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

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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.

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

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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.

Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval

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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.

Object-based Morphological Building Index for Building Extraction from High Resolution Remote Sensing Imagery

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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.

Object-Based Change Detection Using High-Resolution Remotely Sensed Data and GIS

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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.

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

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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.

Estimation of the distribution of Tabebuia guayacan (Bignoniaceae) using high-resolution remote sensing imagery.

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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.

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

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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.

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

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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.

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 ...

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

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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.

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

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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.

Coastal High-resolution Observations and Remote Sensing of Ecosystems (C-HORSE)

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Guild, Liane

2016-01-01

Coastal benthic marine ecosystems, such as coral reefs, seagrass beds, and kelp forests are highly productive as well as ecologically and commercially important resources. These systems are vulnerable to degraded water quality due to coastal development, terrestrial run-off, and harmful algal blooms. Measurements of these features are important for understanding linkages with land-based sources of pollution and impacts to coastal ecosystems. Challenges for accurate remote sensing of coastal benthic (shallow water) ecosystems and water quality are complicated by atmospheric scattering/absorption (approximately 80+% of the signal), sun glint from the sea surface, and water column scattering (e.g., turbidity). Further, sensor challenges related to signal to noise (SNR) over optically dark targets as well as insufficient radiometric calibration thwart the value of coastal remotely-sensed data. Atmospheric correction of satellite and airborne remotely-sensed radiance data is crucial for deriving accurate water-leaving radiance in coastal waters. C-HORSE seeks to optimize coastal remote sensing measurements by using a novel airborne instrument suite that will bridge calibration, validation, and research capabilities of bio-optical measurements from the sea to the high altitude remote sensing platform. The primary goal of C-HORSE is to facilitate enhanced optical observations of coastal ecosystems using state of the art portable microradiometers with 19 targeted spectral channels and flight planning to optimize measurements further supporting current and future remote sensing missions.

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

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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

Research of building information extraction and evaluation based on high-resolution remote-sensing imagery

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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

Remotely Sensed Data for High Resolution Agro-Environmental Policy Analysis

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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.

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

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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

Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

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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.

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

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

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

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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.

Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

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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.

High time resolution boundary layer description using combined remote sensing instruments

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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

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

Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning

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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.

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

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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.

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.

Atmospheric-water absorption features near 2.2 micrometers and their importance in high spectral resolution remote sensing

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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.

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

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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.

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.

High resolution remote sensing of water surface patterns

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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

[Changes of wetland landscape pattern in Dayang River Estuary based on high-resolution remote sensing image].

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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.

S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES

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R. Zhang

2016-06-01

Full Text Available Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs, called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.

High Data Rate Satellite Communications for Environmental Remote Sensing

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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.

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

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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

MODELING AND SIMULATION OF HIGH RESOLUTION OPTICAL REMOTE SENSING SATELLITE GEOMETRIC CHAIN

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Z. Xia

2018-04-01

Full Text Available The high resolution satellite with the longer focal length and the larger aperture has been widely used in georeferencing of the observed scene in recent years. The consistent end to end model of high resolution remote sensing satellite geometric chain is presented, which consists of the scene, the three line array camera, the platform including attitude and position information, the time system and the processing algorithm. The integrated design of the camera and the star tracker is considered and the simulation method of the geolocation accuracy is put forward by introduce the new index of the angle between the camera and the star tracker. The model is validated by the geolocation accuracy simulation according to the test method of the ZY-3 satellite imagery rigorously. The simulation results show that the geolocation accuracy is within 25m, which is highly consistent with the test results. The geolocation accuracy can be improved about 7 m by the integrated design. The model combined with the simulation method is applicable to the geolocation accuracy estimate before the satellite launching.

Random Access Memories: A New Paradigm for Target Detection in High Resolution Aerial Remote Sensing Images.

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Zou, Zhengxia; Shi, Zhenwei

2018-03-01

We propose a new paradigm for target detection in high resolution aerial remote sensing images under small target priors. Previous remote sensing target detection methods frame the detection as learning of detection model + inference of class-label and bounding-box coordinates. Instead, we formulate it from a Bayesian view that at inference stage, the detection model is adaptively updated to maximize its posterior that is determined by both training and observation. We call this paradigm "random access memories (RAM)." In this paradigm, "Memories" can be interpreted as any model distribution learned from training data and "random access" means accessing memories and randomly adjusting the model at detection phase to obtain better adaptivity to any unseen distribution of test data. By leveraging some latest detection techniques e.g., deep Convolutional Neural Networks and multi-scale anchors, experimental results on a public remote sensing target detection data set show our method outperforms several other state of the art methods. We also introduce a new data set "LEarning, VIsion and Remote sensing laboratory (LEVIR)", which is one order of magnitude larger than other data sets of this field. LEVIR consists of a large set of Google Earth images, with over 22 k images and 10 k independently labeled targets. RAM gives noticeable upgrade of accuracy (an mean average precision improvement of 1% ~ 4%) of our baseline detectors with acceptable computational overhead.

Cascade Convolutional Neural Network Based on Transfer-Learning for Aircraft Detection on High-Resolution Remote Sensing Images

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Bin Pan

2017-01-01

Full Text Available Aircraft detection from high-resolution remote sensing images is important for civil and military applications. Recently, detection methods based on deep learning have rapidly advanced. However, they require numerous samples to train the detection model and cannot be directly used to efficiently handle large-area remote sensing images. A weakly supervised learning method (WSLM can detect a target with few samples. However, it cannot extract an adequate number of features, and the detection accuracy requires improvement. We propose a cascade convolutional neural network (CCNN framework based on transfer-learning and geometric feature constraints (GFC for aircraft detection. It achieves high accuracy and efficient detection with relatively few samples. A high-accuracy detection model is first obtained using transfer-learning to fine-tune pretrained models with few samples. Then, a GFC region proposal filtering method improves detection efficiency. The CCNN framework completes the aircraft detection for large-area remote sensing images. The framework first-level network is an image classifier, which filters the entire image, excluding most areas with no aircraft. The second-level network is an object detector, which rapidly detects aircraft from the first-level network output. Compared with WSLM, detection accuracy increased by 3.66%, false detection decreased by 64%, and missed detection decreased by 23.1%.

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

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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

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

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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.

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

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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

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

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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...

Super-Resolution Reconstruction of Remote Sensing Images Using Multifractal Analysis

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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.

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

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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.

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

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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.

Remote Sensing and Reflectance Profiling in Entomology.

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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.

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

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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.

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

Water Extraction in High Resolution Remote Sensing Image Based on Hierarchical Spectrum and Shape Features

International Nuclear Information System (INIS)

Li, Bangyu; Zhang, Hui; Xu, Fanjiang

2014-01-01

This paper addresses the problem of water extraction from high resolution remote sensing images (including R, G, B, and NIR channels), which draws considerable attention in recent years. Previous work on water extraction mainly faced two difficulties. 1) It is difficult to obtain accurate position of water boundary because of using low resolution images. 2) Like all other image based object classification problems, the phenomena of ''different objects same image'' or ''different images same object'' affects the water extraction. Shadow of elevated objects (e.g. buildings, bridges, towers and trees) scattered in the remote sensing image is a typical noise objects for water extraction. In many cases, it is difficult to discriminate between water and shadow in a remote sensing image, especially in the urban region. We propose a water extraction method with two hierarchies: the statistical feature of spectral characteristic based on image segmentation and the shape feature based on shadow removing. In the first hierarchy, the Statistical Region Merging (SRM) algorithm is adopted for image segmentation. The SRM includes two key steps: one is sorting adjacent regions according to a pre-ascertained sort function, and the other one is merging adjacent regions based on a pre-ascertained merging predicate. The sort step is done one time during the whole processing without considering changes caused by merging which may cause imprecise results. Therefore, we modify the SRM with dynamic sort processing, which conducts sorting step repetitively when there is large adjacent region changes after doing merging. To achieve robust segmentation, we apply the merging region with six features (four remote sensing image bands, Normalized Difference Water Index (NDWI), and Normalized Saturation-value Difference Index (NSVDI)). All these features contribute to segment image into region of object. NDWI and NSVDI are discriminate between water and

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

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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

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

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Bin Hou

2016-08-01

Full Text Available Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD methods have been developed to solve them by utilizing remote sensing (RS images. The advent of high resolution (HR remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC segmentation. Then, saliency and morphological building index (MBI extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF. Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

Advancing Atmosphere-Ocean Remote Sensing with Spaceborne High Spectral Resolution Lidar

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Hostetler, C. A.; Behrenfeld, M. J.; Chepfer, H.; Hu, Y.; Hair, J. W.; Trepte, C. R.; Winker, D. M.; Ferrare, R. A.; Burton, S. P.; Scarino, A. J.; Powell, K. A.; Michaud, J.

2016-12-01

More than 1600 publications employing observations from the CALIOP lidar on CALIPSO testify to the value of spaceborne lidar for aerosol and cloud remote sensing. Recent publications have shown the value of CALIOP data for retrievals of key ocean carbon cycle stocks. In this presentation we focus on the advantages of a more advanced technique, High Spectral Resolution Lidar (HSRL), for aerosol, cloud, and ocean remote sensing. An atmosphere-ocean optimized HSRL achieves greater accuracy over the standard backscatter lidar technique for retrievals of aerosol and cloud extinction and backscatter profiles, provides additional capability to retrieve aerosol and cloud microphysical parameters, and enables vertically-resolved characterization of scattering and absorption properties of suspended and dissolved materials in the ocean. Numerous publications highlight the synergy of coincident CALIOP and passive A-train observations for studies of aerosol-cloud radiative effects and cloud-climate feedback. Less appreciated is the complementarity that would exist between an optimized spaceborne lidar and passive ocean color. An optimized HSRL flown in formation with the Plankton, Aerosol, and ocean Ecosystem (PACE) mission would provide phytoplankton vertical distribution, which is needed for accurately estimating net primary productivity but absent in the PACE ocean color data. The HSRL would also provide data needed to improve atmospheric correction schemes in ocean color retrievals. Because lidar provides measurements both night and day, through tenuous clouds and aerosol layers, and in holes between clouds, the sampling achieved is highly complementary to passive radiometry, providing data in important high latitude regions where ocean color data are sparse or nonexistent. In this presentation we will discuss 1) relevant aerosol, cloud, and ocean retrievals from airborne HSRL field missions; 2) the advantages of an optimized spaceborne HSRL for aerosol, cloud, and ocean

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

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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.

[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.

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

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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.

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.

A Method of Road Extraction from High-resolution Remote Sensing Images Based on Shape Features

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LEI Xiaoqi

2016-02-01

Full Text Available Road extraction from high-resolution remote sensing image is an important and difficult task.Since remote sensing images include complicated information,the methods that extract roads by spectral,texture and linear features have certain limitations.Also,many methods need human-intervention to get the road seeds(semi-automatic extraction,which have the great human-dependence and low efficiency.The road-extraction method,which uses the image segmentation based on principle of local gray consistency and integration shape features,is proposed in this paper.Firstly,the image is segmented,and then the linear and curve roads are obtained by using several object shape features,so the method that just only extract linear roads are rectified.Secondly,the step of road extraction is carried out based on the region growth,the road seeds are automatic selected and the road network is extracted.Finally,the extracted roads are regulated by combining the edge information.In experiments,the images that including the better gray uniform of road and the worse illuminated of road surface were chosen,and the results prove that the method of this study is promising.

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

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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.

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).

Remote RemoteRemoteRemote sensing potential for sensing ...

African Journals Online (AJOL)

Remote RemoteRemoteRemote sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing potential for sensing p. A Ngie, F Ahmed, K Abutaleb ...

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

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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.

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.

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

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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.

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

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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.

Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels

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Ovidiu Csillik

2017-03-01

Full Text Available Speed and accuracy are important factors when dealing with time-constraint events for disaster, risk, and crisis-management support. Object-based image analysis can be a time consuming task in extracting information from large images because most of the segmentation algorithms use the pixel-grid for the initial object representation. It would be more natural and efficient to work with perceptually meaningful entities that are derived from pixels using a low-level grouping process (superpixels. Firstly, we tested a new workflow for image segmentation of remote sensing data, starting the multiresolution segmentation (MRS, using ESP2 tool from the superpixel level and aiming at reducing the amount of time needed to automatically partition relatively large datasets of very high resolution remote sensing data. Secondly, we examined whether a Random Forest classification based on an oversegmentation produced by a Simple Linear Iterative Clustering (SLIC superpixel algorithm performs similarly with reference to a traditional object-based classification regarding accuracy. Tests were applied on QuickBird and WorldView-2 data with different extents, scene content complexities, and number of bands to assess how the computational time and classification accuracy are affected by these factors. The proposed segmentation approach is compared with the traditional one, starting the MRS from the pixel level, regarding geometric accuracy of the objects and the computational time. The computational time was reduced in all cases, the biggest improvement being from 5 h 35 min to 13 min, for a WorldView-2 scene with eight bands and an extent of 12.2 million pixels, while the geometric accuracy is kept similar or slightly better. SLIC superpixel-based classification had similar or better overall accuracy values when compared to MRS-based classification, but the results were obtained in a fast manner and avoiding the parameterization of the MRS. These two approaches

Use of Openly Available Satellite Images for Remote Sensing Education

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Wang, C.-K.

2011-09-01

With the advent of Google Earth, Google Maps, and Microsoft Bing Maps, high resolution satellite imagery are becoming more easily accessible than ever. It have been the case that the college students may already have wealth experiences with the high resolution satellite imagery by using these software and web services prior to any formal remote sensing education. It is obvious that the remote sensing education should be adjusted to the fact that the audience are already the customers of remote sensing products (through the use of the above mentioned services). This paper reports the use of openly available satellite imagery in an introductory-level remote sensing course in the Department of Geomatics of National Cheng Kung University as a term project. From the experience learned from the fall of 2009 and 2010, it shows that this term project has effectively aroused the students' enthusiastic toward Remote Sensing.

A Residential Area Extraction Method for High Resolution Remote Sensing Imagery by Using Visual Saliency and Perceptual Organization

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CHEN Yixiang

2017-12-01

Full Text Available Inspired by human visual cognitive mechanism,a method of residential area extraction from high-resolution remote sensing images was proposed based on visual saliency and perceptual organization. Firstly,the data field theory of cognitive physics was introduced to model the visual saliency and the candidate residential areas were produced by adaptive thresholding. Then,the exact residential areas were obtained and refined by perceptual organization based on the high-frequency features of multi-scale wavelet transform. Finally,the validity of the proposed method was verified by experiments conducted on ZY-3 and Quickbird image data sets.

NASA Fluid Lensing & MiDAR: Next-Generation Remote Sensing Technologies for Aquatic Remote Sensing

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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

Analysis of smear in high-resolution remote sensing satellites

Science.gov (United States)

Wahballah, Walid A.; Bazan, Taher M.; El-Tohamy, Fawzy; Fathy, Mahmoud

2016-10-01

High-resolution remote sensing satellites (HRRSS) that use time delay and integration (TDI) CCDs have the potential to introduce large amounts of image smear. Clocking and velocity mismatch smear are two of the key factors in inducing image smear. Clocking smear is caused by the discrete manner in which the charge is clocked in the TDI-CCDs. The relative motion between the HRRSS and the observed object obliges that the image motion velocity must be strictly synchronized with the velocity of the charge packet transfer (line rate) throughout the integration time. During imaging an object off-nadir, the image motion velocity changes resulting in asynchronization between the image velocity and the CCD's line rate. A Model for estimating the image motion velocity in HRRSS is derived. The influence of this velocity mismatch combined with clocking smear on the modulation transfer function (MTF) is investigated by using Matlab simulation. The analysis is performed for cross-track and along-track imaging with different satellite attitude angles and TDI steps. The results reveal that the velocity mismatch ratio and the number of TDI steps have a serious impact on the smear MTF; a velocity mismatch ratio of 2% degrades the MTFsmear by 32% at Nyquist frequency when the TDI steps change from 32 to 96. In addition, the results show that to achieve the requirement of MTFsmear >= 0.95 , for TDI steps of 16 and 64, the allowable roll angles are 13.7° and 6.85° and the permissible pitch angles are no more than 9.6° and 4.8°, respectively.

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.

Remote Sensing Image in the Application of Agricultural Tourism Planning

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Guojing Fan

2013-06-01

Full Text Available This paper introduces the processing technology of high resolution remote sensing image, the specific making process of tourism map and different remote sensing data in the key application of tourism planning and so on. Remote sensing extracts agricultural tourism planning information, improving the scientificalness and operability of agricultural tourism planning. Therefore remote sensing image in the application of agricultural tourism planning will be the inevitable trend of tourism development.

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

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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.

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).

Developing status of satellite remote sensing and its application

International Nuclear Information System (INIS)

Zhang Wanliang; Liu Dechang

2005-01-01

This paper has discussed the latest development of satellite remote sensing in sensor resolutions, satellite motion models, load forms, data processing and its application. The authors consider that sensor resolutions of satellite remote sensing have increased largely. Valid integration of multisensors is a new idea and technology of satellite remote sensing in the 21st century, and post-remote sensing application technology is the important part of deeply applying remote sensing information and has great practical significance. (authors)

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.

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.

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

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

Public Good or Commercial Opportunity: Case Studies in Remote Sensing Commercialization

Science.gov (United States)

Johnston, Shaida; Cordes, Joseph

2002-01-01

The U.S. Government is once again attempting to commercialize the Landsat program and is asking the private sector to develop a next generation mid-resolution remote sensing system that will provide continuity with the thirty-year data archive of Landsat data. Much of the case for commercializing the Landsat program rests on the apparently successful commercialization of high-resolution remote sensing activities coupled with the belief that conditions have changed since the failed attempt to commercialize Landsat in the 1980s. This paper analyzes the economic, political and technical conditions that prevailed in the 1980s as well as conditions that might account for the apparent success of the emerging high-resolution remote sensing industry today. Lessons are gleaned for the future of the Landsat program.

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

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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.

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.

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.

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.

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

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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.

Hyperspectral remote sensing for light pollution monitoring

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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.

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

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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

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)

Noninvasive Remote Sensing Techniques for Infrastructures Diagnostics

Directory of Open Access Journals (Sweden)

Angelo Palombo

2011-01-01

Full Text Available The present paper aims at analyzing the potentialities of noninvasive remote sensing techniques used for detecting the conservation status of infrastructures. The applied remote sensing techniques are ground-based microwave radar interferometer and InfraRed Thermography (IRT to study a particular structure planned and made in the framework of the ISTIMES project (funded by the European Commission in the frame of a joint Call “ICT and Security” of the Seventh Framework Programme. To exploit the effectiveness of the high-resolution remote sensing techniques applied we will use the high-frequency thermal camera to measure the structures oscillations by high-frequency analysis and ground-based microwave radar interferometer to measure the dynamic displacement of several points belonging to a large structure. The paper describes the preliminary research results and discusses on the future applicability and techniques developments for integrating high-frequency time series data of the thermal imagery and ground-based microwave radar interferometer data.

Feasibility of microwave interferometry and fourier-transform spectrometry for high-spectral-resolution sensing

Energy Technology Data Exchange (ETDEWEB)

Gerstl, S.; Cooke, B.; Jacobson, A.; Love, S.; Zardecki, A.

1996-09-01

This is the final report of a one-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The primary objective of this project was to perform the necessary research and development to determine the feasibility of new ideas that, if successful, could lead to the development of future new programs in high-spectral resolution remote sensing. In active remote sensing systems, the solar illumination of a scene is replaced by a man-made source, preferably a laser beam. However, when laser beams are propagated through a scattering medium, like air, random optical path fluctuations comparable to the optical wavelength are generated giving rise to the speckle effect, which is the most severe perturbation in active remote sensing systems. The limitations introduced by the speckle effect degrade or negate the data interpretation. We sought to introduce better physical models of beam scattering that allow a more realistic simulation environment to be developed that, when applied to experimental data sets, improve their interpretability and increase the information content. Improved beam propagation models require improved knowledge of the spatio-temporal distribution of the scattering and absorbing medium. In the free atmosphere the largest contributor is water vapor in the lower troposphere. We tested the feasibility of using microwave interferometry to measure water-vapor irregularities in the boundary layer. Knowledge of these distributions enable much improved atmospheric correction algorithms for satellite imagery of the earth`s surface to be developed. For hyperspectral active remote sensing systems it is necessary to perform very high-resolution spectral measurements of the reflected laser light. Such measurements are possible with optical interferometers.

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.

Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery

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Fan Hu

2015-11-01

Full Text Available Learning efficient image representations is at the core of the scene classification task of remote sensing imagery. The existing methods for solving the scene classification task, based on either feature coding approaches with low-level hand-engineered features or unsupervised feature learning, can only generate mid-level image features with limited representative ability, which essentially prevents them from achieving better performance. Recently, the deep convolutional neural networks (CNNs, which are hierarchical architectures trained on large-scale datasets, have shown astounding performance in object recognition and detection. However, it is still not clear how to use these deep convolutional neural networks for high-resolution remote sensing (HRRS scene classification. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for HRRS scene classification. We propose two scenarios for generating image features via extracting CNN features from different layers. In the first scenario, the activation vectors extracted from fully-connected layers are regarded as the final image features; in the second scenario, we extract dense features from the last convolutional layer at multiple scales and then encode the dense features into global image features through commonly used feature coding approaches. Extensive experiments on two public scene classification datasets demonstrate that the image features obtained by the two proposed scenarios, even with a simple linear classifier, can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the low- and mid-level features. Moreover, we tentatively combine features extracted from different CNN models for better performance.

Object-oriented Method of Hierarchical Urban Building Extraction from High-resolution Remote-Sensing Imagery

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TAO Chao

2016-02-01

Full Text Available An automatic urban building extraction method for high-resolution remote-sensing imagery,which combines building segmentation based on neighbor total variations with object-oriented analysis,is presented in this paper. Aimed at different extraction complexity from various buildings in the segmented image,a hierarchical building extraction strategy with multi-feature fusion is adopted. Firstly,we extract some rectangle buildings which remain intact after segmentation through shape analysis. Secondly,in order to ensure each candidate building target to be independent,multidirectional morphological road-filtering algorithm is designed which can separate buildings from the neighboring roads with similar spectrum. Finally,we take the extracted buildings and the excluded non-buildings as samples to establish probability model respectively,and Bayesian discriminating classifier is used for making judgment of the other candidate building objects to get the ultimate extraction result. The experimental results have shown that the approach is able to detect buildings with different structure and spectral features in the same image. The results of performance evaluation also support the robustness and precision of the approach developed.

An efficient cloud detection method for high resolution remote sensing panchromatic imagery

Science.gov (United States)

Li, Chaowei; Lin, Zaiping; Deng, Xinpu

2018-04-01

In order to increase the accuracy of cloud detection for remote sensing satellite imagery, we propose an efficient cloud detection method for remote sensing satellite panchromatic images. This method includes three main steps. First, an adaptive intensity threshold value combined with a median filter is adopted to extract the coarse cloud regions. Second, a guided filtering process is conducted to strengthen the textural features difference and then we conduct the detection process of texture via gray-level co-occurrence matrix based on the acquired texture detail image. Finally, the candidate cloud regions are extracted by the intersection of two coarse cloud regions above and we further adopt an adaptive morphological dilation to refine them for thin clouds in boundaries. The experimental results demonstrate the effectiveness of the proposed method.

Remote earth sensing experiments

Energy Technology Data Exchange (ETDEWEB)

Trifonov, Yu V

1981-01-01

Description of data devices for deriving multi-spectral measuring television measurement data of middle and high resolution through use of second generation Meteor-type satellites. Options for developing a permanent and active remote sensing system in USSR are discussed. It is noted that the present experiment is an important step in that direction. Design and structural data for this particular device and its application in the experiment are covered.

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

ESA remote-sensing programme - Present activities and future plans

Energy Technology Data Exchange (ETDEWEB)

Plevin, J [ESA, Directorate of Planning and Future Programmes, Paris, France; Pryke, I [ESA, Directorate of Applications Programmes, Toulouse, France

1979-02-01

The present activities and future missions of the ESA program of spaceborne remote sensing of earth resources and environment are discussed. Program objectives have been determined to be the satisfaction of European regional needs by agricultural, land use, water resources, coastal and polar surveys, and meeting the requirements of developing nations in the areas of agricultural production, mineral exploration and disaster warning and assessment. The Earthnet system of data processing centers presently is used for the distribution of remote sensing data acquired by NASA satellites. Remote sensing experiments to be flown aboard Spacelab are the Metric Camera, to test high resolution mapping capabilities of a large format camera, and the Microwave Remote-Sensing Experiment, which operates as a two-frequency scatterometer, a synthetic aperture radar and a passive microwave radiometer. Studies carried out on the definition of future remote sensing satellite systems are described, including studies of system concepts for land applications and coastal monitoring satellites.

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

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.

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

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

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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.

