Applying aerial digital photography as a spectral remote sensing technique for macrophytic cover assessment in small rural streams

Science.gov (United States)

Anker, Y.; Hershkovitz, Y.; Gasith, A.; Ben-Dor, E.

2011-12-01

Although remote sensing of fluvial ecosystems is well developed, the tradeoff between spectral and spatial resolutions prevents its application in small streams (habitat scales classifications, acquisition of aerial digital RGB datasets. B. For section scale classification, hyperspectral (HSR) dataset acquisition. C. For calibration, HSR reflectance measurements of specific ground targets, in close proximity to each dataset acquisition swath. D. For habitat scale classification, manual, in-stream flora grid transects classification. The digital RGB datasets were converted to reflectance units by spectral calibration against colored reference plates. These red, green, blue, white, and black EVA foam reference plates were measured by an ASD field spectrometer and each was given a spectral value. Each spectral value was later applied to the spectral calibration and radiometric correction of spectral RGB (SRGB) cube. Spectral calibration of the HSR dataset was done using the empirical line method, based on reference values of progressive grey scale targets. Differentiation between the vegetation species was done by supervised classification both for the HSR and for the SRGB datasets. This procedure was done using the Spectral Angle Mapper function with the spectral pattern of each vegetation species as a spectral end member. Comparison between the two remote sensing techniques and between the SRGB classification and the in-situ transects indicates that: A. Stream vegetation classification resolution is about 4 cm by the SRGB method compared to about 1 m by HSR. Moreover, this resolution is also higher than of the manual grid transect classification. B. The SRGB method is by far the most cost-efficient. The combination of spectral information (rather than the cognitive color) and high spatial resolution of aerial photography provides noise filtration and better sub-water detection capabilities than the HSR technique. C. Only the SRGB method applies for habitat and

A Method of Particle Swarm Optimized SVM Hyper-spectral Remote Sensing Image Classification

International Nuclear Information System (INIS)

Liu, Q J; Jing, L H; Wang, L M; Lin, Q Z

2014-01-01

Support Vector Machine (SVM) has been proved to be suitable for classification of remote sensing image and proposed to overcome the Hughes phenomenon. Hyper-spectral sensors are intrinsically designed to discriminate among a broad range of land cover classes which may lead to high computational time in SVM mutil-class algorithms. Model selection for SVM involving kernel and the margin parameter values selection which is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyper-spectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, particle swarm algorithm is introduced to the optimal selection of SVM (PSSVM) kernel parameter σ and margin parameter C to improve the modelling efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for evaluating the novel PSSVM, as well as traditional SVM classifier with general Grid-Search cross-validation method (GSSVM). And then, evaluation indexes including SVM model training time, classification Overall Accuracy (OA) and Kappa index of both PSSVM and GSSVM are all analyzed quantitatively. It is demonstrated that OA of PSSVM on test samples and whole image are 85% and 82%, the differences with that of GSSVM are both within 0.08% respectively. And Kappa indexes reach 0.82 and 0.77, the differences with that of GSSVM are both within 0.001. While the modelling time of PSSVM can be only 1/10 of that of GSSVM, and the modelling. Therefore, PSSVM is an fast and accurate algorithm for hyper-spectral image classification and is superior to GSSVM

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

Science.gov (United States)

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

2018-01-15

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

Testing the potential of multi-spectral remote sensing for retrospectively estimating fire severity in African savannahs

Science.gov (United States)

Alistair M.S. Smith; Martin J. Wooster; Nick A. Drake; Frederick M. Dipotso; Michael J. Falkowski; Andrew T. Hudak

2005-01-01

The remote sensing of fire severity is a noted goal in studies of forest and grassland wildfires. Experiments were conducted to discover and evaluate potential relationships between the characteristics of African savannah fires and post-fire surface spectral reflectance in the visible to shortwave infrared spectral region. Nine instrumented experimental fires were...

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.

Spectral reflectance of carbonate sediments and application to remote sensing classification of benthic habitats

Science.gov (United States)

Louchard, Eric Michael

Remote sensing is a valuable tool in marine research that has advanced to the point that images from shallow waters can be used to identify different seafloor types and create maps of benthic habitats. A major goal of this dissertation is to examine differences in spectral reflectance and create new methods of analyzing shallow water remote sensing data to identify different seafloor types quickly and accurately. Carbonate sediments were used as a model system as they presented a relatively uniform, smooth surface for measurement and are a major bottom type in tropical coral reef systems. Experimental results found that sediment reflectance varied in shape and magnitude depending on pigment content, but only varied in magnitude with variations in grain size and shape. Derivative analysis of the reflectance spectra identified wavelength regions that correlate to chlorophyll a and chlorophyllide a as well as accessory pigments, indicating differences in microbial community structure. Derivative peak height also correlated to pigment content in the sediments. In remote sensing data, chlorophyll a, chlorophyllide a, and some xanthophylls were identified in derivative spectra and could be quantified from second derivative peak height. Most accessory pigments were attenuated by the water column, however, and could not be used to quantify pigments in sediments from remote sensing images. Radiative transfer modeling of remote sensing reflectance showed that there was sufficient spectral variation to separate major sediment types, such as ooid shoals and sediment with microbial layers, from different densities of seagrass and pavement bottom communities. Both supervised classification with a spectral library and unsupervised classification with principal component analysis were used to create maps of seafloor type. The results of the experiments were promising; classified seafloor types correlated with ground truth observations taken from underwater video and were

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

Science.gov (United States)

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

2018-04-01

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

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

Science.gov (United States)

Zhang, Qiaoping

For the last three decades, the Geomatics Engineering and Computer Science communities have considered automated road network extraction from remotely-sensed imagery to be a challenging and important research topic. The main objective of this research is to investigate the theory and methodology of automated feature extraction for image-based road database creation, refinement or updating, and to develop a series of algorithms for road network extraction from high resolution multi-spectral imagery. The proposed framework for road network extraction from multi-spectral imagery begins with an image segmentation using the k-means algorithm. This step mainly concerns the exploitation of the spectral information for feature extraction. The road cluster is automatically identified using a fuzzy classifier based on a set of predefined road surface membership functions. These membership functions are established based on the general spectral signature of road pavement materials and the corresponding normalized digital numbers on each multi-spectral band. Shape descriptors of the Angular Texture Signature are defined and used to reduce the misclassifications between roads and other spectrally similar objects (e.g., crop fields, parking lots, and buildings). An iterative and localized Radon transform is developed for the extraction of road centerlines from the classified images. The purpose of the transform is to accurately and completely detect the road centerlines. It is able to find short, long, and even curvilinear lines. The input image is partitioned into a set of subset images called road component images. An iterative Radon transform is locally applied to each road component image. At each iteration, road centerline segments are detected based on an accurate estimation of the line parameters and line widths. Three localization approaches are implemented and compared using qualitative and quantitative methods. Finally, the road centerline segments are grouped into a

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

Science.gov (United States)

Maynard, Jonathan J; Karl, Jason W

2017-01-01

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

Searching remote homology with spectral clustering with symmetry in neighborhood cluster kernels.

Directory of Open Access Journals (Sweden)

Ujjwal Maulik

Full Text Available Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of "recent" paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request.sarkar@labri.fr.

Remote identification of research and educational activities using spectral properties of nighttime light

Science.gov (United States)

Rybnikova, Nataliya A.; Portnov, Boris A.

2017-06-01

Research and educational activities (R&EAs) are major forces behind modern economic growth. However, data on geographic location of such activities are often poorly reported. According to our research hypothesis, intensities and spectral properties of artificial light-at-night (ALAN) can be used for remote identification of R&EAs, due to their unique ALAN signatures. In order to develop activity identification models, we carried out a series of in situ measurements of ALAN intensities and spectral properties in a major metropolitan area in Israel. For this task, we used an illuminance CL-500A spectrophotometer that measures the total intensity and spectral irradiance of ALAN, incremented by a 1-nm pitch, from 360 to 780 nm. As our analysis shows, logistic regressions, incorporating ALAN intensities at the peak or near-peak wavelengths, and geographical attributes of the measurement sites as controls, succeeded to predict correctly up to 98.6% of the actual locations of R&EAs. A digital camera satellite image, obtained from the Astronaut Photography Database, was used for the model's validation. According to the validation results, the actual locations of R&EAs coincided well with the estimated high probability areas, as confirmed by the values of Cohen's Kappa index of up to 64%, which indicate a reasonable level of agreement.

Ground-Based Correction of Remote-Sensing Spectral Imagery

Science.gov (United States)

Alder-Golden, Steven M.; Rochford, Peter; Matthew, Michael; Berk, Alexander

2007-01-01

Software has been developed for an improved method of correcting for the atmospheric optical effects (primarily, effects of aerosols and water vapor) in spectral images of the surface of the Earth acquired by airborne and spaceborne remote-sensing instruments. In this method, the variables needed for the corrections are extracted from the readings of a radiometer located on the ground in the vicinity of the scene of interest. The software includes algorithms that analyze measurement data acquired from a shadow-band radiometer. These algorithms are based on a prior radiation transport software model, called MODTRAN, that has been developed through several versions up to what are now known as MODTRAN4 and MODTRAN5 . These components have been integrated with a user-friendly Interactive Data Language (IDL) front end and an advanced version of MODTRAN4. Software tools for handling general data formats, performing a Langley-type calibration, and generating an output file of retrieved atmospheric parameters for use in another atmospheric-correction computer program known as FLAASH have also been incorporated into the present soft-ware. Concomitantly with the soft-ware described thus far, there has been developed a version of FLAASH that utilizes the retrieved atmospheric parameters to process spectral image data.

Spectral analysis and classification of igneous and metamorphic rocks of Hamedan region for remote sensing studies; using laboratory reflectance spectra (350-2500 nm)

International Nuclear Information System (INIS)

Rangzan, K.; Saki, A.; Hassanshahi, H.; Mojaradi, B.

2012-01-01

Reflectance spectrometry techniques with the integration of remote sensing data help us in identifying and mapping the phenomena on the earth. Using these techniques to discriminate the petrologic units independently and without knowing the spectral behavior of rocks along the electromagnetic wavelengths can not be so much useful. For the purposes of this study, 65 samples of igneous and metamorphic rocks from Hamedan region were collected and their spectra were measured using Fieldspec3 device in laboratory. The spectra were analyzed on the basis of absorption, position and shape. Petrographic analyses were used to interpret the absorption patterns as well. Then the spectra were classified according to spectral patterns. This measurement was done on both freshly cut and exposed surfaces of the samples and except a few samples, the two sets of spectra did not differ significantly. Finally, to evaluate the possibility of recognition of these targets, the responses of two hyper spectral and multispectral sensors were simulated from spectra representative of the spectral classes, showing that significant identification and classification of well exposed rocks are potentially possible using remote instruments providing high quality spectra. Also Aster simulation showed that a preliminary gross discrimination of rocks was however possible.

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

Science.gov (United States)

Cao, Qiong; Gu, Lingjia; Ren, Ruizhi; Wang, Lang

2016-09-01

Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection

Simple luminosity normalization of greenness, yellowness and redness/greenness for comparison of leaf spectral profiles in multi-temporally acquired remote sensing images.

Science.gov (United States)

Doi, Ryoichi

2012-09-01

Observation of leaf colour (spectral profiles) through remote sensing is an effective method of identifying the spatial distribution patterns of abnormalities in leaf colour, which enables appropriate plant management measures to be taken. However, because the brightness of remote sensing images varies with acquisition time, in the observation of leaf spectral profiles in multi-temporally acquired remote sensing images, changes in brightness must be taken into account. This study identified a simple luminosity normalization technique that enables leaf colours to be compared in remote sensing images over time. The intensity values of green and yellow (green+red) exhibited strong linear relationships with luminosity (R2 greater than 0.926) when various invariant rooftops in Bangkok or Tokyo were spectralprofiled using remote sensing images acquired at different time points. The values of the coefficient and constant or the coefficient of the formulae describing the intensity of green or yellow were comparable among the single Bangkok site and the two Tokyo sites, indicating the technique's general applicability. For single rooftops, the values of the coefficient of variation for green, yellow, and red/green were 16% or less (n=6-11), indicating an accuracy not less than those of well-established remote sensing measures such as the normalized difference vegetation index. After obtaining the above linear relationships, raw intensity values were normalized and a temporal comparison of the spectral profiles of the canopies of evergreen and deciduous tree species in Tokyo was made to highlight the changes in the canopies' spectral profiles. Future aspects of this technique are discussed herein.

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

Science.gov (United States)

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.

REMOTE SENSING DATA FUSION TO DETECT ILLICIT CROPS AND UNAUTHORIZED AIRSTRIPS

OpenAIRE

Pena, J. A.; Yumin, T.; Liu, H.; Zhao, B.; Garcia, J. A.; Pinto, J.

2018-01-01

Remote sensing data fusion has been playing a more and more important role in crop planting area monitoring, especially for crop area information acquisition. Multi-temporal data and multi-spectral time series are two major aspects for improving crop identification accuracy. Remote sensing fusion provides high quality multi-spectral and panchromatic images in terms of spectral and spatial information, respectively. In this paper, we take one step further and prove the application of remote se...

SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY

Directory of Open Access Journals (Sweden)

X. Han

2016-06-01

Full Text Available Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE. Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI datasets – Pavia University dataset and the Kennedy Space Centre (KSC dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

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)

ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

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Y.-T. Hsieh

2016-06-01

Full Text Available The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i The DN values in shadow area are much lower than in nonshadow area; (ii DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv The shadow area NIR of vegetation category also shows a strong reflection; (v Generally, vegetation indexes (NDVI still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40 is potential for the extract land cover information of shadow areas.

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.

Rayleigh radiance computations for satellite remote sensing: accounting for the effect of sensor spectral response function.

Science.gov (United States)

Wang, Menghua

2016-05-30

To understand and assess the effect of the sensor spectral response function (SRF) on the accuracy of the top of the atmosphere (TOA) Rayleigh-scattering radiance computation, new TOA Rayleigh radiance lookup tables (LUTs) over global oceans and inland waters have been generated. The new Rayleigh LUTs include spectral coverage of 335-2555 nm, all possible solar-sensor geometries, and surface wind speeds of 0-30 m/s. Using the new Rayleigh LUTs, the sensor SRF effect on the accuracy of the TOA Rayleigh radiance computation has been evaluated for spectral bands of the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) satellite and the Joint Polar Satellite System (JPSS)-1, showing some important uncertainties for VIIRS-SNPP particularly for large solar- and/or sensor-zenith angles as well as for large Rayleigh optical thicknesses (i.e., short wavelengths) and bands with broad spectral bandwidths. To accurately account for the sensor SRF effect, a new correction algorithm has been developed for VIIRS spectral bands, which improves the TOA Rayleigh radiance accuracy to ~0.01% even for the large solar-zenith angles of 70°-80°, compared with the error of ~0.7% without applying the correction for the VIIRS-SNPP 410 nm band. The same methodology that accounts for the sensor SRF effect on the Rayleigh radiance computation can be used for other satellite sensors. In addition, with the new Rayleigh LUTs, the effect of surface atmospheric pressure variation on the TOA Rayleigh radiance computation can be calculated precisely, and no specific atmospheric pressure correction algorithm is needed. There are some other important applications and advantages to using the new Rayleigh LUTs for satellite remote sensing, including an efficient and accurate TOA Rayleigh radiance computation for hyperspectral satellite remote sensing, detector-based TOA Rayleigh radiance computation, Rayleigh radiance calculations for high altitude

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

Science.gov (United States)

Hong, Liang

2013-10-01

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

Estimation of atmospheric columnar organic matter (OM) mass concentration from remote sensing measurements of aerosol spectral refractive indices

Science.gov (United States)

Zhang, Ying; Li, Zhengqiang; Sun, Yele; Lv, Yang; Xie, Yisong

2018-04-01

Aerosols have adverse effects on human health and air quality, changing Earth's energy balance and lead to climate change. The components of aerosol are important because of the different spectral characteristics. Based on the low hygroscopic and high scattering properties of organic matter (OM) in fine modal atmospheric aerosols, we develop an inversion algorithm using remote sensing to obtain aerosol components including black carbon (BC), organic matter (OM), ammonium nitrate-like (AN), dust-like (DU) components and aerosol water content (AW). In the algorithm, the microphysical characteristics (i.e. volume distribution and complex refractive index) of particulates are preliminarily separated to fine and coarse modes, and then aerosol components are retrieved using bimodal parameters. We execute the algorithm using remote sensing measurements of sun-sky radiometer at AERONET site (Beijing RADI) in a period from October of 2014 to January of 2015. The results show a reasonable distribution of aerosol components and a good fit for spectral feature calculations. The mean OM mass concentration in atmospheric column is account for 14.93% of the total and 56.34% of dry and fine-mode aerosol, being a fairly good correlation (R = 0.56) with the in situ observations near the surface layer.

Remote sensing of potential and actual daily transpiration of plant canopies based on spectral reflectance and infrared thermal measurements: Concept with preliminary test

International Nuclear Information System (INIS)

Inoue, Y.; Moran, M.S.; Pinter, P.J.Jr.

1994-01-01

A new concept for estimating potential and actual values of daily transpiration rate of vegetation canopies is presented along with results of an initial test. The method is based on a physical foundation of spectral radiation balance for a vegetation canopy, the key inputs to the model being the remotely sensed spectral reflectance and the surface temperature of the plant canopy. The radiation interception or absorptance is estimated more directly from remotely sensed spectral data than it is from the leaf area index. The potential daily transpiration is defined as a linear function of the absorbed solar radiation, which can be estimated using a linear relationship between the fraction absorptance of solar radiation and the remotely sensed Soil Adjusted Vegetation Index for the canopy. The actual daily transpiration rate is estimated by combining this concept with the Jackson-Idso Crop Water Stress Index, which also can be calculated from remotely sensed plant leaf temperatures measured by infrared thermometry. An initial demonstration with data sets from an alfalfa crop and a rangeland suggests that the method may give reasonable estimates of potential and actual values of daily transpiration rate over diverse vegetation area based on simple remote sensing measurements and basic meteorological parameters

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

Science.gov (United States)

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

2013-12-01

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

Remote sensing of species diversity using Landsat 8 spectral variables

Science.gov (United States)

Madonsela, Sabelo; Cho, Moses Azong; Ramoelo, Abel; Mutanga, Onisimo

2017-11-01

The application of remote sensing in biodiversity estimation has largely relied on the Normalized Difference Vegetation Index (NDVI). The NDVI exploits spectral information from red and near infrared bands of Landsat images and it does not consider canopy background conditions hence it is affected by soil brightness which lowers its sensitivity to vegetation. As such NDVI may be insufficient in explaining tree species diversity. Meanwhile, the Landsat program also collects essential spectral information in the shortwave infrared (SWIR) region which is related to plant properties. The study was intended to: (i) explore the utility of spectral information across Landsat-8 spectrum using the Principal Component Analysis (PCA) and estimate alpha diversity (α-diversity) in the savannah woodland in southern Africa, and (ii) define the species diversity index (Shannon (H‧), Simpson (D2) and species richness (S) - defined as number of species in a community) that best relates to spectral variability on the Landsat-8 Operational Land Imager dataset. We designed 90 m × 90 m field plots (n = 71) and identified all trees with a diameter at breast height (DbH) above 10 cm. H‧, D2 and S were used to quantify tree species diversity within each plot and the corresponding spectral information on all Landsat-8 bands were extracted from each field plot. A stepwise linear regression was applied to determine the relationship between species diversity indices (H‧, D2 and S) and Principal Components (PCs), vegetation indices and Gray Level Co-occurrence Matrix (GLCM) texture layers with calibration (n = 46) and test (n = 23) datasets. The results of regression analysis showed that the Simple Ratio Index derivative had a higher relationship with H‧, D2 and S (r2= 0.36; r2= 0.41; r2= 0.24 respectively) compared to NDVI, EVI, SAVI or their derivatives. Moreover the Landsat-8 derived PCs also had a higher relationship with H‧ and D2 (r2 of 0.36 and 0.35 respectively) than the

Analysis and Mapping of the Spectral Characteristics of Fractional Green Cover in Saline Wetlands (NE Spain Using Field and Remote Sensing Data

Directory of Open Access Journals (Sweden)

Manuela Domínguez-Beisiegel

2016-07-01

Full Text Available Inland saline wetlands are complex systems undergoing continuous changes in moisture and salinity and are especially vulnerable to human pressures. Remote sensing is helpful to identify vegetation change in semi-arid wetlands and to assess wetland degradation. Remote sensing-based monitoring requires identification of the spectral characteristics of soils and vegetation and their correspondence with the vegetation cover and soil conditions. We studied the spectral characteristics of soils and vegetation of saline wetlands in Monegros, NE Spain, through field and satellite images. Radiometric and complementary field measurements in two field surveys in 2007 and 2008 were collected in selected sites deemed as representative of different soil moisture, soil color, type of vegetation, and density. Despite the high local variability, we identified good relationships between field spectral data and Quickbird images. A methodology was established for mapping the fraction of vegetation cover in Monegros and other semi-arid areas. Estimating vegetation cover in arid wetlands is conditioned by the soil background and by the occurrence of dry and senescent vegetation accompanying the green component of perennial salt-tolerant plants. Normalized Difference Vegetation Index (NDVI was appropriate to map the distribution of the vegetation cover if the green and yellow-green parts of the plants are considered.

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

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Y. Di

2017-05-01

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

a Spatio-Spectral Camera for High Resolution Hyperspectral Imaging

Science.gov (United States)

Livens, S.; Pauly, K.; Baeck, P.; Blommaert, J.; Nuyts, D.; Zender, J.; Delauré, B.

2017-08-01

Imaging with a conventional frame camera from a moving remotely piloted aircraft system (RPAS) is by design very inefficient. Less than 1 % of the flying time is used for collecting light. This unused potential can be utilized by an innovative imaging concept, the spatio-spectral camera. The core of the camera is a frame sensor with a large number of hyperspectral filters arranged on the sensor in stepwise lines. It combines the advantages of frame cameras with those of pushbroom cameras. By acquiring images in rapid succession, such a camera can collect detailed hyperspectral information, while retaining the high spatial resolution offered by the sensor. We have developed two versions of a spatio-spectral camera and used them in a variety of conditions. In this paper, we present a summary of three missions with the in-house developed COSI prototype camera (600-900 nm) in the domains of precision agriculture (fungus infection monitoring in experimental wheat plots), horticulture (crop status monitoring to evaluate irrigation management in strawberry fields) and geology (meteorite detection on a grassland field). Additionally, we describe the characteristics of the 2nd generation, commercially available ButterflEYE camera offering extended spectral range (475-925 nm), and we discuss future work.

A SPATIO-SPECTRAL CAMERA FOR HIGH RESOLUTION HYPERSPECTRAL IMAGING

Directory of Open Access Journals (Sweden)

S. Livens

2017-08-01

Full Text Available Imaging with a conventional frame camera from a moving remotely piloted aircraft system (RPAS is by design very inefficient. Less than 1 % of the flying time is used for collecting light. This unused potential can be utilized by an innovative imaging concept, the spatio-spectral camera. The core of the camera is a frame sensor with a large number of hyperspectral filters arranged on the sensor in stepwise lines. It combines the advantages of frame cameras with those of pushbroom cameras. By acquiring images in rapid succession, such a camera can collect detailed hyperspectral information, while retaining the high spatial resolution offered by the sensor. We have developed two versions of a spatio-spectral camera and used them in a variety of conditions. In this paper, we present a summary of three missions with the in-house developed COSI prototype camera (600–900 nm in the domains of precision agriculture (fungus infection monitoring in experimental wheat plots, horticulture (crop status monitoring to evaluate irrigation management in strawberry fields and geology (meteorite detection on a grassland field. Additionally, we describe the characteristics of the 2nd generation, commercially available ButterflEYE camera offering extended spectral range (475–925 nm, and we discuss future work.

Remote Sensing Data Fusion to Detect Illicit Crops and Unauthorized Airstrips

Science.gov (United States)

Pena, J. A.; Yumin, T.; Liu, H.; Zhao, B.; Garcia, J. A.; Pinto, J.

2018-04-01

Remote sensing data fusion has been playing a more and more important role in crop planting area monitoring, especially for crop area information acquisition. Multi-temporal data and multi-spectral time series are two major aspects for improving crop identification accuracy. Remote sensing fusion provides high quality multi-spectral and panchromatic images in terms of spectral and spatial information, respectively. In this paper, we take one step further and prove the application of remote sensing data fusion in detecting illicit crop through LSMM, GOBIA, and MCE analyzing of strategic information. This methodology emerges as a complementary and effective strategy to control and eradicate illicit crops.

[Review of digital ground object spectral library].

Science.gov (United States)

Zhou, Xiao-Hu; Zhou, Ding-Wu

2009-06-01

A higher spectral resolution is the main direction of developing remote sensing technology, and it is quite important to set up the digital ground object reflectance spectral database library, one of fundamental research fields in remote sensing application. Remote sensing application has been increasingly relying on ground object spectral characteristics, and quantitative analysis has been developed to a new stage. The present article summarized and systematically introduced the research status quo and development trend of digital ground object reflectance spectral libraries at home and in the world in recent years. Introducing the spectral libraries has been established, including desertification spectral database library, plants spectral database library, geological spectral database library, soil spectral database library, minerals spectral database library, cloud spectral database library, snow spectral database library, the atmosphere spectral database library, rocks spectral database library, water spectral database library, meteorites spectral database library, moon rock spectral database library, and man-made materials spectral database library, mixture spectral database library, volatile compounds spectral database library, and liquids spectral database library. In the process of establishing spectral database libraries, there have been some problems, such as the lack of uniform national spectral database standard and uniform standards for the ground object features as well as the comparability between different databases. In addition, data sharing mechanism can not be carried out, etc. This article also put forward some suggestions on those problems.

Remote nuclear screening system for hostile environments

International Nuclear Information System (INIS)

Addleman, R.S.; Keele, B.D.

1996-01-01

A remote measurement system has been constructed for in situ gamma and beta isotopic characterization of highly radioactive nuclear material in hostile environments. A small collimated, planar CdZnTe detector is used for gamma-ray spectroscopy. Spectral resolution of 2% full width at half maximum at 662 kiloelectronvolts has been obtained remotely using rise time compensation and limited pulse shape discrimination, Isotopc measurement of high-energy beta emitters was accomplished with a ruggedized, deeply depleted, surface barrier silicon dictator. The primary function of the remote nuclear screening system is to provide fast qualitative and quantitative isotopic assessment of high-level radioactive material

Potential of remote sensing of cirrus optical thickness by airborne spectral radiance measurements at different sideward viewing angles

Science.gov (United States)

Wolf, Kevin; Ehrlich, André; Hüneke, Tilman; Pfeilsticker, Klaus; Werner, Frank; Wirth, Martin; Wendisch, Manfred

2017-03-01

Spectral radiance measurements collected in nadir and sideward viewing directions by two airborne passive solar remote sensing instruments, the Spectral Modular Airborne Radiation measurement sysTem (SMART) and the Differential Optical Absorption Spectrometer (mini-DOAS), are used to compare the remote sensing results of cirrus optical thickness τ. The comparison is based on a sensitivity study using radiative transfer simulations (RTS) and on data obtained during three airborne field campaigns: the North Atlantic Rainfall VALidation (NARVAL) mission, the Mid-Latitude Cirrus Experiment (ML-CIRRUS) and the Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems (ACRIDICON) campaign. Radiative transfer simulations are used to quantify the sensitivity of measured upward radiance I with respect to τ, ice crystal effective radius reff, viewing angle of the sensor θV, spectral surface albedo α, and ice crystal shape. From the calculations it is concluded that sideward viewing measurements are generally better suited than radiance data from the nadir direction to retrieve τ of optically thin cirrus, especially at wavelengths larger than λ = 900 nm. Using sideward instead of nadir-directed spectral radiance measurements significantly improves the sensitivity and accuracy in retrieving τ, in particular for optically thin cirrus of τ ≤ 2. The comparison of retrievals of τ based on nadir and sideward viewing radiance measurements from SMART, mini-DOAS and independent estimates of τ from an additional active remote sensing instrument, the Water Vapor Lidar Experiment in Space (WALES), shows general agreement within the range of measurement uncertainties. For the selected example a mean τ of 0.54 ± 0.2 is derived from SMART, and 0.49 ± 0.2 by mini-DOAS nadir channels, while WALES obtained a mean value of τ = 0.32 ± 0.02 at 532 nm wavelength, respectively. The mean of τ derived from the sideward viewing mini

Spectral characteristics and feature selection of satellite remote sensing data for climate and anthropogenic changes assessment in Bucharest area

Science.gov (United States)

Zoran, Maria; Savastru, Roxana; Savastru, Dan; Tautan, Marina; Miclos, Sorin; Cristescu, Luminita; Carstea, Elfrida; Baschir, Laurentiu

2010-05-01

Urban systems play a vital role in social and economic development in all countries. Their environmental changes can be investigated on different spatial and temporal scales. Urban and peri-urban environment dynamics is of great interest for future planning and decision making as well as in frame of local and regional changes. Changes in urban land cover include changes in biotic diversity, actual and potential primary productivity, soil quality, runoff, and sedimentation rates, and cannot be well understood without the knowledge of land use change that drives them. The study focuses on the assessment of environmental features changes for Bucharest metropolitan area, Romania by satellite remote sensing and in-situ monitoring data. Rational feature selection from the varieties of spectral channels in the optical wavelengths of electromagnetic spectrum (VIS and NIR) is very important for effective analysis and information extraction of remote sensing data. Based on comprehensively analyses of the spectral characteristics of remote sensing data is possibly to derive environmental changes in urban areas. The information quantity contained in a band is an important parameter in evaluating the band. The deviation and entropy are often used to show information amount. Feature selection is one of the most important steps in recognition and classification of remote sensing images. Therefore, it is necessary to select features before classification. The optimal features are those that can be used to distinguish objects easily and correctly. Three factors—the information quantity of bands, the correlation between bands and the spectral characteristic (e.g. absorption specialty) of classified objects in test area Bucharest have been considered in our study. As, the spectral characteristic of an object is influenced by many factors, being difficult to define optimal feature parameters to distinguish all the objects in a whole area, a method of multi-level feature selection

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

Directory of Open Access Journals (Sweden)

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

Determination of Primary Spectral Bands for Remote Sensing of Aquatic Environments

Directory of Open Access Journals (Sweden)

MingXia He

2007-12-01

Full Text Available About 30 years ago, NASA launched the first ocean-color observing satellite:the Coastal Zone Color Scanner. CZCS had 5 bands in the visible-infrared domain with anobjective to detect changes of phytoplankton (measured by concentration of chlorophyll inthe oceans. Twenty years later, for the same objective but with advanced technology, theSea-viewing Wide Field-of-view Sensor (SeaWiFS, 7 bands, the Moderate-ResolutionImaging Spectrometer (MODIS, 8 bands, and the Medium Resolution ImagingSpectrometer (MERIS, 12 bands were launched. The selection of the number of bands andtheir positions was based on experimental and theoretical results achieved before thedesign of these satellite sensors. Recently, Lee and Carder (2002 demonstrated that foradequate derivation of major properties (phytoplankton biomass, colored dissolved organicmatter, suspended sediments, and bottom properties in both oceanic and coastalenvironments from observation of water color, it is better for a sensor to have ~15 bands inthe 400 – 800 nm range. In that study, however, it did not provide detailed analysesregarding the spectral locations of the 15 bands. Here, from nearly 400 hyperspectral (~ 3-nm resolution measurements of remote-sensing reflectance (a measure of water colortaken in both coastal and oceanic waters covering both optically deep and optically shallowwaters, first- and second-order derivatives were calculated after interpolating themeasurements to 1-nm resolution. From these derivatives, the frequency of zero values foreach wavelength was accounted for, and the distribution spectrum of such frequencies wasobtained. Furthermore, the wavelengths that have the highest appearance of zeros wereidentified. Because these spectral locations indicate extrema (a local maximum orminimum of the reflectance spectrum or inflections of the spectral curvature, placing the bands of a sensor at these wavelengths maximizes the potential of capturing (and then restoring

Salinity and spectral reflectance of soils

Science.gov (United States)

Szilagyi, A.; Baumgardner, M. F.

1991-01-01

The basic spectral response related to the salt content of soils in the visible and reflective IR wavelengths is analyzed in order to explore remote sensing applications for monitoring processes of the earth system. The bidirectional reflectance factor (BRF) was determined at 10 nm of increments over the 520-2320-nm spectral range. The effect of salts on reflectance was analyzed on the basis of 162 spectral measurements. MSS and TM bands were simulated within the measured spectral region. A strong relationship was found in variations of reflectance and soil characteristics pertaining to salinization and desalinization. Although the individual MSS bands had high R-squared values and 75-79 percent of soil/treatment combinations were separable, there was a large number of soil/treatment combinations not distinguished by any of the four highly correlated MSS bands under consideration.

System analysis of a tilted field-widened Michelson interferometer for high spectral resolution lidar.

Science.gov (United States)

Liu, Dong; Hostetler, Chris; Miller, Ian; Cook, Anthony; Hair, Johnathan

2012-01-16

High spectral resolution lidars (HSRLs) have shown great value in aircraft aerosol remote sensing application and are planned for future satellite missions. A compact, robust, quasi-monolithic tilted field-widened Michelson interferometer is being developed as the spectral discrimination filter for an second-generation HSRL(HSRL-2) at NASA Langley Research Center. The Michelson interferometer consists of a cubic beam splitter, a solid arm and an air arm. Piezo stacks connect the air arm mirror to the body of the interferometer and can tune the interferometer within a small range. The whole interferometer is tilted so that the standard Michelson output and the reflected complementary output can both be obtained. In this paper, the transmission ratio is proposed to evaluate the performance of the spectral filter for HSRL. The transmission ratios over different types of system imperfections, such as cumulative wavefront error, locking error, reflectance of the beam splitter and anti-reflection coatings, system tilt, and depolarization angle are analyzed. The requirements of each imperfection for good interferometer performance are obtained.

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

Science.gov (United States)

Sánchez-Azofeifa, Arturo; Rivard, Benoit; Wright, Joseph; Feng, Ji-Lu; Li, Peijun; Chong, Mei Mei; Bohlman, Stephanie A

2011-01-01

Species identification and characterization in tropical environments is an emerging field in tropical remote sensing. Significant efforts are currently aimed at the detection of tree species, of levels of forest successional stages, and the extent of liana occurrence at the top of canopies. In this paper we describe our use of high resolution imagery from the Quickbird Satellite to estimate the flowering population of Tabebuia guayacan trees at Barro Colorado Island (BCI), in Panama. The imagery was acquired on 29 April 2002 and 21 March 2004. Spectral Angle Mapping via a One-Class Support Vector machine was used to detect the presence of 422 and 557 flowering tress in the April 2002 and March 2004 imagery. Of these, 273 flowering trees are common to both dates. This study presents a new perspective on the effectiveness of high resolution remote sensing for monitoring a phenological response and its use as a tool for potential conservation and management of natural resources in tropical environments.

Hybrid Image Fusion for Sharpness Enhancement of Multi-Spectral Lunar Images

Science.gov (United States)

Awumah, Anna; Mahanti, Prasun; Robinson, Mark

2016-10-01

Image fusion enhances the sharpness of a multi-spectral (MS) image by incorporating spatial details from a higher-resolution panchromatic (Pan) image [1,2]. Known applications of image fusion for planetary images are rare, although image fusion is well-known for its applications to Earth-based remote sensing. In a recent work [3], six different image fusion algorithms were implemented and their performances were verified with images from the Lunar Reconnaissance Orbiter (LRO) Camera. The image fusion procedure obtained a high-resolution multi-spectral (HRMS) product from the LRO Narrow Angle Camera (used as Pan) and LRO Wide Angle Camera (used as MS) images. The results showed that the Intensity-Hue-Saturation (IHS) algorithm results in a high-spatial quality product while the Wavelet-based image fusion algorithm best preserves spectral quality among all the algorithms. In this work we show the results of a hybrid IHS-Wavelet image fusion algorithm when applied to LROC MS images. The hybrid method provides the best HRMS product - both in terms of spatial resolution and preservation of spectral details. Results from hybrid image fusion can enable new science and increase the science return from existing LROC images.[1] Pohl, Cle, and John L. Van Genderen. "Review article multisensor image fusion in remote sensing: concepts, methods and applications." International journal of remote sensing 19.5 (1998): 823-854.[2] Zhang, Yun. "Understanding image fusion." Photogramm. Eng. Remote Sens 70.6 (2004): 657-661.[3] Mahanti, Prasun et al. "Enhancement of spatial resolution of the LROC Wide Angle Camera images." Archives, XXIII ISPRS Congress Archives (2016).

