal resolution looked thicker on UHSFA than those on HSFA. Lung parenchymal detail with UHSFA was judged equal or superior to HSFA in 95% of images. Lung parenchymal sharpness was improved with UHSFA in all images. Although UHSFA resulted in an increase in visible noise, observers did not found that image noise interfered with image interpretation. The visual CT attenuation of normal lung parenchyma is minimally increased in images with HSFA. The overall visual preference of the images reconstructed on UHSFA was considered equal to or greater than that of those reconstructed on HSFA in 78% of images. The ultrahigh spatial frequency algorithm improved the overall visual quality of the images in pulmonary parenchymal high-resolution CT

  6. Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

    Directory of Open Access Journals (Sweden)

    Marc Cattet

    2010-11-01

    Full Text Available Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC. Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI, inversion algorithm, data fusion, and the integration of remote sensing (RS and geographic information system (GIS.

  7. Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists.

    Science.gov (United States)

    Wang, Kai; Franklin, Steven E; Guo, Xulin; Cattet, Marc

    2010-01-01

    Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).

  8. CHARACTERIZING LANDSCAPE SPATIAL HETEROGENEITY USING SEMIVARIOGRAM PARAMETERS DERIVED FROM NDVI IMAGES

    Directory of Open Access Journals (Sweden)

    Eduarda Martiniano de Oliveira Silveira

    2017-12-01

    Full Text Available Assuming a relationship between landscape heterogeneity and measures of spatial dependence by using remotely sensed data, the aim of this work was to evaluate the potential of semivariogram parameters, derived from satellite images with different spatial resolutions, to characterize landscape spatial heterogeneity of forested and human modified areas. The NDVI (Normalized Difference Vegetation Index was generated in an area of Brazilian amazon tropical forest (1,000 km².We selected samples (1 x 1 km from forested and human modified areas distributed throughout the study area, to generate the semivariogram and extract the sill (σ²-overall spatial variability of the surface property and range (φ-the length scale of the spatial structures of objects parameters. The analysis revealed that image spatial resolution influenced the sill and range parameters. The average sill and range values increase from forested to human modified areas and the greatest between-class variation was found for LANDSAT 8 imagery, indicating that this image spatial resolution is the most appropriate for deriving sill and range parameters with the intention of describing landscape spatial heterogeneity. By combining remote sensing and geostatistical techniques, we have shown that the sill and range parameters of semivariograms derived from NDVI images are a simple indicator of landscape heterogeneity and can be used to provide landscape heterogeneity maps to enable researchers to design appropriate sampling regimes. In the future, more applications combining remote sensing and geostatistical features should be further investigated and developed, such as change detection and image classification using object-based image analysis (OBIA approaches.

  9. Spatial resolution requirements for traffic-related air pollutant exposure evaluations

    Science.gov (United States)

    Batterman, Stuart; Chambliss, Sarah; Isakov, Vlad

    2014-09-01

    Vehicle emissions represent one of the most important air pollution sources in most urban areas, and elevated concentrations of pollutants found near major roads have been associated with many adverse health impacts. To understand these impacts, exposure estimates should reflect the spatial and temporal patterns observed for traffic-related air pollutants. This paper evaluates the spatial resolution and zonal systems required to estimate accurately intraurban and near-road exposures of traffic-related air pollutants. The analyses use the detailed information assembled for a large (800 km2) area centered on Detroit, Michigan, USA. Concentrations of nitrogen oxides (NOx) due to vehicle emissions were estimated using hourly traffic volumes and speeds on 9700 links representing all but minor roads in the city, the MOVES2010 emission model, the RLINE dispersion model, local meteorological data, a temporal resolution of 1 h, and spatial resolution as low as 10 m. Model estimates were joined with the corresponding shape files to estimate residential exposures for 700,000 individuals at property parcel, census block, census tract, and ZIP code levels. We evaluate joining methods, the spatial resolution needed to meet specific error criteria, and the extent of exposure misclassification. To portray traffic-related air pollutant exposure, raster or inverse distance-weighted interpolations are superior to nearest neighbor approaches, and interpolations between receptors and points of interest should not exceed about 40 m near major roads, and 100 m at larger distances. For census tracts and ZIP codes, average exposures are overestimated since few individuals live very near major roads, the range of concentrations is compressed, most exposures are misclassified, and high concentrations near roads are entirely omitted. While smaller zones improve performance considerably, even block-level data can misclassify many individuals. To estimate exposures and impacts of traffic

  10. Optimizing Spatial Resolution of Imagery for Urban Form Detection—The Cases of France and Vietnam

    Directory of Open Access Journals (Sweden)

    Christiane Weber

    2011-09-01

    Full Text Available The multitude of satellite data products available offers a large choice for urban studies. Urban space is known for its high heterogeneity in structure, shape and materials. To approach this heterogeneity, finding the optimal spatial resolution (OSR is needed for urban form detection from remote sensing imagery. By applying the local variance method to our datasets (pan-sharpened images, we can identify OSR at two levels of observation: individual urban elements and urban districts in two agglomerations in West Europe (Strasbourg, France and in Southeast Asia (Da Nang, Vietnam. The OSR corresponds to the minimal variance of largest number of spectral bands. We carry out three categories of interval values of spatial resolutions for identifying OSR: from 0.8 m to 3 m for isolated objects, from 6 m to 8 m for vegetation area and equal or higher than 20 m for urban district. At the urban district level, according to spatial patterns, form, size and material of elements, we propose the range of OSR between 30 m and 40 m for detecting administrative districts, new residential districts and residential discontinuous districts. The detection of industrial districts refers to a coarser OSR from 50 m to 60 m. The residential continuous dense districts effectively need a finer OSR of between 20 m and 30 m for their optimal identification. We also use fractal dimensions to identify the threshold of homogeneity/heterogeneity of urban structure at urban district level. It seems therefore that our approaches are robust and transferable to different urban contexts.

  11. Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks

    Science.gov (United States)

    Gul, M. Shahzeb Khan; Gunturk, Bahadir K.

    2018-05-01

    Light field imaging extends the traditional photography by capturing both spatial and angular distribution of light, which enables new capabilities, including post-capture refocusing, post-capture aperture control, and depth estimation from a single shot. Micro-lens array (MLA) based light field cameras offer a cost-effective approach to capture light field. A major drawback of MLA based light field cameras is low spatial resolution, which is due to the fact that a single image sensor is shared to capture both spatial and angular information. In this paper, we present a learning based light field enhancement approach. Both spatial and angular resolution of captured light field is enhanced using convolutional neural networks. The proposed method is tested with real light field data captured with a Lytro light field camera, clearly demonstrating spatial and angular resolution improvement.

  12. Pushing the limits of spatial resolution with the Kuiper Airborne observatory

    Science.gov (United States)

    Lester, Daniel

    1994-01-01

    The study of astronomical objects at high spatial resolution in the far-IR is one of the most serious limitations to our work at these wavelengths, which carry information about the luminosity of dusty and obscured sources. At IR wavelengths shorter than 30 microns, ground based telescopes with large apertures at superb sites achieve diffraction-limited performance close to the seeing limit in the optical. At millimeter wavelengths, ground based interferometers achieve resolution that is close to this. The inaccessibility of the far-IR from the ground makes it difficult, however, to achieve complementary resolution in the far-IR. The 1983 IRAS survey, while extraordinarily sensitive, provides us with a sky map at a spatial resolution that is limited by detector size on a spatial scale that is far larger than that available in other wavelengths on the ground. The survey resolution is of order 4 min in the 100 micron bandpass, and 2 min at 60 microns (IRAS Explanatory Supplement, 1988). Information on a scale of 1' is available on some sources from the CPC. Deconvolution and image resolution using this database is one of the subjects of this workshop.

  13. HIGH SPATIAL-RESOLUTION IMAGING OF TE INCLUSIONS IN CZT MATERIAL

    International Nuclear Information System (INIS)

    CAMARDA, G.S.; BOLOTNIKOV, A.E.; CARINI, G.A.; CUI, Y.; KOHMAN, K.T.; LI, L.; JAMES, R.B.

    2006-01-01

    We present new results from our studies of defects in current single-crystal CdZnTe material. Our previous measurements, carried out on thin (∼1 mm) and long (>12 mm) CZT detectors, indicated that small (1-20 (micro)m) Te inclusions can significantly degrade the device's energy resolution and detection efficiency. We are conducting detailed studies of the effects of Te inclusions by employing different characterization techniques with better spatial resolution, such as quantitative fluorescence mapping, X-ray micro-diffraction, and TEM. Also, IR microscopy and gamma-mapping with pulse-shape analysis with higher spatial resolution generated more accurate results in the areas surrounding the micro-defects (Te inclusions). Our results reveal how the performance of CdZnTe detectors is influenced by Te inclusions, such as their spatial distribution, concentration, and size. We also discuss a model of charge transport through areas populated with Te inclusions

  14. Preliminary frequency-domain analysis for the reconstructed spatial resolution of muon tomography

    Science.gov (United States)

    Yu, B.; Zhao, Z.; Wang, X.; Wang, Y.; Wu, D.; Zeng, Z.; Zeng, M.; Yi, H.; Luo, Z.; Yue, X.; Cheng, J.

    2014-11-01

    Muon tomography is an advanced technology to non-destructively detect high atomic number materials. It exploits the multiple Coulomb scattering information of muon to reconstruct the scattering density image of the traversed object. Because of the statistics of muon scattering, the measurement error of system and the data incompleteness, the reconstruction is always accompanied with a certain level of interference, which will influence the reconstructed spatial resolution. While statistical noises can be reduced by extending the measuring time, system parameters determine the ultimate spatial resolution that one system can reach. In this paper, an effective frequency-domain model is proposed to analyze the reconstructed spatial resolution of muon tomography. The proposed method modifies the resolution analysis in conventional computed tomography (CT) to fit the different imaging mechanism in muon scattering tomography. The measured scattering information is described in frequency domain, then a relationship between the measurements and the original image is proposed in Fourier domain, which is named as "Muon Central Slice Theorem". Furthermore, a preliminary analytical expression of the ultimate reconstructed spatial is derived, and the simulations are performed for validation. While the method is able to predict the ultimate spatial resolution of a given system, it can also be utilized for the optimization of system design and construction.

  15. Preliminary frequency-domain analysis for the reconstructed spatial resolution of muon tomography

    International Nuclear Information System (INIS)

    Yu, B.; Zhao, Z.; Wang, X.; Wang, Y.; Wu, D.; Zeng, Z.; Zeng, M.; Yi, H.; Luo, Z.; Yue, X.; Cheng, J.

    2014-01-01

    Muon tomography is an advanced technology to non-destructively detect high atomic number materials. It exploits the multiple Coulomb scattering information of muon to reconstruct the scattering density image of the traversed object. Because of the statistics of muon scattering, the measurement error of system and the data incompleteness, the reconstruction is always accompanied with a certain level of interference, which will influence the reconstructed spatial resolution. While statistical noises can be reduced by extending the measuring time, system parameters determine the ultimate spatial resolution that one system can reach. In this paper, an effective frequency-domain model is proposed to analyze the reconstructed spatial resolution of muon tomography. The proposed method modifies the resolution analysis in conventional computed tomography (CT) to fit the different imaging mechanism in muon scattering tomography. The measured scattering information is described in frequency domain, then a relationship between the measurements and the original image is proposed in Fourier domain, which is named as M uon Central Slice Theorem . Furthermore, a preliminary analytical expression of the ultimate reconstructed spatial is derived, and the simulations are performed for validation. While the method is able to predict the ultimate spatial resolution of a given system, it can also be utilized for the optimization of system design and construction

  16. Spatial resolution limits for the isotropic-3D PET detector X’tal cube

    Energy Technology Data Exchange (ETDEWEB)

    Yoshida, Eiji, E-mail: rush@nirs.go.jp; Tashima, Hideaki; Hirano, Yoshiyuki; Inadama, Naoko; Nishikido, Fumihiko; Murayama, Hideo; Yamaya, Taiga

    2013-11-11

    Positron emission tomography (PET) has become a popular imaging method in metabolism, neuroscience, and molecular imaging. For dedicated human brain and small animal PET scanners, high spatial resolution is needed to visualize small objects. To improve the spatial resolution, we are developing the X’tal cube, which is our new PET detector to achieve isotropic 3D positioning detectability. We have shown that the X’tal cube can achieve 1 mm{sup 3} uniform crystal identification performance with the Anger-type calculation even at the block edges. We plan to develop the X’tal cube with even smaller 3D grids for sub-millimeter crystal identification. In this work, we investigate spatial resolution of a PET scanner based on the X’tal cube using Monte Carlo simulations for predicting resolution performance in smaller 3D grids. For spatial resolution evaluation, a point source emitting 511 keV photons was simulated by GATE for all physical processes involved in emission and interaction of positrons. We simulated two types of animal PET scanners. The first PET scanner had a detector ring 14.6 cm in diameter composed of 18 detectors. The second PET scanner had a detector ring 7.8 cm in diameter composed of 12 detectors. After the GATE simulations, we converted the interacting 3D position information to digitalized positions for realistic segmented crystals. We simulated several X’tal cubes with cubic crystals from (0.5 mm){sup 3} to (2 mm){sup 3} in size. Also, for evaluating the effect of DOI resolution, we simulated several X’tal cubes with crystal thickness from (0.5 mm){sup 3} to (9 mm){sup 3}. We showed that sub-millimeter spatial resolution was possible using cubic crystals smaller than (1.0 mm){sup 3} even with the assumed physical processes. Also, the weighted average spatial resolutions of both PET scanners with (0.5 mm){sup 3} cubic crystals were 0.53 mm (14.6 cm ring diameter) and 0.48 mm (7.8 cm ring diameter). For the 7.8 cm ring diameter, spatial

  17. Estimating NOx emissions and surface concentrations at high spatial resolution using OMI

    Science.gov (United States)

    Goldberg, D. L.; Lamsal, L. N.; Loughner, C.; Swartz, W. H.; Saide, P. E.; Carmichael, G. R.; Henze, D. K.; Lu, Z.; Streets, D. G.

    2017-12-01

    In many instances, NOx emissions are not measured at the source. In these cases, remote sensing techniques are extremely useful in quantifying NOx emissions. Using an exponential modified Gaussian (EMG) fitting of oversampled Ozone Monitoring Instrument (OMI) NO2 data, we estimate NOx emissions and lifetimes in regions where these emissions are uncertain. This work also presents a new high-resolution OMI NO2 dataset derived from the NASA retrieval that can be used to estimate surface level concentrations in the eastern United States and South Korea. To better estimate vertical profile shape factors, we use high-resolution model simulations (Community Multi-scale Air Quality (CMAQ) and WRF-Chem) constrained by in situ aircraft observations to re-calculate tropospheric air mass factors and tropospheric NO2 vertical columns during summertime. The correlation between our satellite product and ground NO2 monitors in urban areas has improved dramatically: r2 = 0.60 in new product, r2 = 0.39 in operational product, signifying that this new product is a better indicator of surface concentrations than the operational product. Our work emphasizes the need to use both high-resolution and high-fidelity models in order to re-calculate vertical column data in areas with large spatial heterogeneities in NOx emissions. The methodologies developed in this work can be applied to other world regions and other satellite data sets to produce high-quality region-specific emissions estimates.

  18. Remote Sensing of Vegetation Nitrogen Content for Spatially Explicit Carbon and Water Cycle Estimation

    Science.gov (United States)

    Zhang, Y. L.; Miller, J. R.; Chen, J. M.

    2009-05-01

    Foliage nitrogen concentration is a determinant of photosynthetic capacity of leaves, thereby an important input to ecological models for estimating terrestrial carbon and water budgets. Recently, spectrally continuous airborne hyperspectral remote sensing imagery has proven to be useful for retrieving an important related parameter, total chlorophyll content at both leaf and canopy scales. Thus remote sensing of vegetation biochemical parameters has promising potential for improving the prediction of global carbon and water balance patterns. In this research, we explored the feasibility of estimating leaf nitrogen content using hyperspectral remote sensing data for spatially explicit estimation of carbon and water budgets. Multi-year measurements of leaf biochemical contents of seven major boreal forest species were carried out in northeastern Ontario, Canada. The variation of leaf chlorophyll and nitrogen content in response to various growth conditions, and the relationship between them,were investigated. Despite differences in plant type (deciduous and evergreen), leaf age, stand growth conditions and developmental stages, leaf nitrogen content was strongly correlated with leaf chlorophyll content on a mass basis during the active growing season (r2=0.78). With this general correlation, leaf nitrogen content was estimated from leaf chlorophyll content at an accuracy of RMSE=2.2 mg/g, equivalent to 20.5% of the average measured leaf nitrogen content. Based on this correlation and a hyperspectral remote sensing algorithm for leaf chlorophyll content retrieval, the spatial variation of leaf nitrogen content was inferred from the airborne hyperspectral remote sensing imagery acquired by Compact Airborne Spectrographic Imager (CASI). A process-based ecological model Boreal Ecosystem Productivity Simulator (BEPS) was used for estimating terrestrial carbon and water budgets. In contrast to the scenario with leaf nitrogen content assigned as a constant value without

  19. Site-characterization information using LANDSAT satellite and other remote-sensing data: integration of remote-sensing data with geographic information systems. A case study in Pennsylvania

    International Nuclear Information System (INIS)

    Campbell, W.J.; Imhoff, M.L.; Robinson, J.; Gunther, F.; Boyd, R.; Anuta, M.

    1983-06-01

    The utility and cost effectiveness of incorporating digitized aircraft and satellite remote sensing data into a geographic information system for facility siting and environmental impact assessments was evaluated. This research focused on the evaluation of several types of multisource remotely sensed data representing a variety of spectral band widths and spatial resolution. High resolution aircraft photography, Landsat MSS, and 7 band Thematic Mapper Simulator (TMS) data were acquired, analyzed, and evaluated for their suitability as input to an operational geographic information system (GIS). 78 references, 59 figures, 74 tables

  20. Combining the Pixel-based and Object-based Methods for Building Change Detection Using High-resolution Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    ZHANG Zhiqiang

    2018-01-01

    Full Text Available Timely and accurate change detection of buildings provides important information for urban planning and management.Accompanying with the rapid development of satellite remote sensing technology,detecting building changes from high-resolution remote sensing images have received wide attention.Given that pixel-based methods of change detection often lead to low accuracy while object-based methods are complicated for uses,this research proposes a method that combines pixel-based and object-based methods for detecting building changes from high-resolution remote sensing images.First,based on the multiple features extracted from the high-resolution images,a random forest classifier is applied to detect changed building at the pixel level.Then,a segmentation method is applied to segement the post-phase remote sensing image and to get post-phase image objects.Finally,both changed building at the pixel level and post-phase image objects are fused to recognize the changed building objects.Multi-temporal QuickBird images are used as experiment data for building change detection with high-resolution remote sensing images,the results indicate that the proposed method could reduce the influence of environmental difference,such as light intensity and view angle,on building change detection,and effectively improve the accuracies of building change detection.

  1. Advancing the quantification of humid tropical forest cover loss with multi-resolution optical remote sensing data: Sampling & wall-to-wall mapping

    Science.gov (United States)

    Broich, Mark

    Humid tropical forest cover loss is threatening the sustainability of ecosystem goods and services as vast forest areas are rapidly cleared for industrial scale agriculture and tree plantations. Despite the importance of humid tropical forest in the provision of ecosystem services and economic development opportunities, the spatial and temporal distribution of forest cover loss across large areas is not well quantified. Here I improve the quantification of humid tropical forest cover loss using two remote sensing-based methods: sampling and wall-to-wall mapping. In all of the presented studies, the integration of coarse spatial, high temporal resolution data with moderate spatial, low temporal resolution data enable advances in quantifying forest cover loss in the humid tropics. Imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used as the source of coarse spatial resolution, high temporal resolution data and imagery from the Landsat Enhanced Thematic Mapper Plus (ETM+) sensor are used as the source of moderate spatial, low temporal resolution data. In a first study, I compare the precision of different sampling designs for the Brazilian Amazon using the annual deforestation maps derived by the Brazilian Space Agency for reference. I show that sampling designs can provide reliable deforestation estimates; furthermore, sampling designs guided by MODIS data can provide more efficient estimates than the systematic design used for the United Nations Food and Agricultural Organization Forest Resource Assessment 2010. Sampling approaches, such as the one demonstrated, are viable in regions where data limitations, such as cloud contamination, limit exhaustive mapping methods. Cloud-contaminated regions experiencing high rates of change include Insular Southeast Asia, specifically Indonesia and Malaysia. Due to persistent cloud cover, forest cover loss in Indonesia has only been mapped at a 5-10 year interval using photo interpretation of single

  2. The effects of transient attention on spatial resolution and the size of the attentional cue.

    Science.gov (United States)

    Yeshurun, Yaffa; Carrasco, Marisa

    2008-01-01

    It has been shown that transient attention enhances spatial resolution, but is the effect of transient attention on spatial resolution modulated by the size of the attentional cue? Would a gradual increase in the size of the cue lead to a gradual decrement in spatial resolution? To test these hypotheses, we used a texture segmentation task in which performance depends on spatial resolution, and systematically manipulated the size of the attentional cue: A bar of different lengths (Experiment 1) or a frame of different sizes (Experiments 2-3) indicated the target region in a texture segmentation display. Observers indicated whether a target patch region (oriented line elements in a background of an orthogonal orientation), appearing at a range of eccentricities, was present in the first or the second interval. We replicated the attentional enhancement of spatial resolution found with small cues; attention improved performance at peripheral locations but impaired performance at central locations. However, there was no evidence of gradual resolution decrement with large cues. Transient attention enhanced spatial resolution at the attended location when it was attracted to that location by a small cue but did not affect resolution when it was attracted by a large cue. These results indicate that transient attention cannot adapt its operation on spatial resolution on the basis of the size of the attentional cue.

  3. Study of spatial resolution of coordinate detectors based on Gas Electron Multipliers

    Science.gov (United States)

    Kudryavtsev, V. N.; Maltsev, T. V.; Shekhtman, L. I.

    2017-02-01

    Spatial resolution of GEM-based tracking detectors is determined in the simulation and measured in the experiments. The simulation includes GEANT4 implemented transport of high energy electrons with careful accounting of atomic relaxation processes including emission of fluorescent photons and Auger electrons and custom post-processing with accounting of diffusion, gas amplification fluctuations, distribution of signals on readout electrodes, electronics noise and particular algorithm of final coordinate calculation (center of gravity). The simulation demonstrates that the minimum of spatial resolution of about 10 μm can be achieved with a gas mixture of Ar -CO2 (75-25 %) at a strips pitch from 250 μm to 300 μm. At a larger pitch the resolution quickly degrades reaching 80-100 μm at a pitch of 460-500 μm. Spatial resolution of low-material triple-GEM detectors for the DEUTERON facility at the VEPP-3 storage ring is measured at the extracted beam facility of the VEPP-4 M collider. One-coordinate resolution of the DEUTERON detector is measured with electron beam of 500 MeV, 1 GeV and 3.5 GeV energies. The determined value of spatial resolution varies in the range from approximately 35 μm to 50 μm for orthogonal tracks in the experiments.

  4. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision and hobbyist unmanned aerial vehicles

    Science.gov (United States)

    Dandois, J. P.; Ellis, E. C.

    2013-12-01

    High spatial resolution three-dimensional (3D) measurements of vegetation by remote sensing are advancing ecological research and environmental management. However, substantial economic and logistical costs limit this application, especially for observing phenological dynamics in ecosystem structure and spectral traits. Here we demonstrate a new aerial remote sensing system enabling routine and inexpensive aerial 3D measurements of canopy structure and spectral attributes, with properties similar to those of LIDAR, but with RGB (red-green-blue) spectral attributes for each point, enabling high frequency observations within a single growing season. This 'Ecosynth' methodology applies photogrammetric ''Structure from Motion'' computer vision algorithms to large sets of highly overlapping low altitude (USA. Ecosynth canopy height maps (CHMs) were strong predictors of field-measured tree heights (R2 0.63 to 0.84) and were highly correlated with a LIDAR CHM (R 0.87) acquired 4 days earlier, though Ecosynth-based estimates of aboveground biomass densities included significant errors (31 - 36% of field-based estimates). Repeated scanning of a 0.25 ha forested area at six different times across a 16 month period revealed ecologically significant dynamics in canopy color at different heights and a structural shift upward in canopy density, as demonstrated by changes in vertical height profiles of point density and relative RGB brightness. Changes in canopy relative greenness were highly correlated (R2 = 0.88) with MODIS NDVI time series for the same area and vertical differences in canopy color revealed the early green up of the dominant canopy species, Liriodendron tulipifera, strong evidence that Ecosynth time series measurements capture vegetation structural and spectral dynamics at the spatial scale of individual trees. Observing canopy phenology in 3D at high temporal resolutions represents a breakthrough in forest ecology. Inexpensive user-deployed technologies for

  5. Remote Sensing of River Delta Inundation: Exploiting the Potential of Coarse Spatial Resolution, Temporally-Dense MODIS Time Series

    Directory of Open Access Journals (Sweden)

    Claudia Kuenzer

    2015-07-01

    Full Text Available River deltas belong to the most densely settled places on earth. Although they only account for 5% of the global land surface, over 550 million people live in deltas. These preferred livelihood locations, which feature flat terrain, fertile alluvial soils, access to fluvial and marine resources, a rich wetland biodiversity and other advantages are, however, threatened by numerous internal and external processes. Socio-economic development, urbanization, climate change induced sea level rise, as well as flood pulse changes due to upstream water diversion all lead to changes in these highly dynamic systems. A thorough understanding of a river delta’s general setting and intra-annual as well as long-term dynamic is therefore crucial for an informed management of natural resources. Here, remote sensing can play a key role in analyzing and monitoring these vast areas at a global scale. The goal of this study is to demonstrate the potential of intra-annual time series analyses at dense temporal, but coarse spatial resolution for inundation characterization in five river deltas located in four different countries. Based on 250 m MODIS reflectance data we analyze inundation dynamics in four densely populated Asian river deltas—namely the Yellow River Delta (China, the Mekong Delta (Vietnam, the Irrawaddy Delta (Myanmar, and the Ganges-Brahmaputra (Bangladesh, India—as well as one very contrasting delta: the nearly uninhabited polar Mackenzie Delta Region in northwestern Canada for the complete time span of one year (2013. A complex processing chain of water surface derivation on a daily basis allows the generation of intra-annual time series, which indicate inundation duration in each of the deltas. Our analyses depict distinct inundation patterns within each of the deltas, which can be attributed to processes such as overland flooding, irrigation agriculture, aquaculture, or snowmelt and thermokarst processes. Clear differences between mid

  6. Objective Tuning of Model Parameters in CAM5 Across Different Spatial Resolutions

    Science.gov (United States)

    Bulaevskaya, V.; Lucas, D. D.

    2014-12-01

    Parameterizations of physical processes in climate models are highly dependent on the spatial and temporal resolution and must be tuned for each resolution under consideration. At high spatial resolutions, objective methods for parameter tuning are computationally prohibitive. Our work has focused on calibrating parameters in the Community Atmosphere Model 5 (CAM5) for three spatial resolutions: 1, 2, and 4 degrees. Using perturbed-parameter ensembles and uncertainty quantification methodology, we have identified input parameters that minimize discrepancies of energy fluxes simulated by CAM5 across the three resolutions and with respect to satellite observations. We are also beginning to exploit the parameter-resolution relationships to objectively tune parameters in a high-resolution version of CAM5 by leveraging cheaper, low-resolution simulations and statistical models. We will present our approach to multi-resolution climate model parameter tuning, as well as the key findings. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344 and was supported from the DOE Office of Science through the Scientific Discovery Through Advanced Computing (SciDAC) project on Multiscale Methods for Accurate, Efficient, and Scale-Aware Models of the Earth System.

  7. Methods for Enhancing Geological Structures in Spectral Spatial Difference-Based on Remote-Sensing Image

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    @@In this paper, some image processing methods such as directional template (mask) matching enhancement, pseudocolor or false color enhancement, K-L transform enhancement are used to enhance a geological structure, one of important ore-controlling factors, shown in the remote-sensing images.This geological structure is regarded as image anomaly in the remote-sensing image, since considerable differences, based on the spatial spectral distribution pattern, in gray values (spectral), color tones and texture, are always present between the geological structure and background. Therefore,the enhancement of the geological structure in the remotesensing image is that of the spectral spatial difference.

  8. Scaling of surface energy fluxes using remotely sensed data

    Science.gov (United States)

    French, Andrew Nichols

    Accurate estimates of evapotranspiration (ET) across multiple terrains would greatly ease challenges faced by hydrologists, climate modelers, and agronomists as they attempt to apply theoretical models to real-world situations. One ET estimation approach uses an energy balance model to interpret a combination of meteorological observations taken at the surface and data captured by remote sensors. However, results of this approach have not been accurate because of poor understanding of the relationship between surface energy flux and land cover heterogeneity, combined with limits in available resolution of remote sensors. The purpose of this study was to determine how land cover and image resolution affect ET estimates. Using remotely sensed data collected over El Reno, Oklahoma, during four days in June and July 1997, scale effects on the estimation of spatially distributed ET were investigated. Instantaneous estimates of latent and sensible heat flux were calculated using a two-source surface energy balance model driven by thermal infrared, visible-near infrared, and meteorological data. The heat flux estimates were verified by comparison to independent eddy-covariance observations. Outcomes of observations taken at coarser resolutions were simulated by aggregating remote sensor data and estimated surface energy balance components from the finest sensor resolution (12 meter) to hypothetical resolutions as coarse as one kilometer. Estimated surface energy flux components were found to be significantly dependent on observation scale. For example, average evaporative fraction varied from 0.79, using 12-m resolution data, to 0.93, using 1-km resolution data. Resolution effects upon flux estimates were related to a measure of landscape heterogeneity known as operational scale, reflecting the size of dominant landscape features. Energy flux estimates based on data at resolutions less than 100 m and much greater than 400 m showed a scale-dependent bias. But estimates

  9. Advances in the application of remote sensing and GIS for surveying mountainous land

    NARCIS (Netherlands)

    Mulders, M.A.

    2001-01-01

    Satellite remote sensing has been practised since 1972, starting with broad channels and moderate ground resolution (Landsat MSS). In the 1980s, Landsat TM and SPOT provided for improved spatial and spectral resolutions. Many satellite images were produced in these two decades, offering a synoptic

  10. Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

    Directory of Open Access Journals (Sweden)

    Yansheng Li

    2016-08-01

    Full Text Available With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF. In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP, gray level co-occurrence (GLCM, maximal response 8 (MR8, and scale-invariant feature transform (SIFT. In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM dataset and the Wuhan University (WH dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.

  11. Functional classification of spatially heterogeneous environments: the Land Cover Mosaic approach in remote sensing

    NARCIS (Netherlands)

    Obbink, M.H.

    2011-01-01

    Tropical rainforest areas are difficult to classify in the digital analysis of remote sensing data because of spatial heterogeneity. Often many technical solutions are adopted to reduce the ‘problem’ of spatial heterogeneity. This thesis describes theory and methods that now use this

  12. Spatial Resolution of the ECE for JET Typical Parameters

    International Nuclear Information System (INIS)

    Tribaldos, V.

    2000-01-01

    The purpose of this report is to obtain estimations of the spatial resolution of the electron cyclotron emission (ECE) phenomena for the typical plasmas found in JET tokamak. The analysis of the spatial resolution of the ECE is based on the underlying physical process of emission and a working definition is presented and discussed. In making these estimations a typical JET pulse is being analysed taking into account the magnetic configuration, the density and temperature profiles, obtained with the EFIT code and from the LIDAR diagnostic. Ray tracing simulations are performed for a Maxwellian plasma taking into account the antenna pattern. (Author) 5 refs

  13. A Review of Ocean/Sea Subsurface Water Temperature Studies from Remote Sensing and Non-Remote Sensing Methods

    Directory of Open Access Journals (Sweden)

    Elahe Akbari

    2017-12-01

    Full Text Available Oceans/Seas are important components of Earth that are affected by global warming and climate change. Recent studies have indicated that the deeper oceans are responsible for climate variability by changing the Earth’s ecosystem; therefore, assessing them has become more important. Remote sensing can provide sea surface data at high spatial/temporal resolution and with large spatial coverage, which allows for remarkable discoveries in the ocean sciences. The deep layers of the ocean/sea, however, cannot be directly detected by satellite remote sensors. Therefore, researchers have examined the relationships between salinity, height, and temperature of the oceans/Seas to estimate their subsurface water temperature using dynamical models and model-based data assimilation (numerical based and statistical approaches, which simulate these parameters by employing remotely sensed data and in situ measurements. Due to the requirements of comprehensive perception and the importance of global warming in decision making and scientific studies, this review provides comprehensive information on the methods that are used to estimate ocean/sea subsurface water temperature from remotely and non-remotely sensed data. To clarify the subsurface processes, the challenges, limitations, and perspectives of the existing methods are also investigated.

  14. Context-dependent modulation of hippocampal and cortical recruitment during remote spatial memory retrieval.

    Science.gov (United States)

    Lopez, Joëlle; Herbeaux, Karin; Cosquer, Brigitte; Engeln, Michel; Muller, Christophe; Lazarus, Christine; Kelche, Christian; Bontempi, Bruno; Cassel, Jean-Christophe; de Vasconcelos, Anne Pereira

    2012-04-01

    According to systems consolidation, as hippocampal-dependent memories mature over time, they become additionally (or exclusively) dependent on extra-hippocampal structures. We assessed the recruitment of hippocampal and cortical structures on remote memory retrieval in a performance-degradation resistant (PDR; no performance degradation with time) versus performance-degradation prone (PDP; performance degraded with time) context. Using a water-maze task in two contexts with a hidden platform and three control conditions (home cage, visible platform with or without access to distal cues), we compared neuronal activation (c-Fos imaging) patterns in the dorsal hippocampus and the medial prefrontal cortex (mPFC) after the retrieval of recent (5 days) versus remote (25 days) spatial memory. In the PDR context, the hippocampus exhibited greater c-Fos protein expression on remote than recent memory retrieval, be it in the visible or hidden platform group. In the PDP context, hippocampal activation increased at the remote time point and only in the hidden platform group. In the anterior cingulate cortex, c-Fos expression was greater for remote than for recent memory retrieval and only in the PDR context. The necessity of the mPFC for remote memory retrieval in the PDR context was confirmed using region-specific lidocaine inactivation, which had no impact on recent memory. Conversely, inactivation of the dorsal hippocampus impaired both recent and remote memory in the PDR context, and only recent memory in the PDP context, in which remote memory performance was degraded. While confirming that neuronal circuits supporting spatial memory consolidation are reorganized in a time-dependent manner, our findings further indicate that mPFC and hippocampus recruitment (i) depends on the content and perhaps the strength of the memory and (ii) may be influenced by the environmental conditions (e.g., cue saliency, complexity) in which memories are initially formed and subsequently

  15. Remote sensing data handling to improve the system integration of indonesian national spatial data infrastructure

    International Nuclear Information System (INIS)

    Hari, G. R. V.

    2010-01-01

    With the usage of metadata as a reference for spatial data query, remote sensing images and other spatial datasets have been linked to their related semantic information. In the current catalogue systems, like those or satellite data provides, or clearinghouses, each remote sensing image is maintained as an independent entity. There is a very limited possibility to know the linkage of one image to another, even if one image has actually been derived from the other. It is an advantage for many purposes if the linkage among remote sensing image or other spatial data can be maintained or at least reconstructed. This research will explore how an image is linked to its related information, and how an image can be linked to another images. By exploring links among remote sensing images, a query of remote sensing data collection can be extended, for example, to find the answer of the query: 'which images are used to create certain dataset?', or 'which images have been created from a concrete dataset?', or 'is there a relationship between image A and image B based on their processing steps?'. By building links among spatial datasets in a collection based on their creation process, a further possibility of spatial data organization can be supported. The applicability and compatibility of the proposed method with the current platform is also considered. The proposed method can be implemented using the same standard and protocol and using the same metadata file as used by the existing system. This approach makes it also possible to be implemented in many countries which use the same infrastructure. To prove this purpose, we develop a prototype based on open source platform, including PostgreSQL, Apache Webserver, Mapserver WebGIS, and PHP programming environment. The output of this research leads to an improvement of spatial data handling, where an adjacency list is used to maintain spatial dataset history link. This improvement can enhance the query of spatial data in a

  16. Limits of a spatial resolution of the cascaded GEM based detectors

    International Nuclear Information System (INIS)

    Kudryavtsev, V.N.; Maltsev, T.V.; Shekhtman, L.I.

    2017-01-01

    Spatial resolution of tracking detectors based on GEM cascades is determined in the simulation and measured. The simulation includes GEANT4 implemented transport of high energy electrons with careful accounting for atomic relaxation processes including emission of fluorescent photons and Auger electrons and custom post-processing taking into account diffusion, gas amplification fluctuations, the distribution of signals over readout electrodes, electronics noise and particular algorithm of final coordinate calculation (centre-of-gravity algorithm). The simulation demonstrates that the minimum of the spatial resolution of about 10–20 μm can be achieved with a gas mixture of Ar-CO 2 (75%–25%) at a strip pitch in the range from 250 μm to 300 μm. At a larger pitch the resolution quickly degrades reaching 70–100 μm at a pitch of 450–500 μm. The reasons of such behavior are discussed and corresponding hypothesis is tested. Particularly, the effect of electron cloud modification due to a GEM operation is considered using the ANSYS and Garfield++ simulation programs. The detection efficiency and spatial resolution of low-material triple-GEM detectors for the DEUTERON facility at BINP are measured at the extracted beam facility of the VEPP-4M collider. One-coordinate resolution of two detectors for the DEUTERON facility is measured with a 2 GeV electron beam. The determined values of the detectors' spatial resolution is equal to 46.6 ± 0.1 μm and 38.5 ± 0.2 μm for orthogonal tracks in two detectors, respectively.

  17. Limits of a spatial resolution of the cascaded GEM based detectors

    Science.gov (United States)

    Kudryavtsev, V. N.; Maltsev, T. V.; Shekhtman, L. I.

    2017-06-01

    Spatial resolution of tracking detectors based on GEM cascades is determined in the simulation and measured. The simulation includes GEANT4 implemented transport of high energy electrons with careful accounting for atomic relaxation processes including emission of fluorescent photons and Auger electrons and custom post-processing taking into account diffusion, gas amplification fluctuations, the distribution of signals over readout electrodes, electronics noise and particular algorithm of final coordinate calculation (centre-of-gravity algorithm). The simulation demonstrates that the minimum of the spatial resolution of about 10-20 μm can be achieved with a gas mixture of Ar-CO2 (75%-25%) at a strip pitch in the range from 250 μm to 300 μm. At a larger pitch the resolution quickly degrades reaching 70-100 μm at a pitch of 450-500 μm. The reasons of such behavior are discussed and corresponding hypothesis is tested. Particularly, the effect of electron cloud modification due to a GEM operation is considered using the ANSYS and Garfield++ simulation programs. The detection efficiency and spatial resolution of low-material triple-GEM detectors for the DEUTERON facility at BINP are measured at the extracted beam facility of the VEPP-4M collider. One-coordinate resolution of two detectors for the DEUTERON facility is measured with a 2 GeV electron beam. The determined values of the detectors' spatial resolution is equal to 46.6 ± 0.1 μm and 38.5 ± 0.2 μm for orthogonal tracks in two detectors, respectively.

  18. Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2008-09

    Science.gov (United States)

    ,

    2009-01-01

    Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is useful for analyzing a wide variety of spatial data. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This fact sheet presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup during 2008 and 2009. After a summary of GIS Workgroup capabilities, brief descriptions of activities by project at the local and national levels are presented. Projects are grouped by the fiscal year (October-September 2008 or 2009) the project ends and include overviews, project images, and Internet links to additional project information and related publications or articles.

  19. Remote sensing of potential lunar resources. 2: High spatial resolution mapping of spectral reflectance ratios and implications for nearside mare TiO2 content`

    Science.gov (United States)

    Melendrez, David E.; Johnson, Jeffrey R.; Larson, Stephen M.; Singer, Robert B.

    1994-01-01

    High spatial resolution maps illustrating variations in spectral reflectance 400/560 nm ratio values have been generated for the following mare regions: (1) the border between southern Mare Serenitatis and northern Mare Tranquillitatis (including the MS-2 standard area and Apollo 17 landing site), (2) central Mare Tranquillitatis, (3) Oceanus Procellarum near Seleucus, and (4) southern Oceanus Procellarum and Flamsteed. We have also obtained 320-1000 nm reflectance spectra of several sites relative to MS-2 to facilitate scaling of the images and provide additional information on surface composition. Inferred TiO2 abundances for these mare regions have been determined using an empirical calibration which relates the weight percent TiO2 in mature mare regolith to the observed 400/560 nm ratio. Mare areas with high TiO2 abundances are probably rich in ilmenite (FeTiO3) a potential lunar resource. The highest potential TiO2 concentrations we have identified in the nearside maria occur in central Mare Tranquillitatis. Inferred TiO2 contents for these areas are greater than 9 wt% and are spatially consistent with the highest-TiO2 regions mapped previously at lower spatial resolution. We note that the morphology of surface units with high 400/560 nm ratio values increases in complexity at higher spatial resolutions. Comparisons have been made with previously published geologic maps, Lunar Orbiter IV, and ground-based images, and some possible morphologic correlatins have been found between our mapped 400/560 nm ratio values and volcanic landforms such as lava flows, mare domes, and collapse pits.

  20. Change Detection in High-Resolution Remote Sensing Images Using Levene-Test and Fuzzy Evaluation

    Science.gov (United States)

    Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Liu, H. J.

    2018-04-01

    High-resolution remote sensing images possess complex spatial structure and rich texture information, according to these, this paper presents a new method of change detection based on Levene-Test and Fuzzy Evaluation. It first got map-spots by segmenting two overlapping images which had been pretreated, extracted features such as spectrum and texture. Then, changed information of all map-spots which had been treated by the Levene-Test were counted to obtain the candidate changed regions, hue information (H component) was extracted through the IHS Transform and conducted change vector analysis combined with the texture information. Eventually, the threshold was confirmed by an iteration method, the subject degrees of candidate changed regions were calculated, and final change regions were determined. In this paper experimental results on multi-temporal ZY-3 high-resolution images of some area in Jiangsu Province show that: Through extracting map-spots of larger difference as the candidate changed regions, Levene-Test decreases the computing load, improves the precision of change detection, and shows better fault-tolerant capacity for those unchanged regions which are of relatively large differences. The combination of Hue-texture features and fuzzy evaluation method can effectively decrease omissions and deficiencies, improve the precision of change detection.

  1. Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation

    Science.gov (United States)

    Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong

    2015-03-01

    A cross-scale data integration method was developed and tested based on the theory of geostatistics and multiple-point geostatistics (MPG). The goal was to downscale remotely sensed images while retaining spatial structure by integrating images at different spatial resolutions. During the process of downscaling, a rich spatial correlation model in the form of a training image was incorporated to facilitate reproduction of similar local patterns in the simulated images. Area-to-point cokriging (ATPCK) was used as locally varying mean (LVM) (i.e., soft data) to deal with the change of support problem (COSP) for cross-scale integration, which MPG cannot achieve alone. Several pairs of spectral bands of remotely sensed images were tested for integration within different cross-scale case studies. The experiment shows that MPG can restore the spatial structure of the image at a fine spatial resolution given the training image and conditioning data. The super-resolution image can be predicted using the proposed method, which cannot be realised using most data integration methods. The results show that ATPCK-MPG approach can achieve greater accuracy than methods which do not account for the change of support issue.