Remote sensing for water quality and biological measurements in coastal waters

International Nuclear Information System (INIS)

Johnson, R.W.; Harriss, R.C.

1980-01-01

Recent remote sensing experiments in the United States' coastal waters indicate that certain biological and water quality parameters have distinctive spectral characteristics. Data outputs from remote sensors, to date, include: (1) high resolution measurements to determine concentrations and distributions of total suspended particulates, temperature, salinity, chlorophyll a, and phytoplankton color group associations from airborne and/or satellite platforms, and (2) low resolution measurements of total suspended solids, temperature, ocean color, and possibly chlorophyll from satellite platforms. A summary of platforms, sensors and parameters measured is given. Remote sensing, especially when combined with conventional oceanographic research methods, can be useful in such high priority research areas as estuarine and continental shelf sediment transport dynamics, transport and fate of marine pollutants, marine phytoplankton dynamics, and ocean fronts

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

JEarth | Analytical Remote Sensing Imagery Application for Researchers and Practitioners

Science.gov (United States)

Prashad, L.; Christensen, P. R.; Anwar, S.; Dickenshied, S.; Engle, E.; Noss, D.

2009-12-01

The ASU 100 Cities Project and the ASU Mars Space Flight Facility (MSFF) present JEarth, a set of analytical Geographic Information System (GIS) tools for viewing and processing Earth-based remote sensing imagery and vectors, including high-resolution and hyperspectral imagery such as TIMS and MASTER. JEarth is useful for a wide range of researchers and practitioners who need to access, view, and analyze remote sensing imagery. JEarth stems from existing MSFF applications: the Java application JMars (Java Mission-planning and Analysis for Remote Sensing) for viewing and analyzing remote sensing imagery and THMPROC, a web-based, interactive tool for processing imagery to create band combinations, stretches, and other imagery products. JEarth users can run the application on their desktops by installing Java-based open source software on Windows, Mac, or Linux operating systems.

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.

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

1999 IEEE international geoscience and remote sensing symposium

Energy Technology Data Exchange (ETDEWEB)

NONE

1999-07-01

The theme of IGARSS'99, ``Remote Sensing of the System Earth--A Challenge for the 21st Century,'' shows how earth observation based on satellite remote sensing can significantly contribute to the future study of the environment and the changes it is undergoing, whether from natural causes or human activities. The wide range of topics offers an interdisciplinary approach and suggests integrated techniques and theory in remote sensing are essential for modeling and understanding the environment. Topics covered include: new instrumentation and future systems; high resolution SAR/InSAR; earth system science educational initiative; data fusion; radar sensing of ice sheets; image processing techniques; clouds and ice particles; internal waves; natural hazards and disaster monitoring; advanced passive and active sensors and sensor calibration; radar assessment of rain, oil spills and natural slicks; data standards and distribution; and vegetation monitoring using BRDF approaches.

Remote Sensing

CERN Document Server

Khorram, Siamak; Koch, Frank H; van der Wiele, Cynthia F

2012-01-01

Remote Sensing provides information on how remote sensing relates to the natural resources inventory, management, and monitoring, as well as environmental concerns. It explains the role of this new technology in current global challenges. "Remote Sensing" will discuss remotely sensed data application payloads and platforms, along with the methodologies involving image processing techniques as applied to remotely sensed data. This title provides information on image classification techniques and image registration, data integration, and data fusion techniques. How this technology applies to natural resources and environmental concerns will also be discussed.

First Top-Down Estimates of Anthropogenic NOx Emissions Using High-Resolution Airborne Remote Sensing Observations

Science.gov (United States)

Souri, Amir H.; Choi, Yunsoo; Pan, Shuai; Curci, Gabriele; Nowlan, Caroline R.; Janz, Scott J.; Kowalewski, Matthew G.; Liu, Junjie; Herman, Jay R.; Weinheimer, Andrew J.

2018-03-01

A number of satellite-based instruments have become an essential part of monitoring emissions. Despite sound theoretical inversion techniques, the insufficient samples and the footprint size of current observations have introduced an obstacle to narrow the inversion window for regional models. These key limitations can be partially resolved by a set of modest high-quality measurements from airborne remote sensing. This study illustrates the feasibility of nitrogen dioxide (NO2) columns from the Geostationary Coastal and Air Pollution Events Airborne Simulator (GCAS) to constrain anthropogenic NOx emissions in the Houston-Galveston-Brazoria area. We convert slant column densities to vertical columns using a radiative transfer model with (i) NO2 profiles from a high-resolution regional model (1 × 1 km2) constrained by P-3B aircraft measurements, (ii) the consideration of aerosol optical thickness impacts on radiance at NO2 absorption line, and (iii) high-resolution surface albedo constrained by ground-based spectrometers. We characterize errors in the GCAS NO2 columns by comparing them to Pandora measurements and find a striking correlation (r > 0.74) with an uncertainty of 3.5 × 1015 molecules cm-2. On 9 of 10 total days, the constrained anthropogenic emissions by a Kalman filter yield an overall 2-50% reduction in polluted areas, partly counterbalancing the well-documented positive bias of the model. The inversion, however, boosts emissions by 94% in the same areas on a day when an unprecedented local emissions event potentially occurred, significantly mitigating the bias of the model. The capability of GCAS at detecting such an event ensures the significance of forthcoming geostationary satellites for timely estimates of top-down emissions.

Self-Calibrating High Resolution Tunable Filter for Remote Gas Sensing Applications, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — We propose to develop a compact, robust, optically-based sensor for local and remote sensing of oxygen (O2) at 1.26 µm, carbon dioxide (CO2) at 1.56 µm and other...

High Resolution Spectra of Carbon Monoxide, Propane and Ammonia for Atmospheric Remote Sensing

Science.gov (United States)

Beale, Christopher Andrew

Spectroscopy is a critical tool for analyzing atmospheric data. Identification of atmospheric parameters such as temperature, pressure and the existence and concentrations of constituent gases via remote sensing techniques are only possible with spectroscopic data. These form the basis of model atmospheres which may be compared to observations to determine such parameters. To this end, this dissertation explores the spectroscopy of three molecules: ammonia, propane and carbon monoxide. Infrared spectra have been recorded for ammonia in the region 2400-9000 cm-1. These spectra were recorded at elevated temperatures (from 293-973 K) using a Fourier Transform Spectrometer (FTS). Comparison between the spectra recorded at different temperatures yielded experimental lower state energies. These spectra resulted in the measurement of roughly 30000 lines and about 3000 quantum assignments. In addition spectra of propane were recorded at elevated temperatures (296-700 K) using an FTS. Atmospheres with high temperatures require molecular data at appropriate conditions. This dissertation describes collection of such data and the potential application to atmospheres in our solar system, such as auroral regions in Jupiter, to those of planets orbiting around other stars and cool sub-stellar objects known as brown dwarfs. The spectra of propane and ammonia provide the highest resolution and most complete experimental study of these gases in their respective spectral regions at elevated temperatures. Detection of ammonia in an exoplanet or detection of propane in the atmosphere of Jupiter will most likely rely on the work presented here. The best laboratory that we have to study atmospheres is our own planet. The same techniques that are applied to these alien atmospheres originated on Earth. As such it is appropriate to discuss remote sensing of our own atmosphere. This idea is explored through analysis of spectroscopic data recorded by an FTS on the Atmospheric Chemistry

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

Fast and accurate denoising method applied to very high resolution optical remote sensing images

Science.gov (United States)

Masse, Antoine; Lefèvre, Sébastien; Binet, Renaud; Artigues, Stéphanie; Lassalle, Pierre; Blanchet, Gwendoline; Baillarin, Simon

2017-10-01

Restoration of Very High Resolution (VHR) optical Remote Sensing Image (RSI) is critical and leads to the problem of removing instrumental noise while keeping integrity of relevant information. Improving denoising in an image processing chain implies increasing image quality and improving performance of all following tasks operated by experts (photo-interpretation, cartography, etc.) or by algorithms (land cover mapping, change detection, 3D reconstruction, etc.). In a context of large industrial VHR image production, the selected denoising method should optimized accuracy and robustness with relevant information and saliency conservation, and rapidity due to the huge amount of data acquired and/or archived. Very recent research in image processing leads to a fast and accurate algorithm called Non Local Bayes (NLB) that we propose to adapt and optimize for VHR RSIs. This method is well suited for mass production thanks to its best trade-off between accuracy and computational complexity compared to other state-of-the-art methods. NLB is based on a simple principle: similar structures in an image have similar noise distribution and thus can be denoised with the same noise estimation. In this paper, we describe in details algorithm operations and performances, and analyze parameter sensibilities on various typical real areas observed in VHR RSIs.

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.

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.

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.

REMOTE SENSING FOR ENVIRONMENTAL COMPLIANCE MONITORING

Science.gov (United States)

I. Remote Sensing Basics A. The electromagnetic spectrum demonstrates what we can see both in the visible and beyond the visible part of the spectrum through the use of various types of sensors. B. Resolution refers to what a remote sensor can see and how often. 1. Sp...

Irrigated Area Maps and Statistics of India Using Remote Sensing and National Statistics

Directory of Open Access Journals (Sweden)

Prasad S. Thenkabail

2009-04-01

Full Text Available The goal of this research was to compare the remote-sensing derived irrigated areas with census-derived statistics reported in the national system. India, which has nearly 30% of global annualized irrigated areas (AIAs, and is the leading irrigated area country in the World, along with China, was chosen for the study. Irrigated areas were derived for nominal year 2000 using time-series remote sensing at two spatial resolutions: (a 10-km Advanced Very High Resolution Radiometer (AVHRR and (b 500-m Moderate Resolution Imaging Spectroradiometer (MODIS. These areas were compared with the Indian National Statistical Data on irrigated areas reported by the: (a Directorate of Economics and Statistics (DES of the Ministry of Agriculture (MOA, and (b Ministry of Water Resources (MoWR. A state-by-state comparison of remote sensing derived irrigated areas when compared with MoWR derived irrigation potential utilized (IPU, an equivalent of AIA, provided a high degree of correlation with R2 values of: (a 0.79 with 10-km, and (b 0.85 with MODIS 500-m. However, the remote sensing derived irrigated area estimates for India were consistently higher than the irrigated areas reported by the national statistics. The remote sensing derived total area available for irrigation (TAAI, which does not consider intensity of irrigation, was 101 million hectares (Mha using 10-km and 113 Mha using 500-m. The AIAs, which considers intensity of irrigation, was 132 Mha using 10-km and 146 Mha using 500-m. In contrast the IPU, an equivalent of AIAs, as reported by MoWR was 83 Mha. There are “large variations” in irrigated area statistics reported, even between two ministries (e.g., Directorate of Statistics of Ministry of Agriculture and Ministry of Water Resources of the same national system. The causes include: (a reluctance on part of the states to furnish irrigated area data in view of their vested interests in sharing of water, and (b reporting of large volumes of data

Application of Multi-Source Remote Sensing Image in Yunnan Province Grassland Resources Investigation

Science.gov (United States)

Li, J.; Wen, G.; Li, D.

2018-04-01

Trough mastering background information of Yunnan province grassland resources utilization and ecological conditions to improves grassland elaborating management capacity, it carried out grassland resource investigation work by Yunnan province agriculture department in 2017. The traditional grassland resource investigation method is ground based investigation, which is time-consuming and inefficient, especially not suitable for large scale and hard-to-reach areas. While remote sensing is low cost, wide range and efficient, which can reflect grassland resources present situation objectively. It has become indispensable grassland monitoring technology and data sources and it has got more and more recognition and application in grassland resources monitoring research. This paper researches application of multi-source remote sensing image in Yunnan province grassland resources investigation. First of all, it extracts grassland resources thematic information and conducts field investigation through BJ-2 high space resolution image segmentation. Secondly, it classifies grassland types and evaluates grassland degradation degree through high resolution characteristics of Landsat 8 image. Thirdly, it obtained grass yield model and quality classification through high resolution and wide scanning width characteristics of MODIS images and sample investigate data. Finally, it performs grassland field qualitative analysis through UAV remote sensing image. According to project area implementation, it proves that multi-source remote sensing data can be applied to the grassland resources investigation in Yunnan province and it is indispensable method.

Remote sensing from UAVs for hydrological monitoring

DEFF Research Database (Denmark)

Bandini, Filippo; Garcia, Monica; Bauer-Gottwein, Peter

compared to other technologies: compared to field based techniques, remote sensing with UAVs is a non-destructive technique, less time consuming, ensures a reduced time between acquisition and interpretation of data and gives the possibility to access remote and unsafe areas. Compared to full...... will be able to record the spectral signatures of water and land surfaces with a pixel resolution of around 15 cm, whereas the thermal camera will sense water and land surface temperature with a resolution of 40 cm. Post-processing of data from the thermal camera will allow retrieving vegetation and soil...

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

Remote sensing of water and nitrogen stress in broccoli

Science.gov (United States)

Elsheikha, Diael-Deen Mohamed

Remote sensing is being used in agriculture for crop management. Ground based remote sensing data acquisition system was used for collection of high spatial and temporal resolution data for irrigated broccoli crop. The system was composed of a small cart that ran back and forth on a rail system that was mounted on a linear move irrigation system. The cart was equipped with a sensor that had 4 discrete wavelengths; 550 nm, 660 nm, 720 nm, and 810 nm, and an infrared thermometer, all had 10 nm bandwidth. A global positioning system was used to indicate the cart position. The study consisted of two parts; the first was to evaluate remotely sensed reflectance and indices in broccoli during the growing season, and determine whether remotely sensed indices or standard deviation of indices can distinguish between nitrogen and water stress in broccoli, and the second part of the study was to evaluate remotely sensed indices and standard deviation of remotely sensed indices in broccoli during daily changes in solar zenith angle. Results indicated that nitrogen was detected using Ratio Vegetation index, RVI, Normalized Difference Vegetation Index, NDVI, Canopy Chlorophyll Concentration Index, CCCI, and also using the reflectance in the Near-Infrared, NIR, bands. The Red reflectance band capability of showing stress was not as clear as the previous indices and bands reflectance. The Canopy Chlorophyll Concentration Index, CCCI, was the most successful index. The Crop Water Stress Index was able to detect water stress but it was highly affected by the solar zenith angle change along the day.

Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data

Science.gov (United States)

Gumma, M.K.; Thenkabail, P.S.; Hideto, F.; Nelson, A.; Dheeravath, V.; Busia, D.; Rala, A.

2011-01-01

Maps of irrigated areas are essential for Ghana's agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20-57% higher than irrigated areas reported by Ghana's Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics

China national space remote sensing infrastructure and its application

Science.gov (United States)

Li, Ming

2016-07-01

Space Infrastructure is a space system that provides communication, navigation and remote sensing service for broad users. China National Space Remote Sensing Infrastructure includes remote sensing satellites, ground system and related systems. According to the principle of multiple-function on one satellite, multiple satellites in one constellation and collaboration between constellations, series of land observation, ocean observation and atmosphere observation satellites have been suggested to have high, middle and low resolution and fly on different orbits and with different means of payloads to achieve a high ability for global synthetically observation. With such an infrastructure, we can carry out the research on climate change, geophysics global surveying and mapping, water resources management, safety and emergency management, and so on. I This paper gives a detailed introduction about the planning of this infrastructure and its application in different area, especially the international cooperation potential in the so called One Belt and One Road space information corridor.

Modeling residential lawn fertilization practices: integrating high resolution remote sensing with socioeconomic data

Science.gov (United States)

Weiqi Zhou; Austin Troy; Morgan. Grove

2008-01-01

This article investigates how remotely sensed lawn characteristics, such as parcel lawn area and parcel lawn greenness, combined with household characteristics, can be used to predict household lawn fertilization practices on private residential lands. This study involves two watersheds, Glyndon and Baisman's Run, in Baltimore County, Maryland, USA. Parcel lawn...

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.

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.

A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images.

Science.gov (United States)

Bi, Fukun; Chen, Jing; Zhuang, Yin; Bian, Mingming; Zhang, Qingjun

2017-06-22

With the rapid development of optical remote sensing satellites, ship detection and identification based on large-scale remote sensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance and maritime management. However, because the harbor environment is complex, gray information and texture features between docked ships and their connected dock regions are indistinguishable, most of the popular detection methods are limited by their calculation efficiency and detection accuracy. In this paper, a novel hierarchical method that combines an efficient candidate scanning strategy and an accurate candidate identification mixture model is presented for inshore ship detection in complex harbor areas. First, in the candidate region extraction phase, an omnidirectional intersected two-dimension scanning (OITDS) strategy is designed to rapidly extract candidate regions from the land-water segmented images. In the candidate region identification phase, a decision mixture model (DMM) is proposed to identify real ships from candidate objects. Specifically, to improve the robustness regarding the diversity of ships, a deformable part model (DPM) was employed to train a key part sub-model and a whole ship sub-model. Furthermore, to improve the identification accuracy, a surrounding correlation context sub-model is built. Finally, to increase the accuracy of candidate region identification, these three sub-models are integrated into the proposed DMM. Experiments were performed on numerous large-scale harbor remote sensing images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency.

Remotely Sensed, catchment scale, estimations of flow resistance

Science.gov (United States)

Carbonneau, P.; Dugdale, S. J.

2009-12-01

Despite a decade of progress in the field of fluvial remote sensing, there are few published works using this new technology to advance and explore fundamental ideas and theories in fluvial geomorphology. This paper will apply remote sensing methods in order to re-visit a classic concept in fluvial geomorphology: flow resistance. Classic flow resistance equations such as those of Strickler and Keulegan typically use channel slope, channel depth or hydraulic radius and some measure channel roughness usually equated to the 50th or 84th percentile of the bed material size distribution. In this classic literature, empirical equations such as power laws are usually calibrated and validated with a maximum of a few hundred data points. In contrast, fluvial remote sensing methods are now capable of delivering millions of high resolution data points in continuous, catchment scale, surveys. On the river Tromie in Scotland, a full dataset or river characteristics is now available. Based on low altitude imagery and NextMap topographic data, this dataset has a continuous sampling of channel width at a resolution of 3cm, of depth and median grain size at a resolution of 1m, and of slope at a resolution of 5m. This entire data set is systematic and continuous for the entire 20km length of the river. When combined with discharge at the time of data acquisition, this new dataset offers the opportunity to re-examine flow resistance equations with a 2-4 orders of magnitude increase in calibration data. This paper will therefore re-examine the classic approaches of Strickler and Keulagan along with other more recent flow resistance equations. Ultimately, accurate predictions of flow resistance from remotely sensed parameters could lead to acceptable predictions of velocity. Such a usage of classic equations to predict velocity could allow lotic habitat models to account for microhabitat velocity at catchment scales without the recourse to advanced and computationally intensive

Optical Remote Sensing Potentials for Looting Detection

Directory of Open Access Journals (Sweden)

Athos Agapiou

2017-10-01

Full Text Available Looting of archaeological sites is illegal and considered a major anthropogenic threat for cultural heritage, entailing undesirable and irreversible damage at several levels, such as landscape disturbance, heritage destruction, and adverse social impact. In recent years, the employment of remote sensing technologies using ground-based and/or space-based sensors has assisted in dealing with this issue. Novel remote sensing techniques have tackled heritage destruction occurring in war-conflicted areas, as well as illicit archeological activity in vast areas of archaeological interest with limited surveillance. The damage performed by illegal activities, as well as the scarcity of reliable information are some of the major concerns that local stakeholders are facing today. This study discusses the potential use of remote sensing technologies based on the results obtained for the archaeological landscape of Ayios Mnason in Politiko village, located in Nicosia district, Cyprus. In this area, more than ten looted tombs have been recorded in the last decade, indicating small-scale, but still systematic, looting. The image analysis, including vegetation indices, fusion, automatic extraction after object-oriented classification, etc., was based on high-resolution WorldView-2 multispectral satellite imagery and RGB high-resolution aerial orthorectified images. Google Earth© images were also used to map and diachronically observe the site. The current research also discusses the potential for wider application of the presented methodology, acting as an early warning system, in an effort to establish a systematic monitoring tool for archaeological areas in Cyprus facing similar threats.

Remote sensing of land surface phenology

Science.gov (United States)

Meier, G.A.; Brown, Jesslyn F.

2014-01-01

Remote sensing of land-surface phenology is an important method for studying the patterns of plant and animal growth cycles. Phenological events are sensitive to climate variation; therefore phenology data provide important baseline information documenting trends in ecology and detecting the impacts of climate change on multiple scales. The USGS Remote sensing of land surface phenology program produces annually, nine phenology indicator variables at 250 m and 1,000 m resolution for the contiguous U.S. The 12 year archive is available at http://phenology.cr.usgs.gov/index.php.

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

Semantic Segmentation of Convolutional Neural Network for Supervised Classification of Multispectral Remote Sensing

Science.gov (United States)

Xue, L.; Liu, C.; Wu, Y.; Li, H.

2018-04-01

Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the classification of roads, vegetation, buildings and water from remote Sensing Imagery is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for ground object segmentation and the results could be further improved. This paper used convolution neural network named U-Net, its structure has a contracting path and an expansive path to get high resolution output. In the network , We added BN layers, which is more conducive to the reverse pass. Moreover, after upsampling convolution , we add dropout layers to prevent overfitting. They are promoted to get more precise segmentation results. To verify this network architecture, we used a Kaggle dataset. Experimental results show that U-Net achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.

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

Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool

Science.gov (United States)

McKellip, Rodney; Prados, Donald; Ryan, Robert; Ross, Kenton; Spruce, Joseph; Gasser, Gerald; Greer, Randall

2008-01-01

The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify

It's all in the pixels: high resolution remote sensing data and the mapping and analysis of the archaeological and historical landscape

Directory of Open Access Journals (Sweden)

Erwin Meylemans

2017-03-01

Full Text Available In Flanders (Belgium a large amount of remote-sensing data has been acquired and processed over the past few years, including high-resolution lidar and multi/hyperspectral aerial photography. These new data are contributing to the detection of archaeological sites and the characterisation of the cultural/historical landscape. Of particular use in historically stable areas under forest and pasture, lidar demonstrates the presence of a large number of previously unknown features and sites. The analysis and modelling of these data, combined with other landscape data such as soil maps, augering data, geological and historical maps, and aerial photographs, also provide possible new instruments for the characterisation and evaluation of prehistoric and historic landscapes. This vast amount of new remote-sensing data, plus the information it delivers, however, presents not only obvious opportunities but also a number of challenges. A centralised online system was developed by the 'GIS-Flanders agency', storing both processed and raw data from multispectral recordings, airborne lidar, mobile mapping images etc., and presenting several download and visualisation possibilities and tools. A new system has also been set up to handle specific archaeological and cultural historical data (historical images and aerial photographs, archaeological field data. Dialogue is needed so that the preservation and management needs of the archaeological heritage are also included.

Prospecting for coal in China with remote sensing

Energy Technology Data Exchange (ETDEWEB)

Ke-long Tan; Yu-qing Wan; Sun-xin Sun; Gui-bao Bao; Jing-shui Kuang [Aerophotogrammetry and Remote Sensing Center of China Coal, Xi' an (China)

2008-12-15

In China it is important to explore coal prospecting by taking advantage of modern remote sensing and geographic information system technologies. Given a theoretical basis for coal prospecting by remote sensing, the methodologies and existing problems are demonstrated systematically by summarizing past practices of coal prospecting with remote sensing. A new theory of coal prospecting with remote sensing is proposed. In uncovered areas, coal resources can be prospected by direct interpretation. In coal bearing strata of developed areas covered by thin Quaternary strata or vegetation, prospecting for coal can be carried out by indirect interpretation of geomorphology and vegetation. For deeply buried underground deposits, coal prospecting can rely on tectonic structures, interpretation and analysis of new tectonic clues and regularity of coal formation and preservation controlled by tectonic structures. By applying newly hyper-spectral, multi-polarization, multi-angle, multi-temporal and multi-resolution remote sensing data and carrying out integrated analysis of geographic attributes, ground attributes, geophysical exploration results, geochemical exploration results, geological drilling results and remote sensing data by GIS tools, coal geology resources and mineralogical regularities can be explored and coal resource information can be acquired with some confidence. 12 refs., 4 figs., 3 tabs.

Remote Sensing and the Earth.

Science.gov (United States)

Brosius, Craig A.; And Others

This document is designed to help senior high school students study remote sensing technology and techniques in relation to the environmental sciences. It discusses the acquisition, analysis, and use of ecological remote data. Material is divided into three sections and an appendix. Section One is an overview of the basics of remote sensing.…

Urban Boundary Extraction and Urban Sprawl Measurement Using High-Resolution Remote Sensing Images: a Case Study of China's Provincial

Science.gov (United States)

Wang, H.; Ning, X.; Zhang, H.; Liu, Y.; Yu, F.