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.

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

Science.gov (United States)

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

2018-02-01

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

The Research on the Spectral Characteristics of Sea Fog Based on Caliop and Modis Data

Science.gov (United States)

Wan, J.; Su, J.; Liu, S.; Sheng, H.

2018-04-01

In view of that difficulty of distinguish between sea fog and low cloud by optical remote sensing mean, the research on spectral characteristics of sea fog is focused and carried out. The satellite laser radar CALIOP data and the high spectral MODIS data were obtained from May to December 2017, and the scattering coefficient and the vertical height information were extracted from the atmospheric attenuation of the lower star to extract the sea fog sample points, and the spectral response curve based on MODIS was formed to analyse the spectral response characteristics of the sea fog, thus providing a theoretical basis for the monitoring of sea fog with optical remote sensing image.

THE RESEARCH ON THE SPECTRAL CHARACTERISTICS OF SEA FOG BASED ON CALIOP AND MODIS DATA

Directory of Open Access Journals (Sweden)

J. Wan

2018-04-01

Full Text Available In view of that difficulty of distinguish between sea fog and low cloud by optical remote sensing mean, the research on spectral characteristics of sea fog is focused and carried out。The satellite laser radar CALIOP data and the high spectral MODIS data were obtained from May to December 2017, and the scattering coefficient and the vertical height information were extracted from the atmospheric attenuation of the lower star to extract the sea fog sample points, and the spectral response curve based on MODIS was formed to analyse the spectral response characteristics of the sea fog, thus providing a theoretical basis for the monitoring of sea fog with optical remote sensing image.

Choice of Eye-Safe Radiation Wavelength in UV and Near IR Spectral Bands for Remote Sensing

OpenAIRE

M. L. Belov; V. A. Gorodnichev; D. A. Kravtsov; A. A. Cherpakova

2016-01-01

The introduction of laser remote sensing systems carries a particular risk to the human’s sense of vision. A structure of the eye, and especially the retina, is the main critical organ as related to the laser radiation.The work uses the optical models of the atmosphere, correctly working in both the UV and the near-IR band, to select the eye-safe radiation wavelengths in the UV (0.355 m) and near-IR (~ 1.54 and ~ 2 m) spectral bands from the point of view of recorded lidar signal value to ful...

Characterizing CDOM Spectral Variability Across Diverse Regions and Spectral Ranges

Science.gov (United States)

Grunert, Brice K.; Mouw, Colleen B.; Ciochetto, Audrey B.

2018-01-01

Satellite remote sensing of colored dissolved organic matter (CDOM) has focused on CDOM absorption (aCDOM) at a reference wavelength, as its magnitude provides insight into the underwater light field and large-scale biogeochemical processes. CDOM spectral slope, SCDOM, has been treated as a constant or semiconstant parameter in satellite retrievals of aCDOM despite significant regional and temporal variabilities. SCDOM and other optical metrics provide insights into CDOM composition, processing, food web dynamics, and carbon cycling. To date, much of this work relies on fluorescence techniques or aCDOM in spectral ranges unavailable to current and planned satellite sensors (e.g., global variability in SCDOM and fit deviations in the aCDOM spectra using the recently proposed Gaussian decomposition method. From this, we investigate if global variability in retrieved SCDOM and Gaussian components is significant and regionally distinct. We iteratively decreased the spectral range considered and analyzed the number, location, and magnitude of fitted Gaussian components to understand if a reduced spectral range impacts information obtained within a common spectral window. We compared the fitted slope from the Gaussian decomposition method to absorption-based indices that indicate CDOM composition to determine the ability of satellite-derived slope to inform the analysis and modeling of large-scale biogeochemical processes. Finally, we present implications of the observed variability for remote sensing of CDOM characteristics via SCDOM.

Spectral stratigraphy

Science.gov (United States)

Lang, Harold R.

1991-01-01

A new approach to stratigraphic analysis is described which uses photogeologic and spectral interpretation of multispectral remote sensing data combined with topographic information to determine the attitude, thickness, and lithology of strata exposed at the surface. The new stratigraphic procedure is illustrated by examples in the literature. The published results demonstrate the potential of spectral stratigraphy for mapping strata, determining dip and strike, measuring and correlating stratigraphic sequences, defining lithofacies, mapping biofacies, and interpreting geological structures.

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.

Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

Science.gov (United States)

Ehrlich, André; Bierwirth, Eike; Istomina, Larysa; Wendisch, Manfred

2017-09-01

The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow). Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C. In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S) and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff, C) are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice edge are analysed. The retrieved values of τ, reff

[Road Extraction in Remote Sensing Images Based on Spectral and Edge Analysis].

Science.gov (United States)

Zhao, Wen-zhi; Luo, Li-qun; Guo, Zhou; Yue, Jun; Yu, Xue-ying; Liu, Hui; Wei, Jing

2015-10-01

Roads are typically man-made objects in urban areas. Road extraction from high-resolution images has important applications for urban planning and transportation development. However, due to the confusion of spectral characteristic, it is difficult to distinguish roads from other objects by merely using traditional classification methods that mainly depend on spectral information. Edge is an important feature for the identification of linear objects (e. g. , roads). The distribution patterns of edges vary greatly among different objects. It is crucial to merge edge statistical information into spectral ones. In this study, a new method that combines spectral information and edge statistical features has been proposed. First, edge detection is conducted by using self-adaptive mean-shift algorithm on the panchromatic band, which can greatly reduce pseudo-edges and noise effects. Then, edge statistical features are obtained from the edge statistical model, which measures the length and angle distribution of edges. Finally, by integrating the spectral and edge statistical features, SVM algorithm is used to classify the image and roads are ultimately extracted. A series of experiments are conducted and the results show that the overall accuracy of proposed method is 93% comparing with only 78% overall accuracy of the traditional. The results demonstrate that the proposed method is efficient and valuable for road extraction, especially on high-resolution images.

Spectrally based mapping of riverbed composition

Science.gov (United States)

Legleiter, Carl; Stegman, Tobin K.; Overstreet, Brandon T.

2016-01-01

Remote sensing methods provide an efficient means of characterizing fluvial systems. This study evaluated the potential to map riverbed composition based on in situ and/or remote measurements of reflectance. Field spectra and substrate photos from the Snake River, Wyoming, USA, were used to identify different sediment facies and degrees of algal development and to quantify their optical characteristics. We hypothesized that accounting for the effects of depth and water column attenuation to isolate the reflectance of the streambed would enhance distinctions among bottom types and facilitate substrate classification. A bottom reflectance retrieval algorithm adapted from coastal research yielded realistic spectra for the 450 to 700 nm range; but bottom reflectance-based substrate classifications, generated using a random forest technique, were no more accurate than classifications derived from above-water field spectra. Additional hypothesis testing indicated that a combination of reflectance magnitude (brightness) and indices of spectral shape provided the most accurate riverbed classifications. Convolving field spectra to the response functions of a multispectral satellite and a hyperspectral imaging system did not reduce classification accuracies, implying that high spectral resolution was not essential. Supervised classifications of algal density produced from hyperspectral data and an inferred bottom reflectance image were not highly accurate, but unsupervised classification of the bottom reflectance image revealed distinct spectrally based clusters, suggesting that such an image could provide additional river information. We attribute the failure of bottom reflectance retrieval to yield more reliable substrate maps to a latent correlation between depth and bottom type. Accounting for the effects of depth might have eliminated a key distinction among substrates and thus reduced discriminatory power. Although further, more systematic study across a broader

Analysis of Cross-Seasonal Spectral Response from Kettle Holes: Application of Remote Sensing Techniques for Chlorophyll Estimation

Directory of Open Access Journals (Sweden)

Bernd Lennartz

2012-11-01

Full Text Available Kettle holes, small inland water bodies usually less than 1 ha in size, are subjected to pollution, drainage, and structural alteration by intensive land use practices. This study presents the analysis of spectral signatures from kettle holes based on in situ water sampling and reflectance measurements in application for chlorophyll estimation. Water samples and surface reflectance from kettle holes were collected from 6 ponds in 15 field campaigns (5 in 2007 and 10 in 2008, resulting in a total of 80 spectral datasets. We assessed the existing semi-empirical algorithms to determine chlorophyll content for different types of kettle holes using seasonal and cross-seasonal volume reflectance and derivative spectra. Based on this analysis and optical properties of water leaving reflectance from kettle holes, the following typology of the remote signal interpretation was proposed: Submerged vegetation, Phytoplankton dominated and Mixed type.

Reconstruction of MODIS Spectral Reflectance under Cloudy-Sky Condition

Directory of Open Access Journals (Sweden)

Bo Gao

2016-09-01

Full Text Available Clouds usually cause invalid observations for sensors aboard satellites, which corrupts the spatio-temporal continuity of land surface parameters retrieved from remote sensing data (e.g., MODerate Resolution Imaging Spectroradiometer (MODIS data and prevents the fusing of multi-source remote sensing data in the field of quantitative remote sensing. Based on the requirements of spatio-temporal continuity and the necessity of methods to restore bad pixels, primarily resulting from image processing, this study developed a novel method to derive the spectral reflectance for MODIS band of cloudy pixels in the visual–near infrared (VIS–NIR spectral channel based on the Bidirectional Reflectance Distribution Function (BRDF and multi-spatio-temporal observations. The proposed method first constructs the spatial distribution of land surface reflectance based on the corresponding BRDF and the solar-viewing geometry; then, a geographically weighted regression (GWR is introduced to individually derive the spectral surface reflectance for MODIS band of cloudy pixels. A validation of the proposed method shows that a total root-mean-square error (RMSE of less than 6% and a total R2 of more than 90% are detected, which indicates considerably better precision than those exhibited by other existing methods. Further validation of the retrieved white-sky albedo based on the spectral reflectance for MODIS band of cloudy pixels confirms an RMSE of 3.6% and a bias of 2.2%, demonstrating very high accuracy of the proposed method.

Remote Sensing and Reflectance Profiling in Entomology.

Science.gov (United States)

Nansen, Christian; Elliott, Norman

2016-01-01

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

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.

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

Directory of Open Access Journals (Sweden)

Guizhou Wang

2013-01-01

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

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.

Thermal infrared remote sensing of crude oil slicks

International Nuclear Information System (INIS)

Salisbury, J.W.; D'Aria, D.M.

1993-01-01

It is important to develop a remote sensing technique for reliable detection of oil slicks for reasons of both oil exploration and environmental protection. Yet, unambiguous detection has proven an elusive goal. This article presents new thermal infrared spectra of oil slicks made from five different crude oil samples with a wide range of API gravities and compositions. After a brief outgassing phase, all oil slick spectra are quite similar and little affected by thickness, extended exposure to air or sunlight, and even by emulsification with seawater (mousse formation). Thus, oil slicks provide a remarkably unvarying spectral signature as remote sensing targets in the thermal infrared compared to other regions of the spectrum. This spectral signature in the 8-14 μm atmospheric window is flat, with an average reflectance of 4%. Seawater, on the other hand, has a spectrum that varies in reflectance with wavelength in the 8-14 μm window from 0.90 to 3.65%. In addition, the authors show that sea foam displays a reflectance spectrum quite similar to that of seawater in the 8-14 μm region, because the very high absorption coefficient of water in this wavelength region prevents volume scattering in foam bubbles. This results in a relatively uniform spectral background, against which oil slicks can be detected, based on their different spectral signature. Thus, thermal infrared multispectral remote sensing appears to offer a simple and reliable technique for aircraft or satellite detection of oil slicks

[An improved low spectral distortion PCA fusion method].

Science.gov (United States)

Peng, Shi; Zhang, Ai-Wu; Li, Han-Lun; Hu, Shao-Xing; Meng, Xian-Gang; Sun, Wei-Dong

2013-10-01

Aiming at the spectral distortion produced in PCA fusion process, the present paper proposes an improved low spectral distortion PCA fusion method. This method uses NCUT (normalized cut) image segmentation algorithm to make a complex hyperspectral remote sensing image into multiple sub-images for increasing the separability of samples, which can weaken the spectral distortions of traditional PCA fusion; Pixels similarity weighting matrix and masks were produced by using graph theory and clustering theory. These masks are used to cut the hyperspectral image and high-resolution image into some sub-region objects. All corresponding sub-region objects between the hyperspectral image and high-resolution image are fused by using PCA method, and all sub-regional integration results are spliced together to produce a new image. In the experiment, Hyperion hyperspectral data and Rapid Eye data were used. And the experiment result shows that the proposed method has the same ability to enhance spatial resolution and greater ability to improve spectral fidelity performance.

On spectral distribution of high dimensional covariation matrices

DEFF Research Database (Denmark)

Heinrich, Claudio; Podolskij, Mark

In this paper we present the asymptotic theory for spectral distributions of high dimensional covariation matrices of Brownian diffusions. More specifically, we consider N-dimensional Itô integrals with time varying matrix-valued integrands. We observe n equidistant high frequency data points...... of the underlying Brownian diffusion and we assume that N/n -> c in (0,oo). We show that under a certain mixed spectral moment condition the spectral distribution of the empirical covariation matrix converges in distribution almost surely. Our proof relies on method of moments and applications of graph theory....

Multi-Spectral Remote Sensing of Phytoplankton Pigment Absorption Properties in Cyanobacteria Bloom Waters: A Regional Example in the Western Basin of Lake Erie

Directory of Open Access Journals (Sweden)

Guoqing Wang

2017-12-01

Full Text Available Phytoplankton pigments absorb sunlight for photosynthesis, protect the chloroplast from damage caused by excess light energy, and influence the color of the water. Some pigments act as bio-markers and are important for separation of phytoplankton functional types. Among many efforts that have been made to obtain information on phytoplankton pigments from bio-optical properties, Gaussian curves decomposed from phytoplankton absorption spectrum have been used to represent the light absorption of different pigments. We incorporated the Gaussian scheme into a semi-analytical model and obtained the Gaussian curves from remote sensing reflectance. In this study, a series of sensitivity tests were conducted to explore the potential of obtaining the Gaussian curves from multi-spectral satellite remote sensing. Results showed that the Gaussian curves can be retrieved with 35% or less mean unbiased absolute percentage differences from MEdium Resolution Imaging Spectrometer (MERIS and Moderate Resolution Imaging Spectroradiometer (MODIS-like sensors. Further, using Lake Erie as an example, the spatial distribution of chlorophyll a and phycocyanin concentrations were obtained from the Gaussian curves and used as metrics for the spatial extent of an intense cyanobacterial bloom occurred in Lake Erie in 2014. The seasonal variations of Gaussian absorption properties in 2011 were further obtained from MERIS imagery. This study shows that it is feasible to obtain Gaussian curves from multi-spectral satellite remote sensing data, and the obtained chlorophyll a and phycocyanin concentrations from these Gaussian peak heights demonstrated potential application to monitor harmful algal blooms (HABs and identification of phytoplankton groups from satellite ocean color remote sensing semi-analytically.

Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

Directory of Open Access Journals (Sweden)

A. Ehrlich

2017-09-01

Full Text Available The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow. Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C.In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S, λ2 = 1650 nm (sensitive to τ, and λ3 = 2100 nm (sensitive to reff, C are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012 were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice

High Spectral Resolution Infrared and Raman Lidar Observations for the ARM Program: Clear and Cloudy Sky Applications

Energy Technology Data Exchange (ETDEWEB)

Revercomb, Henry; Tobin, David; Knuteson, Robert; Borg, Lori; Moy, Leslie

2009-06-17

This grant began with the development of the Atmospheric Emitted Radiance Interferometer (AERI) for ARM. The AERI has provided highly accurate and reliable observations of downwelling spectral radiance (Knuteson et al. 2004a, 2004b) for application to radiative transfer, remote sensing of boundary layer temperature and water vapor, and cloud characterization. One of the major contributions of the ARM program has been its success in improving radiation calculation capabilities for models and remote sensing that evolved from the multi-year, clear-sky spectral radiance comparisons between AERI radiances and line-by-line calculations (Turner et al. 2004). This effort also spurred us to play a central role in improving the accuracy of water vapor measurements, again helping ARM lead the way in the community (Turner et al. 2003a, Revercomb et al. 2003). In order to add high-altitude downlooking AERI-like observations over the ARM sites, we began the development of an airborne AERI instrument that has become known as the Scanning High-resolution Interferometer Sounder (Scanning-HIS). This instrument has become an integral part of the ARM Unmanned Aerospace Vehicle (ARM-UAV) program. It provides both a cross-track mapping view of the earth and an uplooking view from the 12-15 km altitude of the Scaled Composites Proteus aircraft when flown over the ARM sites for IOPs. It has successfully participated in the first two legs of the “grand tour” of the ARM sites (SGP and NSA), resulting in a very good comparison with AIRS observations in 2002 and in an especially interesting data set from the arctic during the Mixed-Phase Cloud Experiment (M-PACE) in 2004.

Spectral interdependence of remote-sensing reflectance and its implications on the design of ocean color satellite sensors.

Science.gov (United States)

Lee, Zhongping; Shang, Shaoling; Hu, Chuanmin; Zibordi, Giuseppe

2014-05-20

Using 901 remote-sensing reflectance spectra (R(rs)(λ), sr⁻¹, λ from 400 to 700 nm with a 5 nm resolution), we evaluated the correlations of R(rs)(λ) between neighboring spectral bands in order to characterize (1) the spectral interdependence of R(rs)(λ) at different bands and (2) to what extent hyperspectral R(rs)(λ) can be reconstructed from multiband measurements. The 901 R(rs) spectra were measured over a wide variety of aquatic environments in which water color varied from oceanic blue to coastal green or brown, with chlorophyll-a concentrations ranging from ~0.02 to >100  mg  m⁻³, bottom depths from ~1  m to >1000  m, and bottom substrates including sand, coral reef, and seagrass. The correlation coefficient of R(rs)(λ) between neighboring bands at center wavelengths λ(k) and λ(l), r(Δλ)(λ(k), λ(l)), was evaluated systematically, with the spectral gap (Δλ=λ(l)-λ(k)) changing between 5, 10, 15, 20, 25, and 30 nm, respectively. It was found that r(Δλ) decreased with increasing Δλ, but remained >0.97 for Δλ≤20  nm for all spectral bands. Further, using 15 spectral bands between 400 and 710 nm, we reconstructed, via multivariant linear regression, hyperspectral R(rs)(λ) (from 400 to 700 nm with a 5 nm resolution). The percentage difference between measured and reconstructed R(rs) for each band in the 400-700 nm range was generally less than 1%, with a correlation coefficient close to 1.0. The mean absolute error between measured and reconstructed R(rs) was about 0.00002  sr⁻¹ for each band, which is significantly smaller than the R(rs) uncertainties from all past and current ocean color satellite radiometric products. These results echo findings of earlier studies that R(rs) measurements at ~15 spectral bands in the visible domain can provide nearly identical spectral information as with hyperspectral (contiguous bands at 5 nm spectral resolution) measurements. Such results provide insights for data

Use of high-dimensional spectral data to evaluate organic matter, reflectance relationships in soils

Science.gov (United States)

Henderson, T. L.; Baumgardner, M. F.; Coster, D. C.; Franzmeier, D. P.; Stott, D. E.

1990-01-01

Recent breakthroughs in remote sensing technology have led to the development of a spaceborne high spectral resolution imaging sensor, HIRIS, to be launched in the mid-1990s for observation of earth surface features. The effects of organic carbon content on soil reflectance over the spectral range of HIRIS, and to examine the contributions of humic and fulvic acid fractions to soil reflectance was evaluated. Organic matter from four Indiana agricultural soils was extracted, fractionated, and purified, and six individual components of each soil were isolated and prepared for spectral analysis. The four soils, ranging in organic carbon content from 0.99 percent, represented various combinations of genetic parameters such as parent material, age, drainage, and native vegetation. An experimental procedure was developed to measure reflectance of very small soil and organic component samples in the laboratory, simulating the spectral coverage and resolution of the HIRIS sensor. Reflectance in 210 narrow (10 nm) bands was measured using the CARY 17D spectrophotometer over the 400 to 2500 nm wavelength range. Reflectance data were analyzed statistically to determine the regions of the reflective spectrum which provided useful information about soil organic matter content and composition. Wavebands providing significant information about soil organic carbon content were located in all three major regions of the reflective spectrum: visible, near infrared, and middle infrared. The purified humic acid fractions of the four soils were separable in six bands in the 1600 to 2400 nm range, suggesting that longwave middle infrared reflectance may be useful as a non-destructive laboratory technique for humic acid characterization.

Spectral band discrimination for species observed from hyperspectral remote sensing

CSIR Research Space (South Africa)

Dudeni, N

2009-08-01

Full Text Available across the spectrum. Spectral matching is often achieved by means of matching algorithms such as the Spectral Angle Mapper (SAM), Spectral information divergence (SID) and mixed measures of SAM and SID using either the tangent or the sine trigonometric...

Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics

Directory of Open Access Journals (Sweden)

Angela Lausch

2016-12-01

Full Text Available Anthropogenic stress and disturbance of forest ecosystems (FES has been increasing at all scales from local to global. In rapidly changing environments, in-situ terrestrial FES monitoring approaches have made tremendous progress but they are intensive and often integrate subjective indicators for forest health (FH. Remote sensing (RS bridges the gaps of these limitations, by monitoring indicators of FH on different spatio-temporal scales, and in a cost-effective, rapid, repetitive and objective manner. In this paper, we provide an overview of the definitions of FH, discussing the drivers, processes, stress and adaptation mechanisms of forest plants, and how we can observe FH with RS. We introduce the concept of spectral traits (ST and spectral trait variations (STV in the context of FH monitoring and discuss the prospects, limitations and constraints. Stress, disturbances and resource limitations can cause changes in FES taxonomic, structural and functional diversity; we provide examples how the ST/STV approach can be used for monitoring these FES characteristics. We show that RS based assessments of FH indicators using the ST/STV approach is a competent, affordable, repetitive and objective technique for monitoring. Even though the possibilities for observing the taxonomic diversity of animal species is limited with RS, the taxonomy of forest tree species can be recorded with RS, even though its accuracy is subject to certain constraints. RS has proved successful for monitoring the impacts from stress on structural and functional diversity. In particular, it has proven to be very suitable for recording the short-term dynamics of stress on FH, which cannot be cost-effectively recorded using in-situ methods. This paper gives an overview of the ST/STV approach, whereas the second paper of this series concentrates on discussing in-situ terrestrial monitoring, in-situ RS approaches and RS sensors and techniques for measuring ST/STV for FH.

The study of active tectonic based on hyperspectral remote sensing

Science.gov (United States)

Cui, J.; Zhang, S.; Zhang, J.; Shen, X.; Ding, R.; Xu, S.

2017-12-01

As of the latest technical methods, hyperspectral remote sensing technology has been widely used in each brach of the geosciences. However, it is still a blank for using the hyperspectral remote sensing to study the active structrure. Hyperspectral remote sensing, with high spectral resolution, continuous spectrum, continuous spatial data, low cost, etc, has great potentialities in the areas of stratum division and fault identification. Blind fault identification in plains and invisible fault discrimination in loess strata are the two hot problems in the current active fault research. Thus, the study of active fault based on the hyperspectral technology has great theoretical significance and practical value. Magnetic susceptibility (MS) records could reflect the rhythm alteration of the formation. Previous study shown that MS has correlation with spectral feature. In this study, the Emaokou section, located to the northwest of the town of Huairen, in Shanxi Province, has been chosen for invisible fault study. We collected data from the Emaokou section, including spectral data, hyperspectral image, MS data. MS models based on spectral features were established and applied to the UHD185 image for MS mapping. The results shown that MS map corresponded well to the loess sequences. It can recognize the stratum which can not identity by naked eyes. Invisible fault has been found in this section, which is useful for paleoearthquake analysis. The faults act as the conduit for migration of terrestrial gases, the fault zones, especially the structurally weak zones such as inrtersections or bends of fault, may has different material composition. We take Xiadian fault for study. Several samples cross-fault were collected and these samples were measured by ASD Field Spec 3 spectrometer. Spectral classification method has been used for spectral analysis, we found that the spectrum of the fault zone have four special spectral region(550-580nm, 600-700nm, 700-800nm and 800-900nm

High Spectral Resolution LIDAR as a Tool for Air Quality Research

Science.gov (United States)

Eloranta, E. W.; Spuler, S.; Hayman, M. M.

2017-12-01

Many aspects of air quality research require information on the vertical distribution of pollution. Traditional measurements, obtained from surface based samplers, or passive satellite remote sensing, do not provide vertical profiles. Lidar can provide profiles of aerosol properties. However traditional backscatter lidar suffers from uncertain calibrations with poorly constrained algorithms. These problems are avoided using High Spectral Resolution Lidar (HSRL) which provides absolutely calibrated vertical profiles of aerosol properties. The University of Wisconsin HSRL systems measure 532 nm wavelength aerosol backscatter cross-sections, extinction cross-sections, depolarization, and attenuated 1064 nm backscatter. These instruments are designed for long-term deployment at remote sites with minimal local support. Processed data is provided for public viewing and download in real-time on our web site "http://hsrl.ssec.wisc.edu". Air pollution applications of HSRL data will be illustrated with examples acquired during air quality field programs including; KORUS-AQ, DISCOVER-AQ, LAMOS and FRAPPE. Observations include 1) long range transport of dust, air pollution and smoke. 2) Fumigation episodes where elevated pollution is mixed down to the surface. 3) visibility restrictions by aerosols and 4) diurnal variations in atmospheric optical depth. While HSRL is powerful air quality research tool, its application in routine measurement networks is hindered by the high cost of current systems. Recent technical advances promise a next generation HSRL using telcom components to greatly reduce system cost. This paper will present data generated by a prototype low cost system constructed at NCAR. In addition to lower cost, operation at a non-visible near 780 nm infrared wavelength removes all FAA restrictions on the operation.

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 for light pollution monitoring

Directory of Open Access Journals (Sweden)

P. Marcoionni

2006-06-01

Full Text Available industries. In this paper we introduce the results from a remote sensing campaign performed in September 2001 at night time. For the first time nocturnal light pollution was measured at high spatial and spectral resolution using two airborne hyperspectral sensors, namely the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS and the Visible InfraRed Scanner (VIRS-200. These imagers, generally employed for day-time Earth remote sensing, were flown over the Tuscany coast (Italy on board of a Casa 212/200 airplane from an altitude of 1.5-2.0 km. We describe the experimental activities which preceded the remote sensing campaign, the optimization of sensor configuration, and the images as far acquired. The obtained results point out the novelty of the performed measurements and highlight the need to employ advanced remote sensing techniques as a spectroscopic tool for light pollution monitoring.

[Progress in inversion of vegetation nitrogen concentration by hyperspectral remote sensing].

Science.gov (United States)

Wang, Li-Wen; Wei, Ya-Xing

2013-10-01

Nitrogen is the necessary element in life activity of vegetation, which takes important function in biosynthesis of protein, nucleic acid, chlorophyll, and enzyme etc, and plays a key role in vegetation photosynthesis. The technology about inversion of vegetation nitrogen concentration by hyperspectral remote sensing has been the research hotspot since the 70s of last century. With the development of hyperspectral remote sensing technology in recent years, the advantage of spectral bands subdivision in a certain spectral region provides the powerful technology measure for correlative spectral characteristic research on vegetation nitrogen. In the present paper, combined with the newest research production about monitoring vegetation nitrogen concentration by hyperspectral remote sensing published in main geography science literature in recent several years, the principle and correlated problem about monitoring vegetation nitrogen concentration by hyperspectral remote sensing were introduced. From four aspects including vegetation nitrogen spectral index, vegetation nitrogen content inversion based on chlorophyll index, regression model, and eliminating influence factors to inversion of vegetation nitrogen concentration, main technology methods about inversion of vegetation nitrogen concentration by hyperspectral remote sensing were detailedly introduced. Correlative research conclusions were summarized and analyzed, and research development trend was discussed.

Spectral quality requirements for effluent identification

Science.gov (United States)

Czerwinski, R. N.; Seeley, J. A.; Wack, E. C.

2005-11-01

We consider the problem of remotely identifying gaseous materials using passive sensing of long-wave infrared (LWIR) spectral features at hyperspectral resolution. Gaseous materials are distinguishable in the LWIR because of their unique spectral fingerprints. A sensor degraded in capability by noise or limited spectral resolution, however, may be unable to positively identify contaminants, especially if they are present in low concentrations or if the spectral library used for comparisons includes materials with similar spectral signatures. This paper will quantify the relative importance of these parameters and express the relationships between them in a functional form which can be used as a rule of thumb in sensor design or in assessing sensor capability for a specific task. This paper describes the simulation of remote sensing datacontaining a gas cloud.In each simulation, the spectra are degraded in spectral resolution and through the addition of noise to simulate spectra collected by sensors of varying design and capability. We form a trade space by systematically varying the number of sensor spectral channels and signal-to-noise ratio over a range of values. For each scenario, we evaluate the capability of the sensor for gas identification by computing the ratio of the F-statistic for the truth gas tothe same statistic computed over the rest of the library.The effect of the scope of the library is investigated as well, by computing statistics on the variability of the identification capability as the library composition is varied randomly.

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

Science.gov (United States)

Chirayath, Ved

2018-01-01

We present two recent instrument technology developments at NASA, Fluid Lensing and MiDAR, and their application to remote sensing of Earth's aquatic systems. Fluid Lensing is the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions. MiDAR is a next-generation active hyperspectral remote sensing and optical communications instrument capable of active fluid lensing. Fluid Lensing has been used to provide 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) to enable a new method for remote sensing of living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an objectâ€"TM"s non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Previously, MiDAR was developed into a TRL 2 laboratory instrument capable of imaging in thirty-two narrowband channels across the VNIR spectrum (400-950nm). Recently, MiDAR UV was raised to TRL4 and expanded to include five ultraviolet bands from 280-400nm, permitting UV remote sensing capabilities in UV A, B, and C bands and enabling mineral identification and stimulated fluorescence measurements of organic proteins and compounds, such as green fluorescent proteins in terrestrial and

Spatial, Temporal and Spectral Satellite Image Fusion via Sparse Representation

Science.gov (United States)

Song, Huihui

-MODIS image pairs, we build the corresponding relationship between the difference images of MODIS and ETM+ by training a low- and high-resolution dictionary pair from the given prior image pairs. In the second scenario, i.e., only one Landsat- MODIS image pair being available, we directly correlate MODIS and ETM+ data through an image degradation model. Then, the fusion stage is achieved by super-resolving the MODIS image combining the high-pass modulation in a two-layer fusion framework. Remarkably, the proposed spatial-temporal fusion methods form a unified framework for blending remote sensing images with phenology change or land-cover-type change. Based on the proposed spatial-temporal fusion models, we propose to monitor the land use/land cover changes in Shenzhen, China. As a fast-growing city, Shenzhen faces the problem of detecting the rapid changes for both rational city planning and sustainable development. However, the cloudy and rainy weather in region Shenzhen located makes the capturing circle of high-quality satellite images longer than their normal revisit periods. Spatial-temporal fusion methods are capable to tackle this problem by improving the spatial resolution of images with coarse spatial resolution but frequent temporal coverage, thereby making the detection of rapid changes possible. On two Landsat-MODIS datasets with annual and monthly changes, respectively, we apply the proposed spatial-temporal fusion methods to the task of multiple change detection. Afterward, we propose a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning and sparse non-negative matrix factorization. By combining the spectral information from hyperspectral image, which is characterized by low spatial resolution but high spectral resolution and abbreviated as LSHS, and the spatial information from multispectral image, which is featured by high spatial resolution but low spectral

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

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

Directory of Open Access Journals (Sweden)

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

Assessing diversity of prairie plants using remote sensing

Science.gov (United States)

Gamon, J. A.; Wang, R.

2017-12-01

Biodiversity loss endangers ecosystem services and is considered as a global change that may generate unacceptable environmental consequences for the Earth system. Global biodiversity observations are needed to provide a better understanding of biodiversity - ecosystem services relationships and to provide a stronger foundation for conserving the Earth's biodiversity. While remote sensing metrics have been applied to estimate α biodiversity directly through optical diversity, a better understanding of the mechanisms behind the optical diversity-biodiversity relationship is needed. We designed a series of experiments at Cedar Creek Ecosystem Science Reserve, MN, to investigate the scale dependence of optical diversity and explore how species richness, evenness, and composition affect optical diversity. We collected hyperspectral reflectance of 16 prairie species using both a full-range field spectrometer fitted with a leaf clip, and an imaging spectrometer carried by a tram system to simulate plot-level images with different species richness, evenness, and composition. Two indicators of spectral diversity were explored: the coefficient of variation (CV) of spectral reflectance in space, and spectral classification using a Partial Least Squares Discriminant Analysis (PLS-DA). Our results showed that sampling methods (leaf clip-derived data vs. image-derived data) affected the optical diversity estimation. Both optical diversity indices were affected by species richness and evenness (Pguide regional studies of biodiversity estimation using high spatial and spectral resolution remote sensing.

Spectral Textile Detection in the VNIR/SWIR Band

Science.gov (United States)

2015-03-01

which have spectral similarities to background vegetation , are generally more difficult to detect than animal fibers such as wool and artificial fibers...effectiveness at long ranges, and spectral dismount detection currently relies on detecting skin pixels. In scenarios where skin is not exposed, spectral...training set. Classifiers with optimized parameters are used to classify contact data with artificially added noise and remotely-sensed hyperspectral data

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

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

Design for high productivity remote handling

Energy Technology Data Exchange (ETDEWEB)

Sykes, N., E-mail: nick.sykes@ccfe.ac.uk [Culham Centre For Fusion Energy, Culham Science Centre, OX14 3DB, Abingdon (United Kingdom); Collins, S.; Loving, A.B.; Ricardo, V. [Culham Centre For Fusion Energy, Culham Science Centre, OX14 3DB, Abingdon (United Kingdom); Villedieu, E. [Association Euratom-CEA Cadarache, DSM/IRFM, Saint Paul Les Durance (France)

2011-10-15

As the central part of a programme of enhancements in support of ITER, the Joint European Torus (JET) is being equipped with an all-metal wall. This enhancement programme requires the removal and installation of 6927 tile carriers and tiles, as well as the removal and installation of embedded diagnostics and antennas. The scale of this operation and the necessity to maximise operational availability of the facility added a requirement for high productivity in the remote activities to the existing exigencies of precision, reliability, cleanliness and operational security. This high productivity requirement has been incorporated into the design of the components and associated installation tooling, the design of the installation equipment, the development of installation procedures including the use of a mock-up for optimisation and training. Consideration of the remote handling installation process is vital during the design of the in vessel components. A number of features to meet the need of the high productivity while maintaining the function requirements have been incorporated into the metal wall components and associated tooling including kinematic design with guidance appropriate for remote operation. The component and tools are designed to guide the attachment of the installation tool, the installation path, and the interlocking with adjacent components without contact between the fragile castellated beryllium of the adjacent tiles. Other incorporated ergonomic features are discussed. At JET, the remote maintenance is conducted using end effectors, normally bi-lateral force feed back manipulator, mounted on driven, articulated booms. Prior to the current shutdown one long boom was used to conduct the installation and collect and deliver components to the 'short' boom which was linked to the tile carrier transfer facility. This led to loss of efficiency during these movements. The adoption of a new remote handling philosophy using 'point of

Design for high productivity remote handling

International Nuclear Information System (INIS)

Sykes, N.; Collins, S.; Loving, A.B.; Ricardo, V.; Villedieu, E.