  2. Basic examination of in-plane spatial resolution in multi-slice CT

    International Nuclear Information System (INIS)

    Hara, Takanori; Kato, Hideki; Akiyama, Mitsutoshi; Murata, Katsutoshi

    2002-01-01

    In computed tomography (single-slice spiral CT, conventional CT), in-plane (x-y plane) spatial resolution is consistently identified as depending on the detector density of the in-plane (x-y plane). However, we considered that the in-plane (x-y plane) spatial resolution of multi-slice CT (MSCT) was influenced by an error in the detector's sensitivity to the Z-axis and by the frequency of use of direct row data and complementary row data when the image of spiral pitches (SP) was reconstructed. Our goal in this experiment was to analyze the relationship of the in-plane (x-y plane) spatial resolution of an asymmetric-type detector in MSCT to SP, tube current, and rotation time. By employing a tungsten wire phantom of 0.2 mm in diameter, we examined modulation transfer functions (MTF) by point-spread functions (PSF) of CT-images. Next, using the mean-square-root bandwidth theory, we analyzed the MTF of wire phantoms. The analysis of in-plane (x-y plane) spatial resolution revealed that various tube currents had no effect on the value of the mean-square-root bandwidth. However, rotation time and high spiral pitch did have an effect on mean-square-root bandwidth. Considering the results mentioned above, spiral pitch (z-axis reconstruction algorithm) had a slight effect on in-plane (x-y plane) spatial resolution of asymmetric-type detectors in MSCT. Accordingly, we proposed a new general view of VDDz (view/mm) in MSCT that considered view data density on the Z-axis according to spiral pitch (mm/rotation), rotation time (view/rotation), and slice collimation. (author)

  3. An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

    Directory of Open Access Journals (Sweden)

    Chandi Witharana

    2016-04-01

    Full Text Available The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We systematically gauged the segmentation quality, classification accuracy, and the reproducibility of fuzzy rules. A master ruleset was developed based on one study site and it was re-tasked “without adaptation” and “with adaptation” on candidate image scenes comprising guano stains. Our results suggest that object-based methods incorporating the spectral, textural, spatial, and contextual characteristics of guano are capable of successfully detecting guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while adapted rules produced comparable or superior results compared to the reference image. This work provides a road map to an operational “image-to-assessment pipeline” that will enable Antarctic wildlife researchers to seamlessly integrate VHSR imagery into on-demand penguin population census.

  4. High resolution color imagery for orthomaps and remote sensing

    Energy Technology Data Exchange (ETDEWEB)

    Fricker, Peter [Leica Geosystems GIS and Mapping, LLC (Switzerland); Gallo, M. Guillermo [Leica Geosystems GIS and Mapping, LLC (United States)

    2005-07-01

    The ADS40 Airborne Digital Pushbroom Sensor is currently the only commercial sensor capable of acquiring color and false color strip images in the low decimeter range at the same high resolution as the black and white stereo images. This high resolution of 12,000 pixels across the entire swath and 100% forward overlap in the image strips result in high quality DSM's, True Ortho's and at the same time allow unbiased remote sensing applications due to color strip images unchanged by pan-sharpening. The paper gives details on how the pushbroom sensor achieves these seemingly difficult technical challenges. It describes how a variety of mapping applications benefit from this sensor, a sensor which acts as a satellite pushbroom sensor within the airborne environment. (author)

  5. Analysis of laser-printed spatial resolution for mammographic microcalcification detection

    International Nuclear Information System (INIS)

    Smathers, R.L.; Kowarski, D.

    1987-01-01

    The detectability of microcalfications in mammograms was compared in Kodak Min-R screen-film mammograms versus digitized laser-printed films. Pulverized bone specks were used as the phantoms to produce the original mammograms. The mammograms were then digitized to a spatial resolution of 2,048 x, 2048 with 4,096 gray levels and laser-printed at spatial resolutions of 512 x 512, 1,024 x 1,024, and 2,048 x 2,048 with 256 gray levels. The number of bone specks was determined on a region-by region basis. The 512 x 512 resolution laser-printed images were nondiagnostic, 1,024 x 1,024 images were better, and 2,048 x 2,048 images were quite comparable to the original screen-film mammograms

  6. Tropical forest degradation monitoring using ETM+ and MODIS remote sensing data in the Peninsular Malaysia

    International Nuclear Information System (INIS)

    Hashim, M; Pour, A B; Chong, K W

    2014-01-01

    This study was undertaken in order to test the use of remote sensing technology to assess forest degradation in the Peninsular Malaysia. In order to analyse the effect of spatial resolution on forest degradation assessment, course and moderate spatial resolution remote sensing data were examined in this study. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used as coarse spatial resolution data, while Landsat Enhanced Thematic Mapper + (ETM + ) imagery was used as moderate spatial resolution to compare the accuracy. Geometric and radiometric correction and re-sampling were performed in preprocessing section to enhance the analysis and results. Canopy fractional cover was used as an approach to assess the forest degradation in this study. Then, an optimum vegetation index was selected to apply on canopy fractional cover to enhance the detection of forest canopy damage. At the same time, accuracy assessment for the approach was referred to the location of Neobalanocarpus Heimii and correlate with global evapotranspiration rate. The forest degradation analysis was also applied and compared for all of the states in the Peninsular Malaysia. In conclusion, Landsat ETM + imagery obtained higher accuracy compare to MODIS using canopy fractional cover approach for forest degradation assessment, and can be more broadly applicable to use for forest degradation investigation

  7. Change of spatial information under rescaling: A case study using multi-resolution image series

    Science.gov (United States)

    Chen, Weirong; Henebry, Geoffrey M.

    Spatial structure in imagery depends on a complicated interaction between the observational regime and the types and arrangements of entities within the scene that the image portrays. Although block averaging of pixels has commonly been used to simulate coarser resolution imagery, relatively little attention has been focused on the effects of simple rescaling on spatial structure and the explanation and a possible solution to the problem. Yet, if there are significant differences in spatial variance between rescaled and observed images, it may affect the reliability of retrieved biogeophysical quantities. To investigate these issues, a nested series of high spatial resolution digital imagery was collected at a research site in eastern Nebraska in 2001. An airborne Kodak DCS420IR camera acquired imagery at three altitudes, yielding nominal spatial resolutions ranging from 0.187 m to 1 m. The red and near infrared (NIR) bands of the co-registered image series were normalized using pseudo-invariant features, and the normalized difference vegetation index (NDVI) was calculated. Plots of grain sorghum planted in orthogonal crop row orientations were extracted from the image series. The finest spatial resolution data were then rescaled by averaging blocks of pixels to produce a rescaled image series that closely matched the spatial resolution of the observed image series. Spatial structures of the observed and rescaled image series were characterized using semivariogram analysis. Results for NDVI and its component bands show, as expected, that decreasing spatial resolution leads to decreasing spatial variability and increasing spatial dependence. However, compared to the observed data, the rescaled images contain more persistent spatial structure that exhibits limited variation in both spatial dependence and spatial heterogeneity. Rescaling via simple block averaging fails to consider the effect of scene object shape and extent on spatial information. As the features

  8. Characterizing the Diurnal Cycle of Land Surface Temperature and Evapotranspiration at High Spatial Resolution Using Thermal Observations from sUAS.

    Science.gov (United States)

    Dutta, D.; Drewry, D.; Johnson, W. R.

    2017-12-01

    The surface temperature of plant canopies is an important indicator of the stomatal regulation of plant water use and the associated water flux from plants to atmosphere (evapotranspiration (ET)). Remotely sensed thermal observations using compact, low-cost, lightweight sensors from small unmanned aerial systems (sUAS) have the potential to provide surface temperature (ST) and ET estimates at unprecedented spatial and temporal resolutions, allowing us to characterize the intra-field diurnal variations in canopy ST and ET for a variety of vegetation systems. However, major challenges exist for obtaining accurate surface temperature estimates from low-cost uncooled microbolometer-type sensors. Here we describe the development of calibration methods using thermal chamber experiments, taking into account the ambient optics and sensor temperatures, and applying simple models of spatial non-uniformity correction to the sensor focal-plane-array. We present a framework that can be used to derive accurate surface temperatures using radiometric observations from low-cost sensors, and demonstrate this framework using a sUAS-mounted sensor across a diverse set of calibration and vegetation targets. Further, we demonstrate the use of the Surface Temperature Initiated Closure (STIC) model for computing spatially explicit, high spatial resolution ET estimates across several well-monitored agricultural systems, as driven by sUAS acquired surface temperatures. STIC provides a physically-based surface energy balance framework for the simultaneous retrieval of the surface and atmospheric vapor conductances and surface energy fluxes, by physically integrating radiometric surface temperature information into the Penman-Monteith equation. Results of our analysis over agricultural systems in Ames, IA and Davis, CA demonstrate the power of this approach for quantifying the intra-field spatial variability in the diurnal cycle of plant water use at sub-meter resolutions.

  9. Electron energy-loss spectrometry at the frontier of spatial and energy resolution

    International Nuclear Information System (INIS)

    Hofer, F.; Grogger, W.; Kothleitner, G.

    2004-01-01

    Full text: Electron energy-loss spectroscopy (EELS) in the transmission electron microscope (TEM) is now used routinely as a means of measuring chemical and structural properties of very small regions of a thin specimen. The power of this technique depends significantly on two parameters: its spatial resolution and the energy resolution available in the spectrum and in the energy-filtered TEM (EFTEM) image. The cold field emission source and the Schottky emitter have made an energy resolution below 1 eV possible and it is now feasible to obtain data with a spatial resolution close to atomic dimensions, given the right instrumentation and specimen. EFTEM allows to record elemental maps at sub-nanometre resolution, being mainly limited by chromatic and spherical aberration of the objective lens and by delocalization of inelastic scattering. Recently the possibility of correcting spherical and even chromatic aberrations of electron lenses has become a practical reality thus improving the point resolution of the TEM to below 0.1 nm. The other limiting factor for EFTEM resolution is delocalization. However, recent measurements show that resolution values in the range of 1 nm and below can be achieved, even for energy-losses of only a few eV. In terms of energy-resolution, EELS and EFTEM compare less favourably with other spectroscopies. For common TEMs, the overall energy-resolution is mainly determined by the energy width of the electron source, typically between 0.5 and 1.5 eV. For comparison, synchrotron x-ray sources and beam line spectrometers, provide a resolution well below 0.1 eV for absorption spectroscopy. During the early sixties, the energy spread of an electron beam could be reduced by incorporating an energy-filter into the illumination system, but the system lacked spatial resolution. Later developments combined high energy resolution in the range of 0.1 eV with improved spatial resolution. Recently, FEI introduced a new high resolution EELS system based

  10. Scene Classification Using High Spatial Resolution Multispectral Data

    National Research Council Canada - National Science Library

    Garner, Jamada

    2002-01-01

    ...), High-spatial resolution (8-meter), 4-color MSI data from IKONOS provide a new tool for scene classification, The utility of these data are studied for the purpose of classifying the Elkhorn Slough and surrounding wetlands in central...

  11. A flexible spatiotemporal method for fusing satellite images with different resolutions

    Science.gov (United States)

    Xiaolin Zhu; Eileen H. Helmer; Feng Gao; Desheng Liu; Jin Chen; Michael A. Lefsky

    2016-01-01

    Studies of land surface dynamics in heterogeneous landscapes often require remote sensing datawith high acquisition frequency and high spatial resolution. However, no single sensor meets this requirement. This study presents a new spatiotemporal data fusion method, the Flexible Spatiotemporal DAta Fusion (FSDAF) method, to generate synthesized frequent high spatial...

  12. Connotations of pixel-based scale effect in remote sensing and the modified fractal-based analysis method

    Science.gov (United States)

    Feng, Guixiang; Ming, Dongping; Wang, Min; Yang, Jianyu

    2017-06-01

    Scale problems are a major source of concern in the field of remote sensing. Since the remote sensing is a complex technology system, there is a lack of enough cognition on the connotation of scale and scale effect in remote sensing. Thus, this paper first introduces the connotations of pixel-based scale and summarizes the general understanding of pixel-based scale effect. Pixel-based scale effect analysis is essentially important for choosing the appropriate remote sensing data and the proper processing parameters. Fractal dimension is a useful measurement to analysis pixel-based scale. However in traditional fractal dimension calculation, the impact of spatial resolution is not considered, which leads that the scale effect change with spatial resolution can't be clearly reflected. Therefore, this paper proposes to use spatial resolution as the modified scale parameter of two fractal methods to further analyze the pixel-based scale effect. To verify the results of two modified methods (MFBM (Modified Windowed Fractal Brownian Motion Based on the Surface Area) and MDBM (Modified Windowed Double Blanket Method)); the existing scale effect analysis method (information entropy method) is used to evaluate. And six sub-regions of building areas and farmland areas were cut out from QuickBird images to be used as the experimental data. The results of the experiment show that both the fractal dimension and information entropy present the same trend with the decrease of spatial resolution, and some inflection points appear at the same feature scales. Further analysis shows that these feature scales (corresponding to the inflection points) are related to the actual sizes of the geo-object, which results in fewer mixed pixels in the image, and these inflection points are significantly indicative of the observed features. Therefore, the experiment results indicate that the modified fractal methods are effective to reflect the pixel-based scale effect existing in remote sensing

  13. Illumination invariant feature point matching for high-resolution planetary remote sensing images

    Science.gov (United States)

    Wu, Bo; Zeng, Hai; Hu, Han

    2018-03-01

    Despite its success with regular close-range and remote-sensing images, the scale-invariant feature transform (SIFT) algorithm is essentially not invariant to illumination differences due to the use of gradients for feature description. In planetary remote sensing imagery, which normally lacks sufficient textural information, salient regions are generally triggered by the shadow effects of keypoints, reducing the matching performance of classical SIFT. Based on the observation of dual peaks in a histogram of the dominant orientations of SIFT keypoints, this paper proposes an illumination-invariant SIFT matching method for high-resolution planetary remote sensing images. First, as the peaks in the orientation histogram are generally aligned closely with the sub-solar azimuth angle at the time of image collection, an adaptive suppression Gaussian function is tuned to level the histogram and thereby alleviate the differences in illumination caused by a changing solar angle. Next, the suppression function is incorporated into the original SIFT procedure for obtaining feature descriptors, which are used for initial image matching. Finally, as the distribution of feature descriptors changes after anisotropic suppression, and the ratio check used for matching and outlier removal in classical SIFT may produce inferior results, this paper proposes an improved matching procedure based on cross-checking and template image matching. The experimental results for several high-resolution remote sensing images from both the Moon and Mars, with illumination differences of 20°-180°, reveal that the proposed method retrieves about 40%-60% more matches than the classical SIFT method. The proposed method is of significance for matching or co-registration of planetary remote sensing images for their synergistic use in various applications. It also has the potential to be useful for flyby and rover images by integrating with the affine invariant feature detectors.

  14. Supervised Classification High-Resolution Remote-Sensing Image Based on Interval Type-2 Fuzzy Membership Function

    Directory of Open Access Journals (Sweden)

    Chunyan Wang

    2018-05-01

    Full Text Available Because of the degradation of classification accuracy that is caused by the uncertainty of pixel class and classification decisions of high-resolution remote-sensing images, we proposed a supervised classification method that is based on an interval type-2 fuzzy membership function for high-resolution remote-sensing images. We analyze the data features of a high-resolution remote-sensing image and construct a type-1 membership function model in a homogenous region by supervised sampling in order to characterize the uncertainty of the pixel class. On the basis of the fuzzy membership function model in the homogeneous region and in accordance with the 3σ criterion of normal distribution, we proposed a method for modeling three types of interval type-2 membership functions and analyze the different types of functions to improve the uncertainty of pixel class expressed by the type-1 fuzzy membership function and to enhance the accuracy of classification decision. According to the principle that importance will increase with a decrease in the distance between the original, upper, and lower fuzzy membership of the training data and the corresponding frequency value in the histogram, we use the weighted average sum of three types of fuzzy membership as the new fuzzy membership of the pixel to be classified and then integrated into the neighborhood pixel relations, constructing a classification decision model. We use the proposed method to classify real high-resolution remote-sensing images and synthetic images. Additionally, we qualitatively and quantitatively evaluate the test results. The results show that a higher classification accuracy can be achieved with the proposed algorithm.

  15. Gamma-Ray Imager With High Spatial And Spectral Resolution

    Science.gov (United States)

    Callas, John L.; Varnell, Larry S.; Wheaton, William A.; Mahoney, William A.

    1996-01-01

    Gamma-ray instrument developed to enable both two-dimensional imaging at relatively high spatial resolution and spectroscopy at fractional-photon-energy resolution of about 10 to the negative 3rd power in photon-energy range from 10 keV to greater than 10 MeV. In its spectroscopic aspect, instrument enables identification of both narrow and weak gamma-ray spectral peaks.

  16. A DATA FIELD METHOD FOR URBAN REMOTELY SENSED IMAGERY CLASSIFICATION CONSIDERING SPATIAL CORRELATION

    Directory of Open Access Journals (Sweden)

    Y. Zhang

    2016-06-01

    Full Text Available Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary’s C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM for the classification of multi-features (e.g. the spectral feature and spatial correlation feature. In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.

  17. Long-term monitoring on environmental disasters using multi-source remote sensing technique

    Science.gov (United States)

    Kuo, Y. C.; Chen, C. F.

    2017-12-01

    Environmental disasters are extreme events within the earth's system that cause deaths and injuries to humans, as well as causing damages and losses of valuable assets, such as buildings, communication systems, farmlands, forest and etc. In disaster management, a large amount of multi-temporal spatial data is required. Multi-source remote sensing data with different spatial, spectral and temporal resolutions is widely applied on environmental disaster monitoring. With multi-source and multi-temporal high resolution images, we conduct rapid, systematic and seriate observations regarding to economic damages and environmental disasters on earth. It is based on three monitoring platforms: remote sensing, UAS (Unmanned Aircraft Systems) and ground investigation. The advantages of using UAS technology include great mobility and availability in real-time rapid and more flexible weather conditions. The system can produce long-term spatial distribution information from environmental disasters, obtaining high-resolution remote sensing data and field verification data in key monitoring areas. It also supports the prevention and control on ocean pollutions, illegally disposed wastes and pine pests in different scales. Meanwhile, digital photogrammetry can be applied on the camera inside and outside the position parameters to produce Digital Surface Model (DSM) data. The latest terrain environment information is simulated by using DSM data, and can be used as references in disaster recovery in the future.

  18. Development and features of an X-ray detector with high spatial resolution

    International Nuclear Information System (INIS)

    Hartmann, H.

    1979-09-01

    A laboratory model of an X-ray detector with high spatial resolution was developed and constructed. It has no spectral resolution, but a local resolution of 20 μm which is about ten times as high as that of position-sensitive proportional counters and satisfies the requirements of the very best Wolter telescopes with regard to spatial resolution. The detector will be used for laboratory tests of the 80 cm Wolter telescope which is being developed for Spacelab flights. The theory of the wire grid detector and the physics of the photoelectric effect has been developed, and model calculations and numerical calculations have been carried out. (orig./WB) [de

  19. Practical considerations for high spatial and temporal resolution dynamic transmission electron microscopy

    Energy Technology Data Exchange (ETDEWEB)

    Armstrong, Michael R. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States)], E-mail: armstrong30@llnl.gov; Boyden, Ken [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States); Browning, Nigel D. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States); Department of Chemical Engineering and Materials Science, University of California-Davis, One Shields Avenue, Davis, CA 95616 (United States); Campbell, Geoffrey H.; Colvin, Jeffrey D.; De Hope, William J.; Frank, Alan M. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States); Gibson, David J.; Hartemann, Fred [N Division, Physics and Advanced Technologies Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-280, Livermore, CA 94550 (United States); Kim, Judy S. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States); Department of Chemical Engineering and Materials Science, University of California-Davis, One Shields Avenue, Davis, CA 95616 (United States); King, Wayne E.; La Grange, Thomas B.; Pyke, Ben J.; Reed, Bryan W.; Shuttlesworth, Richard M.; Stuart, Brent C.; Torralva, Ben R. [Materials Science and Technology Division, Chemistry and Materials Science Directorate, Lawrence Livermore National Laboratory, P.O. Box 808, L-356, Livermore, CA 94550 (United States)

    2007-04-15

    Although recent years have seen significant advances in the spatial resolution possible in the transmission electron microscope (TEM), the temporal resolution of most microscopes is limited to video rate at best. This lack of temporal resolution means that our understanding of dynamic processes in materials is extremely limited. High temporal resolution in the TEM can be achieved, however, by replacing the normal thermionic or field emission source with a photoemission source. In this case the temporal resolution is limited only by the ability to create a short pulse of photoexcited electrons in the source, and this can be as short as a few femtoseconds. The operation of the photo-emission source and the control of the subsequent pulse of electrons (containing as many as 5x10{sup 7} electrons) create significant challenges for a standard microscope column that is designed to operate with a single electron in the column at any one time. In this paper, the generation and control of electron pulses in the TEM to obtain a temporal resolution <10{sup -6} s will be described and the effect of the pulse duration and current density on the spatial resolution of the instrument will be examined. The potential of these levels of temporal and spatial resolution for the study of dynamic materials processes will also be discussed.

  20. Practical considerations for high spatial and temporal resolution dynamic transmission electron microscopy

    International Nuclear Information System (INIS)

    Armstrong, Michael R.; Boyden, Ken; Browning, Nigel D.; Campbell, Geoffrey H.; Colvin, Jeffrey D.; De Hope, William J.; Frank, Alan M.; Gibson, David J.; Hartemann, Fred; Kim, Judy S.; King, Wayne E.; La Grange, Thomas B.; Pyke, Ben J.; Reed, Bryan W.; Shuttlesworth, Richard M.; Stuart, Brent C.; Torralva, Ben R.

    2007-01-01

    Although recent years have seen significant advances in the spatial resolution possible in the transmission electron microscope (TEM), the temporal resolution of most microscopes is limited to video rate at best. This lack of temporal resolution means that our understanding of dynamic processes in materials is extremely limited. High temporal resolution in the TEM can be achieved, however, by replacing the normal thermionic or field emission source with a photoemission source. In this case the temporal resolution is limited only by the ability to create a short pulse of photoexcited electrons in the source, and this can be as short as a few femtoseconds. The operation of the photo-emission source and the control of the subsequent pulse of electrons (containing as many as 5x10 7 electrons) create significant challenges for a standard microscope column that is designed to operate with a single electron in the column at any one time. In this paper, the generation and control of electron pulses in the TEM to obtain a temporal resolution -6 s will be described and the effect of the pulse duration and current density on the spatial resolution of the instrument will be examined. The potential of these levels of temporal and spatial resolution for the study of dynamic materials processes will also be discussed

  1. Measurement of the spatial resolution and rate capability of an induction drift chamber

    International Nuclear Information System (INIS)

    Roderburg, E.; Broeders, R.; Dahmen, M.; Decker, G.; Kilian, K.; Kurtenbach, A.; Lippert, C.; Oelert, W.; Sehl, G.; Steinkamp, O.; Stratmann, R.; Walsh, S.; Ziolkowski, M.

    1992-01-01

    The limits of spatial resolution of an induction drift chamber (IDC) lead to the concept of an asymmetric IDC with Flash ADC readout. The construction of a chamber is described. The results of two measurements concerning the spatial resolution and the rate capability are reported. (orig.)

  2. Improving spatial prioritisation for remote marine regions: optimising biodiversity conservation and sustainable development trade-offs

    Science.gov (United States)

    Moore, Cordelia H.; Radford, Ben T.; Possingham, Hugh P.; Heyward, Andrew J.; Stewart, Romola R.; Watts, Matthew E.; Prescott, Jim; Newman, Stephen J.; Harvey, Euan S.; Fisher, Rebecca; Bryce, Clay W.; Lowe, Ryan J.; Berry, Oliver; Espinosa-Gayosso, Alexis; Sporer, Errol; Saunders, Thor

    2016-08-01

    Creating large conservation zones in remote areas, with less intense stakeholder overlap and limited environmental information, requires periodic review to ensure zonation mitigates primary threats and fill gaps in representation, while achieving conservation targets. Follow-up reviews can utilise improved methods and data, potentially identifying new planning options yielding a desirable balance between stakeholder interests. This research explored a marine zoning system in north-west Australia-a biodiverse area with poorly documented biota. Although remote, it is economically significant (i.e. petroleum extraction and fishing). Stakeholder engagement was used to source the best available biodiversity and socio-economic data and advanced spatial analyses produced 765 high resolution data layers, including 674 species distributions representing 119 families. Gap analysis revealed the current proposed zoning system as inadequate, with 98.2% of species below the Convention on Biological Diversity 10% representation targets. A systematic conservation planning algorithm Maxan provided zoning options to meet representation targets while balancing this with industry interests. Resulting scenarios revealed that conservation targets could be met with minimal impacts on petroleum and fishing industries, with estimated losses of 4.9% and 7.2% respectively. The approach addressed important knowledge gaps and provided a powerful and transparent method to reconcile industry interests with marine conservation.

  3. Development of a large-area Multigap RPC with adequate spatial resolution for muon tomography

    Science.gov (United States)

    Wang, J.; Wang, Y.; Wang, X.; Zeng, M.; Xie, B.; Han, D.; Lyu, P.; Wang, F.; Li, Y.

    2016-11-01

    We study the performance of a large-area 2-D Multigap Resistive Plate Chamber (MRPC) designed for muon tomography with high spatial resolution. An efficiency up to 98% and a spatial resolution of around 270 μ m are obtained in cosmic ray and X-ray tests. The performance of the MRPC is also investigated for two working gases: standard gas and pure Freon. The result shows that the MRPC working in pure Freon can provide higher efficiency and better spatial resolution.

  4. A temperature-compensated high spatial resolution distributed strain sensor

    International Nuclear Information System (INIS)

    Belal, Mohammad; Cho, Yuh Tat; Ibsen, Morten; Newson, Trevor P

    2010-01-01

    We propose and demonstrate a scheme which utilizes the temperature dependence of spontaneous Raman scattering to provide temperature compensation for a high spatial resolution Brillouin frequency-based strain sensor

  5. Interactions of collimation, sampling and filtering on spect spatial resolution

    International Nuclear Information System (INIS)

    Tsui, B.M.W.; Jaszczak, R.J.

    1984-01-01

    The major factors which affect the spatial resolution of single-photon emission computer tomography (SPECT) include collimation, sampling and filtering. A theoretical formulation is presented to describe the relationship between these factors and their effects on the projection data. Numerical calculations were made using commercially available SPECT systems and imaging parameters. The results provide an important guide for proper selection of the collimator-detector design, the imaging and the reconstruction parameters to avoid unnecessary spatial resolution degradation and aliasing artifacts in the reconstructed image. In addition, the understanding will help in the fair evaluation of different SPECT systems under specific imaging conditions

  6. Single-acquisition method for simultaneous determination of extrinsic gamma-camera sensitivity and spatial resolution

    Energy Technology Data Exchange (ETDEWEB)

    Santos, J.A.M. [Servico de Fisica Medica, Instituto Portugues de Oncologia Francisco Gentil do Porto, E.P.E., Rua Dr. Antonio Bernardino de Almeida, 4200-072 Porto (Portugal)], E-mail: a.miranda@portugalmail.pt; Sarmento, S. [Servico de Fisica Medica, Instituto Portugues de Oncologia Francisco Gentil do Porto, E.P.E., Rua Dr. Antonio Bernardino de Almeida, 4200-072 Porto (Portugal); Alves, P.; Torres, M.C. [Departamento de Fisica da Universidade do Porto, Rua do Campo Alegre 687, 4169-007 Porto (Portugal); Bastos, A.L. [Servico de Medicina Nuclear, Instituto Portugues de Oncologia Francisco Gentil do Porto, E.P.E., Rua Dr. Antonio Bernardino de Almeida, 4200-072 Porto (Portugal); Ponte, F. [Servico de Fisica Medica, Instituto Portugues de Oncologia Francisco Gentil do Porto, E.P.E., Rua Dr. Antonio Bernardino de Almeida, 4200-072 Porto (Portugal)

    2008-01-15

    A new method for measuring simultaneously both the extrinsic sensitivity and spatial resolution of a gamma-camera in a single planar acquisition was implemented. A dual-purpose phantom (SR phantom; sensitivity/resolution) was developed, tested and the results compared with other conventional methods used for separate determination of these two important image quality parameters. The SR phantom yielded reproducible and accurate results, allowing an immediate visual inspection of the spatial resolution as well as the quantitative determination of the contrast for six different spatial frequencies. It also proved to be useful in the estimation of the modulation transfer function (MTF) of the image formation collimator/detector system at six different frequencies and can be used to estimate the spatial resolution as function of the direction relative to the digital matrix of the detector.

  7. Single-acquisition method for simultaneous determination of extrinsic gamma-camera sensitivity and spatial resolution

    International Nuclear Information System (INIS)

    Santos, J.A.M.; Sarmento, S.; Alves, P.; Torres, M.C.; Bastos, A.L.; Ponte, F.

    2008-01-01

    A new method for measuring simultaneously both the extrinsic sensitivity and spatial resolution of a gamma-camera in a single planar acquisition was implemented. A dual-purpose phantom (SR phantom; sensitivity/resolution) was developed, tested and the results compared with other conventional methods used for separate determination of these two important image quality parameters. The SR phantom yielded reproducible and accurate results, allowing an immediate visual inspection of the spatial resolution as well as the quantitative determination of the contrast for six different spatial frequencies. It also proved to be useful in the estimation of the modulation transfer function (MTF) of the image formation collimator/detector system at six different frequencies and can be used to estimate the spatial resolution as function of the direction relative to the digital matrix of the detector

  8. Modelling the soil microclimate: does the spatial or temporal resolution of input parameters matter?

    Directory of Open Access Journals (Sweden)

    Anna Carter

    2016-01-01

    Full Text Available The urgency of predicting future impacts of environmental change on vulnerable populations is advancing the development of spatially explicit habitat models. Continental-scale climate and microclimate layers are now widely available. However, most terrestrial organisms exist within microclimate spaces that are very small, relative to the spatial resolution of those layers. We examined the effects of multi-resolution, multi-extent topographic and climate inputs on the accuracy of hourly soil temperature predictions for a small island generated at a very high spatial resolution (<1 m2 using the mechanistic microclimate model in NicheMapR. Achieving an accuracy comparable to lower-resolution, continental-scale microclimate layers (within about 2–3°C of observed values required the use of daily weather data as well as high resolution topographic layers (elevation, slope, aspect, horizon angles, while inclusion of site-specific soil properties did not markedly improve predictions. Our results suggest that large-extent microclimate layers may not provide accurate estimates of microclimate conditions when the spatial extent of a habitat or other area of interest is similar to or smaller than the spatial resolution of the layers themselves. Thus, effort in sourcing model inputs should be focused on obtaining high resolution terrain data, e.g., via LiDAR or photogrammetry, and local weather information rather than in situ sampling of microclimate characteristics.

  9. Horizontal Residual Mean Circulation: Evaluation of Spatial Correlations in Coarse Resolution Ocean Models

    Science.gov (United States)

    Li, Y.; McDougall, T. J.

    2016-02-01

    Coarse resolution ocean models lack knowledge of spatial correlations between variables on scales smaller than the grid scale. Some researchers have shown that these spatial correlations play a role in the poleward heat flux. In order to evaluate the poleward transport induced by the spatial correlations at a fixed horizontal position, an equation is obtained to calculate the approximate transport from velocity gradients. The equation involves two terms that can be added to the quasi-Stokes streamfunction (based on temporal correlations) to incorporate the contribution of spatial correlations. Moreover, these new terms do not need to be parameterized and is ready to be evaluated by using model data directly. In this study, data from a high resolution ocean model have been used to estimate the accuracy of this HRM approach for improving the horizontal property fluxes in coarse-resolution ocean models. A coarse grid is formed by sub-sampling and box-car averaging the fine grid scale. The transport calculated on the coarse grid is then compared to the transport on original high resolution grid scale accumulated over a corresponding number of grid boxes. The preliminary results have shown that the estimate on coarse resolution grids roughly match the corresponding transports on high resolution grids.

  10. Exploring the Spatial Resolution of the Photothermal Beam Deflection Technique in the Infrared Region

    CERN Document Server

    Seidel, Wolfgang

    2004-01-01

    In photothermal beam deflection spectroscopy (PTBD) generating and detection of thermal waves occur generally in the sub-millimeter length scale. Therefore, PTBD provides spatial information about the surface of the sample and permits imaging and/or microspectrometry. Recent results of PTBD experiments are presented with a high spatial resolution which is near the diffraction limit of the infrared pump beam (CLIO-FEL). We investigated germanium substrates showing restricted O+-doped regions with an infrared absorption line at a wavelength around 11.6 microns. The spatial resolution was obtained by strongly focusing the probe beam (i.e. a HeNe laser) on a sufficiently small spot. The strong divergence makes it necessary to refocus the probe beam in front of the position detector. The influence of the focusing elements on spatial resolution and signal-to-noise ratio is discussed. In future studies we expect an enhanced spatial resolution due to an extreme focusing of the probe beam leading to a highly sensitive...

  11. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors.

    Science.gov (United States)

    Zheng, Guang; Moskal, L Monika

    2009-01-01

    The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels.

  12. Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush-steppe ecosystem

    Science.gov (United States)

    Wylie, B.K.; Johnson, D.A.; Laca, Emilio; Saliendra, Nicanor Z.; Gilmanov, T.G.; Reed, B.C.; Tieszen, L.L.; Worstell, B.B.

    2003-01-01

    The net ecosystem exchange (NEE) of carbon flux can be partitioned into gross primary productivity (GPP) and respiration (R). The contribution of remote sensing and modeling holds the potential to predict these components and map them spatially and temporally. This has obvious utility to quantify carbon sink and source relationships and to identify improved land management strategies for optimizing carbon sequestration. The objective of our study was to evaluate prediction of 14-day average daytime CO2 fluxes (Fday) and nighttime CO2 fluxes (Rn) using remote sensing and other data. Fday and Rn were measured with a Bowen ratio-energy balance (BREB) technique in a sagebrush (Artemisia spp.)-steppe ecosystem in northeast Idaho, USA, during 1996-1999. Micrometeorological variables aggregated across 14-day periods and time-integrated Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (iNDVI) were determined during four growing seasons (1996-1999) and used to predict Fday and Rn. We found that iNDVI was a strong predictor of Fday (R2 = 0.79, n = 66, P improved predictions of Fday (R2= 0.82, n = 66, P management strategies, carbon certification, and validation and calibration of carbon flux models. ?? 2003 Elsevier Science Inc. All rights reserved.

  13. Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush–steppe ecosystem

    Science.gov (United States)

    Wylie, Bruce K.; Johnson, Douglas A.; Laca, Emilio; Saliendra, Nicanor Z.; Gilmanov, Tagir G.; Reed, Bradley C.; Tieszen, Larry L.; Worstell, Bruce B.

    2003-01-01

    The net ecosystem exchange (NEE) of carbon flux can be partitioned into gross primary productivity (GPP) and respiration (R). The contribution of remote sensing and modeling holds the potential to predict these components and map them spatially and temporally. This has obvious utility to quantify carbon sink and source relationships and to identify improved land management strategies for optimizing carbon sequestration. The objective of our study was to evaluate prediction of 14-day average daytime CO2 fluxes (Fday) and nighttime CO2 fluxes (Rn) using remote sensing and other data. Fday and Rnwere measured with a Bowen ratio–energy balance (BREB) technique in a sagebrush (Artemisia spp.)–steppe ecosystem in northeast Idaho, USA, during 1996–1999. Micrometeorological variables aggregated across 14-day periods and time-integrated Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (iNDVI) were determined during four growing seasons (1996–1999) and used to predict Fday and Rn. We found that iNDVI was a strong predictor of Fday(R2=0.79, n=66, Pimproved predictions of Fday (R2=0.82, n=66, Pmanagement strategies, carbon certification, and validation and calibration of carbon flux models.

  14. Detection of the power lines in UAV remote sensed images using spectral-spatial methods.

    Science.gov (United States)

    Bhola, Rishav; Krishna, Nandigam Hari; Ramesh, K N; Senthilnath, J; Anand, Gautham

    2018-01-15

    In this paper, detection of the power lines on images acquired by Unmanned Aerial Vehicle (UAV) based remote sensing is carried out using spectral-spatial methods. Spectral clustering was performed using Kmeans and Expectation Maximization (EM) algorithm to classify the pixels into the power lines and non-power lines. The spectral clustering methods used in this study are parametric in nature, to automate the number of clusters Davies-Bouldin index (DBI) is used. The UAV remote sensed image is clustered into the number of clusters determined by DBI. The k clustered image is merged into 2 clusters (power lines and non-power lines). Further, spatial segmentation was performed using morphological and geometric operations, to eliminate the non-power line regions. In this study, UAV images acquired at different altitudes and angles were analyzed to validate the robustness of the proposed method. It was observed that the EM with spatial segmentation (EM-Seg) performed better than the Kmeans with spatial segmentation (Kmeans-Seg) on most of the UAV images. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. GRANULOMETRIC MAPS FROM HIGH RESOLUTION SATELLITE IMAGES

    Directory of Open Access Journals (Sweden)

    Catherine Mering

    2011-05-01

    Full Text Available A new method of land cover mapping from satellite images using granulometric analysis is presented here. Discontinuous landscapes such as steppian bushes of semi arid regions and recently growing urban settlements are especially concerned by this study. Spatial organisations of the land cover are quantified by means of the size distribution analysis of the land cover units extracted from high resolution remotely sensed images. A granulometric map is built by automatic classification of every pixel of the image according to the granulometric density inside a sliding neighbourhood. Granulometric mapping brings some advantages over traditional thematic mapping by remote sensing by focusing on fine spatial events and small changes in one peculiar category of the landscape.

  16. Effects of high spatial and temporal resolution Earth observations on simulated hydrometeorological variables in a cropland (southwestern France

    Directory of Open Access Journals (Sweden)

    J. Etchanchu

    2017-11-01

    Full Text Available Agricultural landscapes are often constituted by a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes and must be taken into account in simulations of land surface and distributed hydrological models. The Sentinel-2 mission allows for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy fluxes via the Interactions between the Surface Biosphere Atmosphere (ISBA land surface model included in the EXternalized SURface (SURFEX modeling platform. The study focuses on the effect of the leaf area index (LAI spatial and temporal variability on these fluxes. We compare the use of the LAI climatology from ECOCLIMAP-II, used by default in SURFEX-ISBA, and time series of LAI derived from the high-resolution Formosat-2 satellite data (8 m. The study area is an agricultural zone in southwestern France covering 576 km2 (24 km  ×  24 km. An innovative plot-scale approach is used, in which each computational unit has a homogeneous vegetation type. Evaluation of the simulations quality is done by comparing model outputs with in situ eddy covariance measurements of latent heat flux (LE. Our results show that the use of LAI derived from high-resolution remote sensing significantly improves simulated evapotranspiration with respect to ECOCLIMAP-II, especially when the surface is covered with summer crops. The comparison with in situ measurements shows an improvement of roughly 0.3 in the correlation coefficient and a decrease of around 30 % of the root mean square error (RMSE in the simulated evapotranspiration. This

  17. Improving PET spatial resolution and detectability for prostate cancer imaging

    International Nuclear Information System (INIS)

    Bal, H; Guerin, L; Casey, M E; Conti, M; Eriksson, L; Michel, C; Fanti, S; Pettinato, C; Adler, S; Choyke, P

    2014-01-01

    Prostate cancer, one of the most common forms of cancer among men, can benefit from recent improvements in positron emission tomography (PET) technology. In particular, better spatial resolution, lower noise and higher detectability of small lesions could be greatly beneficial for early diagnosis and could provide a strong support for guiding biopsy and surgery. In this article, the impact of improved PET instrumentation with superior spatial resolution and high sensitivity are discussed, together with the latest development in PET technology: resolution recovery and time-of-flight reconstruction. Using simulated cancer lesions, inserted in clinical PET images obtained with conventional protocols, we show that visual identification of the lesions and detectability via numerical observers can already be improved using state of the art PET reconstruction methods. This was achieved using both resolution recovery and time-of-flight reconstruction, and a high resolution image with 2 mm pixel size. Channelized Hotelling numerical observers showed an increase in the area under the LROC curve from 0.52 to 0.58. In addition, a relationship between the simulated input activity and the area under the LROC curve showed that the minimum detectable activity was reduced by more than 23%. (paper)

  18. High resolution land surface modeling utilizing remote sensing parameters and the Noah-UCM: a case study in the Los Angeles Basin

    Science.gov (United States)

    Vahmani, P.; Hogue, T. S.

    2014-07-01

    In the current work we investigate the utility of remote sensing based surface parameters in the Noah-UCM (urban canopy model) over a highly developed urban area. Landsat and fused Landsat-MODIS data are utilized to generate high resolution (30 m) monthly spatial maps of green vegetation fraction (GVF), impervious surface area (ISA), albedo, leaf area index (LAI), and emissivity in the Los Angeles metropolitan area. The gridded remotely sensed parameter datasets are directly substituted for the land-use/lookup-table values in the Noah-UCM modeling framework. Model performance in reproducing ET (evapotranspiration) and LST (land surface temperature) fields is evaluated utilizing Landsat-based LST and ET estimates from CIMIS (California Irrigation Management Information System) stations as well as in-situ measurements. Our assessment shows that the large deviations between the spatial distributions and seasonal fluctuations of the default and measured parameter sets lead to significant errors in the model predictions of monthly ET fields (RMSE = 22.06 mm month-1). Results indicate that implemented satellite derived parameter maps, particularly GVF, enhance the Noah-UCM capability to reproduce observed ET patterns over vegetated areas in the urban domains (RMSE = 11.77 mm month-1). GVF plays the most significant role in reproducing the observed ET fields, likely due to the interaction with other parameters in the model. Our analysis also shows that remotely sensed GVF and ISA improve the model capability to predict the LST differences between fully vegetated pixels and highly developed areas. However, the model still underestimates remotely sensed LST values over highly developed areas. We hypothesize that the LST underestimation is due to structural formulation in the UCM and cannot be immediately solved with available parameter choices.