2018-04-01

Urban boundary is an important indicator for urban sprawl analysis. However, methods of urban boundary extraction were inconsistent, and construction land or urban impervious surfaces was usually used to represent urban areas with coarse-resolution images, resulting in lower precision and incomparable urban boundary products. To solve above problems, a semi-automatic method of urban boundary extraction was proposed by using high-resolution image and geographic information data. Urban landscape and form characteristics, geographical knowledge were combined to generate a series of standardized rules for urban boundary extraction. Urban boundaries of China's 31 provincial capitals in year 2000, 2005, 2010 and 2015 were extracted with above-mentioned method. Compared with other two open urban boundary products, accuracy of urban boundary in this study was the highest. Urban boundary, together with other thematic data, were integrated to measure and analyse urban sprawl. Results showed that China's provincial capitals had undergone a rapid urbanization from year 2000 to 2015, with the area change from 6520 square kilometres to 12398 square kilometres. Urban area of provincial capital had a remarkable region difference and a high degree of concentration. Urban land became more intensive in general. Urban sprawl rate showed inharmonious with population growth rate. About sixty percent of the new urban areas came from cultivated land. The paper provided a consistent method of urban boundary extraction and urban sprawl measurement using high-resolution remote sensing images. The result of urban sprawl of China's provincial capital provided valuable urbanization information for government and public.

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...

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.

[Object-oriented segmentation and classification of forest gap based on QuickBird remote sensing image.

Science.gov (United States)

Mao, Xue Gang; Du, Zi Han; Liu, Jia Qian; Chen, Shu Xin; Hou, Ji Yu

2018-01-01

Traditional field investigation and artificial interpretation could not satisfy the need of forest gaps extraction at regional scale. High spatial resolution remote sensing image provides the possibility for regional forest gaps extraction. In this study, we used object-oriented classification method to segment and classify forest gaps based on QuickBird high resolution optical remote sensing image in Jiangle National Forestry Farm of Fujian Province. In the process of object-oriented classification, 10 scales (10-100, with a step length of 10) were adopted to segment QuickBird remote sensing image; and the intersection area of reference object (RA or ) and intersection area of segmented object (RA os ) were adopted to evaluate the segmentation result at each scale. For segmentation result at each scale, 16 spectral characteristics and support vector machine classifier (SVM) were further used to classify forest gaps, non-forest gaps and others. The results showed that the optimal segmentation scale was 40 when RA or was equal to RA os . The accuracy difference between the maximum and minimum at different segmentation scales was 22%. At optimal scale, the overall classification accuracy was 88% (Kappa=0.82) based on SVM classifier. Combining high resolution remote sensing image data with object-oriented classification method could replace the traditional field investigation and artificial interpretation method to identify and classify forest gaps at regional scale.

Advances in Remote Sensing for Vegetation Dynamics and Agricultural Management

Science.gov (United States)

Tucker, Compton; Puma, Michael

2015-01-01

Spaceborne remote sensing has led to great advances in the global monitoring of vegetation. For example, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) group has developed widely used datasets from the Advanced Very High Resolution Radiometer (AVHRR) sensors as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) map imagery and normalized difference vegetation index datasets. These data are valuable for analyzing vegetation trends and variability at the regional and global levels. Numerous studies have investigated such trends and variability for both natural vegetation (e.g., re-greening of the Sahel, shifts in the Eurasian boreal forest, Amazonian drought sensitivity) and crops (e.g., impacts of extremes on agricultural production). Here, a critical overview is presented on recent developments and opportunities in the use of remote sensing for monitoring vegetation and crop dynamics.

TerraSAR-X high-resolution radar remote sensing: an operational warning system for Rift Valley fever risk.

Science.gov (United States)

Vignolles, Cécile; Tourre, Yves M; Mora, Oscar; Imanache, Laurent; Lafaye, Murielle

2010-11-01

In the vicinity of the Barkedji village (in the Ferlo region of Senegal), the abundance and aggressiveness of the vector mosquitoes for Rift Valley fever (RVF) are strongly linked to rainfall events and associated ponds dynamics. Initially, these results were obtained from spectral analysis of high-resolution (~10 m) Spot-5 images, but, as a part of the French AdaptFVR project, identification of the free water dynamics within ponds was made with the new high-resolution (down to 3-meter pixels), Synthetic Aperture Radar satellite (TerraSAR-X) produced by Infoterra GmbH, Friedrichshafen/Potsdam, Germany. During summer 2008, within a 30 x 50 km radar image, it was found that identified free water fell well within the footprints of ponds localized by optical data (i.e. Spot-5 images), which increased the confidence in this new and complementary remote sensing technique. Moreover, by using near real-time rainfall data from the Tropical Rainfall Measuring Mission (TRMM), NASA/JAXA joint mission, the filling-up and flushing-out rates of the ponds can be accurately determined. The latter allows for a precise, spatio-temporal mapping of the zones potentially occupied by mosquitoes capable of revealing the variability of pond surfaces. The risk for RVF infection of gathered bovines and small ruminants (~1 park/km(2)) can thus be assessed. This new operational approach (which is independent of weather conditions) is an important development in the mapping of risk components (i.e. hazards plus vulnerability) related to RVF transmission during the summer monsoon, thus contributing to a RVF early warning system.

TerraSAR-X high-resolution radar remote sensing: an operational warning system for Rift Valley fever risk

Directory of Open Access Journals (Sweden)

Cécile Vignolles

2010-11-01

Full Text Available In the vicinity of the Barkedji village (in the Ferlo region of Senegal, the abundance and aggressiveness of the vector mosquitoes for Rift Valley fever (RVF are strongly linked to rainfall events and associated ponds dynamics. Initially, these results were obtained from spectral analysis of high-resolution (~10 m Spot-5 images, but, as a part of the French AdaptFVR project, identification of the free water dynamics within ponds was made with the new high-resolution (down to 3-meter pixels, Synthetic Aperture Radar satellite (TerraSAR-X produced by Infoterra GmbH, Friedrichshafen/Potsdam, Germany. During summer 2008, within a 30 x 50 km radar image, it was found that identified free water fell well within the footprints of ponds localized by optical data (i.e. Spot-5 images, which increased the confidence in this new and complementary remote sensing technique. Moreover, by using near real-time rainfall data from the Tropical Rainfall Measuring Mission (TRMM, NASA/JAXA joint mission, the filling-up and flushingout rates of the ponds can be accurately determined. The latter allows for a precise, spatio-temporal mapping of the zones potentially occupied by mosquitoes capable of revealing the variability of pond surfaces. The risk for RVF infection of gathered bovines and small ruminants (~1 park/km2 can thus be assessed. This new operational approach (which is independent of weather conditions is an important development in the mapping of risk components (i.e. hazards plus vulnerability related to RVF transmission during the summer monsoon, thus contributing to a RVF early warning system.

Upgraded airborne scanner for commercial remote sensing

Science.gov (United States)

Chang, Sheng-Huei; Rubin, Tod D.

1994-06-01

Traditional commercial remote sensing has focused on the geologic market, with primary focus on mineral identification and mapping in the visible through short-wave infrared spectral regions (0.4 to 2.4 microns). Commercial remote sensing users now demand airborne scanning capabilities spanning the entire wavelength range from ultraviolet through thermal infrared (0.3 to 12 microns). This spectral range enables detection, identification, and mapping of objects and liquids on the earth's surface and gases in the air. Applications requiring this range of wavelengths include detection and mapping of oil spills, soil and water contamination, stressed vegetation, and renewable and non-renewable natural resources, and also change detection, natural hazard mitigation, emergency response, agricultural management, and urban planning. GER has designed and built a configurable scanner that acquires high resolution images in 63 selected wave bands in this broad wavelength range.

Time-sensitive remote sensing

CERN Document Server

Lippitt, Christopher; Coulter, Lloyd

2015-01-01

This book documents the state of the art in the use of remote sensing to address time-sensitive information requirements. Specifically, it brings together a group of authors who are both researchers and practitioners, who work toward or are currently using remote sensing to address time-sensitive information requirements with the goal of advancing the effective use of remote sensing to supply time-sensitive information. The book addresses the theoretical implications of time-sensitivity on the remote sensing process, assessments or descriptions of methods for expediting the delivery and improving the quality of information derived from remote sensing, and describes and analyzes time-sensitive remote sensing applications, with an emphasis on lessons learned. This book is intended for remote sensing scientists, practitioners (e.g., emergency responders or administrators of emergency response agencies), and students, but will also be of use to those seeking to understand the potential of remote sensing to addres...

Slums from Space: 15 Years of Slum Mapping Using Remote Sensing

NARCIS (Netherlands)

Kuffer, M.; Pfeffer, K.; Sliuzas, R.

2016-01-01

The body of scientific literature on slum mapping employing remote sensing methods has increased since the availability of more very-high-resolution (VHR) sensors. This improves the ability to produce capable of supporting systematic global slum monitoring required for international policy

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.

Research Status and Development Trend of Remote Sensing in China Using Bibliometric Analysis

Science.gov (United States)

Zeng, Y.; Zhang, J.; Niu, R.

2015-06-01

Remote sensing was introduced into China in 1970s and then began to flourish. At present, China has developed into a big remote sensing country, and remote sensing is increasingly playing an important role in various fields of national economic construction and social development. Based on China Academic Journals Full-text Database and China Citation Database published by China National Knowledge Infrastructure, this paper analyzed academic characteristics of 963 highly cited papers published by 16 professional and academic journals in the field of surveying and mapping from January 2010 to December 2014 in China, which include hot topics, literature authors, research institutions, and fundations. At the same time, it studied a total of 51,149 keywords published by these 16 journals during the same period. Firstly by keyword selection, keyword normalization, keyword consistency and keyword incorporation, and then by analysis of high frequency keywords, the progress and prospect of China's remote sensing technology in data acquisition, data processing and applications during the past five years were further explored and revealed. It can be seen that: highly cited paper analysis and word frequency analysis is complementary on subject progress analysis; in data acquisition phase, research focus is new civilian remote sensing satellite systems and UAV remote sensing system; research focus of data processing and analysis is multi-source information extraction and classification, laser point cloud data processing, objectoriented high resolution image analysis, SAR data and hyper-spectral image processing, etc.; development trend of remote sensing data processing is quantitative, intelligent, automated, and real-time, and the breadth and depth of remote sensing application is gradually increased; parallel computing, cloud computing and geographic conditions monitoring and census are the new research focuses to be paid attention to.

Multi- and hyperspectral geologic remote sensing: A review

Science.gov (United States)

van der Meer, Freek D.; van der Werff, Harald M. A.; van Ruitenbeek, Frank J. A.; Hecker, Chris A.; Bakker, Wim H.; Noomen, Marleen F.; van der Meijde, Mark; Carranza, E. John M.; Smeth, J. Boudewijn de; Woldai, Tsehaie

2012-02-01

Geologists have used remote sensing data since the advent of the technology for regional mapping, structural interpretation and to aid in prospecting for ores and hydrocarbons. This paper provides a review of multispectral and hyperspectral remote sensing data, products and applications in geology. During the early days of Landsat Multispectral scanner and Thematic Mapper, geologists developed band ratio techniques and selective principal component analysis to produce iron oxide and hydroxyl images that could be related to hydrothermal alteration. The advent of the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) with six channels in the shortwave infrared and five channels in the thermal region allowed to produce qualitative surface mineral maps of clay minerals (kaolinite, illite), sulfate minerals (alunite), carbonate minerals (calcite, dolomite), iron oxides (hematite, goethite), and silica (quartz) which allowed to map alteration facies (propylitic, argillic etc.). The step toward quantitative and validated (subpixel) surface mineralogic mapping was made with the advent of high spectral resolution hyperspectral remote sensing. This led to a wealth of techniques to match image pixel spectra to library and field spectra and to unravel mixed pixel spectra to pure endmember spectra to derive subpixel surface compositional information. These products have found their way to the mining industry and are to a lesser extent taken up by the oil and gas sector. The main threat for geologic remote sensing lies in the lack of (satellite) data continuity. There is however a unique opportunity to develop standardized protocols leading to validated and reproducible products from satellite remote sensing for the geology community. By focusing on geologic mapping products such as mineral and lithologic maps, geochemistry, P-T paths, fluid pathways etc. the geologic remote sensing community can bridge the gap with the geosciences community. Increasingly

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.

Remote Sensing of Mangrove Ecosystems: A Review

Directory of Open Access Journals (Sweden)

Stefan Dech

2011-04-01

Full Text Available Mangrove ecosystems dominate the coastal wetlands of tropical and subtropical regions throughout the world. They provide various ecological and economical ecosystem services contributing to coastal erosion protection, water filtration, provision of areas for fish and shrimp breeding, provision of building material and medicinal ingredients, and the attraction of tourists, amongst many other factors. At the same time, mangroves belong to the most threatened and vulnerable ecosystems worldwide and experienced a dramatic decline during the last half century. International programs, such as the Ramsar Convention on Wetlands or the Kyoto Protocol, underscore the importance of immediate protection measures and conservation activities to prevent the further loss of mangroves. In this context, remote sensing is the tool of choice to provide spatio-temporal information on mangrove ecosystem distribution, species differentiation, health status, and ongoing changes of mangrove populations. Such studies can be based on various sensors, ranging from aerial photography to high- and medium-resolution optical imagery and from hyperspectral data to active microwave (SAR data. Remote-sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and changes during the last two decades, which is reflected by the large number of scientific papers published on this topic. To our knowledge, a recent review paper on the remote sensing of mangroves does not exist, although mangrove ecosystems have become the focus of attention in the context of current climate change and discussions of the services provided by these ecosystems. Also, climate change-related remote-sensing studies in coastal zones have increased drastically in recent years. The aim of this review paper is to provide a comprehensive overview and sound summary of all of the work undertaken, addressing the variety of remotely sensed data applied for mangrove

DARLA: Data Assimilation and Remote Sensing for Littoral Applications

Science.gov (United States)

Jessup, A.; Holman, R. A.; Chickadel, C.; Elgar, S.; Farquharson, G.; Haller, M. C.; Kurapov, A. L.; Özkan-Haller, H. T.; Raubenheimer, B.; Thomson, J. M.

2012-12-01

the Field Research Facility at Duck, NC in September 2010 focused on assimilation of tower-based electo-optical, infrared, and radar measurements in predictions of longshore currents. Here we provide an overview of our contribution to the RIVET I experiment at New River Inlet, NC in May 2012. During the course of the 3-week measurement period, continuous tower-based remote sensing measurements were made using electro-optical, infrared, and radar techniques covering the nearshore zone and the inlet mouth. A total of 50 hours of airborne measurements were made using high-resolution infrared imagers and a customized along track interferometric synthetic aperture radar (ATI SAR). The airborne IR imagery provides kilometer-scale mapping of frontal features that evolve as the inlet flow interacts with the oceanic wave and current fields. The ATI SAR provides maps of the two-dimensional surface currents. Near-surface measurements of turbulent velocities and surface waves using SWIFT drifters, designed to measures near-surface properties relevant to remote sensing, complimented the extensive in situ measurements by RIVET investigators.

POLARIZATION REMOTE SENSING PHYSICAL MECHANISM, KEY METHODS AND APPLICATION

Directory of Open Access Journals (Sweden)

B. Yang

2017-09-01

Full Text Available China's long-term planning major projects "high-resolution earth observation system" has been invested nearly 100 billion and the satellites will reach 100 to 2020. As to 2/3 of China's area covered by mountains，it has a higher demand for remote sensing. In addition to light intensity, frequency, phase, polarization is also the main physical characteristics of remote sensing electromagnetic waves. Polarization is an important component of the reflected information from the surface and the atmospheric information, and the polarization effect of the ground object reflection is the basis of the observation of polarization remote sensing. Therefore, the effect of eliminating the polarization effect is very important for remote sensing applications. The main innovations of this paper is as follows: (1 Remote sensing observation method. It is theoretically deduced and verified that the polarization can weaken the light in the strong light region, and then provide the polarization effective information. In turn, the polarization in the low light region can strengthen the weak light, the same can be obtained polarization effective information. (2 Polarization effect of vegetation. By analyzing the structure characteristics of vegetation, polarization information is obtained, then the vegetation structure information directly affects the absorption of biochemical components of leaves. (3 Atmospheric polarization neutral point observation method. It is proved to be effective to achieve the ground-gas separation, which can achieve the effect of eliminating the atmospheric polarization effect and enhancing the polarization effect of the object.

Advanced mineral and lithological mapping using high spectral resolution TIR data from the active CO2 remote sensing system; CO2 laser wo mochiita kosupekutoru bunkaino netsusekigai remote sensing data no ganseki kobutsu shikibetsu eno oyo

Energy Technology Data Exchange (ETDEWEB)

Okada, K [Sumitomo Metal Mining Co. Ltd., Osaka (Japan); Hato, M [Earth Remote Sensing Data Analysis Center, Tokyo (Japan); Cudahy, T; Tapley, I

1997-05-27

A study was conducted on rock/mineral mapping technology for the metal ore deposit survey using MIRACO2LAS, an active type thermal infrared ray remote sensing system which was developed by CSIRO of Australia and is now the highest in spectral resolution in the world, and TIMS of NASA which is a passive type system. The area for the survey is the area of Olary/Broken Hill and Mt. Fitton of Australia. A good correlation is seen between the ground reflectance measured by MIRACO2LAS and the value measured by the chamber CO2 laser of rocks sampled at the above-mentioned area. In case that the width of spectral characteristics is below 300nm, the inspection ability by MIRACO2LAS`s high spectral resolution is more determined in mineral mapping as compared with TIMS which is large in band width. Minerals mapped using MIRACO2LAS are quartz, talc, amphibole, hornblende, garnet, supessartine, dolomite, magnesite, etc. 4 refs., 3 figs.

Remote sensing of glacier- and permafrost-related hazards in high mountains: an overview

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A. Kääb

2005-01-01

Full Text Available Process interactions and chain reactions, the present shift of cryospheric hazard zones due to atmospheric warming, and the potential far reach of glacier disasters make it necessary to apply modern remote sensing techniques for the assessment of glacier and permafrost hazards in high-mountains. Typically, related hazard source areas are situated in remote regions, often difficult to access for physical and/or political reasons. In this contribution we provide an overview of air- and spaceborne remote sensing methods suitable for glacier and permafrost hazard assessment and disaster management. A number of image classification and change detection techniques support high-mountain hazard studies. Digital terrain models (DTMs, derived from optical stereo data, synthetic aperture radar or laserscanning, represent one of the most important data sets for investigating high-mountain processes. Fusion of satellite stereo-derived DTMs with the DTM from the Shuttle Radar Topography Mission (SRTM is a promising way to combine the advantages of both technologies. Large changes in terrain volume such as from avalanche deposits can indeed be measured even by repeat satellite DTMs. Multitemporal data can be used to derive surface displacements on glaciers, permafrost and landslides. Combining DTMs, results from spectral image classification, and multitemporal data from change detection and displacement measurements significantly improves the detection of hazard potentials. Modelling of hazardous processes based on geographic information systems (GIS complements the remote sensing analyses towards an integrated assessment of glacier and permafrost hazards in mountains. Major present limitations in the application of remote sensing to glacier and permafrost hazards in mountains are, on the one hand, of technical nature (e.g. combination and fusion of different methods and data; improved understanding of microwave backscatter. On the other hand, better

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.

A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding

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Maria Tattaris

2016-08-01

Full Text Available Remote sensing (RS of plant canopies permits non-intrusive, high-throughput monitoring of plant physiological characteristics. This study compared three RS approaches using a low flying UAV (unmanned aerial vehicle, with that of proximal sensing, and satellite-based imagery. Two physiological traits were considered, canopy temperature (CT and a vegetation index (NDVI, to determine the most viable approaches for large scale crop genetic improvement. The UAV-based platform achieves plot-level resolution while measuring several hundred plots in one mission via high-resolution thermal and multispectral imagery measured at altitudes of 30-100 m. The satellite measures multispectral imagery from an altitude of 770 km. Information was compared with proximal measurements using IR thermometers and an NDVI sensor at a distance of 0.5-1m above plots. For robust comparisons, CT and NDVI were assessed on panels of elite cultivars under irrigated and drought conditions, in different thermal regimes, and on un-adapted genetic resources under water deficit. Correlations between airborne data and yield/biomass at maturity were generally higher than equivalent proximal correlations. NDVI was derived from high-resolution satellite imagery for only larger sized plots (8.5 x 2.4 m due to restricted pixel density. Results support use of UAV-based RS techniques for high-throughput phenotyping for both precision and efficiency.

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.

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.

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

A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

Science.gov (United States)

Shuxin, Li; Zhilong, Zhang; Biao, Li

2018-01-01

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

High resolution mapping of riffle-pool dynamics based on ADCP and close-range remote sensing data

Science.gov (United States)

Salmela, Jouni; Kasvi, Elina; Alho, Petteri

2017-04-01

Present development of mobile laser scanning (MLS) and close-range photogrammetry with unmanned aerial vehicle (UAV) enable us to create seamless digital elevation models (DEMs) of the riverine environment. Remote-controlled flow measurement platforms have also improved spatio-temporal resolution of the flow field data. In this study, acoustic Doppler current profiler (ADCP) attached to remote-controlled mini-boat, UAV-based bathymetry and MLS techniques were utilized to create the high-resolution DEMs of the river channel. These high-resolution measurements can be used in many fluvial applications such as computational fluid dynamics, channel change detection, habitat mapping or hydro-electric power plant planning. In this study we aim: 1) to analyze morphological changes of river channel especially riffle and pool formations based on fine-scale DEMs and ADCP measurements, 2) to analyze flow fields and their effect on morphological changes. The interest was mainly focused on reach-scale riffle-pool dynamics within two-year period of 2013 and 2014. The study was performed in sub-arctic meandering Pulmankijoki River located in Northern Finland. The river itself has shallow and clear water and sandy bed sediment. Discharge remains typically below 10 m3s-1 most of the year but during snow melt period in spring the discharge may exceed 70 m3s-1. We compared DEMs and ADCP measurements to understand both magnitude and spatio-temporal change of the river bed. Models were accurate enough to study bed form changes and locations and persistence of riffles and pools. We analyzed their locations with relation to flow during the peak and low discharge. Our demonstrated method has improved significantly spatio-temporal resolution of riverine DEMs compared to other cross-sectional and photogrammetry based models. Together with flow field measurements we gained better understanding of riverbed-water interaction

Autonomous Coral Reef Survey in Support of Remote Sensing

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Steven G. Ackleson

2017-10-01

Full Text Available An autonomous surface vehicle instrumented with optical and acoustical sensors was deployed in Kane'ohe Bay, HI, U.S.A., to provide high-resolution, in situ observations of coral reef reflectance with minimal human presence. The data represented a wide range in bottom type, water depth, and illumination and supported more thorough investigations of remote sensing methods for identifying and mapping shallow reef features. The in situ data were used to compute spectral bottom reflectance and remote sensing reflectance, Rrs,λ, as a function of water depth and benthic features. The signals were used to distinguish between live coral and uncolonized sediment within the depth range of the measurements (2.5–5 m. In situRrs, λ were found to compare well with remotely sensed measurements from an imaging spectrometer, the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS, deployed on an aircraft at high altitude. Cloud cover and in situ sensor orientation were found to have minimal impact on in situRrs, λ, suggesting that valid reflectance data may be collected using autonomous surveys even when atmospheric conditions are not favorable for remote sensing operations. The use of reflectance in the red and near infrared portions of the spectrum, expressed as the red edge height, REHλ, was investigated for detecting live aquatic vegetative biomass, including coral symbionts and turf algae. The REHλ signal from live coral was detected in Kane'ohe Bay to a depth of approximately 4 m with in situ measurements. A remote sensing algorithm based on the REHλ signal was defined and applied to AVIRIS imagery of the entire bay and was found to reveal areas of shallow, dense coral and algal cover. The peak wavelength of REHλ decreased with increasing water depth, indicating that a more complete examination of the red edge signal may potentially yield a remote sensing approach to simultaneously estimate vegetative biomass and bathymetry in shallow water.

Conjugate-Gradient Neural Networks in Classification of Multisource and Very-High-Dimensional Remote Sensing Data

Science.gov (United States)

Benediktsson, J. A.; Swain, P. H.; Ersoy, O. K.

1993-01-01

Application of neural networks to classification of remote sensing data is discussed. Conventional two-layer backpropagation is found to give good results in classification of remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of multisource remote sensing and geographic data and very-high-dimensional data. The conjugate-gradient neural networks give excellent performance in classification of multisource data, but do not compare as well with statistical methods in classification of very-high-dimentional data.

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

the type of vegetation and its state of development in a more accurate way than using the ECOCLIMAP database. Finally, the CASA method was applied using the evapotranspiration images with FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) images from LSA-SAF to obtain Dry Matter Productivity (DMP) and crop yield. The potential of using evapotranspiration obtained from remote sensing in crop growth modeling is studied and discussed. Results of comparing the evapotranspiration obtained with ground truth data are shown as well as the influence of using high resolution information to characterize the vegetation in the evapotranspiration estimation. The values of DMP and yield obtained with the CASA method are compared with those obtained using crop growth modeling and field data, showing the potential of using this simplified remote sensing method for crop monitoring and yield forecasting. This methodology could be applied in an operative way to the entire MSG disk, allowing the continuous crop growth monitoring.