2011-01-01

As the central part of a programme of enhancements in support of ITER, the Joint European Torus (JET) is being equipped with an all-metal wall. This enhancement programme requires the removal and installation of 6927 tile carriers and tiles, as well as the removal and installation of embedded diagnostics and antennas. The scale of this operation and the necessity to maximise operational availability of the facility added a requirement for high productivity in the remote activities to the existing exigencies of precision, reliability, cleanliness and operational security. This high productivity requirement has been incorporated into the design of the components and associated installation tooling, the design of the installation equipment, the development of installation procedures including the use of a mock-up for optimisation and training. Consideration of the remote handling installation process is vital during the design of the in vessel components. A number of features to meet the need of the high productivity while maintaining the function requirements have been incorporated into the metal wall components and associated tooling including kinematic design with guidance appropriate for remote operation. The component and tools are designed to guide the attachment of the installation tool, the installation path, and the interlocking with adjacent components without contact between the fragile castellated beryllium of the adjacent tiles. Other incorporated ergonomic features are discussed. At JET, the remote maintenance is conducted using end effectors, normally bi-lateral force feed back manipulator, mounted on driven, articulated booms. Prior to the current shutdown one long boom was used to conduct the installation and collect and deliver components to the 'short' boom which was linked to the tile carrier transfer facility. This led to loss of efficiency during these movements. The adoption of a new remote handling philosophy using 'point of installation

Towards automatic lithological classification from remote sensing data using support vector machines

Science.gov (United States)

Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

2010-05-01

Remote sensing data can be effectively used as a mean to build geological knowledge for poorly mapped terrains. Spectral remote sensing data from space- and air-borne sensors have been widely used to geological mapping, especially in areas of high outcrop density in arid regions. However, spectral remote sensing information by itself cannot be efficiently used for a comprehensive lithological classification of an area due to (1) diagnostic spectral response of a rock within an image pixel is conditioned by several factors including the atmospheric effects, spectral and spatial resolution of the image, sub-pixel level heterogeneity in chemical and mineralogical composition of the rock, presence of soil and vegetation cover; (2) only surface information and is therefore highly sensitive to the noise due to weathering, soil cover, and vegetation. Consequently, for efficient lithological classification, spectral remote sensing data needs to be supplemented with other remote sensing datasets that provide geomorphological and subsurface geological information, such as digital topographic model (DEM) and aeromagnetic data. Each of the datasets contain significant information about geology that, in conjunction, can potentially be used for automated lithological classification using supervised machine learning algorithms. In this study, support vector machine (SVM), which is a kernel-based supervised learning method, was applied to automated lithological classification of a study area in northwestern India using remote sensing data, namely, ASTER, DEM and aeromagnetic data. Several digital image processing techniques were used to produce derivative datasets that contained enhanced information relevant to lithological discrimination. A series of SVMs (trained using k-folder cross-validation with grid search) were tested using various combinations of input datasets selected from among 50 datasets including the original 14 ASTER bands and 36 derivative datasets (including 14

Thermal infrared spectra of surface rocks. Comparison of in the laboratory, in situ, and remote sensing data; Chihyo ganseki no netsusekigaiiki bunko tokusei. Chijo sokutei data to remote sensing data no hikaku

Energy Technology Data Exchange (ETDEWEB)

Ninomiya, Y; Matsunaga, T [Geological Survey of Japan, Tsukuba (Japan)

1996-10-01

An ASTER (advanced spaceborne thermal emission and reflection radiometer) is one of the image sensors. It is to be installed in an earth survey polar orbit platform satellite, EOS-AM1, which is to be launched in 1998, and it is going to start its operation. Data observed by the thermal infrared remote sensing of ASTER include the spectral emissivity, and the spectral emission reflectivity which is expressed by the function of temperature. It is required to overcome technical problems how to extract the spectral emissivity from the observed data. The spectral emissivity extracted from the remote sensing data by the MMD method, measured for samples collected in Cuprite area, Nevada, and/or measured at sampled points were compared to each other and discussed. The hemisphere spectral reflectivity, which is indirect spectral emissivity, agreed well with the direct spectral emissivity. Data suggesting the establishment of Kirchhoff`s law were obtained even for the weathered samples. The spectral emissivity derived from the remote sensing data by the MMD method was in harmony with the spectral characteristics measured strictly on the ground. 14 refs., 3 figs.

Quantum chemical spectral characterization of CH2NH2+ for remote sensing of Titan's atmosphere

Science.gov (United States)

Thackston, Russell; Fortenberry, Ryan C.

2018-01-01

Cassini has shown that CH2NH2+ is likely present in relatively high abundance in Titan's upper atmosphere. Relatively little is known about this molecule even though it contains the same number of electrons as ethylene, a molecule of significance to Titan's chemistry. Any studies on CH2NH2+ with application to Titan or its atmospheric chemistry will have to be done remotely at this point with the end of the fruitful Cassini mission. Consequently, trusted quantum chemical techniques are utilized here to produce the rotational, vibrational, and rovibrational spectroscopic constants for CH2NH2+ for the first time. The methodology produces a tightly fit potential energy surface here that is well-behaved indicating a strong credence in the accuracy for the produced values. Most notably, the 884.1 cm-1 NH2 out-of-plane bend is the brightest of the vibrational frequencies reported here for CH2NH2+ , and an observed and unattributed feature in this spectral region has been documented but never assigned to a molecular carrier. Follow-up IR or radio observations making use of the 540 GHz to 660 GHz range with the 0.45 D molecular dipole moment will have to be undertaken in order to confirm this or any attribution, but the data provided in this work will greatly assist in any such studies related to CH2NH2+.

Hyperspectral remote sensing of wild oyster reefs

Science.gov (United States)

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

2016-04-01

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

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

Miniature Compressive Ultra-spectral Imaging System Utilizing a Single Liquid Crystal Phase Retarder

Science.gov (United States)

August, Isaac; Oiknine, Yaniv; Abuleil, Marwan; Abdulhalim, Ibrahim; Stern, Adrian

2016-03-01

Spectroscopic imaging has been proved to be an effective tool for many applications in a variety of fields, such as biology, medicine, agriculture, remote sensing and industrial process inspection. However, due to the demand for high spectral and spatial resolution it became extremely challenging to design and implement such systems in a miniaturized and cost effective manner. Using a Compressive Sensing (CS) setup based on a single variable Liquid Crystal (LC) retarder and a sensor array, we present an innovative Miniature Ultra-Spectral Imaging (MUSI) system. The LC retarder acts as a compact wide band spectral modulator. Within the framework of CS, a sequence of spectrally modulated images is used to recover ultra-spectral image cubes. Using the presented compressive MUSI system, we demonstrate the reconstruction of gigapixel spatio-spectral image cubes from spectral scanning shots numbering an order of magnitude less than would be required using conventional systems.

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

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

Directory of Open Access Journals (Sweden)

C. Zhang

2017-09-01

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

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

Directory of Open Access Journals (Sweden)

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.

Evaluating productivity-biodiversity relationship and spectral diversity in prairie grasslands under different fire management treatments using in-situ and remote sensing hyperspectral data

Science.gov (United States)

Gholizadeh, H.; Gamon, J. A.; Zygielbaum, A. I.; Schweiger, A. K.; Cavender-Bares, J.; Yang, Y.; Knops, J. M. H.

2017-12-01

Grasslands cover as much as 25% of the Earth's surface and account for approximately 20% of overall terrestrial productivity and contribute to global biodiversity. To optimize the status of grasslands and to counteract their degradation, different management practices have been adopted. Fire has been shown to be an important management practice in the maintenance of grasslands. Our main goals were 1) to evaluate the productivity-biodiversity relationship in grasslands under fire treatment, and 2) to evaluate the capability of hyperspectral remote sensing in estimating biodiversity using spectral data (i.e. spectral diversity). We used above-ground biomass (as a surrogate for productivity), species richness (SR; as a surrogate for biodiversity), and airborne hyperspectral data from a natural grassland with fire treatment (20 plots), and a natural grassland without fire treatment (21 plots), all located at the Cedar Creek Ecosystem Science Reserve in Central Minnesota, USA. The productivity-biodiversity relationship for the fire treatment plots showed a hump-shaped model with adjusted R2=0.37, whereas the relationship for the non-burned plots were non-significant. The relationship between SR and spectral diversity (SD) were positive linear for both treatments; however, the relationship for plots with fire treatment was higher (adjusted R2 = 0.34 vs. 0.19). It is assumed that post-fire foliar nutrients increase soil nitrogen and phosphorus which facilitate post-fire growth and induce higher above-ground biomass and chlorophyll content in plants. Overall, the results of this study showed that management practices affect the productivity-biodiversity relationship and illustrated the effect of fire treatment on remote sensing of biodiversity.

Remote ignitability analysis of high-level radioactive waste

International Nuclear Information System (INIS)

Lundholm, C.W.; Morgan, J.M.; Shurtliff, R.M.; Trejo, L.E.

1992-09-01

The Idaho Chemical Processing Plant (ICPP), was used to reprocess nuclear fuel from government owned reactors to recover the unused uranium-235. These processes generated highly radioactive liquid wastes which are stored in large underground tanks prior to being calcined into a granular solid. The Resource Conservation and Recovery Act (RCRA) and state/federal clean air statutes require waste characterization of these high level radioactive wastes for regulatory permitting and waste treatment purposes. The determination of the characteristic of ignitability is part of the required analyses prior to calcination and waste treatment. To perform this analysis in a radiologically safe manner, a remoted instrument was needed. The remote ignitability Method and Instrument will meet the 60 deg. C. requirement as prescribed for the ignitability in method 1020 of SW-846. The method for remote use will be equivalent to method 1020 of SW-846

Improved spectral absorption coefficient grouping strategy of wide band k-distribution model used for calculation of infrared remote sensing signal of hot exhaust systems

Science.gov (United States)

Hu, Haiyang; Wang, Qiang

2018-07-01

A new strategy for grouping spectral absorption coefficients, considering the influences of both temperature and species mole ratio inhomogeneities on correlated-k characteristics of the spectra of gas mixtures, has been deduced to match the calculation method of spectral overlap parameter used in multiscale multigroup wide band k-distribution model. By comparison with current spectral absorption coefficient grouping strategies, for which only the influence of temperature inhomogeneity on the correlated-k characteristics of spectra of single species was considered, the improvements in calculation accuracies resulting from the new grouping strategy were evaluated using a series of 0D cases in which radiance under 3-5-μm wave band emitted by hot combustion gas of hydrocarbon fuel was attenuated by atmosphere with quite different temperature and mole ratios of water vapor and carbon monoxide to carbon dioxide. Finally, evaluations are presented on the calculation of remote sensing thermal images of transonic hot jet exhausted from a chevron ejecting nozzle with solid wall cooling system.

Spectral Indices to Monitor Nitrogen-Driven Carbon Uptake in Field Corn

Science.gov (United States)

Corp, Lawrence A.; Middleton, Elizabeth M.; Campbell, Peya E.; Huemmrich, K. Fred; Daughtry, Craig S. T.; Russ, Andrew; Cheng, Yen-Ben

2010-01-01

Climate change is heavily impacted by changing vegetation cover and productivity with large scale monitoring of vegetation only possible with remote sensing techniques. The goal of this effort was to evaluate existing reflectance (R) spectroscopic methods for determining vegetation parameters related to photosynthetic function and carbon (C) dynamics in plants. Since nitrogen (N) is a key constituent of photosynthetic pigments and C fixing enzymes, biological C sequestration is regulated in part by N availability. Spectral R information was obtained from field corn grown at four N application rates (0, 70, 140, 280 kg N/ha). A hierarchy of spectral observations were obtained: leaf and canopy with a spectral radiometer; aircraft with the AISA sensor; and satellite with EO-1 Hyperion. A number of spectral R indices were calculated from these hyperspectral observations and compared to geo-located biophysical measures of plant growth and physiological condition. Top performing indices included the R derivative index D730/D705 and the normalized difference of R750 vs. R705 (ND705), both of which differentiated three of the four N fertilization rates at multiple observation levels and yielded high correlations to these carbon parameters: light use efficiency (LUE); C:N ratio; and crop grain yield. These results advocate the use of hyperspectral sensors for remotely monitoring carbon cycle dynamics in managed terrestrial ecosystems.

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

Directory of Open Access Journals (Sweden)

Ran Goldblatt

2017-12-01

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

Diagnostic spectral characteristics of damouritization in granite type uranium deposit

International Nuclear Information System (INIS)

He Jianguo; Mao Yuxian; Li Jianzhong; Wang Changliang; Feng Mingyue; Rong Jiashu; Zhu Minqiang; Rao Minghui

2008-01-01

Spectral characteristics of different alteration type in uranium deposit are the prerequisite of selecting remote sensing spectral bands for uranium reconnaissance and exploration. It is also a basis for mapping alteration zone using imaging spectral data. Taking the No. 201 uranium deposit as example, the paper is focused on the spectral characteristics researching of damouritization in granite type uranium deposite. Through extracting diagnostic spectral feature of damourite and analyzing the reason causing absorption valley, it was found that spectral characteristics of damouritization in Chinese uranium deposit is different from that of illite in the spectral library published abroad. (authors)

Diagnostic spectral characteristics of damouritization in granite type uranium deposit

Energy Technology Data Exchange (ETDEWEB)

Jianguo, He; Yuxian, Mao; Jianzhong, Li; Changliang, Wang; Mingyue, Feng; Jiashu, Rong [Beijing Research Inst. of Uranium Geology, Beijing (China); Minqiang, Zhu; Minghui, Rao [East China Univ. of Technology, Fuzhou (China)

2008-07-15

Spectral characteristics of different alteration type in uranium deposit are the prerequisite of selecting remote sensing spectral bands for uranium reconnaissance and exploration. It is also a basis for mapping alteration zone using imaging spectral data. Taking the No. 201 uranium deposit as example, the paper is focused on the spectral characteristics researching of damouritization in granite type uranium deposite. Through extracting diagnostic spectral feature of damourite and analyzing the reason causing absorption valley, it was found that spectral characteristics of damouritization in Chinese uranium deposit is different from that of illite in the spectral library published abroad. (authors)

On the potential of the 2041–2047 nm spectral region for remote sensing of atmospheric CO2 isotopologues

International Nuclear Information System (INIS)

Reuter, M.; Bovensmann, H.; Buchwitz, M.; Burrows, J.P.; Deutscher, N.M.; Heymann, J.; Rozanov, A.; Schneising, O.; Suto, H.; Toon, G.C.; Warneke, T.

2012-01-01

Pressing open questions about the carbon cycle can be addressed with precise measurements of the three most abundant CO 2 isotopologues 16 O 12 C 16 O, 16 O 13 C 16 O, and 16 O 12 C 18 O. Such measurements can, e.g., help to further constrain oceanic and biospheric net fluxes or to differentiate between the gross biospheric fluxes photosynthesis and respiration. The 2041–2047nm (about 4885–4900cm −1 ) spectral region contains separated absorption lines of the three most abundant CO 2 isotopologues. Their spectral properties make this spectral region well suited for the use of a light path proxy method for the retrieval of δ 13 C and δ 18 O (the ratio of heavier to lighter isotopologues relative to a standard). An optimal estimation based light path proxy retrieval for δ 13 C and δ 18 O has been set up, applicable to GOSAT (Greenhouse gases Observing Satellite) and ground-based FTS (Fourier transform spectrometer) measurements. Initial results show that it is possible to retrieve δ 13 C and δ 18 O from ground-based FTS instruments with a precision of 0.6–1.6‰ and from GOSAT with a precision of about 30‰. Comparison of the achievable precision with the expected atmospheric signals shows that ground-based FTS remote sensing measurements have the potential to gain valuable information on δ 13 C and δ 18 O if averaging a sufficient number of measurements. It seems unlikely that this applies also to GOSAT because of the lower precision and a conceptual larger sensitivity to scattering related errors in satellite viewing geometry. -- Highlights: ► The 2041–2047 nm region is suited for remote sensing atmospheric CO 2 isotopologues. ► A δ 13 C and δ 18 O retrieval was set up for ground-based FTS and the GOSAT satellite. ► The retrieval precision of δ 13 C and δ 18 O is about 0.6–1.6‰ (FTS) and 30‰ (GOSAT). ► FTS measurements can give valuable information on atmospheric δ 13 C and δ 18 O.

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

DETERMINING SPECTRAL REFLECTANCE COEFFICIENTS FROM HYPERSPECTRAL IMAGES OBTAINED FROM LOW ALTITUDES

Directory of Open Access Journals (Sweden)

P. Walczykowski

2016-06-01

Full Text Available Remote Sensing plays very important role in many different study fields, like hydrology, crop management, environmental and ecosystem studies. For all mentioned areas of interest different remote sensing and image processing techniques, such as: image classification (object and pixel- based, object identification, change detection, etc. can be applied. Most of this techniques use spectral reflectance coefficients as the basis for the identification and distinction of different objects and materials, e.g. monitoring of vegetation stress, identification of water pollutants, yield identification, etc. Spectral characteristics are usually acquired using discrete methods such as spectrometric measurements in both laboratory and field conditions. Such measurements however can be very time consuming, which has led many international researchers to investigate the reliability and accuracy of using image-based methods. According to published and ongoing studies, in order to acquire these spectral characteristics from images, it is necessary to have hyperspectral data. The presented article describes a series of experiments conducted using the push-broom Headwall MicroHyperspec A-series VNIR. This hyperspectral scanner allows for registration of images with more than 300 spectral channels with a 1.9 nm spectral bandwidth in the 380- 1000 nm range. The aim of these experiments was to establish a methodology for acquiring spectral reflectance characteristics of different forms of land cover using such sensor. All research work was conducted in controlled conditions from low altitudes. Hyperspectral images obtained with this specific type of sensor requires a unique approach in terms of post-processing, especially radiometric correction. Large amounts of acquired imagery data allowed the authors to establish a new post- processing approach. The developed methodology allowed the authors to obtain spectral reflectance coefficients from a hyperspectral sensor

Determining Spectral Reflectance Coefficients from Hyperspectral Images Obtained from Low Altitudes

Science.gov (United States)

Walczykowski, P.; Jenerowicz, A.; Orych, A.; Siok, K.

2016-06-01

Remote Sensing plays very important role in many different study fields, like hydrology, crop management, environmental and ecosystem studies. For all mentioned areas of interest different remote sensing and image processing techniques, such as: image classification (object and pixel- based), object identification, change detection, etc. can be applied. Most of this techniques use spectral reflectance coefficients as the basis for the identification and distinction of different objects and materials, e.g. monitoring of vegetation stress, identification of water pollutants, yield identification, etc. Spectral characteristics are usually acquired using discrete methods such as spectrometric measurements in both laboratory and field conditions. Such measurements however can be very time consuming, which has led many international researchers to investigate the reliability and accuracy of using image-based methods. According to published and ongoing studies, in order to acquire these spectral characteristics from images, it is necessary to have hyperspectral data. The presented article describes a series of experiments conducted using the push-broom Headwall MicroHyperspec A-series VNIR. This hyperspectral scanner allows for registration of images with more than 300 spectral channels with a 1.9 nm spectral bandwidth in the 380- 1000 nm range. The aim of these experiments was to establish a methodology for acquiring spectral reflectance characteristics of different forms of land cover using such sensor. All research work was conducted in controlled conditions from low altitudes. Hyperspectral images obtained with this specific type of sensor requires a unique approach in terms of post-processing, especially radiometric correction. Large amounts of acquired imagery data allowed the authors to establish a new post- processing approach. The developed methodology allowed the authors to obtain spectral reflectance coefficients from a hyperspectral sensor mounted on an

Bit-rate reduction strategies for noise suppression with a remote wireless microphone

NARCIS (Netherlands)

Cvijanovic, N.; Sadiq, O.; Srinivasan, S.

2012-01-01

In single channel non-stationary noise reduction it is paramount that a good noise reference is available in a timely manner to maintaina high quality speech signal. Using a remote wireless microphone placed close to a noise source, a good estimate of the noise power spectral density (PSD) can be

Bit rate reduction strategies for noise suppression using a remote wireless microphone

NARCIS (Netherlands)

Cvijanovic, N.; Sadiq, O.; Srinivasan, S.

2012-01-01

In single-channel non-stationary noise reduction it is paramount that a good noise reference is available in a timely manner to maintain a high quality speech signal. Using a remote wireless microphone placed close to a noise source, a good estimate of the noise power spectral density (PSD) can be

Analysis of Unmanned Aerial Vehicle (UAV) hyperspectral remote sensing monitoring key technology in coastal wetland

Science.gov (United States)

Ma, Yi; Zhang, Jie; Zhang, Jingyu

2016-01-01

The coastal wetland, a transitional zone between terrestrial ecosystems and marine ecosystems, is the type of great value to ecosystem services. For the recent 3 decades, area of the coastal wetland is decreasing and the ecological function is gradually degraded with the rapid development of economy, which restricts the sustainable development of economy and society in the coastal areas of China in turn. It is a major demand of the national reality to carry out the monitoring of coastal wetlands, to master the distribution and dynamic change. UAV, namely unmanned aerial vehicle, is a new platform for remote sensing. Compared with the traditional satellite and manned aerial remote sensing, it has the advantage of flexible implementation, no cloud cover, strong initiative and low cost. Image-spectrum merging is one character of high spectral remote sensing. At the same time of imaging, the spectral curve of each pixel is obtained, which is suitable for quantitative remote sensing, fine classification and target detection. Aimed at the frontier and hotspot of remote sensing monitoring technology, and faced the demand of the coastal wetland monitoring, this paper used UAV and the new remote sensor of high spectral imaging instrument to carry out the analysis of the key technologies of monitoring coastal wetlands by UAV on the basis of the current situation in overseas and domestic and the analysis of developing trend. According to the characteristic of airborne hyperspectral data on UAV, that is "three high and one many", the key technology research that should develop are promoted as follows: 1) the atmosphere correction of the UAV hyperspectral in coastal wetlands under the circumstance of complex underlying surface and variable geometry, 2) the best observation scale and scale transformation method of the UAV platform while monitoring the coastal wetland features, 3) the classification and detection method of typical features with high precision from multi scale

A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors.

Science.gov (United States)

He, Xiyan; Condat, Laurent; Bioucas-Diaz, Jose; Chanussot, Jocelyn; Xia, Junshi

2014-06-27

The development of multisensor systems in recent years has led to great increase in the amount of available remote sensing data. Image fusion techniques aim at inferring high quality images of a given area from degraded versions of the same area obtained by multiple sensors. This paper focuses on pansharpening, which is the inference of a high spatial resolution multispectral image from two degraded versions with complementary spectral and spatial resolution characteristics: a) a low spatial resolution multispectral image; and b) a high spatial resolution panchromatic image. We introduce a new variational model based on spatial and spectral sparsity priors for the fusion. In the spectral domain we encourage low-rank structure, whereas in the spatial domain we promote sparsity on the local differences. Given the fact that both panchromatic and multispectral images are integrations of the underlying continuous spectra using different channel responses, we propose to exploit appropriate regularizations based on both spatial and spectral links between panchromatic and the fused multispectral images. A weighted version of the vector Total Variation (TV) norm of the data matrix is employed to align the spatial information of the fused image with that of the panchromatic image. With regard to spectral information, two different types of regularization are proposed to promote a soft constraint on the linear dependence between the panchromatic and the fused multispectral images. The first one estimates directly the linear coefficients from the observed panchromatic and low resolution multispectral images by Linear Regression (LR) while the second one employs the Principal Component Pursuit (PCP) to obtain a robust recovery of the underlying low-rank structure. We also show that the two regularizers are strongly related. The basic idea of both regularizers is that the fused image should have low-rank and preserve edge locations. We use a variation of the recently proposed

Berlin Reflectance Spectral Library (BRSL)

Science.gov (United States)

Henckel, D.; Arnold, G.; Kappel, D.; Moroz, L. V.; Markus, K.

2017-09-01

The Berlin Reflectance Spectral Library (BRSL) provides a collection of reflectance spectra between 0.3 and 17 µm. It was originally dedicated to support space missions to small solar system bodies. Meanwhile the library includes selections of biconical reflectance spectra for spectral data analysis of other planetary bodies as well. The library provides reference spectra of well-characterized terrestrial analogue materials and meteorites for interpretation of remote sensing reflectance spectra of planetary surfaces. We introduce the BRSL, summarize the data available, and access to use them for further relevant applications.

High-definition television evaluation for remote handling task performance

International Nuclear Information System (INIS)

Fujita, Y.; Omori, E.; Hayashi, S.; Draper, J.V.; Herndon, J.N.

1986-01-01

In a plant that employs remote handling techniques for equipment maintenance, operators perform maintenance tasks primarily by using the information from television systems. The efficiency of the television system has a significant impact on remote maintenance task performance. High-definition television (HDTV) transmits a video image with more than twice the number of horizontal scan lines as standard-resolution television (1125 for HDTV to 525 for standard-resolution NTSC television). The added scan lines dramatically improve the resolution of images on the HDTV monitors. This paper describes experiments designed to evaluate the impact of HDTV on the performance of typical remote tasks. The experiments described in this paper compared the performance of four operators using HDTV with their performance while using other television systems. The experiments included four television systems: (a) high-definition color television, (b) high-definition monochromatic television, (c) standard-resolution monochromatic television, and (d) standard-resolution stereoscopic monochromatic television

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

Directory of Open Access Journals (Sweden)

Linyi Li

2017-01-01

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

Remote Sensing Image Fusion Based on the Combination Grey Absolute Correlation Degree and IHS Transform

Directory of Open Access Journals (Sweden)

Hui LIN

2014-12-01

Full Text Available An improved fusion algorithm for multi-source remote sensing images with high spatial resolution and multi-spectral capacity is proposed based on traditional IHS fusion and grey correlation analysis. Firstly, grey absolute correlation degree is used to discriminate non-edge pixels and edge pixels in high-spatial resolution images, by which the weight of intensity component is identified in order to combine it with high-spatial resolution image. Therefore, image fusion is achieved using IHS inverse transform. The proposed method is applied to ETM+ multi-spectral images and panchromatic image, and Quickbird’s multi-spectral images and panchromatic image respectively. The experiments prove that the fusion method proposed in the paper can efficiently preserve spectral information of the original multi-spectral images while enhancing spatial resolution greatly. By comparison and analysis, the proposed fusion algorithm is better than traditional IHS fusion and fusion method based on grey correlation analysis and IHS transform.

Remote sensing of oceanic primary production: Computations using a spectral model

Digital Repository Service at National Institute of Oceanography (India)

Sathyendranath, S.; Platt, T.; Caverhill, C.M.; Warnock, R.E.; Lewis, M.R.

A spectral model of underwater irradiance is coupled with a spectral version of the photosynthesis-light relationship to compute oceanic primary production. The results are shown to be significantly different from those obtained using...

Technical Training on High-Order Spectral Analysis and Thermal Anemometry Applications

Science.gov (United States)

Maslov, A. A.; Shiplyuk, A. N.; Sidirenko, A. A.; Bountin, D. A.

2003-01-01

The topics of thermal anemometry and high-order spectral analyses were the subject of the technical training. Specifically, the objective of the technical training was to study: (i) the recently introduced constant voltage anemometer (CVA) for high-speed boundary layer; and (ii) newly developed high-order spectral analysis techniques (HOSA). Both CVA and HOSA are relevant tools for studies of boundary layer transition and stability.

Remote measurement of atmospheric pollutants

Science.gov (United States)

Allario, F.; Hoell, J.; Seals, R. K.

1979-01-01

The concentration and vertical distribution of atmospheric ammonia and ozone are remotely sensed, using dual-C02-laser multichannel infrared Heterodyne Spectrometer (1HS). Innovation makes atmospheric pollution measurements possible with nearly-quantum-noise-limited sensitivity and ultrafine spectral resolution.

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

Directory of Open Access Journals (Sweden)

Mitch Bryson

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

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

Science.gov (United States)

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

2013-01-01

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

Complex of spectral techniques for remote monitoring of oil spills on water surface

International Nuclear Information System (INIS)

Patsayeva, S.; Yuzhakov, V.; Barbini, R.; Fantini, R.; Frassanito, C.; Palucci, A.; Varlamov, V.

1999-01-01

Spectral properties of oil films on water surfaces were studied under laboratory conditions. A laser fluorosensor was used to measure fluorescence response; fluorescence decay measurements were also performed. Differences in decay time were noted for different mineral oils (ranging from 1 ns to 3.5 ns) and for refined oils (which ranged from 3.5 ns to 8 ns). Film thickness was estimated by calculating the wavelength -dependent absorption of the mineral oil. This new approach is independent of many accidental factors, and does not demand the a priori measured signal from clean water which is required by the more conventional method of suppression of the water Raman integral signal. These experiments confirmed the suitability of fluorescent spectroscopy as a very sensitive tool for oil detection and mapping, however, when applied to quantitative measurement or oil recognition in remote sensing, care must be taken to account for the factors influencing fluorescence response of mineral oil. It was also shown that fluorescence decay time is a useful technique to characterize the type of mineral oil spilled on water surface in that it provides a means to distinguish between the various types, using time-resolved spectra. 12 refs., 1 tab., 4 figs

A Fiber-Optic System Generating Pulses of High Spectral Density

Science.gov (United States)

Abramov, A. S.; Zolotovskii, I. O.; Korobko, D. A.; Fotiadi, A. A.

2018-03-01

A cascade fiber-optic system that generates pulses of high spectral density by using the effect of nonlinear spectral compression is proposed. It is demonstrated that the shape of the pulse envelope substantially influences the degree of compression of its spectrum. In so doing, maximum compression is achieved for parabolic pulses. The cascade system includes an optical fiber exhibiting normal dispersion that decreases along the fiber length, thereby ensuring that the pulse envelope evolves toward a parabolic shape, along with diffraction gratings and a fiber spectral compressor. Based on computer simulation, we determined parameters of cascade elements leading to maximum spectral density of radiation originating from a subpicosecond laser pulse of medium energy.

Spatio-spectral analysis of ionization times in high-harmonic generation

Energy Technology Data Exchange (ETDEWEB)

Soifer, Hadas, E-mail: hadas.soifer@weizmann.ac.il [Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100 (Israel); Dagan, Michal; Shafir, Dror; Bruner, Barry D. [Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100 (Israel); Ivanov, Misha Yu. [Department of Physics, Imperial College London, South Kensington Campus, SW7 2AZ London (United Kingdom); Max-Born Institute for Nonlinear Optics and Short Pulse Spectroscopy, Max-Born-Strasse 2A, D-12489 Berlin (Germany); Serbinenko, Valeria; Barth, Ingo; Smirnova, Olga [Max-Born Institute for Nonlinear Optics and Short Pulse Spectroscopy, Max-Born-Strasse 2A, D-12489 Berlin (Germany); Dudovich, Nirit [Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100 (Israel)

2013-03-12

Graphical abstract: A spatio-spectral analysis of the two-color oscillation phase allows us to accurately separate short and long trajectories and reconstruct their ionization times. Highlights: ► We perform a complete spatio-spectral analysis of the high harmonic generation process. ► We analyze the ionization times across the entire spatio-spectral plane of the harmonics. ► We apply this analysis to reconstruct the ionization times of both short and long trajectories. - Abstract: Recollision experiments have been very successful in resolving attosecond scale dynamics. However, such schemes rely on the single atom response, neglecting the macroscopic properties of the interaction and the effects of using multi-cycle laser fields. In this paper we perform a complete spatio-spectral analysis of the high harmonic generation process and resolve the distribution of the subcycle dynamics of the recolliding electron. Specifically, we focus on the measurement of ionization times. Recently, we have demonstrated that the addition of a weak, crossed polarized second harmonic field allows us to resolve the moment of ionization (Shafir, 2012) [1]. In this paper we extend this measurement and perform a complete spatio-spectral analysis. We apply this analysis to reconstruct the ionization times of both short and long trajectories showing good agreement with the quantum path analysis.

The high throughput virtual slit enables compact, inexpensive Raman spectral imagers

Science.gov (United States)

Gooding, Edward; Deutsch, Erik R.; Huehnerhoff, Joseph; Hajian, Arsen R.

2018-02-01

Raman spectral imaging is increasingly becoming the tool of choice for field-based applications such as threat, narcotics and hazmat detection; air, soil and water quality monitoring; and material ID. Conventional fiber-coupled point source Raman spectrometers effectively interrogate a small sample area and identify bulk samples via spectral library matching. However, these devices are very slow at mapping over macroscopic areas. In addition, the spatial averaging performed by instruments that collect binned spectra, particularly when used in combination with orbital raster scanning, tends to dilute the spectra of trace particles in a mixture. Our design, employing free space line illumination combined with area imaging, reveals both the spectral and spatial content of heterogeneous mixtures. This approach is well suited to applications such as detecting explosives and narcotics trace particle detection in fingerprints. The patented High Throughput Virtual Slit1 is an innovative optical design that enables compact, inexpensive handheld Raman spectral imagers. HTVS-based instruments achieve significantly higher spectral resolution than can be obtained with conventional designs of the same size. Alternatively, they can be used to build instruments with comparable resolution to large spectrometers, but substantially smaller size, weight and unit cost, all while maintaining high sensitivity. When used in combination with laser line imaging, this design eliminates sample photobleaching and unwanted photochemistry while greatly enhancing mapping speed, all with high selectivity and sensitivity. We will present spectral image data and discuss applications that are made possible by low cost HTVS-enabled instruments.

Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network

Directory of Open Access Journals (Sweden)

Jie Wang

2017-03-01

Full Text Available Deep convolutional neural networks (CNNs have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained deep CNNs to remote sensing tasks. In the transferring process, generalization power of features in pre-trained deep CNNs plays the key role. In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. These two architectures suggest two directions for improvement. First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet to synthesize spatial information of remote sensing images in each spectral channel. This design shortens the spatial “distance” of target and source datasets for pre-trained deep CNNs. Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral “distance” between remote sensing images and images used to pre-train deep CNNs. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that our proposed framework obtains state-of-the-art results without fine-tuning and feature fusing. This paper also provides baseline for transferring fresh pretrained deep CNNs to other remote sensing tasks.

Mapping of Landscape Cover Using Remote Sensing and GIS in ...

African Journals Online (AJOL)

Tadesse

present study, Remote Sensing (RS) and Geographical Information System (GIS) techniques were used. Remotely sensed .... growing stock in Tahno range of Dehradun Forest Division. Okhandiara (2008) .... areas on an image by identifying 'training' sites of known targets and then extrapolating those spectral signatures to ...

[Research on Oil Sands Spectral Characteristics and Oil Content by Remote Sensing Estimation].

Science.gov (United States)

You, Jin-feng; Xing, Li-xin; Pan, Jun; Shan, Xuan-long; Liang, Li-heng; Fan, Rui-xue

2015-04-01

Visible and near infrared spectroscopy is a proven technology to be widely used in identification and exploration of hydrocarbon energy sources with high spectral resolution for detail diagnostic absorption characteristics of hydrocarbon groups. The most prominent regions for hydrocarbon absorption bands are 1,740-1,780, 2,300-2,340 and 2,340-2,360 nm by the reflectance of oil sands samples. These spectral ranges are dominated by various C-H overlapping overtones and combination bands. Meanwhile, there is relatively weak even or no absorption characteristics in the region from 1,700 to 1,730 nm in the spectra of oil sands samples with low bitumen content. With the increase in oil content, in the spectral range of 1,700-1,730 nm the obvious hydrocarbon absorption begins to appear. The bitumen content is the critical parameter for oil sands reserves estimation. The absorption depth was used to depict the response intensity of the absorption bands controlled by first-order overtones and combinations of the various C-H stretching and bending fundamentals. According to the Pearson and partial correlation relationships of oil content and absorption depth dominated by hydrocarbon groups in 1,740-1,780, 2,300-2,340 and 2,340-2,360 nm wavelength range, the scheme of association mode was established between the intensity of spectral response and bitumen content, and then unary linear regression(ULR) and partial least squares regression (PLSR) methods were employed to model the equation between absorption depth attributed to various C-H bond and bitumen content. There were two calibration equations in which ULR method was employed to model the relationship between absorption depth near 2,350 nm region and bitumen content and PLSR method was developed to model the relationship between absorption depth of 1,758, 2,310, 2,350 nm regions and oil content. It turned out that the calibration models had good predictive ability and high robustness and they could provide the scientific

Time-resolved High Spectral Resolution Observation of 2MASSW J0746425+200032AB

Energy Technology Data Exchange (ETDEWEB)

Wang, Ji; Mawet, Dimitri [Department of Astronomy, California Institute of Technology, MC 249-17, 1200 E. California Boulevard, Pasadena, CA 91106 (United States); Prato, Lisa, E-mail: ji.wang@caltech.edu [Lowell Observatory, 1400 West Mars Hill Road, Flagstaff, AZ 86001 (United States)

2017-03-20

Many brown dwarfs (BDs) exhibit photometric variability at levels from tenths to tens of percents. The photometric variability is related to magnetic activity or patchy cloud coverage, characteristic of BDs near the L–T transition. Time-resolved spectral monitoring of BDs provides diagnostics of cloud distribution and condensate properties. However, current time-resolved spectral studies of BDs are limited to low spectral resolution ( R ∼ 100) with the exception of the study of Luhman 16 AB at a resolution of 100,000 using the VLT+CRIRES. This work yielded the first map of BD surface inhomogeneity, highlighting the importance and unique contribution of high spectral resolution observations. Here, we report on the time-resolved high spectral resolution observations of a nearby BD binary, 2MASSW J0746425+200032AB. We find no coherent spectral variability that is modulated with rotation. Based on simulations, we conclude that the coverage of a single spot on 2MASSW J0746425+200032AB is smaller than 1% or 6.25% if spot contrast is 50% or 80% of its surrounding flux, respectively. Future high spectral resolution observations aided by adaptive optics systems can put tighter constraints on the spectral variability of 2MASSW J0746425+200032AB and other nearby BDs.