  19. Comparative analysis of time efficiency and spatial resolution between different EIT reconstruction algorithms

    International Nuclear Information System (INIS)

    Kacarska, Marija; Loskovska, Suzana

    2002-01-01

    In this paper comparative analysis between different EIT algorithms is presented. Analysis is made for spatial and temporal resolution of obtained images by several different algorithms. Discussions consider spatial resolution dependent on data acquisition method, too. Obtained results show that conventional applied-current EIT is more powerful compared to induced-current EIT. (Author)

  20. Remotely sensed data fusion for offshore wind energy resource mapping; Fusion de donnees satellitaires pour la cartographie du potentiel eolien offshore

    Energy Technology Data Exchange (ETDEWEB)

    Ben Ticha, M.B

    2007-11-15

    Wind energy is a component of an energy policy contributing to a sustainable development. Last years, offshore wind parks have been installed offshore. These parks benefit from higher wind speeds and lower turbulence than onshore. To sit a wind park, it is necessary to have a mapping of wind resource. These maps are needed at high spatial resolution to show wind energy resource variations at the scale of a wind park. Wind resource mapping is achieved through the description of the spatial variations of statistical parameters characterizing wind climatology. For a precise estimation of these statistical parameters, high temporal resolution wind speed and direction measurements are needed. However, presently, there is no data source allying high spatial resolution and high temporal resolution. We propose a data fusion method taking advantage of the high spatial resolution of some remote sensing instruments (synthetic aperture radars) and the high temporal resolution of other remote sensing instruments (scatterometers). The data fusion method is applied to a case study and the results quality is assessed. The results show the pertinence of data fusion for the mapping of wind energy resource offshore. (author)

  1. Satellite microwave remote sensing of North Eurasian inundation dynamics: development of coarse-resolution products and comparison with high-resolution synthetic aperture radar data

    International Nuclear Information System (INIS)

    Schroeder, R; Rawlins, M A; McDonald, K C; Podest, E; Zimmermann, R; Kueppers, M

    2010-01-01

    Wetlands are not only primary producers of atmospheric greenhouse gases but also possess unique features that are favourable for application of satellite microwave remote sensing to monitoring their status and trend. In this study we apply combined passive and active microwave remote sensing data sets from the NASA sensors AMSR-E and QuikSCAT to map surface water dynamics over Northern Eurasia. We demonstrate our method on the evolution of large wetland complexes for two consecutive years from January 2006 to December 2007. We apply river discharge measurements from the Ob River along with land surface runoff simulations derived from the Pan-Arctic Water Balance Model during and after snowmelt in 2006 and 2007 to interpret the abundance of widespread flooding along the River Ob in early summer of 2007 observed in the remote sensing products. The coarse-resolution, 25 km, surface water product is compared to a high-resolution, 30 m, inundation map derived from ALOS PALSAR (Advanced Land Observation Satellite phased array L-band synthetic aperture radar) imagery acquired for 11 July 2006, and extending along a transect in the central Western Siberian Plain. We found that the surface water fraction derived from the combined AMSR-E/QuikSCAT data sets closely tracks the inundation mapped using higher-resolution ALOS PALSAR data.

  2. High-resolution land surface modeling utilizing remote sensing parameters and the Noah UCM: a case study in the Los Angeles Basin

    Science.gov (United States)

    Vahmani, P.; Hogue, T. S.

    2014-12-01

    In the current work we investigate the utility of remote-sensing-based surface parameters in the Noah UCM (urban canopy model) over a highly developed urban area. Landsat and fused Landsat-MODIS data are utilized to generate high-resolution (30 m) monthly spatial maps of green vegetation fraction (GVF), impervious surface area (ISA), albedo, leaf area index (LAI), and emissivity in the Los Angeles metropolitan area. The gridded remotely sensed parameter data sets are directly substituted for the land-use/lookup-table-based values in the Noah-UCM modeling framework. Model performance in reproducing ET (evapotranspiration) and LST (land surface temperature) fields is evaluated utilizing Landsat-based LST and ET estimates from CIMIS (California Irrigation Management Information System) stations as well as in situ measurements. Our assessment shows that the large deviations between the spatial distributions and seasonal fluctuations of the default and measured parameter sets lead to significant errors in the model predictions of monthly ET fields (RMSE = 22.06 mm month-1). Results indicate that implemented satellite-derived parameter maps, particularly GVF, enhance the capability of the Noah UCM to reproduce observed ET patterns over vegetated areas in the urban domains (RMSE = 11.77 mm month-1). GVF plays the most significant role in reproducing the observed ET fields, likely due to the interaction with other parameters in the model. Our analysis also shows that remotely sensed GVF and ISA improve the model's capability to predict the LST differences between fully vegetated pixels and highly developed areas.

  3. Study of the spatial resolution of low-material GEM tracking detectors

    Directory of Open Access Journals (Sweden)

    Kudryavtsev V.N.

    2018-01-01

    Full Text Available The spatial resolution of GEM based tracking detectors has been simulated and measured. The simulation includes the GEANT4 based transport of high energy electrons with careful accounting for atomic relaxation processes including emission of fluorescent photons and Auger electrons and custom post-processing, including accounting for diffusion, gas amplification fluctuations, the distribution of signals on readout electrodes, electronics noise and a particular algorithm of the final coordinate calculation (center of gravity. The simulation demonstrates that a minimum of the spatial resolution of about 10 μm can be achieved with strip pitches from 250 μm to 300 μm. For larger pitches the resolution is quickly degrading reaching 80-100 μm at a pitch of 500 μm. The spatial resolution of low-material triple-GEM detectors for the DEUTRON facility at the VEPP-3 storage ring is measured at the extracted beam facility of the VEPP-4M collider. The amount of material in these detectors is reduced by etching the copper of the GEMs electrodes and using a readout structure on a thin kapton foil rather than on a glass fibre plate. The exact amount of material in one DEUTRON detector is measured by studying multiple scattering of 100 MeV electrons in it. The result of these measurements is X/X0 = 2.4×10−3 corresponding to a thickness of the copper layers of the GEM foils of 3 μm. The spatial resolution of one DEUTRON detector is measured with 500 MeV electrons and the measured value is equal to 35 ± 1 μm for orthogonal tracks.

  4. Study of the spatial resolution of low-material GEM tracking detectors

    Science.gov (United States)

    Kudryavtsev, V. N.; Maltsev, T. V.; Shekhtman, L. I.

    2018-02-01

    The spatial resolution of GEM based tracking detectors has been simulated and measured. The simulation includes the GEANT4 based transport of high energy electrons with careful accounting for atomic relaxation processes including emission of fluorescent photons and Auger electrons and custom post-processing, including accounting for diffusion, gas amplification fluctuations, the distribution of signals on readout electrodes, electronics noise and a particular algorithm of the final coordinate calculation (center of gravity). The simulation demonstrates that a minimum of the spatial resolution of about 10 μm can be achieved with strip pitches from 250 μm to 300 μm. For larger pitches the resolution is quickly degrading reaching 80-100 μm at a pitch of 500 μm. The spatial resolution of low-material triple-GEM detectors for the DEUTRON facility at the VEPP-3 storage ring is measured at the extracted beam facility of the VEPP-4M collider. The amount of material in these detectors is reduced by etching the copper of the GEMs electrodes and using a readout structure on a thin kapton foil rather than on a glass fibre plate. The exact amount of material in one DEUTRON detector is measured by studying multiple scattering of 100 MeV electrons in it. The result of these measurements is X/X0 = 2.4×10-3 corresponding to a thickness of the copper layers of the GEM foils of 3 μm. The spatial resolution of one DEUTRON detector is measured with 500 MeV electrons and the measured value is equal to 35 ± 1 μm for orthogonal tracks.

  5. The French proposal for a high spatial resolution Hyperspectral mission

    Science.gov (United States)

    Carrère, Véronique; Briottet, Xavier; Jacquemoud, Stéphane; Marion, Rodolphe; Bourguignon, Anne; Chami, Malik; Chanussot, Jocelyn; Chevrel, Stéphane; Deliot, Philippe; Dumont, Marie; Foucher, Pierre-Yves; Gomez, Cécile; Roman-Minghelli, Audrey; Sheeren, David; Weber, Christiane; Lefèvre, Marie-José; Mandea, Mioara

    2014-05-01

    More than 25 years of airborne imaging spectroscopy and spaceborne sensors such as Hyperion or HICO have clearly demonstrated the ability of such a remote sensing technique to produce value added information regarding surface composition and physical properties for a large variety of applications. Scheduled missions such as EnMAP and PRISMA prove the increased interest of the scientific community for such a type of remote sensing data. In France, a group of Science and Defence users of imaging spectrometry data (Groupe de Synthèse Hyperspectral, GSH) established an up-to-date review of possible applications, define instrument specifications required for accurate, quantitative retrieval of diagnostic parameters, and identify fields of application where imaging spectrometry is a major contribution. From these conclusions, CNES (French Space Agency) decided a phase 0 study for an hyperspectral mission concept, named at this time HYPXIM (HYPerspectral-X IMagery), the main fields of applications are vegetation biodiversity, coastal and inland waters, geosciences, urban environment, atmospheric sciences, cryosphere and Defence. Results pointed out applications where high spatial resolution was necessary and would not be covered by the other foreseen hyperspectral missions. The phase A started at the beginning of 2013 based on the following HYPXIM characteristics: a hyperspectral camera covering the [0.4 - 2.5 µm] spectral range with a 8 m ground sampling distance (GSD) and a PAN camera with a 1.85 m GSD, onboard a mini-satellite platform. This phase A is currently stopped due to budget constraints. Nevertheless, the Science team is currently focusing on the preparation for the next CNES prospective meeting (March, 2014), an important step for the future of the mission. This paper will provide an update of the status of this mission and of new results obtained by the Science team.

  6. SRXRF analysis with spatial resolution of dental calculus

    International Nuclear Information System (INIS)

    Sanchez, Hector Jorge; Perez, Carlos Alberto; Grenon, Miriam

    2000-01-01

    This work presents elemental-composition studies of dental calculus by X-ray fluorescence analysis using synchrotron radiation. The intrinsic characteristics of synchrotron light allow for a semi-quantitative analysis with spatial resolution. The experiments were carried out in the high-vacuum station of the XRF beamline at the Synchrotron Light National Laboratory (Campinas, Brazil). All the measurements were performed in conventional geometry (45 deg. + 45 deg.) and the micro-collimation was attained via a pair of orthogonal slits mounted in the beamline. In this way, pixels of 50 μmx50 μm were obtained keeping a high flux of photons on the sample. Samples of human dental calculus were measured in different positions along their growing axis, in order to determine variations of the compositions in the pattern of deposit. Intensity ratios of minor elements and traces were obtained, and linear profiles and surface distributions were determined. As a general summary, we can conclude that μXRF experiments with spatial resolution on dental calculus are feasible with simple collimation and adequate positioning systems, keeping a high flux of photon. These results open interesting perspectives for the future station of the line, devoted to μXRF, which will reach resolutions of the order of 10 μm

  7. SRXRF analysis with spatial resolution of dental calculus

    Science.gov (United States)

    Sánchez, Héctor Jorge; Pérez, Carlos Alberto; Grenón, Miriam

    2000-09-01

    This work presents elemental-composition studies of dental calculus by X-ray fluorescence analysis using synchrotron radiation. The intrinsic characteristics of synchrotron light allow for a semi-quantitative analysis with spatial resolution. The experiments were carried out in the high-vacuum station of the XRF beamline at the Synchrotron Light National Laboratory (Campinas, Brazil). All the measurements were performed in conventional geometry (45°+45°) and the micro-collimation was attained via a pair of orthogonal slits mounted in the beamline. In this way, pixels of 50 μm×50 μm were obtained keeping a high flux of photons on the sample. Samples of human dental calculus were measured in different positions along their growing axis, in order to determine variations of the compositions in the pattern of deposit. Intensity ratios of minor elements and traces were obtained, and linear profiles and surface distributions were determined. As a general summary, we can conclude that μXRF experiments with spatial resolution on dental calculus are feasible with simple collimation and adequate positioning systems, keeping a high flux of photon. These results open interesting perspectives for the future station of the line, devoted to μXRF, which will reach resolutions of the order of 10 μm.

  8. Land Cover Change Detection in Urban Lake Areas Using Multi-Temporary Very High Spatial Resolution Aerial Images

    Directory of Open Access Journals (Sweden)

    Wenyuan Zhang

    2018-01-01

    Full Text Available The availability of very high spatial resolution (VHR remote sensing imagery provides unique opportunities to exploit meaningful change information in detail with object-oriented image analysis. This study investigated land cover (LC changes in Shahu Lake of Wuhan using multi-temporal VHR aerial images in the years 1978, 1981, 1989, 1995, 2003, and 2011. A multi-resolution segmentation algorithm and CART (classification and regression trees classifier were employed to perform highly accurate LC classification of the individual images, while a post-classification comparison method was used to detect changes. The experiments demonstrated that significant changes in LC occurred along with the rapid urbanization during 1978–2011. The dominant changes that took place in the study area were lake and vegetation shrinking, replaced by high density buildings and roads. The total area of Shahu Lake decreased from ~7.64 km2 to ~3.60 km2 during the past 33 years, where 52.91% of its original area was lost. The presented results also indicated that urban expansion and inadequate legislative protection are the main factors in Shahu Lake’s shrinking. The object-oriented change detection schema presented in this manuscript enables us to better understand the specific spatial changes of Shahu Lake, which can be used to make reasonable decisions for lake protection and urban development.

  9. Rapid Target Detection in High Resolution Remote Sensing Images Using Yolo Model

    Science.gov (United States)

    Wu, Z.; Chen, X.; Gao, Y.; Li, Y.

    2018-04-01

    Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.

  10. Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement.

    Science.gov (United States)

    Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

    2018-02-07

    There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L ₀ gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.

  11. Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

    Science.gov (United States)

    Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

    2018-01-01

    There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L0 gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements. PMID:29414893

  12. Coherent optical adaptive technique improves the spatial resolution of STED microscopy in thick samples

    Science.gov (United States)

    Yan, Wei; Yang, Yanlong; Tan, Yu; Chen, Xun; Li, Yang; Qu, Junle; Ye, Tong

    2018-01-01

    Stimulated emission depletion microscopy (STED) is one of far-field optical microscopy techniques that can provide sub-diffraction spatial resolution. The spatial resolution of the STED microscopy is determined by the specially engineered beam profile of the depletion beam and its power. However, the beam profile of the depletion beam may be distorted due to aberrations of optical systems and inhomogeneity of specimens’ optical properties, resulting in a compromised spatial resolution. The situation gets deteriorated when thick samples are imaged. In the worst case, the sever distortion of the depletion beam profile may cause complete loss of the super resolution effect no matter how much depletion power is applied to specimens. Previously several adaptive optics approaches have been explored to compensate aberrations of systems and specimens. However, it is hard to correct the complicated high-order optical aberrations of specimens. In this report, we demonstrate that the complicated distorted wavefront from a thick phantom sample can be measured by using the coherent optical adaptive technique (COAT). The full correction can effectively maintain and improve the spatial resolution in imaging thick samples. PMID:29400356

  13. Simulations of the temporal and spatial resolution for a compact time-resolved electron diffractometer

    Science.gov (United States)

    Robinson, Matthew S.; Lane, Paul D.; Wann, Derek A.

    2016-02-01

    A novel compact electron gun for use in time-resolved gas electron diffraction experiments has recently been designed and commissioned. In this paper we present and discuss the extensive simulations that were performed to underpin the design in terms of the spatial and temporal qualities of the pulsed electron beam created by the ionisation of a gold photocathode using a femtosecond laser. The response of the electron pulses to a solenoid lens used to focus the electron beam has also been studied. The simulated results show that focussing the electron beam affects the overall spatial and temporal resolution of the experiment in a variety of ways, and that factors that improve the resolution of one parameter can often have a negative effect on the other. A balance must, therefore, be achieved between spatial and temporal resolution. The optimal experimental time resolution for the apparatus is predicted to be 416 fs for studies of gas-phase species, while the predicted spatial resolution of better than 2 nm-1 compares well with traditional time-averaged electron diffraction set-ups.

  14. Hi-Res scan mode in clinical MDCT systems: Experimental assessment of spatial resolution performance.

    Science.gov (United States)

    Cruz-Bastida, Juan P; Gomez-Cardona, Daniel; Li, Ke; Sun, Heyi; Hsieh, Jiang; Szczykutowicz, Timothy P; Chen, Guang-Hong

    2016-05-01

    The introduction of a High-Resolution (Hi-Res) scan mode and another associated option that combines Hi-Res mode with the so-called High Definition (HD) reconstruction kernels (referred to as a Hi-Res/HD mode in this paper) in some multi-detector CT (MDCT) systems offers new opportunities to increase spatial resolution for some clinical applications that demand high spatial resolution. The purpose of this work was to quantify the in-plane spatial resolution along both the radial direction and tangential direction for the Hi-Res and Hi-Res/HD scan modes at different off-center positions. A technique was introduced and validated to address the signal saturation problem encountered in the attempt to quantify spatial resolution for the Hi-Res and Hi-Res/HD scan modes. Using the proposed method, the modulation transfer functions (MTFs) of a 64-slice MDCT system (Discovery CT750 HD, GE Healthcare) equipped with both Hi-Res and Hi-Res/HD modes were measured using a metal bead at nine different off-centered positions (0-16 cm with a step size of 2 cm); at each position, both conventional scans and Hi-Res scans were performed. For each type of scan and position, 80 repeated acquisitions were performed to reduce noise induced uncertainties in the MTF measurements. A total of 15 reconstruction kernels, including eight conventional kernels and seven HD kernels, were used to reconstruct CT images of the bead. An ex vivo animal study consisting of a bone fracture model was performed to corroborate the MTF results, as the detection of this high-contrast and high frequency task is predominantly determined by spatial resolution. Images of this animal model generated by different scan modes and reconstruction kernels were qualitatively compared with the MTF results. At the centered position, the use of Hi-Res mode resulted in a slight improvement in the MTF; each HD kernel generated higher spatial resolution than its counterpart conventional kernel. However, the MTF along the

  15. Evaluating the Value of High Spatial Resolution in National Capacity Expansion Models using ReEDS

    Energy Technology Data Exchange (ETDEWEB)

    Krishnan, Venkat; Cole, Wesley

    2016-07-18

    This poster is based on the paper of the same name, presented at the IEEE Power & Energy Society General Meeting, July18, 2016. Power sector capacity expansion models (CEMs) have a broad range of spatial resolutions. This paper uses the Regional Energy Deployment System (ReEDS) model, a long-term national scale electric sector CEM, to evaluate the value of high spatial resolution for CEMs. ReEDS models the United States with 134 load balancing areas (BAs) and captures the variability in existing generation parameters, future technology costs, performance, and resource availability using very high spatial resolution data, especially for wind and solar modeled at 356 resource regions. In this paper we perform planning studies at three different spatial resolutions - native resolution (134 BAs), state-level, and NERC region level - and evaluate how results change under different levels of spatial aggregation in terms of renewable capacity deployment and location, associated transmission builds, and system costs. The results are used to ascertain the value of high geographically resolved models in terms of their impact on relative competitiveness among renewable energy resources.

  16. Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

    Directory of Open Access Journals (Sweden)

    C. M. R. Mateo

    2017-10-01

    Full Text Available Global-scale river models (GRMs are core tools for providing consistent estimates of global flood hazard, especially in data-scarce regions. Due to former limitations in computational power and input datasets, most GRMs have been developed to use simplified representations of flow physics and run at coarse spatial resolutions. With increasing computational power and improved datasets, the application of GRMs to finer resolutions is becoming a reality. To support development in this direction, the suitability of GRMs for application to finer resolutions needs to be assessed. This study investigates the impacts of spatial resolution and flow connectivity representation on the predictive capability of a GRM, CaMa-Flood, in simulating the 2011 extreme flood in Thailand. Analyses show that when single downstream connectivity (SDC is assumed, simulation results deteriorate with finer spatial resolution; Nash–Sutcliffe efficiency coefficients decreased by more than 50 % between simulation results at 10 km resolution and 1 km resolution. When multiple downstream connectivity (MDC is represented, simulation results slightly improve with finer spatial resolution. The SDC simulations result in excessive backflows on very flat floodplains due to the restrictive flow directions at finer resolutions. MDC channels attenuated these effects by maintaining flow connectivity and flow capacity between floodplains in varying spatial resolutions. While a regional-scale flood was chosen as a test case, these findings should be universal and may have significant impacts on large- to global-scale simulations, especially in regions where mega deltas exist.These results demonstrate that a GRM can be used for higher resolution simulations of large-scale floods, provided that MDC in rivers and floodplains is adequately represented in the model structure.

  17. Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

    Science.gov (United States)

    Mateo, Cherry May R.; Yamazaki, Dai; Kim, Hyungjun; Champathong, Adisorn; Vaze, Jai; Oki, Taikan

    2017-10-01

    Global-scale river models (GRMs) are core tools for providing consistent estimates of global flood hazard, especially in data-scarce regions. Due to former limitations in computational power and input datasets, most GRMs have been developed to use simplified representations of flow physics and run at coarse spatial resolutions. With increasing computational power and improved datasets, the application of GRMs to finer resolutions is becoming a reality. To support development in this direction, the suitability of GRMs for application to finer resolutions needs to be assessed. This study investigates the impacts of spatial resolution and flow connectivity representation on the predictive capability of a GRM, CaMa-Flood, in simulating the 2011 extreme flood in Thailand. Analyses show that when single downstream connectivity (SDC) is assumed, simulation results deteriorate with finer spatial resolution; Nash-Sutcliffe efficiency coefficients decreased by more than 50 % between simulation results at 10 km resolution and 1 km resolution. When multiple downstream connectivity (MDC) is represented, simulation results slightly improve with finer spatial resolution. The SDC simulations result in excessive backflows on very flat floodplains due to the restrictive flow directions at finer resolutions. MDC channels attenuated these effects by maintaining flow connectivity and flow capacity between floodplains in varying spatial resolutions. While a regional-scale flood was chosen as a test case, these findings should be universal and may have significant impacts on large- to global-scale simulations, especially in regions where mega deltas exist.These results demonstrate that a GRM can be used for higher resolution simulations of large-scale floods, provided that MDC in rivers and floodplains is adequately represented in the model structure.

  18. X-ray diffractometry with spatial resolution

    International Nuclear Information System (INIS)

    Zeiner, K.

    1981-04-01

    X-ray diffractometry is one of the extensively used methods for investigation of the crystalline structure of materials. Line shape and position of a diffracted line are influenced by grain size, deformation and stress. Spatial resolution of one of these specimen characteristics is usually achieved by point-focused X-ray beams and subsequently analyzing different specimen positions. This work uses the method of image reconstruction from projections for the generation of distribution maps. Additional experimental requirements when using a conventional X-ray goniometer are a specimen scanning unit and a computer. The scanning unit repeatedly performs a number of translation steps followed by a rotation step in a fixed X-ray tube/detector (position sensitive detector) arrangement. At each specimen position a diffraction line is recorded using a line-shaped X-ray beam. This network of diffraction lines (showing line resolution) is mathematically converted to a distribution map of diffraction lines and going thus a point resolution. Specimen areas of up to several cm 2 may be analyzed with a linear resolution of 0.1 to 1 mm. Image reconstruction from projections must be modified for generation of ''function-maps''. This theory is discussed and demonstrated by computer simulations. Diffraction line analysis is done for specimen deformation using a deconvolution procedure. The theoretical considerations are experimentally verified. (author)

  19. Spatial Statistical Data Fusion (SSDF)

    Science.gov (United States)

    Braverman, Amy J.; Nguyen, Hai M.; Cressie, Noel

    2013-01-01

    As remote sensing for scientific purposes has transitioned from an experimental technology to an operational one, the selection of instruments has become more coordinated, so that the scientific community can exploit complementary measurements. However, tech nological and scientific heterogeneity across devices means that the statistical characteristics of the data they collect are different. The challenge addressed here is how to combine heterogeneous remote sensing data sets in a way that yields optimal statistical estimates of the underlying geophysical field, and provides rigorous uncertainty measures for those estimates. Different remote sensing data sets may have different spatial resolutions, different measurement error biases and variances, and other disparate characteristics. A state-of-the-art spatial statistical model was used to relate the true, but not directly observed, geophysical field to noisy, spatial aggregates observed by remote sensing instruments. The spatial covariances of the true field and the covariances of the true field with the observations were modeled. The observations are spatial averages of the true field values, over pixels, with different measurement noise superimposed. A kriging framework is used to infer optimal (minimum mean squared error and unbiased) estimates of the true field at point locations from pixel-level, noisy observations. A key feature of the spatial statistical model is the spatial mixed effects model that underlies it. The approach models the spatial covariance function of the underlying field using linear combinations of basis functions of fixed size. Approaches based on kriging require the inversion of very large spatial covariance matrices, and this is usually done by making simplifying assumptions about spatial covariance structure that simply do not hold for geophysical variables. In contrast, this method does not require these assumptions, and is also computationally much faster. This method is

  20. Scaling law for noise variance and spatial resolution in differential phase contrast computed tomography

    International Nuclear Information System (INIS)

    Chen Guanghong; Zambelli, Joseph; Li Ke; Bevins, Nicholas; Qi Zhihua

    2011-01-01

    Purpose: The noise variance versus spatial resolution relationship in differential phase contrast (DPC) projection imaging and computed tomography (CT) are derived and compared to conventional absorption-based x-ray projection imaging and CT. Methods: The scaling law for DPC-CT is theoretically derived and subsequently validated with phantom results from an experimental Talbot-Lau interferometer system. Results: For the DPC imaging method, the noise variance in the differential projection images follows the same inverse-square law with spatial resolution as in conventional absorption-based x-ray imaging projections. However, both in theory and experimental results, in DPC-CT the noise variance scales with spatial resolution following an inverse linear relationship with fixed slice thickness. Conclusions: The scaling law in DPC-CT implies a lesser noise, and therefore dose, penalty for moving to higher spatial resolutions when compared to conventional absorption-based CT in order to maintain the same contrast-to-noise ratio.

  1. Automated road network extraction from high spatial resolution multi-spectral imagery

    Science.gov (United States)

    Zhang, Qiaoping

    road network. The extracted road network is evaluated against a reference dataset using a line segment matching algorithm. The entire process is unsupervised and fully automated. Based on extensive experimentation on a variety of remotely-sensed multi-spectral images, the proposed methodology achieves a moderate success in automating road network extraction from high spatial resolution multi-spectral imagery.

  2. Remote alignment of Low beta quadrupoles with micrometric resolution

    CERN Document Server

    Acar, M; Herty, A; Mainaud-Durand, H; Marin, A; Quesnel, J P

    2008-01-01

    Considering their location in a high radiation environment and the alignment tolerancesrequested, the low beta quadrupoles of LHC will be positioned remotely (controlling 5 degrees of freedom), with a displacement resolution of few microns in horizontal and vertical. Stepping motor gearbox assemblies are plugged into the jacks which support the cryomagnets in order to move them to the desired position regarding the quality of the beam collisions in the detectors. This displacement will be monitored in real time by the sensors located on the magnets. This paper describes the positioning strategy implemented as well as the software tools used to manage it.

  3. High spatial resolution brain functional MRI using submillimeter balanced steady-state free precession acquisition

    International Nuclear Information System (INIS)

    Wu, Pei-Hsin; Chung, Hsiao-Wen; Tsai, Ping-Huei; Wu, Ming-Long; Chuang, Tzu-Chao; Shih, Yi-Yu; Huang, Teng-Yi

    2013-01-01

    Purpose: One of the technical advantages of functional magnetic resonance imaging (fMRI) is its precise localization of changes from neuronal activities. While current practice of fMRI acquisition at voxel size around 3 × 3 × 3 mm 3 achieves satisfactory results in studies of basic brain functions, higher spatial resolution is required in order to resolve finer cortical structures. This study investigated spatial resolution effects on brain fMRI experiments using balanced steady-state free precession (bSSFP) imaging with 0.37 mm 3 voxel volume at 3.0 T. Methods: In fMRI experiments, full and unilateral visual field 5 Hz flashing checkerboard stimulations were given to healthy subjects. The bSSFP imaging experiments were performed at three different frequency offsets to widen the coverage, with functional activations in the primary visual cortex analyzed using the general linear model. Variations of the spatial resolution were achieved by removing outerk-space data components. Results: Results show that a reduction in voxel volume from 3.44 × 3.44 × 2 mm 3 to 0.43 × 0.43 × 2 mm 3 has resulted in an increase of the functional activation signals from (7.7 ± 1.7)% to (20.9 ± 2.0)% at 3.0 T, despite of the threefold SNR decreases in the original images, leading to nearly invariant functional contrast-to-noise ratios (fCNR) even at high spatial resolution. Activation signals aligning nicely with gray matter sulci at high spatial resolution would, on the other hand, have possibly been mistaken as noise at low spatial resolution. Conclusions: It is concluded that the bSSFP sequence is a plausible technique for fMRI investigations at submillimeter voxel widths without compromising fCNR. The reduction of partial volume averaging with nonactivated brain tissues to retain fCNR is uniquely suitable for high spatial resolution applications such as the resolving of columnar organization in the brain

  4. High spatial resolution brain functional MRI using submillimeter balanced steady-state free precession acquisition

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Pei-Hsin; Chung, Hsiao-Wen [Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan (China); Tsai, Ping-Huei [Imaging Research Center, Taipei Medical University, Taipei 11031, Taiwan and Department of Medical Imaging, Taipei Medical University Hospital, Taipei Medical University, Taipei 11031, Taiwan (China); Wu, Ming-Long, E-mail: minglong.wu@csie.ncku.edu.tw [Institute of Medical Informatics, National Cheng-Kung University, Tainan 70101, Taiwan and Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan 70101, Taiwan (China); Chuang, Tzu-Chao [Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan (China); Shih, Yi-Yu [Siemens Limited Healthcare Sector, Taipei 11503, Taiwan (China); Huang, Teng-Yi [Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan (China)

    2013-12-15

    Purpose: One of the technical advantages of functional magnetic resonance imaging (fMRI) is its precise localization of changes from neuronal activities. While current practice of fMRI acquisition at voxel size around 3 × 3 × 3 mm{sup 3} achieves satisfactory results in studies of basic brain functions, higher spatial resolution is required in order to resolve finer cortical structures. This study investigated spatial resolution effects on brain fMRI experiments using balanced steady-state free precession (bSSFP) imaging with 0.37 mm{sup 3} voxel volume at 3.0 T. Methods: In fMRI experiments, full and unilateral visual field 5 Hz flashing checkerboard stimulations were given to healthy subjects. The bSSFP imaging experiments were performed at three different frequency offsets to widen the coverage, with functional activations in the primary visual cortex analyzed using the general linear model. Variations of the spatial resolution were achieved by removing outerk-space data components. Results: Results show that a reduction in voxel volume from 3.44 × 3.44 × 2 mm{sup 3} to 0.43 × 0.43 × 2 mm{sup 3} has resulted in an increase of the functional activation signals from (7.7 ± 1.7)% to (20.9 ± 2.0)% at 3.0 T, despite of the threefold SNR decreases in the original images, leading to nearly invariant functional contrast-to-noise ratios (fCNR) even at high spatial resolution. Activation signals aligning nicely with gray matter sulci at high spatial resolution would, on the other hand, have possibly been mistaken as noise at low spatial resolution. Conclusions: It is concluded that the bSSFP sequence is a plausible technique for fMRI investigations at submillimeter voxel widths without compromising fCNR. The reduction of partial volume averaging with nonactivated brain tissues to retain fCNR is uniquely suitable for high spatial resolution applications such as the resolving of columnar organization in the brain.

  5. Assimilation of remote sensing observations into a sediment transport model of China's largest freshwater lake: spatial and temporal effects.

    Science.gov (United States)

    Zhang, Peng; Chen, Xiaoling; Lu, Jianzhong; Zhang, Wei

    2015-12-01

    Numerical models are important tools that are used in studies of sediment dynamics in inland and coastal waters, and these models can now benefit from the use of integrated remote sensing observations. This study explores a scheme for assimilating remotely sensed suspended sediment (from charge-coupled device (CCD) images obtained from the Huanjing (HJ) satellite) into a two-dimensional sediment transport model of Poyang Lake, the largest freshwater lake in China. Optimal interpolation is used as the assimilation method, and model predictions are obtained by combining four remote sensing images. The parameters for optimal interpolation are determined through a series of assimilation experiments evaluating the sediment predictions based on field measurements. The model with assimilation of remotely sensed sediment reduces the root-mean-square error of the predicted sediment concentrations by 39.4% relative to the model without assimilation, demonstrating the effectiveness of the assimilation scheme. The spatial effect of assimilation is explored by comparing model predictions with remotely sensed sediment, revealing that the model with assimilation generates reasonable spatial distribution patterns of suspended sediment. The temporal effect of assimilation on the model's predictive capabilities varies spatially, with an average temporal effect of approximately 10.8 days. The current velocities which dominate the rate and direction of sediment transport most likely result in spatial differences in the temporal effect of assimilation on model predictions.

  6. Mapping wood density globally using remote sensing and climatological data

    Science.gov (United States)

    Moreno, A.; Camps-Valls, G.; Carvalhais, N.; Kattge, J.; Robinson, N.; Reichstein, M.; Allred, B. W.; Running, S. W.

    2017-12-01

    Wood density (WD) is defined as the oven-dry mass divided by fresh volume, varies between individuals, and describes the carbon investment per unit volume of stem. WD has been proven to be a key functional trait in carbon cycle research and correlates with numerous morphological, mechanical, physiological, and ecological properties. In spite of the utility and importance of this trait, there is a lack of an operational framework to spatialize plant WD measurements at a global scale. In this work, we present a consistent modular processing chain to derive global maps (500 m) of WD using modern machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data using the Google Earth Engine platform. The developed approach uses a hierarchical Bayesian approach to fill in gaps in the plant measured WD data set to maximize its global representativeness. WD plant species are then aggregated to Plant Functional Types (PFT). The spatial abundance of PFT at 500 m spatial resolution (MODIS) is calculated using a high resolution (30 m) PFT map developed using Landsat data. Based on these PFT abundances, representative WD values are estimated for each MODIS pixel with nearby measured data. Finally, random forests are used to globally estimate WD from these MODIS pixels using remote sensing and climate. The validation and assessment of the applied methods indicate that the model explains more than 72% of the spatial variance of the calculated community aggregated WD estimates with virtually unbiased estimates and low RMSE (<15%). The maps thus offer new opportunities to study and analyze the global patterns of variation of WD at an unprecedented spatial coverage and spatial resolution.

  7. Real-time and quantitative isotropic spatial resolution susceptibility imaging for magnetic nanoparticles

    Science.gov (United States)

    Pi, Shiqiang; Liu, Wenzhong; Jiang, Tao

    2018-03-01

    The magnetic transparency of biological tissue allows the magnetic nanoparticle (MNP) to be a promising functional sensor and contrast agent. The complex susceptibility of MNPs, strongly influenced by particle concentration, excitation magnetic field and their surrounding microenvironment, provides significant implications for biomedical applications. Therefore, magnetic susceptibility imaging of high spatial resolution will give more detailed information during the process of MNP-aided diagnosis and therapy. In this study, we present a novel spatial magnetic susceptibility extraction method for MNPs under a gradient magnetic field, a low-frequency drive magnetic field, and a weak strength high-frequency magnetic field. Based on this novel method, a magnetic particle susceptibility imaging (MPSI) of millimeter-level spatial resolution (<3 mm) was achieved using our homemade imaging system. Corroborated by the experimental results, the MPSI shows real-time (1 s per frame acquisition) and quantitative abilities, and isotropic high resolution.

  8. EBSD spatial resolution for detecting sigma phase in steels

    Energy Technology Data Exchange (ETDEWEB)

    Bordín, S. Fernandez; Limandri, S. [Instituto de Física Enrique Gaviola, CONICET. M. Allende s/n, Ciudad Universitaria, 5000 Córdoba (Argentina); Ranalli, J.M. [Comisión Nacional de Energía Atómica, Av. Gral. Paz 1499, San Martín, 1650 Buenos Aires (Argentina); Castellano, G. [Instituto de Física Enrique Gaviola, CONICET. M. Allende s/n, Ciudad Universitaria, 5000 Córdoba (Argentina)

    2016-12-15

    The spatial resolution of the electron backscatter diffraction signal is explored by Monte Carlo simulation for the sigma phase in steel at a typical instrumental set-up. In order to estimate the active volume corresponding to the diffracted electrons, the fraction of the backscattered electrons contributing to the diffraction signal was inferred by extrapolating the Kikuchi pattern contrast measured by other authors, as a function of the diffracted electron energy. In the resulting estimation, the contribution of the intrinsic incident beam size and the software capability to deconvolve patterns were included. A strong influence of the beam size on the lateral resolution was observed, resulting in 20 nm for the aperture considered. For longitudinal and depth directions the resolutions obtained were 75 nm and 16 nm, respectively. The reliability of this last result is discussed in terms of the survey of the last large-angle deflection undergone by the backscattered electrons involved in the diffraction process. Bearing in mind the mean transversal resolution found, it was possible to detect small area grains of sigma phase by EBSD measurements, for a stabilized austenitic AISI 347 stainless steel under heat treatments, simulating post welding (40 h at 600 °C) and aging (284 h at 484 °C) effects—as usually occurring in nuclear reactor pressure vessels. - Highlights: • EBSD spatial resolution is studied by Monte Carlo simulation for σ-phase in steel. • The contribution of the intrinsic incident beam size was included. • A stabilized austenitic stainless steel under heat treatments was measured by EBSD. • With the transversal resolution found, small area σ-phase grains could be identified.

  9. The measurement and calculation of the X-ray spatial resolution obtained in the analytical electron microscope

    International Nuclear Information System (INIS)

    Michael, J.R.; Williams, D.B.

    1990-01-01

    The X-ray microanalytical spatial resolution is determined experimentally in various analytical electron microscopes by measuring the degradation of an atomically discrete composition profile across an interphase interface in a thin-foil of Ni-Cr-Fe. The experimental spatial resolutions are then compared with calculated values. The calculated spatial resolutions are obtained by the mathematical convolution of the electron probe size with an assumed beam-broadening distribution and the single-scattering model of beam broadening. The probe size is measured directly from an image of the probe in a TEM/SETEM and indirectly from dark-field signal changes resulting from scanning the probe across the edge of an MgO crystal in a dedicated STEM. This study demonstrates the applicability of the convolution technique to the calculation of the microanalytical spatial resolution obtained in the analytical electron microscope. It is demonstrated that, contrary to popular opinion, the electron probe size has a major impact on the measured spatial resolution in foils < 150 nm thick. (author)

  10. Evaluating the Value of High Spatial Resolution in National Capacity Expansion Models using ReEDS: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Krishnan, Venkat; Cole, Wesley

    2016-07-01

    Power sector capacity expansion models (CEMs) have a broad range of spatial resolutions. This paper uses the Regional Energy Deployment System (ReEDS) model, a long-term national scale electric sector CEM, to evaluate the value of high spatial resolution for CEMs. ReEDS models the United States with 134 load balancing areas (BAs) and captures the variability in existing generation parameters, future technology costs, performance, and resource availability using very high spatial resolution data, especially for wind and solar modeled at 356 resource regions. In this paper we perform planning studies at three different spatial resolutions--native resolution (134 BAs), state-level, and NERC region level--and evaluate how results change under different levels of spatial aggregation in terms of renewable capacity deployment and location, associated transmission builds, and system costs. The results are used to ascertain the value of high geographically resolved models in terms of their impact on relative competitiveness among renewable energy resources.

  11. Design and study of a coplanar grid array CdZnTe detector for improved spatial resolution

    International Nuclear Information System (INIS)

    Ma, Yuedong; Xiao, Shali; Yang, Guoqiang; Zhang, Liuqiang

    2014-01-01

    Coplanar grid (CPG) CdZnTe detectors have been used as gamma-ray spectrometers for years. Comparing with pixelated CdZnTe detectors, CPG CdZnTe detectors have either no or poor spatial resolution, which directly limits its use in imaging applications. To address the issue, a 2×2 CPG array CdZnTe detector with dimensions of 7×7×5 mm 3 was fabricated. Each of the CPG pairs in the detector was moderately shrunk in size and precisely designed to improve the spatial resolution while maintaining good energy resolution, considering the charge loss at the surface between the strips of each CPG pairs. Preliminary measurements were demonstrated at an energy resolution of 2.7–3.9% for the four CPG pairs using 662 keV gamma rays and with a spatial resolution of 3.3 mm, which is the best spatial resolution ever achieved for CPG CdZnTe detectors. The results reveal that the CPG CdZnTe detector can also be applied to imaging applications at a substantially higher spatial resolution. - Highlights: • A novel structure of coplanar grid CdZnTe detector was designed to evaluate the possibility of applying the detector to gamma-ray imaging applications. • The best spatial resolution of coplanar grid CdZnTe detectors ever reported has been achieved, along with good spectroscopic performance. • Depth correction of the energy spectra using a new algorithm is presented

  12. Remote-controlling chemical reactions by light: towards chemistry with high spatio-temporal resolution.

    Science.gov (United States)

    Göstl, Robert; Senf, Antti; Hecht, Stefan

    2014-03-21

    The foundation of the chemical enterprise has always been the creation of new molecular entities, such as pharmaceuticals or polymeric materials. Over the past decades, this continuing effort of designing compounds with improved properties has been complemented by a strong effort to render their preparation (more) sustainable by implementing atom as well as energy economic strategies. Therefore, synthetic chemistry is typically concerned with making specific bonds and connections in a highly selective and efficient manner. However, to increase the degree of sophistication and expand the scope of our work, we argue that the modern aspiring chemist should in addition be concerned with attaining (better) control over when and where chemical bonds are being made or broken. For this purpose, photoswitchable molecular systems, which allow for external modulation of chemical reactions by light, are being developed and in this review we are covering the current state of the art of this exciting new field. These "remote-controlled synthetic tools" provide a remarkable opportunity to perform chemical transformations with high spatial and temporal resolution and should therefore allow regulating biological processes as well as material and device performance.

  13. Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia

    NARCIS (Netherlands)

    Schmidt, M.; Lucas, R.; Bunting, P.; Verbesselt, J.; Armston, J.

    2015-01-01

    High spatio-temporal resolution optical remote sensing data provide unprecedented opportunities to monitor and detect forest disturbance and loss. To demonstrate this potential, a 12-year time series (2000 to 2011) with an 8-day interval of a 30 m spatial resolution data was generated by the use of

  14. Study and optimization of the spatial resolution for detectors with binary readout

    Energy Technology Data Exchange (ETDEWEB)

    Yonamine, R., E-mail: ryo.yonamine@ulb.ac.be; Maerschalk, T.; Lentdecker, G. De

    2016-09-11

    Using simulations and analytical approaches, we have studied single hit resolutions obtained with a binary readout, which is often proposed for high granularity detectors to reduce the generated data volume. Our simulations considering several parameters (e.g. strip pitch) show that the detector geometry and an electronics parameter of the binary readout chips could be optimized for binary readout to offer an equivalent spatial resolution to the one with an analog readout. To understand the behavior as a function of simulation parameters, we developed analytical models that reproduce simulation results with a few parameters. The models can be used to optimize detector designs and operation conditions with regard to the spatial resolution.