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...

Suppression of Instability on Sensing Signal of Optical Pulse Correlation Measurement in Remote Fiber Sensing

Directory of Open Access Journals (Sweden)

Hirokazu Kobayashi

2012-01-01

response and improve the accuracy of signals at the focused sensing regions. We also experimentally demonstrate remote temperature monitoring over a 30 km-long distance using a remote reference technique, and we estimate the resolution and the measurable span of the temperature variation as (1.1/L∘C and (5.9×10/L°C, respectively, where L is the length of the fiber in the sensing region.

Information Extraction of High-Resolution Remotely Sensed Image Based on Multiresolution Segmentation

Directory of Open Access Journals (Sweden)

Peng Shao

2014-08-01

Full Text Available The principle of multiresolution segmentation was represented in detail in this study, and the canny algorithm was applied for edge-detection of a remotely sensed image based on this principle. The target image was divided into regions based on object-oriented multiresolution segmentation and edge-detection. Furthermore, object hierarchy was created, and a series of features (water bodies, vegetation, roads, residential areas, bare land and other information were extracted by the spectral and geometrical features. The results indicate that the edge-detection has a positive effect on multiresolution segmentation, and overall accuracy of information extraction reaches to 94.6% by the confusion matrix.

Commercial future: making remote sensing a media event

Science.gov (United States)

Lurie, Ian

1999-12-01

The rapid growth of commercial remote sensing has made high quality digital sensing data widely available -- now, remote sensing must become and remain a strong, commercially viable industry. However, this new industry cannot survive without an educated consumer base. To access markets, remote sensing providers must make their product more accessible, both literally and figuratively: Potential customers must be able to find the data they require, when they require it, and they must understand the utility of the information available to them. The Internet and the World Wide Web offer the perfect medium to educate potential customers and to sell remote sensing data to those customers. A well-designed web presence can provide both an information center and a market place for companies offering their data for sale. A very high potential web-based market for remote sensing lies in media. News agencies, web sites, and a host of other visual media services can use remote sensing data to provide current, relevant information regarding news around the world. This paper will provide a model for promotion and sale of remote sensing data via the Internet.

Remote sensing application for property tax evaluation

Science.gov (United States)

Jain, Sadhana

2008-02-01

This paper presents a study for linking remotely sensed data with property tax related issues. First, it discusses the key attributes required for property taxation and evaluates the capabilities of remote sensing technology to measure these attributes accurately at parcel level. Next, it presents a detailed case study of six representative wards of different characteristics in Dehradun, India, that illustrates how measurements of several of these attributes supported by field survey can be combined to address the issues related to property taxation. Information derived for various factors quantifies the property taxation contributed by an average dwelling unit of the different income groups. Results show that the property tax calculated in different wards varies between 55% for the high-income group, 32% for the middle-income group, 12% for the low-income group and 1% for squatter units. The study concludes that higher spatial resolution satellite data and integrates social survey helps to assess the socio-economic status of the population for tax contribution purposes.

International Conference on Remote Sensing Applications for Archaeological Research and World Heritage Conservation

Science.gov (United States)

2002-01-01

. Archaeology through Space: Experience in Indian Subcontinent. The creation of a GIS Archaeological Site Location Catalogue in Yucatan: A Tool to preserve its Cultural Heritage. Mapping the Ancient Anasazi Roads of Southeast Utah. Remote Sensing and GIS Technology for Identification of Conservation and Heritage sites in Urban Planning. Mapping Angkor: For a new appraisal of the Angkor region. Angkor and radar imaging: seeing a vast pre-industrial low-density, dispersed urban complex. Technical and methodological aspects of archaeological CRM integrating high resolution satellite imagery. The contribution of satellite imagery to archaeological survey: an example from western Syria. The use of satellite images, digital elevation models and ground truth for the monitoring of land degradation in the "Cinque Terre" National park. Remote Sensing and GIS Applications for Protection and Conservation of World Heritage Site on the coast - Case Study of Tamil Nadu Coast, India. Multispectral high resolution satellite imagery in combination with "traditional" remote sensing and ground survey methods to the study of archaeological landscapes. The case study of Tuscany. Use of Remotely-Sensed Imagery in Cultural Landscape. Characterisation at Fort Hood, Texas. Heritage Learning and Data Collection: Biodiversity & Heritage Conservation through Collaborative Monitoring & Research. A collaborative project by UNESCO's WHC (World Heritage Center) & The GLOBE Program (Global Learning and Observations to Benefit the Environment). Practical Remote Sensing Activities in an Interdisciplinary Master-Level Space Course.

Introduction to remote sensing

CERN Document Server

Cracknell, Arthur P

2007-01-01

Addressing the need for updated information in remote sensing, Introduction to Remote Sensing, Second Edition provides a full and authoritative introduction for scientists who need to know the scope, potential, and limitations in the field. The authors discuss the physical principles of common remote sensing systems and examine the processing, interpretation, and applications of data. This new edition features updated and expanded material, including greater coverage of applications from across earth, environmental, atmospheric, and oceanographic sciences. Illustrated with remotely sensed colo

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

High-resolution satellite remote sensing of provincial PM2.5 trends in China from 2001 to 2015

Science.gov (United States)

Lin, C. Q.; Liu, G.; Lau, A. K. H.; Li, Y.; Li, C. C.; Fung, J. C. H.; Lao, X. Q.

2018-05-01

Given the vast territory of China, the long-term PM2.5 trends may substantially differ among the provinces. In this study, we aim to assess the provincial PM2.5 trends in China during the past few Five-Year Plan (FYP) periods. The lack of long-term PM2.5 measurements, however, makes such assessment difficult. Satellite remote sensing of PM2.5 concentration is an important step toward filling this data gap. In this study, a PM2.5 data set was built over China at a resolution of 1 km from 2001 to 2015 using satellite remote sensing. Analyses show that the national average of PM2.5 concentration increased by 0.04 μg·m-3·yr-1 during the 10th FYP period (2001-2005) and started to decline by -0.65 μg·m-3·yr-1 and -2.33 μg·m-3·yr-1 during the 11th (2006-2010) and the 12th (2011-2015) FYP period, respectively. In addition, substantial differences in the PM2.5 trends were observed among the provinces. Provinces in the Beijing-Tianjin-Hebei (BTH) region had the largest reduction of PM2.5 concentrations during the 10th and 12th FYP period. The greatest reduction rate of PM2.5 concentration during the 10th and 12th FYP period was observed in Beijing (-3.68 μg·m-3·yr-1) and Tianjin (-6.62 μg·m-3·yr-1), respectively. In contrast, PM2.5 concentrations remained steady for provinces in eastern and southeastern China (e.g., Shanghai) during the 12th FYP period. In overall, great efforts are still required to effectively reduce the PM2.5 concentrations in future.

Combining Remote Temperature Sensing with in-Situ Sensing to Track Marine/Freshwater Mixing Dynamics.

Science.gov (United States)

McCaul, Margaret; Barland, Jack; Cleary, John; Cahalane, Conor; McCarthy, Tim; Diamond, Dermot

2016-08-31

The ability to track the dynamics of processes in natural water bodies on a global scale, and at a resolution that enables highly localised behaviour to be visualized, is an ideal scenario for understanding how local events can influence the global environment. While advances in in-situ chem/bio-sensing continue to be reported, costs and reliability issues still inhibit the implementation of large-scale deployments. In contrast, physical parameters like surface temperature can be tracked on a global scale using satellite remote sensing, and locally at high resolution via flyovers and drones using multi-spectral imaging. In this study, we show how a much more complete picture of submarine and intertidal groundwater discharge patterns in Kinvara Bay, Galway can be achieved using a fusion of data collected from the Earth Observation satellite (Landsat 8), small aircraft and in-situ sensors. Over the course of the four-day field campaign, over 65,000 in-situ temperatures, salinity and nutrient measurements were collected in parallel with high-resolution thermal imaging from aircraft flyovers. The processed in-situ data show highly correlated patterns between temperature and salinity at the southern end of the bay where freshwater springs can be identified at low tide. Salinity values range from 1 to 2 ppt at the southern end of the bay to 30 ppt at the mouth of the bay, indicating the presence of a freshwater wedge. The data clearly show that temperature differences can be used to track the dynamics of freshwater and seawater mixing in the inner bay region. This outcome suggests that combining the tremendous spatial density and wide geographical reach of remote temperature sensing (using drones, flyovers and satellites) with ground-truthing via appropriately located in-situ sensors (temperature, salinity, chemical, and biological) can produce a much more complete and accurate picture of the water dynamics than each modality used in isolation.

The Study of Land Use Classification Based on SPOT6 High Resolution Data

OpenAIRE

Wu Song; Jiang Qigang

2016-01-01

A method is carried out to quick classification extract of the type of land use in agricultural areas, which is based on the spot6 high resolution remote sensing classification data and used of the good nonlinear classification ability of support vector machine. The results show that the spot6 high resolution remote sensing classification data can realize land classification efficiently, the overall classification accuracy reached 88.79% and Kappa factor is 0.8632 which means that the classif...

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

Intelligent Detection of Structure from Remote Sensing Images Based on Deep Learning Method

Science.gov (United States)

Xin, L.

2018-04-01

Utilizing high-resolution remote sensing images for earth observation has become the common method of land use monitoring. It requires great human participation when dealing with traditional image interpretation, which is inefficient and difficult to guarantee the accuracy. At present, the artificial intelligent method such as deep learning has a large number of advantages in the aspect of image recognition. By means of a large amount of remote sensing image samples and deep neural network models, we can rapidly decipher the objects of interest such as buildings, etc. Whether in terms of efficiency or accuracy, deep learning method is more preponderant. This paper explains the research of deep learning method by a great mount of remote sensing image samples and verifies the feasibility of building extraction via experiments.

Geographic information system for fusion and analysis of high-resolution remote sensing and ground data

Science.gov (United States)

Freeman, Anthony; Way, Jo Bea; Dubois, Pascale; Leberl, Franz

1993-01-01

We seek to combine high-resolution remotely sensed data with models and ground truth measurements, in the context of a Geographical Information System (GIS), integrated with specialized image processing software. We will use this integrated system to analyze the data from two Case Studies, one at a boreal forest site, the other a tropical forest site. We will assess the information content of the different components of the data, determine the optimum data combinations to study biogeophysical changes in the forest, assess the best way to visualize the results, and validate the models for the forest response to different radar wavelengths/polarizations. During the 1990's, unprecedented amounts of high-resolution images from space of the Earth's surface will become available to the applications scientist from the LANDSAT/TM series, European and Japanese ERS-1 satellites, RADARSAT and SIR-C missions. When the Earth Observation Systems (EOS) program is operational, the amount of data available for a particular site can only increase. The interdisciplinary scientist, seeking to use data from various sensors to study his site of interest, may be faced with massive difficulties in manipulating such large data sets, assessing their information content, determining the optimum combinations of data to study a particular parameter, visualizing his results and validating his model of the surface. The techniques to deal with these problems are also needed to support the analysis of data from NASA's current program of Multi-sensor Airborne Campaigns, which will also generate large volumes of data. In the Case Studies outlined in this proposal, we will have somewhat unique data sets. For the Bonanza Creek Experimental Forest (Case 1) calibrated DC-8 SAR (Synthetic Aperture Radar) data and extensive ground truth measurement are already at our disposal. The data set shows documented evidence to temporal change. The Belize Forest Experiment (Case 2) will produce calibrated DC-8 SAR

Crowdsourcing earthquake damage assessment using remote sensing imagery

Directory of Open Access Journals (Sweden)

Stuart Gill

2011-06-01

Full Text Available This paper describes the evolution of recent work on using crowdsourced analysis of remote sensing imagery, particularly high-resolution aerial imagery, to provide rapid, reliable assessments of damage caused by earthquakes and potentially other disasters. The initial effort examined online imagery taken after the 2008 Wenchuan, China, earthquake. A more recent response to the 2010 Haiti earthquake led to the formation of an international consortium: the Global Earth Observation Catastrophe Assessment Network (GEO-CAN. The success of GEO-CAN in contributing to the official damage assessments made by the Government of Haiti, the United Nations, and the World Bank led to further development of a web-based interface. A current initiative in Christchurch, New Zealand, is underway where remote sensing experts are analyzing satellite imagery, geotechnical engineers are marking liquefaction areas, and structural engineers are identifying building damage. The current site includes online training to improve the accuracy of the assessments and make it possible for even novice users to contribute to the crowdsourced solution. The paper discusses lessons learned from these initiatives and presents a way forward for using crowdsourced remote sensing as a tool for rapid assessment of damage caused by natural disasters around the world.

Low-cost multispectral imaging for remote sensing of lettuce health

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Ren, David D. W.; Tripathi, Siddhant; Li, Larry K. B.

2017-01-01

In agricultural remote sensing, unmanned aerial vehicle (UAV) platforms offer many advantages over conventional satellite and full-scale airborne platforms. One of the most important advantages is their ability to capture high spatial resolution images (1-10 cm) on-demand and at different viewing angles. However, UAV platforms typically rely on the use of multiple cameras, which can be costly and difficult to operate. We present the development of a simple low-cost imaging system for remote sensing of crop health and demonstrate it on lettuce (Lactuca sativa) grown in Hong Kong. To identify the optimal vegetation index, we recorded images of both healthy and unhealthy lettuce, and used them as input in an expectation maximization cluster analysis with a Gaussian mixture model. Results from unsupervised and supervised clustering show that, among four widely used vegetation indices, the blue wide-dynamic range vegetation index is the most accurate. This study shows that it is readily possible to design and build a remote sensing system capable of determining the health status of lettuce at a reasonably low cost (lettuce growers.

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.

A Multi-Temporal Remote Sensing Approach to Freshwater Turtle Conservation

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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

Spatial-Spectral Approaches to Edge Detection in Hyperspectral Remote Sensing

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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

Using High Spatio-Temporal Optical Remote Sensing to Monitor Dissolved Organic Carbon in the Arctic River Yenisei

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Pierre-Alexis Herrault

2016-09-01

Full Text Available In Arctic regions, a major concern is the release of carbon from melting permafrost that could greatly exceed current human carbon emissions. Arctic rivers drain these organic-rich watersheds (Ob, Lena, Yenisei, Mackenzie, Yukon but field measurements at the outlets of these great Arctic rivers are constrained by limited accessibility of sampling sites. In particular, the highest dissolved organic carbon (DOC fluxes are observed throughout the ice breakup period that occurs over a short two to three-week period in late May or early June during the snowmelt-generated peak flow. The colored fraction of dissolved organic carbon (DOC which absorbs UV and visible light is designed as chromophoric dissolved organic matter (CDOM. It is highly correlated to DOC in large arctic rivers and streams, allowing for remote sensing to monitor DOC concentrations from satellite imagery. High temporal and spatial resolutions remote sensing tools are highly relevant for the study of DOC fluxes in a large Arctic river. The high temporal resolution allows for correctly assessing this highly dynamic process, especially the spring freshet event (a few weeks in May. The high spatial resolution allows for assessing the spatial variability within the stream and quantifying DOC transfer during the ice break period when the access to the river is almost impossible. In this study, we develop a CDOM retrieval algorithm at a high spatial and a high temporal resolution in the Yenisei River. We used extensive DOC and DOM spectral absorbance datasets from 2014 and 2015. Twelve SPOT5 (Take5 and Landsat 8 (OLI images from 2014 and 2015 were examined for this investigation. Relationships between CDOM and spectral variables were explored using linear models (LM. Results demonstrated the capacity of a CDOM algorithm retrieval to monitor DOC fluxes in the Yenisei River during a whole open water season with a special focus on the peak flow period. Overall, future Sentinel2/Landsat8

Computational Ghost Imaging for Remote Sensing

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Erkmen, Baris I.

2012-01-01

This work relates to the generic problem of remote active imaging; that is, a source illuminates a target of interest and a receiver collects the scattered light off the target to obtain an image. Conventional imaging systems consist of an imaging lens and a high-resolution detector array [e.g., a CCD (charge coupled device) array] to register the image. However, conventional imaging systems for remote sensing require high-quality optics and need to support large detector arrays and associated electronics. This results in suboptimal size, weight, and power consumption. Computational ghost imaging (CGI) is a computational alternative to this traditional imaging concept that has a very simple receiver structure. In CGI, the transmitter illuminates the target with a modulated light source. A single-pixel (bucket) detector collects the scattered light. Then, via computation (i.e., postprocessing), the receiver can reconstruct the image using the knowledge of the modulation that was projected onto the target by the transmitter. This way, one can construct a very simple receiver that, in principle, requires no lens to image a target. Ghost imaging is a transverse imaging modality that has been receiving much attention owing to a rich interconnection of novel physical characteristics and novel signal processing algorithms suitable for active computational imaging. The original ghost imaging experiments consisted of two correlated optical beams traversing distinct paths and impinging on two spatially-separated photodetectors: one beam interacts with the target and then illuminates on a single-pixel (bucket) detector that provides no spatial resolution, whereas the other beam traverses an independent path and impinges on a high-resolution camera without any interaction with the target. The term ghost imaging was coined soon after the initial experiments were reported, to emphasize the fact that by cross-correlating two photocurrents, one generates an image of the target. In

What is a picture worth? A history of remote sensing

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Moore, Gerald K.

1979-01-01

Remote sensing is the use of electromagnetic energy to measure the physical properties of distant objects. It includes photography and geophysical surveying as well as newer techniques that use other parts of the electromagnetic spectrum. The history of remote sensing begins with photography. The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors have been designed to operate in virtually all of the electromagnetic spectrum. Today a wide variety of remote sensing instruments are available for use in hydrological studies; satellite data, such as Skylab photographs and Landsat images are particularly suitable for regional problems and studies. Planned future satellites will provide a ground resolution of 10–80 m. Remote sensing is currently used for hydrological applications in most countries of the world. The range of applications includes groundwater exploration determination of physical water quality, snowfield mapping, flood-inundation delineation, and making inventories of irrigated land. The use of remote sensing commonly results in considerable hydrological information at minimal cost. This information can be used to speed-up the development of water resources, to improve management practices, and to monitor environmental problems.

[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.

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

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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.

Validating firn compaction model with remote sensing data

DEFF Research Database (Denmark)

Simonsen, S. B.; Stenseng, Lars; Sørensen, Louise Sandberg

A comprehensive understanding of firn processes is of outmost importance, when estimating present and future changes of the Greenland Ice Sheet. Especially, when remote sensing altimetry is used to assess the state of ice sheets and their contribution to global sea level rise, firn compaction...... models have been shown to be a key component. Now, remote sensing data can also be used to validate the firn models. Radar penetrating the upper part of the firn column in the interior part of Greenland shows a clear layering. The observed layers from the radar data can be used as an in-situ validation...... correction relative to the changes in the elevation of the surface observed with remote sensing altimetry? What model time resolution is necessary to resolved the observed layering? What model refinements are necessary to give better estimates of the surface mass balance of the Greenland ice sheet from...

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification

Science.gov (United States)

Bradbury, Kyle; Saboo, Raghav; L. Johnson, Timothy; Malof, Jordan M.; Devarajan, Arjun; Zhang, Wuming; M. Collins, Leslie; G. Newell, Richard

2016-12-01

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

Advanced Remote Sensing Research

Science.gov (United States)

Slonecker, Terrence; Jones, John W.; Price, Susan D.; Hogan, Dianna

2008-01-01

'Remote sensing' is a generic term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth's surface. Remotely sensed data are fundamental to geographic science. The Eastern Geographic Science Center (EGSC) of the U.S. Geological Survey (USGS) is currently conducting and promoting the research and development of three different aspects of remote sensing science: spectral analysis, automated orthorectification of historical imagery, and long wave infrared (LWIR) polarimetric imagery (PI).

Hyperspectral remote sensing

CERN Document Server

Eismann, Michael

2012-01-01

Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications that builds on the principles of material spectroscopy, radiative transfer, imaging spectrometry, and hyperspectral data processing. This book provides a holistic treatment that captures its multidisciplinary nature, emphasizing the physical principles of hyperspectral remote sensing.

Redefining nondiscriminatory access to remote sensing imagery and its impact on global transparency

Science.gov (United States)

Aten, Michelle L.

2003-04-01

Global transparency is founded on the Open Skies philosophy and its precept of non-discriminatory access. Global transparency implies that anyone can have anytime, anyplace access to a wide-array of remotely sensed imagery. The custom of non-discriminatory access requires that datasets of interest must be affordable, usable, and obtainable in a timely fashion devoid of political, economic or technical obstacles. Thus, an assessment of the correlation between the availability of satellite imagery and changes in governmental policies, pricing fluctuations of data, and advances in technology is critical to assessing the viability of global transparency. The Open Skies philosophy was originally proposed at the 1955 Geneva Summit to advocate mutually beneficial aerial reconnaissance missions over the USSR and the US as a verification tool for arms control and non-proliferation agreements. However, due to Cold War tensions, this philosophy and the custom of non-discriminatory were not widely adopted in the civilian remote sensing community until the commissioning of the Landsat Program in 1972. Since this time, commercial high-resolution satellites have drastically changed the circumstances on which the fundamental tenets of this philosophy are based. Since the successful launch of the first of this satellite class, the IKONOS satellite, high-resolution imagery is now available to non-US governments and an unlimited set of non-state actors. As more advanced capabilities are added to the growing assortment of remote sensing satellites, the reality of global transparency will rapidly evolve. This assessment includes an overview of historical precedents and a brief explanation of relevant US policy decisions that define non-discriminatory access with respect to US government and US based corporate assets. It also presents the dynamics of the political, economic, and technical barriers that may dictate or influence the remote sensing community's access to satellite data. In

A change detection method for remote sensing image based on LBP and SURF feature

Science.gov (United States)

Hu, Lei; Yang, Hao; Li, Jin; Zhang, Yun

2018-04-01

Finding the change in multi-temporal remote sensing image is important in many the image application. Because of the infection of climate and illumination, the texture of the ground object is more stable relative to the gray in high-resolution remote sensing image. And the texture features of Local Binary Patterns (LBP) and Speeded Up Robust Features (SURF) are outstanding in extracting speed and illumination invariance. A method of change detection for matched remote sensing image pair is present, which compares the similarity by LBP and SURF to detect the change and unchanged of the block after blocking the image. And region growing is adopted to process the block edge zone. The experiment results show that the method can endure some illumination change and slight texture change of the ground object.

Water column correction for coral reef studies by remote sensing.

Science.gov (United States)

Zoffoli, Maria Laura; Frouin, Robert; Kampel, Milton

2014-09-11

Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.

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

Multi-Beam Focal Plane Arrays with Digital Beamforming for High Precision Space-Borne Ocean Remote Sensing

DEFF Research Database (Denmark)

Iupikov, Oleg A.; Ivashina, Mariana V.; Skou, Niels

2018-01-01

alternative radiometer systems: a conical scanner with an off-set parabolic reflector, and stationary wide-scan torus reflector system; each operating at C, X and Ku bands. Numerical results predict excellent beam performance for both systems with as low as 0:14 % total received power over the land.......The present-day ocean remote sensing instruments that operate at low microwave frequencies are limited in spatial resolution and do not allow for monitoring of the coastal waters. This is due the difficulties of employing a large reflector antenna on a satellite platform, and generating high-quality...

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.)

Remote Sensing of Coral Reefs for Monitoring and Management: A Review

Directory of Open Access Journals (Sweden)

John D. Hedley

2016-02-01

Full Text Available Coral reefs are in decline worldwide and monitoring activities are important for assessing the impact of disturbance on reefs and tracking subsequent recovery or decline. Monitoring by field surveys provides accurate data but at highly localised scales and so is not cost-effective for reef scale monitoring at frequent time points. Remote sensing from satellites is an alternative and complementary approach. While remote sensing cannot provide the level of detail and accuracy at a single point than a field survey, the statistical power for inferring large scale patterns benefits in having complete areal coverage. This review considers the state of the art of coral reef remote sensing for the diverse range of objectives relevant for management, ranging from the composition of the reef: physical extent, benthic cover, bathymetry, rugosity; to environmental parameters: sea surface temperature, exposure, light, carbonate chemistry. In addition to updating previous reviews, here we also consider the capability to go beyond basic maps of habitats or environmental variables, to discuss concepts highly relevant to stakeholders, policy makers and public communication: such as biodiversity, environmental threat and ecosystem services. A clear conclusion of the review is that advances in both sensor technology and processing algorithms continue to drive forward remote sensing capability for coral reef mapping, particularly with respect to spatial resolution of maps, and synthesis across multiple data products. Both trends can be expected to continue.

Mapping Palm Swamp Wetland Ecosystems in the Peruvian Amazon: a Multi-Sensor Remote Sensing Approach

Science.gov (United States)

Podest, E.; McDonald, K. C.; Schroeder, R.; Pinto, N.; Zimmerman, R.; Horna, V.