[Research on symmetrical optical waveguide based surface plasmon resonance sensing with spectral interrogation].

Science.gov (United States)

Zhang, Yi-long; Liu, Le; Guo, Jun; Zhang, Peng-fei; Guo, Ji-hua; Ma, Hui; He, Yong-hong

2015-02-01

Surface plasmon resonance (SPR) sensors with spectral interrogation can adopt fiber to transmit light signals, thus leaving the sensing part separated, which is very convenient for miniaturization, remote-sensing and on-site analysis. Symmetrical optical waveguide (SOW) SPR has the same refractive index of the-two buffer media layers adjacent to the metal film, resulting in longer propagation distance, deeper penetration depth and better performance compared to conventional SPR In the present paper, we developed a symmetrical optical, waveguide (SOW) SPR sensor with wavelength interrogation. In the system, MgF2-Au-MgF2 film was used as SOW module for glucose sensing, and a fiber based light source and detection was used in the spectral interrogation. In the experiment, a refractive index resolution of 2.8 x 10(-7) RIU in fluid protocol was acquired. This technique provides advantages of high resolution and could have potential use in compact design, on-site analysis and remote sensing.

High spectral resolution X-ray observations of AGN

NARCIS (Netherlands)

Kaastra, J.S.

2008-01-01

brief overview of some highlights of high spectral resolution X-ray observations of AGN is given, mainly obtained with the RGS of XMM-Newton. Future prospects for such observations with XMM-Newton are given.

Determination of Primary Spectral Bands for Remote Sensing of Aquatic Environments

Science.gov (United States)

2007-12-20

spectral bands are always facing the possibility of missing important spectral features of special cases, such as some coral reefs and/or seagrass ...Res. 1998, 103(C 10), 21,601-621,609. 16. Kirk, J. T. 0. 1994 Light & Photosynthesis in Aquatic Ecosystems, University Press, Cambridge. 17. Lee, Z

Hyperspectral remote sensing techniques for early detection of plant diseases

Science.gov (United States)

Krezhova, Dora; Maneva, Svetla; Zdravev, Tomas

Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications in Earth observation. Nowadays spectral remote sensing techniques allow presymptomatic monitoring of changes in the physiological state of plants with high spectral resolution. Hyperspectral leaf reflectance and chlorophyll fluorescence proved to be highly suitable for identification of growth anomalies of cultural plants that result from the environmental changes and different stress factors. Hyperspectral technologies can find place in many scientific areas, as well as for monitoring of plants status and functioning to help in making timely management decisions. This research aimed to detect a presence of viral infection in young pepper plants (Capsicum annuum L.) caused by Cucumber Mosaic Virus (CMV) by using hyperspectral reflectance and fluorescence data and to assess the effect of some growth regulators on the development of the disease. In Bulgaria CMV is one of the widest spread pathogens, causing the biggest economical losses in crop vegetable production. Leaf spectral reflectance and fluorescence data were collected by a portable fibre-optics spectrometer in the spectral ranges 450÷850 nm and 600-900 nm. Greenhouse experiment with pepper plants of two cultivars, Sivria (sensitive to CMV) and Ostrion (resistant to CMV) were used. The plants were divided into six groups. The first group consisted of healthy (control) plants. At growth stage 4-6 expanded leaf, the second group was inoculated with CMV. The other four groups were treated with growth regulators: Spermine, MEIA (beta-monomethyl ester of itaconic acid), BTH (benzo(1,2,3)thiadiazole-7-carbothioic acid-S-methyl ester) and Phytoxin. On the next day, the pepper plants of these four groups were inoculated with CMV. The viral concentrations in the plants were determined by the serological method DAS-ELISA. Statistical, first derivative and cluster analysis were applied and several vegetation indices were

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.

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

Science.gov (United States)

McCullough, Ian M.; Loftin, Cynthia S.; Sader, Steven A.

2012-01-01

Satellite-based remote monitoring programs of regional lake water quality largely have relied on Landsat Thematic Mapper (TM) owing to its long image archive, moderate spatial resolution (30 m), and wide sensitivity in the visible portion of the electromagnetic spectrum, despite some notable limitations such as temporal resolution (i.e., 16 days), data pre-processing requirements to improve data quality, and aging satellites. Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors on Aqua/Terra platforms compensate for these shortcomings, although at the expense of spatial resolution. We developed and evaluated a remote monitoring protocol for water clarity of large lakes using MODIS 500 m data and compared MODIS utility to Landsat-based methods. MODIS images captured during May–September 2001, 2004 and 2010 were analyzed with linear regression to identify the relationship between lake water clarity and satellite-measured surface reflectance. Correlations were strong (R² = 0.72–0.94) throughout the study period; however, they were the most consistent in August, reflecting seasonally unstable lake conditions and inter-annual differences in algal productivity during the other months. The utility of MODIS data in remote water quality estimation lies in intra-annual monitoring of lake water clarity in inaccessible, large lakes, whereas Landsat is more appropriate for inter-annual, regional trend analyses of lakes ≥ 8 ha. Model accuracy is improved when ancillary variables are included to reflect seasonal lake dynamics and weather patterns that influence lake clarity. The identification of landscape-scale drivers of regional water quality is a useful way to supplement satellite-based remote monitoring programs relying on spectral data alone.

Selective suppression of high-order harmonics within phase-matched spectral regions.

Science.gov (United States)

Lerner, Gavriel; Diskin, Tzvi; Neufeld, Ofer; Kfir, Ofer; Cohen, Oren

2017-04-01

Phase matching in high-harmonic generation leads to enhancement of multiple harmonics. It is sometimes desired to control the spectral structure within the phase-matched spectral region. We propose a scheme for selective suppression of high-order harmonics within the phase-matched spectral region while weakly influencing the other harmonics. The method is based on addition of phase-mismatched segments within a phase-matched medium. We demonstrate the method numerically in two examples. First, we show that one phase-mismatched segment can significantly suppress harmonic orders 9, 15, and 21. Second, we show that two phase-mismatched segments can efficiently suppress circularly polarized harmonics with one helicity over the other when driven by a bi-circular field. The new method may be useful for various applications, including the generation of highly helical bright attosecond pulses.

High-quality remote interactive imaging in the operating theatre

Science.gov (United States)

Grimstead, Ian J.; Avis, Nick J.; Evans, Peter L.; Bocca, Alan

2009-02-01

We present a high-quality display system that enables the remote access within an operating theatre of high-end medical imaging and surgical planning software. Currently, surgeons often use printouts from such software for reference during surgery; our system enables surgeons to access and review patient data in a sterile environment, viewing real-time renderings of MRI & CT data as required. Once calibrated, our system displays shades of grey in Operating Room lighting conditions (removing any gamma correction artefacts). Our system does not require any expensive display hardware, is unobtrusive to the remote workstation and works with any application without requiring additional software licenses. To extend the native 256 levels of grey supported by a standard LCD monitor, we have used the concept of "PseudoGrey" where slightly off-white shades of grey are used to extend the intensity range from 256 to 1,785 shades of grey. Remote access is facilitated by a customized version of UltraVNC, which corrects remote shades of grey for display in the Operating Room. The system is successfully deployed at Morriston Hospital, Swansea, UK, and is in daily use during Maxillofacial surgery. More formal user trials and quantitative assessments are being planned for the future.

On the use of spectral minutiae in high-resolution palmprint recognition

NARCIS (Netherlands)

Wang, Ruifang; Veldhuis, Raymond N.J.; Ramos, Daniel; Spreeuwers, Lieuwe Jan; Fierrez, Julian; Xu, H.

2013-01-01

The spectral minutiae representation has been proposed as a novel method to minutiae-based fingerprint recognition, which can handle minutiae translation and rotation and improve matching speed. As high-resolution palmprint recognition is also mainly based on minutiae sets, we apply spectral

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.

Monitoring of Gangotri glacier using remote sensing and ground ...

Indian Academy of Sciences (India)

Dozier J 1989a Remote sensing of snow in the visible and near-infrared wavelengths; In: Theory and Applications of. Optical Remote Sensing (ed.) Asrar G (New York: John. Wiley and Sons), pp. 527–547. Dozier J 1989b Spectral signature of alpine snow cover from the Landsat Thematic Mapper; Rem. Sens. Environ. 28.

Temperature Dependence of Quasiparticle Spectral Weight and Coherence in High Tc Superconductors

Science.gov (United States)

He, Yang; Zhang, Jessie; Hoffman, Jennifer; Hoffman Lab Team

2014-03-01

Superconductivity arises from the Cooper pairing of quasiparticles on the Fermi surface. Understanding the formation of Cooper pairs is an essential step towards unveiling the mechanism of high Tc superconductivity. We compare scanning tunneling microscope investigations of the temperature dependence of quasiparticle spectral weight and quasiparticle interference in several families of high Tc materials. We calculate the coherent spectral weight related to superconductivity, despite the coexistence of competing orders. The relation between pairing temperature and coherent spectral weight is discussed. We acknowledge support by the New York Community Trust-George Merck Fund.

Research on Remote Sensing Image Classification Based on Feature Level Fusion

Science.gov (United States)

Yuan, L.; Zhu, G.

2018-04-01

Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

High order spectral difference lattice Boltzmann method for incompressible hydrodynamics

Science.gov (United States)

Li, Weidong

2017-09-01

This work presents a lattice Boltzmann equation (LBE) based high order spectral difference method for incompressible flows. In the present method, the spectral difference (SD) method is adopted to discretize the convection and collision term of the LBE to obtain high order (≥3) accuracy. Because the SD scheme represents the solution as cell local polynomials and the solution polynomials have good tensor-product property, the present spectral difference lattice Boltzmann method (SD-LBM) can be implemented on arbitrary unstructured quadrilateral meshes for effective and efficient treatment of complex geometries. Thanks to only first oder PDEs involved in the LBE, no special techniques, such as hybridizable discontinuous Galerkin method (HDG), local discontinuous Galerkin method (LDG) and so on, are needed to discrete diffusion term, and thus, it simplifies the algorithm and implementation of the high order spectral difference method for simulating viscous flows. The proposed SD-LBM is validated with four incompressible flow benchmarks in two-dimensions: (a) the Poiseuille flow driven by a constant body force; (b) the lid-driven cavity flow without singularity at the two top corners-Burggraf flow; and (c) the unsteady Taylor-Green vortex flow; (d) the Blasius boundary-layer flow past a flat plate. Computational results are compared with analytical solutions of these cases and convergence studies of these cases are also given. The designed accuracy of the proposed SD-LBM is clearly verified.

Relationship of transpiration and evapotranspiration to solar radiation and spectral reflectance in soybean [Glycine max] canopies: A simple method for remote sensing of canopy transpiration

International Nuclear Information System (INIS)

Choi, E.N.; Inoue, Y.

2004-01-01

Abstract The study investigated diurnal and seasonal dynamics of evapotranspiration (ET) and transpiration (Tr) in a soybean canopy, as well as the relationships among ET, Tr, solar radiation and remotely sensed spectral reflectance. The eddy covariance method (ECM) and stem heat balance method (SHBM) were used for independent measurement of ET and Tr, respectively. Micrometeorological, soil, and spectral reflectance data were acquired for the entire growing season. The instantaneous values of canopy-Tr estimated by SHBM and ET by ECM were well synchronized with each other, and both were strongly affected by the solar radiation. The daily values canopy-Tr increased rapidly with increasing leaf area index (LAI), and got closer to the ET even at a low value of LAI such as 1.5-2. The daily values of ET were moderately correlated with global solar radiation (Rs), and more closely with the potential evapotranspiration (ETp), estimated by the 'radiation method.' This fact supported the effectiveness of the simple radiation method in estimation of evapotranspiration. The ratio of Tr/ET as well as the ratio of ground heat flux (G) to Rs (G/Rs) was closely related to LAI, and LAI was a key variable in determining the energy partitioning to soil and vegetation. It was clearly shown that a remotely sensed vegetation index such as SAVI (soil adjusted vegetation index) was effective for estimating LAI, and further useful for directly estimating energy partitioning to soil and vegetation. The G and Tr/ET were both well estimated by the vegetation index. It was concluded that the combination of a simple radiation method with remotely sensed information can provide useful information on energy partitioning and Tr/ET in vegetation canopies

[Differences of vegetation phenology monitoring by remote sensing based on different spectral vegetation indices.

Science.gov (United States)

Zuo, Lu; Wang, Huan Jiong; Liu, Rong Gao; Liu, Yang; Shang, Rong

2018-02-01

Vegetation phenology is a comprehensive indictor for the responses of terrestrial ecosystem to climatic and environmental changes. Remote sensing spectrum has been widely used in the extraction of vegetation phenology information. However, there are many differences between phenology extracted by remote sensing and site observations, with their physical meaning remaining unclear. We selected one tile of MODIS data in northeastern China (2000-2014) to examine the SOS and EOS differences derived from the normalized difference vegetation index (NDVI) and the simple ratio vegetation index (SR) based on both the red and near-infrared bands. The results showed that there were significant differences between NDVI-phenology and SR-phenology. SOS derived from NDVI averaged 18.9 days earlier than that from SR. EOS derived from NDVI averaged 19.0 days later than from SR. NDVI-phenology had a longer growing season. There were significant differences in the inter-annual variation of phenology from NDVI and SR. More than 20% of the pixel SOS and EOS derived from NDVI and SR showed the opposite temporal trend. These results caused by the seasonal curve characteristics and noise resistance differences of NDVI and SR. The observed data source of NDVI and SR were completely consistent, only the mathematical expressions were different, but phenology results were significantly different. Our results indicated that vegetation phenology monitoring by remote sensing is highly dependent on the mathematical expression of vegetation index. How to establish a reliable method for extracting vegetation phenology by remote sensing needs further research.

ASTER spectral sensitivity of carbonate rocks - Study in Sultanate of Oman

Science.gov (United States)

Rajendran, Sankaran; Nasir, Sobhi

2014-02-01

Remote sensing satellite data plays a vital role and capable in detecting minerals and discriminating rock types for explorations of mineral resources and geological studies. Study of spectral absorption characters of remotely sensed data are under consideration by the exploration and mining companies, and demonstrating the spectral absorption characters of carbonates on the cost-effective multispectral image (rather than the hyperspectral, Lidar image) for easy understanding of all geologists and exploration communities of carbonates is very much important. The present work is an integrated study and an outcome of recently published works on the economic important carbonate rocks, includes limestone, marl, listwaenites and carbonatites occurred in parts of the Sultanate of Oman. It demonstrates the spectral sensitivity of such rocks for simple interpretation over satellite data and describes and distinguishes them based on the absorptions of carbonate minerals in the spectral bands of advanced spaceborne thermal emission and reflection radiometer (ASTER) for mapping and exploration studies. The study results that the ASTER spectral band 8 discriminates the carbonate rocks due to the presence of predominantly occurred carbonate minerals; the ASTER band 5 distinguishes the limestones and marls (more hydroxyl clay minerals) from listwaenite (hydrothermally altered rock) due to the presence of altered minerals and the ASTER band 4 detects carbonatites (ultramafic intrusive alkaline rocks) which contain relatively more silicates. The study on the intensity of the total absorptions against the reflections of these rocks shows that the limestones and marls have low intensity in absorptions (and high reflection values) due to the presence of carbonate minerals (calcite and dolomite) occurred in different proportions. The listwaenites and carbonatites have high intensity of absorptions (low reflection values) due to the occurrence of Mn-oxide in listwaenites and carbonates

International Conference on Spectral and High-Order Methods

CERN Document Server

Dumont, Ney; Hesthaven, Jan

2017-01-01

This book features a selection of high-quality papers chosen from the best presentations at the International Conference on Spectral and High-Order Methods (2016), offering an overview of the depth and breadth of the activities within this important research area. The carefully reviewed papers provide a snapshot of the state of the art, while the extensive bibliography helps initiate new research directions.

High-Speed Target Identification System Based on the Plume’s Spectral Distribution

Directory of Open Access Journals (Sweden)

Wenjie Lang

2015-01-01

Full Text Available In order to recognize the target of high speed quickly and accurately, an identification system was designed based on analysis of the distribution characteristics of the plume spectrum. In the system, the target was aligned with visible light tracking module, and the spectral analysis of the target’s plume radiation was achieved by interference module. The distinguishing factor recognition algorithm was designed on basis of ratio of multifeature band peaks and valley mean values. Effective recognition of the high speed moving target could be achieved after partition of the active region and the influence of target motion on spectral acquisition was analyzed. In the experiment the small rocket combustion was used as the target. The spectral detection experiment was conducted at different speeds 2.0 km away from the detection system. Experimental results showed that spectral distribution had significant spectral offset in the same sampling period for the target with different speeds, but the spectral distribution was basically consistent. Through calculation of the inclusion relationship between distinguishing factor and distinction interval of the peak value and the valley value at the corresponding wave-bands, effective identification of target could be achieved.

HARD - The High Assurance Remote Authentication Device Project

OpenAIRE

2006-01-01

The HARD project will build and evaluate a high assurance network access device. The purpose of this device is to provide an unforgeable trusted path with which network clients can securely interact with security-enabled remote servers.

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

Biological and remote sensing perspectives of pigmentation in coral reef organisms.

Science.gov (United States)

Hedley, John D; Mumby, Peter J

2002-01-01

Coral reef communities face unprecedented pressures on local, regional and global scales as a consequence of climate change and anthropogenic disturbance. Optical remote sensing, from satellites or aircraft, is possibly the only means of measuring the effects of such stresses at appropriately large spatial scales (many thousands of square kilometres). To map key variables such as coral community structure, percentages of living coral or percentages of dead coral, a remote sensing instrument must be able to distinguish the reflectance spectra (i.e. "spectral signature", reflected light as a function of wavelength) of each category. For biotic classes, reflectance is a complex function of pigmentation, structure and morphology. Studies of coral "colour" fall into two disparate but potentially complementary types. Firstly, biological studies tend to investigate the structure and significance of pigmentation in reef organisms. These studies often lack details that would be useful from a remote sensing perspective such as intraspecific variation in pigment concentration or the contribution of fluorescence to reflectance. Secondly, remote sensing studies take empirical measurements of spectra and seek wavelengths that discriminate benthic categories. Benthic categories used in remote sensing sometimes consist of species groupings that are biologically or spectrally inappropriate (e.g. merging of algal phyla with distinct pigments). Here, we attempt to bridge the gap between biological and remote sensing perspectives of pigmentation in reef taxa. The aim is to assess the extent to which spectral discrimination can be given a biological foundation, to reduce the ad hoc nature of discriminatory criteria, and to understand the fundamental (biological) limitations in the spectral separability of biotic classes. Sources of pigmentation in reef biota are reviewed together with remote sensing studies where spectral discrimination has been effectively demonstrated between benthic

Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning

Science.gov (United States)

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

2017-12-01

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

[Analysis of related factors of slope plant hyperspectral remote sensing].

Science.gov (United States)

Sun, Wei-Qi; Zhao, Yun-Sheng; Tu, Lin-Ling

2014-09-01

In the present paper, the slope gradient, aspect, detection zenith angle and plant types were analyzed. In order to strengthen the theoretical discussion, the research was under laboratory condition, and modeled uniform slope for slope plant. Through experiments we found that these factors indeed have influence on plant hyperspectral remote sensing. When choosing slope gradient as the variate, the blade reflection first increases and then decreases as the slope gradient changes from 0° to 36°; When keeping other factors constant, and only detection zenith angle increasing from 0° to 60°, the spectral characteristic of slope plants do not change significantly in visible light band, but decreases gradually in near infrared band; With only slope aspect changing, when the dome meets the light direction, the blade reflectance gets maximum, and when the dome meets the backlit direction, the blade reflectance gets minimum, furthermore, setting the line of vertical intersection of incidence plane and the dome as an axis, the reflectance on the axis's both sides shows symmetric distribution; In addition, spectral curves of different plant types have a lot differences between each other, which means that the plant types also affect hyperspectral remote sensing results of slope plants. This research breaks through the limitations of the traditional vertical remote sensing data collection and uses the multi-angle and hyperspectral information to analyze spectral characteristics of slope plants. So this research has theoretical significance to the development of quantitative remote sensing, and has application value to the plant remote sensing monitoring.

Remote Sensing of Rock Type in the Visible and Near-Infrared,

Science.gov (United States)

Visible and near-infrared spectra of minerals and rocks have been measured and evaluated in terms of remote sensing applications. The authors...difficult or impossible to use in a generalized remote sensing effort in which the composition of all rocks is to be mapped. Instead, this spectral

Spatial variability of oceanic phycoerythrin spectral types derived from airborne laser-induced fluorescence emissions

Science.gov (United States)

Hoge, Frank E.; Wright, C. Wayne; Kana, Todd M.; Swift, Robert N.; Yungel, James K.

1998-07-01

We report spatial variability of oceanic phycoerythrin spectral types detected by means of a blue spectral shift in airborne laser-induced fluorescence emission. The blue shift of the phycoerythrobilin fluorescence is known from laboratory studies to be induced by phycourobilin chromophore substitution at phycoerythrobilin chromophore sites in some strains of phycoerythrin-containing marine cyanobacteria. The airborne 532-nm laser-induced phycoerythrin fluorescence of the upper oceanic volume showed distinct segregation of cyanobacterial chromophore types in a flight transect from coastal water to the Sargasso Sea in the western North Atlantic. High phycourobilin levels were restricted to the oceanic (oligotrophic) end of the flight transect, in agreement with historical ship findings. These remotely observed phycoerythrin spectral fluorescence shifts have the potential to permit rapid, wide-area studies of the spatial variability of spectrally distinct cyanobacteria, especially across interfacial regions of coastal and oceanic water masses. Airborne laser-induced phytoplankton spectral fluorescence observations also further the development of satellite algorithms for passive detection of phytoplankton pigments. Optical modifications to the NASA Airborne Oceanographic Lidar are briefly described that permitted observation of the fluorescence spectral shifts.

Impact of the cameras radiometric resolution on the accuracy of determining spectral reflectance coefficients

Science.gov (United States)

Orych, A.; Walczykowski, P.; Jenerowicz, A.; Zdunek, Z.

2014-11-01

Nowadays remote sensing plays a very important role in many different study fields, i.e. environmental studies, hydrology, mineralogy, ecosystem studies, etc. One of the key areas of remote sensing applications is water quality monitoring. Understanding and monitoring of the water quality parameters and detecting different water contaminants is an important issue in water management and protection of whole environment and especially the water ecosystem. There are many remote sensing methods to monitor water quality and detect water pollutants. One of the most widely used method for substance detection with remote sensing techniques is based on usage of spectral reflectance coefficients. They are usually acquired using discrete methods such as spectrometric measurements. These however can be very time consuming, therefore image-based methods are used more and more often. In order to work out the proper methodology of obtaining spectral reflectance coefficients from hyperspectral and multispectral images, it is necessary to verify the impact of cameras radiometric resolution on the accuracy of determination of them. This paper presents laboratory experiments that were conducted using two monochromatic XEVA video sensors (400-1700 nm spectral data registration) with two different radiometric resolutions (12 and 14 bits). In view of determining spectral characteristics from images, the research team used set of interferometric filters. All data collected with multispectral digital video cameras were compared with spectral reflectance coefficients obtained with spectroradiometer. The objective of this research is to find the impact of cameras radiometric resolution on reflectance values in chosen wavelength. The main topic of this study is the analysis of accuracy of spectral coefficients from sensors with different radiometric resolution. By comparing values collected from images acquired with XEVA sensors and with the curves obtained with spectroradiometer it

High Spectral Resolution, High Cadence, Imaging X-ray Microcalorimeters for Solar Physics - Phase 2 Project

Data.gov (United States)

National Aeronautics and Space Administration — Microcalorimeter x-ray instruments are non-dispersive, high spectral resolution, broad-band, high cadence imaging spectrometers. We have been developing these...

[Hyperspectral remote sensing in monitoring the vegetation heavy metal pollution].

Science.gov (United States)

Li, Na; Lü, Jian-sheng; Altemann, W

2010-09-01

Mine exploitation aggravates the environment pollution. The large amount of heavy metal element in the drainage of slag from the mine pollutes the soil seriously, doing harm to the vegetation growing and human health. The investigation of mining environment pollution is urgent, in which remote sensing, as a new technique, helps a lot. In the present paper, copper mine in Dexing was selected as the study area and China sumac as the study plant. Samples and spectral data in field were gathered and analyzed in lab. The regression model from spectral characteristics for heavy metal content was built, and the feasibility of hyperspectral remote sensing in environment pollution monitoring was testified.

SPAM- SPECTRAL ANALYSIS MANAGER (UNIX VERSION)

Science.gov (United States)

Solomon, J. E.

1994-01-01

The Spectral Analysis Manager (SPAM) was developed to allow easy qualitative analysis of multi-dimensional imaging spectrometer data. Imaging spectrometers provide sufficient spectral sampling to define unique spectral signatures on a per pixel basis. Thus direct material identification becomes possible for geologic studies. SPAM provides a variety of capabilities for carrying out interactive analysis of the massive and complex datasets associated with multispectral remote sensing observations. In addition to normal image processing functions, SPAM provides multiple levels of on-line help, a flexible command interpretation, graceful error recovery, and a program structure which can be implemented in a variety of environments. SPAM was designed to be visually oriented and user friendly with the liberal employment of graphics for rapid and efficient exploratory analysis of imaging spectrometry data. SPAM provides functions to enable arithmetic manipulations of the data, such as normalization, linear mixing, band ratio discrimination, and low-pass filtering. SPAM can be used to examine the spectra of an individual pixel or the average spectra over a number of pixels. SPAM also supports image segmentation, fast spectral signature matching, spectral library usage, mixture analysis, and feature extraction. High speed spectral signature matching is performed by using a binary spectral encoding algorithm to separate and identify mineral components present in the scene. The same binary encoding allows automatic spectral clustering. Spectral data may be entered from a digitizing tablet, stored in a user library, compared to the master library containing mineral standards, and then displayed as a timesequence spectral movie. The output plots, histograms, and stretched histograms produced by SPAM can be sent to a lineprinter, stored as separate RGB disk files, or sent to a Quick Color Recorder. SPAM is written in C for interactive execution and is available for two different

A High Energy and High Efficiency Spectral Shaping Single Frequency Fiber Laser, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — This SBIR phase II project proposes a single frequency high energy fiber laser system for coherent Lidar systems for remote sensing. Current state-of-art...

Feasibility study of a novel miniaturized spectral imaging system architecture in UAV surveillance

Science.gov (United States)

Liu, Shuyang; Zhou, Tao; Jia, Xiaodong; Cui, Hushan; Huang, Chengjun

2016-01-01

The spectral imaging technology is able to analysis the spectral and spatial geometric character of the target at the same time. To break through the limitation brought by the size, weight and cost of the traditional spectral imaging instrument, a miniaturized novel spectral imaging based on CMOS processing has been introduced in the market. This technology has enabled the possibility of applying spectral imaging in the UAV platform. In this paper, the relevant technology and the related possible applications have been presented to implement a quick, flexible and more detailed remote sensing system.

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

Science.gov (United States)

Caras, Tamir; Hedley, John; Karnieli, Arnon

2017-12-01

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

High Spectral Density Optical Communication Technologies

CERN Document Server

Nakazawa, Masataka; Miyazaki, Tetsuya

2010-01-01

The latest hot topics of high-spectral density optical communication systems using digital coherent optical fibre communication technologies are covered by this book. History and meaning of a "renaissance" of the technology, requirements to the Peta-bit/s class "new generation network" are also covered in the first part of this book. The main topics treated are electronic and optical devices, digital signal processing including forward error correction, modulation formats as well as transmission and application systems. The book serves as a reference to researchers and engineers.

A High Energy and High Efficiency Spectral Shaping Single Frequency Fiber Laser, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — This SBIR phase I project proposes a tunable single frequency high energy fiber laser system for coherent Lidar systems for remote sensing. Current state-of-art...

Influence of spectral resolution, spectral range and signal-to-noise ratio of Fourier transform infra-red spectra on identification of high explosive substances

Science.gov (United States)

Banas, Krzysztof; Banas, Agnieszka M.; Heussler, Sascha P.; Breese, Mark B. H.

2018-01-01

In the contemporary spectroscopy there is a trend to record spectra with the highest possible spectral resolution. This is clearly justified if the spectral features in the spectrum are very narrow (for example infra-red spectra of gas samples). However there is a plethora of samples (in the liquid and especially in the solid form) where there is a natural spectral peak broadening due to collisions and proximity predominately. Additionally there is a number of portable devices (spectrometers) with inherently restricted spectral resolution, spectral range or both, which are extremely useful in some field applications (archaeology, agriculture, food industry, cultural heritage, forensic science). In this paper the investigation of the influence of spectral resolution, spectral range and signal-to-noise ratio on the identification of high explosive substances by applying multivariate statistical methods on the Fourier transform infra-red spectral data sets is studied. All mathematical procedures on spectral data for dimension reduction, clustering and validation were implemented within R open source environment.

[Modeling and Simulation of Spectral Polarimetric BRDF].

Science.gov (United States)

Ling, Jin-jiang; Li, Gang; Zhang, Ren-bin; Tang, Qian; Ye, Qiu

2016-01-01

Under the conditions of the polarized light, The reflective surface of the object is affected by many factors, refractive index, surface roughness, and so the angle of incidence. For the rough surface in the different wavelengths of light exhibit different reflection characteristics of polarization, a spectral polarimetric BRDF based on Kirchhof theory is proposee. The spectral model of complex refraction index is combined with refraction index and extinction coefficient spectral model which were got by using the known complex refraction index at different value. Then get the spectral model of surface roughness derived from the classical surface roughness measuring method combined with the Fresnel reflection function. Take the spectral model of refraction index and roughness into the BRDF model, then the spectral polarimetirc BRDF model is proposed. Compare the simulation results of the refractive index varies with wavelength, roughness is constant, the refraction index and roughness both vary with wavelength and origin model with other papers, it shows that, the spectral polarimetric BRDF model can show the polarization characteristics of the surface accurately, and can provide a reliable basis for the application of polarization remote sensing, and other aspects of the classification of substances.

Choice of Eye-Safe Radiation Wavelength in UV and Near IR Spectral Bands for Remote Sensing

Directory of Open Access Journals (Sweden)

M. L. Belov

2016-01-01

Full Text Available The introduction of laser remote sensing systems carries a particular risk to the human’s sense of vision. A structure of the eye, and especially the retina, is the main critical organ as related to the laser radiation.The work uses the optical models of the atmosphere, correctly working in both the UV and the near-IR band, to select the eye-safe radiation wavelengths in the UV (0.355 m and near-IR (~ 1.54 and ~ 2 m spectral bands from the point of view of recorded lidar signal value to fulfill the tasks of laser sensing the natural formations and laser aerosol sensing in the atmosphere.It is shown that the remote sensing lasers with appropriate characteristics can be selected both in the UV band (at a wavelength of 0.355 μm and in the near-IR band (at wavelengths of 1.54 ~ or ~ 2 μm.Molecular scattering has its maximum (for the selected wavelength at a wavelength of 0.355 μm in the UV band, and the minimum at the wavelengths of 1.54 and 2.09 μm in the near -IR band. The main contribution to the molecular absorption at a wavelength of 0.355 μm is made by ozone. In the near-IR spectral band the radiation is absorbed due to water vapor and carbon dioxide.Calculations show that the total effect of the molecular absorption and scattering has no influence on radiation transmission for both the wavelength of 0.355 μm in the UV band, and the wavelengths of 1.54 and 2.09 μm in the near-IR band for sensing trails ~ 1 km.One of the main factors of laser radiation attenuation in the Earth's atmosphere is radiation scattering by aerosol particles.The results of calculations at wavelengths of 0.355 μm, 1.54 μm and 2.09 μm for the several models of the atmosphere show that a choice of the most effective (in terms of the recorded signal of lidar and eye-safe radiation wavelength depends strongly on the task of sensing.To fulfill the task of laser sensing the natural formations, among the eye-safe wavelengths there is one significantly advantageous

On the feasibility of comprehensive high-resolution 3D remote dosimetry

International Nuclear Information System (INIS)

Juang, Titania; Grant, Ryan; Adamovics, John; Ibbott, Geoffrey; Oldham, Mark

2014-01-01

Purpose: This study investigates the feasibility of remote high-resolution 3D dosimetry with the PRESAGE®/Optical-CT system. In remote dosimetry, dosimeters are shipped out from a central base institution to a remote institution for irradiation, then shipped back to the base institution for subsequent readout and analysis. Methods: Two nominally identical optical-CT scanners for 3D dosimetry were constructed and placed at the base (Duke University) and remote (Radiological Physics Center) institutions. Two formulations of PRESAGE® (SS1, SS2) radiochromic dosimeters were investigated. Higher sensitivity was expected in SS1, which had higher initiator content (0.25% bromotrichloromethane), while greater temporal stability was expected in SS2. Four unirradiated PRESAGE® dosimeters (two per formulation, cylindrical dimensions 11 cm diameter, 8.5–9.5 cm length) were imaged at the base institution, then shipped to the remote institution for planning and irradiation. Each dosimeter was irradiated with the same simple treatment plan: an isocentric 3-field “cross” arrangement of 4 × 4 cm open 6 MV beams configured as parallel opposed laterals with an anterior beam. This simple plan was amenable to accurate and repeatable setup, as well as accurate dose modeling by a commissioned treatment planning system (Pinnacle). After irradiation and subsequent (within 1 h) optical-CT readout at the remote institution, the dosimeters were shipped back to the base institution for remote dosimetry readout 3 days postirradiation. Measured on-site and remote relative 3D dose distributions were registered to the Pinnacle dose calculation, which served as the reference distribution for 3D gamma calculations with passing criteria of 5%/2 mm, 3%/3 mm, and 3%/2 mm with a 10% dose threshold. Gamma passing rates, dose profiles, and color-maps were all used to assess and compare the performance of both PRESAGE® formulations for remote dosimetry. Results: The best agreements between the

Remote Sensing of Irrigated Agriculture: Opportunities and Challenges

Directory of Open Access Journals (Sweden)

Chelsea Cervantes

2010-09-01

Full Text Available Over the last several decades, remote sensing has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. The objective of this review, which summarizes the methods and the results of existing remote sensing studies, is to synthesize principle findings and assess the state of the art. We take a taxonomic approach to group studies based on location, scale, inputs, and methods, in an effort to categorize different approaches within a logical framework. We seek to evaluate the ability of remote sensing to provide synoptic and timely coverage of irrigated lands in several spectral regions. We also investigate the value of archived data that enable comparison of images through time. This overview of the studies to date indicates that remote sensing-based monitoring of irrigation is at an intermediate stage of development at local scales. For instance, there is overwhelming consensus on the efficacy of vegetation indices in identifying irrigated fields. Also, single date imagery, acquired at peak growing season, may suffice to identify irrigated lands, although to multi-date image data are necessary for improved classification and to distinguish different crop types. At local scales, the mapping of irrigated lands with remote sensing is also strongly affected by the timing of image acquisition and the number of images used. At the regional and global scales, on the other hand, remote sensing has not been fully operational, as methods that work in one place and time are not necessarily transferable to other locations and periods. Thus, at larger scales, more work is required to indentify the best spectral indices, best time periods, and best classification methods under different climatological and cultural environments. Existing studies at regional scales also establish the fact that both remote sensing and national statistical approaches require further refinement with a substantial investment of

Performance of spectral fitting methods for vegetation fluorescence quantification

NARCIS (Netherlands)

Meroni, M.; Busetto, D.; Colombo, R.; Guanter, L.; Moreno, J.; Verhoef, W.