  15. Front-illuminated versus back-illuminated photon-counting CCD-based gamma camera: important consequences for spatial resolution and energy resolution

    International Nuclear Information System (INIS)

    Heemskerk, Jan W T; Westra, Albert H; Linotte, Peter M; Ligtvoet, Kees M; Zbijewski, Wojciech; Beekman, Freek J

    2007-01-01

    Charge-coupled devices (CCDs) coupled to scintillation crystals can be used for high-resolution imaging with x-rays and gamma rays. When the CCD images can be read out fast enough, the energy and interaction position of individual gamma quanta can be estimated by a real-time image analysis of the scintillation light flashes ('photon-counting mode'). The electron-multiplying CCD (EMCCD) is well suited for fast read out, since even at high frame rates it has extremely low read-out noise. Back-illuminated (BI) EMCCDs have much higher quantum efficiency than front-illuminated (FI) EMCCDs. Here we compare the spatial and energy resolution of gamma cameras based on FI and BI EMCCDs. The CCDs are coupled to a 1000 μm thick columnar CsI(Tl) crystal for the purpose of Tc-99m and I-125 imaging. Intrinsic spatial resolutions of 44 μm for I-125 and 49 μm for Tc-99m were obtained when using a BI EMCCD, which is an improvement by a factor of about 1.2-2 over the FI EMCCD. Furthermore, in the energy spectrum of the BI EMCCD, the I-125 signal could be clearly separated from the background noise, which was not the case for the FI EMCCD. The energy resolution of a BI EMCCD for Tc-99m was estimated to be approximately 36 keV, full width at half maximum, at 141 keV. The excellent results for the BI EMCCD encouraged us to investigate the cooling requirements for our setup. We have found that for the BI EMCCD, the spatial and energy resolution, as well as image noise, remained stable over a range of temperatures from -50 deg. C to -15 deg. C. This is a significant advantage over the FI EMCCD, which suffered from loss of spatial and especially energy resolution at temperatures as low as -40 deg. C. We conclude that the use of BI EMCCDs may significantly improve the imaging capabilities and the cost efficiency of CCD-based high-resolution gamma cameras. (note)

  16. Remote sensing science - new concepts and applications

    Energy Technology Data Exchange (ETDEWEB)

    Gerstl, S.A.; Cooke, B.J.; Henderson, B.G.; Love, S.P.; Zardecki, A.

    1996-10-01

    This is the final report of a one-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The science and technology of satellite remote sensing is an emerging interdisciplinary field that is growing rapidly with many global and regional applications requiring quantitative sensing of earth`s surface features as well as its atmosphere from space. It is possible today to resolve structures on the earth`s surface as small as one meter from space. If this high spatial resolution is coupled with high spectral resolution, instant object identification can also be achieved. To interpret these spectral signatures correctly, it is necessary to perform a computational correction on the satellite imagery that removes the distorting effects of the atmosphere. This project studied such new concepts and applied innovative new approaches in remote sensing science.

  17. Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Fan Hu

    2016-06-01

    Full Text Available Scene classification of high-resolution remote sensing (HRRS imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal “filters” from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy.

  18. Implications of sensor design for coral reef detection: Upscaling ground hyperspectral imagery in spatial and spectral scales

    Science.gov (United States)

    Caras, Tamir; Hedley, John; Karnieli, Arnon

    2017-12-01

    Remote sensing offers a potential tool for large scale environmental surveying and monitoring. However, remote observations of coral reefs are difficult especially due to the spatial and spectral complexity of the target compared to sensor specifications as well as the environmental implications of the water medium above. The development of sensors is driven by technological advances and the desired products. Currently, spaceborne systems are technologically limited to a choice between high spectral resolution and high spatial resolution, but not both. The current study explores the dilemma of whether future sensor design for marine monitoring should prioritise on improving their spatial or spectral resolution. To address this question, a spatially and spectrally resampled ground-level hyperspectral image was used to test two classification elements: (1) how the tradeoff between spatial and spectral resolutions affects classification; and (2) how a noise reduction by majority filter might improve classification accuracy. The studied reef, in the Gulf of Aqaba (Eilat), Israel, is heterogeneous and complex so the local substrate patches are generally finer than currently available imagery. Therefore, the tested spatial resolution was broadly divided into four scale categories from five millimeters to one meter. Spectral resolution resampling aimed to mimic currently available and forthcoming spaceborne sensors such as (1) Environmental Mapping and Analysis Program (EnMAP) that is characterized by 25 bands of 6.5 nm width; (2) VENμS with 12 narrow bands; and (3) the WorldView series with broadband multispectral resolution. Results suggest that spatial resolution should generally be prioritized for coral reef classification because the finer spatial scale tested (pixel size mind, while the focus in this study was on the technologically limited spaceborne design, aerial sensors may presently provide an opportunity to implement the suggested setup.

  19. Offshore wind potential evaluation and remote sensing imagery; Evaluation du potentiel eolien offshore et imagerie satellitale

    Energy Technology Data Exchange (ETDEWEB)

    Fichaux, N.

    2003-12-15

    Offshore wind energy may help to contribute to the respect of the Kyoto objectives by Europe. It is a key issue to struggle against global change. To sit the future offshore wind parks, it is necessary to accurately evaluate the spatial repartition of the wind potential. We demonstrate that the offshore wind potential shall be represented by maps of wind statistics. As remote sensing is a tool for measuring space physical phenomena, we evaluate its potentialities for mapping wind statistics. Space-borne scatterometers enables the obtention of wind statistics, but far from our areas of interest and at low spatial resolution. Synthetic Aperture Radar (SAR) enables the computation of high resolution wind maps over our areas of interest, but are unsuitable to compute wind statistics. We define the mathematical framework of a statistical method. That method enables to take advantage of both scatterometer and SAR to compute maps of wind statistics at high spatial resolution over the areas of interest. It enables remote sensing to be used operationally to map the offshore wind potential. (author)

  20. Spatial Resolution of a Wedge Shaped MSGC Module

    CERN Document Server

    Bachmann, Sebastian

    1997-01-01

    A banana shaped closed design MSGC detector module was tested together with silicon detectors and other MSGCs in a 100 GeV muon beam. Despite of an undesirable geometry of the test setup, a spatial resolution below 40 micron m was reached. The efficiency of the module, defined by track reconstruction, shows to be 95,6 percent

  1. High-spatial resolution and high-spectral resolution detector for use in the measurement of solar flare hard x rays

    International Nuclear Information System (INIS)

    Desai, U.D.; Orwig, L.E.

    1988-01-01

    In the areas of high spatial resolution, the evaluation of a hard X-ray detector with 65 micron spatial resolution for operation in the energy range from 30 to 400 keV is proposed. The basic detector is a thick large-area scintillator faceplate, composed of a matrix of high-density scintillating glass fibers, attached to a proximity type image intensifier tube with a resistive-anode digital readout system. Such a detector, combined with a coded-aperture mask, would be ideal for use as a modest-sized hard X-ray imaging instrument up to X-ray energies as high as several hundred keV. As an integral part of this study it was also proposed that several techniques be critically evaluated for X-ray image coding which could be used with this detector. In the area of high spectral resolution, it is proposed to evaluate two different types of detectors for use as X-ray spectrometers for solar flares: planar silicon detectors and high-purity germanium detectors (HPGe). Instruments utilizing these high-spatial-resolution detectors for hard X-ray imaging measurements from 30 to 400 keV and high-spectral-resolution detectors for measurements over a similar energy range would be ideally suited for making crucial solar flare observations during the upcoming maximum in the solar cycle

  2. Spatial probabilistic approach on landslide susceptibility assessment from high resolution sensors derived parameters

    International Nuclear Information System (INIS)

    Aman, S N A; Latif, Z Abd; Pradhan, B

    2014-01-01

    Landslide occurrence depends on various interrelating factors which consequently initiate to massive mass of soil and rock debris that move downhill due to the gravity action. LiDAR has come with a progressive approach in mitigating landslide by permitting the formation of more accurate DEM compared to other active space borne and airborne remote sensing techniques. The objective of this research is to assess the susceptibility of landslide in Ulu Klang area by investigating the correlation between past landslide events with geo environmental factors. A high resolution LiDAR DEM was constructed to produce topographic attributes such as slope, curvature and aspect. These data were utilized to derive second deliverables of landslide parameters such as topographic wetness index (TWI), surface area ratio (SAR) and stream power index (SPI) as well as NDVI generated from IKONOS imagery. Subsequently, a probabilistic based frequency ratio model was applied to establish the spatial relationship between the landslide locations and each landslide related factor. Factor ratings were summed up to obtain Landslide Susceptibility Index (LSI) to construct the landslide susceptibility map

  3. Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms

    Directory of Open Access Journals (Sweden)

    K. Parvathi

    2009-01-01

    Full Text Available The watershed transformation is a useful morphological segmentation tool for a variety of grey-scale images. However, over segmentation and under segmentation have become the key problems for the conventional algorithm. In this paper, an efficient segmentation method for high-resolution remote sensing image analysis is presented. Wavelet analysis is one of the most popular techniques that can be used to detect local intensity variation and hence the wavelet transformation is used to analyze the image. Wavelet transform is applied to the image, producing detail (horizontal, vertical, and diagonal and Approximation coefficients. The image gradient with selective regional minima is estimated with the grey-scale morphology for the Approximation image at a suitable resolution, and then the watershed is applied to the gradient image to avoid over segmentation. The segmented image is projected up to high resolutions using the inverse wavelet transform. The watershed segmentation is applied to small subset size image, demanding less computational time. We have applied our new approach to analyze remote sensing images. The algorithm was implemented in MATLAB. Experimental results demonstrated the method to be effective.

  4. Minimum detection limit and spatial resolution of thin-sample field-emission electron probe microanalysis

    International Nuclear Information System (INIS)

    Kubo, Yugo; Hamada, Kotaro; Urano, Akira

    2013-01-01

    The minimum detection limit and spatial resolution for a thinned semiconductor sample were determined by electron probe microanalysis (EPMA) using a Schottky field emission (FE) electron gun and wavelength dispersive X-ray spectrometry. Comparison of the FE-EPMA results with those obtained using energy dispersive X-ray spectrometry in conjunction with scanning transmission electron microscopy, confirmed that FE-EPMA is largely superior in terms of detection sensitivity. Thin-sample FE-EPMA is demonstrated as a very effective method for high resolution, high sensitivity analysis in a laboratory environment because a high probe current and high signal-to-noise ratio can be achieved. - Highlights: • Minimum detection limit and spatial resolution determined for FE-EPMA. • Detection sensitivity of FE-EPMA greatly superior to that of STEM-EDX. • Minimum detection limit and spatial resolution controllable by probe current

  5. Does the Data Resolution/origin Matter? Satellite, Airborne and Uav Imagery to Tackle Plant Invasions

    Science.gov (United States)

    Müllerová, Jana; Brůna, Josef; Dvořák, Petr; Bartaloš, Tomáš; Vítková, Michaela

    2016-06-01

    Invasive plant species represent a serious threat to biodiversity and landscape as well as human health and socio-economy. To successfully fight plant invasions, new methods enabling fast and efficient monitoring, such as remote sensing, are needed. In an ongoing project, optical remote sensing (RS) data of different origin (satellite, aerial and UAV), spectral (panchromatic, multispectral and color), spatial (very high to medium) and temporal resolution, and various technical approaches (object-, pixelbased and combined) are tested to choose the best strategies for monitoring of four invasive plant species (giant hogweed, black locust, tree of heaven and exotic knotweeds). In our study, we address trade-offs between spectral, spatial and temporal resolutions required for balance between the precision of detection and economic feasibility. For the best results, it is necessary to choose best combination of spatial and spectral resolution and phenological stage of the plant in focus. For species forming distinct inflorescences such as giant hogweed iterative semi-automated object-oriented approach was successfully applied even for low spectral resolution data (if pixel size was sufficient) whereas for lower spatial resolution satellite imagery or less distinct species with complicated architecture such as knotweed, combination of pixel and object based approaches was used. High accuracies achieved for very high resolution data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparably precise, fast and efficient. This knowledge serves as a basis for prediction, monitoring and prioritization of management targets.

  6. High-frequency remote monitoring of large lakes with MODIS 500 m imagery

    Science.gov (United States)

    McCullough, Ian M.; Loftin, Cynthia S.; Sader, Steven A.

    2012-01-01

    Satellite-based remote monitoring programs of regional lake water quality largely have relied on Landsat Thematic Mapper (TM) owing to its long image archive, moderate spatial resolution (30 m), and wide sensitivity in the visible portion of the electromagnetic spectrum, despite some notable limitations such as temporal resolution (i.e., 16 days), data pre-processing requirements to improve data quality, and aging satellites. Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors on Aqua/Terra platforms compensate for these shortcomings, although at the expense of spatial resolution. We developed and evaluated a remote monitoring protocol for water clarity of large lakes using MODIS 500 m data and compared MODIS utility to Landsat-based methods. MODIS images captured during May–September 2001, 2004 and 2010 were analyzed with linear regression to identify the relationship between lake water clarity and satellite-measured surface reflectance. Correlations were strong (R² = 0.72–0.94) throughout the study period; however, they were the most consistent in August, reflecting seasonally unstable lake conditions and inter-annual differences in algal productivity during the other months. The utility of MODIS data in remote water quality estimation lies in intra-annual monitoring of lake water clarity in inaccessible, large lakes, whereas Landsat is more appropriate for inter-annual, regional trend analyses of lakes ≥ 8 ha. Model accuracy is improved when ancillary variables are included to reflect seasonal lake dynamics and weather patterns that influence lake clarity. The identification of landscape-scale drivers of regional water quality is a useful way to supplement satellite-based remote monitoring programs relying on spectral data alone.

  7. Hyperspectral remote sensing of canopy biodiversity in Hawaiian lowland rainforests

    Science.gov (United States)

    Kimberly M. Carlson; Gregory P. Asner; R. Flint Hughes; Rebecca Ostertag; Roberta E. Martin

    2007-01-01

    Mapping biological diversity is a high priority for conservation research, management and policy development, but few studies have provided diversity data at high spatial resolution from remote sensing. We used airborne imaging spectroscopy to map woody vascular plant species richness in lowland tropical forest ecosystems in Hawaii. Hyperspectral signatures spanning...

  8. Science with High Spatial Resolution Far-Infrared Data

    Science.gov (United States)

    Terebey, Susan (Editor); Mazzarella, Joseph M. (Editor)

    1994-01-01

    The goal of this workshop was to discuss new science and techniques relevant to high spatial resolution processing of far-infrared data, with particular focus on high resolution processing of IRAS data. Users of the maximum correlation method, maximum entropy, and other resolution enhancement algorithms applicable to far-infrared data gathered at the Infrared Processing and Analysis Center (IPAC) for two days in June 1993 to compare techniques and discuss new results. During a special session on the third day, interested astronomers were introduced to IRAS HIRES processing, which is IPAC's implementation of the maximum correlation method to the IRAS data. Topics discussed during the workshop included: (1) image reconstruction; (2) random noise; (3) imagery; (4) interacting galaxies; (5) spiral galaxies; (6) galactic dust and elliptical galaxies; (7) star formation in Seyfert galaxies; (8) wavelet analysis; and (9) supernova remnants.

  9. Theme issue ;State-of-the-art in photogrammetry, remote sensing and spatial information science;

    Science.gov (United States)

    Heipke, Christian; Madden, Marguerite; Li, Zhilin; Dowman, Ian

    2016-05-01

    Over the past few years, photogrammetry, remote sensing and spatial information science have witnessed great changes in virtually every stage of information from imagery. Indeed, we have seen, for example, a sharply increased interest in unmanned aerial vehicles,

  10. Object-based Morphological Building Index for Building Extraction from High Resolution Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    LIN Xiangguo

    2017-06-01

    Full Text Available Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. In this article, an object-based morphological building index (OBMBI is constructed based on both image segmentation and graph-based top-hat reconstruction, and OBMBI is used for building extraction from high resolution remote sensing images. First, bidirectional mapping relationship between pixels, objects and graph-nodes are constructed. Second, the OBMBI image is built based on both graph-based top-hat reconstruction and the above mapping relationship. Third, a binary thresholding is performed on the OBMBI image, and the binary image is converted into vector format to derive the building polygons. Finally, the post-processing is made to optimize the extracted building polygons. Two images, including an aerial image and a panchromatic satellite image, are used to test both the proposed method and classic PanTex method. The experimental results suggest that our proposed method has a higher accuracy in building extraction than the classic PanTex method. On average, the correctness, the completeness and the quality of our method are respectively 9.49%, 11.26% and 14.11% better than those of the PanTex.

  11. Normal mitochondrial respiratory function is essential for spatial remote memory in mice

    Directory of Open Access Journals (Sweden)

    Tanaka Daisuke

    2008-12-01

    Full Text Available Abstract Background Mitochondrial DNA (mtDNA with pathogenic mutations has been found in patients with cognitive disorders. However, little is known about whether pathogenic mtDNA mutations and the resultant mitochondrial respiration deficiencies contribute to the expression of cognitive alterations, such as impairments of learning and memory. To address this point, we used two groups of trans-mitochondrial mice (mito-mice with heteroplasmy for wild-type and pathogenically deleted (Δ mtDNA; the "low" group carried 50% or less ΔmtDNA, and the "high" group carried more than 50% ΔmtDNA. Results Both groups had normal phenotypes for not only spatial learning, but also memory at short retention delays, indicating that ΔmtDNA load did not affect learning and temporal memory. The high group, however, showed severe impairment of memory at long retention delays. In the visual cortex and dentate gyrus of these mice, we observed mitochondrial respiration deficiencies, and reduced Ca2+/calmodulin-dependent kinase II-α (α-CaMKII, a protein important for the establishment of spatial remote memory. Conclusion Our results indicated that normal mitochondrial respiratory function is necessary for retention and consolidation of memory trace; deficiencies in this function due to high loads of pathogenically mutated mtDNA are responsible for the preferential impairment of spatial remote memory.

  12. Accelerator-based Single-shot Ultrafast Transmission Electron Microscope with Picosecond Temporal Resolution and Nanometer Spatial Resolution

    OpenAIRE

    Xiang, D.; Fu, F.; Zhang, J.; Huang, X.; Wang, L.; Wang, X.; Wan, W.

    2014-01-01

    We present feasibility study of an accelerator-based ultrafast transmission electron microscope (u-TEM) capable of producing a full field image in a single-shot with simultaneous picosecond temporal resolution and nanometer spatial resolution. We study key physics related to performance of u-TEMs, and discuss major challenges as well as possible solutions for practical realization of u-TEMs. The feasibility of u-TEMs is confirmed through simulations using realistic electron beam parameters. W...

  13. Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance

    Energy Technology Data Exchange (ETDEWEB)

    Li, Ke; Chen, Guang-Hong, E-mail: gchen7@wisc.edu [Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, Wisconsin 53705 and Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, Wisconsin 53792 (United States); Garrett, John; Ge, Yongshuai [Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, Wisconsin 53705 (United States)

    2014-07-15

    Purpose: Statistical model based iterative reconstruction (MBIR) methods have been introduced to clinical CT systems and are being used in some clinical diagnostic applications. The purpose of this paper is to experimentally assess the unique spatial resolution characteristics of this nonlinear reconstruction method and identify its potential impact on the detectabilities and the associated radiation dose levels for specific imaging tasks. Methods: The thoracic section of a pediatric phantom was repeatedly scanned 50 or 100 times using a 64-slice clinical CT scanner at four different dose levels [CTDI{sub vol} =4, 8, 12, 16 (mGy)]. Both filtered backprojection (FBP) and MBIR (Veo{sup ®}, GE Healthcare, Waukesha, WI) were used for image reconstruction and results were compared with one another. Eight test objects in the phantom with contrast levels ranging from 13 to 1710 HU were used to assess spatial resolution. The axial spatial resolution was quantified with the point spread function (PSF), while the z resolution was quantified with the slice sensitivity profile. Both were measured locally on the test objects and in the image domain. The dependence of spatial resolution on contrast and dose levels was studied. The study also features a systematic investigation of the potential trade-off between spatial resolution and locally defined noise and their joint impact on the overall image quality, which was quantified by the image domain-based channelized Hotelling observer (CHO) detectability index d′. Results: (1) The axial spatial resolution of MBIR depends on both radiation dose level and image contrast level, whereas it is supposedly independent of these two factors in FBP. The axial spatial resolution of MBIR always improved with an increasing radiation dose level and/or contrast level. (2) The axial spatial resolution of MBIR became equivalent to that of FBP at some transitional contrast level, above which MBIR demonstrated superior spatial resolution than

  14. Patient dose rate: An ultimate limit for spatial and density resolution of scanning systems

    International Nuclear Information System (INIS)

    Kowalski, G.; Wagner, W.

    1979-01-01

    In X-ray scanning systems, picture quality of the reconstructed slices is limited to a maximum spatial as well as density resolution by the applied radiation dose. Density resolution can be improved in proportion to the root of the patient dose, whereas a doubled spatial resolving power requires an eight times higher patient dose, assuming a fixed slice thickness. Only a careful trade-off between the applied patient dose, density resolution and spatial resolution yields a maximal diagnostic value for the physician. Specifications of a scanning system have to take into account these ultimate restrictions, so that picture quality really is limited by the patient's dose rather than by technical constraints. In addition a method is given by which the applied dose can be reduced by focusing the main intensity onto the region of interest, in case that region is known a priori. (orig.) [de

  15. THE INFLUENCE OF SPATIAL RESOLUTION ON NONLINEAR FORCE-FREE MODELING

    Energy Technology Data Exchange (ETDEWEB)

    DeRosa, M. L.; Schrijver, C. J. [Lockheed Martin Solar and Astrophysics Laboratory, 3251 Hanover St. B/252, Palo Alto, CA 94304 (United States); Wheatland, M. S.; Gilchrist, S. A. [Sydney Institute for Astronomy, School of Physics, The University of Sydney, Sydney, NSW 2006 (Australia); Leka, K. D.; Barnes, G. [NorthWest Research Associates, 3380 Mitchell Ln., Boulder, CO 80301 (United States); Amari, T.; Canou, A. [CNRS, Centre de Physique Théorique de l’École Polytechnique, F-91128, Palaiseau Cedex (France); Thalmann, J. K. [Institute of Physics/IGAM, University of Graz, Universitätsplatz 5, A-8010 Graz (Austria); Valori, G. [Mullard Space Science Laboratory, University College London, Holmbury St. Mary, Dorking, Surrey, RH5 6NT (United Kingdom); Wiegelmann, T. [Max-Planck-Institut für Sonnensystemforschung, Justus-von-Liebig-Weg 3, D-37077, Göttingen (Germany); Malanushenko, A. [Department of Physics, Montana State University, Bozeman, MT 59717 (United States); Sun, X. [W. W. Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305 (United States); Régnier, S. [Department of Mathematics and Information Sciences, Faculty of Engineering and Environment, Northumbria University, Newcastle-Upon-Tyne, NE1 8ST (United Kingdom)

    2015-10-01

    The nonlinear force-free field (NLFFF) model is often used to describe the solar coronal magnetic field, however a series of earlier studies revealed difficulties in the numerical solution of the model in application to photospheric boundary data. We investigate the sensitivity of the modeling to the spatial resolution of the boundary data, by applying multiple codes that numerically solve the NLFFF model to a sequence of vector magnetogram data at different resolutions, prepared from a single Hinode/Solar Optical Telescope Spectro-Polarimeter scan of NOAA Active Region 10978 on 2007 December 13. We analyze the resulting energies and relative magnetic helicities, employ a Helmholtz decomposition to characterize divergence errors, and quantify changes made by the codes to the vector magnetogram boundary data in order to be compatible with the force-free model. This study shows that NLFFF modeling results depend quantitatively on the spatial resolution of the input boundary data, and that using more highly resolved boundary data yields more self-consistent results. The free energies of the resulting solutions generally trend higher with increasing resolution, while relative magnetic helicity values vary significantly between resolutions for all methods. All methods require changing the horizontal components, and for some methods also the vertical components, of the vector magnetogram boundary field in excess of nominal uncertainties in the data. The solutions produced by the various methods are significantly different at each resolution level. We continue to recommend verifying agreement between the modeled field lines and corresponding coronal loop images before any NLFFF model is used in a scientific setting.

  16. People and pixels in the Sahel: a study linking coarse-resolution remote sensing observations to land users' perceptions of their changing environment in Senegal

    Directory of Open Access Journals (Sweden)

    Stefanie M. Herrmann

    2014-09-01

    Full Text Available Mounting evidence from satellite observations of a re-greening across much of the Sahel and Sudan zones over the past three decades has raised questions about the extent and reversibility of desertification. Historical ground data that could help in interpreting the re-greening are scarce. To fill that void, we tapped into the collective memories of local land users from central and western Senegal in 39 focus groups and assessed the spatial association between their perceptions of vegetation changes over time and remote sensing-derived trends. To provide context to the vegetation changes, we also explored the land users' perspective on the evolution of other environmental and human variables that are potentially related to the greening, using participatory research methods. While increases in vegetation were confirmed by the study participants for certain areas, which spatially corresponded to satellite-observed re-greening, vegetation degradation dominated their perceptions of change. This degradation, although spatially extensive according to land users, flies under the radar of coarse-resolution remote sensing data because it is not necessarily associated with a decrease in biomass but rather with undesired changes in species composition. Few significant differences were found in the perceived trends of population pressure, environmental, and livelihood variables between communities that have greened up according to satellite data and those that have not. Our findings challenge the prevailing chain of assumptions of the satellite-observed greening trend indicating an improvement of environmental conditions in the sense of a rehabilitation of the vegetation cover after the great droughts of the 1970s and 1980s, and the improvement of environmental conditions possibly translating into more stable livelihoods and greater well-being of the populations. For monitoring desertification and rehabilitation, there is a need to develop remote sensing

  17. Estimation of the distribution of Tabebuia guayacan (Bignoniaceae) using high-resolution remote sensing imagery.

    Science.gov (United States)

    Sánchez-Azofeifa, Arturo; Rivard, Benoit; Wright, Joseph; Feng, Ji-Lu; Li, Peijun; Chong, Mei Mei; Bohlman, Stephanie A

    2011-01-01

    Species identification and characterization in tropical environments is an emerging field in tropical remote sensing. Significant efforts are currently aimed at the detection of tree species, of levels of forest successional stages, and the extent of liana occurrence at the top of canopies. In this paper we describe our use of high resolution imagery from the Quickbird Satellite to estimate the flowering population of Tabebuia guayacan trees at Barro Colorado Island (BCI), in Panama. The imagery was acquired on 29 April 2002 and 21 March 2004. Spectral Angle Mapping via a One-Class Support Vector machine was used to detect the presence of 422 and 557 flowering tress in the April 2002 and March 2004 imagery. Of these, 273 flowering trees are common to both dates. This study presents a new perspective on the effectiveness of high resolution remote sensing for monitoring a phenological response and its use as a tool for potential conservation and management of natural resources in tropical environments.

  18. A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors.

    Science.gov (United States)

    He, Xiyan; Condat, Laurent; Bioucas-Diaz, Jose; Chanussot, Jocelyn; Xia, Junshi

    2014-06-27

    The development of multisensor systems in recent years has led to great increase in the amount of available remote sensing data. Image fusion techniques aim at inferring high quality images of a given area from degraded versions of the same area obtained by multiple sensors. This paper focuses on pansharpening, which is the inference of a high spatial resolution multispectral image from two degraded versions with complementary spectral and spatial resolution characteristics: a) a low spatial resolution multispectral image; and b) a high spatial resolution panchromatic image. We introduce a new variational model based on spatial and spectral sparsity priors for the fusion. In the spectral domain we encourage low-rank structure, whereas in the spatial domain we promote sparsity on the local differences. Given the fact that both panchromatic and multispectral images are integrations of the underlying continuous spectra using different channel responses, we propose to exploit appropriate regularizations based on both spatial and spectral links between panchromatic and the fused multispectral images. A weighted version of the vector Total Variation (TV) norm of the data matrix is employed to align the spatial information of the fused image with that of the panchromatic image. With regard to spectral information, two different types of regularization are proposed to promote a soft constraint on the linear dependence between the panchromatic and the fused multispectral images. The first one estimates directly the linear coefficients from the observed panchromatic and low resolution multispectral images by Linear Regression (LR) while the second one employs the Principal Component Pursuit (PCP) to obtain a robust recovery of the underlying low-rank structure. We also show that the two regularizers are strongly related. The basic idea of both regularizers is that the fused image should have low-rank and preserve edge locations. We use a variation of the recently proposed

  19. Tactile spatial resolution in blind braille readers.

    Science.gov (United States)

    Van Boven, R W; Hamilton, R H; Kauffman, T; Keenan, J P; Pascual-Leone, A

    2000-06-27

    To determine if blind people have heightened tactile spatial acuity. Recently, studies using magnetic source imaging and somatosensory evoked potentials have shown that the cortical representation of the reading fingers of blind Braille readers is expanded compared to that of fingers of sighted subjects. Furthermore, the visual cortex is activated during certain tactile tasks in blind subjects but not sighted subjects. The authors hypothesized that the expanded cortical representation of fingers used in Braille reading may reflect an enhanced fidelity in the neural transmission of spatial details of a stimulus. If so, the quantitative limit of spatial acuity would be superior in blind people. The authors employed a grating orientation discrimination task in which threshold performance is accounted for by the spatial resolution limits of the neural image evoked by a stimulus. The authors quantified the psychophysical limits of spatial acuity at the middle and index fingers of 15 blind Braille readers and 15 sighted control subjects. The mean grating orientation threshold was significantly (p = 0.03) lower in the blind group (1.04 mm) compared to the sighted group (1.46 mm). The self-reported dominant reading finger in blind subjects had a mean grating orientation threshold of 0.80 mm, which was significantly better than other fingers tested. Thresholds at non-Braille reading fingers in blind subjects averaged 1.12 mm, which were also superior to sighted subjects' performances. Superior tactile spatial acuity in blind Braille readers may represent an adaptive, behavioral correlate of cortical plasticity.

  20. WE-DE-207B-05: Measuring Spatial Resolution in Digital Breast Tomosynthesis: Update of AAPM Task Group 245

    Energy Technology Data Exchange (ETDEWEB)

    Scaduto, DA; Hu, Y-H; Zhao, W [Stony Brook Medicine, Stony Brook, NY (United States); Goodsitt, M; Chan, H-P [University Michigan, Ann Arbor, MI (United States); Olafsdottir, H [Image Owl, 105 Reykjavik (Iceland); Das, M [University Houston, Houston, TX (United States); Fredenberg, E [Philips Healthcare, Solna (Sweden); Geiser, W [UT MD Anderson Cancer Center, Houston, TX (United States); Goodenough, D [The George Washington University, Washington, DC (United States); Heid, P [ARCADES, Marseille (France); Liu, B [Massachusetts General Hospital, Boston, MA (United States); Mainprize, J [Sunnybrook Health Sciences Centre, North York, ON (Canada); Reiser, I [The University of Chicago, Chicago, IL (United States); Van Engen, R [LRCB, Nijmegen (Netherlands); Varchena, V [CIRS Inc., Norfolk, VA (United States); Vecchio, S [I.M.S., Pontecchio Marconi (Italy); Glick, S [Food and Drug Administration, Silver Spring, MD (United States)

    2016-06-15

    Purpose: Spatial resolution in digital breast tomosynthesis (DBT) is affected by inherent/binned detector resolution, oblique entry of x-rays, and focal spot size/motion; the limited angular range further limits spatial resolution in the depth-direction. While DBT is being widely adopted clinically, imaging performance metrics and quality control protocols have not been standardized. AAPM Task Group 245 on Tomosynthesis Quality Control has been formed to address this deficiency. Methods: Methods of measuring spatial resolution are evaluated using two prototype quality control phantoms for DBT. Spatial resolution in the detector plane is measured in projection and reconstruction domains using edge-spread function (ESF), point-spread function (PSF) and modulation transfer function (MTF). Spatial resolution in the depth-direction and effective slice thickness are measured in the reconstruction domain using slice sensitivity profile (SSP) and artifact spread function (ASF). An oversampled PSF in the depth-direction is measured using a 50 µm angulated tungsten wire, from which the MTF is computed. Object-dependent PSF is derived and compared with ASF. Sensitivity of these measurements to phantom positioning, imaging conditions and reconstruction algorithms is evaluated. Results are compared from systems of varying acquisition geometry (9–25 projections over 15–60°). Dependence of measurements on feature size is investigated. Results: Measurements of spatial resolution using PSF and LSF are shown to depend on feature size; depth-direction spatial resolution measurements are shown to similarly depend on feature size for ASF, though deconvolution with an object function removes feature size-dependence. A slanted wire may be used to measure oversampled PSFs, from which MTFs may be computed for both in-plane and depth-direction resolution. Conclusion: Spatial resolution measured using PSF is object-independent with sufficiently small object; MTF is object

  1. Using remote sensing and machine learning for the spatial modelling of a bluetongue virus vector

    Science.gov (United States)

    Van doninck, J.; Peters, J.; De Baets, B.; Ducheyne, E.; Verhoest, N. E. C.

    2012-04-01

    Bluetongue is a viral vector-borne disease transmitted between hosts, mostly cattle and small ruminants, by some species of Culicoides midges. Within the Mediterranean basin, C. imicola is the main vector of the bluetongue virus. The spatial distribution of this species is limited by a number of environmental factors, including temperature, soil properties and land cover. The identification of zones at risk of bluetongue outbreaks thus requires detailed information on these environmental factors, as well as appropriate epidemiological modelling techniques. We here give an overview of the environmental factors assumed to be constraining the spatial distribution of C. imicola, as identified in different studies. Subsequently, remote sensing products that can be used as proxies for these environmental constraints are presented. Remote sensing data are then used together with species occurrence data from the Spanish Bluetongue National Surveillance Programme to calibrate a supervised learning model, based on Random Forests, to model the probability of occurrence of the C. imicola midge. The model will then be applied for a pixel-based prediction over the Iberian peninsula using remote sensing products for habitat characterization.

  2. Research on active imaging information transmission technology of satellite borne quantum remote sensing

    Science.gov (United States)

    Bi, Siwen; Zhen, Ming; Yang, Song; Lin, Xuling; Wu, Zhiqiang

    2017-08-01

    According to the development and application needs of Remote Sensing Science and technology, Prof. Siwen Bi proposed quantum remote sensing. Firstly, the paper gives a brief introduction of the background of quantum remote sensing, the research status and related researches at home and abroad on the theory, information mechanism and imaging experiments of quantum remote sensing and the production of principle prototype.Then, the quantization of pure remote sensing radiation field, the state function and squeezing effect of quantum remote sensing radiation field are emphasized. It also describes the squeezing optical operator of quantum light field in active imaging information transmission experiment and imaging experiments, achieving 2-3 times higher resolution than that of coherent light detection imaging and completing the production of quantum remote sensing imaging prototype. The application of quantum remote sensing technology can significantly improve both the signal-to-noise ratio of information transmission imaging and the spatial resolution of quantum remote sensing .On the above basis, Prof.Bi proposed the technical solution of active imaging information transmission technology of satellite borne quantum remote sensing, launched researches on its system composition and operation principle and on quantum noiseless amplifying devices, providing solutions and technical basis for implementing active imaging information technology of satellite borne Quantum Remote Sensing.

  3. Modeling the impacts of phenological and inter-annual changes in landscape metrics on local biodiversity of agricultural lands of Eastern Ontario using multi-spatial and multi-temporal remote sensing data

    Science.gov (United States)

    Alavi-Shoushtari, N.; King, D.

    2017-12-01

    Agricultural landscapes are highly variable ecosystems and are home to many local farmland species. Seasonal, phenological and inter-annual agricultural landscape dynamics have potential to affect the richness and abundance of farmland species. Remote sensing provides data and techniques which enable monitoring landscape changes in multiple temporal and spatial scales. MODIS high temporal resolution remote sensing images enable detection of seasonal and phenological trends, while Landsat higher spatial resolution images, with its long term archive enables inter-annual trend analysis over several decades. The objective of this study to use multi-spatial and multi-temporal remote sensing data to model the response of farmland species to landscape metrics. The study area is the predominantly agricultural region of eastern Ontario. 92 sample landscapes were selected within this region using a protocol designed to maximize variance in composition and configuration heterogeneity while controlling for amount of forest and spatial autocorrelation. Two sample landscape extents (1×1km and 3×3km) were selected to analyze the impacts of spatial scale on biodiversity response. Gamma diversity index data for four taxa groups (birds, butterflies, plants, and beetles) were collected during the summers of 2011 and 2012 within the cropped area of each landscape. To extract the seasonal and phenological metrics a 2000-2012 MODIS NDVI time-series was used, while a 1985-2012 Landsat time-series was used to model the inter-annual trends of change in the sample landscapes. The results of statistical modeling showed significant relationships between farmland biodiversity for several taxa and the phenological and inter-annual variables. The following general results were obtained: 1) Among the taxa groups, plant and beetles diversity was most significantly correlated with the phenological variables; 2) Those phenological variables which are associated with the variability in the start of

  4. Improvement of range spatial resolution of medical ultrasound imaging by element-domain signal processing

    Science.gov (United States)

    Hasegawa, Hideyuki

    2017-07-01

    The range spatial resolution is an important factor determining the image quality in ultrasonic imaging. The range spatial resolution in ultrasonic imaging depends on the ultrasonic pulse length, which is determined by the mechanical response of the piezoelectric element in an ultrasonic probe. To improve the range spatial resolution without replacing the transducer element, in the present study, methods based on maximum likelihood (ML) estimation and multiple signal classification (MUSIC) were proposed. The proposed methods were applied to echo signals received by individual transducer elements in an ultrasonic probe. The basic experimental results showed that the axial half maximum of the echo from a string phantom was improved from 0.21 mm (conventional method) to 0.086 mm (ML) and 0.094 mm (MUSIC).

  5. Remote sensing inputs to landscape models which predict future spatial land use patterns for hydrologic models

    Science.gov (United States)

    Miller, L. D.; Tom, C.; Nualchawee, K.

    1977-01-01

    A tropical forest area of Northern Thailand provided a test case of the application of the approach in more natural surroundings. Remote sensing imagery subjected to proper computer analysis has been shown to be a very useful means of collecting spatial data for the science of hydrology. Remote sensing products provide direct input to hydrologic models and practical data bases for planning large and small-scale hydrologic developments. Combining the available remote sensing imagery together with available map information in the landscape model provides a basis for substantial improvements in these applications.

  6. Multi- and hyperspectral remote sensing of tropical marine benthic habitats

    Science.gov (United States)

    Mishra, Deepak R.

    Tropical marine benthic habitats such as coral reef and associated environments are severely endangered because of the environmental degradation coupled with hurricanes, El Nino events, coastal pollution and runoff, tourism, and economic development. To monitor and protect this diverse environment it is important to not only develop baseline maps depicting their spatial distribution but also to document their changing conditions over time. Remote sensing offers an important means of delineating and monitoring coral reef ecosystems. Over the last twenty years the scientific community has been investigating the use and potential of remote sensing techniques to determine the conditions of the coral reefs by analyzing their spectral characteristics from space. One of the problems in monitoring coral reefs from space is the effect of the water column on the remotely sensed signal. When light penetrates water its intensity decreases exponentially with increasing depth. This process, known as water column attenuation, exerts a profound effect on remotely sensed data collected over water bodies. The approach presented in this research focuses on the development of semi-analytical models that resolves the confounding influence water column attenuation on substrate reflectance to characterize benthic habitats from high resolution remotely sensed imagery on a per-pixel basis. High spatial resolution satellite and airborne imagery were used as inputs in the models to derive water depth and water column optical properties (e.g., absorption and backscattering coefficients). These parameters were subsequently used in various bio-optical algorithms to deduce bottom albedo and then to classify the benthos, generating a detailed map of benthic habitats. IKONOS and QuickBird multispectral satellite data and AISA Eagle hyperspectral airborne data were used in this research for benthic habitat mapping along the north shore of Roatan Island, Honduras. The AISA Eagle classification was

  7. A New Perspective: Assessing the Spatial Distribution of Coral Bleaching with Unmanned Low Altitude Remote Sensing Systems

    Science.gov (United States)

    Levy, J.; Franklin, E. C.; Hunter, C. L.

    2016-12-01

    Coral reefs are biodiversity hotspots that are vital to the function of global economic and biological processes. Coral bleaching is a significant contributor to the global decline of reefs and can impact an expansive reef area over short timescales. In order to understand the dynamics of coral bleaching and how these stress events impact reef ecosystems, it is important to conduct rapid bleaching surveys at functionally important spatial scales. Due to the inherent heterogeneity, size, and in some cases, remoteness of coral reefs, it is difficult to routinely monitor coral bleaching dynamics before, during, and after bleaching. Additionally, current in situ survey methods only collect snippets of discrete reef data over small reef areas, which are unable to accurately represent the reef as a whole. We present a new technique using small unmanned aerial systems (sUAS) as cost effective, efficient monitoring tools that target small to intermediate-scale reef dynamics to understand the spatial distribution of bleached coral colonies during the 2015 bleaching event on patch reefs in Kaneohe Bay, Oahu. Overlapping low altitude aerial images were collected at four reefs during the bleaching period and processed using Structure-from-Motion techniques to produce georeferenced and spatially accurate orthomosaics of complete reef areas. Mosaics were analyzed using manual and heuristic neural network classification schemes to identify comprehensive populations of bleached and live coral on each patch reef. We found that bleached colonies had random and clumped distributions on patch reefs in Kaneohe Bay depending on local environmental conditions. Our work demonstrates that sUAS provide a low cost, efficient platform that can rapidly and repeatedly collect high-resolution imagery (1 cm/pixel) and map large areas of shallow reef ecosystems (5 hectares). This study proves the feasibility of utilizing sUAS as a tool to collect spatially rich reef data that will provide reef

  8. 3D high spectral and spatial resolution imaging of ex vivo mouse brain

    International Nuclear Information System (INIS)

    Foxley, Sean; Karczmar, Gregory S.; Domowicz, Miriam; Schwartz, Nancy

    2015-01-01

    Purpose: Widely used MRI methods show brain morphology both in vivo and ex vivo at very high resolution. Many of these methods (e.g., T 2 * -weighted imaging, phase-sensitive imaging, or susceptibility-weighted imaging) are sensitive to local magnetic susceptibility gradients produced by subtle variations in tissue composition. However, the spectral resolution of commonly used methods is limited to maintain reasonable run-time combined with very high spatial resolution. Here, the authors report on data acquisition at increased spectral resolution, with 3-dimensional high spectral and spatial resolution MRI, in order to analyze subtle variations in water proton resonance frequency and lineshape that reflect local anatomy. The resulting information compliments previous studies based on T 2 * and resonance frequency. Methods: The proton free induction decay was sampled at high resolution and Fourier transformed to produce a high-resolution water spectrum for each image voxel in a 3D volume. Data were acquired using a multigradient echo pulse sequence (i.e., echo-planar spectroscopic imaging) with a spatial resolution of 50 × 50 × 70 μm 3 and spectral resolution of 3.5 Hz. Data were analyzed in the spectral domain, and images were produced from the various Fourier components of the water resonance. This allowed precise measurement of local variations in water resonance frequency and lineshape, at the expense of significantly increased run time (16–24 h). Results: High contrast T 2 * -weighted images were produced from the peak of the water resonance (peak height image), revealing a high degree of anatomical detail, specifically in the hippocampus and cerebellum. In images produced from Fourier components of the water resonance at −7.0 Hz from the peak, the contrast between deep white matter tracts and the surrounding tissue is the reverse of the contrast in water peak height images. This indicates the presence of a shoulder in the water resonance that is not

  9. Spatial quantification of groundwater abstraction in the irrigated indus basin

    NARCIS (Netherlands)

    Cheema, M. J M; Immerzeel, W. W.; Bastiaanssen, W. G M

    2014-01-01

    Groundwater abstraction and depletion were assessed at a 1-km resolution in the irrigated areas of the Indus Basin using remotely sensed evapotranspiration (ET) and precipitation; a process-based hydrological model and spatial information on canal water supplies. A calibrated Soil and Water

  10. Spatial Quantification of Groundwater Abstraction in the Irrigated Indus Basin

    NARCIS (Netherlands)

    Cheema, M.J.M.; Immerzeel, W.W.; Bastiaanssen, W.G.M.