2012-12-01

Wetland ecosystems are prevalent in the Amazon basin, especially in northern Peru. Of specific interest are palm swamp wetlands because they are characterized by constant surface inundation and moderate seasonal water level variation. This combination of constantly saturated soils 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, it is critical to develop methods to quantify their spatial extent and inundation state in order to assess their carbon dynamics. 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 developed a remote sensing methodology using multi-sensor remote sensing data from the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR), Shuttle Radar Topography Mission (SRTM) DEM, and Landsat to derive maps at 100 meter resolution of palm swamp extent and inundation based on ground data collections; and combined active and passive microwave data from AMSR-E and QuikSCAT to derive inundation extent at 25 kilometer resolution on a weekly basis. We then compared information content and accuracy of the coarse resolution products relative to the high-resolution datasets. The synergistic combination of high and low resolution datasets allowed for characterization of palm swamps and assessment of their flooding status. This work has been undertaken partly within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data have been provided by JAXA. Portions of this work were carried out at the Jet Propulsion Laboratory

REMOTE SENSING IN OCEANOGRAPHY.

Science.gov (United States)

remote sensing from satellites. Sensing of oceanographic variables from aircraft began with the photographing of waves and ice. Since then remote measurement of sea surface temperatures and wave heights have become routine. Sensors tested for oceanographic applications include multi-band color cameras, radar scatterometers, infrared spectrometers and scanners, passive microwave radiometers, and radar imagers. Remote sensing has found its greatest application in providing rapid coverage of large oceanographic areas for synoptic and analysis and

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.

An integrated approach to the remote sensing of floating ice

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Campbell, W. J.; Ramseier, R. O.; Weeks, W. F.; Gloersen, P.

1976-01-01

Review article on remote sensing applications to glaciology. Ice parameters sensed include: ice cover vs open water, ice thickness, distribution and morphology of ice formations, vertical resolution of ice thickness, ice salinity (percolation and drainage of brine; flushing of ice body with fresh water), first-year ice and multiyear ice, ice growth rate and surface heat flux, divergence of ice packs, snow cover masking ice, behavior of ice shelves, icebergs, lake ice and river ice; time changes. Sensing techniques discussed include: satellite photographic surveys, thermal IR, passive and active microwave studies, microwave radiometry, microwave scatterometry, side-looking radar, and synthetic aperture radar. Remote sensing of large aquatic mammals and operational ice forecasting are also discussed.

Urban Shanty Town Recognition Based on High-Resolution Remote Sensing Images and National Geographical Monitoring Features - a Case Study of Nanning City

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He, Y.; He, Y.

2018-04-01

Urban shanty towns are communities that has contiguous old and dilapidated houses with more than 2000 square meters built-up area or more than 50 households. This study makes attempts to extract shanty towns in Nanning City using the product of Census and TripleSat satellite images. With 0.8-meter high-resolution remote sensing images, five texture characteristics (energy, contrast, maximum probability, and inverse difference moment) of shanty towns are trained and analyzed through GLCM. In this study, samples of shanty town are well classified with 98.2 % producer accuracy of unsupervised classification and 73.2 % supervised classification correctness. Low-rise and mid-rise residential blocks in Nanning City are classified into 4 different types by using k-means clustering and nearest neighbour classification respectively. This study initially establish texture feature descriptions of different types of residential areas, especially low-rise and mid-rise buildings, which would help city administrator evaluate residential blocks and reconstruction shanty towns.

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.

Geographic information system for fusion and analysis of high-resolution remote sensing and ground truth data

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Freeman, Anthony; Way, Jo Bea; Dubois, Pascale; Leberl, Franz

1992-01-01

We seek to combine high-resolution remotely sensed data with models and ground truth measurements, in the context of a Geographical Information System, integrated with specialized image processing software. We will use this integrated system to analyze the data from two Case Studies, one at a bore Al forest site, the other a tropical forest site. We will assess the information content of the different components of the data, determine the optimum data combinations to study biogeophysical changes in the forest, assess the best way to visualize the results, and validate the models for the forest response to different radar wavelengths/polarizations. During the 1990's, unprecedented amounts of high-resolution images from space of the Earth's surface will become available to the applications scientist from the LANDSAT/TM series, European and Japanese ERS-1 satellites, RADARSAT and SIR-C missions. When the Earth Observation Systems (EOS) program is operational, the amount of data available for a particular site can only increase. The interdisciplinary scientist, seeking to use data from various sensors to study his site of interest, may be faced with massive difficulties in manipulating such large data sets, assessing their information content, determining the optimum combinations of data to study a particular parameter, visualizing his results and validating his model of the surface. The techniques to deal with these problems are also needed to support the analysis of data from NASA's current program of Multi-sensor Airborne Campaigns, which will also generate large volumes of data. In the Case Studies outlined in this proposal, we will have somewhat unique data sets. For the Bonanza Creek Experimental Forest (Case I) calibrated DC-8 SAR data and extensive ground truth measurement are already at our disposal. The data set shows documented evidence to temporal change. The Belize Forest Experiment (Case II) will produce calibrated DC-8 SAR and AVIRIS data, together with

Combining Remote Temperature Sensing with in-Situ Sensing to Track Marine/Freshwater Mixing Dynamics

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Margaret McCaul

2016-08-01

Full Text Available The ability to track the dynamics of processes in natural water bodies on a global scale, and at a resolution that enables highly localised behaviour to be visualized, is an ideal scenario for understanding how local events can influence the global environment. While advances in in-situ chem/bio-sensing continue to be reported, costs and reliability issues still inhibit the implementation of large-scale deployments. In contrast, physical parameters like surface temperature can be tracked on a global scale using satellite remote sensing, and locally at high resolution via flyovers and drones using multi-spectral imaging. In this study, we show how a much more complete picture of submarine and intertidal groundwater discharge patterns in Kinvara Bay, Galway can be achieved using a fusion of data collected from the Earth Observation satellite (Landsat 8, small aircraft and in-situ sensors. Over the course of the four-day field campaign, over 65,000 in-situ temperatures, salinity and nutrient measurements were collected in parallel with high-resolution thermal imaging from aircraft flyovers. The processed in-situ data show highly correlated patterns between temperature and salinity at the southern end of the bay where freshwater springs can be identified at low tide. Salinity values range from 1 to 2 ppt at the southern end of the bay to 30 ppt at the mouth of the bay, indicating the presence of a freshwater wedge. The data clearly show that temperature differences can be used to track the dynamics of freshwater and seawater mixing in the inner bay region. This outcome suggests that combining the tremendous spatial density and wide geographical reach of remote temperature sensing (using drones, flyovers and satellites with ground-truthing via appropriately located in-situ sensors (temperature, salinity, chemical, and biological can produce a much more complete and accurate picture of the water dynamics than each modality used in isolation.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

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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.

REMOTE SENSING APPLICATIONS WITH HIGH RELIABILITY IN CHANGJIANG WATER RESOURCE MANAGEMENT

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L. Ma

2018-04-01

Full Text Available Remote sensing technology has been widely used in many fields. But most of the applications cannot get the information with high reliability and high accuracy in large scale, especially for the applications using automatic interpretation methods. We have designed an application-oriented technology system (PIR composed of a series of accurate interpretation techniques,which can get over 85 % correctness in Water Resource Management from the view of photogrammetry and expert knowledge. The techniques compose of the spatial positioning techniques from the view of photogrammetry, the feature interpretation techniques from the view of expert knowledge, and the rationality analysis techniques from the view of data mining. Each interpreted polygon is accurate enough to be applied to the accuracy sensitive projects, such as the Three Gorge Project and the South - to - North Water Diversion Project. In this paper, we present several remote sensing applications with high reliability in Changjiang Water Resource Management,including water pollution investigation, illegal construction inspection, and water conservation monitoring, etc.

Remote Sensing Applications with High Reliability in Changjiang Water Resource Management

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Ma, L.; Gao, S.; Yang, A.

2018-04-01

Remote sensing technology has been widely used in many fields. But most of the applications cannot get the information with high reliability and high accuracy in large scale, especially for the applications using automatic interpretation methods. We have designed an application-oriented technology system (PIR) composed of a series of accurate interpretation techniques,which can get over 85 % correctness in Water Resource Management from the view of photogrammetry and expert knowledge. The techniques compose of the spatial positioning techniques from the view of photogrammetry, the feature interpretation techniques from the view of expert knowledge, and the rationality analysis techniques from the view of data mining. Each interpreted polygon is accurate enough to be applied to the accuracy sensitive projects, such as the Three Gorge Project and the South - to - North Water Diversion Project. In this paper, we present several remote sensing applications with high reliability in Changjiang Water Resource Management,including water pollution investigation, illegal construction inspection, and water conservation monitoring, etc.

Hyperspectral remote sensing of wild oyster reefs

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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

Levee Health Monitoring With Radar Remote Sensing

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Jones, C. E.; Bawden, G. W.; Deverel, S. J.; Dudas, J.; Hensley, S.; Yun, S.

2012-12-01

Remote sensing offers the potential to augment current levee monitoring programs by providing rapid and consistent data collection over large areas irrespective of the ground accessibility of the sites of interest, at repeat intervals that are difficult or costly to maintain with ground-based surveys, and in rapid response to emergency situations. While synthetic aperture radar (SAR) has long been used for subsidence measurements over large areas, applying this technique directly to regional levee monitoring is a new endeavor, mainly because it requires both a wide imaging swath and fine spatial resolution to resolve individual levees within the scene, a combination that has not historically been available. Application of SAR remote sensing directly to levee monitoring has only been attempted in a few pilot studies. Here we describe how SAR remote sensing can be used to assess levee conditions, such as seepage, drawing from the results of two levee studies: one of the Sacramento-San Joaquin Delta levees in California that has been ongoing since July 2009 and a second that covered the levees near Vicksburg, Mississippi, during the spring 2011 floods. These studies have both used data acquired with NASA's UAVSAR L-band synthetic aperture radar, which has the spatial resolution needed for this application (1.7 m single-look), sufficiently wide imaging swath (22 km), and the longer wavelength (L-band, 0.238 m) required to maintain phase coherence between repeat collections over levees, an essential requirement for applying differential interferometry (DInSAR) to a time series of repeated collections for levee deformation measurement. We report the development and demonstration of new techniques that employ SAR polarimetry and differential interferometry to successfully assess levee health through the quantitative measurement of deformation on and near levees and through detection of areas experiencing seepage. The Sacramento-San Joaquin Delta levee study, which covers

Remote sensing of high-latitude ionization profiles by ground-based and spaceborne instrumentation

International Nuclear Information System (INIS)

Vondrak, R.R.

1981-01-01

Ionospheric specification and modeling are now largely based on data provided by active remote sensing with radiowave techniques (ionosondes, incoherent-scatter radars, and satellite beacons). More recently, passive remote sensing techniques have been developed that can be used to monitor quantitatively the spatial distribution of high-latitude E-region ionization. These passive methods depend on the measurement, or inference, of the energy distribution of precipitating kilovolt electrons, the principal source of the nighttime E-region at high latitudes. To validate these techniques, coordinated measurements of the auroral ionosphere have been made with the Chatanika incoherent-scatter radar and a variety of ground-based and spaceborne sensors

Geometric registration of remotely sensed data with SAMIR

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Gianinetto, Marco; Barazzetti, Luigi; Dini, Luigi; Fusiello, Andrea; Toldo, Roberto

2015-06-01

The commercial market offers several software packages for the registration of remotely sensed data through standard one-to-one image matching. Although very rapid and simple, this strategy does not take into consideration all the interconnections among the images of a multi-temporal data set. This paper presents a new scientific software, called Satellite Automatic Multi-Image Registration (SAMIR), able to extend the traditional registration approach towards multi-image global processing. Tests carried out with high-resolution optical (IKONOS) and high-resolution radar (COSMO-SkyMed) data showed that SAMIR can improve the registration phase with a more rigorous and robust workflow without initial approximations, user's interaction or limitation in spatial/spectral data size. The validation highlighted a sub-pixel accuracy in image co-registration for the considered imaging technologies, including optical and radar imagery.

Water Column Correction for Coral Reef Studies by Remote Sensing

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Maria Laura Zoffoli

2014-09-01

Full Text Available Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.

Water Column Correction for Coral Reef Studies by Remote Sensing

Science.gov (United States)

Zoffoli, Maria Laura; Frouin, Robert; Kampel, Milton

2014-01-01

Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application. PMID:25215941

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.

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

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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.

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

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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.

Study on the Coastline Change of Jiaozhou Bay Based on High Resolution Remote Sensing Image

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Zhu, H.; Xing, B.; Ni, S.; Wei, P.

2018-05-01

In recent years, with the rapid development of the Jiaozhou Bay area of Qingdao, the influence of human activities on the coastline of Jiaozhou Bay is becoming more and more serious. Based on the high resolution remote sensing image data of 10 periods from 2001 to 2017 in the Jiaozhou Bay area, and combined with the data of on-the-spot survey and expert knowledge, this paper have completed the interpretation and extraction of coastline data of each year, and analyzed the distribution, size, rate of change, and trend of the increase and decrease of the coastal area of Jiaozhou Bay in different time periods, combined with the economic construction and the marine hydrodynamic environment of the region to analyze the reasons for the change of the coastline of Jiaozhou Bay. The results show that the increase and reduction of the coastal area of Jiaozhou Bay was mainly affected by human activities such as sea reclamation and marine aquaculture, resulting in a gradual change in the rate of increase and decrease with human development. For coastal advance part,2001-2013, the average increase rate on the coastal area of Jiaozhou Bay was 2.30 km2/a, showing a trend of rapid growth, 2013-2017 the average increase rate of 0.53 km2/a, and the growth rate slowed down. For coastal retreat part, 2001-2013, the average decrease rate was 2.58 × 10-3 km2/a. 2013-2014, the decrease rate reached a peak value of 1.11 km2/a. 2014-2017, the average decrease rate was 0.14 km2/a. The decrease rate shows a trend of increasing first and then slowing down.

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

Remote Sensing of Suspended Sediment Dynamics in the Mississippi Sound

Science.gov (United States)

Merritt, D. N.; Skarke, A. D.; Silwal, S.; Dash, P.

2016-02-01

The Mississippi Sound is a semi-enclosed estuary between the coast of Mississippi and a chain of offshore barrier islands with relatively shallow water depths and high marine biodiversity that is wildly utilized for commercial fishing and public recreation. The discharge of sediment-laden rivers into the Mississippi Sound and the adjacent Northern Gulf of Mexico creates turbid plumes that can extend hundreds of square kilometers along the coast and persist for multiple days. The concentration of suspended sediment in these coastal waters is an important parameter in the calculation of regional sediment budgets as well as analysis of water-quality factors such as primary productivity, nutrient dynamics, and the transport of pollutants as well as pathogens. The spectral resolution, sampling frequency, and regional scale spatial domain associated with satellite based sensors makes remote sensing an ideal tool to monitor suspended sediment dynamics in the Northern Gulf of Mexico. Accordingly, the presented research evaluates the validity of published models that relate remote sensing reflectance with suspended sediment concentrations (SSC), for similar environmental settings, with 51 in situ observations of SSC from the Mississippi Sound. Additionally, regression analysis is used to correlate additional in situ observations of SSC in Mississippi Sound with coincident observations of visible and near-infrared band reflectance collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Aqua satellite, in order to develop a site-specific empirical predictive model for SSC. Finally, specific parameters of the sampled suspended sediment such as grain size and mineralogy are analyzed in order to quantify their respective contributions to total remotely sensed reflectance.

Compressed sensing cine imaging with high spatial or high temporal resolution for analysis of left ventricular function.

Science.gov (United States)

Goebel, Juliane; Nensa, Felix; Schemuth, Haemi P; Maderwald, Stefan; Gratz, Marcel; Quick, Harald H; Schlosser, Thomas; Nassenstein, Kai

2016-08-01

To assess two compressed sensing cine magnetic resonance imaging (MRI) sequences with high spatial or high temporal resolution in comparison to a reference steady-state free precession cine (SSFP) sequence for reliable quantification of left ventricular (LV) volumes. LV short axis stacks of two compressed sensing breath-hold cine sequences with high spatial resolution (SPARSE-SENSE HS: temporal resolution: 40 msec, in-plane resolution: 1.0 × 1.0 mm(2) ) and high temporal resolution (SPARSE-SENSE HT: temporal resolution: 11 msec, in-plane resolution: 1.7 × 1.7 mm(2) ) and of a reference cine SSFP sequence (standard SSFP: temporal resolution: 40 msec, in-plane resolution: 1.7 × 1.7 mm(2) ) were acquired in 16 healthy volunteers on a 1.5T MR system. LV parameters were analyzed semiautomatically twice by one reader and once by a second reader. The volumetric agreement between sequences was analyzed using paired t-test, Bland-Altman plots, and Passing-Bablock regression. Small differences were observed between standard SSFP and SPARSE-SENSE HS for stroke volume (SV; -7 ± 11 ml; P = 0.024), ejection fraction (EF; -2 ± 3%; P = 0.019), and myocardial mass (9 ± 9 g; P = 0.001), but not for end-diastolic volume (EDV; P = 0.079) and end-systolic volume (ESV; P = 0.266). No significant differences were observed between standard SSFP and SPARSE-SENSE HT regarding EDV (P = 0.956), SV (P = 0.088), and EF (P = 0.103), but for ESV (3 ± 5 ml; P = 0.039) and myocardial mass (8 ± 10 ml; P = 0.007). Bland-Altman analysis showed good agreement between the sequences (maximum bias ≤ -8%). Two compressed sensing cine sequences, one with high spatial resolution and one with high temporal resolution, showed good agreement with standard SSFP for LV volume assessment. J. Magn. Reson. Imaging 2016;44:366-374. © 2016 Wiley Periodicals, Inc.

Training Small Networks for Scene Classification of Remote Sensing Images via Knowledge Distillation

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Guanzhou Chen

2018-05-01

Full Text Available Scene classification, aiming to identify the land-cover categories of remotely sensed image patches, is now a fundamental task in the remote sensing image analysis field. Deep-learning-model-based algorithms are widely applied in scene classification and achieve remarkable performance, but these high-level methods are computationally expensive and time-consuming. Consequently in this paper, we introduce a knowledge distillation framework, currently a mainstream model compression method, into remote sensing scene classification to improve the performance of smaller and shallower network models. Our knowledge distillation training method makes the high-temperature softmax output of a small and shallow student model match the large and deep teacher model. In our experiments, we evaluate knowledge distillation training method for remote sensing scene classification on four public datasets: AID dataset, UCMerced dataset, NWPU-RESISC dataset, and EuroSAT dataset. Results show that our proposed training method was effective and increased overall accuracy (3% in AID experiments, 5% in UCMerced experiments, 1% in NWPU-RESISC and EuroSAT experiments for small and shallow models. We further explored the performance of the student model on small and unbalanced datasets. Our findings indicate that knowledge distillation can improve the performance of small network models on datasets with lower spatial resolution images, numerous categories, as well as fewer training samples.

REMOTE SENSING IMAGE CLASSIFICATION APPLIED TO THE FIRST NATIONAL GEOGRAPHICAL INFORMATION CENSUS OF CHINA

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X. Yu

2016-06-01

Full Text Available Image classification will still be a long way in the future, although it has gone almost half a century. In fact, researchers have gained many fruits in the image classification domain, but there is still a long distance between theory and practice. However, some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. Usually, networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Tree Augmented Naive Bayesian Networks (TAN to texture classification of High-resolution remote sensing images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. Since 2013, China government has started the first national geographical information census project, which mainly interprets geographical information based on high-resolution remote sensing images. Therefore, this paper tries to apply Bayesian network to remote sensing image classification, in order to improve image interpretation in the first national geographical information census project. In the experiment, we choose some remote sensing images in Beijing. Experimental results demonstrate TAN outperform than Naive Bayesian Classifier (NBC and Maximum Likelihood Classification Method (MLC in the overall classification accuracy. In addition, the proposed method can reduce the workload of field workers and improve the work efficiency. Although it is time consuming, it will be an attractive and effective method for assisting office operation of image interpretation.

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.

Remote Sensing Tertiary Education Meets High Intensity Interval Training

Science.gov (United States)

Joyce, K. E.; White, B.

2015-04-01

Enduring a traditional lecture is the tertiary education equivalent of a long, slow, jog. There are certainly some educational benefits if the student is able to maintain concentration, but they are just as likely to get caught napping and fall off the back end of the treadmill. Alternatively, a pre-choreographed interactive workshop style class requires students to continually engage with the materials. Appropriately timed breaks or intervals allow students to recover briefly before being increasingly challenged throughout the class. Using an introductory remote sensing class at Charles Darwin University, this case study presents a transition from the traditional stand and deliver style lecture to an active student-led learning experience. The class is taught at undergraduate and postgraduate levels, with both on-campus as well as online distance learning students. Based on the concept that active engagement in learning materials promotes 'stickiness' of subject matter, the remote sensing class was re-designed to encourage an active style of learning. Critically, class content was reviewed to identify the key learning outcomes for the students. This resulted in a necessary sacrifice of topic range for depth of understanding. Graduates of the class reported high levels of enthusiasm for the materials, and the style in which the class was taught. This paper details a number of techniques that were used to engage students in active and problem based learning throughout the semester. It suggests a number of freely available tools that academics in remote sensing and related fields can readily incorporate into their teaching portfolios. Moreover, it shows how simple it can be to provide a far more enjoyable and effective learning experience for students than the one dimensional lecture.

Investigating the relationship between tree heights derived from SIBBORK forest model and remote sensing measurements

Science.gov (United States)

Osmanoglu, B.; Feliciano, E. A.; Armstrong, A. H.; Sun, G.; Montesano, P.; Ranson, K.

2017-12-01

Tree heights are one of the most commonly used remote sensing parameters to measure biomass of a forest. In this project, we investigate the relationship between remotely sensed tree heights (e.g. G-LiHT lidar and commercially available high resolution satellite imagery, HRSI) and the SIBBORK modeled tree heights. G-LiHT is a portable, airborne imaging system that simultaneously maps the composition, structure, and function of terrestrial ecosystems using lidar, imaging spectroscopy and thermal mapping. Ground elevation and canopy height models were generated using the lidar data acquired in 2012. A digital surface model was also generated using the HRSI technique from the commercially available WorldView data in 2016. The HRSI derived height and biomass products are available at the plot (10x10m) level. For this study, we parameterized the SIBBORK individual-based gap model for Howland forest, Maine. The parameterization was calibrated using field data for the study site and results show that the simulated forest reproduces the structural complexity of Howland old growth forest, based on comparisons of key variables including, aboveground biomass, forest height and basal area. Furthermore carbon cycle and ecosystem observational capabilities will be enhanced over the next 6 years via the launch of two LiDAR (NASA's GEDI and ICESAT 2) and two SAR (NASA's ISRO NiSAR and ESA's Biomass) systems. Our aim is to present the comparison of canopy height models obtained with SIBBORK forest model and remote sensing techniques, highlighting the synergy between individual-based forest modeling and high-resolution remote sensing.

Educational activities of remote sensing archaeology (Conference Presentation)

Science.gov (United States)

Hadjimitsis, Diofantos G.; Agapiou, Athos; Lysandrou, Vasilki; Themistocleous, Kyriacos; Cuca, Branka; Nisantzi, Argyro; Lasaponara, Rosa; Masini, Nicola; Krauss, Thomas; Cerra, Daniele; Gessner, Ursula; Schreier, Gunter

2016-10-01

Remote sensing science is increasingly being used to support archaeological and cultural heritage research in various ways. Satellite sensors either passive or active are currently used in a systematic basis to detect buried archaeological remains and to systematic monitor tangible heritage. In addition, airborne and low altitude systems are being used for documentation purposes. Ground surveys using remote sensing tools such as spectroradiometers and ground penetrating radars can detect variations of vegetation and soil respectively, which are linked to the presence of underground archaeological features. Education activities and training of remote sensing archaeology to young people is characterized of highly importance. Specific remote sensing tools relevant for archaeological research can be developed including web tools, small libraries, interactive learning games etc. These tools can be then combined and aligned with archaeology and cultural heritage. This can be achieved by presenting historical and pre-historical records, excavated sites or even artifacts under a "remote sensing" approach. Using such non-form educational approach, the students can be involved, ask, read, and seek to learn more about remote sensing and of course to learn about history. The paper aims to present a modern didactical concept and some examples of practical implementation of remote sensing archaeology in secondary schools in Cyprus. The idea was built upon an ongoing project (ATHENA) focused on the sue of remote sensing for archaeological research in Cyprus. Through H2020 ATHENA project, the Remote Sensing Science and Geo-Environment Research Laboratory at the Cyprus University of Technology (CUT), with the support of the National Research Council of Italy (CNR) and the German Aerospace Centre (DLR) aims to enhance its performance in all these new technologies.