2010-01-01

The Fraunhofer Line Discriminator (FLD) principle has long been considered as the reference method to quantify solar-induced chlorophyll fluorescence (F) from passive remote sensing measurements. Recently, alternative retrieval algorithms based on the spectral fitting of hyperspectral radiance

Cloud Masking for Remotely Sensed Data Using Spectral and Principal Components Analysis

Directory of Open Access Journals (Sweden)

A. Ahmad

2012-06-01

Full Text Available Two methods of cloud masking tuned to tropical conditions have been developed, based on spectral analysis and Principal Components Analysis (PCA of Moderate Resolution Imaging Spectroradiometer (MODIS data. In the spectral approach, thresholds were applied to four reflective bands (1, 2, 3, and 4, three thermal bands (29, 31 and 32, the band 2/band 1 ratio, and the difference between band 29 and 31 in order to detect clouds. The PCA approach applied a threshold to the first principal component derived from the seven quantities used for spectral analysis. Cloud detections were compared with the standard MODIS cloud mask, and their accuracy was assessed using reference images and geographical information on the study area.

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.

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

Directory of Open Access Journals (Sweden)

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.

Some Insights of Spectral Optimization in Ocean Color Inversion

Science.gov (United States)

Lee, Zhongping; Franz, Bryan; Shang, Shaoling; Dong, Qiang; Arnone, Robert

2011-01-01

In the past decades various algorithms have been developed for the retrieval of water constituents from the measurement of ocean color radiometry, and one of the approaches is spectral optimization. This approach defines an error target (or error function) between the input remote sensing reflectance and the output remote sensing reflectance, with the latter modeled with a few variables that represent the optically active properties (such as the absorption coefficient of phytoplankton and the backscattering coefficient of particles). The values of the variables when the error reach a minimum (optimization is achieved) are considered the properties that form the input remote sensing reflectance; or in other words, the equations are solved numerically. The applications of this approach implicitly assume that the error is a monotonic function of the various variables. Here, with data from numerical simulation and field measurements, we show the shape of the error surface, in a way to justify the possibility of finding a solution of the various variables. In addition, because the spectral properties could be modeled differently, impacts of such differences on the error surface as well as on the retrievals are also presented.

[Applications of spectral analysis technique to monitoring grasshoppers].

Science.gov (United States)

Lu, Hui; Han, Jian-guo; Zhang, Lu-da

2008-12-01

Grasshopper monitoring is of great significance in protecting environment and reducing economic loss. However, how to predict grasshoppers accurately and effectively is a difficult problem for a long time. In the present paper, the importance of forecasting grasshoppers and its habitat is expounded, and the development in monitoring grasshopper populations and the common arithmetic of spectral analysis technique are illustrated. Meanwhile, the traditional methods are compared with the spectral technology. Remote sensing has been applied in monitoring the living, growing and breeding habitats of grasshopper population, and can be used to develop a forecast model combined with GIS. The NDVI values can be analyzed throughout the remote sensing data and be used in grasshopper forecasting. Hyper-spectra remote sensing technique which can be used to monitor grasshoppers more exactly has advantages in measuring the damage degree and classifying damage areas of grasshoppers, so it can be adopted to monitor the spatial distribution dynamic of rangeland grasshopper population. Differentialsmoothing can be used to reflect the relations between the characteristic parameters of hyper-spectra and leaf area index (LAI), and indicate the intensity of grasshopper damage. The technology of near infrared reflectance spectroscopy has been employed in judging grasshopper species, examining species occurrences and monitoring hatching places by measuring humidity and nutrient of soil, and can be used to investigate and observe grasshoppers in sample research. According to this paper, it is concluded that the spectral analysis technique could be used as a quick and exact tool in monitoring and forecasting the infestation of grasshoppers, and will become an important means in such kind of research for their advantages in determining spatial orientation, information extracting and processing. With the rapid development of spectral analysis methodology, the goal of sustainable monitoring

High spectral resolution infrared observations of V1057 Cygni

International Nuclear Information System (INIS)

Hartmann, L.; Kenyon, S.J.

1987-01-01

High-resolution near-infrared spectra of V1057 Cygni obtained in 1986 with the KPNO 4-m Fourier transform spectrometer provide support for a previously proposed accretion disk model. The model predicts that the observed rotational broadening of spectral lines should be smaller in the infrared than in the optical. The present observations show that V1057 Cyg rotates more slowly at 2.3 microns than at 6000 A by an amount quantitatively consistent with the simple disk models. The absence of any radial velocity variations in either the infrared or optical spectral regions supports the suggestion that the accreted material arises from a remnant disk of protostellar material. 19 references

VNIR spectral modeling of Mars analogue rocks: first results

Science.gov (United States)

Pompilio, L.; Roush, T.; Pedrazzi, G.; Sgavetti, M.

a study applying the Modified Gaussian Model [MGM, 13] to solid mafic rock spectral modeling. Reflectance measurements were acquired on rock slabs, rock powder, and mineral separates. The results of spectral modeling were evaluated using compositional data determined from techniques other than reflectance spectroscopy. The rocks studied, melanorite (cumulate analog) and basalt (effusive analog), have different textural characteristics. The modal composition of melanorite includes relatively high opaque content. Opaque minerals strongly affect reflectance 1 spectra of transparent minerals and the criteria for their identification from remotely acquired data are not clearly established. Detailed studies of the melanorite slab spectrum, which includes accounting for the opaque content, can be extended to the basalt spectrum, which must also account for the spectral influence of glass. The spectral analyses reveal the MGM decompositions of solid rock samples can be used to obtain qualitative estimates of the main mineral compositions, for the melanorite, but become more problematic for the basalt. Statistically objective evaluation of the spectral models is complicated by the increased observational error due to the heterogeneity of the rock surfaces relative to mixtures of powders. This suggests additional efforts are required to provide a better understanding regarding the spectral modeling of both laboratory and in-situ measurements of bulk rocks. REFERENCES - [1] Bandfield, J., P. Christensen, and V. Hamilton (2000) Science, 287, 1626-1630. [2] Christensen, P., J. Bandfield, V. Hamilton, and 23 others (2001) J. Geophys. Res.-Planets., 106 (E10), 23823-23871. [3] Wyatt, M. and H. McSween (2002) Nature, 417, 263-266. [4] Hamilton, V. and M. Minitti (2003) Geophys. Res. Lett., 30, PLA 1-1. [5] Bibring, J., Y. Langevin, A. Gendrin, and 9 others (2005) Science, 307, 1576-1581. [6] Mustard, J., F. Poulet, A. Gendrin, and 6 others (2005) Science, 307, 1594-1597. [7] Hapke

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

Directory of Open Access Journals (Sweden)

Helingjie Huang

2017-03-01

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

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.

3D high spectral and spatial resolution imaging of ex vivo mouse brain

International Nuclear Information System (INIS)

Foxley, Sean; Karczmar, Gregory S.; Domowicz, Miriam; Schwartz, Nancy

2015-01-01

Purpose: Widely used MRI methods show brain morphology both in vivo and ex vivo at very high resolution. Many of these methods (e.g., T 2 * -weighted imaging, phase-sensitive imaging, or susceptibility-weighted imaging) are sensitive to local magnetic susceptibility gradients produced by subtle variations in tissue composition. However, the spectral resolution of commonly used methods is limited to maintain reasonable run-time combined with very high spatial resolution. Here, the authors report on data acquisition at increased spectral resolution, with 3-dimensional high spectral and spatial resolution MRI, in order to analyze subtle variations in water proton resonance frequency and lineshape that reflect local anatomy. The resulting information compliments previous studies based on T 2 * and resonance frequency. Methods: The proton free induction decay was sampled at high resolution and Fourier transformed to produce a high-resolution water spectrum for each image voxel in a 3D volume. Data were acquired using a multigradient echo pulse sequence (i.e., echo-planar spectroscopic imaging) with a spatial resolution of 50 × 50 × 70 μm 3 and spectral resolution of 3.5 Hz. Data were analyzed in the spectral domain, and images were produced from the various Fourier components of the water resonance. This allowed precise measurement of local variations in water resonance frequency and lineshape, at the expense of significantly increased run time (16–24 h). Results: High contrast T 2 * -weighted images were produced from the peak of the water resonance (peak height image), revealing a high degree of anatomical detail, specifically in the hippocampus and cerebellum. In images produced from Fourier components of the water resonance at −7.0 Hz from the peak, the contrast between deep white matter tracts and the surrounding tissue is the reverse of the contrast in water peak height images. This indicates the presence of a shoulder in the water resonance that is not

Analysis of X-ray Spectra of High-Z Elements obtained on Nike with high spectral and spatial resolution

Science.gov (United States)

Aglitskiy, Yefim; Weaver, J. L.; Karasik, M.; Serlin, V.; Obenschain, S. P.; Ralchenko, Yu.

2014-10-01

The spectra of multi-charged ions of Hf, Ta, W, Pt, Au and Bi have been studied on Nike krypton-fluoride laser facility with the help of two kinds of X-ray spectrometers. First, survey instrument covering a spectral range from 0.5 to 19.5 angstroms which allows simultaneous observation of both M- and N- spectra of above mentioned elements with high spectral resolution. Second, an imaging spectrometer with interchangeable spherically bent Quartz crystals that added higher efficiency, higher spectral resolution and high spatial resolution to the qualities of the former one. Multiple spectral lines with X-ray energies as high as 4 keV that belong to the isoelectronic sequences of Fe, Co, Ni, Cu and Zn were identified with the help of NOMAD package developed by Dr. Yu. Ralchenko and colleagues. In our continuous effort to support DOE-NNSA's inertial fusion program, this campaign covered a wide range of plasma conditions that result in production of relatively energetic X-rays. Work supported by the US DOE/NNSA.

High-performance technology for indexing of high volumes of Earth remote sensing data

Science.gov (United States)

Strotov, Valery V.; Taganov, Alexander I.; Kolesenkov, Aleksandr N.; Kostrov, Boris V.

2017-10-01

The present paper has suggested a technology for search, indexing, cataloging and distribution of aerospace images on the basis of geo-information approach, cluster and spectral analysis. It has considered information and algorithmic support of the system. Functional circuit of the system and structure of the geographical data base have been developed on the basis of the geographical online portal technology. Taking into account heterogeneity of information obtained from various sources it is reasonable to apply a geoinformation platform that allows analyzing space location of objects and territories and executing complex processing of information. Geoinformation platform is based on cartographic fundamentals with the uniform coordinate system, the geographical data base, a set of algorithms and program modules for execution of various tasks. The technology for adding by particular users and companies of images taken by means of professional and amateur devices and also processed by various software tools to the array system has been suggested. Complex usage of visual and instrumental approaches allows significantly expanding an application area of Earth remote sensing data. Development and implementation of new algorithms based on the complex usage of new methods for processing of structured and unstructured data of high volumes will increase periodicity and rate of data updating. The paper has shown that application of original algorithms for search, indexing and cataloging of aerospace images will provide an easy access to information spread by hundreds of suppliers and allow increasing an access rate to aerospace images up to 5 times in comparison with current analogues.

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

International Nuclear Information System (INIS)

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

2014-01-01

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

Thematic Conference on Geologic Remote Sensing, 8th, Denver, CO, Apr. 29-May 2, 1991, Proceedings. Vols. 1 & 2

Science.gov (United States)

1991-01-01

The proceedings contain papers discussing the state-of-the-art exploration, engineering, and environmental applications of geologic remote sensing, along with the research and development activities aimed at increasing the future capabilities of this technology. The following topics are addressed: spectral geology, U.S. and international hydrocarbon exporation, radar and thermal infrared remote sensing, engineering geology and hydrogeology, mineral exploration, remote sensing for marine and environmental applications, image processing and analysis, geobotanical remote sensing, and data integration and geographic information systems. Particular attention is given to spectral alteration mapping with imaging spectrometers, mapping the coastal plain of the Congo with airborne digital radar, applications of remote sensing techniques to the assessment of dam safety, remote sensing of ferric iron minerals as guides for gold exploration, principal component analysis for alteration mappping, and the application of remote sensing techniques for gold prospecting in the north Fujian province.

Remote sensing applications for monitoring rangeland vegetation ...

African Journals Online (AJOL)

Remote sensing techniques hold considerable promise for the inventory and monitoring of natural resources on rangelands. A significant lack of information concerning basic spectral characteristics of range vegetation and soils has resulted in a lack of rangeland applications. The parameters of interest for range condition ...

Review of spectral imaging technology in biomedical engineering: achievements and challenges.

Science.gov (United States)

Li, Qingli; He, Xiaofu; Wang, Yiting; Liu, Hongying; Xu, Dongrong; Guo, Fangmin

2013-10-01

Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This review introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide the reader with an insight into the current technological advances and its potential for biomedical research.

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

Directory of Open Access Journals (Sweden)

C. Yao

2017-09-01

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

Decision tree approach for classification of remotely sensed satellite

Indian Academy of Sciences (India)

DTC) algorithm for classification of remotely sensed satellite data (Landsat TM) using open source support. The decision tree is constructed by recursively partitioning the spectral distribution of the training dataset using WEKA, open source ...

Advanced remote handling developments for high radiation applications

International Nuclear Information System (INIS)

Herndon, J.N.; Kring, C.T.; Feldman, M.J.; Kuban, D.P.; Martin, H.L.; Rowe, J.C.; Hamel, W.R.

1985-01-01

The Remote Control Engineering Task of the Consolidated Fuel Reprocessing Program at Oak Ridge National Laboratory has been developing advanced techniques for remote maintenance of future US fuel reprocessing plants. These efforts are based on the application of teleoperated, force-reflecting servomanipulators for dexterous remote handling with television viewing for large-volume hazardous applications. These developments fully address the nonrepetitive nature of remote maintenance in the unstructured environments encountered in fuel reprocessing. This paper covers the primary emphasis in the present program; the design, fabrication, and installation of a prototype remote handling system for reprocessing applications, the Advanced Integrated Maintenance System

Atmospheric weighting functions and surface partial derivatives for remote sensing of scattering planetary atmospheres in thermal spectral region: general adjoint approach

International Nuclear Information System (INIS)

Ustinov, Eugene A.

2005-01-01

An approach to formulation of inversion algorithms for remote sensing in the thermal spectral region in the case of a scattering planetary atmosphere, based on the adjoint equation of radiative transfer (Ustinov (JQSRT 68 (2001) 195; JQSRT 73 (2002) 29); referred to as Papers 1 and 2, respectively, in the main text), is applied to the general case of retrievals of atmospheric and surface parameters for the scattering atmosphere with nadir viewing geometry. Analytic expressions for corresponding weighting functions for atmospheric parameters and partial derivatives for surface parameters are derived. The case of pure atmospheric absorption with a scattering underlying surface is considered and convergence to results obtained for the non-scattering atmospheres (Ustinov (JQSRT 74 (2002) 683), referred to as Paper 3 in the main text) is demonstrated

a Novel Deep Convolutional Neural Network for Spectral-Spatial Classification of Hyperspectral Data

Science.gov (United States)

Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.

2018-04-01

Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.

A New Fusion Technique of Remote Sensing Images for Land Use/Cover

Institute of Scientific and Technical Information of China (English)

WU Lian-Xi; SUN Bo; ZHOU Sheng-Lu; HUANG Shu-E; ZHAO Qi-Guo

2004-01-01

In China,accelerating industrialization and urbanization following high-speed economic development and population increases have greatly impacted land use/cover changes,making it imperative to obtain accurate and up to date information on changes so as to evaluate their environmental effects. The major purpose of this study was to develop a new method to fuse lower spatial resolution multispectral satellite images with higher spatial resolution panchromatic ones to assist in land use/cover mapping. An algorithm of a new fusion method known as edge enhancement intensity modulation (EEIM) was proposed to merge two optical image data sets of different spectral ranges. The results showed that the EEIM image was quite similar in color to lower resolution multispectral images,and the fused product was better able to preserve spectral information. Thus,compared to conventional approaches,the spectral distortion of the fused images was markedly reduced. Therefore,the EEIM fusion method could be utilized to fuse remote sensing data from the same or different sensors,including TM images and SPOT5 panchromatic images,providing high quality land use/cover images.

Remote systems requirements of the high-yield lithium injection fusion energy converter concept

International Nuclear Information System (INIS)

Walker, P.E.

1978-01-01

Remote systems will be required in the high-yield lithium injection fusion energy converter power plant proposed by Lawrence Livermore Laboratory. During inspection operations, viewing of the chamber interior and certain pumps, valve fittings, and welds must be done remotely. Ideas for remote maintenance of laser-beam blast baffles, optics, and target material traps are described. Radioisotope sources, their distributions, and exposure rates at various points in the reactor vicinity are presented

Regional geology mapping using satellite-based remote sensing approach in Northern Victoria Land, Antarctica

Science.gov (United States)

Pour, Amin Beiranvand; Park, Yongcheol; Park, Tae-Yoon S.; Hong, Jong Kuk; Hashim, Mazlan; Woo, Jusun; Ayoobi, Iman

2018-06-01

Satellite remote sensing imagery is especially useful for geological investigations in Antarctica because of its remoteness and extreme environmental conditions that constrain direct geological survey. The highest percentage of exposed rocks and soils in Antarctica occurs in Northern Victoria Land (NVL). Exposed Rocks in NVL were part of the paleo-Pacific margin of East Gondwana during the Paleozoic time. This investigation provides a satellite-based remote sensing approach for regional geological mapping in the NVL, Antarctica. Landsat-8 and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) datasets were used to extract lithological-structural and mineralogical information. Several spectral-band ratio indices were developed using Landsat-8 and ASTER bands and proposed for Antarctic environments to map spectral signatures of snow/ice, iron oxide/hydroxide minerals, Al-OH-bearing and Fe, Mg-OH and CO3 mineral zones, and quartz-rich felsic and mafic-to-ultramafic lithological units. The spectral-band ratio indices were tested and implemented to Level 1 terrain-corrected (L1T) products of Landsat-8 and ASTER datasets covering the NVL. The surface distribution of the mineral assemblages was mapped using the spectral-band ratio indices and verified by geological expeditions and laboratory analysis. Resultant image maps derived from spectral-band ratio indices that developed in this study are fairly accurate and correspond well with existing geological maps of the NVL. The spectral-band ratio indices developed in this study are especially useful for geological investigations in inaccessible locations and poorly exposed lithological units in Antarctica environments.

High resolution remote sensing of water surface patterns

Science.gov (United States)

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

2012-12-01

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

3D high spectral and spatial resolution imaging of ex vivo mouse brain

Energy Technology Data Exchange (ETDEWEB)

Foxley, Sean, E-mail: sean.foxley@ndcn.ox.ac.uk; Karczmar, Gregory S. [Department of Radiology, University of Chicago, Chicago, Illinois 60637 (United States); Domowicz, Miriam [Department of Pediatrics, University of Chicago, Chicago, Illinois 60637 (United States); Schwartz, Nancy [Department of Pediatrics, Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois 60637 (United States)

2015-03-15

Purpose: Widely used MRI methods show brain morphology both in vivo and ex vivo at very high resolution. Many of these methods (e.g., T{sub 2}{sup *}-weighted imaging, phase-sensitive imaging, or susceptibility-weighted imaging) are sensitive to local magnetic susceptibility gradients produced by subtle variations in tissue composition. However, the spectral resolution of commonly used methods is limited to maintain reasonable run-time combined with very high spatial resolution. Here, the authors report on data acquisition at increased spectral resolution, with 3-dimensional high spectral and spatial resolution MRI, in order to analyze subtle variations in water proton resonance frequency and lineshape that reflect local anatomy. The resulting information compliments previous studies based on T{sub 2}{sup *} and resonance frequency. Methods: The proton free induction decay was sampled at high resolution and Fourier transformed to produce a high-resolution water spectrum for each image voxel in a 3D volume. Data were acquired using a multigradient echo pulse sequence (i.e., echo-planar spectroscopic imaging) with a spatial resolution of 50 × 50 × 70 μm{sup 3} and spectral resolution of 3.5 Hz. Data were analyzed in the spectral domain, and images were produced from the various Fourier components of the water resonance. This allowed precise measurement of local variations in water resonance frequency and lineshape, at the expense of significantly increased run time (16–24 h). Results: High contrast T{sub 2}{sup *}-weighted images were produced from the peak of the water resonance (peak height image), revealing a high degree of anatomical detail, specifically in the hippocampus and cerebellum. In images produced from Fourier components of the water resonance at −7.0 Hz from the peak, the contrast between deep white matter tracts and the surrounding tissue is the reverse of the contrast in water peak height images. This indicates the presence of a shoulder in

Land cover mapping at Alkali Flat and Lake Lucero, White Sands, New Mexico, USA using multi-temporal and multi-spectral remote sensing data

Science.gov (United States)

Ghrefat, Habes A.; Goodell, Philip C.

2011-08-01

analysis between the various datasets helped to uncover the most optimum spatial-spectral-temporal and radiometric-resolution sensor characteristics for remote sensing based on monitoring of seasonal land cover, surface water, groundwater, and alluvial sediment input changes within the basin. The results demonstrated good agreement between ground truth data and XRD analysis of samples, and the results of Matched Filtering (MF) mapping method.

Measurement of high-temperature spectral emissivity using integral blackbody approach

Science.gov (United States)

Pan, Yijie; Dong, Wei; Lin, Hong; Yuan, Zundong; Bloembergen, Pieter

2016-11-01

Spectral emissivity is one of the most critical thermophysical properties of a material for heat design and analysis. Especially in the traditional radiation thermometry, normal spectral emissivity is very important. We developed a prototype instrument based upon an integral blackbody method to measure material's spectral emissivity at elevated temperatures. An optimized commercial variable-high-temperature blackbody, a high speed linear actuator, a linear pyrometer, and an in-house designed synchronization circuit was used to implemented the system. A sample was placed in a crucible at the bottom of the blackbody furnace, by which the sample and the tube formed a simulated reference blackbody which had an effective total emissivity greater than 0.985. During the measurement, a pneumatic cylinder pushed a graphite rode and then the sample crucible to the cold opening within hundreds of microseconds. The linear pyrometer was used to monitor the brightness temperature of the sample surface, and the corresponding opto-converted voltage was fed and recorded by a digital multimeter. To evaluate the temperature drop of the sample along the pushing process, a physical model was proposed. The tube was discretized into several isothermal cylindrical rings, and the temperature of each ring was measurement. View factors between sample and rings were utilized. Then, the actual surface temperature of the sample at the end opening was obtained. Taking advantages of the above measured voltage signal and the calculated actual temperature, normal spectral emissivity under the that temperature point was obtained. Graphite sample at 1300°C was measured to prove the validity of the method.

Remote sampling and analysis of highly radioactive samples in shielded boxes

International Nuclear Information System (INIS)

Kirpikov, D.A.; Miroshnichenko, I.V.; Pykhteev, O.Yu.

2010-01-01

The sampling procedure used for highly radioactive coolant water is associated with high risk of personnel irradiation and uncontrolled radioactive contamination. Remote sample manipulation with provision for proper radiation shielding is intended for safety enhancement of the sampling procedure. The sampling lines are located in an isolated compartment, a shielded box. Various equipment which enables remote or automatic sample manipulation is used for this purpose. The main issues of development of the shielded box equipment intended for a wider ranger of remote chemical analyses and manipulation techniques for highly radioactive water samples are considered in the paper. There were three principal directions of work: Transfer of chemical analysis performed in the laboratory inside the shielded box; Prevalence of computer-aided and remote techniques of highly radioactive sample manipulation inside the shielded box; and, Increase in control over sampling and determination of thermal-hydraulic parameters of the coolant water in the sampling lines. The developed equipment and solutions enable remote chemical analysis in the restricted volume of the shielded box by using ion-chromatographic, amperometrical, fluorimetric, flow injection, phototurbidimetric, conductometric and potentiometric methods. Extent of control performed in the shielded box is determined taking into account the requirements of the regulatory documents as well as feasibility and cost of the technical adaptation of various methods to the shielded box conditions. The work resulted in highly precise determination of more than 15 indexes of the coolant water quality performed in on-line mode in the shielded box. It averages to 80% of the total extent of control performed at the prototype reactor plants. The novel solutions for highly radioactive sample handling are implemented in the shielded box (for example, packaging, sample transportation to the laboratory, volume measurement). The shielded box is

Proceedings of the eighth thematic conference on geologic remote sensing

International Nuclear Information System (INIS)

Balmer, M.L.; Lange, F.F.; Levi, C.G.

1991-01-01

These proceedings contain papers presented at the Eighth Thematic Conference on Geologic Remote Sensing. This meeting was held April 29-May 2, 1991, in Denver, Colorado, USA. The conference was organized by the Environmental Research Institute of Michigan, in Cooperation with an international program committee composed primarily of geologic remote sensing specialists. The meeting was convened to discuss state-of-the-art exploration, engineering, and environmental applications of geologic remote sensing as well as research and development activities aimed at increasing the future capabilities of this technology. The presentations in these volumes address the following topics: Spectral Geology; U.S. and International Hydrocarbon Exploration; Radar and Thermal Infrared Remote Sensing; Engineering Geology and Hydrogeology; Minerals Exploration; Remote Sensing for Marine and Environmental Applications; Image Processing and Analysis; Geobotanical Remote Sensing; Data Integration and Geographic Information Systems

Spectral Phase Modulation and chirped pulse amplification in High Gain Harmonic Generation

CERN Document Server

Wu, Zilu; Krinsky, Sam; Loos, Henrik; Murphy, James; Shaftan, Timur; Sheehy, Brian; Shen, Yuzhen; Wang, Xijie; Yu Li Hua

2004-01-01

High Gain Harmonic Generation (HGHG), because it produces longitudinally coherent pulses derived from a coherent seed, presents remarkable possibilities for manipulating FEL pulses. If spectral phase modulation imposed on the seed modulates the spectral phase of the HGHG in a deterministic fashion, then chirped pulse amplification, pulse shaping, and coherent control experiments at short wavelengths become possible. In addition, the details of the transfer function will likely depend on electron beam and radiator dynamics and so prove to be a useful tool for studying these. Using the DUVFEL at the National Synchrotron Light Source at Brookhaven National Laboratory, we present spectral phase analyses of both coherent HGHG and incoherent SASE ultraviolet FEL radiation, applying Spectral Interferometry for Direct Electric Field Reconstruction (SPIDER), and assess the potential for employing compression and shaping techniques.

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

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

Science.gov (United States)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Development of a theory of the spectral reflectance of minerals, part 4

Science.gov (United States)

Aronson, J. R.; Emslie, A. G.; Smith, E. M.

1972-01-01

A theory of the spectral reflectance or emittance of particulate minerals was developed. The theory is expected to prove invaluable in the interpretation of the remote infrared spectra of planetary surfaces.

Spectral estimates of net radiation and soil heat flux

International Nuclear Information System (INIS)

Daughtry, C.S.T.; Kustas, W.P.; Moran, M.S.; Pinter, P.J. Jr.; Jackson, R.D.; Brown, P.W.; Nichols, W.D.; Gay, L.W.

1990-01-01

Conventional methods of measuring surface energy balance are point measurements and represent only a small area. Remote sensing offers a potential means of measuring outgoing fluxes over large areas at the spatial resolution of the sensor. The objective of this study was to estimate net radiation (Rn) and soil heat flux (G) using remotely sensed multispectral data acquired from an aircraft over large agricultural fields. Ground-based instruments measured Rn and G at nine locations along the flight lines. Incoming fluxes were also measured by ground-based instruments. Outgoing fluxes were estimated using remotely sensed data. Remote Rn, estimated as the algebraic sum of incoming and outgoing fluxes, slightly underestimated Rn measured by the ground-based net radiometers. The mean absolute errors for remote Rn minus measured Rn were less than 7%. Remote G, estimated as a function of a spectral vegetation index and remote Rn, slightly overestimated measured G; however, the mean absolute error for remote G was 13%. Some of the differences between measured and remote values of Rn and G are associated with differences in instrument designs and measurement techniques. The root mean square error for available energy (Rn - G) was 12%. Thus, methods using both ground-based and remotely sensed data can provide reliable estimates of the available energy which can be partitioned into sensible and latent heat under non advective conditions

Importance of Surface Texture to Infrared Remote Sensing Interpretations

Science.gov (United States)

Kirkland, L. E.; Adams, P. M.; Herr, K. C.; Salisbury, J. W.

2001-11-01

Thermal infrared remote sensing may be used to identify minerals present on the surface using diagnostic spectral bands. As band depth (spectral contrast) exhibited by the mineral increases, the mineral is easier to detect. In order to determine the expected spectral contrast, thermal infrared spectra of typical mineral endmembers are commonly measured in the laboratory. For example, for calcite, well-crystalline limestone is commonly studied. However, carbonates occur in several forms, including thin coatings, indurated carbonate (calcrete), and hot springs deposits. Different formation pathways may cause different microstructures and surface textures. This in turn can also affect the surface texture of the weathered material. Different surface textures can affect the measured band contrast, through roughness that causes a cavity (hohlraum) effect, and particle size and roughness on a scale that causes volume scattering. Thus since detection limits vary with the spectral contrast, surface texture can be an important variable in how detectable a mineral is. To study these issues, we have examined limestone and calcrete deposits at Mormon Mesa, Nevada that have two distinctly different microstructures and surface texture [Kirkland et al., 2001]. The limestone studied has larger grains and the grains frequently have flat, smooth surfaces on the order of 10-50 microns in cross-section length. The calcrete has smaller, more angular calcite grains, which exhibit almost no flat surfaces longer than 5 microns in cross-section length. We will show scanning electron microscope images to compare the different microstructures and surface textures of both the fresh and weathered surfaces, and we will show corresponding thermal infrared spectra to illustrate the different spectral signatures. The results demonstrate the importance of understanding the microstructure of mineral deposits to accurately interpret infrared remote sensing data, especially for studies that lack ground

Endoscopic hyperspectral imaging: light guide optimization for spectral light source

Science.gov (United States)

Browning, Craig M.; Mayes, Samuel; Rich, Thomas C.; Leavesley, Silas J.

2018-02-01

Hyperspectral imaging (HSI) is a technology used in remote sensing, food processing and documentation recovery. Recently, this approach has been applied in the medical field to spectrally interrogate regions of interest within respective substrates. In spectral imaging, a two (spatial) dimensional image is collected, at many different (spectral) wavelengths, to sample spectral signatures from different regions and/or components within a sample. Here, we report on the use of hyperspectral imaging for endoscopic applications. Colorectal cancer is the 3rd leading cancer for incidences and deaths in the US. One factor of severity is the miss rate of precancerous/flat lesions ( 65% accuracy). Integrating HSI into colonoscopy procedures could minimize misdiagnosis and unnecessary resections. We have previously reported a working prototype light source with 16 high-powered light emitting diodes (LEDs) capable of high speed cycling and imaging. In recent testing, we have found our current prototype is limited by transmission loss ( 99%) through the multi-furcated solid light guide (lightpipe) and the desired framerate (20-30 fps) could not be achieved. Here, we report on a series of experimental and modeling studies to better optimize the lightpipe and the spectral endoscopy system as a whole. The lightpipe was experimentally evaluated using an integrating sphere and spectrometer (Ocean Optics). Modeling the lightpipe was performed using Monte Carlo optical ray tracing in TracePro (Lambda Research Corp.). Results of these optimization studies will aid in manufacturing a revised prototype with the newly designed light guide and increased sensitivity. Once the desired optical output (5-10 mW) is achieved then the HIS endoscope system will be able to be implemented without adding onto the procedure time.

High temperature spectral emissivity measurement using integral blackbody method

Science.gov (United States)

Pan, Yijie; Dong, Wei; Lin, Hong; Yuan, Zundong; Bloembergen, Pieter

2016-10-01

Spectral emissivity is a critical material's thermos-physical property for heat design and radiation thermometry. A prototype instrument based upon an integral blackbody method was developed to measure material's spectral emissivity above 1000 °. The system was implemented with an optimized commercial variable-high-temperature blackbody, a high speed linear actuator, a linear pyrometer, and an in-house designed synchronization circuit. A sample was placed in a crucible at the bottom of the blackbody furnace, by which the sample and the tube formed a simulated blackbody which had an effective total emissivity greater than 0.985. During the measurement, the sample was pushed to the end opening of the tube by a graphite rod which was actuated through a pneumatic cylinder. A linear pyrometer was used to monitor the brightness temperature of the sample surface through the measurement. The corresponding opto-converted voltage signal was fed and recorded by a digital multi-meter. A physical model was proposed to numerically evaluate the temperature drop along the process. Tube was discretized as several isothermal cylindrical rings, and the temperature profile of the tube was measurement. View factors between sample and rings were calculated and updated along the whole pushing process. The actual surface temperature of the sample at the end opening was obtained. Taking advantages of the above measured voltage profile and the calculated true temperature, spectral emissivity under this temperature point was calculated.

[Remote results of high myopia surgical correction by tunnel keratoplasty ].

Science.gov (United States)

Dushin, N V; Beliaev, V S; Gonchar, P A; Barashkov, V I; Kravchinina, V V; Frolov, M A

2000-01-01

Remote results evidence high refraction efficiency of tunnel keratoplasty, stable results being observed for up to 15 years. A total of 104 operations (58 patients) were analyzed for a period of observation of more than 10 years. The patients' ages varied from 17 to 52 years, there were 34 women and 24 men. The main advantage of interlamellar refraction meridional keratoplasty is easiness of operation. At present it is the operation of choice for dosed reduction of eye refraction aimed at correction of high myopia and astigmatism. The possibility of correcting residual myopia after keratotomy and repair of refraction abnormalities resultant from perforating keratoplasty is particularly interesting. The possibility of regulating the corrective effect in remote periods by replacing the implants also deserves attention. Hence, low traumatism, high efficiency, and stability of the refraction effect once more confirm our recommendation to use tunnel keratoplasty in clinical practice.

Remote Sensing of Parasitic Nematodes in Plants

Science.gov (United States)

Lawrence, Gary W.; King, Roger; Kelley, Amber T.; Vickery, John

2007-01-01

A method and apparatus for remote sensing of parasitic nematodes in plants, now undergoing development, is based on measurement of visible and infrared spectral reflectances of fields where the plants are growing. Initial development efforts have been concentrated on detecting reniform nematodes (Rotylenchulus reniformis) in cotton plants, because of the economic importance of cotton crops. The apparatus includes a hand-held spectroradiometer. The readings taken by the radiometer are processed to extract spectral reflectances at sixteen wavelengths between 451 and 949 nm that, taken together, have been found to be indicative of the presence of Rotylenchulus reniformis. The intensities of the spectral reflectances are used to estimate the population density of the nematodes in an area from which readings were taken.

Mapping SOC in a river catchment by integrating laboratory spectra wavelength with remote sensing spectra

DEFF Research Database (Denmark)

Peng, Yi; Xiong, Xiong; Knadel, Maria

There is potential to use soil ·-proximal and remote sensing derived spectra concomitantly to develop soil organic carbon (SOC) models. Yet mixing spectral data from different sources and technologies to improve soil models is still in its infancy. The objective of this study was to incorporate...... soil spectral features indicative of SOC from laboratory visible near-infrared reflectance (vis-NlR) spectra and incorporate them with remote sensing (RS) images to improve predictions of top SOC in the Skjem river catchment, Denmark. The secondary objective was to improve prediction results...

Remote sensing for non-renewable resources - Satellite and airborne multiband scanners for mineral exploration

Science.gov (United States)

Goetz, Alexander F. H.

1986-01-01

The application of remote sensing techniques to mineral exploration involves the use of both spatial (morphological) as well as spectral information. This paper is directed toward a discussion of the uses of spectral image information and emphasizes the newest airborne and spaceborne sensor developments involving imaging spectrometers.

Assessing and monitoring of urban vegetation using multiple endmember spectral mixture analysis

Science.gov (United States)

Zoran, M. A.; Savastru, R. S.; Savastru, D. M.