    2013-01-01

    Groundwater abstraction and depletion were assessed at a 1-km resolution in the irrigated areas of the Indus Basin using remotely sensed evapotranspiration (ET) and precipitation; a process-based hydrological model and spatial information on canal water supplies. A calibrated Soil and Water

  11. Developing a CCD camera with high spatial resolution for RIXS in the soft X-ray range

    Science.gov (United States)

    Soman, M. R.; Hall, D. J.; Tutt, J. H.; Murray, N. J.; Holland, A. D.; Schmitt, T.; Raabe, J.; Schmitt, B.

    2013-12-01

    The Super Advanced X-ray Emission Spectrometer (SAXES) at the Swiss Light Source contains a high resolution Charge-Coupled Device (CCD) camera used for Resonant Inelastic X-ray Scattering (RIXS). Using the current CCD-based camera system, the energy-dispersive spectrometer has an energy resolution (E/ΔE) of approximately 12,000 at 930 eV. A recent study predicted that through an upgrade to the grating and camera system, the energy resolution could be improved by a factor of 2. In order to achieve this goal in the spectral domain, the spatial resolution of the CCD must be improved to better than 5 μm from the current 24 μm spatial resolution (FWHM). The 400 eV-1600 eV energy X-rays detected by this spectrometer primarily interact within the field free region of the CCD, producing electron clouds which will diffuse isotropically until they reach the depleted region and buried channel. This diffusion of the charge leads to events which are split across several pixels. Through the analysis of the charge distribution across the pixels, various centroiding techniques can be used to pinpoint the spatial location of the X-ray interaction to the sub-pixel level, greatly improving the spatial resolution achieved. Using the PolLux soft X-ray microspectroscopy endstation at the Swiss Light Source, a beam of X-rays of energies from 200 eV to 1400 eV can be focused down to a spot size of approximately 20 nm. Scanning this spot across the 16 μm square pixels allows the sub-pixel response to be investigated. Previous work has demonstrated the potential improvement in spatial resolution achievable by centroiding events in a standard CCD. An Electron-Multiplying CCD (EM-CCD) has been used to improve the signal to effective readout noise ratio achieved resulting in a worst-case spatial resolution measurement of 4.5±0.2 μm and 3.9±0.1 μm at 530 eV and 680 eV respectively. A method is described that allows the contribution of the X-ray spot size to be deconvolved from these

  12. Hyperspectral remote sensing for light pollution monitoring

    Directory of Open Access Journals (Sweden)

    P. Marcoionni

    2006-06-01

    Full Text Available industries. In this paper we introduce the results from a remote sensing campaign performed in September 2001 at night time. For the first time nocturnal light pollution was measured at high spatial and spectral resolution using two airborne hyperspectral sensors, namely the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS and the Visible InfraRed Scanner (VIRS-200. These imagers, generally employed for day-time Earth remote sensing, were flown over the Tuscany coast (Italy on board of a Casa 212/200 airplane from an altitude of 1.5-2.0 km. We describe the experimental activities which preceded the remote sensing campaign, the optimization of sensor configuration, and the images as far acquired. The obtained results point out the novelty of the performed measurements and highlight the need to employ advanced remote sensing techniques as a spectroscopic tool for light pollution monitoring.

  13. Spatially explicit rangeland erosion monitoring using high-resolution digital aerial imagery

    Science.gov (United States)

    Gillan, Jeffrey K.; Karl, Jason W.; Barger, Nichole N.; Elaksher, Ahmed; Duniway, Michael C.

    2016-01-01

    Nearly all of the ecosystem services supported by rangelands, including production of livestock forage, carbon sequestration, and provisioning of clean water, are negatively impacted by soil erosion. Accordingly, monitoring the severity, spatial extent, and rate of soil erosion is essential for long-term sustainable management. Traditional field-based methods of monitoring erosion (sediment traps, erosion pins, and bridges) can be labor intensive and therefore are generally limited in spatial intensity and/or extent. There is a growing effort to monitor natural resources at broad scales, which is driving the need for new soil erosion monitoring tools. One remote-sensing technique that can be used to monitor soil movement is a time series of digital elevation models (DEMs) created using aerial photogrammetry methods. By geographically coregistering the DEMs and subtracting one surface from the other, an estimate of soil elevation change can be created. Such analysis enables spatially explicit quantification and visualization of net soil movement including erosion, deposition, and redistribution. We constructed DEMs (12-cm ground sampling distance) on the basis of aerial photography immediately before and 1 year after a vegetation removal treatment on a 31-ha Piñon-Juniper woodland in southeastern Utah to evaluate the use of aerial photography in detecting soil surface change. On average, we were able to detect surface elevation change of ± 8−9cm and greater, which was sufficient for the large amount of soil movement exhibited on the study area. Detecting more subtle soil erosion could be achieved using the same technique with higher-resolution imagery from lower-flying aircraft such as unmanned aerial vehicles. DEM differencing and process-focused field methods provided complementary information and a more complete assessment of soil loss and movement than any single technique alone. Photogrammetric DEM differencing could be used as a technique to

  14. Linking terrace geomorphology and canopy characteristics in the Peruvian Amazon using high resolution airborne remote sensing (Invited)

    Science.gov (United States)

    Chadwick, K.; Asner, G. P.

    2013-12-01

    The Peruvian Amazon is home to over half a million square kilometers of forest, nearly three quarters of which is supported by terrace landforms with variable histories. Characteristics of these terrace ecosystems have been contrasted with neighboring floodplain systems along riverine transportation corridors, but the ecological complexity within these terrace landscapes has remained largely unexplored. Airborne remote measurements provide an opportunity to consider the relationship between forest canopy characteristics and geomorphic gradients at high resolution over large spatial extents. In 2011 the Carnegie Airborne Observatory (CAO) was used to map a large section of intact lowland humid tropical forest in the southwestern Peruvian Amazon, including over nine thousand hectares of terrace forest. The CAO collected high-fidelity imaging spectroscopy data with its Visible-Shortwave Imaging Spectrometer (VSWIR) and digital elevation and canopy structure data with its high-resolution dual waveform LiDAR. These data, supplemented with field data collection, were used to quantify relationships between forest canopy traits and geomorphic gradients. Results suggest that both spectral properties of the canopy with known relationships to canopy chemistry, including pigment and nutrient concentrations, and canopy structural traits, including vegetation height and leaf area, are associated with geomorphic characteristics of this terrace landscape.

  15. The cognitive neuroscience of remote episodic, semantic and spatial memory.

    Science.gov (United States)

    Moscovitch, Morris; Nadel, Lynn; Winocur, Gordon; Gilboa, Asaf; Rosenbaum, R Shayna

    2006-04-01

    The processes and mechanisms implicated in retention and retrieval of memories as they age is an enduring problem in cognitive neuroscience. Research from lesion and functional neuroimaging studies on remote episodic, semantic and spatial memory in humans is crucial for evaluating three theories of hippocampal and/or medial temporal lobe-neocortical interaction in memory retention and retrieval: cognitive map theory, standard consolidation theory and multiple trace theory. Each theory makes different predictions regarding first, the severity and extent of retrograde amnesia following lesions to some or all of the structures mentioned; second, the extent of activation of these structures to retrieval of memory across time; and third, the type of memory being retrieved. Each of these theories has strengths and weaknesses, and there are various unresolved issues. We propose a unified account based on multiple trace theory. This theory states that the hippocampus is needed for re-experiencing detailed episodic and spatial memories no matter how old they are, and that it contributes to the formation and assimilation of semantic memories and schematic spatial maps.

  16. Impact of precipitation spatial resolution on the hydrological response of an integrated distributed water resources model

    DEFF Research Database (Denmark)

    Fu, Suhua; Sonnenborg, Torben; Jensen, Karsten Høgh

    2011-01-01

    Precipitation is a key input variable to hydrological models, and the spatial variability of the input is expected to impact the hydrological response predicted by a distributed model. In this study, the effect of spatial resolution of precipitation on runoff , recharge and groundwater head...... of the total catchment and runoff discharge hydrograph at watershed outlet. On the other hand, groundwater recharge and groundwater head were both aff ected. The impact of the spatial resolution of precipitation input is reduced with increasing catchment size. The effect on stream discharge is relatively low...... was analyzed in the Alergaarde catchment in Denmark. Six different precipitation spatial resolutions were used as inputs to a physically based, distributed hydrological model, the MIKE SHE model. The results showed that the resolution of precipitation input had no apparent effect on annual water balance...

  17. High spatial resolution soft-x-ray microscopy

    Energy Technology Data Exchange (ETDEWEB)

    Meyer-Ilse, W.; Medecki, H.; Brown, J.T. [Ernest Orlando Lawrence Berkeley National Lab., CA (United States)] [and others

    1997-04-01

    A new soft x-ray microscope (XM-1) with high spatial resolution has been constructed by the Center for X-ray Optics. It uses bending magnet radiation from beamline 6.1 at the Advanced Light Source, and is used in a variety of projects and applications in the life and physical sciences. Most of these projects are ongoing. The instrument uses zone plate lenses and achieves a resolution of 43 nm, measured over 10% to 90% intensity with a knife edge test sample. X-ray microscopy permits the imaging of relatively thick samples, up to 10 {mu}m thick, in water. XM-1 has an easy to use interface, that utilizes visible light microscopy to precisely position and focus the specimen. The authors describe applications of this device in the biological sciences, as well as in studying industrial applications including structured polymer samples.

  18. Influence of backscattering on the spatial resolution of semiconductor X-ray detectors

    International Nuclear Information System (INIS)

    Hoheisel, M.; Korn, A.; Giersch, J.

    2005-01-01

    Pixelated X-ray detectors using semiconductor layers or scintillators as absorbers are widely used in high-energy physics, medical diagnosis, or non-destructive testing. Their good spatial resolution performance makes them particularly suitable for applications where fine details have to be resolved. Intrinsic limitations of the spatial resolution have been studied in previous simulations. These simulations focused on interactions inside the conversion layer. Transmitted photons were treated as a loss. In this work, we also implemented the structure behind the conversion layer to investigate the impact of backscattering inside the detector setup. We performed Monte Carlo simulations with the program ROSI (Roentgen Simulation) which is based on the well-established EGS4 algorithm. Line-spread functions of different fully implemented detectors were simulated. In order to characterize the detectors' spatial resolution, the modulation transfer functions (MTF) were calculated. The additional broadening of the line-spread function by carrier transport has been ignored in this work. We investigated two different detector types: a directly absorbing pixel detector where a semiconductor slab is bump-bonded to a readout ASIC such as the Medipix-2 setup with Si or GaAs as an absorbing semiconductor layer, and flat-panel detectors with a Se or a CsI converter. We found a significant degradation of the MTF compared to the case without backscattering. At energies above the K-edge of the backscattering material the spatial resolution drops and can account for the observed low-frequency drop of the MTF. Ignoring this backscatter effect might lead to misinterpretations of the charge sharing effect in counting pixel detectors

  19. High spatial resolution mapping of folds and fractures using Unmanned Aerial Vehicle (UAV) photogrammetry

    Science.gov (United States)

    Cruden, A. R.; Vollgger, S.

    2016-12-01

    The emerging capability of UAV photogrammetry combines a simple and cost-effective method to acquire digital aerial images with advanced computer vision algorithms that compute spatial datasets from a sequence of overlapping digital photographs from various viewpoints. Depending on flight altitude and camera setup, sub-centimeter spatial resolution orthophotographs and textured dense point clouds can be achieved. Orientation data can be collected for detailed structural analysis by digitally mapping such high-resolution spatial datasets in a fraction of time and with higher fidelity compared to traditional mapping techniques. Here we describe a photogrammetric workflow applied to a structural study of folds and fractures within alternating layers of sandstone and mudstone at a coastal outcrop in SE Australia. We surveyed this location using a downward looking digital camera mounted on commercially available multi-rotor UAV that autonomously followed waypoints at a set altitude and speed to ensure sufficient image overlap, minimum motion blur and an appropriate resolution. The use of surveyed ground control points allowed us to produce a geo-referenced 3D point cloud and an orthophotograph from hundreds of digital images at a spatial resolution automatically extracted from these high-resolution datasets using open-source software. This resulted in an extensive and statistically relevant orientation dataset that was used to 1) interpret the progressive development of folds and faults in the region, and 2) to generate a 3D structural model that underlines the complex internal structure of the outcrop and quantifies spatial variations in fold geometries. Overall, our work highlights how UAV photogrammetry can contribute to new insights in structural analysis.

  20. Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty

    Science.gov (United States)

    Katharine White; Jennifer Pontius; Paul Schaberg

    2014-01-01

    Current remote sensing studies of phenology have been limited to coarse spatial or temporal resolution and often lack a direct link to field measurements. To address this gap, we compared remote sensing methodologies using Landsat Thematic Mapper (TM) imagery to extensive field measurements in a mixed northern hardwood forest. Five vegetation indices, five mathematical...

  1. Accessing High Spatial Resolution in Astronomy Using Interference Methods

    Science.gov (United States)

    Carbonel, Cyril; Grasset, Sébastien; Maysonnave, Jean

    2018-01-01

    In astronomy, methods such as direct imaging or interferometry-based techniques (Michelson stellar interferometry for example) are used for observations. A particular advantage of interferometry is that it permits greater spatial resolution compared to direct imaging with a single telescope, which is limited by diffraction owing to the aperture of…

  2. Benefits of GMR sensors for high spatial resolution NDT applications

    Science.gov (United States)

    Pelkner, M.; Stegemann, R.; Sonntag, N.; Pohl, R.; Kreutzbruck, M.

    2018-04-01

    Magneto resistance sensors like GMR (giant magneto resistance) or TMR (tunnel magneto resistance) are widely used in industrial applications; examples are position measurement and read heads of hard disk drives. However, in case of non-destructive testing (NDT) applications these sensors, although their properties are outstanding like high spatial resolution, high field sensitivity, low cost and low energy consumption, never reached a technical transfer to an application beyond scientific scope. This paper deals with benefits of GMR/TMR sensors in terms of high spatial resolution testing for different NDT applications. The first example demonstrates the preeminent advantages of MR-elements compared with conventional coils used in eddy current testing (ET). The probe comprises one-wire excitation with an array of MR elements. This led to a better spatial resolution in terms of neighboring defects. The second section concentrates on MFL-testing (magnetic flux leakage) with active field excitation during and before testing. The latter illustrated the capability of highly resolved crack detection of a crossed notch. This example is best suited to show the ability of tiny magnetic field sensors for magnetic material characterization of a sample surface. Another example is based on characterization of samples after tensile test. Here, no external field is applied. The magnetization is only changed due to external load and magnetostriction leading to a field signature which GMR sensors can resolve. This gives access to internal changes of the magnetization state of the sample under test.

  3. Effects of spatial aggregation on the multi-scale estimation of evapotranspiration

    KAUST Repository

    Ershadi, Ali; McCabe, Matthew; Evans, Jason P.; Walker, Jeffrey P.

    2013-01-01

    The influence of spatial resolution on the estimation of land surface heat fluxes from remote sensing is poorly understood. In this study, the effects of aggregation from fine (< 100 m) to medium (approx. 1. km) scales are investigated using high

  4. Estimation of the high-spatial-resolution variability in extreme wind speeds for forestry applications

    Directory of Open Access Journals (Sweden)

    A. Venäläinen

    2017-07-01

    Full Text Available The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In Finland, a forested country, over 50 % of the current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high-spatial-resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 m spatial resolution CORINE land-use dataset and high-resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalyzed data. Using a geospatial re-mapping technique the data were downscaled to 26 meteorological station locations to represent very diverse environments. Applying a comparison, we find that the downscaled 10-year return levels represent 66 % of the observed variation among the stations examined. In addition, the spatial variation in wind-multiplier-downscaled 10-year return level wind was compared with the WAsP model-simulated wind. The heterogeneous test area was situated in northern Finland, and it was found that the major features of the spatial variation were similar, but in some locations, there were relatively large differences. The results indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations with the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount of wind damage for certain forest stand configurations.

  5. An overview of passive remote sensing for post-fire monitoring

    Directory of Open Access Journals (Sweden)

    2005-01-01

    Full Text Available Monitoring of forest burnt areas has several aims: to locate and estimate the extent of such areas; to assess the damages suffered by the forest stands; to check the ability of the ecosystem to naturally recover after the fire; to support the planning of reclamation interventions; to assess the dynamics (pattern and speed of the natural recovery; to check the outcome of any eventual restoration intervention. Remote sensing is an important source of information to support all such tasks. In the last decades, the effectiveness of remotely sensed imagery is increasing due to the advancement of tools and techniques, and to the lowering of the costs, in relative terms. For an effective support to post-fire management (burnt scar perimeter mapping, damage severity assessment, post-fire vegetation monitoring, a mapping scale of at least 1:10000-1:20000 is required: hence, the selection of remotely sensed data is restricted to aerial imagery and to satellite imagery characterized by high (HR and, above all, very high (VHR spatial resolution. In the last decade, HR and VHR passive remote sensing has widespread, providing affordable multitemporal and multispectral pictures of the considered phenomena, at different scales (spatial, temporal and spectral resolutions with reference to the monitoring needs. In the light of such a potential, the integration of GPS field survey and HR (Landsat 7, Spot HVR and VHR satellite imagery (Ikonos, Quickbird, Spot 5 is currently sought as a highly viable option for the post-fire monitoring.

  6. Remote Sensing Image Fusion Based on the Combination Grey Absolute Correlation Degree and IHS Transform

    Directory of Open Access Journals (Sweden)

    Hui LIN

    2014-12-01

    Full Text Available An improved fusion algorithm for multi-source remote sensing images with high spatial resolution and multi-spectral capacity is proposed based on traditional IHS fusion and grey correlation analysis. Firstly, grey absolute correlation degree is used to discriminate non-edge pixels and edge pixels in high-spatial resolution images, by which the weight of intensity component is identified in order to combine it with high-spatial resolution image. Therefore, image fusion is achieved using IHS inverse transform. The proposed method is applied to ETM+ multi-spectral images and panchromatic image, and Quickbird’s multi-spectral images and panchromatic image respectively. The experiments prove that the fusion method proposed in the paper can efficiently preserve spectral information of the original multi-spectral images while enhancing spatial resolution greatly. By comparison and analysis, the proposed fusion algorithm is better than traditional IHS fusion and fusion method based on grey correlation analysis and IHS transform.

  7. [Remote sensing monitoring and screening for urban black and odorous water body: A review.

    Science.gov (United States)

    Shen, Qian; Zhu, Li; Cao, Hong Ye

    2017-10-01

    Continuous improvement of urban water environment and overall control of black and odorous water body are not merely national strategic needs with the action plan for prevention and treatment of water pollution, but also the hot issues attracting the attention of people. Most previous researches concentrated on the study of cause, evaluation and treatment measures of this phenomenon, and there are few researches on the monitoring using remote sensing, which is often a strain to meet the national needs of operational monitoring. This paper mainly summarized the urgent research problems, mainly including the identification and classification standard, research on the key technologies, and the frame of remote sensing screening systems for the urban black and odorous water body. The main key technologies were concluded too, including the high spatial resolution image preprocessing and extraction technique for black and odorous water body, the extraction of water information in city zones, the classification of the black and odorous water, and the identification and classification technique based on satellite-sky-ground remote sensing. This paper summarized the research progress and put forward research ideas of monitoring and screening urban black and odorous water body via high spatial resolution remote sensing technology, which would be beneficial to having an overall grasp of spatial distribution and improvement progress of black and odorous water body, and provide strong technical support for controlling urban black and odorous water body.

  8. Linear mixing model applied to coarse spatial resolution data from multispectral satellite sensors

    Science.gov (United States)

    Holben, Brent N.; Shimabukuro, Yosio E.

    1993-01-01

    A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55-3.95 micron channel was used with the two reflective channels 0.58-0.68 micron and 0.725-1.1 micron to run a constrained least squares model to generate fraction images for an area in the west central region of Brazil. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse spatial resolution data for global studies.

  9. Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval

    Directory of Open Access Journals (Sweden)

    Weixun Zhou

    2017-05-01

    Full Text Available Learning powerful feature representations for image retrieval has always been a challenging task in the field of remote sensing. Traditional methods focus on extracting low-level hand-crafted features which are not only time-consuming but also tend to achieve unsatisfactory performance due to the complexity of remote sensing images. In this paper, we investigate how to extract deep feature representations based on convolutional neural networks (CNNs for high-resolution remote sensing image retrieval (HRRSIR. To this end, several effective schemes are proposed to generate powerful feature representations for HRRSIR. In the first scheme, a CNN pre-trained on a different problem is treated as a feature extractor since there are no sufficiently-sized remote sensing datasets to train a CNN from scratch. In the second scheme, we investigate learning features that are specific to our problem by first fine-tuning the pre-trained CNN on a remote sensing dataset and then proposing a novel CNN architecture based on convolutional layers and a three-layer perceptron. The novel CNN has fewer parameters than the pre-trained and fine-tuned CNNs and can learn low dimensional features from limited labelled images. The schemes are evaluated on several challenging, publicly available datasets. The results indicate that the proposed schemes, particularly the novel CNN, achieve state-of-the-art performance.

  10. Super-resolution mapping using multi-viewing CHRIS/PROBA data

    Science.gov (United States)

    Dwivedi, Manish; Kumar, Vinay

    2016-04-01

    High-spatial resolution Remote Sensing (RS) data provides detailed information which ensures high-definition visual image analysis of earth surface features. These data sets also support improved information extraction capabilities at a fine scale. In order to improve the spatial resolution of coarser resolution RS data, the Super Resolution Reconstruction (SRR) technique has become widely acknowledged which focused on multi-angular image sequences. In this study multi-angle CHRIS/PROBA data of Kutch area is used for SR image reconstruction to enhance the spatial resolution from 18 m to 6m in the hope to obtain a better land cover classification. Various SR approaches like Projection onto Convex Sets (POCS), Robust, Iterative Back Projection (IBP), Non-Uniform Interpolation and Structure-Adaptive Normalized Convolution (SANC) chosen for this study. Subjective assessment through visual interpretation shows substantial improvement in land cover details. Quantitative measures including peak signal to noise ratio and structural similarity are used for the evaluation of the image quality. It was observed that SANC SR technique using Vandewalle algorithm for the low resolution image registration outperformed the other techniques. After that SVM based classifier is used for the classification of SRR and data resampled to 6m spatial resolution using bi-cubic interpolation. A comparative analysis is carried out between classified data of bicubic interpolated and SR derived images of CHRIS/PROBA and SR derived classified data have shown a significant improvement of 10-12% in the overall accuracy. The results demonstrated that SR methods is able to improve spatial detail of multi-angle images as well as the classification accuracy.

  11. Accessing High Spatial Resolution in Astronomy Using Interference Methods

    Science.gov (United States)

    Carbonel, Cyril; Grasset, Sébastien; Maysonnave, Jean

    2018-04-01

    In astronomy, methods such as direct imaging or interferometry-based techniques (Michelson stellar interferometry for example) are used for observations. A particular advantage of interferometry is that it permits greater spatial resolution compared to direct imaging with a single telescope, which is limited by diffraction owing to the aperture of the instrument as shown by Rueckner et al. in a lecture demonstration. The focus of this paper, addressed to teachers and/or students in high schools and universities, is to easily underline both an application of interferometry in astronomy and stress its interest for resolution. To this end very simple optical experiments are presented to explain all the concepts. We show how an interference pattern resulting from the combined signals of two telescopes allows us to measure the distance between two stars with a resolution beyond the diffraction limit. Finally this work emphasizes the breathtaking resolution obtained in state-of-the-art instruments such as the VLTi (Very Large Telescope interferometer).

  12. 3D high spectral and spatial resolution imaging of ex vivo mouse brain

    Energy Technology Data Exchange (ETDEWEB)

    Foxley, Sean, E-mail: sean.foxley@ndcn.ox.ac.uk; Karczmar, Gregory S. [Department of Radiology, University of Chicago, Chicago, Illinois 60637 (United States); Domowicz, Miriam [Department of Pediatrics, University of Chicago, Chicago, Illinois 60637 (United States); Schwartz, Nancy [Department of Pediatrics, Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois 60637 (United States)

    2015-03-15

    Purpose: Widely used MRI methods show brain morphology both in vivo and ex vivo at very high resolution. Many of these methods (e.g., T{sub 2}{sup *}-weighted imaging, phase-sensitive imaging, or susceptibility-weighted imaging) are sensitive to local magnetic susceptibility gradients produced by subtle variations in tissue composition. However, the spectral resolution of commonly used methods is limited to maintain reasonable run-time combined with very high spatial resolution. Here, the authors report on data acquisition at increased spectral resolution, with 3-dimensional high spectral and spatial resolution MRI, in order to analyze subtle variations in water proton resonance frequency and lineshape that reflect local anatomy. The resulting information compliments previous studies based on T{sub 2}{sup *} and resonance frequency. Methods: The proton free induction decay was sampled at high resolution and Fourier transformed to produce a high-resolution water spectrum for each image voxel in a 3D volume. Data were acquired using a multigradient echo pulse sequence (i.e., echo-planar spectroscopic imaging) with a spatial resolution of 50 × 50 × 70 μm{sup 3} and spectral resolution of 3.5 Hz. Data were analyzed in the spectral domain, and images were produced from the various Fourier components of the water resonance. This allowed precise measurement of local variations in water resonance frequency and lineshape, at the expense of significantly increased run time (16–24 h). Results: High contrast T{sub 2}{sup *}-weighted images were produced from the peak of the water resonance (peak height image), revealing a high degree of anatomical detail, specifically in the hippocampus and cerebellum. In images produced from Fourier components of the water resonance at −7.0 Hz from the peak, the contrast between deep white matter tracts and the surrounding tissue is the reverse of the contrast in water peak height images. This indicates the presence of a shoulder in

  13. REMOTE SENSING-BASED DETECTION AND SPATIAL PATTERN ANALYSIS FOR GEO-ECOLOGICAL NICHE MODELING OF TILLANDSIA SPP. IN THE ATACAMA, CHILE

    Directory of Open Access Journals (Sweden)

    N. Wolf

    2016-06-01

    Full Text Available In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called ‘fog oases’ dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of Tillandsia spp. in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of Tillandsia spp. Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.

  14. Remote Sensing-Based Detection and Spatial Pattern Analysis for Geo-Ecological Niche Modeling of Tillandsia SPP. In the Atacama, Chile

    Science.gov (United States)

    Wolf, N.; Siegmund, A.; del Río, C.; Osses, P.; García, J. L.

    2016-06-01

    In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called `fog oases' dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of Tillandsia spp. in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of Tillandsia spp. Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF) are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.

  15. Advances in remote sensing of vegetation function and traits

    KAUST Repository

    Houborg, Rasmus

    2015-07-09

    Remote sensing of vegetation function and traits has advanced significantly over the past half-century in the capacity to retrieve useful plant biochemical, physiological and structural quantities across a range of spatial and temporal scales. However, the translation of remote sensing signals into meaningful descriptors of vegetation function and traits is still associated with large uncertainties due to complex interactions between leaf, canopy, and atmospheric mediums, and significant challenges in the treatment of confounding factors in spectrum-trait relations. This editorial provides (1) a background on major advances in the remote sensing of vegetation, (2) a detailed timeline and description of relevant historical and planned satellite missions, and (3) an outline of remaining challenges, upcoming opportunities and key research objectives to be tackled. The introduction sets the stage for thirteen Special Issue papers here that focus on novel approaches for exploiting current and future advancements in remote sensor technologies. The described enhancements in spectral, spatial and temporal resolution and radiometric performance provide exciting opportunities to significantly advance the ability to accurately monitor and model the state and function of vegetation canopies at multiple scales on a timely basis.

  16. Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image

    OpenAIRE

    Wang, Jianhua; Qin, Qiming; Zhao, Jianghua; Ye, Xin; Feng, Xiao; Qin, Xuebin; Yang, Xiucheng

    2015-01-01

    Road damage detection and assessment from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pairing of pre-disaster and post-disaster road data for change detection and assessment is difficult to achieve due to the mismatch of different data sources, especially for rural areas where the pre-disaster data (i.e., remote sensing imagery or vector map) are hard to obtain. In this study, a knowledge-based method for ...

  17. HIGH SPATIAL RESOLUTION OBSERVATIONS OF LOOPS IN THE SOLAR CORONA

    Energy Technology Data Exchange (ETDEWEB)

    Brooks, David H.; Ugarte-Urra, Ignacio [College of Science, George Mason University, 4400 University Drive, Fairfax, VA 22030 (United States); Warren, Harry P. [Space Science Division, Naval Research Laboratory, Washington, DC 20375 (United States); Winebarger, Amy R. [NASA Marshall Space Flight Center, ZP 13, Huntsville, AL 35812 (United States)

    2013-08-01

    Understanding how the solar corona is structured is of fundamental importance to determine how the Sun's upper atmosphere is heated to high temperatures. Recent spectroscopic studies have suggested that an instrument with a spatial resolution of 200 km or better is necessary to resolve coronal loops. The High Resolution Coronal Imager (Hi-C) achieved this performance on a rocket flight in 2012 July. We use Hi-C data to measure the Gaussian widths of 91 loops observed in the solar corona and find a distribution that peaks at about 270 km. We also use Atmospheric Imaging Assembly data for a subset of these loops and find temperature distributions that are generally very narrow. These observations provide further evidence that loops in the solar corona are often structured at a scale of several hundred kilometers, well above the spatial scale of many proposed physical mechanisms.

  18. Long-Term Monitoring of Desert Land and Natural Resources and Application of Remote Sensing Technologies

    Energy Technology Data Exchange (ETDEWEB)

    Hamada, Yuki [Argonne National Lab. (ANL), Argonne, IL (United States); Rollins, Katherine E. [Argonne National Lab. (ANL), Argonne, IL (United States)

    2016-11-01

    Monitoring environmental impacts over large, remote desert regions for long periods of time can be very costly. Remote sensing technologies present a promising monitoring tool because they entail the collection of spatially contiguous data, automated processing, and streamlined data analysis. This report provides a summary of remote sensing products and refinement of remote sensing data interpretation methodologies that were generated as part of the U.S. Department of the Interior Bureau of Land Management Solar Energy Program. In March 2015, a team of researchers from Argonne National Laboratory (Argonne) collected field data of vegetation and surface types from more than 5,000 survey points within the eastern part of the Riverside East Solar Energy Zone (SEZ). Using the field data, remote sensing products that were generated in 2014 using very high spatial resolution (VHSR; 15 cm) multispectral aerial images were validated in order to evaluate potential refinements to the previous methodologies to improve the information extraction accuracy.

  19. Spatial resolution of the HRRT PET scanner using 3D-OSEM PSF reconstruction

    DEFF Research Database (Denmark)

    Olesen, Oline Vinter; Sibomana, Merence; Keller, Sune Høgild

    2009-01-01

    The spatial resolution of the Siemens High Resolution Research Tomograph (HRRT) dedicated brain PET scanner installed at Copenhagen University Hospital (Rigshospitalet) was measured using a point-source phantom with high statistics. Further, it was demonstrated how the newly developed 3D-OSEM PSF...

  20. Combining remote sensing and water-balance evapotranspiration estimates for the conterminous United States

    Science.gov (United States)

    Reitz, Meredith; Senay, Gabriel; Sanford, Ward E.

    2017-01-01

    Evapotranspiration (ET) is a key component of the hydrologic cycle, accounting for ~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to significant advances in the frequency and spatial resolution of ET estimates, derived from energy balance principles with variables such as temperature used to estimate surface latent heat flux. Although remote sensing methods excel at depicting spatial and temporal variability, estimation of ET independently of other water budget components can lead to inconsistency with other budget terms. Methods that rely on ground-based data better constrain long-term ET, but are unable to provide the same temporal resolution. Here we combine long-term ET estimates from a water-balance approach with the SSEBop (operational Simplified Surface Energy Balance) remote sensing-based ET product for 2000–2015. We test the new combined method, the original SSEBop product, and another remote sensing ET product (MOD16) against monthly measurements from 119 flux towers. The new product showed advantages especially in non-irrigated areas where the new method showed a coefficient of determination R2 of 0.44, compared to 0.41 for SSEBop or 0.35 for MOD16. The resulting monthly data set will be a useful, unique contribution to ET estimation, due to its combination of remote sensing-based variability and ground-based long-term water balance constraints.

  1. The spatial resolution in dosimetry with normoxic polymer-gels investigated with the dose modulation transfer approach

    International Nuclear Information System (INIS)

    Bayreder, Christian; Schoen, Robert; Wieland, M.; Georg, Dietmar; Moser, Ewald; Berg, Andreas

    2008-01-01

    The verification of dose distributions with high dose gradients as appearing in brachytherapy or stereotactic radiotherapy for example, calls for dosimetric methods with sufficiently high spatial resolution. Polymer gels in combination with a MR or optical scanner as a readout device have the potential of performing the verification of a three-dimensional dose distribution within a single measurement. The purpose of this work is to investigate the spatial resolution achievable in MR-based polymer gel dosimetry. The authors show that dosimetry on a very small spatial scale (voxel size: 94x94x1000 μm 3 ) can be performed with normoxic polymer gels using parameter selective T2 imaging. In order to prove the spatial resolution obtained we are relying on the dose-modulation transfer function (DMTF) concept based on very fine dose modulations at half periods of 200 μm. Very fine periodic dose modulations of a 60 Co photon field were achieved by means of an absorption grid made of tungsten-carbide, specifically designed for quality control. The dose modulation in the polymer gel is compared with that of film dosimetry in one plane via the DMTF concept for general access to the spatial resolution of a dose imaging system. Additionally Monte Carlo simulations were performed and used for the calculation of the DMTF of both, the polymer gel and film dosimetry. The results obtained by film dosimetry agree well with those of Monte Carlo simulations, whereas polymer gel dosimetry overestimates the amplitude value of the fine dose modulations. The authors discuss possible reasons. The in-plane resolution achieved in this work competes with the spatial resolution of standard clinical film-scanner systems

  2. HIGH SPATIAL RESOLUTION IMAGING OF INERTIAL FUSION TARGET PLASMAS USING BUBBLE NEUTRON DETECTORS

    International Nuclear Information System (INIS)

    FISHER, R.K.

    2003-01-01

    OAK B202 HIGH SPATIAL RESOLUTION IMAGING OF INERTIAL FUSION TARGET PLASMAS USING BUBBLE NEUTRON DETECTORS. Bubble detectors, which can detect neutrons with a spatial 5 to 30 (micro), are the most promising approach to imaging NIF target plasmas with the desired 5 (micro) spatial resolution in the target plane. Gel bubble detectors are being tested to record neutron images of ICF implosions in OMEGA experiments. By improving the noise reduction techniques used in analyzing the data taken in June 2000, we have been able to image the neutron emission from 6 · 10 13 yield DT target plasmas with a target plane spatial resolution of ∼ 140 (micro). As expected, the spatial resolution was limited by counting statistics as a result of the low neutron detection efficiency of the easy-to-use gel bubble detectors. The results have been submitted for publication and will be the subject of an invited talk at the October 2001 Meeting of the Division of Plasma Physics of the American Physical Society. To improve the counting statistics, data was taken in May 2001 using a stack of four gel detectors and integrated over a series of up to seven high-yield DT shots. Analysis of the 2001 data is still in its early stages. Gel detectors were chosen for these initial tests since the bubbles can be photographed several hours after the neutron exposure. They consist of ∼ 5000 drops (∼ 100 (micro) in diameter) of bubble detector liquid/cm 3 suspended in an inactive support gel that occupies ∼ 99% of the detector volume. Using a liquid bubble chamber detector and a light scattering system to record the bubble locations a few microseconds after the neutron exposure when the bubbles are ∼ 10 (micro) in diameter, should result in ∼ 1000 times higher neutron detection efficiency and a target plane resolution on OMEGA of ∼ 10 to 50 (micro)

  3. Spatial resolution of wedge shaped silicon microstrip detectors

    International Nuclear Information System (INIS)

    Anticic, T.; Barnett, B.; Blumenfeld, B.; Chien, C.Y.; Fisher, P.; Gougas, A.; Krizmanic, J.; Madansky, L.; Newman, D.; Orndorff, J.; Pevsner, A.; Spangler, J.

    1995-01-01

    Several wedge-shaped silicon microstrip detectors with pitches from 30 to 100 μm have been designed by our group and beam tested at the CERN SPS. We find the spatial resolution σ becomes larger at the rate of 0.21 μm per 1 μm increase in pitch, but the number of strips per cluster remains about the same as the pitch varies from 30 to 100 μm. (orig.)

  4. Advancing High Spatial and Spectral Resolution Remote Sensing for Observing Plant Community Response to Environmental Variability and Change in the Alaskan Arctic

    Science.gov (United States)

    Vargas Zesati, Sergio A.

    landscape level ecosystem structure and phenological dynamics at multiple temporal scales. Overall, this study has furthered our knowledge of how tundra ecosystems in the Arctic change seasonally and how such change could impact remote sensing studies conducted from multiple platforms and across multiple spatial scales. Additionally, this study also highlights the urgent need for research into the validation of satellite products in order to better understand the causes and consequences of the changing Arctic and its potential effects on global processes. This study focused on sites located in northern Alaska and was formed in collaboration with Florida International University (FIU) and Grand Valley State University (GVSU) as a contribution to the US Arctic Observing Network (AON). All efforts were supported through the National Science Foundation (NSF), the Cyber-ShARE Center of Excellence, and the International Tundra Experiment (ITEX).

  5. Persistent increased PKMζ in long-term and remote spatial memory.

    Science.gov (United States)

    Hsieh, Changchi; Tsokas, Panayiotis; Serrano, Peter; Hernández, A Iván; Tian, Dezhi; Cottrell, James E; Shouval, Harel Z; Fenton, André Antonio; Sacktor, Todd Charlton

    2017-02-01

    PKMζ is an autonomously active PKC isoform that is thought to maintain both LTP and long-term memory. Whereas persistent increases in PKMζ protein sustain the kinase's action in LTP, the molecular mechanism for the persistent action of PKMζ during long-term memory has not been characterized. PKMζ inhibitors disrupt spatial memory when introduced into the dorsal hippocampus from 1day to 1month after training. Therefore, if the mechanisms of PKMζ's persistent action in LTP maintenance and long-term memory were similar, persistent increases in PKMζ would last for the duration of the memory, far longer than most other learning-induced gene products. Here we find that spatial conditioning by aversive active place avoidance or appetitive radial arm maze induces PKMζ increases in dorsal hippocampus that persist from 1day to 1month, coinciding with the strength and duration of memory retention. Suppressing the increase by intrahippocampal injections of PKMζ-antisense oligodeoxynucleotides prevents the formation of long-term memory. Thus, similar to LTP maintenance, the persistent increase in the amount of autonomously active PKMζ sustains the kinase's action during long-term and remote spatial memory maintenance. Copyright © 2016. Published by Elsevier Inc.

  6. PIV study of flow field in Rushton turbine stirred vessel influenced by spatial resolution

    Czech Academy of Sciences Publication Activity Database

    Kotek, M.; Jašíková, D.; Kysela, Bohuš; Šulc, R.; Kopecký, V.

    2017-01-01

    Roč. 2, č. 2017 (2017), s. 79-84 ISSN 2367-8992 R&D Projects: GA ČR GA16-20175S Grant - others:GA MŠk(CZ) LO1201 Institutional support: RVO:67985874 Keywords : mixing process * PIV measurement * spatial resolution Subject RIV: JP - Industrial Processing OBOR OECD: Fluids and plasma physics (including surface physics) http://www.iaras.org/iaras/home/caijtam/piv-study-of-flow-field-in-rushton-turbine-stirred-vessel-influenced-by-spatial-resolution

  7. Parallelizing remote sensing image geometric correction

    OpenAIRE

    Bernabeu i Altayó, Gerard; Universitat Autònoma de Barcelona. Departament d'Arquitectura de Computadors i Sistemes Operatius

    2012-01-01

    Remote sensing spatial, spectral, and temporal resolutions of images, acquired Les resolucions espacials, espectrals i temporals d'imatges de teledetecci ó, adquirides a una mida raonable, donen com a resultat imatges que es poden processar per a representar grans àrees de terreny amb un nivell de detall espacial que es Las resoluciones espaciales, espectrales y temporales de imágenes de teledetección, adquiridas a un tamaño razonable, dan como resultado imágenes que se pueden procesar ...

  8. Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis

    Science.gov (United States)

    Alonso, Carmelo; Tarquis, Ana M.; Zúñiga, Ignacio; Benito, Rosa M.

    2017-03-01

    Several studies have shown that vegetation indexes can be used to estimate root zone soil moisture. Earth surface images, obtained by high-resolution satellites, presently give a lot of information on these indexes, based on the data of several wavelengths. Because of the potential capacity for systematic observations at various scales, remote sensing technology extends the possible data archives from the present time to several decades back. Because of this advantage, enormous efforts have been made by researchers and application specialists to delineate vegetation indexes from local scale to global scale by applying remote sensing imagery. In this work, four band images have been considered, which are involved in these vegetation indexes, and were taken by satellites Ikonos-2 and Landsat-7 of the same geographic location, to study the effect of both spatial (pixel size) and radiometric (number of bits coding the image) resolution on these wavelength bands as well as two vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). In order to do so, a multi-fractal analysis of these multi-spectral images was applied in each of these bands and the two indexes derived. The results showed that spatial resolution has a similar scaling effect in the four bands, but radiometric resolution has a larger influence in blue and green bands than in red and near-infrared bands. The NDVI showed a higher sensitivity to the radiometric resolution than EVI. Both were equally affected by the spatial resolution. From both factors, the spatial resolution has a major impact in the multi-fractal spectrum for all the bands and the vegetation indexes. This information should be taken in to account when vegetation indexes based on different satellite sensors are obtained.