Exploitation of commercial remote sensing images: reality ignored?

Science.gov (United States)

Allen, Paul C.

1999-12-01

The remote sensing market is on the verge of being awash in commercial high-resolution images. Market estimates are based on the growing numbers of planned commercial remote sensing electro-optical, radar, and hyperspectral satellites and aircraft. EarthWatch, Space Imaging, SPOT, and RDL among others are all working towards launch and service of one to five meter panchromatic or radar-imaging satellites. Additionally, new advances in digital air surveillance and reconnaissance systems, both manned and unmanned, are also expected to expand the geospatial customer base. Regardless of platform, image type, or location, each system promises images with some combination of increased resolution, greater spectral coverage, reduced turn-around time (request-to- delivery), and/or reduced image cost. For the most part, however, market estimates for these new sources focus on the raw digital images (from collection to the ground station) while ignoring the requirements for a processing and exploitation infrastructure comprised of exploitation tools, exploitation training, library systems, and image management systems. From this it would appear the commercial imaging community has failed to learn the hard lessons of national government experience choosing instead to ignore reality and replicate the bias of collection over processing and exploitation. While this trend may be not impact the small quantity users that exist today it will certainly adversely affect the mid- to large-sized users of the future.

RESEARCH ON REMOTE SENSING GEOLOGICAL INFORMATION EXTRACTION BASED ON OBJECT ORIENTED CLASSIFICATION

Directory of Open Access Journals (Sweden)

H. Gao

2018-04-01

Full Text Available The northern Tibet belongs to the Sub cold arid climate zone in the plateau. It is rarely visited by people. The geological working conditions are very poor. However, the stratum exposures are good and human interference is very small. Therefore, the research on the automatic classification and extraction of remote sensing geological information has typical significance and good application prospect. Based on the object-oriented classification in Northern Tibet, using the Worldview2 high-resolution remote sensing data, combined with the tectonic information and image enhancement, the lithological spectral features, shape features, spatial locations and topological relations of various geological information are excavated. By setting the threshold, based on the hierarchical classification, eight kinds of geological information were classified and extracted. Compared with the existing geological maps, the accuracy analysis shows that the overall accuracy reached 87.8561 %, indicating that the classification-oriented method is effective and feasible for this study area and provides a new idea for the automatic extraction of remote sensing geological information.

Remote Sensing for Wind Energy

DEFF Research Database (Denmark)

The Remote Sensing in Wind Energy Compendium provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind this compendium began in year 2008 at Risø DTU during the first PhD Summer School: Remote Sensing in Wind Energy. Thus......-of-the-art compendium available for people involved in Remote Sensing in Wind Energy....

Tunnel-Site Selection by Remote Sensing Techniques

Science.gov (United States)

A study of the role of remote sensing for geologic reconnaissance for tunnel-site selection was commenced. For this study, remote sensing was defined...conventional remote sensing . Future research directions are suggested, and the extension of remote sensing to include airborne passive microwave

Remote Sensing Best Paper Award 2013

OpenAIRE

Prasad Thenkabail

2013-01-01

Remote Sensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remote sensing techniques, design and applications published in Remote Sensing. We are pleased to announce the first “Remote Sensing Best Paper Award” for 2013. Nominations were selected by the Editor-in-Chief and selected editorial board members from among all the papers published in 2009. Reviews and research papers were evaluated separately.

Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing.

Science.gov (United States)

Walz, Yvonne; Wegmann, Martin; Dech, Stefan; Vounatsou, Penelope; Poda, Jean-Noël; N'Goran, Eliézer K; Utzinger, Jürg; Raso, Giovanna

2015-11-01

Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health. We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in Côte d'Ivoire and validated against readily available survey data from school-aged children. Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of Côte d'Ivoire. A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail

High efficient optical remote sensing images acquisition for nano-satellite: reconstruction algorithms

Science.gov (United States)

Liu, Yang; Li, Feng; Xin, Lei; Fu, Jie; Huang, Puming

2017-10-01

Large amount of data is one of the most obvious features in satellite based remote sensing systems, which is also a burden for data processing and transmission. The theory of compressive sensing(CS) has been proposed for almost a decade, and massive experiments show that CS has favorable performance in data compression and recovery, so we apply CS theory to remote sensing images acquisition. In CS, the construction of classical sensing matrix for all sparse signals has to satisfy the Restricted Isometry Property (RIP) strictly, which limits applying CS in practical in image compression. While for remote sensing images, we know some inherent characteristics such as non-negative, smoothness and etc.. Therefore, the goal of this paper is to present a novel measurement matrix that breaks RIP. The new sensing matrix consists of two parts: the standard Nyquist sampling matrix for thumbnails and the conventional CS sampling matrix. Since most of sun-synchronous based satellites fly around the earth 90 minutes and the revisit cycle is also short, lots of previously captured remote sensing images of the same place are available in advance. This drives us to reconstruct remote sensing images through a deep learning approach with those measurements from the new framework. Therefore, we propose a novel deep convolutional neural network (CNN) architecture which takes in undersampsing measurements as input and outputs an intermediate reconstruction image. It is well known that the training procedure to the network costs long time, luckily, the training step can be done only once, which makes the approach attractive for a host of sparse recovery problems.

Active landslide monitoring using remote sensing data, GPS measurements and cameras on board UAV

Science.gov (United States)

Nikolakopoulos, Konstantinos G.; Kavoura, Katerina; Depountis, Nikolaos; Argyropoulos, Nikolaos; Koukouvelas, Ioannis; Sabatakakis, Nikolaos

2015-10-01

An active landslide can be monitored using many different methods: Classical geotechnical measurements like inclinometer, topographical survey measurements with total stations or GPS and photogrammetric techniques using airphotos or high resolution satellite images. As the cost of the aerial photo campaign and the acquisition of very high resolution satellite data is quite expensive the use of cameras on board UAV could be an identical solution. Small UAVs (Unmanned Aerial Vehicles) have started their development as expensive toys but they currently became a very valuable tool in remote sensing monitoring of small areas. The purpose of this work is to demonstrate a cheap but effective solution for an active landslide monitoring. We present the first experimental results of the synergistic use of UAV, GPS measurements and remote sensing data. A six-rotor aircraft with a total weight of 6 kg carrying two small cameras has been used. Very accurate digital airphotos, high accuracy DSM, DGPS measurements and the data captured from the UAV are combined and the results are presented in the current study.

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.

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.

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.

Tree health monitoring: perspectives from the visible and near infrared remote sensing

Directory of Open Access Journals (Sweden)

Gonthier P

2012-05-01

Full Text Available Based on a comprehensive literature analysis, we present a critical review of those optical remote sensing techniques operating with the visible (VIS and near infrared (NIR bands for the assessment of health in forest trees. Physical, biological and physio-pathological issues of VIS-NIR reflectance of leaves are described pointing out that a decrease of NIR reflectance is highly influenced by stress conditions on tree caused by abiotic and biotic factors. In many cases the NIR spectral band is more sensitive than the VIS one, allowing to detect plant stress long before the appearance of visible symptoms. A description of the main remote sensing methods is provided, including radiometric measurements and multispectral imaging approaches. False colour infrared (FCIR images collection and their photointerpretation and processing are shown as they represent the most relevant means to acquire information of canopy from its reflectance properties. The amount and the quality of the obtainable data depend on: (i field conditions; (ii the type of the adopted instrument (camera, radiometer; (iii the recording system position (ground platforms, aircraft, satellite; (iv the format of the data (analogical, digitalised or digital; and (v the photointerpretation technique. Results from literature are discussed stressing the limits of remote sensing methods. Remote sensing in VIS and NIR spectral bands is generally a powerful classification tool to detect and score tree stress. Nevertheless, it is not a diagnostic tool in that it does not provide information on the cause of stress. Moreover, the method should be adequately tested at single tree level for many important pathogens, in particular root rot, butt rot and stem rot fungi. In perspective, new high spatial resolution satellite images and their GIS software elaboration might be suitable to improve remote sensing analysis.

Remote Sensing for Wind Energy

DEFF Research Database (Denmark)

Peña, Alfredo; Hasager, Charlotte Bay; Lange, Julia

The Remote Sensing in Wind Energy report provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind it began in year 2008 at DTU Wind Energy (formerly Risø) during the first PhD Summer School: Remote Sensing in Wind Energy...... state-of-the-art ‘guideline’ available for people involved in Remote Sensing in Wind Energy....

Remote Sensing and Imaging Physics

Science.gov (United States)

2012-03-07

Program Manager AFOSR/RSE Air Force Research Laboratory Remote Sensing and Imaging Physics 7 March 2012 Report Documentation Page Form...00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Remote Sensing And Imaging Physics 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT...Imaging of Space Objects •Information without Imaging •Predicting the Location of Space Objects • Remote Sensing in Extreme Conditions •Propagation

Road Segmentation of Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields

Directory of Open Access Journals (Sweden)

Teerapong Panboonyuen

2017-07-01

Full Text Available Object segmentation of remotely-sensed aerial (or very-high resolution, VHS images and satellite (or high-resolution, HR images, has been applied to many application domains, especially in road extraction in which the segmented objects are served as a mandatory layer in geospatial databases. Several attempts at applying the deep convolutional neural network (DCNN to extract roads from remote sensing images have been made; however, the accuracy is still limited. In this paper, we present an enhanced DCNN framework specifically tailored for road extraction of remote sensing images by applying landscape metrics (LMs and conditional random fields (CRFs. To improve the DCNN, a modern activation function called the exponential linear unit (ELU, is employed in our network, resulting in a higher number of, and yet more accurate, extracted roads. To further reduce falsely classified road objects, a solution based on an adoption of LMs is proposed. Finally, to sharpen the extracted roads, a CRF method is added to our framework. The experiments were conducted on Massachusetts road aerial imagery as well as the Thailand Earth Observation System (THEOS satellite imagery data sets. The results showed that our proposed framework outperformed Segnet, a state-of-the-art object segmentation technique, on any kinds of remote sensing imagery, in most of the cases in terms of precision, recall, and F 1 .

Remote sensing: best practice

Energy Technology Data Exchange (ETDEWEB)

Brown, Gareth [Sgurr Energy (Canada)

2011-07-01

This paper presents remote sensing best practice in the wind industry. Remote sensing is a technique whereby measurements are obtained from the interaction of laser or acoustic pulses with the atmosphere. There is a vast diversity of tools and techniques available and they offer wide scope for reducing project uncertainty and risk but best practice must take into account versatility and flexibility. It should focus on the outcome in terms of results and data. However, traceability of accuracy requires comparison with conventional instruments. The framework for the Boulder protocol is given. Overviews of the guidelines for IEA SODAR and IEA LIDAR are also mentioned. The important elements of IEC 61400-12-1, an international standard for wind turbines, are given. Bankability is defined based on the Boulder protocol and a pie chart is presented that illustrates the uncertainty area covered by remote sensing. In conclusion it can be said that remote sensing is changing perceptions about how wind energy assessments can be made.

Research on fast Fourier transforms algorithm of huge remote sensing image technology with GPU and partitioning technology.

Science.gov (United States)

Yang, Xue; Li, Xue-You; Li, Jia-Guo; Ma, Jun; Zhang, Li; Yang, Jan; Du, Quan-Ye

2014-02-01

Fast Fourier transforms (FFT) is a basic approach to remote sensing image processing. With the improvement of capacity of remote sensing image capture with the features of hyperspectrum, high spatial resolution and high temporal resolution, how to use FFT technology to efficiently process huge remote sensing image becomes the critical step and research hot spot of current image processing technology. FFT algorithm, one of the basic algorithms of image processing, can be used for stripe noise removal, image compression, image registration, etc. in processing remote sensing image. CUFFT function library is the FFT algorithm library based on CPU and FFTW. FFTW is a FFT algorithm developed based on CPU in PC platform, and is currently the fastest CPU based FFT algorithm function library. However there is a common problem that once the available memory or memory is less than the capacity of image, there will be out of memory or memory overflow when using the above two methods to realize image FFT arithmetic. To address this problem, a CPU and partitioning technology based Huge Remote Fast Fourier Transform (HRFFT) algorithm is proposed in this paper. By improving the FFT algorithm in CUFFT function library, the problem of out of memory and memory overflow is solved. Moreover, this method is proved rational by experiment combined with the CCD image of HJ-1A satellite. When applied to practical image processing, it improves effect of the image processing, speeds up the processing, which saves the time of computation and achieves sound result.

Comprehensive UAV agricultural remote-sensing research at Texas A M University

Science.gov (United States)

Thomasson, J. Alex; Shi, Yeyin; Olsenholler, Jeffrey; Valasek, John; Murray, Seth C.; Bishop, Michael P.

2016-05-01

Unmanned aerial vehicles (UAVs) have advantages over manned vehicles for agricultural remote sensing. Flying UAVs is less expensive, is more flexible in scheduling, enables lower altitudes, uses lower speeds, and provides better spatial resolution for imaging. The main disadvantage is that, at lower altitudes and speeds, only small areas can be imaged. However, on large farms with contiguous fields, high-quality images can be collected regularly by using UAVs with appropriate sensing technologies that enable high-quality image mosaics to be created with sufficient metadata and ground-control points. In the United States, rules governing the use of aircraft are promulgated and enforced by the Federal Aviation Administration (FAA), and rules governing UAVs are currently in flux. Operators must apply for appropriate permissions to fly UAVs. In the summer of 2015 Texas A&M University's agricultural research agency, Texas A&M AgriLife Research, embarked on a comprehensive program of remote sensing with UAVs at its 568-ha Brazos Bottom Research Farm. This farm is made up of numerous fields where various crops are grown in plots or complete fields. The crops include cotton, corn, sorghum, and wheat. After gaining FAA permission to fly at the farm, the research team used multiple fixed-wing and rotary-wing UAVs along with various sensors to collect images over all parts of the farm at least once per week. This article reports on details of flight operations and sensing and analysis protocols, and it includes some lessons learned in the process of developing a UAV remote-sensing effort of this sort.

Remote sensing in meteorology, oceanography and hydrology

Energy Technology Data Exchange (ETDEWEB)

Cracknell, A P [ed.

1981-01-01

Various aspects of remote sensing are discussed. Topics include: the EARTHNET data acquisition, processing, and distribution facility the design and implementation of a digital interactive image processing system geometrical aspects of remote sensing and space cartography remote sensing of a complex surface legal aspects of remote sensing remote sensing of pollution, dust storms, ice masses, and ocean waves and currents use of satellite images for weather forecasting. Notes on field trips and work-sheets for laboratory exercises are included.

Space remote sensing systems an introduction

CERN Document Server

Chen, H S

1985-01-01

Space Remote Sensing Systems: An Introduction discusses the space remote sensing system, which is a modern high-technology field developed from earth sciences, engineering, and space systems technology for environmental protection, resource monitoring, climate prediction, weather forecasting, ocean measurement, and many other applications. This book consists of 10 chapters. Chapter 1 describes the science of the atmosphere and the earth's surface. Chapter 2 discusses spaceborne radiation collector systems, while Chapter 3 focuses on space detector and CCD systems. The passive space optical rad

High resolution spectroscopy in the microwave and far infrared

Science.gov (United States)

Pickett, Herbert M.

1990-01-01

High resolution rotational spectroscopy has long been central to remote sensing techniques in atmospheric sciences and astronomy. As such, laboratory measurements must supply the required data to make direct interpretation of data for instruments which sense atmospheres using rotational spectra. Spectral measurements in the microwave and far infrared regions are also very powerful tools when combined with infrared measurements for characterizing the rotational structure of vibrational spectra. In the past decade new techniques were developed which have pushed high resolution spectroscopy into the wavelength region between 25 micrometers and 2 mm. Techniques to be described include: (1) harmonic generation of microwave sources, (2) infrared laser difference frequency generation, (3) laser sideband generation, and (4) ultrahigh resolution interferometers.

Estimating Discharge in Low-Order Rivers With High-Resolution Aerial Imagery

OpenAIRE

King, Tyler V.; Neilson, Bethany T.; Rasmussen, Mitchell T.

2018-01-01

Remote sensing of river discharge promises to augment in situ gauging stations, but the majority of research in this field focuses on large rivers (>50 m wide). We present a method for estimating volumetric river discharge in low-order (wide) rivers from remotely sensed data by coupling high-resolution imagery with one-dimensional hydraulic modeling at so-called virtual gauging stations. These locations were identified as locations where the river contracted under low flows, exposing a substa...

Passive microwave remote sensing of soil moisture

International Nuclear Information System (INIS)

Jackson, T.J.; Schmugge, T.J.

1986-01-01

Microwave remote sensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive microwave soil moisture sensors currently considered for space operation are in the range 10–20 km. The most useful frequency range for soil moisture sensing is 1–5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods. Particularly promising are methods for optimally assimilating passive microwave data into hydrologic models. Further studies are needed to investigate the effects on microwave observations of within-footprint spatial heterogeneity of vegetation cover and subsurface soil characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale soil moisture information from remote observations

Advances in Small Remotely Piloted Aircraft Communications and Remote Sensing in Maritime Environments including the Arctic

Science.gov (United States)

McGillivary, P. A.; Borges de Sousa, J.; Wackowski, S.; Walker, G.

2011-12-01

highlight use in the arctic of two different small remotely piloted aircraft (ScanEagle and RAVEN) for remote sensing of ice and ocean conditions as well as surveys of marine mammals. Finally, we explain how these can be used in future networked environments with DTN support not only for the collection of ocean and ice data for maritime domain awareness, but also for monitoring oil spill dynamics in high latitude environments, including spills in and under sea ice. The networked operation of heterogeneous air and ocean vehicle systems using DTN communications methods can provide unprecedented levels of spatial-temporal sampling resolution important to improving arctic remote sensing and maritime domain awareness capabilities.

Unmanned aerial systems for photogrammetry and remote sensing: A review

Science.gov (United States)

Colomina, I.; Molina, P.

2014-06-01

We discuss the evolution and state-of-the-art of the use of Unmanned Aerial Systems (UAS) in the field of Photogrammetry and Remote Sensing (PaRS). UAS, Remotely-Piloted Aerial Systems, Unmanned Aerial Vehicles or simply, drones are a hot topic comprising a diverse array of aspects including technology, privacy rights, safety and regulations, and even war and peace. Modern photogrammetry and remote sensing identified the potential of UAS-sourced imagery more than thirty years ago. In the last five years, these two sister disciplines have developed technology and methods that challenge the current aeronautical regulatory framework and their own traditional acquisition and processing methods. Navety and ingenuity have combined off-the-shelf, low-cost equipment with sophisticated computer vision, robotics and geomatic engineering. The results are cm-level resolution and accuracy products that can be generated even with cameras costing a few-hundred euros. In this review article, following a brief historic background and regulatory status analysis, we review the recent unmanned aircraft, sensing, navigation, orientation and general data processing developments for UAS photogrammetry and remote sensing with emphasis on the nano-micro-mini UAS segment.

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.

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.

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

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.

Monitoring Nuclear Facilities Using Satellite Imagery and Associated Remote Sensing Techniques

International Nuclear Information System (INIS)

Lafitte, Marc; Robin, Jean‑Philippe

2015-01-01

The mission of the European Union Satellite Centre (SatCen) is “to support the decision making and actions of the European Union in the field of the CFSP and in particular the CSDP, including European Union crisis management missions and operations, by providing, at the request of the Council or the European Union High Representative, products and services resulting from the exploitation of relevant space assets and collateral data, including satellite and aerial imagery, and related services”. The SatCen Non‑Proliferation Team, part of the SatCen Operations Division, is responsible for the analysis of installations that are involved, or could be involved, in the preparation or acquisition of capabilities intended to divert the production of nuclear material for military purposes and, in particular, regarding the spread of Weapons of Mass destruction and their means of delivery. For the last four decades, satellite imagery and associated remote sensing and geospatial techniques have increasingly expanded their capabilities. The unprecedented Very High Resolution (VHR) data currently available, the improved spectral capabilities, the increasing number of sensors and ever increasing computing capacity, has opened up a wide range of new perspectives for remote sensing applications. Concurrently, the availability of open source information (OSINF), has increased exponentially through the medium of the internet. This range of new capabilities for sensors and associated remote sensing techniques have strengthened the SatCen analysis capabilities for the monitoring of suspected proliferation installations for the detection of undeclared nuclear facilities, processes and activities. The combination of these remote sensing techniques, imagery analysis, open source investigation and their integration into Geographic Information Systems (GIS), undoubtedly improve the efficiency and comprehensive analysis capability provided by the SatCen to the EU stake‑holders. The

Potential for remote sensing of agriculture from the international space station

International Nuclear Information System (INIS)

Morgenthaler, George W.; Khatib, Nader

1999-01-01

Today's spatial resolution of orbital sensing systems is too coarse to economically serve the yield-improvement/contamination-reduction needs of the small to mid-size farm enterprise. Remote sensing from aircraft is being pressed into service. However, satellite remote sensing constellations with greater resolution and more spectral bands, i.e., with resolutions of 1 m in the panchromatic, 4 m in the multi-spectral, and 8 m in the hyper-spectral are expected to be in orbit by the year 2000. Such systems coupled with Global Positioning System (GPS) capability will make 'precision agriculture', i.e., the identification of specific and timely fertilizer, irrigation, herbicide, and insecticide needs on an acre-by-acre basis and the ability to meet these needs with precision delivery systems at affordable costs, is what is needed and can be achieved. Current plans for remote sensing systems on the International Space Station (ISS) include externally attached payloads and a window observation platform. The planned orbit of the Space Station will result in overflight of a specific latitude and longitude at the same clock time every 3 months. However, a pass over a specific latitude and longitude during 'daylight hours' could occur much more frequently. The ISS might thus be a space platform for experimental and developmental testing of future commercial space remote sensing precision agriculture systems. There is also a need for agricultural 'truth' sites so that predictive crop yield and pollution models can be devised and corrective suggestions delivered to farmers at affordable costs. In Summer 1998, the University of Colorado at Boulder and the Center for the Study of Terrestrial and Extraterrestrial Atmospheres (CSTEA) at Howard University, under NASA Goddard Space Flight Center funding, established an agricultural 'truth' site in eastern Colorado. The 'truth' site was highly instrumented for measuring trace gas concentrations (NO x , SO x , CO 2 , O 3 , organics

Application of Remote Sensing in Building Damages Assessment after Moderate and Strong Earthquake

Science.gov (United States)

Tian, Y.; Zhang, J.; Dou, A.

2003-04-01

- Earthquake is a main natural disaster in modern society. However, we still cannot predict the time and place of its occurrence accurately. Then it is of much importance to survey the damages information when an earthquake occurs, which can help us to mitigate losses and implement fast damage evaluation. In this paper, we use remote sensing techniques for our purposes. Remotely sensed satellite images often view a large scale of land at a time. There are several kinds of satellite images, which of different spatial and spectral resolutions. Landsat-4/5 TM sensor can view ground at 30m resolution, while Landsat-7 ETM Plus has a resolution of 15m in panchromatic waveband. SPOT satellite can provide images with higher resolutions. Those images obtained pre- and post-earthquake can help us greatly in identifying damages of moderate and large-size buildings. In this paper, we bring forward a method to implement quick damages assessment by analyzing both pre- and post-earthquake satellite images. First, those images are geographically registered together with low RMS (Root Mean Square) error. Then, we clip out residential areas by overlaying images with existing vector layers through Geographic Information System (GIS) software. We present a new change detection algorithm to quantitatively identify damages degree. An empirical or semi-empirical model is then established by analyzing the real damage degree and changes of pixel values of the same ground objects. Experimental result shows that there is a good linear relationship between changes of pixel values and ground damages, which proves the potentials of remote sensing in post-quake fast damage assessment. Keywords: Damages Assessment, Earthquake Hazard, Remote Sensing

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)

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

Combining low level features and visual attributes for VHR remote sensing image classification

Science.gov (United States)

Zhao, Fumin; Sun, Hao; Liu, Shuai; Zhou, Shilin

2015-12-01

Semantic classification of very high resolution (VHR) remote sensing images is of great importance for land use or land cover investigation. A large number of approaches exploiting different kinds of low level feature have been proposed in the literature. Engineers are often frustrated by their conclusions and a systematic assessment of various low level features for VHR remote sensing image classification is needed. In this work, we firstly perform an extensive evaluation of eight features including HOG, dense SIFT, SSIM, GIST, Geo color, LBP, Texton and Tiny images for classification of three public available datasets. Secondly, we propose to transfer ground level scene attributes to remote sensing images. Thirdly, we combine both low-level features and mid-level visual attributes to further improve the classification performance. Experimental results demonstrate that i) Dene SIFT and HOG features are more robust than other features for VHR scene image description. ii) Visual attribute competes with a combination of low level features. iii) Multiple feature combination achieves the best performance under different settings.

Radar Remote Sensing

Science.gov (United States)

Rosen, Paul A.