2013-08-01

During last years urban vegetation with significant health, biological and economical values had experienced dramatic changes due to urbanization and human activities in the metropolitan area of Bucharest in Romania. We investigated the utility of remote sensing approaches of multiple endmember spectral mixture analysis (MESMA) applied to IKONOS and Landsat TM/ETM satellite data for estimating fractional cover of urban/periurban forest, parks, agricultural vegetation areas. Because of the spectral heterogeneity of same physical features of urban vegetation increases with the increase of image resolution, the traditional spectral information-based statistical method may not be useful to classify land cover dynamics from high resolution imageries like IKONOS. So we used hierarchy tree classification method in classification and MESMA for vegetation land cover dynamics assessment based on available IKONOS high-resolution imagery of Bucharest town. This study employs thirty two endmembers and six hundred and sixty spectral models to identify all Earth's features (vegetation, water, soil, impervious) and shade in the Bucharest area. The mean RMS error for the selected vegetation land cover classes range from 0.0027 to 0.018. The Pearson correlation between the fraction outputs from MESMA and reference data from all IKONOS images 1m panchromatic resolution data for urban/periurban vegetation were ranging in the domain 0.7048 - 0.8287. The framework in this study can be applied to other urban vegetation areas in Romania.

Deep learning decision fusion for the classification of urban remote sensing data

Science.gov (United States)

Abdi, Ghasem; Samadzadegan, Farhad; Reinartz, Peter

2018-01-01

Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral-spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

Utility of BRDF Models for Estimating Optimal View Angles in Classification of Remotely Sensed Images

Science.gov (United States)

Valdez, P. F.; Donohoe, G. W.

1997-01-01

Statistical classification of remotely sensed images attempts to discriminate between surface cover types on the basis of the spectral response recorded by a sensor. It is well known that surfaces reflect incident radiation as a function of wavelength producing a spectral signature specific to the material under investigation. Multispectral and hyperspectral sensors sample the spectral response over tens and even hundreds of wavelength bands to capture the variation of spectral response with wavelength. Classification algorithms then exploit these differences in spectral response to distinguish between materials of interest. Sensors of this type, however, collect detailed spectral information from one direction (usually nadir); consequently, do not consider the directional nature of reflectance potentially detectable at different sensor view angles. Improvements in sensor technology have resulted in remote sensing platforms capable of detecting reflected energy across wavelengths (spectral signatures) and from multiple view angles (angular signatures) in the fore and aft directions. Sensors of this type include: the moderate resolution imaging spectroradiometer (MODIS), the multiangle imaging spectroradiometer (MISR), and the airborne solid-state array spectroradiometer (ASAS). A goal of this paper, then, is to explore the utility of Bidirectional Reflectance Distribution Function (BRDF) models in the selection of optimal view angles for the classification of remotely sensed images by employing a strategy of searching for the maximum difference between surface BRDFs. After a brief discussion of directional reflect ante in Section 2, attention is directed to the Beard-Maxwell BRDF model and its use in predicting the bidirectional reflectance of a surface. The selection of optimal viewing angles is addressed in Section 3, followed by conclusions and future work in Section 4.

From spectral holeburning memory to spatial-spectral microwave signal processing

International Nuclear Information System (INIS)

Babbitt, Wm Randall; Barber, Zeb W; Harrington, Calvin; Mohan, R Krishna; Sharpe, Tia; Bekker, Scott H; Chase, Michael D; Merkel, Kristian D; Stiffler, Colton R; Traxinger, Aaron S; Woidtke, Alex J

2014-01-01

Many storage and processing systems based on spectral holeburning have been proposed that access the broad bandwidth and high dynamic range of spatial-spectral materials, but only recently have practical systems been developed that exceed the performance and functional capabilities of electronic devices. This paper reviews the history of the proposed applications of spectral holeburning and spatial-spectral materials, from frequency domain optical memory to microwave photonic signal processing systems. The recent results of a 20 GHz bandwidth high performance spectrum monitoring system with the additional capability of broadband direction finding demonstrates the potential for spatial-spectral systems to be the practical choice for solving demanding signal processing problems in the near future. (paper)

SPAM- SPECTRAL ANALYSIS MANAGER (DEC VAX/VMS VERSION)

Science.gov (United States)

Solomon, J. E.

1994-01-01

The Spectral Analysis Manager (SPAM) was developed to allow easy qualitative analysis of multi-dimensional imaging spectrometer data. Imaging spectrometers provide sufficient spectral sampling to define unique spectral signatures on a per pixel basis. Thus direct material identification becomes possible for geologic studies. SPAM provides a variety of capabilities for carrying out interactive analysis of the massive and complex datasets associated with multispectral remote sensing observations. In addition to normal image processing functions, SPAM provides multiple levels of on-line help, a flexible command interpretation, graceful error recovery, and a program structure which can be implemented in a variety of environments. SPAM was designed to be visually oriented and user friendly with the liberal employment of graphics for rapid and efficient exploratory analysis of imaging spectrometry data. SPAM provides functions to enable arithmetic manipulations of the data, such as normalization, linear mixing, band ratio discrimination, and low-pass filtering. SPAM can be used to examine the spectra of an individual pixel or the average spectra over a number of pixels. SPAM also supports image segmentation, fast spectral signature matching, spectral library usage, mixture analysis, and feature extraction. High speed spectral signature matching is performed by using a binary spectral encoding algorithm to separate and identify mineral components present in the scene. The same binary encoding allows automatic spectral clustering. Spectral data may be entered from a digitizing tablet, stored in a user library, compared to the master library containing mineral standards, and then displayed as a timesequence spectral movie. The output plots, histograms, and stretched histograms produced by SPAM can be sent to a lineprinter, stored as separate RGB disk files, or sent to a Quick Color Recorder. SPAM is written in C for interactive execution and is available for two different

Assessing Cd-induced stress from plant spectral response

Science.gov (United States)

Kancheva, Rumiana; Georgiev, Georgi

2014-10-01

Remote sensing plays a significant role in local, regional and global monitoring of land covers. Ecological concerns worldwide determine the importance of remote sensing applications for the assessment of soil conditions, vegetation health and identification of stress-induced changes. The extensive industrial growth and intensive agricultural land-use arise the serious ecological problem of environmental pollution associated with the increasing anthropogenic pressure on the environment. Soil contamination is a reason for degradation processes and temporary or permanent decrease of the productive capacity of land. Heavy metals are among the most dangerous pollutants because of their toxicity, persistent nature, easy up-take by plants and long biological half-life. This paper takes as its focus the study of crop species spectral response to Cd pollution. Ground-based experiments were performed, using alfalfa, spring barley and pea grown in Cd contaminated soils and in different hydroponic systems under varying concentrations of the heavy metal. Cd toxicity manifested itself by inhibition of plant growth and synthesis of photosynthetic pigments. Multispectral reflectance, absorbance and transmittance, as well as red and far red fluorescence were measured and examined for their suitability to detect differences in plant condition. Statistical analysis was performed and empirical relationships were established between Cd concentration, plant growth variables and spectral response Various spectral properties proved to be indicators of plant performance and quantitative estimators of the degree of the Cd-induced stress.

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...

The remote atmospheric and ionospheric detection system

International Nuclear Information System (INIS)

McCoy, R.P.; Wolfram, K.D.; Meier, R.R.

1986-01-01

The Remote Atmospheric and Ionospheric Detection System (RAIDS) experiment, to fly on a TIROS spacecraft in the late 1980's, consists of a comprehensive set of one limb imaging and seven limb scanning optical sensors. These eight instruments span the spectral range from the extreme ultraviolet to the near infrared, allowing simultaneous observations of the neutral and ion composition on the day and night side as well as in the auroral region. The primary objective of RAIDS is to demonstrate a system for remote sensing of the ionosphere to produce global maps of the electron density, peak altitude and critical frequency

Robotics and remote handling concepts for disposal of high-level nuclear waste

International Nuclear Information System (INIS)

McAffee, Douglas; Raczka, Norman; Schwartztrauber, Keith

1997-01-01

This paper summarizes preliminary remote handling and robotic concepts being developed as part of the US Department of Energy's (DOE) Yucca Mountain Project. The DOE is currently evaluating the Yucca Mountain Nevada site for suitability as a possible underground geologic repository for the disposal of high level nuclear waste. The current advanced conceptual design calls for the disposal of more than 12,000 high level nuclear waste packages within a 225 km underground network of tunnels and emplacement drifts. Many of the waste packages may weigh as much as 66 tonnes and measure 1.8 m in diameter and 5.6 m long. The waste packages will emit significant levels of radiation and heat. Therefore, remote handling is a cornerstone of the repository design and operating concepts. This paper discusses potential applications areas for robotics and remote handling technologies within the subsurface repository. It also summarizes the findings of a preliminary technology survey which reviewed available robotic and remote handling technologies developed within the nuclear, mining, rail and industrial robotics and automation industries, and at national laboratories, universities, and related research institutions and government agencies

SPECTRAL FILTRATION OF IMAGES BY MEANS OF DISPERSIVE SYSTEMS

Directory of Open Access Journals (Sweden)

I. M. Gulis

2016-01-01

Full Text Available Instruments for spectral filtration of images are an important element of the systems used in remote sensing, medical diagnostics, in-process measurements. The aim of this study is analysis of the functional features and characteristics of the proposed two image monochromator versions which are based on dispersive spectral filtering. The first is based on the use of a dispersive monochromator, where collimating and camera lenses form a telescopic system, the dispersive element of which is within the intermediate image plane. The second version is based on an imaging double monochromator with dispersion subtraction by back propagation. For the telescopic system version, the spectral and spatial resolutions are estimated, the latter being limited by aberrations and diffraction from the entrance slit. The device has been numerically simulated and prototyped. It is shown that for the spectral bandwidth 10 nm (visible spectral range, the aberration-limited spot size is from 10–20 μm at the image center to about 30 μm at the image periphery for the image size 23–27 mm. The monochromator with dispersion subtraction enables one to vary the spectral resolution (up to 1 nm and higher by changing the intermediate slit width. But the distinctive feature is a significant change in the selected central wavelength over the image field. The considered designs of dispersive image monochromators look very promising due to the particular advantages over the systems based on tunable filters as regards the spectral resolution, fast tuning, and the spectral contrast. The monochromator based on a telescopic system has a simple design and a rather large image field but it also has a limited light throughput due to small aperture size. The monochromator with dispersion subtraction has higher light throughput, can provide high spectral resolution when recording a full data cube in a series of measuring acts for different dispersive element positions. 

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.

Spectral Band Characterization for Hyperspectral Monitoring of Water Quality

Science.gov (United States)

Vermillion, Stephanie C.; Raqueno, Rolando; Simmons, Rulon

2001-01-01

A method for selecting the set of spectral characteristics that provides the smallest increase in prediction error is of interest to those using hyperspectral imaging (HSI) to monitor water quality. The spectral characteristics of interest to these applications are spectral bandwidth and location. Three water quality constituents of interest that are detectable via remote sensing are chlorophyll (CHL), total suspended solids (TSS), and colored dissolved organic matter (CDOM). Hyperspectral data provides a rich source of information regarding the content and composition of these materials, but often provides more data than an analyst can manage. This study addresses the spectral characteristics need for water quality monitoring for two reasons. First, determination of the greatest contribution of these spectral characteristics would greatly improve computational ease and efficiency. Second, understanding the spectral capabilities of different spectral resolutions and specific regions is an essential part of future system development and characterization. As new systems are developed and tested, water quality managers will be asked to determine sensor specifications that provide the most accurate and efficient water quality measurements. We address these issues using data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and a set of models to predict constituent concentrations.

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

Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery

Science.gov (United States)

Chen, Gang; Metz, Margaret R.; Rizzo, David M.; Dillon, Whalen W.; Meentemeyer, Ross K.

2015-04-01

Forest ecosystems are subject to a variety of disturbances with increasing intensities and frequencies, which may permanently change the trajectories of forest recovery and disrupt the ecosystem services provided by trees. Fire and invasive species, especially exotic disease-causing pathogens and insects, are examples of disturbances that together could pose major threats to forest health. This study examines the impacts of fire and exotic disease (sudden oak death) on forests, with an emphasis on the assessment of post-fire burn severity in a forest where trees have experienced three stages of disease progression pre-fire: early-stage (trees retaining dried foliage and fine twigs), middle-stage (trees losing fine crown fuels), and late-stage (trees falling down). The research was conducted by applying Geographic Object-Based Image Analysis (GEOBIA) to MASTER airborne images that were acquired immediately following the fire for rapid assessment and contained both high-spatial (4 m) and high-spectral (50 bands) resolutions. Although GEOBIA has gradually become a standard tool for analyzing high-spatial resolution imagery, high-spectral resolution data (dozens to hundreds of bands) can dramatically reduce computation efficiency in the process of segmentation and object-based variable extraction, leading to complicated variable selection for succeeding modeling. Hence, we also assessed two widely used band reduction algorithms, PCA (principal component analysis) and MNF (minimum noise fraction), for the delineation of image objects and the subsequent performance of burn severity models using either PCA or MNF derived variables. To increase computation efficiency, only the top 5 PCA and MNF and top 10 PCA and MNF components were evaluated, which accounted for 10% and 20% of the total number of the original 50 spectral bands, respectively. Results show that if no band reduction was applied the models developed for the three stages of disease progression had relatively

USGS Spectral Library Version 7

Science.gov (United States)

Kokaly, Raymond F.; Clark, Roger N.; Swayze, Gregg A.; Livo, K. Eric; Hoefen, Todd M.; Pearson, Neil C.; Wise, Richard A.; Benzel, William M.; Lowers, Heather A.; Driscoll, Rhonda L.; Klein, Anna J.

2017-04-10

We have assembled a library of spectra measured with laboratory, field, and airborne spectrometers. The instruments used cover wavelengths from the ultraviolet to the far infrared (0.2 to 200 microns [μm]). Laboratory samples of specific minerals, plants, chemical compounds, and manmade materials were measured. In many cases, samples were purified, so that unique spectral features of a material can be related to its chemical structure. These spectro-chemical links are important for interpreting remotely sensed data collected in the field or from an aircraft or spacecraft. This library also contains physically constructed as well as mathematically computed mixtures. Four different spectrometer types were used to measure spectra in the library: (1) Beckman™ 5270 covering the spectral range 0.2 to 3 µm, (2) standard, high resolution (hi-res), and high-resolution Next Generation (hi-resNG) models of Analytical Spectral Devices (ASD) field portable spectrometers covering the range from 0.35 to 2.5 µm, (3) Nicolet™ Fourier Transform Infra-Red (FTIR) interferometer spectrometers covering the range from about 1.12 to 216 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.37 to 2.5 µm. Measurements of rocks, soils, and natural mixtures of minerals were made in laboratory and field settings. Spectra of plant components and vegetation plots, comprising many plant types and species with varying backgrounds, are also in this library. Measurements by airborne spectrometers are included for forested vegetation plots, in which the trees are too tall for measurement by a field spectrometer. This report describes the instruments used, the organization of materials into chapters, metadata descriptions of spectra and samples, and possible artifacts in the spectral measurements. To facilitate greater application of the spectra, the library has also been convolved to selected spectrometer and imaging spectrometers sampling and

Determination of Primary Bands for Global Ocean-Color Remote Sensing

National Research Council Canada - National Science Library

Lee, ZhongPing; Arnone, Robert; Carder, Kendall; He, MingXia

2007-01-01

...) from remote sensing of its color, a sensor with roughly 17 spectral bands in the 400 - 800 nm range can provide acceptable results compared to a sensor with 81 consecutive bands (in a 5-nm step...

New experimental device for high-temperature normal spectral emissivity measurements of coatings

International Nuclear Information System (INIS)

Honnerová, Petra; Martan, Jiří; Kučera, Martin; Honner, Milan; Hameury, Jacques

2014-01-01

A new experimental device for normal spectral emissivity measurements of coatings in the infrared spectral range from 1.38 μm to 26 μm and in the temperature range from 550 K to 1250 K is presented. A Fourier transform infrared spectrometer (FTIR) is used for the detection of sample and blackbody spectral radiation. Sample heating is achieved by a fiber laser with a scanning head. Surface temperature is measured by two methods. The first method uses an infrared camera and a reference coating with known effective emissivity, the second method is based on the combination of Christiansen wavelength with contact and noncontact surface temperature measurement. Application of the method is shown on the example of a high-temperature high-emissivity coating. Experimental results obtained with this apparatus are compared with the results performed by a direct method of Laboratoire National d’Essais (LNE) in France. The differences in the spectra are analyzed. (paper)

[Estimation of Hunan forest carbon density based on spectral mixture analysis of MODIS data].

Science.gov (United States)

Yan, En-ping; Lin, Hui; Wang, Guang-xing; Chen, Zhen-xiong

2015-11-01

With the fast development of remote sensing technology, combining forest inventory sample plot data and remotely sensed images has become a widely used method to map forest carbon density. However, the existence of mixed pixels often impedes the improvement of forest carbon density mapping, especially when low spatial resolution images such as MODIS are used. In this study, MODIS images and national forest inventory sample plot data were used to conduct the study of estimation for forest carbon density. Linear spectral mixture analysis with and without constraint, and nonlinear spectral mixture analysis were compared to derive the fractions of different land use and land cover (LULC) types. Then sequential Gaussian co-simulation algorithm with and without the fraction images from spectral mixture analyses were employed to estimate forest carbon density of Hunan Province. Results showed that 1) Linear spectral mixture analysis with constraint, leading to a mean RMSE of 0.002, more accurately estimated the fractions of LULC types than linear spectral and nonlinear spectral mixture analyses; 2) Integrating spectral mixture analysis model and sequential Gaussian co-simulation algorithm increased the estimation accuracy of forest carbon density to 81.5% from 74.1%, and decreased the RMSE to 5.18 from 7.26; and 3) The mean value of forest carbon density for the province was 30.06 t · hm(-2), ranging from 0.00 to 67.35 t · hm(-2). This implied that the spectral mixture analysis provided a great potential to increase the estimation accuracy of forest carbon density on regional and global level.

Remote sensing, airborne radiometric survey and aeromagnetic survey data processing and analysis

International Nuclear Information System (INIS)

Dong Xiuzhen; Liu Dechang; Ye Fawang; Xuan Yanxiu

2009-01-01

Taking remote sensing data, airborne radiometric data and aero magnetic survey data as an example, the authors elaborate about basic thinking of remote sensing data processing methods, spectral feature analysis and adopted processing methods, also explore the remote sensing data combining with the processing of airborne radiometric survey and aero magnetic survey data, and analyze geological significance of processed image. It is not only useful for geological environment research and uranium prospecting in the study area, but also reference to applications in another area. (authors)

[The design and implementation of the web typical surface object spectral information system in arid areas based on .NET and SuperMap].

Science.gov (United States)

Xia, Jun; Tashpolat, Tiyip; Zhang, Fei; Ji, Hong-jiang

2011-07-01

The characteristic of object spectrum is not only the base of the quantification analysis of remote sensing, but also the main content of the basic research of remote sensing. The typical surface object spectral database in arid areas oasis is of great significance for applied research on remote sensing in soil salinization. In the present paper, the authors took the Ugan-Kuqa River Delta Oasis as an example, unified .NET and the SuperMap platform with SQL Server database stored data, used the B/S pattern and the C# language to design and develop the typical surface object spectral information system, and established the typical surface object spectral database according to the characteristics of arid areas oasis. The system implemented the classified storage and the management of typical surface object spectral information and the related attribute data of the study areas; this system also implemented visualized two-way query between the maps and attribute data, the drawings of the surface object spectral response curves and the processing of the derivative spectral data and its drawings. In addition, the system initially possessed a simple spectral data mining and analysis capabilities, and this advantage provided an efficient, reliable and convenient data management and application platform for the Ugan-Kuqa River Delta Oasis's follow-up study in soil salinization. Finally, It's easy to maintain, convinient for secondary development and practically operating in good condition.

Assessing leaf spectral properties of Phragmites australis impacted by acid mine drainage

CSIR Research Space (South Africa)

Van Deventer, Heidi

2014-07-01

Full Text Available decrease in the NIR24, and similarly the radionuclides Cs and Sr at Chernobyl were highly negatively correlated to the REP, green and NIR regions26. An opposite trend was observed in plants exposed to Cd24, Pb27, a combination of heavy metals (As, Cd, Cr.... Davids C, Tyler AN. Detecting contamination-induced tree stress within the Chernobyl exclusion zone. Remote Sens Environ. 2003;85(1):30–38. http:// dx.doi.org/10.1016/S0034-4257(02)00184-0 Research Article Assessing leaf spectral properties of Phragmites...

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.

[Application of hyperspectral remote sensing in research on ecological boundary in north farming-pasturing transition in China].

Science.gov (United States)

Wang, Hong-Mei; Wang, Kun; Xie, Ying-Zhong

2009-06-01

Studies of ecological boundaries are important and have become a rapidly evolving part of contemporary ecology. The ecotones are dynamic and play several functional roles in ecosystem dynamics, and the changes in their locations can be used as an indicator of environment changes, and for these reasons, ecotones have recently become a focus of investigation of landscape ecology and global climate change. As the interest in ecotone increases, there is an increased need for formal techniques to detect it. Hence, to better study and understand the functional roles and dynamics of ecotones in ecosystem, we need quantitative methods to characterize them. In the semi-arid region of northern China, there exists a farming-pasturing transition resulting from grassland reclamation and deforestation. With the fragmentation of grassland landscape, the structure and function of the grassland ecosystem are changing. Given this perspective; new-image processing approaches are needed to focus on transition themselves. Hyperspectral remote sensing data, compared with wide-band remote sensing data, has the advantage of high spectral resolution. Hyperspectral remote sensing can be used to visualize transitional zones and to detect ecotone based on surface properties (e. g. vegetation, soil type, and soil moisture etc). In this paper, the methods of hyperspectral remote sensing information processing, spectral analysis and its application in detecting the vegetation classifications, vegetation growth state, estimating the canopy biochemical characteristics, soil moisture, soil organic matter etc are reviewed in detail. Finally the paper involves further application of hyperspectral remote sensing information in research on local climate in ecological boundary in north farming-pasturing transition in China.

Remote systems requirements of the High Yield Lithium Injection Fusion Energy (HYLIFE) converter concept

International Nuclear Information System (INIS)

Walker, P.E.

1978-10-01

Remote systems will be required in the High Yield Lithium Injection Fusion Energy Converter power plant proposed by Lawrence Livermore Laboratory. During inspection operations, viewing of the chamber interior and certain pumps, valve fittings and welds must be done remotely. Ideas for remote maintenance of laser beam blast baffles, optics, and target material traps are described. Radioisotope sources and their distributions, and exposure rates at various points in the reactor vicinity are presented

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

Spatial and spectral coherence in propagating high-intensity twin beams

Czech Academy of Sciences Publication Activity Database

Haderka, O.; Machulka, R.; Peřina ml., Jan; Allevi, A.; Bondani, M.

2015-01-01

Roč. 5, Sep (2015), s. 14365 ISSN 2045-2322 R&D Projects: GA ČR GAP205/12/0382 Institutional support: RVO:68378271 Keywords : spatial and spectral coherence * high-intensity twin beams Subject RIV: BH - Optics, Masers, Lasers Impact factor: 5.228, year: 2015

Spectrally-Corrected Estimation for High-Dimensional Markowitz Mean-Variance Optimization

NARCIS (Netherlands)

Z. Bai (Zhidong); H. Li (Hua); M.J. McAleer (Michael); W.-K. Wong (Wing-Keung)

2016-01-01

textabstractThis paper considers the portfolio problem for high dimensional data when the dimension and size are both large. We analyze the traditional Markowitz mean-variance (MV) portfolio by large dimension matrix theory, and find the spectral distribution of the sample covariance is the main

Spectral beam combining of diode lasers with high efficiency

DEFF Research Database (Denmark)

Müller, André; Vijayakumar, Deepak; Jensen, Ole Bjarlin

2012-01-01

Based on spectral beam combining we obtain 16 W of output power, combining two 1063 nm DBR-tapered diode lasers. The spectral separation within the combined beam can be used for subsequent sum-frequency generation.......Based on spectral beam combining we obtain 16 W of output power, combining two 1063 nm DBR-tapered diode lasers. The spectral separation within the combined beam can be used for subsequent sum-frequency generation....

A theoretical-experimental methodology for assessing the sensitivity of biomedical spectral imaging platforms, assays, and analysis methods.

Science.gov (United States)

Leavesley, Silas J; Sweat, Brenner; Abbott, Caitlyn; Favreau, Peter; Rich, Thomas C

2018-01-01

Spectral imaging technologies have been used for many years by the remote sensing community. More recently, these approaches have been applied to biomedical problems, where they have shown great promise. However, biomedical spectral imaging has been complicated by the high variance of biological data and the reduced ability to construct test scenarios with fixed ground truths. Hence, it has been difficult to objectively assess and compare biomedical spectral imaging assays and technologies. Here, we present a standardized methodology that allows assessment of the performance of biomedical spectral imaging equipment, assays, and analysis algorithms. This methodology incorporates real experimental data and a theoretical sensitivity analysis, preserving the variability present in biomedical image data. We demonstrate that this approach can be applied in several ways: to compare the effectiveness of spectral analysis algorithms, to compare the response of different imaging platforms, and to assess the level of target signature required to achieve a desired performance. Results indicate that it is possible to compare even very different hardware platforms using this methodology. Future applications could include a range of optimization tasks, such as maximizing detection sensitivity or acquisition speed, providing high utility for investigators ranging from design engineers to biomedical scientists. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

Directory of Open Access Journals (Sweden)

Fengchen Huang

2012-01-01

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

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

Science.gov (United States)

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

2012-01-01

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

Using GIS servers and interactive maps in spectral data sharing and administration: Case study of Ahvaz Spectral Geodatabase Platform (ASGP)

Science.gov (United States)

Karami, Mojtaba; Rangzan, Kazem; Saberi, Azim

2013-10-01

With emergence of air-borne and space-borne hyperspectral sensors, spectroscopic measurements are gaining more importance in remote sensing. Therefore, the number of available spectral reference data is constantly increasing. This rapid increase often exhibits a poor data management, which leads to ultimate isolation of data on disk storages. Spectral data without precise description of the target, methods, environment, and sampling geometry cannot be used by other researchers. Moreover, existing spectral data (in case it accompanied with good documentation) become virtually invisible or unreachable for researchers. Providing documentation and a data-sharing framework for spectral data, in which researchers are able to search for or share spectral data and documentation, would definitely improve the data lifetime. Relational Database Management Systems (RDBMS) are main candidates for spectral data management and their efficiency is proven by many studies and applications to date. In this study, a new approach to spectral data administration is presented based on spatial identity of spectral samples. This method benefits from scalability and performance of RDBMS for storage of spectral data, but uses GIS servers to provide users with interactive maps as an interface to the system. The spectral files, photographs and descriptive data are considered as belongings of a geospatial object. A spectral processing unit is responsible for evaluation of metadata quality and performing routine spectral processing tasks for newly-added data. As a result, by using internet browser software the users would be able to visually examine availability of data and/or search for data based on descriptive attributes associated to it. The proposed system is scalable and besides giving the users good sense of what data are available in the database, it facilitates participation of spectral reference data in producing geoinformation.

Assessing the potential of remote sensing to discriminate invasive ...

African Journals Online (AJOL)

The usefulness of remote sensing to discriminate Seriphium plumosum from grass using a field spectrometer data was investigated in this study. Analysis focused on wavelength regions that showed potential of discriminating S. plumosum from grass which were determined from global pair spectral comparison between S.

Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest

Science.gov (United States)

Chen, Xuexia; Vogelmann, James E.; Rollins, Matt; Ohlen, Donald; Key, Carl H.; Yang, Limin; Huang, Chengquan; Shi, Hua

2011-01-01

It is challenging to detect burn severity and vegetation recovery because of the relatively long time period required to capture the ecosystem characteristics. Multitemporal remote sensing data can providemultitemporal observations before, during and after a wildfire, and can improve the change detection accuracy. The goal of this study is to examine the correlations between multitemporal spectral indices and field-observed burn severity, and to provide a practical method to estimate burn severity and vegetation recovery. The study site is the Jasper Fire area in the Black Hills National Forest, South Dakota, that burned during August and September 2000. Six multitemporal Landsat images acquired from 2000 (pre-fire), 2001 (post-fire), 2002, 2003, 2005 and 2007 were used to assess burn severity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized burn ratio (NBR), integrated forest index (IFI) and the differences of these indices between the pre-fire and post-fire years were computed and analysed with 66 field-based composite burn index (CBI) plots collected in 2002. Results showed that differences of NDVI and differences of EVI between the pre-fire year and the first two years post-fire were highly correlated with the CBI scores. The correlations were low beyond the second year post-fire. Differences of NBR had good correlation with CBI scores in all study years. Differences of IFI had low correlation with CBI in the first year post-fire and had good correlation in later years. A CBI map of the burnt area was produced using regression tree models and the multitemporal images. The dynamics of four spectral indices from 2000 to 2007 indicated that both NBR and IFI are valuable for monitoring long-term vegetation recovery. The high burn severity areas had a much slower recovery than the moderate and low burn areas.

Gamma-Ray Imager With High Spatial And Spectral Resolution

Science.gov (United States)

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

1996-01-01

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

Spectral broadening of acoustic tones generated by unmanned aerial vehicles in a turbulent atmosphere

DEFF Research Database (Denmark)

Ostashev, Vladimir E.; Wilson, D. K.; Finn, Anthony

2016-01-01

The acoustic spectrum emitted by unmanned aerial vehicles (UAVs) and other aircraft can be distorted by propagation through atmospheric turbulence. Since most UAVs are propeller-based, they generate a series of acoustic tones and harmonics. In this paper, spectral broadening of these tones due......, spectral broadening is calculated and analyzed for typical meteorological regimes of the atmospheric boundary layer and different flight trajectories of UAVs. Experimental results are presented and compared with theoretical predictions. Spectral broadening might also provide a means for remotely sensing...

Remote sensing reflectance of Pomeranian lakes and the Baltic

Directory of Open Access Journals (Sweden)

Dariusz Ficek

2011-11-01

Full Text Available The remote sensing reflectance Rrs, concentrations of chlorophyll a and other pigments Ci, suspended particulate matter concentrations CSPM and coloured dissolved organic matter absorption coefficient aCDOM(λ were measured in the euphotic zones of 15 Pomeranian lakes in 2007-2010. On the basis of 235 sets of data points obtained from simultaneous estimates of these quantities, we classified the lake waters into three types. The first one, with the lowest aCDOM(440 nm (usually between 0.1 and 1.3 m-1 and chlorophyll a concentrations 1.3 10 m-1, up to 17.4 m-1 in Lake Pyszne; it has a relatively low reflectance (Rrs 4 mg m-3, up to 336 mg m-3 in Lake Gardno. The remote sensing reflectance spectra in these waters always exhibit three peaks (Rrs > 0.005 sr-1: a broad one at 560-580 nm, a smaller one at ca 650 nm and a well-pronounced one at 690-720 nm. These Rrs(λ peaks correspond to the relatively low absorption of light by the various optically active components of the lake water and the considerable scattering (over the entire spectral range investigated due to the high SPM concentrations there. The remote sensing maximum at λ ≈ 690-720 nm is higher still as a result of the natural fluorescence of chlorophyll a. Empirical relationships between the spectral reflectance band ratios at selected wavelengths and the various optically active components for these lake waters are also established: for example, the chlorophyll a concentration in surface water layer Ca = 6.432 e4.556X, where X = [max Rrs (695 ≤ λ ≤ 720 - Rrs(λ = 670] / max Rrs (695 ≤ λ ≤ 720, and the coefficient of determination R2 = 0.95.

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.

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.

Evaluation of a New Remote Handling Design for High Throughput Annular Centrifugal Contactors

International Nuclear Information System (INIS)

Meikrantz, David H.; Garn, Troy G.; Law, Jack D.; Macaluso, Lawrence L.

2009-01-01

Advanced designs of nuclear fuel recycling plants are expected to include more ambitious goals for aqueous based separations including; higher separations efficiency, high-level waste minimization, and a greater focus on continuous processes to minimize cost and footprint. Therefore, Annular Centrifugal Contactors (ACCs) are destined to play a more important role for such future processing schemes. Previous efforts defined and characterized the performance of commercial 5 cm and 12.5 cm single-stage ACCs in a 'cold' environment. The next logical step, the design and evaluation of remote capable pilot scale ACCs in a 'hot' or radioactive environment was reported earlier. This report includes the development of remote designs for ACCs that can process the large throughput rates needed in future nuclear fuel recycling plants. Novel designs were developed for the remote interconnection of contactor units, clean-in-place and drain connections, and a new solids removal collection chamber. A three stage, 12.5 cm diameter rotor module has been constructed and evaluated for operational function and remote handling in highly radioactive environments. This design is scalable to commercial CINC ACC models from V-05 to V-20 with total throughput rates ranging from 20 to 650 liters per minute. The V-05R three stage prototype was manufactured by the commercial vendor for ACCs in the U.S., CINC mfg. It employs three standard V-05 clean-in-place (CIP) units modified for remote service and replacement via new methods of connection for solution inlets, outlets, drain and CIP. Hydraulic testing and functional checks were successfully conducted and then the prototype was evaluated for remote handling and maintenance suitability. Removal and replacement of the center position V-05R ACC unit in the three stage prototype was demonstrated using an overhead rail mounted PaR manipulator. This evaluation confirmed the efficacy of this innovative design for interconnecting and cleaning

Quantitative mapping of particulate iron in an ocean dump using remotely sensed data

Science.gov (United States)

Ohlhorst, C. W.; Bahn, G. S.

1978-01-01

A remote sensing experiment was conducted at the industrial acid waste ocean dump site located approximately 38 n mi SE of Cape Henlopen, Delaware, to see if there was a relationship between aircraft remotely sensed spectral signatures and the iron concentration measured in the plume. Results are presented which show that aircraft remotely sensed spectral data can be used to quantify and map an acid waste dump in terms of its particulate iron concentration. A single variable equation using the ratio of band 2 (440-490 nm) radiance to band 4 (540-580 nm) radiance was used to quantify the acid plume and the surrounding water. The acid waste varied in age from freshly dumped to 3 1/2 hours old. Particulate iron concentrations in the acid waste were estimated to range up to 1.1 mg/liter at the 0.46 meter depth. A classification technique was developed to remove sunglitter-affected pixels from the data set.

Remote spectrometry with optical fibers, ten years of development and prospects for on-line control

International Nuclear Information System (INIS)

Boisde, G.; Perez, J.J.

1984-09-01

This paper describes, with examples uranium and plutonium spectra, how optical fibers have raised new concepts in spectrometry, such as the internal spectral reference, instantaneous measurements on the sides of the absorption spectra, and the modelling of spectral variations. With optical fibers, original technical solutions are used for remote chemical analysis

Global Learning Spectral Archive- A new Way to deal with Unknown Urban Spectra -

Science.gov (United States)

Jilge, M.; Heiden, U.; Habermeyer, M.; Jürgens, C.

2015-12-01

Rapid urbanization processes and the need of identifying urban materials demand urban planners and the remote sensing community since years. Urban planners cannot overcome the issue of up-to-date information of urban materials due to time-intensive fieldwork. Hyperspectral remote sensing can facilitate this issue by interpreting spectral signals to provide information of occurring materials. However, the complexity of urban areas and the occurrence of diverse urban materials vary due to regional and cultural aspects as well as the size of a city, which makes identification of surface materials a challenging analysis task. For the various surface material identification approaches, spectral libraries containing pure material spectra are commonly used, which are derived from field, laboratory or the hyperspectral image itself. One of the requirements for successful image analysis is that all spectrally different surface materials are represented by the library. Currently, a universal library, applicable in every urban area worldwide and taking each spectral variability into account, is and will not be existent. In this study, the issue of unknown surface material spectra and the demand of an urban site-specific spectral library is tackled by the development of a learning spectral archive tool. Starting with an incomplete library of labelled image spectra from several German cities, surface materials of pure image pixels will be identified in a hyperspectral image based on a similarity measure (e.g. SID-SAM). Additionally, unknown image spectra of urban objects are identified based on an object- and spectral-based-rule set. The detected unknown surface material spectra are entered with additional metadata, such as regional occurrence into the existing spectral library and thus, are reusable for further studies. Our approach is suitable for pure surface material detection of urban hyperspectral images that is globally applicable by taking incompleteness into account

Remote detection of radioactive material using high-power pulsed electromagnetic radiation.