  9. On the feasibility of comprehensive high-resolution 3D remote dosimetry

    International Nuclear Information System (INIS)

    Juang, Titania; Grant, Ryan; Adamovics, John; Ibbott, Geoffrey; Oldham, Mark

    2014-01-01

    Purpose: This study investigates the feasibility of remote high-resolution 3D dosimetry with the PRESAGE®/Optical-CT system. In remote dosimetry, dosimeters are shipped out from a central base institution to a remote institution for irradiation, then shipped back to the base institution for subsequent readout and analysis. Methods: Two nominally identical optical-CT scanners for 3D dosimetry were constructed and placed at the base (Duke University) and remote (Radiological Physics Center) institutions. Two formulations of PRESAGE® (SS1, SS2) radiochromic dosimeters were investigated. Higher sensitivity was expected in SS1, which had higher initiator content (0.25% bromotrichloromethane), while greater temporal stability was expected in SS2. Four unirradiated PRESAGE® dosimeters (two per formulation, cylindrical dimensions 11 cm diameter, 8.5–9.5 cm length) were imaged at the base institution, then shipped to the remote institution for planning and irradiation. Each dosimeter was irradiated with the same simple treatment plan: an isocentric 3-field “cross” arrangement of 4 × 4 cm open 6 MV beams configured as parallel opposed laterals with an anterior beam. This simple plan was amenable to accurate and repeatable setup, as well as accurate dose modeling by a commissioned treatment planning system (Pinnacle). After irradiation and subsequent (within 1 h) optical-CT readout at the remote institution, the dosimeters were shipped back to the base institution for remote dosimetry readout 3 days postirradiation. Measured on-site and remote relative 3D dose distributions were registered to the Pinnacle dose calculation, which served as the reference distribution for 3D gamma calculations with passing criteria of 5%/2 mm, 3%/3 mm, and 3%/2 mm with a 10% dose threshold. Gamma passing rates, dose profiles, and color-maps were all used to assess and compare the performance of both PRESAGE® formulations for remote dosimetry. Results: The best agreements between the

  10. Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

    Directory of Open Access Journals (Sweden)

    Yun Du

    2016-04-01

    Full Text Available Monitoring open water bodies accurately is an important and basic application in remote sensing. Various water body mapping approaches have been developed to extract water bodies from multispectral images. The method based on the spectral water index, especially the Modified Normalized Difference Water Index (MDNWI calculated from the green and Shortwave-Infrared (SWIR bands, is one of the most popular methods. The recently launched Sentinel-2 satellite can provide fine spatial resolution multispectral images. This new dataset is potentially of important significance for regional water bodies’ mapping, due to its free access and frequent revisit capabilities. It is noted that the green and SWIR bands of Sentinel-2 have different spatial resolutions of 10 m and 20 m, respectively. Straightforwardly, MNDWI can be produced from Sentinel-2 at the spatial resolution of 20 m, by upscaling the 10-m green band to 20 m correspondingly. This scheme, however, wastes the detailed information available at the 10-m resolution. In this paper, to take full advantage of the 10-m information provided by Sentinel-2 images, a novel 10-m spatial resolution MNDWI is produced from Sentinel-2 images by downscaling the 20-m resolution SWIR band to 10 m based on pan-sharpening. Four popular pan-sharpening algorithms, including Principle Component Analysis (PCA, Intensity Hue Saturation (IHS, High Pass Filter (HPF and À Trous Wavelet Transform (ATWT, were applied in this study. The performance of the proposed method was assessed experimentally using a Sentinel-2 image located at the Venice coastland. In the experiment, six water indexes, including 10-m NDWI, 20-m MNDWI and 10-m MNDWI, produced by four pan-sharpening algorithms, were compared. Three levels of results, including the sharpened images, the produced MNDWI images and the finally mapped water bodies, were analysed quantitatively. The results showed that MNDWI can enhance water bodies and suppressbuilt

  11. Research of building information extraction and evaluation based on high-resolution remote-sensing imagery

    Science.gov (United States)

    Cao, Qiong; Gu, Lingjia; Ren, Ruizhi; Wang, Lang

    2016-09-01

    Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection

  12. High spatial resolution Kelvin probe force microscopy with coaxial probes

    International Nuclear Information System (INIS)

    Brown, Keith A; Westervelt, Robert M; Satzinger, Kevin J

    2012-01-01

    Kelvin probe force microscopy (KPFM) is a widely used technique to measure the local contact potential difference (CPD) between an AFM probe and the sample surface via the electrostatic force. The spatial resolution of KPFM is intrinsically limited by the long range of the electrostatic interaction, which includes contributions from the macroscopic cantilever and the conical tip. Here, we present coaxial AFM probes in which the cantilever and cone are shielded by a conducting shell, confining the tip–sample electrostatic interaction to a small region near the end of the tip. We have developed a technique to measure the true CPD despite the presence of the shell electrode. We find that the behavior of these probes agrees with an electrostatic model of the force, and we observe a factor of five improvement in spatial resolution relative to unshielded probes. Our discussion centers on KPFM, but the field confinement offered by these probes may improve any variant of electrostatic force microscopy. (paper)

  13. Hard X-ray Microscopy with sub 30 nm Spatial Resolution

    International Nuclear Information System (INIS)

    Tang, M.-T.; Song, Y.-F.; Yin, G.-C.; Chen, J.-H.; Chen, Y.-M.; Liang, Keng S.; Chen, F.-R.; Duewer, F.; Yun Wenbing

    2007-01-01

    A transmission X-ray microscope (TXM) has been installed at the BL01B beamline at National Synchrotron Radiation Research Center in Taiwan. This state-of-the-art TXM operational in a range 8-11 keV provides 2D images and 3D tomography with spatial resolution 60 nm, and with the Zernike-phase contrast mode for imaging light materials such as biological specimens. A spatial resolution of the TXM better than 30 nm, apparently the best result in hard X-ray microscopy, has been achieved by employing the third diffraction order of the objective zone plate. The TXM has been applied in diverse research fields, including analysis of failure mechanisms in microelectronic devices, tomographic structures of naturally grown photonic specimens, and the internal structure of fault zone gouges from an earthquake core. Here we discuss the scope and prospects of the project, and the progress of the TXM in NSRRC

  14. Preliminary work of mangrove ecosystem carbon stock mapping in small island using remote sensing: above and below ground carbon stock mapping on medium resolution satellite image

    Science.gov (United States)

    Wicaksono, Pramaditya; Danoedoro, Projo; Hartono, Hartono; Nehren, Udo; Ribbe, Lars

    2011-11-01

    Mangrove forest is an important ecosystem located in coastal area that provides various important ecological and economical services. One of the services provided by mangrove forest is the ability to act as carbon sink by sequestering CO2 from atmosphere through photosynthesis and carbon burial on the sediment. The carbon buried on mangrove sediment may persist for millennia before return to the atmosphere, and thus act as an effective long-term carbon sink. Therefore, it is important to understand the distribution of carbon stored within mangrove forest in a spatial and temporal context. In this paper, an effort to map carbon stocks in mangrove forest is presented using remote sensing technology to overcome the handicap encountered by field survey. In mangrove carbon stock mapping, the use of medium spatial resolution Landsat 7 ETM+ is emphasized. Landsat 7 ETM+ images are relatively cheap, widely available and have large area coverage, and thus provide a cost and time effective way of mapping mangrove carbon stocks. Using field data, two image processing techniques namely Vegetation Index and Linear Spectral Unmixing (LSU) were evaluated to find the best method to explain the variation in mangrove carbon stocks using remote sensing data. In addition, we also tried to estimate mangrove carbon sequestration rate via multitemporal analysis. Finally, the technique which produces significantly better result was used to produce a map of mangrove forest carbon stocks, which is spatially extensive and temporally repetitive.

  15. Comparison of MODIS and SWAT evapotranspiration over a complex terrain at different spatial scales

    Science.gov (United States)

    Abiodun, Olanrewaju O.; Guan, Huade; Post, Vincent E. A.; Batelaan, Okke

    2018-05-01

    In most hydrological systems, evapotranspiration (ET) and precipitation are the largest components of the water balance, which are difficult to estimate, particularly over complex terrain. In recent decades, the advent of remotely sensed data based ET algorithms and distributed hydrological models has provided improved spatially upscaled ET estimates. However, information on the performance of these methods at various spatial scales is limited. This study compares the ET from the MODIS remotely sensed ET dataset (MOD16) with the ET estimates from a SWAT hydrological model on graduated spatial scales for the complex terrain of the Sixth Creek Catchment of the Western Mount Lofty Ranges, South Australia. ET from both models was further compared with the coarser-resolution AWRA-L model at catchment scale. The SWAT model analyses are performed on daily timescales with a 6-year calibration period (2000-2005) and 7-year validation period (2007-2013). Differences in ET estimation between the SWAT and MOD16 methods of up to 31, 19, 15, 11 and 9 % were observed at respectively 1, 4, 9, 16 and 25 km2 spatial resolutions. Based on the results of the study, a spatial scale of confidence of 4 km2 for catchment-scale evapotranspiration is suggested in complex terrain. Land cover differences, HRU parameterisation in AWRA-L and catchment-scale averaging of input climate data in the SWAT semi-distributed model were identified as the principal sources of weaker correlations at higher spatial resolution.

  16. Improved wetland classification using eight-band high-resolution satellite imagery and a hybrid approach

    Science.gov (United States)

    Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of derived wetland maps were limited or often unsatisfactory largely due to the relatively coarse spatial resolution of conventional satellite imagery. This re...

  17. Application of spatially resolved high resolution crystal spectrometry to inertial confinement fusion plasmas.

    Science.gov (United States)

    Hill, K W; Bitter, M; Delgado-Aparacio, L; Pablant, N A; Beiersdorfer, P; Schneider, M; Widmann, K; Sanchez del Rio, M; Zhang, L

    2012-10-01

    High resolution (λ∕Δλ ∼ 10 000) 1D imaging x-ray spectroscopy using a spherically bent crystal and a 2D hybrid pixel array detector is used world wide for Doppler measurements of ion-temperature and plasma flow-velocity profiles in magnetic confinement fusion plasmas. Meter sized plasmas are diagnosed with cm spatial resolution and 10 ms time resolution. This concept can also be used as a diagnostic of small sources, such as inertial confinement fusion plasmas and targets on x-ray light source beam lines, with spatial resolution of micrometers, as demonstrated by laboratory experiments using a 250-μm (55)Fe source, and by ray-tracing calculations. Throughput calculations agree with measurements, and predict detector counts in the range 10(-8)-10(-6) times source x-rays, depending on crystal reflectivity and spectrometer geometry. Results of the lab demonstrations, application of the technique to the National Ignition Facility (NIF), and predictions of performance on NIF will be presented.

  18. Multispectral remote-sensing algorithms for particulate organic carbon (POC): The Gulf of Mexico

    OpenAIRE

    Son, Young Baek; Gardner, Wilford D.; Mishonov, Alexey V.; Richardson, Mary Jo

    2009-01-01

    To greatly increase the spatial and temporal resolution for studying carbon dynamics in the marine environment, we have developed remote-sensing algorithms for particulate organic carbon (POC) by matching in situ POC measurements in the Gulf of Mexico with matching SeaWiFS remote-sensing reflectance. Data on total particulate matter (PM) as well as POC collected during nine cruises in spring, summer and early winter from 1997-2000 as part of the Northeastern Gulf of Mexico (NEGOM) study were ...

  19. Iterative algorithm for reconstructing rotationally asymmetric surface deviation with pixel-level spatial resolution

    Science.gov (United States)

    Quan, Haiyang; Wu, Fan; Hou, Xi

    2015-10-01

    New method for reconstructing rotationally asymmetric surface deviation with pixel-level spatial resolution is proposed. It is based on basic iterative scheme and accelerates the Gauss-Seidel method by introducing an acceleration parameter. This modified Successive Over-relaxation (SOR) is effective for solving the rotationally asymmetric components with pixel-level spatial resolution, without the usage of a fitting procedure. Compared to the Jacobi and Gauss-Seidel method, the modified SOR method with an optimal relaxation factor converges much faster and saves more computational costs and memory space without reducing accuracy. It has been proved by real experimental results.

  20. A patch-based convolutional neural network for remote sensing image classification.

    Science.gov (United States)

    Sharma, Atharva; Liu, Xiuwen; Yang, Xiaojun; Shi, Di

    2017-11-01

    Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Total porosity of carbonate reservoir rocks by X-ray microtomography in two different spatial resolutions

    International Nuclear Information System (INIS)

    Nagata, Rodrigo; Appoloni, Carlos R.; Marques, Leonardo C.; Fernandes, Celso P.

    2011-01-01

    Carbonate reservoir rocks contain more than 50% of world's petroleum. To know carbonate rocks' structural properties is quite important to petroleum extraction. One of their main structural properties is the total porosity, which shows the rock's capacity to stock petroleum. In recent years, the X-ray microtomography had been used to analyze the structural parameters of reservoir rocks. Such nondestructive technique generates images of the samples' internal structure, allowing the evaluation of its properties. The spatial resolution is a measurement parameter that indicates the smallest structure size observable in a sample. It is possible to measure one sample using two or more different spatial resolutions in order to evaluate the samples' pore scale. In this work, two samples of the same sort of carbonate rock were measured, and in each measurement a different spatial resolution (17 μm and 7 μm) was applied. The obtained results showed that with the better resolution it was possible to measure 8% more pores than with the poorer resolution. Such difference provides us with good expectations about such approach to study the pore scale of carbonate rocks. (author)

  2. Working memory-driven attention improves spatial resolution: Support for perceptual enhancement.

    Science.gov (United States)

    Pan, Yi; Luo, Qianying; Cheng, Min

    2016-08-01

    Previous research has indicated that attention can be biased toward those stimuli matching the contents of working memory and thereby facilitates visual processing at the location of the memory-matching stimuli. However, whether this working memory-driven attentional modulation takes place on early perceptual processes remains unclear. Our present results showed that working memory-driven attention improved identification of a brief Landolt target presented alone in the visual field. Because the suprathreshold target appeared without any external noise added (i.e., no distractors or masks), the results suggest that working memory-driven attention enhances the target signal at early perceptual stages of visual processing. Furthermore, given that performance in the Landolt target identification task indexes spatial resolution, this attentional facilitation indicates that working memory-driven attention can boost early perceptual processing via enhancement of spatial resolution at the attended location.

  3. Linear and nonlinear optical spectroscopy: Spectral, temporal and spatial resolution

    DEFF Research Database (Denmark)

    Hvam, Jørn Marcher

    1997-01-01

    Selected linear and nonlinear optical spectroscopies are being described with special emphasis on the possibility of obtaining simultaneous spectral, temporal and spatial resolution. The potential of various experimental techniques is being demonstrated by specific examples mostly taken from inve...... investigations of the electronic, and opto-electronic, properties of semiconductor nanostructures....

  4. High time resolution boundary layer description using combined remote sensing instruments

    Directory of Open Access Journals (Sweden)

    C. Gaffard

    2008-09-01

    Full Text Available Ground based remote sensing systems for future observation operations will allow continuous monitoring of the lower troposphere at temporal resolutions much better than every 30 min. Observations which may be considered spurious from an individual instrument can be validated or eliminated when considered in conjunction with measurements from other instruments observing at the same location. Thus, improved quality control of atmospheric profiles from microwave radiometers and wind profilers should be sought by considering the measurements from different systems together rather than individually. In future test bed deployments for future operational observing systems, this should be aided by observations from laser ceilometers and cloud radars. Observations of changes in atmospheric profiles at high temporal resolution in the lower troposphere are presented from a 12 channel microwave radiometer and 1290 MHz UHF wind profiler deployed in southern England during the CSIP field experiment in July/August 2005. The observations chosen were from days when thunderstorms occurred in southern England. Rapid changes near the surface in dry layers are considered, both when rain/hail may be falling from above and where the dry air is associated with cold pools behind organised thunderstorms. Also, short term variations in atmospheric profiles and vertical stability are presented on a day with occasional low cloud, when thunderstorms triggered 50 km down wind of the observing site Improved quality control of the individual remote sensing systems need to be implemented, examining the basic quality of the underlying observations as well as the final outputs, and so for instance eliminating ground clutter as far as possible from the basic Doppler spectra measurements of the wind profiler. In this study, this was performed manually. The potential of incorporating these types of instruments in future upper air observational networks leads to the challenge to

  5. Remote distractor effects and saccadic inhibition: spatial and temporal modulation.

    Science.gov (United States)

    Walker, Robin; Benson, Valerie

    2013-09-12

    The onset of a visual distractor remote from a saccade target is known to increase saccade latency (the remote distractor effect [RDE]). In addition, distractors may also selectively inhibit saccades that would be initiated about 90 ms after distractor onset (termed saccadic inhibition [SI]). Recently, it has been proposed that the transitory inhibition of saccades (SI) may underlie the increase in mean latency (RDE). In a first experiment, the distractor eccentricity was manipulated, and a robust RDE that was strongly modulated by distractor eccentricity was observed. However, the underlying latency distributions did not reveal clear evidence of SI. A second experiment manipulated distractor spatial location and the timing of the distractor onset in relation to the target. An RDE was again observed with remote distractors away from the target axis and under conditions with early-onset distractors that would be unlikely to produce SI, whereas later distractor onsets produced an RDE along with some evidence of an SI effect. A third experiment using a mixed block of target-distractor stimulus-onset asynchronies (SOAs) revealed an RDE that varied with both distractor eccentricity and SOA and changes to latency distributions consistent with the timing of SI. We argue that the notion that SI underpins the RDE is similar to the earlier argument that express saccades underlie the fixation offset (gap) effect and that changes in mean latency and to the shape of the underlying latency distributions following a visual onset may involve more than one inhibitory process.

  6. Spatial resolution dependence on spectral frequency in human speech cortex electrocorticography

    Science.gov (United States)

    Muller, Leah; Hamilton, Liberty S.; Edwards, Erik; Bouchard, Kristofer E.; Chang, Edward F.

    2016-10-01

    Objective. Electrocorticography (ECoG) has become an important tool in human neuroscience and has tremendous potential for emerging applications in neural interface technology. Electrode array design parameters are outstanding issues for both research and clinical applications, and these parameters depend critically on the nature of the neural signals to be recorded. Here, we investigate the functional spatial resolution of neural signals recorded at the human cortical surface. We empirically derive spatial spread functions to quantify the shared neural activity for each frequency band of the electrocorticogram. Approach. Five subjects with high-density (4 mm center-to-center spacing) ECoG grid implants participated in speech perception and production tasks while neural activity was recorded from the speech cortex, including superior temporal gyrus, precentral gyrus, and postcentral gyrus. The cortical surface field potential was decomposed into traditional EEG frequency bands. Signal similarity between electrode pairs for each frequency band was quantified using a Pearson correlation coefficient. Main results. The correlation of neural activity between electrode pairs was inversely related to the distance between the electrodes; this relationship was used to quantify spatial falloff functions for cortical subdomains. As expected, lower frequencies remained correlated over larger distances than higher frequencies. However, both the envelope and phase of gamma and high gamma frequencies (30-150 Hz) are largely uncorrelated (<90%) at 4 mm, the smallest spacing of the high-density arrays. Thus, ECoG arrays smaller than 4 mm have significant promise for increasing signal resolution at high frequencies, whereas less additional gain is achieved for lower frequencies. Significance. Our findings quantitatively demonstrate the dependence of ECoG spatial resolution on the neural frequency of interest. We demonstrate that this relationship is consistent across patients and

  7. Using Very High Resolution Remotely Sensed Imagery to Estimate Agricultural Production: A comparison of food insecure and secure growing areas in Kenya

    Science.gov (United States)

    Grace, K.; Husak, G. J.; Bogle, S.

    2013-12-01

    Determining the amount of food produced in a food insecure, isolated, subsistence farming community can be used to help identify households or communities who may be in need of additional food resources. Measuring annual food production in developing countries, much less at a sub-national level, is complicated by lack of data. It can be difficult and costly to access all of the farming households engaged in subsistence farming. However, recent research has focused on the use of remotely sensed data to aid in the estimation of area under cultivation and because food production is the measure of yield (production per hectare) multiplied by area (number of hectares), we can use the area measure to reduce uncertainty in food production estimates. One strategy for estimating cultivated area relies on a fairly time intensive manual interpretation of very high resolution data. Due to the availability of very high resolution data it is possible to construct estimates of cultivated area, even in communities where fields are small. While this strategy has been used to effectively estimate cultivated area in a timely manner, questions remain about the spatial and temporal generalizability of this approach. The purpose of this paper is to produce and compare estimates of cultivated area in two very different agricultural areas of Kenya, a highly food insecure country in East Africa, during two different agricultural seasons. The areas selected represent two different livelihood zones: a marginal growing area where poor farmers rely on inconsistent rainfall and a lush growing area near the mountainous region of the middle-West area of the country where rainfall is consistent and therefore more suited to cultivation. The overarching goal is to determine the effectiveness of very high resolution remotely sensed imagery in calculating estimates of cultivated area in areas where food production strategies are different. Additionally the results of this research will explore the

  8. Improving the analysis of biogeochemical patterns associated with internal waves in the strait of Gibraltar using remote sensing images

    Science.gov (United States)

    Navarro, Gabriel; Vicent, Jorge; Caballero, Isabel; Gómez-Enri, Jesús; Morris, Edward P.; Sabater, Neus; Macías, Diego; Bolado-Penagos, Marina; Gomiz, Juan Jesús; Bruno, Miguel; Caldeira, Rui; Vázquez, Águeda

    2018-05-01

    High Amplitude Internal Waves (HAIWs) are physical processes observed in the Strait of Gibraltar (the narrow channel between the Atlantic Ocean and the Mediterranean Sea). These internal waves are generated over the Camarinal Sill (western side of the strait) during the tidal outflow (toward the Atlantic Ocean) when critical hydraulic conditions are established. HAIWs remain over the sill for up to 4 h until the outflow slackens, being then released (mostly) towards the Mediterranean Sea. These have been previously observed using Synthetic Aperture Radar (SAR), which captures variations in surface water roughness. However, in this work we use high resolution optical remote sensing, with the aim of examining the influence of HAIWs on biogeochemical processes. We used hyperspectral images from the Hyperspectral Imager for the Coastal Ocean (HICO) and high spatial resolution (10 m) images from the MultiSpectral Instrument (MSI) onboard the Sentinel-2A satellite. This work represents the first attempt to examine the relation between internal wave generation and the water constituents of the Camarinal Sill using hyperspectral and high spatial resolution remote sensing images. This enhanced spatial and spectral resolution revealed the detailed biogeochemical patterns associated with the internal waves and suggests local enhancements of productivity associated with internal waves trains.

  9. High Resolution Insights into Snow Distribution Provided by Drone Photogrammetry

    Science.gov (United States)

    Redpath, T.; Sirguey, P. J.; Cullen, N. J.; Fitzsimons, S.

    2017-12-01

    Dynamic in time and space, New Zealand's seasonal snow is largely confined to remote alpine areas, complicating ongoing in situ measurement and characterisation. Improved understanding and modeling of the seasonal snowpack requires fine scale resolution of snow distribution and spatial variability. The potential of remotely piloted aircraft system (RPAS) photogrammetry to resolve spatial and temporal variability of snow depth and water equivalent in a New Zealand alpine catchment is assessed in the Pisa Range, Central Otago. This approach yielded orthophotomosaics and digital surface models (DSM) at 0.05 and 0.15 m spatial resolution, respectively. An autumn reference DSM allowed mapping of winter (02/08/2016) and spring (10/09/2016) snow depth at 0.15 m spatial resolution, via DSM differencing. The consistency and accuracy of the RPAS-derived surface was assessed by comparison of snow-free regions of the spring and autumn DSMs, while accuracy of RPAS retrieved snow depth was assessed with 86 in situ snow probe measurements. Results show a mean vertical residual of 0.024 m between DSMs acquired in autumn and spring. This residual approximated a Laplace distribution, reflecting the influence of large outliers on the small overall bias. Propagation of errors associated with successive DSMs saw snow depth mapped with an accuracy of ± 0.09 m (95% c.l.). Comparing RPAS and in situ snow depth measurements revealed the influence of geo-location uncertainty and interactions between vegetation and the snowpack on snow depth uncertainty and bias. Semi-variogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine scale spatial variability. Despite limitations accompanying RPAS photogrammetry, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological basin ( 0.5 km2), at high resolution. Resolving snowpack features associated with re-distribution and preferential

  10. Study on the Spatial Resolution of Single and Multiple Coincidences Compton Camera

    Science.gov (United States)

    Andreyev, Andriy; Sitek, Arkadiusz; Celler, Anna

    2012-10-01

    In this paper we study the image resolution that can be obtained from the Multiple Coincidences Compton Camera (MCCC). The principle of MCCC is based on a simultaneous acquisition of several gamma-rays emitted in cascade from a single nucleus. Contrary to a standard Compton camera, MCCC can theoretically provide the exact location of a radioactive source (based only on the identification of the intersection point of three cones created by a single decay), without complicated tomographic reconstruction. However, practical implementation of the MCCC approach encounters several problems, such as low detection sensitivities result in very low probability of coincident triple gamma-ray detection, which is necessary for the source localization. It is also important to evaluate how the detection uncertainties (finite energy and spatial resolution) influence identification of the intersection of three cones, thus the resulting image quality. In this study we investigate how the spatial resolution of the reconstructed images using the triple-cone reconstruction (TCR) approach compares to images reconstructed from the same data using standard iterative method based on single-cone. Results show, that FWHM for the point source reconstructed with TCR was 20-30% higher than the one obtained from the standard iterative reconstruction based on expectation maximization (EM) algorithm and conventional single-cone Compton imaging. Finite energy and spatial resolutions of the MCCC detectors lead to errors in conical surfaces definitions (“thick” conical surfaces) which only amplify in image reconstruction when intersection of three cones is being sought. Our investigations show that, in spite of being conceptually appealing, the identification of triple cone intersection constitutes yet another restriction of the multiple coincidence approach which limits the image resolution that can be obtained with MCCC and TCR algorithm.

  11. Modeling the Hydrological Regime of Turkana Lake (Kenya, Ethiopia) by Combining Spatially Distributed Hydrological Modeling and Remote Sensing Datasets

    Science.gov (United States)

    Anghileri, D.; Kaelin, A.; Peleg, N.; Fatichi, S.; Molnar, P.; Roques, C.; Longuevergne, L.; Burlando, P.

    2017-12-01

    Hydrological modeling in poorly gauged basins can benefit from the use of remote sensing datasets although there are challenges associated with the mismatch in spatial and temporal scales between catchment scale hydrological models and remote sensing products. We model the hydrological processes and long-term water budget of the Lake Turkana catchment, a transboundary basin between Kenya and Ethiopia, by integrating several remote sensing products into a spatially distributed and physically explicit model, Topkapi-ETH. Lake Turkana is the world largest desert lake draining a catchment of 145'500 km2. It has three main contributing rivers: the Omo river, which contributes most of the annual lake inflow, the Turkwel river, and the Kerio rivers, which contribute the remaining part. The lake levels have shown great variations in the last decades due to long-term climate fluctuations and the regulation of three reservoirs, Gibe I, II, and III, which significantly alter the hydrological seasonality. Another large reservoir is planned and may be built in the next decade, generating concerns about the fate of Lake Turkana in the long run because of this additional anthropogenic pressure and increasing evaporation driven by climate change. We consider different remote sensing datasets, i.e., TRMM-V7 for precipitation, MERRA-2 for temperature, as inputs to the spatially distributed hydrological model. We validate the simulation results with other remote sensing datasets, i.e., GRACE for total water storage anomalies, GLDAS-NOAH for soil moisture, ERA-Interim/Land for surface runoff, and TOPEX/Poseidon for satellite altimetry data. Results highlight how different remote sensing products can be integrated into a hydrological modeling framework accounting for their relative uncertainties. We also carried out simulations with the artificial reservoirs planned in the north part of the catchment and without any reservoirs, to assess their impacts on the catchment hydrological

  12. Spatial unmixing for environmental impact monitoring of mining using UAS and WV-2

    Science.gov (United States)

    Delalieux, S.; Livens, S.; Goossens, M.; Reusen, I.; Tote, C.

    2012-04-01

    The three principal activities of the mineral resources mining industry - mining, mineral processing and metallurgical extraction - all produce waste. The environmental impact of these activities depends on many factors, in particular, the type of mining and the size of the operation. The effects of the mining (extraction) stage tend to be mainly local, associated with surface disturbance, the production of large amounts of solid waste material, and the spread of chemically reactive particulate matter to the atmosphere and hydrosphere. Many studies have shown the potential of remote sensing for environmental impact monitoring, e.g., [1]. However, its applicability has been limited due to the inherent spatial-spectral and temporal trade-off of most sensors. More recently, miniaturization of sensors makes it possible to capture color images from unmanned aerial systems (UAS) with a very high spatial resolution. In addition, the UAS can be deployed in a very flexible manner, allowing high temporal resolution imaging. More detailed spectral information is available from multispectral images, albeit at lower spatial resolution. Combining both types of images using image fusion can help to overcome the spatial-spectral trade-off and provide a new tool for more detailed monitoring of environmental impacts. Within the framework of the ImpactMin project, funded by the Framework Programme 7 of the European Commission, the objective of this study is to implement and apply the spatial unmixing algorithm, as proposed by [2], on images of the 'Vihovici Coal Mine' area, located in the Mostar Valley, Bosnia and Herzegovina. A WorldView2 (WV2) satellite image will be employed, which provides 8-band multispectral data at a spatial resolution of 2m. High spatial resolution images, obtained by a SmartPlanes UAS, will provide RGB data with 0.05m spatial resolution. The spatial unmixing technique is based on the idea that a linear mixing model can be used to perform the downscaling of

  13. Resolving mass flux at high spatial and temporal resolution using GRACE intersatellite measurements

    DEFF Research Database (Denmark)

    Rowlands, D. D.; Luthcke, S. B.; Klosko, S. M.

    2005-01-01

    resolution. Using 4° × 4° blocks at 10-day intervals, we estimate the mass of surplus or deficit water over a 52° × 60° grid centered on the Amazon basin for July 2003. We demonstrate that the recovered signals are coherent and correlate well with the expected hydrological signal....... the estimation of static monthly parameters. Through an analysis of the GRACE data residuals, we show that the fundamental temporal and spatial resolution of the GRACE data is 10 days and 400 km. We present an approach similar in concept to altimetric methods that recovers submonthly mass flux at a high spatial...

  14. Concept for room temperature single-spin tunneling force microscopy with atomic spatial resolution

    Science.gov (United States)

    Payne, Adam

    A study of a force detected single-spin magnetic resonance measurement concept with atomic spatial resolution is presented. The method is based upon electrostatic force detection of spin-selection rule controlled single electron tunneling between two electrically isolated paramagnetic states. Single-spin magnetic resonance detection is possible by measuring the force detected tunneling charge noise on and off spin resonance. Simulation results of this charge noise, based upon physical models of the tunneling and spin physics, are directly compared to measured atomic force microscopy (AFM) system noise. The results show that the approach could provide single-spin measurement of electrically isolated defect states with atomic spatial resolution at room temperature.

  15. Remote sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed pixels

    Directory of Open Access Journals (Sweden)

    Z. Q. Peng

    2016-11-01

    Full Text Available Evapotranspiration (ET plays an important role in surface–atmosphere interactions and can be monitored using remote sensing data. However, surface heterogeneity, including the inhomogeneity of landscapes and surface variables, significantly affects the accuracy of ET estimated from satellite data. The objective of this study is to assess and reduce the uncertainties resulting from surface heterogeneity in remotely sensed ET using Chinese HJ-1B satellite data, which is of 30 m spatial resolution in VIS/NIR bands and 300 m spatial resolution in the thermal-infrared (TIR band. A temperature-sharpening and flux aggregation scheme (TSFA was developed to obtain accurate heat fluxes from the HJ-1B satellite data. The IPUS (input parameter upscaling and TRFA (temperature resampling and flux aggregation methods were used to compare with the TSFA in this study. The three methods represent three typical schemes used to handle mixed pixels from the simplest to the most complex. IPUS handles all surface variables at coarse resolution of 300 m in this study, TSFA handles them at 30 m resolution, and TRFA handles them at 30 and 300 m resolution, which depends on the actual spatial resolution. Analyzing and comparing the three methods can help us to get a better understanding of spatial-scale errors in remote sensing of surface heat fluxes. In situ data collected during HiWATER-MUSOEXE (Multi-Scale Observation Experiment on Evapotranspiration over heterogeneous land surfaces of the Heihe Watershed Allied Telemetry Experimental Research were used to validate and analyze the methods. ET estimated by TSFA exhibited the best agreement with in situ observations, and the footprint validation results showed that the R2, MBE, and RMSE values of the sensible heat flux (H were 0.61, 0.90, and 50.99 W m−2, respectively, and those for the latent heat flux (LE were 0.82, −20.54, and 71.24 W m−2, respectively. IPUS yielded the largest errors

  16. Applications of high-resolution spatial discretization scheme and Jacobian-free Newton–Krylov method in two-phase flow problems

    International Nuclear Information System (INIS)

    Zou, Ling; Zhao, Haihua; Zhang, Hongbin

    2015-01-01

    Highlights: • Using high-resolution spatial scheme in solving two-phase flow problems. • Fully implicit time integrations scheme. • Jacobian-free Newton–Krylov method. • Analytical solution for two-phase water faucet problem. - Abstract: The majority of the existing reactor system analysis codes were developed using low-order numerical schemes in both space and time. In many nuclear thermal–hydraulics applications, it is desirable to use higher-order numerical schemes to reduce numerical errors. High-resolution spatial discretization schemes provide high order spatial accuracy in smooth regions and capture sharp spatial discontinuity without nonphysical spatial oscillations. In this work, we adapted an existing high-resolution spatial discretization scheme on staggered grids in two-phase flow applications. Fully implicit time integration schemes were also implemented to reduce numerical errors from operator-splitting types of time integration schemes. The resulting nonlinear system has been successfully solved using the Jacobian-free Newton–Krylov (JFNK) method. The high-resolution spatial discretization and high-order fully implicit time integration numerical schemes were tested and numerically verified for several two-phase test problems, including a two-phase advection problem, a two-phase advection with phase appearance/disappearance problem, and the water faucet problem. Numerical results clearly demonstrated the advantages of using such high-resolution spatial and high-order temporal numerical schemes to significantly reduce numerical diffusion and therefore improve accuracy. Our study also demonstrated that the JFNK method is stable and robust in solving two-phase flow problems, even when phase appearance/disappearance exists

  17. Spatial resolution and chest nodule detection: an interesting incidental finding

    Science.gov (United States)

    Toomey, R. J.; McEntee, M. F.; Ryan, J. T.; Evanoff, M. G.; Hayes, A.; Brennan, P. C.

    2010-02-01

    This study reports an incidental finding from a larger work. It examines the relationship between spatial resolution and nodule detection for chest radiographs. Twelve examining radiologists with the American Board of Radiology read thirty chest radiographs in two conditions - full (1500 × 1500 pixel) resolution, and 300 × 300 pixel resolution linearly interpolated to 1500 × 1500 pixels. All images were surrounded by a 10-pixel sharp grey border to aid in focussing the observer's eye when viewing the comparatively unsharp interpolated images. Fifteen of the images contained a single simulated pulmonary nodule. Observers were asked to rate their confidence that a nodule was present on each radiograph on a scale of 1 (least confidence, certain no lesion is present) to 6 (most confidence, certain a lesion was present). All other abnormalities were to be ignored. No windowing, levelling or magnification of the images was permitted and viewing distance was constrained to approximately 70cm. Images were displayed on a 3 megapixel greyscale monitor. Receiver operating characteristic (ROC) analysis was applied to the results of the readings using the Dorfman-Berbaum-Metz multiplereader, multiple-case method. No statistically significant differences were found with either readers and cases treated as random or with cases treated as fixed. Low spatial frequency information appears to be sufficient for the detection of chest lesion of the type used in this study.

  18. Improving the spatial resolution of the multiple multiwire proportional chamber gamma camera

    International Nuclear Information System (INIS)

    Bateman, J.E.; Connolly, J.F.

    1978-03-01

    Results are presented showing how the spatial resolution of the multiple multiwire proportional chamber (MMPC) gamma camera may be improved. Under the best conditions 1.6 mm bars can be resolved. (author)

  19. Identifying landscape features associated with Rift Valley fever virus transmission, Ferlo region, Senegal, using very high spatial resolution satellite imagery.

    Science.gov (United States)

    Soti, Valérie; Chevalier, Véronique; Maura, Jonathan; Bégué, Agnès; Lelong, Camille; Lancelot, Renaud; Thiongane, Yaya; Tran, Annelise

    2013-03-01

    Dynamics of most of vector-borne diseases are strongly linked to global and local environmental changes. Landscape changes are indicators of human activities or natural processes that are likely to modify the ecology of the diseases. Here, a landscape approach developed at a local scale is proposed for extracting mosquito favourable biotopes, and for testing ecological parameters when identifying risk areas of Rift Valley fever (RVF) transmission. The study was carried out around Barkedji village, Ferlo region, Senegal. In order to test whether pond characteristics may influence the density and the dispersal behaviour of RVF vectors, and thus the spatial variation in RVFV transmission, we used a very high spatial resolution remote sensing image (2.4 m resolution) provided by the Quickbird sensor to produce a detailed land-cover map of the study area. Based on knowledge of vector and disease ecology, seven landscape attributes were defined at the pond level and computed from the land-cover map. Then, the relationships between landscape attributes and RVF serologic incidence rates in small ruminants were analyzed through a beta-binomial regression. Finally, the best statistical model according to the Akaike Information Criterion corrected for small samples (AICC), was used to map areas at risk for RVF. Among the derived landscape variables, the vegetation density index (VDI) computed within a 500 m buffer around ponds was positively correlated with serologic incidence (premote sensing data for identifying environmental risk factors and mapping RVF risk areas at a local scale.

  20. Object-Based Change Detection Using High-Resolution Remotely Sensed Data and GIS

    Science.gov (United States)

    Sofina, N.; Ehlers, M.

    2012-08-01

    High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same sensor which should be acquired at the same time of season, at the same time of a day, and - for electro-optical sensors - in cloudless conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. A promising alternative is the use of vector-based maps containing information about the original urban layout which can be related to a single image obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' (DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show the high reliability of the DPC feature as an indicator for change.

  1. The influence of spatial resolution on human health risk co-benefit estimates for global climate policy assessments.

    Science.gov (United States)

    Shih, Hsiu-Ching; Crawford-Brown, Douglas; Ma, Hwong-wen

    2015-03-15

    Assessment of the ability of climate policies to produce desired improvements in public health through co-benefits of air pollution reduction can consume resources in both time and research funds. These resources increase significantly as the spatial resolution of models increases. In addition, the level of spatial detail available in macroeconomic models at the heart of climate policy assessments is much lower than that available in traditional human health risk modeling. It is therefore important to determine whether increasing spatial resolution considerably affects risk-based decisions; which kinds of decisions might be affected; and under what conditions they will be affected. Human health risk co-benefits from carbon emissions reductions that bring about concurrent reductions in Particulate Matter (PM10) emissions is therefore examined here at four levels of spatial resolution (Uniform Nation, Uniform Region, Uniform County/city, Health Risk Assessment) in a case study of Taiwan as one of the geographic regions of a global macroeceonomic model, with results that are representative of small, industrialized nations within that global model. A metric of human health risk mortality (YOLL, years of life lost in life expectancy) is compared under assessments ranging from a "uniform simulation" in which there is no spatial resolution of changes in ambient air concentration under a policy to a "highly spatially resolved simulation" (called here Health Risk Assessment). PM10 is chosen in this study as the indicator of air pollution for which risks are assessed due to its significance as a co-benefit of carbon emissions reductions within climate mitigation policy. For the policy examined, the four estimates of mortality in the entirety of Taiwan are 747 YOLL, 834 YOLL, 984 YOLL and 916 YOLL, under Uniform Taiwan, Uniform Region, Uniform County and Health Risk Assessment respectively; or differences of 18%, 9%, 7% if the HRA methodology is taken as the baseline. While

  2. Dynamic Raman imaging system with high spatial and temporal resolution

    Science.gov (United States)

    Wang, Lei; Dai, Yinzhen; He, Hao; Lv, Ruiqi; Zong, Cheng; Ren, Bin

    2017-09-01

    There is an increasing need to study dynamic changing systems with significantly high spatial and temporal resolutions. In this work, we integrated point-scanning, line-scanning, and wide-field Raman imaging techniques into a single system. By using an Electron Multiplying CCD (EMCCD) with a high gain and high frame rate, we significantly reduced the time required for wide-field imaging, making it possible to monitor the electrochemical reactions in situ. The highest frame rate of EMCDD was ˜50 fps, and the Raman images for a specific Raman peak can be obtained by passing the signal from the sample through the Liquid Crystal Tunable Filter. The spatial resolutions of scanning imaging and wide-field imaging with a 100× objective (NA = 0.9) are 0.5 × 0.5 μm2 and 0.36 × 0.36 μm2, respectively. The system was used to study the surface plasmon resonance of Au nanorods, the surface-enhanced Raman scattering signal distribution for Au Nanoparticle aggregates, and dynamic Raman imaging of an electrochemical reacting system.

  3. Study and design of a very high spatial resolution beta imaging system

    International Nuclear Information System (INIS)

    Donnard, J.

    2008-01-01

    The b autoradiography is a widely used technique in pharmacology or biological fields. It is able to locate in two dimensions molecules labeled with beta emitters. The development of a gaseous detector incorporating micro-mesh called PIM in the Subatech laboratory leads to the construction of a very high spatial resolution apparatus dedicated to b imaging. This device is devoted to small analysis surface of a half microscope slide in particular of 3 H or 14 C and the measured spatial resolution is 20 μm FWHM. The recent development of a new reconstruction method allows enlarging the field of investigation to high energy beta emitters such as 131 I, 18 F or 46 Sc. A new device with a large active area of 18*18 cm 2 has been built with a user friendly design. This allows to image simultaneously 10 microscope slides. Thanks to a multi-modality solution, it retains the good characteristics of spatial resolution obtained previously on a small surface. Moreover, different kinds of samples, like microscope slides or scotches can be analysed. The simulation and experimentation work achieved during this thesis led to an optimal disposition of the inner structure of the detector. These results and characterization show that the PIM structure has to be considered for a next generation of b-Imager. (author)

  4. Simulation study of spatial resolution in phase-contrast X-ray imaging with Takagi-Taupin equation

    International Nuclear Information System (INIS)

    Koyama, Ichiro; Momose, Atsushi

    2003-01-01

    To evaluate attainable spatial resolution of phase-contrast X-ray imaging using an LLL X-ray interferometer with a thin crystal wafer, a computer simulation study with Takagi-Taupin equation was performed. Modulation transfer function of the wafer for X-ray phase was evaluated. For a polyester film whose thickness is 0.1 mm, it was concluded that the spatial resolution can be improved up to 3 μm by thinning the wafer, under our experimental condition

  5. Extraction Method for Earthquake-Collapsed Building Information Based on High-Resolution Remote Sensing

    International Nuclear Information System (INIS)

    Chen, Peng; Wu, Jian; Liu, Yaolin; Wang, Jing

    2014-01-01

    At present, the extraction of earthquake disaster information from remote sensing data relies on visual interpretation. However, this technique cannot effectively and quickly obtain precise and efficient information for earthquake relief and emergency management. Collapsed buildings in the town of Zipingpu after the Wenchuan earthquake were used as a case study to validate two kinds of rapid extraction methods for earthquake-collapsed building information based on pixel-oriented and object-oriented theories. The pixel-oriented method is based on multi-layer regional segments that embody the core layers and segments of the object-oriented method. The key idea is to mask layer by layer all image information, including that on the collapsed buildings. Compared with traditional techniques, the pixel-oriented method is innovative because it allows considerably rapid computer processing. As for the object-oriented method, a multi-scale segment algorithm was applied to build a three-layer hierarchy. By analyzing the spectrum, texture, shape, location, and context of individual object classes in different layers, the fuzzy determined rule system was established for the extraction of earthquake-collapsed building information. We compared the two sets of results using three variables: precision assessment, visual effect, and principle. Both methods can extract earthquake-collapsed building information quickly and accurately. The object-oriented method successfully overcomes the pepper salt noise caused by the spectral diversity of high-resolution remote sensing data and solves the problem of same object, different spectrums and that of same spectrum, different objects. With an overall accuracy of 90.38%, the method achieves more scientific and accurate results compared with the pixel-oriented method (76.84%). The object-oriented image analysis method can be extensively applied in the extraction of earthquake disaster information based on high-resolution remote sensing

  6. Comparison of MODIS and SWAT evapotranspiration over a complex terrain at different spatial scales

    Directory of Open Access Journals (Sweden)

    O. O. Abiodun

    2018-05-01

    Full Text Available In most hydrological systems, evapotranspiration (ET and precipitation are the largest components of the water balance, which are difficult to estimate, particularly over complex terrain. In recent decades, the advent of remotely sensed data based ET algorithms and distributed hydrological models has provided improved spatially upscaled ET estimates. However, information on the performance of these methods at various spatial scales is limited. This study compares the ET from the MODIS remotely sensed ET dataset (MOD16 with the ET estimates from a SWAT hydrological model on graduated spatial scales for the complex terrain of the Sixth Creek Catchment of the Western Mount Lofty Ranges, South Australia. ET from both models was further compared with the coarser-resolution AWRA-L model at catchment scale. The SWAT model analyses are performed on daily timescales with a 6-year calibration period (2000–2005 and 7-year validation period (2007–2013. Differences in ET estimation between the SWAT and MOD16 methods of up to 31, 19, 15, 11 and 9 % were observed at respectively 1, 4, 9, 16 and 25 km2 spatial resolutions. Based on the results of the study, a spatial scale of confidence of 4 km2 for catchment-scale evapotranspiration is suggested in complex terrain. Land cover differences, HRU parameterisation in AWRA-L and catchment-scale averaging of input climate data in the SWAT semi-distributed model were identified as the principal sources of weaker correlations at higher spatial resolution.