2012-01-01

This lecture was just a taste of radar remote sensing techniques and applications. Other important areas include Stereo radar grammetry. PolInSAR for volumetric structure mapping. Agricultural monitoring, soil moisture, ice-mapping, etc. The broad range of sensor types, frequencies of observation and availability of sensors have enabled radar sensors to make significant contributions in a wide area of earth and planetary remote sensing sciences. The range of applications, both qualitative and quantitative, continue to expand with each new generation of sensors.

Ultraviolet spectrographs for thermospheric and ionospheric remote sensing

International Nuclear Information System (INIS)

Dymond, K.F.; McCoy, R.P.

1993-01-01

The Naval Research Laboratory (NRL) has been developing far- and extreme-ultraviolet spectrographs for remote sensing the Earth's upper atmosphere and ionosphere. The first of these sensors, called the Special Sensor Ultraviolet Limb Imager (SSULI), will be flying on the Air Force's Defense Meteorological Satellite Program (DMSP) block 5D3 satellites as an operational sensor in the 1997-2010 time frame. A second sensor, called the High-resolution ionospheric and Thermospheric Spectrograph (HITS), will fly in late 1995 on the Air Force Space Test Program's Advanced Research and Global Observation Satellite (ARGOS, also known as P91-1) as part of NRL's High Resolution Airglow and Auroral Spectroscopy (HIRAAS) experiment. Both of these instruments are compact and do not draw much power and would be good candidates for small satellite applications. The instruments and their capabilities are discussed. Possible uses of these instruments in small satellite applications are also presented

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.

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)

Remote sensing fire and fuels in southern California

Science.gov (United States)

Philip Riggan; Lynn Wolden; Bob Tissell; David Weise; J. Coen

2011-01-01

Airborne remote sensing at infrared wavelengths has the potential to quantify large-fire properties related to energy release or intensity, residence time, fuel-consumption rate, rate of spread, and soil heating. Remote sensing at a high temporal rate can track fire-line outbreaks and acceleration and spotting ahead of a fire front. Yet infrared imagers and imaging...

Remote sensing applications in environmental research

CERN Document Server

Srivastava, Prashant K; Gupta, Manika; Islam, Tanvir

2014-01-01

Remote Sensing Applications in Environmental Research is the basis for advanced Earth Observation (EO) datasets used in environmental monitoring and research. Now that there are a number of satellites in orbit, EO has become imperative in today's sciences, weather and natural disaster prediction. This highly interdisciplinary reference work brings together diverse studies on remote sensing and GIS, from a theoretical background to its applications, represented through various case studies and the findings of new models. The book offers a comprehensive range of contributions by well-known scientists from around the world and opens a new window for students in presenting interdisciplinary and methodological resources on the latest research. It explores various key aspects and offers state-of-the-art research in a simplified form, describing remote sensing and GIS studies for those who are new to the field, as well as for established researchers.

Remote Sensing Analysis Techniques and Sensor Requirements to Support the Mapping of Illegal Domestic Waste Disposal Sites in Queensland, Australia

Directory of Open Access Journals (Sweden)

Katharine Glanville

2015-10-01

Full Text Available Illegal disposal of waste is a significant management issue for contemporary governments with waste posing an economic, social, and environmental risk. An improved understanding of the distribution of illegal waste disposal sites is critical to enhance the cost-effectiveness and efficiency of waste management efforts. Remotely sensed data has the potential to address this knowledge gap. However, the literature regarding the use of remote sensing to map illegal waste disposal sites is incomplete. This paper aims to analyze existing remote sensing methods and sensors used to monitor and map illegal waste disposal sites. The purpose of this paper is to support the evaluation of existing remote sensing methods for mapping illegal domestic waste sites in Queensland, Australia. Recent advances in technology and the acquisition of very high-resolution remote sensing imagery provide an important opportunity to (1 revisit established analysis techniques for identifying illegal waste disposal sites, (2 examine the applicability of different remote sensors for illegal waste disposal detection, and (3 identify opportunities for future research to increase the accuracy of any illegal waste disposal mapping products.

Some technical notes on using UAV-based remote sensing for post disaster assessment

Science.gov (United States)

Rokhmana, Catur Aries; Andaru, Ruli

2017-07-01

Indonesia is located in an area prone to disasters, which are various kinds of natural disasters happen. In disaster management, the geoinformation data are needed to be able to evaluate the impact area. The UAV (Unmanned Aerial Vehicle)-Based remote sensing technology is a good choice to produce a high spatial resolution of less than 15 cm, while the current resolution of the satellite imagery is still greater than 50 cm. This paper shows some technical notes that should be considered when using UAV-Based remote sensing system in post disaster for rapid assessment. Some cases are Aceh Earthquake in years 2013 for seeing infrastructure damages, Banjarnegara landslide in year 2014 for seeing the impact; and Kelud volcano eruption in year 2014 for seeing the impact and volumetric material calculation. The UAV-Based remote sensing system should be able to produce the Orthophoto image that can provide capabilities for visual interpretation the individual damage objects, and the changes situation. Meanwhile the DEM (digital Elevation model) product can derive terrain topography, and volumetric calculation with accuracy 3-5 pixel or sub-meter also. The UAV platform should be able for working remotely and autonomously in dangerous area and limited infrastructures. In mountainous or volcano area, an unconventional flight plan should implemented. Unfortunately, not all impact can be seen from above such as wall crack, some parcel boundaries, and many objects that covered by others higher object. The previous existing geoinformation data are also needed to be able to evaluate the change detection automatically.

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.

Directory of Open Access Journals (Sweden)

Gerald Forkuor

Full Text Available 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

Remote sensing estimates of impervious surfaces for pluvial flood modelling

DEFF Research Database (Denmark)

Kaspersen, Per Skougaard; Drews, Martin

This paper investigates the accuracy of medium resolution (MR) satellite imagery in estimating impervious surfaces for European cities at the detail required for pluvial flood modelling. Using remote sensing techniques enables precise and systematic quantification of the influence of the past 30...

Remote sensing for water quality

International Nuclear Information System (INIS)

Giardino, Claudia

2006-01-01

The application of remote sensing to the study of lakes is begun in years 80 with the lunch of the satellites of second generation. Many experiences have indicated the contribution of remote sensing for the limnology [it

Corn and sorghum phenotyping using a fixed-wing UAV-based remote sensing system

Science.gov (United States)

Shi, Yeyin; Murray, Seth C.; Rooney, William L.; Valasek, John; Olsenholler, Jeff; Pugh, N. Ace; Henrickson, James; Bowden, Ezekiel; Zhang, Dongyan; Thomasson, J. Alex

2016-05-01

Recent development of unmanned aerial systems has created opportunities in automation of field-based high-throughput phenotyping by lowering flight operational cost and complexity and allowing flexible re-visit time and higher image resolution than satellite or manned airborne remote sensing. In this study, flights were conducted over corn and sorghum breeding trials in College Station, Texas, with a fixed-wing unmanned aerial vehicle (UAV) carrying two multispectral cameras and a high-resolution digital camera. The objectives were to establish the workflow and investigate the ability of UAV-based remote sensing for automating data collection of plant traits to develop genetic and physiological models. Most important among these traits were plant height and number of plants which are currently manually collected with high labor costs. Vegetation indices were calculated for each breeding cultivar from mosaicked and radiometrically calibrated multi-band imagery in order to be correlated with ground-measured plant heights, populations and yield across high genetic-diversity breeding cultivars. Growth curves were profiled with the aerial measured time-series height and vegetation index data. The next step of this study will be to investigate the correlations between aerial measurements and ground truth measured manually in field and from lab tests.

Remote sensing terminology: past experience and recent needs

Science.gov (United States)

Kancheva, Rumiana

2013-10-01

Terminology is a key issue for a better understanding among people using various languages. Terminology accuracy is essential during all phases of international cooperation. It is crucial to keep up with the latest quantitative and qualitative developments and novelties of the terminology in advanced technology fields such as aerospace science and industry. This is especially true in remote sensing and geoinformatics which develop rapidly and have wide and ever extending applications in various domains of human activity. The importance of the correct use of remote sensing terms refers not only to people working in this field but also to experts in many disciplines who handle remote sensing data and information products. The paper is devoted to terminology issues that refer to all aspects of remote sensing research and application areas. The attention is drawn on the recent needs and peculiarities of compiling specialized dictionaries in the subject area of remote sensing. Details are presented about the work in progress on the preparation of an English-Bulgarian dictionary of remote sensing terms focusing on Earth observations and geoinformation science. Our belief is that the elaboration of bilingual and multilingual dictionaries and glossaries in this spreading, most technically advanced and promising field of human expertise is of great practical importance. Any interest in cooperation and initiating of suchlike collaborative multilingual projects is welcome and highly appreciated.

Environmental mapping and monitoring of Iceland by remote sensing (EMMIRS)

Science.gov (United States)

Pedersen, Gro B. M.; Vilmundardóttir, Olga K.; Falco, Nicola; Sigurmundsson, Friðþór S.; Rustowicz, Rose; Belart, Joaquin M.-C.; Gísladóttir, Gudrun; Benediktsson, Jón A.

2016-04-01

Iceland is exposed to rapid and dynamic landscape changes caused by natural processes and man-made activities, which impact and challenge the country. Fast and reliable mapping and monitoring techniques are needed on a big spatial scale. However, currently there is lack of operational advanced information processing techniques, which are needed for end-users to incorporate remote sensing (RS) data from multiple data sources. Hence, the full potential of the recent RS data explosion is not being fully exploited. The project Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS) bridges the gap between advanced information processing capabilities and end-user mapping of the Icelandic environment. This is done by a multidisciplinary assessment of two selected remote sensing super sites, Hekla and Öræfajökull, which encompass many of the rapid natural and man-made landscape changes that Iceland is exposed to. An open-access benchmark repository of the two remote sensing supersites is under construction, providing high-resolution LIDAR topography and hyperspectral data for land-cover and landform classification. Furthermore, a multi-temporal and multi-source archive stretching back to 1945 allows a decadal evaluation of landscape and ecological changes for the two remote sensing super sites by the development of automated change detection techniques. The development of innovative pattern recognition and machine learning-based approaches to image classification and change detection is one of the main tasks of the EMMIRS project, aiming to extract and compute earth observation variables as automatically as possible. Ground reference data collected through a field campaign will be used to validate the implemented methods, which outputs are then inferred with geological and vegetation models. Here, preliminary results of an automatic land-cover classification based on hyperspectral image analysis are reported. Furthermore, the EMMIRS project

Realization of daily evapotranspiration in arid ecosystems based on remote sensing techniques

Science.gov (United States)

Elhag, Mohamed; Bahrawi, Jarbou A.

2017-03-01

Daily evapotranspiration is a major component of water resources management plans. In arid ecosystems, the quest for an efficient water budget is always hard to achieve due to insufficient irrigational water and high evapotranspiration rates. Therefore, monitoring of daily evapotranspiration is a key practice for sustainable water resources management, especially in arid environments. Remote sensing techniques offered a great help to estimate the daily evapotranspiration on a regional scale. Existing open-source algorithms proved to estimate daily evapotranspiration comprehensively in arid environments. The only deficiency of these algorithms is the course scale of the used remote sensing data. Consequently, the adequate downscaling algorithm is a compulsory step to rationalize an effective water resources management plan. Daily evapotranspiration was estimated fairly well using an Advance Along-Track Scanner Radiometer (AATSR) in conjunction with (MEdium Resolution Imaging Spectrometer) MERIS data acquired in July 2013 with 1 km spatial resolution and 3 days of temporal resolution under a surface energy balance system (SEBS) model. Results were validated against reference evapotranspiration ground truth values using standardized Penman-Monteith method with R2 of 0.879. The findings of the current research successfully monitor turbulent heat fluxes values estimated from AATSR and MERIS data with a temporal resolution of 3 days only in conjunction with reliable meteorological data. Research verdicts are necessary inputs for a well-informed decision-making processes regarding sustainable water resource management.

Advanced Ecosystem Mapping Techniques for Large Arctic Study Domains Using Calibrated High-Resolution Imagery

Science.gov (United States)

Macander, M. J.; Frost, G. V., Jr.

2015-12-01

Regional-scale mapping of vegetation and other ecosystem properties has traditionally relied on medium-resolution remote sensing such as Landsat (30 m) and MODIS (250 m). Yet, the burgeoning availability of high-resolution (environments has not been previously evaluated. Image segmentation, or object-based image analysis, automatically partitions high-resolution imagery into homogeneous image regions that can then be analyzed based on spectral, textural, and contextual information. We applied eCognition software to delineate waterbodies and vegetation classes, in combination with other techniques. Texture metrics were evaluated to determine the feasibility of using high-resolution imagery to algorithmically characterize periglacial surface forms (e.g., ice-wedge polygons), which are an important physical characteristic of permafrost-dominated regions but which cannot be distinguished by medium-resolution remote sensing. These advanced mapping techniques provide products which can provide essential information supporting a broad range of ecosystem science and land-use planning applications in northern Alaska and elsewhere in the circumpolar Arctic.

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 ...

Sensing our Environment: Remote sensing in a physics classroom

Science.gov (United States)

Isaacson, Sivan; Schüttler, Tobias; Cohen-Zada, Aviv L.; Blumberg, Dan G.; Girwidz, Raimund; Maman, Shimrit

2017-04-01

Remote sensing is defined as data acquisition of an object, deprived physical contact. Fundamentally, most remote sensing applications are referred to as the use of satellite- or aircraft-based sensor technologies to detect and classify objects mainly on Earth or other planets. In the last years there have been efforts to bring the important subject of remote sensing into schools, however, most of these attempts focused on geography disciplines - restricting to the applications of remote sensing and to a less extent the technique itself and the physics behind it. Optical remote sensing is based on physical principles and technical devices, which are very meaningful from a theoretical point of view as well as for "hands-on" teaching. Some main subjects are radiation, atom and molecular physics, spectroscopy, as well as optics and the semiconductor technology used in modern digital cameras. Thus two objectives were outlined for this project: 1) to investigate the possibilities of using remote sensing techniques in physics teaching, and 2) to identify its impact on pupil's interest in the field of natural sciences. This joint project of the DLR_School_Lab, Oberpfaffenhofen of the German Aerospace Center (DLR) and the Earth and Planetary Image Facility (EPIF) at BGU, was conducted in 2016. Thirty teenagers (ages 16-18) participated in the project and were exposed to the cutting edge methods of earth observation. The pupils on both sides participated in the project voluntarily, knowing that at least some of the project's work had to be done in their leisure time. The pupil's project started with a day at EPIF and DLR respectively, where the project task was explained to the participants and an introduction to remote sensing of vegetation was given. This was realized in lectures and in experimental workshops. During the following two months both groups took several measurements with modern optical remote sensing systems in their home region with a special focus on flora

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.

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

Introductory remote sensing principles and concepts principles and concepts

CERN Document Server

Gibson, Paul

2013-01-01

Introduction to Remote Sensing Principles and Concepts provides a comprehensive student introduction to both the theory and application of remote sensing. This textbook* introduces the field of remote sensing and traces its historical development and evolution* presents detailed explanations of core remote sensing principles and concepts providing the theory required for a clear understanding of remotely sensed images.* describes important remote sensing platforms - including Landsat, SPOT and NOAA * examines and illustrates many of the applications of remotely sensed images in various fields.

Remote sensing time series analysis for crop monitoring with the SPIRITS software: new functionalities and use examples

Directory of Open Access Journals (Sweden)

Felix eRembold

2015-07-01

Full Text Available Monitoring crop and natural vegetation conditions is highly relevant, particularly in the food insecure areas of the world. Data from remote sensing image time series at high temporal and medium to low spatial resolution can assist this monitoring as they provide key information about vegetation status in near real-time over large areas. The Software for the Processing and Interpretation of Remotely sensed Image Time Series (SPIRITS is a stand-alone flexible analysis environment created to facilitate the processing and analysis of large image time series and ultimately for providing clear information about vegetation status in various graphical formats to crop production analysts and decision makers. In this paper we present the latest functional developments of SPIRITS and we illustrate recent applications. The main new developments include: HDF5 importer, Image re-projection, additional options for temporal Smoothing and Periodicity conversion, computation of a rainfall-based probability index (Standardized Precipitation Index for drought detection and extension of the Graph composer functionalities.In particular,. The examples of operational analyses are taken from several recent agriculture and food security monitoring reports and bulletins. We conclude with considerations on future SPIRITS developments also in view of the data processing requirements imposed by the coming generation of remote sensing products at high spatial and temporal resolution, such as those provided by the Sentinel sensors of the European Copernicus programme.

[Comparison of precision in retrieving soybean leaf area index based on multi-source remote sensing data].

Science.gov (United States)

Gao, Lin; Li, Chang-chun; Wang, Bao-shan; Yang Gui-jun; Wang, Lei; Fu, Kui

2016-01-01

With the innovation of remote sensing technology, remote sensing data sources are more and more abundant. The main aim of this study was to analyze retrieval accuracy of soybean leaf area index (LAI) based on multi-source remote sensing data including ground hyperspectral, unmanned aerial vehicle (UAV) multispectral and the Gaofen-1 (GF-1) WFV data. Ratio vegetation index (RVI), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), difference vegetation index (DVI), and triangle vegetation index (TVI) were used to establish LAI retrieval models, respectively. The models with the highest calibration accuracy were used in the validation. The capability of these three kinds of remote sensing data for LAI retrieval was assessed according to the estimation accuracy of models. The experimental results showed that the models based on the ground hyperspectral and UAV multispectral data got better estimation accuracy (R² was more than 0.69 and RMSE was less than 0.4 at 0.01 significance level), compared with the model based on WFV data. The RVI logarithmic model based on ground hyperspectral data was little superior to the NDVI linear model based on UAV multispectral data (The difference in E(A), R² and RMSE were 0.3%, 0.04 and 0.006, respectively). The models based on WFV data got the lowest estimation accuracy with R2 less than 0.30 and RMSE more than 0.70. The effects of sensor spectral response characteristics, sensor geometric location and spatial resolution on the soybean LAI retrieval were discussed. The results demonstrated that ground hyperspectral data were advantageous but not prominent over traditional multispectral data in soybean LAI retrieval. WFV imagery with 16 m spatial resolution could not meet the requirements of crop growth monitoring at field scale. Under the condition of ensuring the high precision in retrieving soybean LAI and working efficiently, the approach to acquiring agricultural information by UAV remote

Mississippi Sound Remote Sensing Study

Science.gov (United States)

Atwell, B. H.

1973-01-01

The Mississippi Sound Remote Sensing Study was initiated as part of the research program of the NASA Earth Resources Laboratory. The objective of this study is development of remote sensing techniques to study near-shore marine waters. Included within this general objective are the following: (1) evaluate existing techniques and instruments used for remote measurement of parameters of interest within these waters; (2) develop methods for interpretation of state-of-the-art remote sensing data which are most meaningful to an understanding of processes taking place within near-shore waters; (3) define hardware development requirements and/or system specifications; (4) develop a system combining data from remote and surface measurements which will most efficiently assess conditions in near-shore waters; (5) conduct projects in coordination with appropriate operating agencies to demonstrate applicability of this research to environmental and economic problems.

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.

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

Fluid Lensing, Applications to High-Resolution 3D Subaqueous Imaging & Automated Remote Biosphere Assessment from Airborne and Space-borne Platforms

Science.gov (United States)

Chirayath, V.

2014-12-01

Fluid Lensing is a theoretical model and algorithm I present for fluid-optical interactions in turbulent flows as well as two-fluid surface boundaries that, when coupled with an unique computer vision and image-processing pipeline, may be used to significantly enhance the angular resolution of a remote sensing optical system with applicability to high-resolution 3D imaging of subaqueous regions and through turbulent fluid flows. This novel remote sensing technology has recently been implemented on a quadcopter-based UAS for imaging shallow benthic systems to create the first dataset of a biosphere with unprecedented sub-cm-level imagery in 3D over areas as large as 15 square kilometers. Perturbed two-fluid boundaries with different refractive indices, such as the surface between the ocean and air, may be exploited for use as lensing elements for imaging targets on either side of the interface with enhanced angular resolution. I present theoretical developments behind Fluid Lensing and experimental results from its recent implementation for the Reactive Reefs project to image shallow reef ecosystems at cm scales. Preliminary results from petabyte-scale aerial survey efforts using Fluid Lensing to image at-risk coral reefs in American Samoa (August, 2013) show broad applicability to large-scale automated species identification, morphology studies and reef ecosystem characterization for shallow marine environments and terrestrial biospheres, of crucial importance to understanding climate change's impact on coastal zones, global oxygen production and carbon sequestration.

Photogrammetry - Remote Sensing and Geoinformation

Science.gov (United States)

Lazaridou, M. A.; Patmio, E. N.

2012-07-01

Earth and its environment are studied by different scientific disciplines as geosciences, science of engineering, social sciences, geography, etc. The study of the above, beyond pure scientific interest, is useful for the practical needs of man. Photogrammetry and Remote Sensing (defined by Statute II of ISPRS) is the art, science, and technology of obtaining reliable information from non-contact imaging and other sensor systems about the Earth and its environment, and other physical objects and of processes through recording, measuring, analyzing and representation. Therefore, according to this definition, photogrammetry and remote sensing can support studies of the above disciplines for acquisition of geoinformation. This paper concerns basic concepts of geosciences (geomorphology, geology, hydrology etc), and the fundamentals of photogrammetry-remote sensing, in order to aid the understanding of the relationship between photogrammetry-remote sensing and geoinformation and also structure curriculum in a brief, concise and coherent way. This curriculum can represent an appropriate research and educational outline and help to disseminate knowledge in various directions and levels. It resulted from our research and educational experience in graduate and post-graduate level (post-graduate studies relative to the protection of environment and protection of monuments and historical centers) in the Lab. of Photogrammetry - Remote Sensing in Civil Engineering Faculty of Aristotle University of Thessaloniki.

Comparision of Bathymetry and Bottom Characteristics From Hyperspectral Remote Sensing Data and Shipborne Acoustic Measurements

Science.gov (United States)

McIntyre, M. L.; Naar, D. F.; Carder, K. L.; Howd, P. A.; Lewis, J. M.; Donahue, B. T.; Chen, F. R.

2002-12-01

There is growing interest in applying optical remote sensing techniques to shallow-water geological applications such as bathymetry and bottom characterization. Model inversions of hyperspectral remote-sensing reflectance imagery can provide estimates of bottom albedo and depth. This research was conducted in support of the HyCODE (Hyperspectral Coupled Ocean Dynamics Experiment) project in order to test optical sensor performance and the use of a hyperspectral remote-sensing reflectance algorithm for shallow waters in estimating bottom depths and reflectance. The objective of this project was to compare optically derived products of bottom depths and reflectance to shipborne acoustic measurements of bathymetry and backscatter. A set of three high-resolution, multibeam surveys within an 18 km by 1.5 km shore-perpendicular transect 5 km offshore of Sarasota, Florida were collected at water depths ranging from 8 m to 16 m. These products are compared to bottom depths derived from aircraft remote-sensing data collected with the AVIRIS (Airborne Visible-Infrared Imaging Spectrometer) instrument data by means of a semi-analytical remote sensing reflectance model. The pixel size of the multibeam bathymetry and AVIRIS data are 0.25 m and 10 m, respectively. When viewed at full resolution, the multibeam bathymetry data show small-scale sedimentary bedforms (wavelength ~10m, amplitude ~1m) that are not observed in the lower resolution hyperspectral bathymetry. However, model-derived bottom depths agree well with a smoothed version of the multibeam bathymetry. Depths derived from shipborne hyperspectral measurements were accurate within 13%. In areas where diver observations confirmed biological growth and bioturbation, derived bottom depths were less accurate. Acoustic backscatter corresponds well with the aircraft hyperspectral imagery and in situ measurements of bottom reflectance. Acoustic backscatter was used to define the distribution of different bottom types

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 ...

Near-Space Microwave Radar Remote Sensing: Potentials and Challenge Analysis

Directory of Open Access Journals (Sweden)

Qicong Peng

2010-03-01

Full Text Available Near-space, defined as the region between 20 km and 100 km, offers many new capabilities that are not accessible to low earth orbit (LEO satellites and airplanes, because it is above storm and not constrained by either the orbital mechanics of satellites or the high fuel consumption of airplanes. By placing radar transmitter/receiver in near-space platforms, many functions that are currently performed with satellites or airplanes could be performed in a cheaper way. Inspired by these advantages, this paper introduces several near-space vehicle-based radar configurations, such as near-space passive bistatic radar and high-resolution wide-swath (HRWS synthetic aperture radar (SAR. Their potential applications, technical challenges and possible solutions are investigated. It is shown that near-space is a satisfactory solution to some specific remote sensing applications. Firstly, near-space passive bistatic radar using opportunistic illuminators offers a solution to persistent regional remote sensing, which is particularly interest for protecting homeland security or monitoring regional environment. Secondly, near-space provides an optimal solution to relative HRWS SAR imaging. Moreover, as motion compensation is a common technical challenge for the described radars, an active transponder-based motion compensation is also described.