Science.gov (United States)

Kim, Dongsung; Yu, Dongho; Sawant, Ashwini; Choe, Mun Seok; Lee, Ingeun; Kim, Sung Gug; Choi, EunMi

2017-05-09

Remote detection of radioactive materials is impossible when the measurement location is far from the radioactive source such that the leakage of high-energy photons or electrons from the source cannot be measured. Current technologies are less effective in this respect because they only allow the detection at distances to which the high-energy photons or electrons can reach the detector. Here we demonstrate an experimental method for remote detection of radioactive materials by inducing plasma breakdown with the high-power pulsed electromagnetic waves. Measurements of the plasma formation time and its dispersion lead to enhanced detection sensitivity compared to the theoretically predicted one based only on the plasma on and off phenomena. We show that lower power of the incident electromagnetic wave is sufficient for plasma breakdown in atmospheric-pressure air and the elimination of the statistical distribution is possible in the presence of radioactive material.

16 W output power by high-efficient spectral beam combining of DBR-tapered diode lasers

DEFF Research Database (Denmark)

Müller, André; Vijayakumar, Deepak; Jensen, Ole Bjarlin

2011-01-01

output power achieved by spectral beam combining of two single element tapered diode lasers. Since spectral beam combining does not affect beam propagation parameters, M2-values of 1.8 (fast axis) and 3.3 (slow axis) match the M2- values of the laser with lowest spatial coherence. The principle......Up to 16 W output power has been obtained using spectral beam combining of two 1063 nm DBR-tapered diode lasers. Using a reflecting volume Bragg grating, a combining efficiency as high as 93.7% is achieved, resulting in a single beam with high spatial coherence. The result represents the highest...... of spectral beam combining used in our experiments can be expanded to combine more than two tapered diode lasers and hence it is expected that the output power may be increased even further in the future....

16 W output power by high-efficient spectral beam combining of DBR-tapered diode lasers.

Science.gov (United States)

Müller, André; Vijayakumar, Deepak; Jensen, Ole Bjarlin; Hasler, Karl-Heinz; Sumpf, Bernd; Erbert, Götz; Andersen, Peter E; Petersen, Paul Michael

2011-01-17

Up to 16 W output power has been obtained using spectral beam combining of two 1063 nm DBR-tapered diode lasers. Using a reflecting volume Bragg grating, a combining efficiency as high as 93.7% is achieved, resulting in a single beam with high spatial coherence. The result represents the highest output power achieved by spectral beam combining of two single element tapered diode lasers. Since spectral beam combining does not affect beam propagation parameters, M2-values of 1.8 (fast axis) and 3.3 (slow axis) match the M2-values of the laser with lowest spatial coherence. The principle of spectral beam combining used in our experiments can be expanded to combine more than two tapered diode lasers and hence it is expected that the output power may be increased even further in the future.

Autonomous Coral Reef Survey in Support of Remote Sensing

Directory of Open Access Journals (Sweden)

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.

Improving the analysis of biogeochemical patterns associated with internal waves in the strait of Gibraltar using remote sensing images

Science.gov (United States)

Navarro, Gabriel; Vicent, Jorge; Caballero, Isabel; Gómez-Enri, Jesús; Morris, Edward P.; Sabater, Neus; Macías, Diego; Bolado-Penagos, Marina; Gomiz, Juan Jesús; Bruno, Miguel; Caldeira, Rui; Vázquez, Águeda

2018-05-01

High Amplitude Internal Waves (HAIWs) are physical processes observed in the Strait of Gibraltar (the narrow channel between the Atlantic Ocean and the Mediterranean Sea). These internal waves are generated over the Camarinal Sill (western side of the strait) during the tidal outflow (toward the Atlantic Ocean) when critical hydraulic conditions are established. HAIWs remain over the sill for up to 4 h until the outflow slackens, being then released (mostly) towards the Mediterranean Sea. These have been previously observed using Synthetic Aperture Radar (SAR), which captures variations in surface water roughness. However, in this work we use high resolution optical remote sensing, with the aim of examining the influence of HAIWs on biogeochemical processes. We used hyperspectral images from the Hyperspectral Imager for the Coastal Ocean (HICO) and high spatial resolution (10 m) images from the MultiSpectral Instrument (MSI) onboard the Sentinel-2A satellite. This work represents the first attempt to examine the relation between internal wave generation and the water constituents of the Camarinal Sill using hyperspectral and high spatial resolution remote sensing images. This enhanced spatial and spectral resolution revealed the detailed biogeochemical patterns associated with the internal waves and suggests local enhancements of productivity associated with internal waves trains.

Pristine Survey : High-Resolution Spectral Analyses of New Metal-poor Stars

Science.gov (United States)

Venn, Kim; Starkenburg, Else; Martin, Nicolas; Kielty, Collin; Youakim, Kris; Arnetsen, Anke

2018-06-01

The Pristine survey (Starkenburg et al. 2017) is a new and very successful metal-poor star survey. Combining high-quality narrow-band CaHK CFHT/MegaCam photometry with existing broadband photometry from SDSS, then very metal-poor stars have been found as confirmed from low-resolution spectroscopy (Youakim et al. 2017). Furthermore, we have extended this survey towards the Galactic bulge in a pilot program to test the capabilities in the highly crowded and (inhomogeneously) extincted bulge (Arentsen et al. 2018). High resolution spectral follow-up analyses have been initiated at the CFHT with Espadons (Vevolution or changes in the IMF, e.g., carbon enrichment, high [alpha/Fe] ratios vs alpha-challenged stars, and details in the neutron capture element ratios. While these early studies are being carried out using classical model atmospheres and synthetic spectral fitting (Venn et al. 2017, 2018), we are also exploring the use of a neural network for the fast, efficient, and precise determination of these stellar parameters and chemical abundances (e.g., StarNet, Fabbro et al. 2018).

Remotely-Controlled Shear for Dismantling Highly Radioactive Tools In Rokkasho Vitrification Facility - 12204

Energy Technology Data Exchange (ETDEWEB)

Mitsui, Takashi; Sawa, Shusuke; Sadaki, Akira; Awano, Toshihiko [IHI Corporation, 1 Shin-Nakahara-cho, Isogo-ku, Yokohama, Kanagawa (Japan); Cole, Matt [S.A. Technology Inc, 3985 S. Lincoln Ave., Ste. 100, Loveland CO 80537 (United States); Miura, Yasuhiko; Ino, Tooru [Japan Nuclear Fuel Limited, 4-108, Aza Okitsuke, Oaza Obuchi, Rokkasho-Mura, Kamikita-gun, Aomori (Japan)

2012-07-01

A high-level liquid waste vitrification facility in the Japanese Rokkasho Reprocessing Plant (RRP) is right in the middle of hot commissioning tests toward starting operation in fall of 2012. In these tests, various tools were applied to address issues occurring in the vitrification cell. Because of these tools' unplanned placement in the cell it has been necessary to dismantle and dispose of them promptly. One of the tools requiring removal is a rod used in the glass melter to improve glass pouring. It is composed of a long rod made of Inconel 601 or 625 and has been highly contaminated. In order to dismantle these tools and to remotely put them in a designated waste basket, a custom electric shear machine was developed. It was installed in a dismantling area of the vitrification cell by remote cranes and manipulators and has been successfully operated. It can be remotely dismantled and placed in a waste basket for interim storage. This is a very good example of a successful deployment of a specialty remote tool in a hot cell environment. This paper also highlights how commissioning and operations are done in the Japanese Rokkasho Reprocessing Plant. (authors)

A Concept of Multi-Mode High Spectral Resolution Lidar Using Mach-Zehnder Interferometer

Directory of Open Access Journals (Sweden)

Jin Yoshitaka

2016-01-01

Full Text Available In this paper, we present the design of a High Spectral Resolution Lidar (HSRL using a laser that oscillates in a multi-longitudinal mode. Rayleigh and Mie scattering components are separated using a Mach-Zehnder Interferometer (MZI with the same free spectral range (FSR as the transmitted laser. The transmitted laser light is measured as a reference signal with the same MZI. By scanning the MZI periodically with a scanning range equal to the mode spacing, we can identify the maximum Mie and the maximum Rayleigh signals using the reference signal. The cross talk due to the spectral width of each laser mode can also be estimated.

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

Directory of Open Access Journals (Sweden)

Yang Chen

2018-05-01

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

A Methodology to Assess the Accuracy with which Remote Data Characterize a Specific Surface, as a Function of Full Width at Half Maximum (FWHM: Application to Three Italian Coastal Waters

Directory of Open Access Journals (Sweden)

Rosa Maria Cavalli

2014-01-01

Full Text Available This methodology assesses the accuracy with which remote data characterizes a surface, as a function of Full Width at Half Maximum (FWHM. The purpose is to identify the best remote data that improves the characterization of a surface, evaluating the number of bands in the spectral range. The first step creates an accurate dataset of remote simulated data, using in situ hyperspectral reflectances. The second step evaluates the capability of remote simulated data to characterize this surface. The spectral similarity measurements, which are obtained using classifiers, provide this capability. The third step examines the precision of this capability. The assumption is that in situ hyperspectral reflectances are considered the “real” reflectances. They are resized with the same spectral range of the remote data. The spectral similarity measurements which are obtained from “real” resized reflectances, are considered “real” measurements. Therefore, the quantity and magnitude of “errors” (i.e., differences between spectral similarity measurements obtained from “real” resized reflectances and from remote data provide the accuracy as a function of FWHM. This methodology was applied to evaluate the accuracy with which CHRIS-mode1, CHRIS-mode2, Landsat5-TM, MIVIS and PRISMA data characterize three coastal waters. Their mean values of uncertainty are 1.59%, 3.79%, 7.75%, 3.15% and 1.18%, respectively.

Unmixing of spectral components affecting AVIRIS imagery of Tampa Bay

Science.gov (United States)

Carder, Kendall L.; Lee, Z. P.; Chen, Robert F.; Davis, Curtiss O.

1993-09-01

According to Kirk's as well as Morel and Gentili's Monte Carlo simulations, the popular simple expression, R approximately equals 0.33 bb/a, relating subsurface irradiance reflectance (R) to the ratio of the backscattering coefficient (bb) to absorption coefficient (a), is not valid for bb/a > 0.25. This means that it may no longer be valid for values of remote-sensing reflectance (above-surface ratio of water-leaving radiance to downwelling irradiance) where Rrs4/ > 0.01. Since there has been no simple Rrs expression developed for very turbid waters, we developed one based in part on Monte Carlo simulations and empirical adjustments to an Rrs model and applied it to rather turbid coastal waters near Tampa Bay to evaluate its utility for unmixing the optical components affecting the water- leaving radiance. With the high spectral (10 nm) and spatial (20 m2) resolution of Airborne Visible-InfraRed Imaging Spectrometer (AVIRIS) data, the water depth and bottom type were deduced using the model for shallow waters. This research demonstrates the necessity of further research to improve interpretations of scenes with highly variable turbid waters, and it emphasizes the utility of high spectral-resolution data as from AVIRIS for better understanding complicated coastal environments such as the west Florida shelf.

Geometric registration of remotely sensed data with SAMIR

Science.gov (United States)

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.

First Application of the Zeeman Technique to Remotely Measure Auroral Electrojet Intensity From Space

Science.gov (United States)

Yee, J. H.; Gjerloev, J.; Wu, D.; Schwartz, M. J.

2017-01-01

Using the O2 118 GHz spectral radiance measurements obtained by the Microwave Limb Sounder instrument on board the Aura spacecraft, we demonstrate that the Zeeman effect can be used to remotely measure the magnetic field perturbations produced by the auroral electrojet near the Hall current closure altitudes. Our derived current-induced magnetic field perturbations are found to be highly correlated with those coincidently obtained by ground magnetometers. These perturbations are also found to be linearly correlated with auroral electrojet strength. The statistically derived polar maps of our measured magnetic field perturbation reveal a spatial-temporal morphology consistent with that produced by the Hall current during substorms and storms. With today's technology, a constellation of compact, low-power, high spectral-resolution cubesats would have the capability to provide high precision and spatiotemporal magnetic field samplings needed for auroral electrojet measurements to gain insights into the spatiotemporal behavior of the auroral electrojet system.

The multi-spectral line-polarization MSE system on Alcator C-Mod

Energy Technology Data Exchange (ETDEWEB)

Mumgaard, R. T., E-mail: mumgaard@psfc.mit.edu; Khoury, M. [Plasma Science and Fusion Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Scott, S. D. [Princeton Plasma Physics Laboratory, Princeton, New Jersey 08540 (United States)

2016-11-15

A multi-spectral line-polarization motional Stark effect (MSE-MSLP) diagnostic has been developed for the Alcator C-Mod tokamak wherein the Stokes vector is measured in multiple wavelength bands simultaneously on the same sightline to enable better polarized background subtraction. A ten-sightline, four wavelength MSE-MSLP detector system was designed, constructed, and qualified. This system consists of a high-throughput polychromator for each sightline designed to provide large étendue and precise spectral filtering in a cost-effective manner. Each polychromator utilizes four narrow bandpass interference filters and four custom large diameter avalanche photodiode detectors. Two filters collect light to the red and blue of the MSE emission spectrum while the remaining two filters collect the beam pi and sigma emission generated at the same viewing volume. The filter wavelengths are temperature tuned using custom ovens in an automated manner. All system functions are remote controllable and the system can be easily retrofitted to existing single-wavelength line-polarization MSE systems.

The multi-spectral line-polarization MSE system on Alcator C-Mod

International Nuclear Information System (INIS)

Mumgaard, R. T.; Khoury, M.; Scott, S. D.

2016-01-01

A multi-spectral line-polarization motional Stark effect (MSE-MSLP) diagnostic has been developed for the Alcator C-Mod tokamak wherein the Stokes vector is measured in multiple wavelength bands simultaneously on the same sightline to enable better polarized background subtraction. A ten-sightline, four wavelength MSE-MSLP detector system was designed, constructed, and qualified. This system consists of a high-throughput polychromator for each sightline designed to provide large étendue and precise spectral filtering in a cost-effective manner. Each polychromator utilizes four narrow bandpass interference filters and four custom large diameter avalanche photodiode detectors. Two filters collect light to the red and blue of the MSE emission spectrum while the remaining two filters collect the beam pi and sigma emission generated at the same viewing volume. The filter wavelengths are temperature tuned using custom ovens in an automated manner. All system functions are remote controllable and the system can be easily retrofitted to existing single-wavelength line-polarization MSE systems.

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.

Very High Spectral Resolution Imaging Spectroscopy: the Fluorescence Explorer (FLEX) Mission

Science.gov (United States)

Moreno, Jose F.; Goulas, Yves; Huth, Andreas; Middleton, Elizabeth; Miglietta, Franco; Mohammed, Gina; Nedbal, Ladislav; Rascher, Uwe; Verhoef, Wouter; Drusch, Matthias

2016-01-01

The Fluorescence Explorer (FLEX) mission has been recently selected as the 8th Earth Explorer by the European Space Agency (ESA). It will be the first mission specifically designed to measure from space vegetation fluorescence emission, by making use of very high spectral resolution imaging spectroscopy techniques. Vegetation fluorescence is the best proxy to actual vegetation photosynthesis which can be measurable from space, allowing an improved quantification of vegetation carbon assimilation and vegetation stress conditions, thus having key relevance for global mapping of ecosystems dynamics and aspects related with agricultural production and food security. The FLEX mission carries the FLORIS spectrometer, with a spectral resolution in the range of 0.3 nm, and is designed to fly in tandem with Copernicus Sentinel-3, in order to provide all the necessary spectral / angular information to disentangle emitted fluorescence from reflected radiance, and to allow proper interpretation of the observed fluorescence spatial and temporal dynamics.

Review of commonly used remote sensing and ground-based ...

African Journals Online (AJOL)

This review provides an overview of the use of remote sensing data, the development of spectral reflectance indices for detecting plant water stress, and the usefulness of field measurements for ground-truthing purposes. Reliable measurements of plant water stress over large areas are often required for management ...

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 ...

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.

[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.

INTEGRATED FUSION METHOD FOR MULTIPLE TEMPORAL-SPATIAL-SPECTRAL IMAGES

Directory of Open Access Journals (Sweden)

H. Shen

2012-08-01

Full Text Available Data fusion techniques have been widely researched and applied in remote sensing field. In this paper, an integrated fusion method for remotely sensed images is presented. Differently from the existed methods, the proposed method has the performance to integrate the complementary information in multiple temporal-spatial-spectral images. In order to represent and process the images in one unified framework, two general image observation models are firstly presented, and then the maximum a posteriori (MAP framework is used to set up the fusion model. The gradient descent method is employed to solve the fused image. The efficacy of the proposed method is validated using simulated images.

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

Recent Advances in Registration, Integration and Fusion of Remotely Sensed Data: Redundant Representations and Frames

Science.gov (United States)

Czaja, Wojciech; Le Moigne-Stewart, Jacqueline

2014-01-01

In recent years, sophisticated mathematical techniques have been successfully applied to the field of remote sensing to produce significant advances in applications such as registration, integration and fusion of remotely sensed data. Registration, integration and fusion of multiple source imagery are the most important issues when dealing with Earth Science remote sensing data where information from multiple sensors, exhibiting various resolutions, must be integrated. Issues ranging from different sensor geometries, different spectral responses, differing illumination conditions, different seasons, and various amounts of noise need to be dealt with when designing an image registration, integration or fusion method. This tutorial will first define the problems and challenges associated with these applications and then will review some mathematical techniques that have been successfully utilized to solve them. In particular, we will cover topics on geometric multiscale representations, redundant representations and fusion frames, graph operators, diffusion wavelets, as well as spatial-spectral and operator-based data fusion. All the algorithms will be illustrated using remotely sensed data, with an emphasis on current and operational instruments.

A Remote Sensing Image Fusion Method based on adaptive dictionary learning

Science.gov (United States)

He, Tongdi; Che, Zongxi

2018-01-01

This paper discusses using a remote sensing fusion method, based on' adaptive sparse representation (ASP)', to provide improved spectral information, reduce data redundancy and decrease system complexity. First, the training sample set is formed by taking random blocks from the images to be fused, the dictionary is then constructed using the training samples, and the remaining terms are clustered to obtain the complete dictionary by iterated processing at each step. Second, the self-adaptive weighted coefficient rule of regional energy is used to select the feature fusion coefficients and complete the reconstruction of the image blocks. Finally, the reconstructed image blocks are rearranged and an average is taken to obtain the final fused images. Experimental results show that the proposed method is superior to other traditional remote sensing image fusion methods in both spectral information preservation and spatial resolution.

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

Science.gov (United States)

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.

Advancing High Spatial and Spectral Resolution Remote Sensing for Observing Plant Community Response to Environmental Variability and Change in the Alaskan Arctic

Science.gov (United States)

Vargas Zesati, Sergio A.

The Arctic is being impacted by climate change more than any other region on Earth. Impacts to terrestrial ecosystems have the potential to manifest through feedbacks with other components of the Earth System. Of particular concern is the potential for the massive store of soil organic carbon to be released from arctic permafrost to the atmosphere where it could exacerbate greenhouse warming and impact global climate and biogeochemical cycles. Even though substantial gains to our understanding of the changing Arctic have been made, especially over the past decade, linking research results from plot to regional scales remains a challenge due to the lack of adequate low/mid-altitude sampling platforms, logistic constraints, and the lack of cross-scale validation of research methodologies. The prime motivation of this study is to advance observational capacities suitable for documenting multi-scale environmental change in arctic terrestrial landscapes through the development and testing of novel ground-based and low altitude remote sensing methods. Specifically this study addressed the following questions: • How well can low-cost kite aerial photography and advanced computer vision techniques model the microtopographic heterogeneity of changing tundra surfaces? • How does imagery from kite aerial photography and fixed time-lapse digital cameras (pheno-cams) compare in their capacity to monitor plot-level phenological dynamics of arctic vegetation communities? • Can the use of multi-scale digital imaging systems be scaled to improve measurements of ecosystem properties and processes at the landscape level? • How do results from ground-based and low altitude digital remote sensing of the spatiotemporal variability in ecosystem processes compare with those from satellite remote sensing platforms? Key findings from this study suggest that cost-effective alternative digital imaging and remote sensing methods are suitable for monitoring and quantifying plot to

Evaluation of high-definition television for remote task performance

International Nuclear Information System (INIS)

Draper, J.V.; Fujita, Y.; Herndon, J.N.

1987-04-01

High-definition television (HDTV) transmits a video image with more than twice the number (1125 for HDTV to 525 for standard-resolution TV) of horizontal scan lines that standard-resolution TV provides. The improvement in picture quality (compared to standard-resolution TV) that the extra scan lines provide is impressive. Objects in the HDTV picture have more sharply defined edges, better contrast, and more accurate reproduction of shading and color patterns than do those in the standard-resolution TV picture. Because the TV viewing system is a key component for teleoperator performance, an improvement in TV picture quality could mean an improvement in the speed and accuracy with which teleoperators perform tasks. This report describes three experiments designed to evaluate the impact of HDTV on the performance of typical remote tasks. The performance of HDTV was compared to that of standard-resolution, monochromatic TV and standard-resolution, stereoscopic, monochromatic TV in the context of judgment of depth in a televised scene, visual inspection of an object, and performance of a typical remote handling task. The results of the three experiments show that in some areas HDTV can lead to improvement in teleoperator performance. Observers inspecting a small object for a flaw were more accurate with HDTV than with either of the standard-resolution systems. High resolution is critical for detection of small-scale flaws of the type in the experiment (a scratch on a glass bottle). These experiments provided an evaluation of HDTV television for use in tasks that must be routinely performed to remotely maintain a nuclear fuel reprocessing facility. 5 refs., 7 figs., 9 tabs

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

Interferometric filters for spectral discrimination in high-spectral-resolution lidar: performance comparisons between Fabry-Perot interferometer and field-widened Michelson interferometer.

Science.gov (United States)

Cheng, Zhongtao; Liu, Dong; Yang, Yongying; Yang, Liming; Huang, Hanlu

2013-11-10

Thanks to wavelength flexibility, interferometric filters such as Fabry-Perot interferometers (FPIs) and field-widened Michelson interferometers (FWMIs) have shown great convenience for spectrally separating the molecule and aerosol scattering components in the high-spectral-resolution lidar (HSRL) return signal. In this paper, performance comparisons between the FPI and FWMI as a spectroscopic discrimination filter in HSRL are performed. We first present a theoretical method for spectral transmission analysis and quantitative evaluation on the spectral discrimination. Then the process in determining the parameters of the FPI and FWMI for the performance comparisons is described. The influences from the incident field of view (FOV), the cumulative wavefront error induced by practical imperfections, and the frequency locking error on the spectral discrimination performance of the two filters are discussed in detail. Quantitative analyses demonstrate that FPI can produce higher transmittance while the remarkable spectral discrimination is one of the most appealing advantages of FWMI. As a result of the field-widened design, the FWMI still performs well even under the illumination with large FOV while the FPI is only qualified for a small incident angle. The cumulative wavefront error attaches a great effect on the spectral discrimination performance of the interferometric filters. We suggest if a cumulative wavefront error is less than 0.05 waves RMS, it is beneficial to employ the FWMI; otherwise, FPI may be more proper. Although the FWMI shows much more sensitivity to the frequency locking error, it can outperform the FPI given a locking error less than 0.1 GHz is achieved. In summary, the FWMI is very competent in HSRL applications if these practical engineering and control problems can be solved, theoretically. Some other estimations neglected in this paper can also be carried out through the analytical method illustrated herein.

The Use of Remote Sensing to Resolve the Aerosol Radiative Forcing

Science.gov (United States)

Kaufman, Y. J.; Tanre, D.; Remer, Lorraine

1999-01-01

Satellites are used for remote sensing of aerosol optical thickness and optical properties in order to derive the aerosol direct and indirect radiative forcing of climate. Accuracy of the derived aerosol optical thickness is used as a measure of the accuracy in deriving the aerosol radiative forcing. Several questions can be asked to challenge this concept. Is the accuracy of the satellite-derived aerosol direct forcing limited to the accuracy of the measured optical thickness? What are the spectral bands needed to derive the total aerosol forcing? Does most of the direct or indirect aerosol forcing of climate originate from regions with aerosol concentrations that are high enough to be detected from space? What should be the synergism ground-based and space-borne remote sensing to solve the problem? We shall try to answer some of these questions, using AVIRIS airborne measurements and simulations.

High aspect ratio, remote controlled pumping assembly

Science.gov (United States)

Brown, Steve B.; Milanovich, Fred P.

1995-01-01

A miniature dual syringe-type pump assembly which has a high aspect ratio and which is remotely controlled, for use such as in a small diameter penetrometer cone or well packer used in water contamination applications. The pump assembly may be used to supply and remove a reagent to a water contamination sensor, for example, and includes a motor, gearhead and motor encoder assembly for turning a drive screw for an actuator which provides pushing on one syringe and pulling on the other syringe for injecting new reagent and withdrawing used reagent from an associated sensor.

A NDVI assisted remote sensing image adaptive scale segmentation method

Science.gov (United States)

Zhang, Hong; Shen, Jinxiang; Ma, Yanmei

2018-03-01

Multiscale segmentation of images can effectively form boundaries of different objects with different scales. However, for the remote sensing image which widely coverage with complicated ground objects, the number of suitable segmentation scales, and each of the scale size is still difficult to be accurately determined, which severely restricts the rapid information extraction of the remote sensing image. A great deal of experiments showed that the normalized difference vegetation index (NDVI) can effectively express the spectral characteristics of a variety of ground objects in remote sensing images. This paper presents a method using NDVI assisted adaptive segmentation of remote sensing images, which segment the local area by using NDVI similarity threshold to iteratively select segmentation scales. According to the different regions which consist of different targets, different segmentation scale boundaries could be created. The experimental results showed that the adaptive segmentation method based on NDVI can effectively create the objects boundaries for different ground objects of remote sensing images.

Remote Sensing of shallow sea floor for digital earth environment

International Nuclear Information System (INIS)

Yahya, N N; Hashim, M; Ahmad, S

2014-01-01

Understanding the sea floor biodiversity requires spatial information that can be acquired from remote sensing satellite data. Species volume, spatial patterns and species coverage are some of the information that can be derived. Current approaches for mapping sea bottom type have evolved from field observation, visual interpretation from aerial photography, mapping from remote sensing satellite data along with field survey and hydrograhic chart. Remote sensing offers most versatile technique to map sea bottom type up to a certain scale. This paper reviews the technical characteristics of signal and light interference within marine features, space and remote sensing satellite. In addition, related image processing techniques that are applicable to remote sensing satellite data for sea bottom type digital mapping is also presented. The sea bottom type can be differentiated by classification method using appropriate spectral bands of satellite data. In order to verify the existence of particular sea bottom type, field observations need to be carried out with proper technique and equipment

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

Directory of Open Access Journals (Sweden)

H. Ru

2016-06-01

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

Field-widened Michelson interferometer for spectral discrimination in high-spectral-resolution lidar: theoretical framework.

Science.gov (United States)

Cheng, Zhongtao; Liu, Dong; Luo, Jing; Yang, Yongying; Zhou, Yudi; Zhang, Yupeng; Duan, Lulin; Su, Lin; Yang, Liming; Shen, Yibing; Wang, Kaiwei; Bai, Jian

2015-05-04

A field-widened Michelson interferometer (FWMI) is developed to act as the spectral discriminator in high-spectral-resolution lidar (HSRL). This realization is motivated by the wide-angle Michelson interferometer (WAMI) which has been used broadly in the atmospheric wind and temperature detection. This paper describes an independent theoretical framework about the application of the FWMI in HSRL for the first time. In the framework, the operation principles and application requirements of the FWMI are discussed in comparison with that of the WAMI. Theoretical foundations for designing this type of interferometer are introduced based on these comparisons. Moreover, a general performance estimation model for the FWMI is established, which can provide common guidelines for the performance budget and evaluation of the FWMI in the both design and operation stages. Examples incorporating many practical imperfections or conditions that may degrade the performance of the FWMI are given to illustrate the implementation of the modeling. This theoretical framework presents a complete and powerful tool for solving most of theoretical or engineering problems encountered in the FWMI application, including the designing, parameter calibration, prior performance budget, posterior performance estimation, and so on. It will be a valuable contribution to the lidar community to develop a new generation of HSRLs based on the FWMI spectroscopic filter.

Field-widened Michelson interferometer for spectral discrimination in high-spectral-resolution lidar: practical development.

Science.gov (United States)

Cheng, Zhongtao; Liu, Dong; Zhang, Yupeng; Yang, Yongying; Zhou, Yudi; Luo, Jing; Bai, Jian; Shen, Yibing; Wang, Kaiwei; Liu, Chong; Su, Lin; Yang, Liming

2016-04-04

A field-widened Michelson interferometer (FWMI), which is intended as the spectroscopic discriminator in ground-based high-spectral-resolution lidar (HSRL) for atmospheric aerosol detection, is described in this paper. The structure, specifications and design of the developed prototype FWMI are introduced, and an experimental approach is proposed to optimize the FWMI assembly and evaluate its comprehensive characteristic simultaneously. Experimental results show that, after optimization process, the peak-to-valley (PV) value and root-mean-square (RMS) value of measured OPD variation for the FWMI are 0.04λ and 0.008λ respectively among the half divergent angle range of 1.5 degree. Through an active locking technique, the frequency of the FWMI can be locked to the laser transmitter with accuracy of 27 MHz for more than one hour. The practical spectral discrimination ratio (SDR) for the developed FWMI is evaluated to be larger than 86 if the divergent angle of incident beam is smaller than 0.5 degree. All these results demonstrate the great potential of the developed FWMI as the spectroscopic discriminator for HSRLs, as well as the feasibility of the proposed design and optimization process. This paper is expected to provide a good entrance for the lidar community in future HSRL developments using the FWMI technique.

Methods for measuring the spectral reflectivity of advanced materials at high temperature

International Nuclear Information System (INIS)

Salikhov, T.P.; Kan, V.V.

1993-01-01

For investigation in the domain of advanced materials as well as for new technologies there is an urgent need for knowledge of the spectral reflectivity of the materials specially at high temperatures. However the methods available are mostly intended for measuring the model materials with specular or diffuse reflection surface. This is not quite correct since advanced materials have mixed specular diffuse reflection surfaces. New methods for reflectivity measurements of materials in the visible, near and middle infrared range at high temperature, regardless of surface texture, have been developed. The advantages of the methods proposed are as flows: (a) the facility of performing the reflectivity measurements for materials with mixed specular diffuse reflectance; (b) wide spectral range 0,38-8 micro m; (c) wide temperature range 300-3000 K; (d) high accuracy and rapid measurements. The methods are based on the following principals (i) Diffuse irradiation of the sample surface and the use of Helkholtz reciprocity principle to determine the directional hemispherical reflectivity ii) Pulse polychromatic probing of the sample by additional light source. The first principle excludes the influence of the angular reflection distribution of sample surface on data obtained. The second principle gives the possibility of simultaneous measurements of the reflectivity. The second principle gives the possibility of simultaneous measurements of the reflectivity in wide spectral range. On the basis of these principles for high temperature reflectometers have been developed and discussed here. (author)

Empirical Analysis of High Efficient Remote Cloud Data Center Backup Using HBase and Cassandra

Directory of Open Access Journals (Sweden)

Bao Rong Chang

2015-01-01

Full Text Available HBase, a master-slave framework, and Cassandra, a peer-to-peer (P2P framework, are the two most commonly used large-scale distributed NoSQL databases, especially applicable to the cloud computing with high flexibility and scalability and the ease of big data processing. Regarding storage structure, different structure adopts distinct backup strategy to reduce the risks of data loss. This paper aims to realize high efficient remote cloud data center backup using HBase and Cassandra, and in order to verify the high efficiency backup they have applied Thrift Java for cloud data center to take a stress test by performing strictly data read/write and remote database backup in the large amounts of data. Finally, in terms of the effectiveness-cost evaluation to assess the remote datacenter backup, a cost-performance ratio has been evaluated for several benchmark databases and the proposed ones. As a result, the proposed HBase approach outperforms the other databases.

Development of a high spectral resolution surface albedo product for the ARM Southern Great Plains Central Facility

Energy Technology Data Exchange (ETDEWEB)

McFarlane, Sally A.; Gaustad, Krista L.; Mlawer, Eli J.; Long, Charles N.; Delamere, Jennifer

2011-09-01

We present a method for identifying dominant surface type and estimating high spectral resolution surface albedo at the Atmospheric Radiation Measurement (ARM) facility at the Southern Great Plains (SGP) site in Oklahoma for use in radiative transfer calculations. Given a set of 6-channel narrowband visible and near-infrared irradiance measurements from upward and downward looking multi-filter radiometers (MFRs), four different surface types (snow-covered, green vegetation, partial vegetation, non-vegetated) can be identified. A normalized difference vegetation index (NDVI) is used to distinguish between vegetated and non-vegetated surfaces, and a scaled NDVI index is used to estimate the percentage of green vegetation in partially vegetated surfaces. Based on libraries of spectral albedo measurements, a piecewise continuous function is developed to estimate the high spectral resolution surface albedo for each surface type given the MFR albedo values as input. For partially vegetated surfaces, the albedo is estimated as a linear combination of the green vegetation and non-vegetated surface albedo values. The estimated albedo values are evaluated through comparison to high spectral resolution albedo measurements taken during several Intensive Observational Periods (IOPs) and through comparison of the integrated spectral albedo values to observed broadband albedo measurements. The estimated spectral albedo values agree well with observations for the visible wavelengths constrained by the MFR measurements, but have larger biases and variability at longer wavelengths. Additional MFR channels at 1100 nm and/or 1600 nm would help constrain the high resolution spectral albedo in the near infrared region.

Development of a high spectral resolution surface albedo product for the ARM Southern Great Plains central facility

Science.gov (United States)

McFarlane, S. A.; Gaustad, K. L.; Mlawer, E. J.; Long, C. N.; Delamere, J.

2011-09-01

We present a method for identifying dominant surface type and estimating high spectral resolution surface albedo at the Atmospheric Radiation Measurement (ARM) facility at the Southern Great Plains (SGP) site in Oklahoma for use in radiative transfer calculations. Given a set of 6-channel narrowband visible and near-infrared irradiance measurements from upward and downward looking multi-filter radiometers (MFRs), four different surface types (snow-covered, green vegetation, partial vegetation, non-vegetated) can be identified. A normalized difference vegetation index (NDVI) is used to distinguish between vegetated and non-vegetated surfaces, and a scaled NDVI index is used to estimate the percentage of green vegetation in partially vegetated surfaces. Based on libraries of spectral albedo measurements, a piecewise continuous function is developed to estimate the high spectral resolution surface albedo for each surface type given the MFR albedo values as input. For partially vegetated surfaces, the albedo is estimated as a linear combination of the green vegetation and non-vegetated surface albedo values. The estimated albedo values are evaluated through comparison to high spectral resolution albedo measurements taken during several Intensive Observational Periods (IOPs) and through comparison of the integrated spectral albedo values to observed broadband albedo measurements. The estimated spectral albedo values agree well with observations for the visible wavelengths constrained by the MFR measurements, but have larger biases and variability at longer wavelengths. Additional MFR channels at 1100 nm and/or 1600 nm would help constrain the high resolution spectral albedo in the near infrared region.

Development of a high spectral resolution surface albedo product for the ARM Southern Great Plains central facility

Directory of Open Access Journals (Sweden)

J. Delamere

2011-09-01

Full Text Available We present a method for identifying dominant surface type and estimating high spectral resolution surface albedo at the Atmospheric Radiation Measurement (ARM facility at the Southern Great Plains (SGP site in Oklahoma for use in radiative transfer calculations. Given a set of 6-channel narrowband visible and near-infrared irradiance measurements from upward and downward looking multi-filter radiometers (MFRs, four different surface types (snow-covered, green vegetation, partial vegetation, non-vegetated can be identified. A normalized difference vegetation index (NDVI is used to distinguish between vegetated and non-vegetated surfaces, and a scaled NDVI index is used to estimate the percentage of green vegetation in partially vegetated surfaces. Based on libraries of spectral albedo measurements, a piecewise continuous function is developed to estimate the high spectral resolution surface albedo for each surface type given the MFR albedo values as input. For partially vegetated surfaces, the albedo is estimated as a linear combination of the green vegetation and non-vegetated surface albedo values. The estimated albedo values are evaluated through comparison to high spectral resolution albedo measurements taken during several Intensive Observational Periods (IOPs and through comparison of the integrated spectral albedo values to observed broadband albedo measurements. The estimated spectral albedo values agree well with observations for the visible wavelengths constrained by the MFR measurements, but have larger biases and variability at longer wavelengths. Additional MFR channels at 1100 nm and/or 1600 nm would help constrain the high resolution spectral albedo in the near infrared region.