  7. Experimental investigation on spontaneously active hippocampal cultures recorded by means of high-density MEAs: analysis of the spatial resolution effects

    Directory of Open Access Journals (Sweden)

    Alessandro Maccione

    2010-05-01

    Full Text Available Based on experiments performed with high-resolution Active Pixel Sensor microelectrode arrays (APS-MEAs coupled with spontaneously active hippocampal cultures, this work investigates the spatial resolution effects of the neuroelectronic interface on the analysis of the recorded electrophysiological signals. The adopted methodology consists, first, in recording the spontaneous activity at the highest spatial resolution (inter-electrode separation of 21 µm from the whole array of 4096 microelectrodes. Then, the full resolution dataset is spatially down sampled in order to evaluate the effects on raster plot representation, array-wide spike rate (AWSR, mean firing rate (MFR and mean bursting rate (MBR. Furthermore, the effects of the array-to-network relative position are evaluated by shifting a subset of equally spaced electrodes on the entire recorded area. Results highlight that MFR and MBR are particularly influenced by the spatial resolution provided by the neuroelectronic interface. On high-resolution large MEAs, such analysis better represent the time-based parameterization of the network dynamics. Finally, this work suggest interesting capabilities of high-resolution MEAs for spatial-based analysis in dense and low-dense neuronal preparation for investigating signalling at both local and global neuronal circuitries.

  8. Snowpack spatial and temporal variability assessment using SMP high-resolution penetrometer

    Science.gov (United States)

    Komarov, Anton; Seliverstov, Yuriy; Sokratov, Sergey; Grebennikov, Pavel

    2017-04-01

    This research is focused on study of spatial and temporal variability of structure and characteristics of snowpack, quick identification of layers based on hardness and dispersion values received from snow micro penetrometer (SMP). We also discuss the detection of weak layers and definition of their parameters in non-alpine terrain. As long as it is the first SMP tool available in Russia, our intent is to test it in different climate and weather conditions. During two separate snowpack studies in plain and mountain landscapes, we derived density and grain size profiles by comparing snow density and grain size from snowpits and SMP measurements. The first case study was MSU meteorological observatory test site in Moscow. SMP data was obtained by 6 consecutive measurements along 10 m transects with a horizontal resolution of approximately 50 cm. The detailed description of snowpack structure, density, grain size, air and snow temperature was also performed. By comparing this information, the detailed scheme of snowpack evolution was created. The second case study was in Khibiny mountains. One 10-meter-long transect was made. SMP, density, grain size and snow temperature data was obtained with horizontal resolution of approximately 50 cm. The high-definition profile of snowpack density variation was acquired using received data. The analysis of data reveals high spatial and temporal variability in snow density and layer structure in both horizontal and vertical dimensions. It indicates that the spatial variability is exhibiting similar spatial patterns as surface topology. This suggests a strong influence from such factors as wind and liquid water pressure on the temporal and spatial evolution of snow structure. It was also defined, that spatial variation of snowpack characteristics is substantial even within homogeneous plain landscape, while in high-latitude mountain regions it grows significantly.

  9. High spatial resolution distributed fiber system for multi-parameter sensing based on modulated pulses.

    Science.gov (United States)

    Zhang, Jingdong; Zhu, Tao; Zhou, Huan; Huang, Shihong; Liu, Min; Huang, Wei

    2016-11-28

    We demonstrate a cost-effective distributed fiber sensing system for the multi-parameter detection of the vibration, the temperature, and the strain by integrating phase-sensitive optical time domain reflectometry (φ-OTDR) and Brillouin optical time domain reflectometry (B-OTDR). Taking advantage of the fast changing property of the vibration and the static properties of the temperature and the strain, both the width and intensity of the laser pulses are modulated and injected into the single-mode sensing fiber proportionally, so that three concerned parameters can be extracted simultaneously by only one photo-detector and one data acquisition channel. A data processing method based on Gaussian window short time Fourier transform (G-STFT) is capable of achieving high spatial resolution in B-OTDR. The experimental results show that up to 4.8kHz vibration sensing with 3m spatial resolution at 10km standard single-mode fiber can be realized, as well as the distributed temperature and stress profiles along the same fiber with 80cm spatial resolution.

  10. High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

    Science.gov (United States)

    Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

    2017-01-01

    Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness

  11. THE IMPACT OF SPATIAL AND TEMPORAL RESOLUTIONS IN TROPICAL SUMMER RAINFALL DISTRIBUTION: PRELIMINARY RESULTS

    Directory of Open Access Journals (Sweden)

    Q. Liu

    2017-10-01

    Full Text Available The abundance or lack of rainfall affects peoples’ life and activities. As a major component of the global hydrological cycle (Chokngamwong & Chiu, 2007, accurate representations at various spatial and temporal scales are crucial for a lot of decision making processes. Climate models show a warmer and wetter climate due to increases of Greenhouse Gases (GHG. However, the models’ resolutions are often too coarse to be directly applicable to local scales that are useful for mitigation purposes. Hence disaggregation (downscaling procedures are needed to transfer the coarse scale products to higher spatial and temporal resolutions. The aim of this paper is to examine the changes in the statistical parameters of rainfall at various spatial and temporal resolutions. The TRMM Multi-satellite Precipitation Analysis (TMPA at 0.25 degree, 3 hourly grid rainfall data for a summer is aggregated to 0.5,1.0, 2.0 and 2.5 degree and at 6, 12, 24 hourly, pentad (five days and monthly resolutions. The probability distributions (PDF and cumulative distribution functions(CDF of rain amount at these resolutions are computed and modeled as a mixed distribution. Parameters of the PDFs are compared using the Kolmogrov-Smironov (KS test, both for the mixed and the marginal distribution. These distributions are shown to be distinct. The marginal distributions are fitted with Lognormal and Gamma distributions and it is found that the Gamma distributions fit much better than the Lognormal.

  12. The Impact of Spatial and Temporal Resolutions in Tropical Summer Rainfall Distribution: Preliminary Results

    Science.gov (United States)

    Liu, Q.; Chiu, L. S.; Hao, X.

    2017-10-01

    The abundance or lack of rainfall affects peoples' life and activities. As a major component of the global hydrological cycle (Chokngamwong & Chiu, 2007), accurate representations at various spatial and temporal scales are crucial for a lot of decision making processes. Climate models show a warmer and wetter climate due to increases of Greenhouse Gases (GHG). However, the models' resolutions are often too coarse to be directly applicable to local scales that are useful for mitigation purposes. Hence disaggregation (downscaling) procedures are needed to transfer the coarse scale products to higher spatial and temporal resolutions. The aim of this paper is to examine the changes in the statistical parameters of rainfall at various spatial and temporal resolutions. The TRMM Multi-satellite Precipitation Analysis (TMPA) at 0.25 degree, 3 hourly grid rainfall data for a summer is aggregated to 0.5,1.0, 2.0 and 2.5 degree and at 6, 12, 24 hourly, pentad (five days) and monthly resolutions. The probability distributions (PDF) and cumulative distribution functions(CDF) of rain amount at these resolutions are computed and modeled as a mixed distribution. Parameters of the PDFs are compared using the Kolmogrov-Smironov (KS) test, both for the mixed and the marginal distribution. These distributions are shown to be distinct. The marginal distributions are fitted with Lognormal and Gamma distributions and it is found that the Gamma distributions fit much better than the Lognormal.

  13. [Changes of wetland landscape pattern in Dayang River Estuary based on high-resolution remote sensing image].

    Science.gov (United States)

    Wu, Tao; Zhao, Dong-zhi; Zhang, Feng-shou; Wei, Bao-quan

    2011-07-01

    Based on the comprehensive consideration of the high resolution characteristics of remote sensing data and the current situation of land cover and land use in Dayang River Estuary wetland, a classification system with different resolutions of wetland landscape in the Estuary was established. The landscape pattern indices and landscape transition matrix were calculated by using the high resolution remote sensing data, and the dynamic changes of the landscape pattern from 1984 to 2008 were analyzed. In the study period, the wetland landscape components changed drastically. Wetland landscape transferred from natural wetland into artificial wetland, and wetland core regional area decreased. Natural wetland's largest patch area index descended, and the fragmentation degree ascended; while artificial wetland area expanded, its patch number decreased, polymerization degree increased, and the maximum patch area index had an obvious increasing trend. Increasing human activities, embankment construction, and reclamation for aquaculture were the main causes for the decrease of wetland area and the degradation of the ecological functions of Dayang River Estuary. To constitute long-term scientific and reasonable development plan, establish wetland nature reserves, protect riverway, draft strict inspective regimes for aquaculture reclamation, and energetically develop resource-based tourism industry would be the main strategies for the protection of the estuarine wetland.

  14. THE MORPHOLOGICAL PYRAMID AND ITS APPLICATIONS TO REMOTE SENSING: MULTIRESOLUTION DATA ANALYSIS AND FEATURES EXTRACTION

    Directory of Open Access Journals (Sweden)

    Laporterie Florence

    2011-05-01

    Full Text Available In remote sensing, sensors are more and more numerous, and their spatial resolution is higher and higher. Thus, the availability of a quick and accurate characterisation of the increasing amount of data is now a quite important issue. This paper deals with an approach combining a pyramidal algorithm and mathematical morphology to study the physiographic characteristics of terrestrial ecosystems. Our pyramidal strategy involves first morphological filters, then extraction at each level of resolution of well-known landscapes features. The approach is applied to a digitised aerial photograph representing an heterogeneous landscape of orchards and forests along the Garonne river (France. This example, simulating very high spatial resolution imagery, highlights the influence of the parameters of the pyramid according to the spatial properties of the studied patterns. It is shown that, the morphological pyramid approach is a promising attempt for multi-level features extraction by modelling geometrical relevant parameters.

  15. Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities.

    Science.gov (United States)

    Al-Kindi, Khalifa M; Kwan, Paul; R Andrew, Nigel; Welch, Mitchell

    2017-01-01

    In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus . An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops.

  16. Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae habitat and population densities

    Directory of Open Access Journals (Sweden)

    Khalifa M. Al-Kindi

    2017-08-01

    Full Text Available In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops.

  17. Impact of Spatial Resolution on Wind Field Derived Estimates of Air Pressure Depression in the Hurricane Eye

    Directory of Open Access Journals (Sweden)

    Linwood Jones

    2010-03-01

    Full Text Available Measurements of the near surface horizontal wind field in a hurricane with spatial resolution of order 1–10 km are possible using airborne microwave radiometer imagers. An assessment is made of the information content of the measured winds as a function of the spatial resolution of the imager. An existing algorithm is used which estimates the maximum surface air pressure depression in the hurricane eye from the maximum wind speed. High resolution numerical model wind fields from Hurricane Frances 2004 are convolved with various HIRAD antenna spatial filters to observe the impact of the antenna design on the central pressure depression in the eye that can be deduced from it.

  18. Incoherent improvement of the spatial resolution in digital holography

    International Nuclear Information System (INIS)

    Garcia-Sucerquia, J.; Herrera-Ramirez, J.; Castaneda, R.

    2005-10-01

    We report on a technique for increasing the spatial resolution of digitally recorded and reconstructed holograms of macroscopic objects, via the reduction of the contrast of the speckle noise present in the coherent imaging techniques. The contrast of the speckle noise is reduced through the superposition on an intensity basis of digitally reconstructed holograms of the same static scene. The reconstruction of a very poor contrasted object illustrates the performance of the technique. (author)

  19. Development of temperature profile sensor at high temporal and spatial resolution

    International Nuclear Information System (INIS)

    Takiguchi, Hiroki; Furuya, Masahiro; Arai, Takahiro

    2017-01-01

    In order to quantify thermo-physical flow field for the industrial applications such as nuclear and chemical reactors, high temporal and spatial measurements for temperature, pressure, phase velocity, viscosity and so on are required to validate computational fluid dynamics (CFD) and subchannel analyses. The paper proposes a novel temperature profile sensor, which can acquire temperature distribution in water at high temporal (a millisecond) and spatial (millimeter) resolutions. The devised sensor acquires electric conductance between transmitter and receiver wires, which is a function of temperature. The sensor comprise wire mesh structure for multipoint and simultaneous temperature measurement in water, which indicated that three-dimensional temperature distribution can be detected in flexible resolutions. For the demonstration of the principle, temperature profile in water was estimated according to pre-determined temperature calibration line against time-averaged impedance. The 16×16 grid sensor visualized fast and multi-dimensional mixing process of a hot water jet into a cold water pool. (author)

  20. A Multi-Temporal Remote Sensing Approach to Freshwater Turtle Conservation

    Science.gov (United States)

    Mui, Amy B.

    Freshwater turtles are a globally declining taxa, and estimates of population status are not available for many species. Primary causes of decline stem from widespread habitat loss and degradation, and obtaining spatially-explicit information on remaining habitat across a relevant spatial scale has proven challenging. The discipline of remote sensing science has been employed widely in studies of biodiversity conservation, but it has not been utilized as frequently for cryptic, and less vagile species such as turtles, despite their vulnerable status. The work presented in this thesis investigates how multi-temporal remote sensing imagery can contribute key information for building spatially-explicit and temporally dynamic models of habitat and connectivity for the threatened, Blanding's turtle (Emydoidea blandingii) in southern Ontario, Canada. I began with outlining a methodological approach for delineating freshwater wetlands from high spatial resolution remote sensing imagery, using a geographic object-based image analysis (GEOBIA) approach. This method was applied to three different landscapes in southern Ontario, and across two biologically relevant seasons during the active (non-hibernating) period of Blanding's turtles. Next, relevant environmental variables associated with turtle presence were extracted from remote sensing imagery, and a boosted regression tree model was developed to predict the probability of occurrence of this species. Finally, I analysed the movement potential for Blanding's turtles in a disturbed landscape using a combination of approaches. Results indicate that (1) a parsimonious GEOBIA approach to land cover mapping, incorporating texture, spectral indices, and topographic information can map heterogeneous land cover with high accuracy, (2) remote-sensing derived environmental variables can be used to build habitat models with strong predictive power, and (3) connectivity potential is best estimated using a variety of approaches

  1. High spatial resolution upgrade of the electron cyclotron emission radiometer for the DIII-D tokamak.

    Science.gov (United States)

    Truong, D D; Austin, M E

    2014-11-01

    The 40-channel DIII-D electron cyclotron emission (ECE) radiometer provides measurements of Te(r,t) at the tokamak midplane from optically thick, second harmonic X-mode emission over a frequency range of 83-130 GHz. The frequency spacing of the radiometer's channels results in a spatial resolution of ∼1-3 cm, depending on local magnetic field and electron temperature. A new high resolution subsystem has been added to the DIII-D ECE radiometer to make sub-centimeter (0.6-0.8 cm) resolution Te measurements. The high resolution subsystem branches off from the regular channels' IF bands and consists of a microwave switch to toggle between IF bands, a switched filter bank for frequency selectivity, an adjustable local oscillator and mixer for further frequency down-conversion, and a set of eight microwave filters in the 2-4 GHz range. Higher spatial resolution is achieved through the use of a narrower (200 MHz) filter bandwidth and closer spacing between the filters' center frequencies (250 MHz). This configuration allows for full coverage of the 83-130 GHz frequency range in 2 GHz bands. Depending on the local magnetic field, this translates into a "zoomed-in" analysis of a ∼2-4 cm radial region. Expected uses of these channels include mapping the spatial dependence of Alfven eigenmodes, geodesic acoustic modes, and externally applied magnetic perturbations. Initial Te measurements, which demonstrate that the desired resolution is achieved, are presented.

  2. Mapping Monthly Water Scarcity in Global Transboundary Basins at Country-Basin Mesh Based Spatial Resolution.

    Science.gov (United States)

    Degefu, Dagmawi Mulugeta; Weijun, He; Zaiyi, Liao; Liang, Yuan; Zhengwei, Huang; Min, An

    2018-02-01

    Currently fresh water scarcity is an issue with huge socio-economic and environmental impacts. Transboundary river and lake basins are among the sources of fresh water facing this challenge. Previous studies measured blue water scarcity at different spatial and temporal resolutions. But there is no global water availability and footprint assessment done at country-basin mesh based spatial and monthly temporal resolutions. In this study we assessed water scarcity at these spatial and temporal resolutions. Our results showed that around 1.6 billion people living within the 328 country-basin units out of the 560 we assessed in this study endures severe water scarcity at least for a month within the year. In addition, 175 country-basin units goes through severe water scarcity for 3-12 months in the year. These sub-basins include nearly a billion people. Generally, the results of this study provide insights regarding the number of people and country-basin units experiencing low, moderate, significant and severe water scarcity at a monthly temporal resolution. These insights might help these basins' sharing countries to design and implement sustainable water management and sharing schemes.

  3. Ultra-High Resolution Spectroscopic Remote Sensing: A Microscope on Planetary Atmospheres

    Science.gov (United States)

    Kostiuk, Theodor

    2010-01-01

    Remote sensing of planetary atmospheres is not complete without studies of all levels of the atmosphere, including the dense cloudy- and haze filled troposphere, relatively clear and important stratosphere and the upper atmosphere, which are the first levels to experience the effects of solar radiation. High-resolution spectroscopy can provide valuable information on these regions of the atmosphere. Ultra-high spectral resolution studies can directly measure atmospheric winds, composition, temperature and non-thermal phenomena, which describe the physics and chemistry of the atmosphere. Spectroscopy in the middle to long infrared wavelengths can also probe levels where dust of haze limit measurements at shorter wavelength or can provide ambiguous results on atmospheric species abundances or winds. A spectroscopic technique in the middle infrared wavelengths analogous to a radio receiver. infrared heterodyne spectroscopy [1], will be describe and used to illustrate the detailed study of atmospheric phenomena not readily possible with other methods. The heterodyne spectral resolution with resolving power greater than 1,000.000 measures the true line shapes of emission and absorption lines in planetary atmospheres. The information on the region of line formation is contained in the line shapes. The absolute frequency of the lines can be measured to I part in 100 ,000,000 and can be used to accurately measure the Doppler frequency shift of the lines, directly measuring the line-of-sight velocity of the gas to --Im/s precision (winds). The technical and analytical methods developed and used to measure and analyze infrared heterodyne measurements will be described. Examples of studies on Titan, Venus, Mars, Earth, and Jupiter will be presented. 'These include atmospheric dynamics on slowly rotating bodies (Titan [2] and Venus [3] and temperature, composition and chemistry on Mars 141, Venus and Earth. The discovery and studies of unique atmospheric phenomena will also be

  4. High spatial resolution in laser-induced breakdown spectroscopy of expanding plasmas

    International Nuclear Information System (INIS)

    Siegel, J.; Epurescu, G.; Perea, A.; Gordillo-Vazquez, F.J.; Gonzalo, J.; Afonso, C.N.

    2005-01-01

    We report a technique that is able to achieve high spatial resolution in the measurement of the temporal and spectral emission characteristics of laser-induced expanding plasmas. The plasma is imaged directly onto the slit of an imaging spectrograph coupled to a time-gated intensified camera, with the plasma expansion direction being parallel to the slit extension. In this way, a single hybrid detection system is used to acquire the spatial, spectral and temporal characteristics of the laser induced plasma. The parallel acquisition approach of this technique ensures a much better spatial resolution in the expansion direction, reproducibility and data acquisition speed than commonly obtained by sequential measurements at different distances from the target. We have applied this technique to study the laser-induced plasma in LiNbO 3 and Bi 12 Ge 1 O 20 , revealing phenomena not seen in such detail with standard instruments. These include extreme line broadening up to a few nanometers accompanied by self-absorption near the target surface, as well as different ablation and expansion dynamics for the different species ejected. Overall, the high precision and wealth of quantitative information accessible with this technique open up new possibilities for the study of fundamental plasma expansion processes during pulsed laser ablation

  5. Measuring the spatial resolution of an optical system in an undergraduate optics laboratory

    Science.gov (United States)

    Leung, Calvin; Donnelly, T. D.

    2017-06-01

    Two methods of quantifying the spatial resolution of a camera are described, performed, and compared, with the objective of designing an imaging-system experiment for students in an undergraduate optics laboratory. With the goal of characterizing the resolution of a typical digital single-lens reflex (DSLR) camera, we motivate, introduce, and show agreement between traditional test-target contrast measurements and the technique of using Fourier analysis to obtain the modulation transfer function (MTF). The advantages and drawbacks of each method are compared. Finally, we explore the rich optical physics at work in the camera system by calculating the MTF as a function of wavelength and f-number. For example, we find that the Canon 40D demonstrates better spatial resolution at short wavelengths, in accordance with scalar diffraction theory, but is not diffraction-limited, being significantly affected by spherical aberration. The experiment and data analysis routines described here can be built and written in an undergraduate optics lab setting.

  6. SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY

    Directory of Open Access Journals (Sweden)

    X. Han

    2016-06-01

    Full Text Available Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE. Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI datasets – Pavia University dataset and the Kennedy Space Centre (KSC dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

  7. Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models

    Science.gov (United States)

    Schlögel, R.; Marchesini, I.; Alvioli, M.; Reichenbach, P.; Rossi, M.; Malet, J.-P.

    2018-01-01

    We perform landslide susceptibility zonation with slope units using three digital elevation models (DEMs) of varying spatial resolution of the Ubaye Valley (South French Alps). In so doing, we applied a recently developed algorithm automating slope unit delineation, given a number of parameters, in order to optimize simultaneously the partitioning of the terrain and the performance of a logistic regression susceptibility model. The method allowed us to obtain optimal slope units for each available DEM spatial resolution. For each resolution, we studied the susceptibility model performance by analyzing in detail the relevance of the conditioning variables. The analysis is based on landslide morphology data, considering either the whole landslide or only the source area outline as inputs. The procedure allowed us to select the most useful information, in terms of DEM spatial resolution, thematic variables and landslide inventory, in order to obtain the most reliable slope unit-based landslide susceptibility assessment.

  8. Spatial resolution test of a beam diagnostic system for DESIREE

    Science.gov (United States)

    Das, Susanta; Kallberg, A.

    2010-11-01

    A diagnostic system based on the observation of low energy ( ˜ 10 eV) secondary electrons (SE) produced by a beam, striking a metallic foil has been built to monitor and to cover the wide range of beam intensities and energies for Double ElectroStatic Ion Ring ExpEriment [1,2].The system consists of a Faraday cup to measure the beam current, a collimator with circular apertures of different diameters to measure the spatial resolution of the system, a beam profile monitoring system (BPMS), and a control unit. The BPMS, in turn, consists of an aluminim (Al) foil, a grid placed in front of the Al foil to accelerate the SE, position sensitive MCP, fluorescent screen, and a CCD camera to capture the images. The collimator contains a set of circular holes of different diameters and separations (d) between them. The collimator cuts out from the beam areas equal to the holes with separation d mm between the beams centers and creates well separated (distinguishable) narrow beams of approximately same intensity close to each other. A 10 keV proton beam was used. The spatial resolution of the system was tested for different Al plate and MCP voltages and resolution of better than 2 mm was achieved. Ref.: 1. K. Kruglov {et al}., NIM A 441 (2000) 595; 701 (2002) 193c, 2. MSL and Atomic Physics, Stockholm Univ.(www.msl.se, http://www.atom.physto.se/Cederquist/desiree/web/hc.html).

  9. Effects of satellite image spatial aggregation and resolution on estimates of forest land area

    Science.gov (United States)

    M.D. Nelson; R.E. McRoberts; G.R. Holden; M.E. Bauer

    2009-01-01

    Satellite imagery is being used increasingly in association with national forest inventories (NFIs) to produce maps and enhance estimates of forest attributes. We simulated several image spatial resolutions within sparsely and heavily forested study areas to assess resolution effects on estimates of forest land area, independent of other sensor characteristics. We...

  10. Bathymetry mapping using a GPS-sonar equipped remote control boat: Application in waste stabilisation ponds

    Science.gov (United States)

    Coggins, Liah; Ghadouani, Anas; Ghisalberti, Marco

    2014-05-01

    Traditionally, bathymetry mapping of ponds, lakes and rivers have used techniques which are low in spatial resolution, sometimes subjective in terms of precision and accuracy, labour intensive, and that require a high level of safety precautions. In waste stabilisation ponds (WSP) in particular, sludge heights, and thus sludge volume, are commonly measured using a sludge judge (a clear plastic pipe with length markings). A remote control boat fitted with a GPS-equipped sonar unit can improve the resolution of depth measurements, and reduce safety and labour requirements. Sonar devices equipped with GPS technology, also known as fish finders, are readily available and widely used by people in boating. Through the use of GPS technology in conjunction with sonar, the location and depth can be recorded electronically onto a memory card. However, despite its high applicability to the field, this technology has so far been underutilised. In the case of WSP, the sonar can measure the water depth to the top of the sludge layer, which can then be used to develop contour maps of sludge distribution and to determine sludge volume. The coupling of sonar technology with a remotely operative vehicle has several advantages of traditional measurement techniques, particularly in removing human subjectivity of readings, and the sonar being able to collect more data points in a shorter period of time, and continuously, with a much higher spatial resolution. The GPS-sonar equipped remote control boat has been tested on in excess of 50 WSP within Western Australia, and has shown a very strong correlation (R2 = 0.98) between spot readings taken with the sonar compared to a sludge judge. This has shown that the remote control boat with GPS-sonar device is capable of providing sludge bathymetry with greatly increased spatial resolution, while greatly reducing profiling time. Remotely operated vehicles, such as the one built in this study, are useful for not only determining sludge

  11. Enhancement of Spatial Resolution Using a Metamaterial Sensor in Nondestructive Evaluation

    Directory of Open Access Journals (Sweden)

    Adriana Savin

    2015-11-01

    Full Text Available The current stage of non-destructive evaluation techniques imposes the development of new electromagnetic methods that are based on high spatial resolution and increased sensitivity. Printed circuit boards, integrated circuit boards, composite materials with polymeric matrix containing conductive fibers, as well as some types of biosensors are devices of interest in using such evaluation methods. In order to achieve high performance, the work frequencies must be either radiofrequencies or microwaves. At these frequencies, at the dielectric/conductor interface, plasmon polaritons can appear, propagating between conductive regions as evanescent waves. Detection of these waves, containing required information, can be done using sensors with metamaterial lenses. We propose in this paper the enhancement of the spatial resolution using electromagnetic methods, which can be accomplished in this case using evanescent waves that appear in the current study in slits of materials such as the spaces between carbon fibers in Carbon Fibers Reinforced Plastics or in materials of interest in the nondestructive evaluation field with industrial applications, where microscopic cracks are present. We propose herein a unique design of the metamaterials for use in nondestructive evaluation based on Conical Swiss Rolls configurations, which assure the robust concentration/focusing of the incident electromagnetic waves (practically impossible to be focused using classical materials, as well as the robust manipulation of evanescent waves. Applying this testing method, spatial resolution of approximately λ/2000 can be achieved. This testing method can be successfully applied in a variety of applications of paramount importance such as defect/damage detection in materials used in a variety of industrial applications, such as automotive and aviation technologies.

  12. A method for examining temporal changes in cyanobacterial harmful algal bloom spatial extent using satellite remote sensing..

    Science.gov (United States)

    Cyanobacterial harmful algal blooms (CyanoHAB) are thought to be increasing globally over the past few decades, but relatively little quantitative information is available about the spatial extent of blooms. Satellite remote sensing provides a potential technology for identifying...

  13. A method for geological hazard extraction using high-resolution remote sensing

    International Nuclear Information System (INIS)

    Wang, Q J; Chen, Y; Bi, J T; Lin, Q Z; Li, M X

    2014-01-01

    Taking Yingxiu, the epicentre of the Wenchuan earthquake, as the study area, a method for geological disaster extraction using high-resolution remote sensing imagery was proposed in this study. A high-resolution Digital Elevation Model (DEM) was used to create mask imagery to remove interfering factors such as buildings and water at low altitudes. Then, the mask imagery was diced into several small parts to reduce the large images' inconsistency, and they were used as the sources to be classified. After that, vector conversion was done on the classified imagery in ArcGIS. Finally, to ensure accuracy, other interfering factors such as buildings at high altitudes, bare land, and land covered by little vegetation were removed manually. Because the method can extract geological hazards in a short time, it is of great importance for decision-makers and rescuers who need to know the degree of damage in the disaster area, especially within 72 hours after an earthquake. Therefore, the method will play an important role in decision making, rescue, and disaster response planning

  14. Improvements in agricultural water decision support using remote sensing

    Science.gov (United States)

    Marshall, M. T.

    2012-12-01

    Population driven water scarcity, aggravated by climate-driven evaporative demand in dry regions of the world, has the potential of transforming ecological and social systems to the point of armed conflict. Water shortages will be most severe in agricultural areas, as the priority shifts to urban and industrial use. In order to design, evaluate, and monitor appropriate mitigation strategies, predictive models must be developed that quantify exposure to water shortage. Remote sensing data has been used for more than three decades now to parametrize these models, because field measurements are costly and difficult in remote regions of the world. In the past decade, decision-makers for the first time can make accurate and near real-time evaluations of field conditions with the advent of hyper- spatial and spectral and coarse resolution continuous remote sensing data. Here, we summarize two projects representing diverse applications of remote sensing to improve agricultural water decision support. The first project employs MODIS (coarse resolution continuous data) to drive an evapotranspiration index, which is combined with the Standardized Precipitation Index driven by meteorological satellite data to improve famine early warning in Africa. The combined index is evaluated using district-level crop yield data from Kenya and Malawi and national-level crop yield data from the United Nations Food and Agriculture Organization. The second project utilizes hyper- spatial (GeoEye 1, Quickbird, IKONOS, and RapidEye) and spectral (Hyperion/ALI), as well as multi-spectral (Landsat ETM+, SPOT, and MODIS) data to develop biomass estimates for key crops (alfalfa, corn, cotton, and rice) in the Central Valley of California. Crop biomass is an important indicator of crop water productivity. The remote sensing data is combined using various data fusion techniques and evaluated with field data collected in the summer of 2012. We conclude with a brief discussion on implementation of

  15. Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    ZHENG Zhuo

    2018-05-01

    Full Text Available High resolution remote sensing imagery scene classification is important for automatic complex scene recognition, which is the key technology for military and disaster relief, etc. In this paper, we propose a novel joint multi-scale convolution neural network (JMCNN method using a limited amount of image data for high resolution remote sensing imagery scene classification. Different from traditional convolutional neural network, the proposed JMCNN is an end-to-end training model with joint enhanced high-level feature representation, which includes multi-channel feature extractor, joint multi-scale feature fusion and Softmax classifier. Multi-channel and scale convolutional extractors are used to extract scene middle features, firstly. Then, in order to achieve enhanced high-level feature representation in a limit dataset, joint multi-scale feature fusion is proposed to combine multi-channel and scale features using two feature fusions. Finally, enhanced high-level feature representation can be used for classification by Softmax. Experiments were conducted using two limit public UCM and SIRI datasets. Compared to state-of-the-art methods, the JMCNN achieved improved performance and great robustness with average accuracies of 89.3% and 88.3% on the two datasets.

  16. A method for generating high resolution satellite image time series

    Science.gov (United States)

    Guo, Tao

    2014-10-01

    There is an increasing demand for satellite remote sensing data with both high spatial and temporal resolution in many applications. But it still is a challenge to simultaneously improve spatial resolution and temporal frequency due to the technical limits of current satellite observation systems. To this end, much R&D efforts have been ongoing for years and lead to some successes roughly in two aspects, one includes super resolution, pan-sharpen etc. methods which can effectively enhance the spatial resolution and generate good visual effects, but hardly preserve spectral signatures and result in inadequate analytical value, on the other hand, time interpolation is a straight forward method to increase temporal frequency, however it increase little informative contents in fact. In this paper we presented a novel method to simulate high resolution time series data by combing low resolution time series data and a very small number of high resolution data only. Our method starts with a pair of high and low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and then projected onto the high resolution data plane and assigned to each high resolution pixel according to the predefined temporal change patterns of each type of ground objects. Finally the simulated high resolution data is generated. A preliminary experiment shows that our method can simulate a high resolution data with a reasonable accuracy. The contribution of our method is to enable timely monitoring of temporal changes through analysis of time sequence of low resolution images only, and usage of costly high resolution data can be reduces as much as possible, and it presents a highly effective way to build up an economically operational monitoring solution for agriculture, forest, land use investigation

  17. Technique for evaluation of spatial resolution and microcalcifications in digital and scanned images of a standard breast phantom

    International Nuclear Information System (INIS)

    Santana, Priscila do C.; Gomes, Danielle S.; Oliveira, Marcio A.; Oliveira, Paulo Marcio C. de; Meira-Belo, Luiz C.; Nogueira-Tavares, Maria S.

    2011-01-01

    In this work, an automated methodology to evaluate digital and scanned images of a standard phantom (Phantom Mama) was studied. The Phantom Mama was used as an important tool to check the quality of mammographs. The scanned images were digitized using a ScanMaker 9800XL, with resolution of 900 dpi. The aim of this work is to test an automatic methodology for evaluation of spatial resolution and microcalcifications group of phantom mama images acquired with the same parameters in the same equipment. In order to analyze the images we have used the ImageJ software (in Java) which is public domain. We have used the Fast Fourier transform technique to evaluate the spatial resolution and used the ImageJ function Subtract Background and the Light Background plus Sliding Paraboloid on the evaluation of the five groups of microcalcifications on the breast phantom to assess the viability of using automated methods for both types of images. The methodology was adequate for evaluated the microcalcifications group and the spatial resolution in scanned and digital images, but the Phantom Mama doesn't provide sufficient parameters to evaluate the spatial resolution in this images. (author)

  18. Atmospheric-water absorption features near 2.2 micrometers and their importance in high spectral resolution remote sensing

    Science.gov (United States)

    Kruse, F. A.; Clark, R. N.

    1986-01-01

    Selective absorption of electromagnetic radiation by atmospheric gases and water vapor is an accepted fact in terrestrial remote sensing. Until recently, only a general knowledge of atmospheric effects was required for analysis of remote sensing data; however, with the advent of high spectral resolution imaging devices, detailed knowledge of atmospheric absorption bands has become increasingly important for accurate analysis. Detailed study of high spectral resolution aircraft data at the U.S. Geological Survey has disclosed narrow absorption features centered at approximately 2.17 and 2.20 micrometers not caused by surface mineralogy. Published atmospheric transmission spectra and atmospheric spectra derived using the LOWTRAN-5 computer model indicate that these absorption features are probably water vapor. Spectral modeling indicates that the effects of atmospheric absorption in this region are most pronounced in spectrally flat materials with only weak absorption bands. Without correction and detailed knowledge of the atmospheric effects, accurate mapping of surface mineralogy (particularly at low mineral concentrations) is not possible.

  19. Spatial resolution studies of a GEM-TPC

    Energy Technology Data Exchange (ETDEWEB)

    Berger, Martin [TU Muenchen, 85748 Garching (Germany); Collaboration: GEM-TPC-Collaboration

    2015-07-01

    A GEM-TPC can exploit the intrinsic suppression of back drifting ions from the amplification stage of the GEM (Gas Electron Multiplier) foils to overcome the problem of drift-field distortions in an ungated operation. To explore the possibility of such a continuously running TPC (Time Projection Chamber) a large-size detector was built. This detector, with a drift length of 728 mm and a radius of 308 mm and a total of 10254 electronic channels, was designed as an upgrade for the FOPI experiment at GSI (Darmstadt, Germany) to improve the secondary vertex resolution especially for K{sup 0}{sub S}- and Λ-reconstruction and the PID capabilities. After commissioning a large statistics of cosmic data and beam-target reactions has been collected and the obtained tracks in the TPC have been used to improve the tracking algorithms. During the track finding and fitting procedure a clustering algorithm which takes into account the track topology as well as the full 3D spatial information is employed. The the clustering algorithm, the cluster error calculation and the tracking resolution are discussed in this contribution.

  20. Assessing Greater Sage-Grouse Selection of Brood-Rearing Habitat Using Remotely-Sensed Imagery: Can Readily Available High-Resolution Imagery Be Used to Identify Brood-Rearing Habitat Across a Broad Landscape?

    Science.gov (United States)

    Westover, Matthew; Baxter, Jared; Baxter, Rick; Day, Casey; Jensen, Ryan; Petersen, Steve; Larsen, Randy

    2016-01-01

    Greater sage-grouse populations have decreased steadily since European settlement in western North America. Reduced availability of brood-rearing habitat has been identified as a limiting factor for many populations. We used radio-telemetry to acquire locations of sage-grouse broods from 1998 to 2012 in Strawberry Valley, Utah. Using these locations and remotely-sensed NAIP (National Agricultural Imagery Program) imagery, we 1) determined which characteristics of brood-rearing habitat could be used in widely available, high resolution imagery 2) assessed the spatial extent at which sage-grouse selected brood-rearing habitat, and 3) created a predictive habitat model to identify areas of preferred brood-rearing habitat. We used AIC model selection to evaluate support for a list of variables derived from remotely-sensed imagery. We examined the relationship of these explanatory variables at three spatial extents (45, 200, and 795 meter radii). Our top model included 10 variables (percent shrub, percent grass, percent tree, percent paved road, percent riparian, meters of sage/tree edge, meters of riparian/tree edge, distance to tree, distance to transmission lines, and distance to permanent structures). Variables from each spatial extent were represented in our top model with the majority being associated with the larger (795 meter) spatial extent. When applied to our study area, our top model predicted 75% of naïve brood locations suggesting reasonable success using this method and widely available NAIP imagery. We encourage application of our methodology to other sage-grouse populations and species of conservation concern.

  1. Long-distance super-resolution imaging assisted by enhanced spatial Fourier transform.

    Science.gov (United States)

    Tang, Heng-He; Liu, Pu-Kun

    2015-09-07

    A new gradient-index (GRIN) lens that can realize enhanced spatial Fourier transform (FT) over optically long distances is demonstrated. By using an anisotropic GRIN metamaterial with hyperbolic dispersion, evanescent wave in free space can be transformed into propagating wave in the metamaterial and then focused outside due to negative-refraction. Both the results based on the ray tracing and the finite element simulation show that the spatial frequency bandwidth of the spatial FT can be extended to 2.7k(0) (k(0) is the wave vector in free space). Furthermore, assisted by the enhanced spatial FT, a new long-distance (in the optical far-field region) super-resolution imaging scheme is also proposed and the super resolved capability of λ/5 (λ is the wavelength in free space) is verified. The work may provide technical support for designing new-type high-speed microscopes with long working distances.

  2. Improving the spatial and temporal resolution with quantification of uncertainty and errors in earth observation data sets using Data Interpolating Empirical Orthogonal Functions methodology

    Science.gov (United States)

    El Serafy, Ghada; Gaytan Aguilar, Sandra; Ziemba, Alexander

    2016-04-01

    There is an increasing use of process-based models in the investigation of ecological systems and scenario predictions. The accuracy and quality of these models are improved when run with high spatial and temporal resolution data sets. However, ecological data can often be difficult to collect which manifests itself through irregularities in the spatial and temporal domain of these data sets. Through the use of Data INterpolating Empirical Orthogonal Functions(DINEOF) methodology, earth observation products can be improved to have full spatial coverage within the desired domain as well as increased temporal resolution to daily and weekly time step, those frequently required by process-based models[1]. The DINEOF methodology results in a degree of error being affixed to the refined data product. In order to determine the degree of error introduced through this process, the suspended particulate matter and chlorophyll-a data from MERIS is used with DINEOF to produce high resolution products for the Wadden Sea. These new data sets are then compared with in-situ and other data sources to determine the error. Also, artificial cloud cover scenarios are conducted in order to substantiate the findings from MERIS data experiments. Secondly, the accuracy of DINEOF is explored to evaluate the variance of the methodology. The degree of accuracy is combined with the overall error produced by the methodology and reported in an assessment of the quality of DINEOF when applied to resolution refinement of chlorophyll-a and suspended particulate matter in the Wadden Sea. References [1] Sirjacobs, D.; Alvera-Azcárate, A.; Barth, A.; Lacroix, G.; Park, Y.; Nechad, B.; Ruddick, K.G.; Beckers, J.-M. (2011). Cloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology. J. Sea Res. 65(1): 114-130. Dx.doi.org/10.1016/j.seares.2010.08.002

  3. Assessment of Acacia Koa Forest Health across Environmental Gradients in Hawai‘i Using Fine Resolution Remote Sensing and GIS

    Science.gov (United States)

    Morales, Rodolfo Martinez; Idol, Travis; Friday, James B.