Image Fusion Technologies In Commercial Remote Sensing Packages

OpenAIRE

Al-Wassai, Firouz Abdullah; Kalyankar, N. V.

2013-01-01

Several remote sensing software packages are used to the explicit purpose of analyzing and visualizing remotely sensed data, with the developing of remote sensing sensor technologies from last ten years. Accord-ing to literature, the remote sensing is still the lack of software tools for effective information extraction from remote sensing data. So, this paper provides a state-of-art of multi-sensor image fusion technologies as well as review on the quality evaluation of the single image or f...

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.

Remote Sensing Digital Image Analysis An Introduction

CERN Document Server

Richards, John A

2013-01-01

Remote Sensing Digital Image Analysis provides the non-specialist with a treatment of the quantitative analysis of satellite and aircraft derived remotely sensed data. Since the first edition of the book there have been significant developments in the algorithms used for the processing and analysis of remote sensing imagery; nevertheless many of the fundamentals have substantially remained the same.  This new edition presents material that has retained value since those early days, along with new techniques that can be incorporated into an operational framework for the analysis of remote sensing data. The book is designed as a teaching text for the senior undergraduate and postgraduate student, and as a fundamental treatment for those engaged in research using digital image processing in remote sensing.  The presentation level is for the mathematical non-specialist.  Since the very great number of operational users of remote sensing come from the earth sciences communities, the text is pitched at a leve...

STUDY ON BUILDING EXTRACTION FROM HIGH-RESOLUTION IMAGES USING MBI

Directory of Open Access Journals (Sweden)

Z. Ding

2018-04-01

Full Text Available Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. However, the diversity and complexity of buildings make building extraction methods still face challenges in terms of accuracy, efficiency, and so on. In this study, a new building extraction framework based on MBI and combined with image segmentation techniques, spectral constraint, shadow constraint, and shape constraint is proposed. In order to verify the proposed method, worldview-2, GF-2, GF-1 remote sensing images covered Xiamen Software Park were used for building extraction experiments. Experimental results indicate that the proposed method improve the original MBI significantly, and the correct rate is over 86 %. Furthermore, the proposed framework reduces the false alarms by 42 % on average compared to the performance of the original MBI.

Multiscale and Multitemporal Urban Remote Sensing

Science.gov (United States)

Mesev, V.

2012-07-01

The remote sensing of urban areas has received much attention from scientists conducting studies on measuring sprawl, congestion, pollution, poverty, and environmental encroachment. Yet much of the research is case and data-specific where results are greatly influenced by prevailing local conditions. There seems to be a lack of epistemological links between remote sensing and conventional theoretical urban geography; in other words, an oversight for the appreciation of how urban theory fuels urban change and how urban change is measured by remotely sensed data. This paper explores basic urban theories such as centrality, mobility, materiality, nature, public space, consumption, segregation and exclusion, and how they can be measured by remote sensing sources. In particular, the link between structure (tangible objects) and function (intangible or immaterial behavior) is addressed as the theory that supports the wellknow contrast between land cover and land use classification from remotely sensed data. The paper then couches these urban theories and contributions from urban remote sensing within two analytical fields. The first is the search for an "appropriate" spatial scale of analysis, which is conveniently divided between micro and macro urban remote sensing for measuring urban structure, understanding urban processes, and perhaps contributions to urban theory at a variety of scales of analysis. The second is on the existence of a temporal lag between materiality of urban objects and the planning process that approved their construction, specifically how time-dependence in urban structural-functional models produce temporal lags that alter the causal links between societal and political functional demands and structural ramifications.

Intercomparison of Remotely Sensed Vegetation Indices, Ground Spectroscopy, and Foliar Chemistry Data from NEON

Science.gov (United States)

Hulslander, D.; Warren, J. N.; Weintraub, S. R.

2017-12-01

Hyperspectral imaging systems can be used to produce spectral reflectance curves giving rich information about composition, relative abundances of materials, mixes and combinations. Indices based on just a few spectral bands have been used for over 40 years to study vegetation health, mineral abundance, and more. These indices are much simpler to visualize and use than a full hyperspectral data set which may contain over 400 bands. Yet historically, it has been difficult to directly relate remotely sensed spectral indices to quantitative biophysical properties significant to forest ecology such as canopy nitrogen, lignin, and chlorophyll. This linkage is a critical piece in enabling the detection of high value ecological information, usually only available from labor-intensive canopy foliar chemistry sampling, to the geographic and temporal coverage available via remote sensing. Previous studies have shown some promising results linking ground-based data and remotely sensed indices, but are consistently limited in time, geographic extent, and land cover type. Moreover, previous studies are often focused on tuning linkage algorithms for the purpose of achieving good results for only one study site or one type of vegetation, precluding development of more generalized algorithms. The National Ecological Observatory Network (NEON) is a unique system of 47 terrestrial sites covering all of the major eco-climatic domains of the US, including AK, HI, and Puerto Rico. These sites are regularly monitored and sampled using uniform instrumentation and protocols, including both foliar chemistry sampling and remote sensing flights for high resolution hyperspectral, LiDAR, and digital camera data acquisition. In this study we compare the results of foliar chemistry analysis to the remote sensing vegetation indices and investigate possible sources for variance and difference through the use of the larger hyperspectral dataset as well as ground based spectrometer measurements of

Remote Sensing of Suspended Sediments and Shallow Coastal Waters

Science.gov (United States)

Li, Rong-Rong; Kaufman, Yoram J.; Gao, Bo-Cai; Davis, Curtiss O.

2002-01-01

Ocean color sensors were designed mainly for remote sensing of chlorophyll concentrations over the clear open oceanic areas (case 1 water) using channels between 0.4 and 0.86 micrometers. The Moderate Resolution Imaging Spectroradiometer (MODIS) launched on the NASA Terra and Aqua Spacecrafts is equipped with narrow channels located within a wider wavelength range between 0.4 and 2.5 micrometers for a variety of remote sensing applications. The wide spectral range can provide improved capabilities for remote sensing of the more complex and turbid coastal waters (case 2 water) and for improved atmospheric corrections for Ocean scenes. In this article, we describe an empirical algorithm that uses this wide spectral range to identifying areas with suspended sediments in turbid waters and shallow waters with bottom reflections. The algorithm takes advantage of the strong water absorption at wavelengths longer than 1 micrometer that does not allow illumination of sediments in the water or a shallow ocean floor. MODIS data acquired over the east coast of China, west coast of Africa, Arabian Sea, Mississippi Delta, and west coast of Florida are used in this study.

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.

Exploiting LF/MF signals of opportunity for lower ionospheric remote sensing

Science.gov (United States)

Higginson-Rollins, Marc A.; Cohen, Morris B.

2017-08-01

We introduce a method to diagnose and track the D region ionosphere (60-100 km). This region is important for long-distance terrestrial communication and is impacted by a variety of geophysical phenomena, but it is traditionally very difficult to detect. Modern remote sensing methods used to study the D region are predominately near the very low frequency (VLF, 3-30 kHz) band, with some work also done in the high-frequency and very high frequency bands (HF/VHF, 3-300 MHz). However, the frequency band between VLF and HF has been largely ignored as a diagnostic tool for the ionosphere. In this paper, we evaluate the use of 300 kHz radio reflections as a diagnostic tool for characterizing the D region of the ionosphere. We present radio receiver data, analyze diurnal trends in the signal from these transmitters, and identify ionospheric disturbances impacting LF/MF propagation. We find that 300 kHz remote sensing may allow a unique method for D region diagnostics compared to both the VLF and HF/VHF frequency bands, due to a more direct ionospheric reflection coefficient calculation method with high temporal resolution without the use of forward modeling.

Landscape Pattern Detection in Archaeological Remote Sensing

Directory of Open Access Journals (Sweden)

Arianna Traviglia

2017-12-01

Full Text Available Automated detection of landscape patterns on Remote Sensing imagery has seen virtually little or no development in the archaeological domain, notwithstanding the fact that large portion of cultural landscapes worldwide are characterized by land engineering applications. The current extraordinary availability of remotely sensed images makes it now urgent to envision and develop automatic methods that can simplify their inspection and the extraction of relevant information from them, as the quantity of information is no longer manageable by traditional “human” visual interpretation. This paper expands on the development of automatic methods for the detection of target landscape features—represented by field system patterns—in very high spatial resolution images, within the framework of an archaeological project focused on the landscape engineering embedded in Roman cadasters. The targets of interest consist of a variety of similarly oriented objects of diverse nature (such as roads, drainage channels, etc. concurring to demark the current landscape organization, which reflects the one imposed by Romans over two millennia ago. The proposed workflow exploits the textural and shape properties of real-world elements forming the field patterns using multiscale analysis of dominant oriented response filters. Trials showed that this approach provides accurate localization of target linear objects and alignments signaled by a wide range of physical entities with very different characteristics.

Remote Sensing for Wind Energy

DEFF Research Database (Denmark)

The Remote Sensing in Wind Energy Compendium provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind this compendium began in year 2008 at Risø DTU during the first PhD Summer School: Remote Sensing in Wind Energy. Thus...... in the Meteorology and Test and Measurements Programs from the Wind Energy Division at Risø DTU in the PhD Summer Schools. We hope to add more topics in future editions and to update as necessary, to provide a truly state-of-the-art compendium available for people involved in Remote Sensing in Wind Energy....

Remote Sensing for Wind Energy

DEFF Research Database (Denmark)

Peña, Alfredo; Hasager, Charlotte Bay; Badger, Merete

The Remote Sensing in Wind Energy report provides a description of several topics and it is our hope that students and others interested will learn from it. The idea behind it began in year 2008 at DTU Wind Energy (formerly Risø) during the first PhD Summer School: Remote Sensing in Wind Energy...... colleagues in the Meteorology and Test and Measurements Sections from DTU Wind Energy in the PhD Summer Schools. We hope to continue adding more topics in future editions and to update and improve as necessary, to provide a truly state-of-the-art ‘guideline’ available for people involved in Remote Sensing...

Application of the remote-sensing communication model to a time-sensitive wildfire remote-sensing system

Science.gov (United States)

Christopher D. Lippitt; Douglas A. Stow; Philip J. Riggan

2016-01-01

Remote sensing for hazard response requires a priori identification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the Remote Sensing Communication...

City of Flagstaff Project: Ground Water Resource Evaluation, Remote Sensing Component

Science.gov (United States)

Chavez, Pat S.; Velasco, Miguel G.; Bowell, Jo-Ann; Sides, Stuart C.; Gonzalez, Rosendo R.; Soltesz, Deborah L.

1996-01-01

Many regions, cities, and towns in the Western United States need new or expanded water resources because of both population growth and increased development. Any tools or data that can help in the evaluation of an area's potential water resources must be considered for this increasingly critical need. Remotely sensed satellite images and subsequent digital image processing have been under-utilized in ground water resource evaluation and exploration. Satellite images can be helpful in detecting and mapping an area's regional structural patterns, including major fracture and fault systems, two important geologic settings for an area's surface to ground water relations. Within the United States Geological Survey's (USGS) Flagstaff Field Center, expertise and capabilities in remote sensing and digital image processing have been developed over the past 25 years through various programs. For the City of Flagstaff project, this expertise and these capabilities were combined with traditional geologic field mapping to help evaluate ground water resources in the Flagstaff area. Various enhancement and manipulation procedures were applied to the digital satellite images; the results, in both digital and hardcopy format, were used for field mapping and analyzing the regional structure. Relative to surface sampling, remotely sensed satellite and airborne images have improved spatial coverage that can help study, map, and monitor the earth surface at local and/or regional scales. Advantages offered by remotely sensed satellite image data include: 1. a synoptic/regional view compared to both aerial photographs and ground sampling, 2. cost effectiveness, 3. high spatial resolution and coverage compared to ground sampling, and 4. relatively high temporal coverage on a long term basis. Remotely sensed images contain both spectral and spatial information. The spectral information provides various properties and characteristics about the surface cover at a given location or pixel

Kingfisher: a system for remote sensing image database management

Science.gov (United States)

Bruzzo, Michele; Giordano, Ferdinando; Dellepiane, Silvana G.

2003-04-01

At present retrieval methods in remote sensing image database are mainly based on spatial-temporal information. The increasing amount of images to be collected by the ground station of earth observing systems emphasizes the need for database management with intelligent data retrieval capabilities. The purpose of the proposed method is to realize a new content based retrieval system for remote sensing images database with an innovative search tool based on image similarity. This methodology is quite innovative for this application, at present many systems exist for photographic images, as for example QBIC and IKONA, but they are not able to extract and describe properly remote image content. The target database is set by an archive of images originated from an X-SAR sensor (spaceborne mission, 1994). The best content descriptors, mainly texture parameters, guarantees high retrieval performances and can be extracted without losses independently of image resolution. The latter property allows DBMS (Database Management System) to process low amount of information, as in the case of quick-look images, improving time performance and memory access without reducing retrieval accuracy. The matching technique has been designed to enable image management (database population and retrieval) independently of dimensions (width and height). Local and global content descriptors are compared, during retrieval phase, with the query image and results seem to be very encouraging.

Deriving harmonised forest information in Europe using remote sensing methods

DEFF Research Database (Denmark)

Seebach, Lucia Maria

the need for harmonised forest information can be satisfied using remote sensing methods. In conclusion, the study showed that it is possible to derive harmonised forest information of high spatial detail in Europe with remote sensing. The study also highlighted the imperative provision of accuracy...

STANDARDIZING QUALITY ASSESSMENT OF FUSED REMOTELY SENSED IMAGES

Directory of Open Access Journals (Sweden)

C. Pohl

2017-09-01

Full Text Available The multitude of available operational remote sensing satellites led to the development of many image fusion techniques to provide high spatial, spectral and temporal resolution images. The comparison of different techniques is necessary to obtain an optimized image for the different applications of remote sensing. There are two approaches in assessing image quality: 1. Quantitatively by visual interpretation and 2. Quantitatively using image quality indices. However an objective comparison is difficult due to the fact that a visual assessment is always subject and a quantitative assessment is done by different criteria. Depending on the criteria and indices the result varies. Therefore it is necessary to standardize both processes (qualitative and quantitative assessment in order to allow an objective image fusion quality evaluation. Various studies have been conducted at the University of Osnabrueck (UOS to establish a standardized process to objectively compare fused image quality. First established image fusion quality assessment protocols, i.e. Quality with No Reference (QNR and Khan's protocol, were compared on varies fusion experiments. Second the process of visual quality assessment was structured and standardized with the aim to provide an evaluation protocol. This manuscript reports on the results of the comparison and provides recommendations for future research.

Standardizing Quality Assessment of Fused Remotely Sensed Images

Science.gov (United States)

Pohl, C.; Moellmann, J.; Fries, K.

2017-09-01

The multitude of available operational remote sensing satellites led to the development of many image fusion techniques to provide high spatial, spectral and temporal resolution images. The comparison of different techniques is necessary to obtain an optimized image for the different applications of remote sensing. There are two approaches in assessing image quality: 1. Quantitatively by visual interpretation and 2. Quantitatively using image quality indices. However an objective comparison is difficult due to the fact that a visual assessment is always subject and a quantitative assessment is done by different criteria. Depending on the criteria and indices the result varies. Therefore it is necessary to standardize both processes (qualitative and quantitative assessment) in order to allow an objective image fusion quality evaluation. Various studies have been conducted at the University of Osnabrueck (UOS) to establish a standardized process to objectively compare fused image quality. First established image fusion quality assessment protocols, i.e. Quality with No Reference (QNR) and Khan's protocol, were compared on varies fusion experiments. Second the process of visual quality assessment was structured and standardized with the aim to provide an evaluation protocol. This manuscript reports on the results of the comparison and provides recommendations for future research.

Best practices in Remote Sensing for REDD+

DEFF Research Database (Denmark)

Dons, Klaus; Grogan, Kenneth

2012-01-01

due to steep terrain, • phenological gradients across natural, agricultural and forestry ecosystems including plantations and • the need to serve the REDD-specific context of deforestation and forest degradation across spatial and temporal scales make remote sensing based approaches particularly...... be expected from remote sensing imagery and the provided information shall help to better anticipate problems that will be encountered when acquiring, analyzing and interpreting remote sensing data. Beyond remote sensing, it may be a good point of departure for a large group of scientists with a diverse...... and governance, and deforestation and forest degradation processes. The second part summarizes the available literature on remote sensing based good practices for REDD. It largely draws from the documents of the Intergovernmental Panel on Climate Change (IPCC), the United Nations Framework Convention on Climate...

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

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.

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

Ten ways remote sensing can contribute to conservation

Science.gov (United States)

Rose, Robert A.; Byler, Dirck; Eastman, J. Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A.; Laporte, Nadine; Leidner, Allison K.; Leimgruber, Peter; Morisette, Jeffrey T.; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C.; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2014-01-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to

Ten ways remote sensing can contribute to conservation.

Science.gov (United States)

Rose, Robert A; Byler, Dirck; Eastman, J Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A; Laporte, Nadine; Leidner, Allison; Leimgruber, Peter; Morisette, Jeffrey; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2015-04-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to

Remote sensing for oil spill detection and response

International Nuclear Information System (INIS)

Engelhardt, F.R.

1999-01-01

This paper focuses on the use of remote sensing for marine oil spill detection and response. The surveillance and monitoring of discharges, and the main elements of effective surveillance are discussed. Tactical emergency response and the requirements for selecting a suitable remote sensing approach, airborne remote sensing systems, and the integration of satellite and airborne imaging are examined. Specifications of satellite surveillance systems potentially usable for oil spill detection, and specifications of airborne remote sensing systems suitable for oil spill detection, monitoring and supplemental actions are tabulated, and a schema of integrated satellite-airborne remote sensing (ISARS) is presented. (UK)

Satellite Remote Sensing: Aerosol Measurements

Science.gov (United States)

Kahn, Ralph A.

2013-01-01

Aerosols are solid or liquid particles suspended in the air, and those observed by satellite remote sensing are typically between about 0.05 and 10 microns in size. (Note that in traditional aerosol science, the term "aerosol" refers to both the particles and the medium in which they reside, whereas for remote sensing, the term commonly refers to the particles only. In this article, we adopt the remote-sensing definition.) They originate from a great diversity of sources, such as wildfires, volcanoes, soils and desert sands, breaking waves, natural biological activity, agricultural burning, cement production, and fossil fuel combustion. They typically remain in the atmosphere from several days to a week or more, and some travel great distances before returning to Earth's surface via gravitational settling or washout by precipitation. Many aerosol sources exhibit strong seasonal variability, and most experience inter-annual fluctuations. As such, the frequent, global coverage that space-based aerosol remote-sensing instruments can provide is making increasingly important contributions to regional and larger-scale aerosol studies.

Optical Remote Sensing of Glacier Characteristics: A Review with Focus on the Himalaya

Science.gov (United States)

Racoviteanu, Adina E.; Williams, Mark W.; Barry, Roger G.

2008-01-01

The increased availability of remote sensing platforms with appropriate spatial and temporal resolution, global coverage and low financial costs allows for fast, semi-automated, and cost-effective estimates of changes in glacier parameters over large areas. Remote sensing approaches allow for regular monitoring of the properties of alpine glaciers such as ice extent, terminus position, volume and surface elevation, from which glacier mass balance can be inferred. Such methods are particularly useful in remote areas with limited field-based glaciological measurements. This paper reviews advances in the use of visible and infrared remote sensing combined with field methods for estimating glacier parameters, with emphasis on volume/area changes and glacier mass balance. The focus is on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor and its applicability for monitoring Himalayan glaciers. The methods reviewed are: volumetric changes inferred from digital elevation models (DEMs), glacier delineation algorithms from multi-spectral analysis, changes in glacier area at decadal time scales, and AAR/ELA methods used to calculate yearly mass balances. The current limitations and on-going challenges in using remote sensing for mapping characteristics of mountain glaciers also discussed, specifically in the context of the Himalaya. PMID:27879883

Thermal signatures of urban land cover types: High-resolution thermal infrared remote sensing of urban heat island in Huntsville, AL

Science.gov (United States)

Lo, Chor Pang

1996-01-01

The main objective of this research is to apply airborne high-resolution thermal infrared imagery for urban heat island studies, using Huntsville, AL, a medium-sized American city, as the study area. The occurrence of urban heat islands represents human-induced urban/rural contrast, which is caused by deforestation and the replacement of the land surface by non-evaporating and non-porous materials such as asphalt and concrete. The result is reduced evapotranspiration and more rapid runoff of rain water. The urban landscape forms a canopy acting as a transitional zone between the atmosphere and the land surface. The composition and structure of this canopy have a significant impact on the thermal behavior of the urban environment. Research on the trends of surface temperature at rapidly growing urban sites in the United States during the last 30 to 50 years suggests that significant urban heat island effects have caused the temperatures at these sites to rise by 1 to 2 C. Urban heat islands have caused changes in urban precipitation and temperature that are at least similar to, if not greater than, those predicted to develop over the next 100 years by global change models. Satellite remote sensing, particularly NOAA AVHRR thermal data, has been used in the study of urban heat islands. Because of the low spatial resolution (1.1 km at nadir) of the AVHRR data, these studies can only examine and map the phenomenon at the macro-level. The present research provides the rare opportunity to utilize 5-meter thermal infrared data acquired from an airplane to characterize more accurately the thermal responses of different land cover types in the urban landscape as input to urban heat island studies.

Accurate and emergent applications for high precision light small aerial remote sensing system

Science.gov (United States)

Pei, Liu; Yingcheng, Li; Yanli, Xue; Qingwu, Hu; Xiaofeng, Sun

2014-03-01

In this paper, we focus on the successful applications of accurate and emergent surveying and mapping for high precision light small aerial remote sensing system. First, the remote sensing system structure and three integrated operation modes will be introduced. It can be combined to three operation modes depending on the application requirements. Second, we describe the preliminary results of a precision validation method for POS direct orientation in 1:500 mapping. Third, it presents two fast response mapping products- regional continuous three-dimensional model and digital surface model, taking the efficiency and accuracy evaluation of the two products as an important point. The precision of both products meets the 1:2 000 topographic map accuracy specifications in Pingdingshan area. In the end, conclusions and future work are summarized.

Accurate and emergent applications for high precision light small aerial remote sensing system

International Nuclear Information System (INIS)

Pei, Liu; Yingcheng, Li; Yanli, Xue; Xiaofeng, Sun; Qingwu, Hu

2014-01-01

In this paper, we focus on the successful applications of accurate and emergent surveying and mapping for high precision light small aerial remote sensing system. First, the remote sensing system structure and three integrated operation modes will be introduced. It can be combined to three operation modes depending on the application requirements. Second, we describe the preliminary results of a precision validation method for POS direct orientation in 1:500 mapping. Third, it presents two fast response mapping products- regional continuous three-dimensional model and digital surface model, taking the efficiency and accuracy evaluation of the two products as an important point. The precision of both products meets the 1:2 000 topographic map accuracy specifications in Pingdingshan area. In the end, conclusions and future work are summarized

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.

High-Resolution Graphene Films for Electrochemical Sensing via Inkjet Maskless Lithography.

Science.gov (United States)

Hondred, John A; Stromberg, Loreen R; Mosher, Curtis L; Claussen, Jonathan C

2017-10-24

Solution-phase printing of nanomaterial-based graphene inks are rapidly gaining interest for fabrication of flexible electronics. However, scalable manufacturing techniques for high-resolution printed graphene circuits are still lacking. Here, we report a patterning technique [i.e., inkjet maskless lithography (IML)] to form high-resolution, flexible, graphene films (line widths down to 20 μm) that significantly exceed the current inkjet printing resolution of graphene (line widths ∼60 μm). IML uses an inkjet printed polymer lacquer as a sacrificial pattern, viscous spin-coated graphene, and a subsequent graphene lift-off to pattern films without the need for prefabricated stencils, templates, or cleanroom technology (e.g., photolithography). Laser annealing is employed to increase conductivity on thermally sensitive, flexible substrates [polyethylene terephthalate (PET)]. Laser annealing and subsequent platinum nanoparticle deposition substantially increases the electroactive nature of graphene as illustrated by electrochemical hydrogen peroxide (H 2 O 2 ) sensing [rapid response (5 s), broad linear sensing range (0.1-550 μm), high sensitivity (0.21 μM/μA), and low detection limit (0.21 μM)]. Moreover, high-resolution, complex graphene circuits [i.e., interdigitated electrodes (IDE) with varying finger width and spacing] were created with IML and characterized via potassium chloride (KCl) electrochemical impedance spectroscopy (EIS). Results indicated that sensitivity directly correlates to electrode feature size as the IDE with the smallest finger width and spacing (50 and 50 μm) displayed the largest response to changes in KCl concentration (∼21 kΩ). These results indicate that the developed IML patterning technique is well-suited for rapid, solution-phase graphene film prototyping on flexible substrates for numerous applications including electrochemical sensing.