    2011-01-01

    Koa (Acacia koa) forests are found across broad environmental gradients in the Hawai‘ian Islands. Previous studies have identified koa forest health problems and dieback at the plot level, but landscape level patterns remain unstudied. The availability of high-resolution satellite images from the new GeoEye1 satellite offers the opportunity to conduct landscape-level assessments of forest health. The goal of this study was to develop integrated remote sensing and geographic information systems (GIS) methodologies to characterize the health of koa forests and model the spatial distribution and variability of koa forest dieback patterns across an elevation range of 600–1,000 m asl in the island of Kaua‘i, which correspond to gradients of temperature and rainfall ranging from 17–20 °C mean annual temperature and 750–1,500 mm mean annual precipitation. GeoEye1 satellite imagery of koa stands was analyzed using supervised classification techniques based on the analysis of 0.5-m pixel multispectral bands. There was clear differentiation of native koa forest from areas dominated by introduced tree species and differentiation of healthy koa stands from those exhibiting dieback symptoms. The area ratio of healthy koa to koa dieback corresponded linearly to changes in temperature across the environmental gradient, with koa dieback at higher relative abundance in warmer areas. A landscape-scale map of healthy koa forest and dieback distribution demonstrated both the general trend with elevation and the small-scale heterogeneity that exists within particular elevations. The application of these classification techniques with fine spatial resolution imagery can improve the accuracy of koa forest inventory and mapping across the islands of Hawai‘i. Such findings should also improve ecological restoration, conservation and silviculture of this important native tree species. PMID:22163920

  4. Assessment of Acacia koa forest health across environmental gradients in Hawai'i using fine resolution remote sensing and GIS.

    Science.gov (United States)

    Morales, Rodolfo Martinez; Idol, Travis; Friday, James B

    2011-01-01

    Koa (Acacia koa) forests are found across broad environmental gradients in the Hawai'ian Islands. Previous studies have identified koa forest health problems and dieback at the plot level, but landscape level patterns remain unstudied. The availability of high-resolution satellite images from the new GeoEye1 satellite offers the opportunity to conduct landscape-level assessments of forest health. The goal of this study was to develop integrated remote sensing and geographic information systems (GIS) methodologies to characterize the health of koa forests and model the spatial distribution and variability of koa forest dieback patterns across an elevation range of 600-1,000 m asl in the island of Kaua'i, which correspond to gradients of temperature and rainfall ranging from 17-20 °C mean annual temperature and 750-1,500 mm mean annual precipitation. GeoEye1 satellite imagery of koa stands was analyzed using supervised classification techniques based on the analysis of 0.5-m pixel multispectral bands. There was clear differentiation of native koa forest from areas dominated by introduced tree species and differentiation of healthy koa stands from those exhibiting dieback symptoms. The area ratio of healthy koa to koa dieback corresponded linearly to changes in temperature across the environmental gradient, with koa dieback at higher relative abundance in warmer areas. A landscape-scale map of healthy koa forest and dieback distribution demonstrated both the general trend with elevation and the small-scale heterogeneity that exists within particular elevations. The application of these classification techniques with fine spatial resolution imagery can improve the accuracy of koa forest inventory and mapping across the islands of Hawai'i. Such findings should also improve ecological restoration, conservation and silviculture of this important native tree species.

  5. Stimulated Emission Pumping Enablling Sub-Diffraction-Limited Spatial Resolution in CARS Microscopy

    NARCIS (Netherlands)

    Cleff, C.; Gross, P.; Fallnich, C.; Offerhaus, Herman L.; Herek, Jennifer Lynn; Kruse, K.; Beeker, W.P; Beeker, W.P.; Lee, Christopher James; Boller, Klaus J.; Dobner, S.

    2012-01-01

    Suppression of CARS signal generation is demonstrated by equalization of the ground and Raman states via a control state in a theoretical investigation. Using donut-shaped control light fields for population transfer results in sub-diffraction-limited spatial resolution CARS microscopy.

  6. A new framework for UAV-based remote sensing data processing and its application in almond water stress quantification

    Science.gov (United States)

    With the rapid development of small imaging sensors and unmanned aerial vehicles (UAVs), remote sensing is undergoing a revolution with greatly increased spatial and temporal resolutions. While more relevant detail becomes available, it is a challenge to analyze the large number of images to extract...

  7. High spatial resolution upgrade of the electron cyclotron emission radiometer for the DIII-D tokamak

    Energy Technology Data Exchange (ETDEWEB)

    Truong, D. D., E-mail: dtruong@wisc.edu [Department of Engineering Physics, University of Wisconsin-Madison, Madison, Wisconsin 53706 (United States); Austin, M. E. [Institute for Fusion Studies, University of Texas, Austin, Texas, 78712 (United States)

    2014-11-15

    The 40-channel DIII-D electron cyclotron emission (ECE) radiometer provides measurements of T{sub e}(r,t) at the tokamak midplane from optically thick, second harmonic X-mode emission over a frequency range of 83–130 GHz. The frequency spacing of the radiometer's channels results in a spatial resolution of ∼1–3 cm, depending on local magnetic field and electron temperature. A new high resolution subsystem has been added to the DIII-D ECE radiometer to make sub-centimeter (0.6–0.8 cm) resolution T{sub e} measurements. The high resolution subsystem branches off from the regular channels’ IF bands and consists of a microwave switch to toggle between IF bands, a switched filter bank for frequency selectivity, an adjustable local oscillator and mixer for further frequency down-conversion, and a set of eight microwave filters in the 2–4 GHz range. Higher spatial resolution is achieved through the use of a narrower (200 MHz) filter bandwidth and closer spacing between the filters’ center frequencies (250 MHz). This configuration allows for full coverage of the 83–130 GHz frequency range in 2 GHz bands. Depending on the local magnetic field, this translates into a “zoomed-in” analysis of a ∼2–4 cm radial region. Expected uses of these channels include mapping the spatial dependence of Alfven eigenmodes, geodesic acoustic modes, and externally applied magnetic perturbations. Initial T{sub e} measurements, which demonstrate that the desired resolution is achieved, are presented.

  8. Vegetation cover in relation to socioeconomic factors in a tropical city assessed from sub-meter resolution imagery.

    Science.gov (United States)

    Martinuzzi, Sebastián; Ramos-González, Olga M; Muñoz-Erickson, Tischa A; Locke, Dexter H; Lugo, Ariel E; Radeloff, Volker C

    2018-04-01

    Fine-scale information about urban vegetation and social-ecological relationships is crucial to inform both urban planning and ecological research, and high spatial resolution imagery is a valuable tool for assessing urban areas. However, urban ecology and remote sensing have largely focused on cities in temperate zones. Our goal was to characterize urban vegetation cover with sub-meter (urban vegetation patterns in a tropical city, the San Juan Metropolitan Area, Puerto Rico. Our specific objectives were to (1) map vegetation cover using sub-meter spatial resolution (0.3-m) imagery, (2) quantify the amount of residential and non-residential vegetation, and (3) investigate the relationship between patterns of urban vegetation vs. socioeconomic and environmental factors. We found that 61% of the San Juan Metropolitan Area was green and that our combination of high spatial resolution imagery and object-based classification was highly successful for extracting vegetation cover in a moist tropical city (97% accuracy). In addition, simple spatial pattern analysis allowed us to separate residential from non-residential vegetation with 76% accuracy, and patterns of residential and non-residential vegetation varied greatly across the city. Both socioeconomic (e.g., population density, building age, detached homes) and environmental variables (e.g., topography) were important in explaining variations in vegetation cover in our spatial regression models. However, important socioeconomic drivers found in cities in temperate zones, such as income and home value, were not important in San Juan. Climatic and cultural differences between tropical and temperate cities may result in different social-ecological relationships. Our study provides novel information for local land use planners, highlights the value of high spatial resolution remote sensing data to advance ecological research and urban planning in tropical cities, and emphasizes the need for more studies in tropical

  9. Macrophysical properties of continental cumulus clouds from active and passive remote sensing

    Energy Technology Data Exchange (ETDEWEB)

    Kassianov, Evgueni I.; Riley, Erin A.; Kleiss, Jessica; Long, Charles N.; Riihimaki, Laura D.; Flynn, Donna M.; Flynn, Connor J M.; Berg, Larry K.

    2017-10-06

    Cloud amount is an essential and extensively used macrophysical parameter of cumulus clouds. It is commonly defined as a cloud fraction (CF) from zenith-pointing ground-based active and passive remote sensing. However, conventional retrievals of CF from the remote sensing data with very narrow field-of-view (FOV) may not be representative of the surrounding area. Here we assess its representativeness using an integrated dataset collected at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site in Oklahoma, USA. For our assessment with focus on selected days with single-layer cumulus clouds (2005-2016), we include the narrow-FOV ARM Active Remotely Sensed Clouds Locations (ARSCL) and large-FOV Total Sky Imager (TSI) cloud products, the 915-MHz Radar Wind Profiler (RWP) measurements of wind speed and direction, and also high-resolution satellite images from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS). We demonstrate that a root-mean-square difference (RMSD) between the 15-min averaged ARSCL cloud fraction (CF) and the 15-min averaged TSI fractional sky cover (FSC) is large (up to 0.3). We also discuss how the horizontal distribution of clouds can modify the obtained large RMSD using a new uniformity metric. The latter utilizes the spatial distribution of the FSC over the 100° FOV TSI images obtained with high temporal resolution (30 sec sampling). We demonstrate that cases with more uniform spatial distribution of FSC show better agreement between the narrow-FOV CF and large-FOV FSC, reducing the RMSD by up to a factor of 2.

  10. Improving the spatial resolution in CZT detectors using charge sharing effect and transient signal analysis: Simulation study

    Energy Technology Data Exchange (ETDEWEB)

    Zheng, Xiaoqing; Cheng, Zeng [Department of Electrical and Computer Engineering, McMaster University (Canada); Deen, M. Jamal, E-mail: jamal@mcmaster.ca [Department of Electrical and Computer Engineering, McMaster University (Canada); School of Biomedical Engineering, McMaster University (Canada); Peng, Hao, E-mail: penghao@mcmaster.ca [Department of Electrical and Computer Engineering, McMaster University (Canada); School of Biomedical Engineering, McMaster University (Canada); Department of Medical Physics, McMaster University, Ontario L8S 4K1, Hamilton (Canada)

    2016-02-01

    Cadmium Zinc Telluride (CZT) semiconductor detectors are capable of providing superior energy resolution and three-dimensional position information of gamma ray interactions in a large variety of fields, including nuclear physics, gamma-ray imaging and nuclear medicine. Some dedicated Positron Emission Tomography (PET) systems, for example, for breast cancer detection, require higher contrast recovery and more accurate event location compared with a whole-body PET system. The spatial resolution is currently limited by electrode pitch in CZT detectors. A straightforward approach to increase the spatial resolution is by decreasing the detector electrode pitch, but this leads to higher fabrication cost and a larger number of readout channels. In addition, inter-electrode charge spreading can negate any improvement in spatial resolution. In this work, we studied the feasibility of achieving sub-pitch spatial resolution in CZT detectors using two methods: charge sharing effect and transient signal analysis. We noted that their valid ranges of usage were complementary. The dependences of their corresponding valid ranges on electrode design, depth-of-interaction (DOI), voltage bias and signal triggering threshold were investigated. The implementation of these two methods in both pixelated and cross-strip configuration of CZT detectors were discussed. Our results show that the valid range of charge sharing effect increases as a function of DOI, but decreases with increasing gap width and bias voltage. For a CZT detector of 5 mm thickness, 100 µm gap and biased at 400 V, the valid range of charge sharing effect was found to be about 112.3 µm around the gap center. This result complements the valid range of the transient signal analysis within one electrode pitch. For a signal-to-noise ratio (SNR) of ~17 and preliminary measurements, the sub-pitch spatial resolution is expected to be ~30 µm and ~250 µm for the charge sharing and transient signal analysis methods

  11. Spatial Variability of Soil-Water Storage in the Southern Sierra Critical Zone Observatory: Measurement and Prediction

    Science.gov (United States)

    Oroza, C.; Bales, R. C.; Zheng, Z.; Glaser, S. D.

    2017-12-01

    Predicting the spatial distribution of soil moisture in mountain environments is confounded by multiple factors, including complex topography, spatial variably of soil texture, sub-surface flow paths, and snow-soil interactions. While remote-sensing tools such as passive-microwave monitoring can measure spatial variability of soil moisture, they only capture near-surface soil layers. Large-scale sensor networks are increasingly providing soil-moisture measurements at high temporal resolution across a broader range of depths than are accessible from remote sensing. It may be possible to combine these in-situ measurements with high-resolution LIDAR topography and canopy cover to estimate the spatial distribution of soil moisture at high spatial resolution at multiple depths. We study the feasibility of this approach using six years (2009-2014) of daily volumetric water content measurements at 10-, 30-, and 60-cm depths from the Southern Sierra Critical Zone Observatory. A non-parametric, multivariate regression algorithm, Random Forest, was used to predict the spatial distribution of depth-integrated soil-water storage, based on the in-situ measurements and a combination of node attributes (topographic wetness, northness, elevation, soil texture, and location with respect to canopy cover). We observe predictable patterns of predictor accuracy and independent variable ranking during the six-year study period. Predictor accuracy is highest during the snow-cover and early recession periods but declines during the dry period. Soil texture has consistently high feature importance. Other landscape attributes exhibit seasonal trends: northness peaks during the wet-up period, and elevation and topographic-wetness index peak during the recession and dry period, respectively.

  12. Ambiguity of Quality in Remote Sensing Data

    Science.gov (United States)

    Lynnes, Christopher; Leptoukh, Greg

    2010-01-01

    This slide presentation reviews some of the issues in quality of remote sensing data. Data "quality" is used in several different contexts in remote sensing data, with quite different meanings. At the pixel level, quality typically refers to a quality control process exercised by the processing algorithm, not an explicit declaration of accuracy or precision. File level quality is usually a statistical summary of the pixel-level quality but is of doubtful use for scenes covering large areal extents. Quality at the dataset or product level, on the other hand, usually refers to how accurately the dataset is believed to represent the physical quantities it purports to measure. This assessment often bears but an indirect relationship at best to pixel level quality. In addition to ambiguity at different levels of granularity, ambiguity is endemic within levels. Pixel-level quality terms vary widely, as do recommendations for use of these flags. At the dataset/product level, quality for low-resolution gridded products is often extrapolated from validation campaigns using high spatial resolution swath data, a suspect practice at best. Making use of quality at all levels is complicated by the dependence on application needs. We will present examples of the various meanings of quality in remote sensing data and possible ways forward toward a more unified and usable quality framework.

  13. Gaussian Multiple Instance Learning Approach for Mapping the Slums of the World Using Very High Resolution Imagery

    Energy Technology Data Exchange (ETDEWEB)

    Vatsavai, Raju [ORNL

    2013-01-01

    In this paper, we present a computationally efficient algo- rithm based on multiple instance learning for mapping infor- mal settlements (slums) using very high-resolution remote sensing imagery. From remote sensing perspective, infor- mal settlements share unique spatial characteristics that dis- tinguish them from other urban structures like industrial, commercial, and formal residential settlements. However, regular pattern recognition and machine learning methods, which are predominantly single-instance or per-pixel classi- fiers, often fail to accurately map the informal settlements as they do not capture the complex spatial patterns. To overcome these limitations we employed a multiple instance based machine learning approach, where groups of contigu- ous pixels (image patches) are modeled as generated by a Gaussian distribution. We have conducted several experi- ments on very high-resolution satellite imagery, represent- ing four unique geographic regions across the world. Our method showed consistent improvement in accurately iden- tifying informal settlements.

  14. Intensity-Duration-Frequency curves from remote sensing datasets: direct comparison of weather radar and CMORPH over the Eastern Mediterranean

    Science.gov (United States)

    Morin, Efrat; Marra, Francesco; Peleg, Nadav; Mei, Yiwen; Anagnostou, Emmanouil N.

    2017-04-01

    Rainfall frequency analysis is used to quantify the probability of occurrence of extreme rainfall and is traditionally based on rain gauge records. The limited spatial coverage of rain gauges is insufficient to sample the spatiotemporal variability of extreme rainfall and to provide the areal information required by management and design applications. Conversely, remote sensing instruments, even if quantitative uncertain, offer coverage and spatiotemporal detail that allow overcoming these issues. In recent years, remote sensing datasets began to be used for frequency analyses, taking advantage of increased record lengths and quantitative adjustments of the data. However, the studies so far made use of concepts and techniques developed for rain gauge (i.e. point or multiple-point) data and have been validated by comparison with gauge-derived analyses. These procedures add further sources of uncertainty and prevent from isolating between data and methodological uncertainties and from fully exploiting the available information. In this study, we step out of the gauge-centered concept presenting a direct comparison between at-site Intensity-Duration-Frequency (IDF) curves derived from different remote sensing datasets on corresponding spatial scales, temporal resolutions and records. We analyzed 16 years of homogeneously corrected and gauge-adjusted C-Band weather radar estimates, high-resolution CMORPH and gauge-adjusted high-resolution CMORPH over the Eastern Mediterranean. Results of this study include: (a) good spatial correlation between radar and satellite IDFs ( 0.7 for 2-5 years return period); (b) consistent correlation and dispersion in the raw and gauge adjusted CMORPH; (c) bias is almost uniform with return period for 12-24 h durations; (d) radar identifies thicker tail distributions than CMORPH and the tail of the distributions depends on the spatial and temporal scales. These results demonstrate the potential of remote sensing datasets for rainfall

  15. Process and device for remote inspection of parts of a nuclear plant

    International Nuclear Information System (INIS)

    Schmalfuss, H.

    1987-01-01

    The process is suitable for remote position inspection of the parts of the plant in a large hot cell, for example of a reprocessing plant. A device with a TV camera was selected as the inspection system, where pictures obtained by photography are compared (photogrammetry). The possible resolution is considerably increased by using two spatially movable separate picture systems with the associated drawing media. (DG) [de

  16. Uniform competency-based local feature extraction for remote sensing images

    Science.gov (United States)

    Sedaghat, Amin; Mohammadi, Nazila

    2018-01-01

    Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

  17. Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

    Directory of Open Access Journals (Sweden)

    Yongyang Xu

    2018-01-01

    Full Text Available Very high resolution (VHR remote sensing imagery has been used for land cover classification, and it tends to a transition from land-use classification to pixel-level semantic segmentation. Inspired by the recent success of deep learning and the filter method in computer vision, this work provides a segmentation model, which designs an image segmentation neural network based on the deep residual networks and uses a guided filter to extract buildings in remote sensing imagery. Our method includes the following steps: first, the VHR remote sensing imagery is preprocessed and some hand-crafted features are calculated. Second, a designed deep network architecture is trained with the urban district remote sensing image to extract buildings at the pixel level. Third, a guided filter is employed to optimize the classification map produced by deep learning; at the same time, some salt-and-pepper noise is removed. Experimental results based on the Vaihingen and Potsdam datasets demonstrate that our method, which benefits from neural networks and guided filtering, achieves a higher overall accuracy when compared with other machine learning and deep learning methods. The method proposed shows outstanding performance in terms of the building extraction from diversified objects in the urban district.

  18. NASA Fluid Lensing & MiDAR: Next-Generation Remote Sensing Technologies for Aquatic Remote Sensing

    Science.gov (United States)

    Chirayath, Ved

    2018-01-01

    We present two recent instrument technology developments at NASA, Fluid Lensing and MiDAR, and their application to remote sensing of Earth's aquatic systems. Fluid Lensing is the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions. MiDAR is a next-generation active hyperspectral remote sensing and optical communications instrument capable of active fluid lensing. Fluid Lensing has been used to provide 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) to enable a new method for remote sensing of living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an objectâ€"TM"s non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Previously, MiDAR was developed into a TRL 2 laboratory instrument capable of imaging in thirty-two narrowband channels across the VNIR spectrum (400-950nm). Recently, MiDAR UV was raised to TRL4 and expanded to include five ultraviolet bands from 280-400nm, permitting UV remote sensing capabilities in UV A, B, and C bands and enabling mineral identification and stimulated fluorescence measurements of organic proteins and compounds, such as green fluorescent proteins in terrestrial and

  19. Crowding in Visual Working Memory Reveals Its Spatial Resolution and the Nature of Its Representations.

    Science.gov (United States)

    Tamber-Rosenau, Benjamin J; Fintzi, Anat R; Marois, René

    2015-09-01

    Spatial resolution fundamentally limits any image representation. Although this limit has been extensively investigated for perceptual representations by assessing how neighboring flankers degrade the perception of a peripheral target with visual crowding, the corresponding limit for representations held in visual working memory (VWM) is unknown. In the present study, we evoked crowding in VWM and directly compared resolution in VWM and perception. Remarkably, the spatial resolution of VWM proved to be no worse than that of perception. However, mixture modeling of errors caused by crowding revealed the qualitatively distinct nature of these representations. Perceptual crowding errors arose from both increased imprecision in target representations and substitution of flankers for targets. By contrast, VWM crowding errors arose exclusively from substitutions, which suggests that VWM transforms analog perceptual representations into discrete items. Thus, although perception and VWM share a common resolution limit, exceeding this limit reveals distinct mechanisms for perceiving images and holding them in mind. © The Author(s) 2015.

  20. Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation

    Science.gov (United States)

    Jana, Raghavendra B.; Mohanty, Binayak P.

    2011-03-01

    SummaryUse of remotely sensed data products in the earth science and water resources fields is growing due to increasingly easy availability of the data. Traditionally, pedotransfer functions (PTFs) employed for soil hydraulic parameter estimation from other easily available data have used basic soil texture and structure information as inputs. Inclusion of surrogate/supplementary data such as topography and vegetation information has shown some improvement in the PTF's ability to estimate more accurate soil hydraulic parameters. Artificial neural networks (ANNs) are a popular tool for PTF development, and are usually applied across matching spatial scales of inputs and outputs. However, different hydrologic, hydro-climatic, and contaminant transport models require input data at different scales, all of which may not be easily available from existing databases. In such a scenario, it becomes necessary to scale the soil hydraulic parameter values estimated by PTFs to suit the model requirements. Also, uncertainties in the predictions need to be quantified to enable users to gauge the suitability of a particular dataset in their applications. Bayesian Neural Networks (BNNs) inherently provide uncertainty estimates for their outputs due to their utilization of Markov Chain Monte Carlo (MCMC) techniques. In this paper, we present a PTF methodology to estimate soil water retention characteristics built on a Bayesian framework for training of neural networks and utilizing several in situ and remotely sensed datasets jointly. The BNN is also applied across spatial scales to provide fine scale outputs when trained with coarse scale data. Our training data inputs include ground/remotely sensed soil texture, bulk density, elevation, and Leaf Area Index (LAI) at 1 km resolutions, while similar properties measured at a point scale are used as fine scale inputs. The methodology was tested at two different hydro-climatic regions. We also tested the effect of varying the support

  1. Integration of In Situ Radon Modeling with High Resolution Aerial Remote Sensing for Mapping and Quantifying Local to Regional Flow and Transport of Submarine Groundwater Discharge from Coastal Aquifers

    Science.gov (United States)

    Glenn, C. R.; Kennedy, J. J.; Dulaiova, H.; Kelly, J. L.; Lucey, P. G.; Lee, E.; Fackrell, J.

    2015-12-01

    Submarine groundwater discharge (SGD) is a principal conduit for huge volumes of fresh groundwater loss and is a key transport mechanism for nutrient and contaminant pollution to coastal zones worldwide. However, the volumes and spatially and temporally variable nature of SGD is poorly known and requires rapid and high-resolution data acquisition at the scales in which it is commonly observed. Airborne thermal infrared (TIR) remote sensing, using high-altitude manned aircraft and low-altitude remote-controlled unmanned aerial vehicles (UAVs or "Drones") are uniquely qualified for this task, and applicable wherever 0.1°C temperature contrasts exist between discharging and receiving waters. We report on the use of these technologies in combination with in situ radon model studies of SGD volume and nutrient flux from three of the largest Hawaiian Islands. High altitude manned aircraft results produce regional (~300m wide x 100s km coastline) 0.5 to 3.2 m-resolution sea-surface temperature maps accurate to 0.7°C that show point-source and diffuse flow in exquisite detail. Using UAVs offers cost-effective advantages of higher spatial and temporal resolution and instantaneous deployments that can be coordinated simultaneously with any ground-based effort. We demonstrate how TIR-mapped groundwater discharge plume areas may be linearly and highly correlated to in situ groundwater fluxes. We also illustrate how in situ nutrient data may be incorporated into infrared imagery to produce nutrient distribution maps of regional worth. These results illustrate the potential for volumetric quantification and up-scaling of small- to regional-scale SGD. These methodologies provide a tremendous advantage for identifying and differentiating spring-fed, point-sourced, and/or diffuse groundwater discharge into oceans, estuaries, and streams. The integrative techniques are also important precursors for developing best-use and cost-effective strategies for otherwise time-consuming in

  2. Spatial Growth Modeling and High Resolution Remote Sensing Data Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

    Science.gov (United States)

    Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William; Johnson, Hoyt; Khan, Maudood

    2006-01-01

    The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 60 percent of the world s population will live in cities. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes within an urban ecosystems perspective. A reduction in air quality over cities is a major result of these impacts. Because of its complexity, the urban landscape is not adequately captured in air quality models such as the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to a meteorological/air quality modeling system focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include business as usual and smart growth scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the CMAQ modeling schemes. Use of these data has been found to better characterize low density/suburban development as compared

  3. Technical Note: Measuring contrast- and noise-dependent spatial resolution of an iterative reconstruction method in CT using ensemble averaging

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Lifeng, E-mail: yu.lifeng@mayo.edu; Vrieze, Thomas J.; Leng, Shuai; Fletcher, Joel G.; McCollough, Cynthia H. [Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905 (United States)

    2015-05-15

    Purpose: The spatial resolution of iterative reconstruction (IR) in computed tomography (CT) is contrast- and noise-dependent because of the nonlinear regularization. Due to the severe noise contamination, it is challenging to perform precise spatial-resolution measurements at very low-contrast levels. The purpose of this study was to measure the spatial resolution of a commercially available IR method using ensemble-averaged images acquired from repeated scans. Methods: A low-contrast phantom containing three rods (7, 14, and 21 HU below background) was scanned on a 128-slice CT scanner at three dose levels (CTDI{sub vol} = 16, 8, and 4 mGy). Images were reconstructed using two filtered-backprojection (FBP) kernels (B40 and B20) and a commercial IR method (sinogram affirmed iterative reconstruction, SAFIRE, Siemens Healthcare) with two strength settings (I40-3 and I40-5). The same scan was repeated 100 times at each dose level. The modulation transfer function (MTF) was calculated based on the edge profile measured on the ensemble-averaged images. Results: The spatial resolution of the two FBP kernels, B40 and B20, remained relatively constant across contrast and dose levels. However, the spatial resolution of the two IR kernels degraded relative to FBP as contrast or dose level decreased. For a given dose level at 16 mGy, the MTF{sub 50%} value normalized to the B40 kernel decreased from 98.4% at 21 HU to 88.5% at 7 HU for I40-3 and from 97.6% to 82.1% for I40-5. At 21 HU, the relative MTF{sub 50%} value decreased from 98.4% at 16 mGy to 90.7% at 4 mGy for I40-3 and from 97.6% to 85.6% for I40-5. Conclusions: A simple technique using ensemble averaging from repeated CT scans can be used to measure the spatial resolution of IR techniques in CT at very low contrast levels. The evaluated IR method degraded the spatial resolution at low contrast and high noise levels.

  4. Downscaling of coarse resolution LAI products to achieve both high spatial and temporal resolution for regions of interest

    KAUST Repository

    Houborg, Rasmus; McCabe, Matthew; Gao, Feng

    2015-01-01

    This paper presents a flexible tool for spatio-temporal enhancement of coarse resolution leaf area index (LAI) products, which is readily adaptable to different land cover types, landscape heterogeneities and cloud cover conditions. The framework integrates a rule-based regression tree approach for estimating Landsat-scale LAI from existing 1 km resolution LAI products, and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to intelligently interpolate the downscaled LAI between Landsat acquisitions. Comparisons against in-situ records of LAI measured over corn and soybean highlights its utility for resolving sub-field LAI dynamics occurring over a range of plant development stages.

  5. Downscaling of coarse resolution LAI products to achieve both high spatial and temporal resolution for regions of interest

    KAUST Repository

    Houborg, Rasmus

    2015-11-12

    This paper presents a flexible tool for spatio-temporal enhancement of coarse resolution leaf area index (LAI) products, which is readily adaptable to different land cover types, landscape heterogeneities and cloud cover conditions. The framework integrates a rule-based regression tree approach for estimating Landsat-scale LAI from existing 1 km resolution LAI products, and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to intelligently interpolate the downscaled LAI between Landsat acquisitions. Comparisons against in-situ records of LAI measured over corn and soybean highlights its utility for resolving sub-field LAI dynamics occurring over a range of plant development stages.

  6. The role of remote sensing and GIS for spatial prediction of vector-borne diseases transmission: a systematic review.

    Science.gov (United States)

    Palaniyandi, M

    2012-12-01

    There have been several attempts made to the appreciation of remote sensing and GIS for the study of vectors, biodiversity, vector presence, vector abundance and the vector-borne diseases with respect to space and time. This study was made for reviewing and appraising the potential use of remote sensing and GIS applications for spatial prediction of vector-borne diseases transmission. The nature of the presence and the abundance of vectors and vector-borne diseases, disease infection and the disease transmission are not ubiquitous and are confined with geographical, environmental and climatic factors, and are localized. The presence of vectors and vector-borne diseases is most complex in nature, however, it is confined and fueled by the geographical, climatic and environmental factors including man-made factors. The usefulness of the present day availability of the information derived from the satellite data including vegetation indices of canopy cover and its density, soil types, soil moisture, soil texture, soil depth, etc. is integrating the information in the expert GIS engine for the spatial analysis of other geoclimatic and geoenvironmental variables. The present study gives the detailed information on the classical studies of the past and present, and the future role of remote sensing and GIS for the vector-borne diseases control. The ecological modeling directly gives us the relevant information to understand the spatial variation of the vector biodiversity, vector presence, vector abundance and the vector-borne diseases in association with geoclimatic and the environmental variables. The probability map of the geographical distribution and seasonal variations of horizontal and vertical distribution of vector abundance and its association with vector -borne diseases can be obtained with low cost remote sensing and GIS tool with reliable data and speed.

  7. Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

    Science.gov (United States)

    Diao, Chunyuan

    In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of

  8. Reprocessing the Historical Satellite Passive Microwave Record at Enhanced Spatial Resolutions using Image Reconstruction

    Science.gov (United States)

    Hardman, M.; Brodzik, M. J.; Long, D. G.; Paget, A. C.; Armstrong, R. L.

    2015-12-01

    Beginning in 1978, the satellite passive microwave data record has been a mainstay of remote sensing of the cryosphere, providing twice-daily, near-global spatial coverage for monitoring changes in hydrologic and cryospheric parameters that include precipitation, soil moisture, surface water, vegetation, snow water equivalent, sea ice concentration and sea ice motion. Currently available global gridded passive microwave data sets serve a diverse community of hundreds of data users, but do not meet many requirements of modern Earth System Data Records (ESDRs) or Climate Data Records (CDRs), most notably in the areas of intersensor calibration, quality-control, provenance and consistent processing methods. The original gridding techniques were relatively primitive and were produced on 25 km grids using the original EASE-Grid definition that is not easily accommodated in modern software packages. Further, since the first Level 3 data sets were produced, the Level 2 passive microwave data on which they were based have been reprocessed as Fundamental CDRs (FCDRs) with improved calibration and documentation. We are funded by NASA MEaSUREs to reprocess the historical gridded data sets as EASE-Grid 2.0 ESDRs, using the most mature available Level 2 satellite passive microwave (SMMR, SSM/I-SSMIS, AMSR-E) records from 1978 to the present. We have produced prototype data from SSM/I and AMSR-E for the year 2003, for review and feedback from our Early Adopter user community. The prototype data set includes conventional, low-resolution ("drop-in-the-bucket" 25 km) grids and enhanced-resolution grids derived from the two candidate image reconstruction techniques we are evaluating: 1) Backus-Gilbert (BG) interpolation and 2) a radiometer version of Scatterometer Image Reconstruction (SIR). We summarize our temporal subsetting technique, algorithm tuning parameters and computational costs, and include sample SSM/I images at enhanced resolutions of up to 3 km. We are actively

  9. ESTAR: The Electronically Scanned Thinned Array Radiometer for remote sensing measurement of soil moisture and ocean salinity

    Science.gov (United States)

    Swift, C. T.

    1993-01-01

    The product of a working group assembled to help define the science objectives and measurement requirements of a spaceborne L-band microwave radiometer devoted to remote sensing of surface soil moisture and sea surface salinity is presented. Remote sensing in this long-wavelength portion of the microwave spectrum requires large antennas in low-Earth orbit to achieve acceptable spatial resolution. The proposed radiometer, ESTAR, is unique in that it employs aperture synthesis to reduce the antenna area requirements for a space system.

  10. Satellite remote sensing of landscape freeze/thaw state dynamics for complex Topography and Fire Disturbance Areas Using multi-sensor radar and SRTM digital elevation models

    Science.gov (United States)

    Podest, Erika; McDonald, Kyle; Kimball, John; Randerson, James

    2003-01-01

    We characterize differences in radar-derived freeze/thaw state, examining transitions over complex terrain and landscape disturbance regimes. In areas of complex terrain, we explore freezekhaw dynamics related to elevation, slope aspect and varying landcover. In the burned regions, we explore the timing of seasonal freeze/thaw transition as related to the recovering landscape, relative to that of a nearby control site. We apply in situ biophysical measurements, including flux tower measurements to validate and interpret the remotely sensed parameters. A multi-scale analysis is performed relating high-resolution SAR backscatter and moderate resolution scatterometer measurements to assess trade-offs in spatial and temporal resolution in the remotely sensed fields.

  11. Synopsis Session-I 'Chemical information under high spatial resolution'

    International Nuclear Information System (INIS)

    2013-01-01

    High spatial resolution in this research field is a prerequisite for a better understanding of governing processes, as individual clay particles are very small in dimension (clay size fraction typically defined as < 2μm). The phenomena that have been looked at under this resolution are narrow alteration zones either due to relatively short term laboratory experiments compared to the repository evolution time scale and/or due to the low reaction rates observed for clay minerals at ambient temperature. Another challenge in the field of chemical information to be extracted from compacted clay systems is that mostly the research is focused on the potential contaminants released from the repository near-field, which will be in the trace element concentration and analytical systems have to be tuned to increase the sensitivity under this high spatial resolution. Therefore, chemical information in form of element maps or correlation maps were shown on the initial clay material composition and its heterogeneities (e.g. phase assemblages), the water composition, sorption and migration effects of trace elements, reaction products of geochemical perturbation/alteration and the surface speciation/binding environment of the radionuclides or their chemical homologues. Overall, six invited presentation were given in this session plus additional poster presentations. Beside this, a number of presentations in the other sessions showed a great overlap presenting also chemical data under high spatial resolution. All these oral contributions have shown the progress in this field focusing on the current resolution limits set by the physics and instrumentation available (C. Jacobsen, APS), the micro-focusing instrumentation available at the Karlsruhe Institute of Technology (KIT) synchrotron light source ANKA (J. Goettlicher, KIT-ISS), the application of nano-SIMS to retrieve elemental/isotope maps on complex organo-mineral structures (C. Hoeschen, TU Muenchen), the application of a

  12. Analysis of flood inundation in ungauged basins based on multi-source remote sensing data.

    Science.gov (United States)

    Gao, Wei; Shen, Qiu; Zhou, Yuehua; Li, Xin

    2018-02-09

    Floods are among the most expensive natural hazards experienced in many places of the world and can result in heavy losses of life and economic damages. The objective of this study is to analyze flood inundation in ungauged basins by performing near-real-time detection with flood extent and depth based on multi-source remote sensing data. Via spatial distribution analysis of flood extent and depth in a time series, the inundation condition and the characteristics of flood disaster can be reflected. The results show that the multi-source remote sensing data can make up the lack of hydrological data in ungauged basins, which is helpful to reconstruct hydrological sequence; the combination of MODIS (moderate-resolution imaging spectroradiometer) surface reflectance productions and the DFO (Dartmouth Flood Observatory) flood database can achieve the macro-dynamic monitoring of the flood inundation in ungauged basins, and then the differential technique of high-resolution optical and microwave images before and after floods can be used to calculate flood extent to reflect spatial changes of inundation; the monitoring algorithm for the flood depth combining RS and GIS is simple and easy and can quickly calculate the depth with a known flood extent that is obtained from remote sensing images in ungauged basins. Relevant results can provide effective help for the disaster relief work performed by government departments.

  13. The measurement of the presampled MTF of a high spatial resolution neutron imaging system

    International Nuclear Information System (INIS)

    Cao, Raymond Lei; Biegalski, Steven R.

    2007-01-01

    A high spatial resolution neutron imaging device was developed at the Mark II TRIGA reactor at University of Texas at Austin. As the modulation transfer function (MTF) is recognized as a well-established parameter for evaluation of imaging system resolution, the aliasing associated with digital sampling adds complexity to its measurement. Aliasing is especially problematic when using a high spatial resolution micro-channel plate (MCP) neutron detector that has a pixel grid size similar to that of a CCD array. To compensate for the aliasing an angulated edge method was used to evaluate the neutron imaging facility, overcoming aliasing by obtaining an oversampled edge spread function (ESF). Baseline correction was applied to the ESF to remove the noticeable trends and the LSF was multiplied by Hann window to obtain a smoothed version of presampled MTF. The computing procedure is confirmed by visual inspection of a testing phantom; in addition, it is confirmed by comparison to the MTF measurement of a scintillation screen with a known MTF curve

  14. High resolution radar satellite imagery analysis for safeguards applications

    Energy Technology Data Exchange (ETDEWEB)

    Minet, Christian; Eineder, Michael [German Aerospace Center, Remote Sensing Technology Institute, Department of SAR Signal Processing, Wessling, (Germany); Rezniczek, Arnold [UBA GmbH, Herzogenrath, (Germany); Niemeyer, Irmgard [Forschungszentrum Juelich, Institue of Energy and Climate Research, IEK-6: Nuclear Waste Management and Reactor Safety, Juelich, (Germany)

    2011-12-15

    For monitoring nuclear sites, the use of Synthetic Aperture Radar (SAR) imagery shows essential promises. Unlike optical remote sensing instruments, radar sensors operate under almost all weather conditions and independently of the sunlight, i.e. time of the day. Such technical specifications are required both for continuous and for ad-hoc, timed surveillance tasks. With Cosmo-Skymed, TerraSARX and Radarsat-2, high-resolution SAR imagery with a spatial resolution up to 1m has recently become available. Our work therefore aims to investigate the potential of high-resolution TerraSAR data for nuclear monitoring. This paper focuses on exploiting amplitude of a single acquisition, assessing amplitude changes and phase differences between two acquisitions, and PS-InSAR processing of an image stack.

  15. A Multi-Resolution Spatial Model for Large Datasets Based on the Skew-t Distribution

    KAUST Repository

    Tagle, Felipe

    2017-12-06

    Large, non-Gaussian spatial datasets pose a considerable modeling challenge as the dependence structure implied by the model needs to be captured at different scales, while retaining feasible inference. Skew-normal and skew-t distributions have only recently begun to appear in the spatial statistics literature, without much consideration, however, for the ability to capture dependence at multiple resolutions, and simultaneously achieve feasible inference for increasingly large data sets. This article presents the first multi-resolution spatial model inspired by the skew-t distribution, where a large-scale effect follows a multivariate normal distribution and the fine-scale effects follow a multivariate skew-normal distributions. The resulting marginal distribution for each region is skew-t, thereby allowing for greater flexibility in capturing skewness and heavy tails characterizing many environmental datasets. Likelihood-based inference is performed using a Monte Carlo EM algorithm. The model is applied as a stochastic generator of daily wind speeds over Saudi Arabia.

  16. Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

    Science.gov (United States)

    Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

    2018-02-01

    The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

  17. High-resolution spatial databases of monthly climate variables (1961-2010) over a complex terrain region in southwestern China

    Science.gov (United States)

    Wu, Wei; Xu, An-Ding; Liu, Hong-Bin

    2015-01-01

    Climate data in gridded format are critical for understanding climate change and its impact on eco-environment. The aim of the current study is to develop spatial databases for three climate variables (maximum, minimum temperatures, and relative humidity) over a large region with complex topography in southwestern China. Five widely used approaches including inverse distance weighting, ordinary kriging, universal kriging, co-kriging, and thin-plate smoothing spline were tested. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) showed that thin-plate smoothing spline with latitude, longitude, and elevation outperformed other models. Average RMSE, MAE, and MAPE of the best models were 1.16 °C, 0.74 °C, and 7.38 % for maximum temperature; 0.826 °C, 0.58 °C, and 6.41 % for minimum temperature; and 3.44, 2.28, and 3.21 % for relative humidity, respectively. Spatial datasets of annual and monthly climate variables with 1-km resolution covering the period 1961-2010 were then obtained using the best performance methods. Comparative study showed that the current outcomes were in well agreement with public datasets. Based on the gridded datasets, changes in temperature variables were investigated across the study area. Future study might be needed to capture the uncertainty induced by environmental conditions through remote sensing and knowledge-based methods.

  18. Development and applications of coherent imaging with improved temporal and spatial resolution

    International Nuclear Information System (INIS)

    Mokso, Rajmund

    2006-01-01

    This work has 2 purposes: the improvement of both temporal and spatial resolution of X-ray tomography. The first part is devoted to the technical aspects of the tomographic technique, particularly at the ESRF (European Synchrotron Radiation Facility) beamline ID19, and the application of the new acquisition scheme to the imaging of liquid foams. We have improved the temporal resolution and field of view of the setup, which allowed to obtain for the first time experimental data with good statistics on three dimensional liquid foams. In the second part of the thesis we have described the Kirkpatrick-Baez focusing system and its first applications. In terms of stability and image quality the developments presented in this part of the thesis provide valuable evidence for the feasibility of phase contrast tomography in magnifying geometry. Since the ultimate goal of this research is to improve the spatial resolution in tomography for applications, four different contributions are important for the characterization of the imaging system: 1) the thermal stability and mechanical imperfections, 2) effects of distortion induced by mirror imperfections, 3) effects of refraction on sample borders, and 4) phase propagation effects with the influence of the magnification. Each of these factors has been studied

  19. Study of spatial resolution of YAG:Ce cathodoluminescent imaging screens

    Czech Academy of Sciences Publication Activity Database

    Schauer, Petr; Bok, Jan

    2013-01-01

    Roč. 308, 1 August (2013), s. 68-73 ISSN 0168-583X R&D Projects: GA TA ČR TE01020118; GA ČR GAP102/10/1410; GA MŠk EE.2.3.20.0103 Institutional support: RVO:68081731 Keywords : Spatial resolution * Imaging screen * Electron microscope * Cathodoluminescence * YAG:Ce single crystal * Line spread function * Modulation transfer function Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 1.186, year: 2013

  20. Initial results of the spatial distribution of rubber trees in Peninsular Malaysia using remotely sensed data for biomass estimate

    International Nuclear Information System (INIS)

    Shidiq, I P A; Ismail, M H; Kamarudin, N

    2014-01-01

    The preservation and sustainable management of forest and other land cover ecosystems such as rubber trees will help addressing two major recent issues: climate change and bio-resource energy. The rubber trees are dominantly distributed in the Negeri Sembilan and Kedah on the west coast side of Peninsular Malaysia. This study is aimed to analyse the spatial distribution and biomass of rubber trees in Peninsular Malaysia with special emphasis in Negeri Sembilan State. Geospatial data from remote sensors are used to tackle the time and labour consuming problem due to the large spatial coverage and the need of continuous temporal data. Remote sensing imagery used in this study is a Landsat 5 TM. The image from optical sensor was used to sense the rubber trees and further classified rubber tree by different age