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

Science.gov (United States)

Gao, F.; Anderson, M. C.

2017-12-01

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

Advances in estimation methods of vegetation water content based on optical remote sensing techniques

Institute of Scientific and Technical Information of China (English)

无

2010-01-01

Quantitative estimation of vegetation water content(VWC) using optical remote sensing techniques is helpful in forest fire as-sessment,agricultural drought monitoring and crop yield estimation.This paper reviews the research advances of VWC retrieval using spectral reflectance,spectral water index and radiative transfer model(RTM) methods.It also evaluates the reli-ability of VWC estimation using spectral water index from the observation data and the RTM.Focusing on two main definitions of VWC-the fuel moisture content(FMC) and the equivalent water thickness(EWT),the retrieval accuracies of FMC and EWT using vegetation water indices are analyzed.Moreover,the measured information and the dataset are used to estimate VWC,the results show there are significant correlations among three kinds of vegetation water indices(i.e.,WSI,NDⅡ,NDWI1640,WI/NDVI) and canopy FMC of winter wheat(n=45).Finally,the future development directions of VWC detection based on optical remote sensing techniques are also summarized.

Installation of spectrally selective imaging system in RF negative ion source

International Nuclear Information System (INIS)

Ikeda, K.; Kisaki, M.; Nagaoka, K.; Nakano, H.; Osakabe, M.; Tsumori, K.; Kaneko, O.; Takeiri, Y.; Wünderlich, D.; Fantz, U.; Heinemann, B.; Geng, S.

2016-01-01

A spectrally selective imaging system has been installed in the RF negative ion source in the International Thermonuclear Experimental Reactor-relevant negative ion beam test facility ELISE (Extraction from a Large Ion Source Experiment) to investigate distribution of hydrogen Balmer-α emission (H α ) close to the production surface of hydrogen negative ion. We selected a GigE vision camera coupled with an optical band-path filter, which can be controlled remotely using high speed network connection. A distribution of H α emission near the bias plate has been clearly observed. The same time trend on H α intensities measured by the imaging diagnostic and the optical emission spectroscopy is confirmed

Highly Efficient Spectrally Stable Red Perovskite Light-Emitting Diodes.

Science.gov (United States)

Tian, Yu; Zhou, Chenkun; Worku, Michael; Wang, Xi; Ling, Yichuan; Gao, Hanwei; Zhou, Yan; Miao, Yu; Guan, Jingjiao; Ma, Biwu

2018-05-01

Perovskite light-emitting diodes (LEDs) have recently attracted great research interest for their narrow emissions and solution processability. Remarkable progress has been achieved in green perovskite LEDs in recent years, but not blue or red ones. Here, highly efficient and spectrally stable red perovskite LEDs with quasi-2D perovskite/poly(ethylene oxide) (PEO) composite thin films as the light-emitting layer are reported. By controlling the molar ratios of organic salt (benzylammonium iodide) to inorganic salts (cesium iodide and lead iodide), luminescent quasi-2D perovskite thin films are obtained with tunable emission colors from red to deep red. The perovskite/polymer composite approach enables quasi-2D perovskite/PEO composite thin films to possess much higher photoluminescence quantum efficiencies and smoothness than their neat quasi-2D perovskite counterparts. Electrically driven LEDs with emissions peaked at 638, 664, 680, and 690 nm have been fabricated to exhibit high brightness and external quantum efficiencies (EQEs). For instance, the perovskite LED with an emission peaked at 680 nm exhibits a brightness of 1392 cd m -2 and an EQE of 6.23%. Moreover, exceptional electroluminescence spectral stability under continuous device operation has been achieved for these red perovskite LEDs. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Spectral reflectance of five hardwood tree species in southern Indiana

Science.gov (United States)

Dale R. Weigel; J.C. Randolph

2013-01-01

The use of remote sensing to identify forest species has been ongoing since the launch of Landsat-1 using MSS imagery. The ability to separate hardwoods from conifers was accomplished by the 1980s. However, distinguishing individual hardwood species is more problematic due to similar spectral and phenological characteristics. With the launch of commercial satellites...

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

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Wu, Bo; Zeng, Hai; Hu, Han

2018-03-01

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

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

Ultra-High Resolution Spectroscopic Remote Sensing: A Microscope on Planetary Atmospheres

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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

Ultra-High Density Spectral Storage Materials

National Research Council Canada - National Science Library

Hasan, Zameer U

2002-01-01

.... Being atomic scale storage, spectral storage has the potential of providing orders of magnitude denser memories than present day memories that depend on the hulk properties of the storage medium...

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

Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques.

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Gao, Yongnian; Gao, Junfeng; Yin, Hongbin; Liu, Chuansheng; Xia, Ting; Wang, Jing; Huang, Qi

2015-03-15

Remote sensing has been widely used for ater quality monitoring, but most of these monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating the phosphorus concentration in water. The total phosphorus (TP) in lakes has been estimated from remotely sensed observations, primarily using the simple individual band ratio or their natural logarithm and the statistical regression method based on the field TP data and the spectral reflectance. In this study, we investigated the possibility of establishing a spatial modeling scheme to estimate the TP concentration of a large lake from multi-spectral satellite imagery using band combinations and regional multivariate statistical modeling techniques, and we tested the applicability of the spatial modeling scheme. The results showed that HJ-1A CCD multi-spectral satellite imagery can be used to estimate the TP concentration in a lake. The correlation and regression analysis showed a highly significant positive relationship between the TP concentration and certain remotely sensed combination variables. The proposed modeling scheme had a higher accuracy for the TP concentration estimation in the large lake compared with the traditional individual band ratio method and the whole-lake scale regression-modeling scheme. The TP concentration values showed a clear spatial variability and were high in western Lake Chaohu and relatively low in eastern Lake Chaohu. The northernmost portion, the northeastern coastal zone and the southeastern portion of western Lake Chaohu had the highest TP concentrations, and the other regions had the lowest TP concentration values, except for the coastal zone of eastern Lake Chaohu. These results strongly suggested that the proposed modeling scheme, i.e., the band combinations and the regional multivariate

Kent mixture model for classification of remote sensing data on spherical manifolds

CSIR Research Space (South Africa)

Lunga, D

2011-10-01

Full Text Available Modern remote sensing imaging sensor technology provides detailed spectral and spatial information that enables precise analysis of land cover usage. From a research point of view, traditional widely used statistical models are often limited...

Report of second LASFLEUR field campaign for remote sensing of vegetation health: ENEA contribution

Energy Technology Data Exchange (ETDEWEB)

Barbini, R; Colao, F; Fantoni, R; Palucci, A; Ribezzo, S [ENEA, Frascati (Italy). Dipt. Sviluppo Tecnologie di Punta

1993-09-15

The second European joint field campaign for the remote sensing of vegetation health was held in Oberpfaffenhofen (D) (30 Jun-9 Jul 1992) within the framework of the EUREKA/LASFLEUR Project. Italian groups, from ENEA (Italian Agency for Energy, New Technologies and the Environment), CNR (Italian National Research Council) and Viterbo University participated in this campaign together with German, French and Swedish groups from different institutes. On the occasion of this campaign, the lidar (light detection and ranging) fluorosensor system built at ENEA Frascati for the remote sensing of water and territory was improved, on the basis of the former field experience on plant fluorescence remote detection gained during the first LASFLEUR campaign held in Viterbo, and carried out on-site by means of a movable container. The new version of the set-up is presented here, together with the measurements performed on the available targets (spruce, maple, elm and cornel trees, and mais plants). Data analysis is discussed in detail, attempting to correlate the present spectral domain measurements with the plant photosynthetic activity under different weather and (nutrition or water) stress conditions. Several correlations were found between different pigment concentrations in various vegetables and spectrally resolved remote sensed data on the same species. It was demonstrated that the measurements, when performed from an airborne platform, would allow for a remote vegetation recognition across large areas (monitoring cultivations or forests). Part of the campaign was dedicated to the inter-calibration of different lidar systems operating in the spectrally resolved mode: this point is discussed here as well. Some conclusions drawn at the end of the LASFLEUR project Phase 1 are presented at the end of this report, as discussed during the last Project Workshop held in Florence from October 22nd to 26th, 1992.

A method of incident angle estimation for high resolution spectral recovery in filter-array-based spectrometers

Science.gov (United States)

Kim, Cheolsun; Lee, Woong-Bi; Ju, Gun Wu; Cho, Jeonghoon; Kim, Seongmin; Oh, Jinkyung; Lim, Dongsung; Lee, Yong Tak; Lee, Heung-No

2017-02-01

In recent years, there has been an increasing interest in miniature spectrometers for research and development. Especially, filter-array-based spectrometers have advantages of low cost and portability, and can be applied in various fields such as biology, chemistry and food industry. Miniaturization in optical filters causes degradation of spectral resolution due to limitations on spectral responses and the number of filters. Nowadays, many studies have been reported that the filter-array-based spectrometers have achieved resolution improvements by using digital signal processing (DSP) techniques. The performance of the DSP-based spectral recovery highly depends on the prior information of transmission functions (TFs) of the filters. The TFs vary with respect to an incident angle of light onto the filter-array. Conventionally, it is assumed that the incident angle of light on the filters is fixed and the TFs are known to the DSP. However, the incident angle is inconstant according to various environments and applications, and thus TFs also vary, which leads to performance degradation of spectral recovery. In this paper, we propose a method of incident angle estimation (IAE) for high resolution spectral recovery in the filter-array-based spectrometers. By exploiting sparse signal reconstruction of the L1- norm minimization, IAE estimates an incident angle among all possible incident angles which minimizes the error of the reconstructed signal. Based on IAE, DSP effectively provides a high resolution spectral recovery in the filter-array-based spectrometers.

Remote Research Mentoring of Virginia High School Students

Science.gov (United States)

Corby, Joanna; Dirienzo, W. J.; Beaton, R.; Pennucci, T.; Zasowski, G.

2013-01-01

Graduate students at the University of Virginia (UVa) are volunteering as research advisors on astronomy projects for Virginia's science and technology high schools. In previous years, we have worked with more than a dozen students through a research class at Central Virginia Governor's School in Lynchburg to develop an astronomy research curriculum that teaches background concepts and terminology, guides students in data analysis, and prepares them to present material in poster and oral forums. In our fourth year of operation, we are continuing to work with Central Virginia Governor's School and adapting the research curriculum to an independent course at Roanoke Valley Governor's School in Roanoke. Because both schools are far from UVa in Charlottesville, the program operates remotely; graduate advisors and high school students interact through "virtual" means, establishing a successful framework for meaningful remote mentoring. In the current year, six students will complete projects on astrophysical topics including megamasers, astrochemistry, and pulsars using data taken by the Green Bank Telescope (GBT). Students at Roanoke Valley were directly trained on the GBT as part of a separate outreach program called the Pulsar Search Collaboratory, and all six students will receive hands-on experience in handling GBT data. The current projects are components of larger research efforts by graduate student and professional level researchers, so that the projects contribute to high-level projects only possible with the GBT. This stands as a rare outreach program that uses the principle of “deliberative practice” to train high school students in the development of skills that are crucial to success in science. Furthermore, it provides graduate students with an opportunity to plan and advise research projects, developing a skill set that is required in more advanced academic positions. Our poster discusses the implementation of our online curriculum in two distinct

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

Recent Characterization of the Night-Sky Irradiance in the Visible/Near-Infrared Spectral Band

Science.gov (United States)

Moore, Carolynn; Wood, Michael; Bender, Edward; Hart, Steve

2018-01-01

The U.S. Army RDECOM CERDEC NVESD has made numerous characterizations of the night sky over the past 45 years. Up until the last four years, the measurement devices were highly detector-limited, which led to low spectral resolution, marginal sensitivity in no-moon conditions, and the need for inferential analysis of the resulting data. In 2014, however, the PhotoResearch Model PR-745 spectro-radiometer established a new state of the art for measurement of the integrated night-sky irradiance over the Visible-to-Near-Infrared (VNIR) spectral band (400-1050nm). This has enabled characterization of no-moon night-sky irradiance with a spectral bandwidth less than 15 nanometers, even when this irradiance is attenuated by heavy clouds or forest canopy. Since 2014, we have conducted a series of night-sky data collections at remote sites across the United States. The resulting data has provided new insights into natural radiance variations, cultural lighting impacts, and the spectrally-varying attenuation caused by cloud cover and forest canopy. Several new metrics have also been developed to provide insight into these newly-found components and temporal variations. The observations, findings and conclusions of the above efforts will be presented, including planned near-term efforts to further characterize the night-sky irradiance in the Visible/Near-Infrared spectral band.

An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

Directory of Open Access Journals (Sweden)

Youkyung Han

2017-01-01

Full Text Available Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.

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

Science.gov (United States)

Tigges, Jan; Lakes, Tobia

2017-10-04

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

High-Selectivity Filter Banks for Spectral Analysis of Music Signals

Directory of Open Access Journals (Sweden)

Luiz W. P. Biscainho

2007-01-01

Full Text Available This paper approaches, under a unified framework, several algorithms for the spectral analysis of musical signals. Such algorithms include the fast Fourier transform (FFT, the fast filter bank (FFB, the constant-Q transform (CQT, and the bounded-Q transform (BQT, previously known from the associated literature. Two new methods are then introduced, namely, the constant-Q fast filter bank (CQFFB and the bounded-Q fast filter bank (BQFFB, combining the positive characteristics of the previously mentioned algorithms. The provided analyses indicate that the proposed BQFFB achieves an excellent compromise between the reduced computational effort of the FFT, the high selectivity of each output channel of the FFB, and the efficient distribution of frequency channels associated to the CQT and BQT methods. Examples are included to illustrate the performances of these methods in the spectral analysis of music signals.

Nontronite mineral identification in nilgiri hills of tamil nadu using hyperspectral remote sensing

Science.gov (United States)

Vigneshkumar, M.; Yarakkula, Kiran

2017-11-01

Hyperspectral Remote sensing is a tool to identify the minerals along with field investigation. Tamil Nadu has abundant minerals like 30% titanium, 52% molybdenum, 59% garnet, 69% dunite, 75% vermiculite and 81% lignite. To enhance the user and industry requirements, mineral extraction is required. To identify the minerals properly, sophisticated tools are required. Hyperspectral remote sensing provides continuous extraction of earth surface information in an accurate manner. Nontronite is an iron-rich mineral mainly available in Nilgiri hills, Tamil Nadu, India. Due to the large number of bands, hyperspectral data require various preprocessing steps such as bad bands removal, destriping, radiance conversion and atmospheric correction. The atmospheric correction is performed using FLAASH method. The spectral data reduction is carried out with minimum noise fraction (MNF) method. The spatial information is reduced using pixel purity index (PPI) with 10000 iterations. The selected end members are compared with spectral libraries like USGS, JPL, and JHU. In the Nontronite mineral gives the probability of 0.85. Finally the classification is accomplished using spectral angle mapper (SAM) method.

High Frequency High Spectral Resolution Focal Plane Arrays for AtLAST

Science.gov (United States)

Baryshev, Andrey

2018-01-01

Large collecting area single dish telescope such as ATLAST will be especially effective for medium (R 1000) and high (R 50000) spectral resolution observations. Large focal plane array is a natural solution to increase mapping speed. For medium resolution direct detectors with filter banks (KIDs) and or heterodyne technology can be employed. We will analyze performance limits of comparable KID and SIS focal plane array taking into account quantum limit and high background condition of terrestrial observing site. For large heterodyne focal plane arrays, a high current density AlN junctions open possibility of large instantaneous bandwidth >40%. This and possible multi frequency band FPSs presents a practical challenge for spatial sampling and scanning strategies. We will discuss phase array feeds as a possible solution, including a modular back-end system, which can be shared between KID and SIS based FPA. Finally we will discuss achievable sensitivities and pixel co unts for a high frequency (>500 GHz) FPAs and address main technical challenges: LO distribution, wire counts, bias line multiplexing, and monolithic vs. discrete mixer component integration.

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

Science.gov (United States)

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

2016-01-01

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

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

Science.gov (United States)

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

2018-04-01

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

High order spectral volume and spectral difference methods on unstructured grids

Science.gov (United States)

Kannan, Ravishekar

The spectral volume (SV) and the spectral difference (SD) methods were developed by Wang and Liu and their collaborators for conservation laws on unstructured grids. They were introduced to achieve high-order accuracy in an efficient manner. Recently, these methods were extended to three-dimensional systems and to the Navier Stokes equations. The simplicity and robustness of these methods have made them competitive against other higher order methods such as the discontinuous Galerkin and residual distribution methods. Although explicit TVD Runge-Kutta schemes for the temporal advancement are easy to implement, they suffer from small time step limited by the Courant-Friedrichs-Lewy (CFL) condition. When the polynomial order is high or when the grid is stretched due to complex geometries or boundary layers, the convergence rate of explicit schemes slows down rapidly. Solution strategies to remedy this problem include implicit methods and multigrid methods. A novel implicit lower-upper symmetric Gauss-Seidel (LU-SGS) relaxation method is employed as an iterative smoother. It is compared to the explicit TVD Runge-Kutta smoothers. For some p-multigrid calculations, combining implicit and explicit smoothers for different p-levels is also studied. The multigrid method considered is nonlinear and uses Full Approximation Scheme (FAS). An overall speed-up factor of up to 150 is obtained using a three-level p-multigrid LU-SGS approach in comparison with the single level explicit method for the Euler equations for the 3rd order SD method. A study of viscous flux formulations was carried out for the SV method. Three formulations were used to discretize the viscous fluxes: local discontinuous Galerkin (LDG), a penalty method and the 2nd method of Bassi and Rebay. Fourier analysis revealed some interesting advantages for the penalty method. These were implemented in the Navier Stokes solver. An implicit and p-multigrid method was also implemented for the above. An overall speed

Piecewise spectrally band-pass for compressive coded aperture spectral imaging

International Nuclear Information System (INIS)

Qian Lu-Lu; Lü Qun-Bo; Huang Min; Xiang Li-Bin

2015-01-01

Coded aperture snapshot spectral imaging (CASSI) has been discussed in recent years. It has the remarkable advantages of high optical throughput, snapshot imaging, etc. The entire spatial-spectral data-cube can be reconstructed with just a single two-dimensional (2D) compressive sensing measurement. On the other hand, for less spectrally sparse scenes, the insufficiency of sparse sampling and aliasing in spatial-spectral images reduce the accuracy of reconstructed three-dimensional (3D) spectral cube. To solve this problem, this paper extends the improved CASSI. A band-pass filter array is mounted on the coded mask, and then the first image plane is divided into some continuous spectral sub-band areas. The entire 3D spectral cube could be captured by the relative movement between the object and the instrument. The principle analysis and imaging simulation are presented. Compared with peak signal-to-noise ratio (PSNR) and the information entropy of the reconstructed images at different numbers of spectral sub-band areas, the reconstructed 3D spectral cube reveals an observable improvement in the reconstruction fidelity, with an increase in the number of the sub-bands and a simultaneous decrease in the number of spectral channels of each sub-band. (paper)

Spectral Detection of Soybean Aphid (Hemiptera: Aphididae) and Confounding Insecticide Effects in Soybean

Science.gov (United States)

Alves, Tavvs Micael

Soybean aphid, Aphis glycines (Hemiptera: Aphididae) is the primary insect pest of soybean in the northcentral United States. Soybean aphid may cause stunted plants, leaf discoloration, plant death, and decrease soybean yield by 40%. Sampling plans have been developed for supporting soybean aphid management. However, growers' perception about time involved in direct insect counts has been contributing to a lower adoption of traditional pest scouting methods and may be associated with the use of prophylactic insecticide applications in soybean. Remote sensing of plant spectral (light-derived) responses to soybean aphid feeding is a promising alternative to estimate injury without direct insect counts and, thus, increase adoption and efficiency of scouting programs. This research explored the use of remote sensing of soybean reflectance for detection of soybean aphids and showed that foliar insecticides may have implications for subsequent use of soybean spectral reflectance for pest detection. (Abstract shortened by ProQuest.).

Spectral identification and quantification of salts in the Atacama Desert

Science.gov (United States)

Harris, J. K.; Cousins, C. R.; Claire, M. W.

2016-10-01

Salt minerals are an important natural resource. The ability to quickly and remotely identify and quantify salt deposits and salt contaminated soils and sands is therefore a priority goal for the various industries and agencies that utilise salts. The advent of global hyperspectral imagery from instruments such as Hyperion on NASA's Earth-Observing 1 satellite has opened up a new source of data that can potentially be used for just this task. This study aims to assess the ability of Visible and Near Infrared (VNIR) spectroscopy to identify and quantify salt minerals through the use of spectral mixture analysis. The surface and near-surface soils of the Atacama Desert in Chile contain a variety of well-studied salts, which together with low cloud coverage, and high aridity, makes this region an ideal testbed for this technique. Two forms of spectral data ranging 0.35 - 2.5 μm were collected: laboratory spectra acquired using an ASD FieldSpec Pro instrument on samples from four locations in the Atacama desert known to have surface concentrations of sulfates, nitrates, chlorides and perchlorates; and images from the EO-1 satellite's Hyperion instrument taken over the same four locations. Mineral identifications and abundances were confirmed using quantitative XRD of the physical samples. Spectral endmembers were extracted from within the laboratory and Hyperion spectral datasets and together with additional spectral library endmembers fed into a linear mixture model. The resulting identification and abundances from both dataset types were verified against the sample XRD values. Issues of spectral scale, SNR and how different mineral spectra interact are considered, and the utility of VNIR spectroscopy and Hyperion in particular for mapping specific salt concentrations in desert environments is established. Overall, SMA was successful at estimating abundances of sulfate minerals, particularly calcium sulfate, from both hyperspectral image and laboratory sample spectra

Effect of water content and organic carbon on remote sensing of crop residue cover

Science.gov (United States)

Serbin, G.; Hunt, E. R., Jr.; Daughtry, C. S. T.; McCarty, G. W.; Brown, D. J.; Doraiswamy, P. C.

2009-04-01

Crop residue cover is an important indicator of tillage method. Remote sensing of crop residue cover is an attractive and efficient method when compared with traditional ground-based methods, e.g., the line-point transect or windshield survey. A number of spectral indices have been devised for residue cover estimation. Of these, the most effective are those in the shortwave infrared portion of the spectrum, situated between 1950 and 2500 nm. These indices include the hyperspectral Cellulose Absorption Index (CAI), and advanced multispectral indices, i.e., the Lignin-Cellulose Absorption (LCA) index and the Shortwave Infrared Normalized Difference Residue Index (SINDRI), which were devised for the NASA Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Spectra of numerous soils from U.S. Corn Belt (Indiana and Iowa) were acquired under wetness conditions varying from saturation to oven-dry conditions. The behavior of soil reflectance with water content was also dependent on the soil organic carbon content (SOC) of the soils, and the location of the spectral bands relative to significant water absorptions. High-SOC soils showed the least change in spectral index values with increase in soil water content. Low-SOC soils, on the other hand, showed measurable difference. For CAI, low-SOC soils show an initial decrease in index value followed by an increase, due to the way that water content affects CAI spectral bands. Crop residue CAI values decrease with water content. For LCA, water content increases decrease crop residue index values and increase them for soils, resulting in decreased contrast. SINDRI is also affected by SOC and water content. As such, spatial information on the distribution of surface soil water content and SOC, when used in a geographic information system (GIS), will improve the accuracy of remotely-sensed crop residue cover estimates.

Mapping Weathering and Alteration Minerals in the Comstock and Geiger Grade Areas using Visible to Thermal Infrared Airborne Remote Sensing Data

Science.gov (United States)

Vaughan, Greg R.; Calvin, Wendy M.

2005-01-01

To support research into both precious metal exploration and environmental site characterization a combination of high spatial/spectral resolution airborne visible, near infrared, short wave infrared (VNIR/SWIR) and thermal infrared (TIR) image data were acquired to remotely map hydrothermal alteration minerals around the Geiger Grade and Comstock alteration regions, and map the mineral by-products of weathered mine dumps in Virginia City. Remote sensing data from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS), SpecTIR Corporation's airborne hyperspectral imager (HyperSpecTIR), the MODIS-ASTER airborne simulator (MASTER), and the Spatially Enhanced Broadband Array Spectrograph System (SEBASS) were acquired and processed into mineral maps based on the unique spectral signatures of image pixels. VNIR/SWIR and TIR field spectrometer data were collected for both calibration and validation of the remote data sets, and field sampling, laboratory spectral analyses and XRD analyses were made to corroborate the surface mineralogy identified by spectroscopy. The resulting mineral maps show the spatial distribution of several important alteration minerals around each study area including alunite, quartz, pyrophyllite, kaolinite, montmorillonite/muscovite, and chlorite. In the Comstock region the mineral maps show acid-sulfate alteration, widespread propylitic alteration and extensive faulting that offsets the acid-sulfate areas, in contrast to the larger, dominantly acid-sulfate alteration exposed along Geiger Grade. Also, different mineral zones within the intense acid-sulfate areas were mapped. In the Virginia City historic mining district the important weathering minerals mapped include hematite, goethite, jarosite and hydrous sulfate minerals (hexahydrite, alunogen and gypsum) located on mine dumps. Sulfate minerals indicate acidic water forming in the mine dump environment. While there is not an immediate threat to the community, there are clearly sources of

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

Science.gov (United States)

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

2018-06-01

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

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

Science.gov (United States)

Sofina, N.; Ehlers, M.

2012-08-01

High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same sensor which should be acquired at the same time of season, at the same time of a day, and - for electro-optical sensors - in cloudless conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. A promising alternative is the use of vector-based maps containing information about the original urban layout which can be related to a single image obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' (DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show the high reliability of the DPC feature as an indicator for change.

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.

Remote detection system

International Nuclear Information System (INIS)

Nixon, K.V.; France, S.W.; Garcia, C.; Hastings, R.D.

1981-05-01

A newly designed remote detection system has been developed at Los Alamos that allows the collection of high-resolution gamma-ray spectra and neutron data from a remote location. The system consists of the remote unit and a command unit. The remote unit collects data in a potentially hostile environment while the operator controls the unit by either radio or wire link from a safe position. Both units are battery powered and are housed in metal carrying cases

Hyperspectral remote sensing application for monitoring and preservation of plant ecosystems

Science.gov (United States)

Krezhova, Dora; Maneva, Svetla; Zdravev, Tomas; Petrov, Nikolay; Stoev, Antoniy

Remote sensing technologies have advanced significantly at last decade and have improved the capability to gather information about Earth’s resources and environment. They have many applications in Earth observation, such as mapping and updating land-use and cover, weather forecasting, biodiversity determination, etc. Hyperspectral remote sensing offers unique opportunities in the environmental monitoring and sustainable use of natural resources. Remote sensing sensors on space-based platforms, aircrafts, or on ground, are capable of providing detailed spectral, spatial and temporal information on terrestrial ecosystems. Ground-based sensors are used to record detailed information about the land surface and to create a data base for better characterizing the objects which are being imaged by the other sensors. In this paper some applications of two hyperspectral remote sensing techniques, leaf reflectance and chlorophyll fluorescence, for monitoring and assessment of the effects of adverse environmental conditions on plant ecosystems are presented. The effect of stress factors such as enhanced UV-radiation, acid rain, salinity, viral infections applied to some young plants (potato, pea, tobacco) and trees (plums, apples, paulownia) as well as of some growth regulators were investigated. Hyperspectral reflectance and fluorescence data were collected by means of a portable fiber-optics spectrometer in the visible and near infrared spectral ranges (450-850 nm and 600-900 nm), respectively. The differences between the reflectance data of healthy (control) and injured (stressed) plants were assessed by means of statistical (Student’s t-criterion), first derivative, and cluster analysis and calculation of some vegetation indices in four most informative for the investigated species regions: green (520-580 nm), red (640-680 nm), red edge (690-720 nm) and near infrared (720-780 nm). Fluorescence spectra were analyzed at five characteristic wavelengths located at the

A review of hyperspectral remote sensing and its application in ...

African Journals Online (AJOL)

Multispectral imagery has been used as the data source for water and land observational remote sensing from airborne and satellite systems since the early 1960s. Over the past two decades, advances in sensor technology have made it possible for the collection of several hundred spectral bands. This is commonly ...

Object-based vegetation classification with high resolution remote sensing imagery

Science.gov (United States)

Yu, Qian

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

Improved crop residue cover estimates by coupling spectral indices for residue and moisture

Science.gov (United States)

Remote sensing assessment of soil residue cover (fR) and tillage intensity will improve our predictions of the impact of agricultural practices and promote sustainable management. Spectral indices for estimating fR are sensitive to soil and residue water content, therefore, the uncertainty of estima...

Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale

Directory of Open Access Journals (Sweden)

Anne Clasen

2015-11-01

Full Text Available Forest biochemical and biophysical variables and their spatial and temporal distribution are essential inputs to process-orientated ecosystem models. To provide this information, imaging spectroscopy appears to be a promising tool. In this context, the present study investigates the potential of spectral unmixing to derive sub-pixel crown component fractions in a temperate deciduous forest ecosystem. However, the high proportion of foliage in this complex vegetation structure leads to the problem of saturation effects, when applying broadband vegetation indices. This study illustrates that multiple endmember spectral mixture analysis (MESMA can contribute to overcoming this challenge. Reference fractional abundances, as well as spectral measurements of the canopy components, could be precisely determined from a crane measurement platform situated in a deciduous forest in North-East Germany. In contrast to most other studies, which only use leaf and soil endmembers, this experimental setup allowed for the inclusion of a bark endmember for the unmixing of components within the canopy. This study demonstrates that the inclusion of additional endmembers markedly improves the accuracy. A mean absolute error of 7.9% could be achieved for the fractional occurrence of the leaf endmember and 5.9% for the bark endmember. In order to evaluate the results of this field-based study for airborne and satellite-based remote sensing applications, a transfer to Airborne Imaging Spectrometer for Applications (AISA and simulated Environmental Mapping and Analysis Program (EnMAP and Sentinel-2 imagery was carried out. All sensors were capable of unmixing crown components with a mean absolute error ranging between 3% and 21%.

[Research on Spectral Polarization Imaging System Based on Static Modulation].

Science.gov (United States)

Zhao, Hai-bo; Li, Huan; Lin, Xu-ling; Wang, Zheng

2015-04-01

The main disadvantages of traditional spectral polarization imaging system are: complex structure, with moving parts, low throughput. A novel method of spectral polarization imaging system is discussed, which is based on static polarization intensity modulation combined with Savart polariscope interference imaging. The imaging system can obtain real-time information of spectral and four Stokes polarization messages. Compared with the conventional methods, the advantages of the imaging system are compactness, low mass and no moving parts, no electrical control, no slit and big throughput. The system structure and the basic theory are introduced. The experimental system is established in the laboratory. The experimental system consists of reimaging optics, polarization intensity module, interference imaging module, and CCD data collecting and processing module. The spectral range is visible and near-infrared (480-950 nm). The white board and the plane toy are imaged by using the experimental system. The ability of obtaining spectral polarization imaging information is verified. The calibration system of static polarization modulation is set up. The statistical error of polarization degree detection is less than 5%. The validity and feasibility of the basic principle is proved by the experimental result. The spectral polarization data captured by the system can be applied to object identification, object classification and remote sensing detection.

An improved feature extraction algorithm based on KAZE for multi-spectral image

Science.gov (United States)

Yang, Jianping; Li, Jun

2018-02-01

Multi-spectral image contains abundant spectral information, which is widely used in all fields like resource exploration, meteorological observation and modern military. Image preprocessing, such as image feature extraction and matching, is indispensable while dealing with multi-spectral remote sensing image. Although the feature matching algorithm based on linear scale such as SIFT and SURF performs strong on robustness, the local accuracy cannot be guaranteed. Therefore, this paper proposes an improved KAZE algorithm, which is based on nonlinear scale, to raise the number of feature and to enhance the matching rate by using the adjusted-cosine vector. The experiment result shows that the number of feature and the matching rate of the improved KAZE are remarkably than the original KAZE algorithm.

Liquid crystal-based Mueller matrix spectral imaging polarimetry for parameterizing mineral structural organization.

Science.gov (United States)

Gladish, James C; Duncan, Donald D

2017-01-20

Herein, we discuss the remote assessment of the subwavelength organizational structure of a medium. Specifically, we use spectral imaging polarimetry, as the vector nature of polarized light enables it to interact with optical anisotropies within a medium, while the spectral aspect of polarization is sensitive to small-scale structure. The ability to image these effects allows for inference of spatial structural organization parameters. This work describes a methodology for revealing structural organization by exploiting the Stokes/Mueller formalism and by utilizing measurements from a spectral imaging polarimeter constructed from liquid crystal variable retarders and a liquid crystal tunable filter. We provide results to validate the system and then show results from measurements on a mineral sample.

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.

Spectral classifying base on color of live corals and dead corals covered with algae

Science.gov (United States)

Nurdin, Nurjannah; Komatsu, Teruhisa; Barille, Laurent; Akbar, A. S. M.; Sawayama, Shuhei; Fitrah, Muh. Nur; Prasyad, Hermansyah

2016-05-01

Pigments in the host tissues of corals can make a significant contribution to their spectral signature and can affect their apparent color as perceived by a human observer. The aim of this study is classifying the spectral reflectance of corals base on different color. It is expected that they can be used as references in discriminating between live corals, dead coral covered with algae Spectral reflectance data was collected in three small islands, Spermonde Archipelago, Indonesia by using a hyperspectral radiometer underwater. First and second derivative analysis resolved the wavelength locations of dominant features contributing to reflectance in corals and support the distinct differences in spectra among colour existed. Spectral derivative analysis was used to determine the specific wavelength regions ideal for remote identification of substrate type. The analysis results shown that yellow, green, brown and violet live corals are spectrally separable from each other, but they are similar with dead coral covered with algae spectral.

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 ...

Multiscale finite element methods for high-contrast problems using local spectral basis functions

KAUST Repository

Efendiev, Yalchin

2011-02-01

In this paper we study multiscale finite element methods (MsFEMs) using spectral multiscale basis functions that are designed for high-contrast problems. Multiscale basis functions are constructed using eigenvectors of a carefully selected local spectral problem. This local spectral problem strongly depends on the choice of initial partition of unity functions. The resulting space enriches the initial multiscale space using eigenvectors of local spectral problem. The eigenvectors corresponding to small, asymptotically vanishing, eigenvalues detect important features of the solutions that are not captured by initial multiscale basis functions. Multiscale basis functions are constructed such that they span these eigenfunctions that correspond to small, asymptotically vanishing, eigenvalues. We present a convergence study that shows that the convergence rate (in energy norm) is proportional to (H/Λ*)1/2, where Λ* is proportional to the minimum of the eigenvalues that the corresponding eigenvectors are not included in the coarse space. Thus, we would like to reach to a larger eigenvalue with a smaller coarse space. This is accomplished with a careful choice of initial multiscale basis functions and the setup of the eigenvalue problems. Numerical results are presented to back-up our theoretical results and to show higher accuracy of MsFEMs with spectral multiscale basis functions. We also present a hierarchical construction of the eigenvectors that provides CPU savings. © 2010.

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

Science.gov (United States)

Selkowitz, D.J.

2010-01-01

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km2 study area in Arctic Alaska. The study area is centered at 69 ??N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10??, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets

Quantitative interpretation of great lakes remote sensing data

International Nuclear Information System (INIS)

Shook, D.F.; Salzman, J.; Svehla, R.A.; Gedney, R.T.

1980-01-01

Remote sensing has been applied in the past to the surveillance of Great Lakes water quality, but it has been only partially successful because of the completely empirical approach taken in relating the multispectral scanning data at visible and near-infrared wavelengths to water parameters. Any remote sensing approach using water color information must take into account (1) the existence of many different organic and inorganic species throughtout the Greak Lakes, (2) the occurrence of a mixture of species in most locations, and (3) spatial (inter- and interlake as well as vertical) variations in types and concentrations of species. The radiative transfer model provides a potential method for an orderly analysis of remote sensing data and a physical basis for developing quantitative algorithms. Predictions and field measurements of volume reflectances are presented which clearly show the advantage of using a radiative transfer model. Spectral absorptance and backscattering coefficients for two inorganic sediments are reported

Techniques of uranium mineralization alteration remote sensing information identification and its application in Taoshan area, Jiangxi province

International Nuclear Information System (INIS)

Xuan Yanxiu; Zhang Jielin

2010-01-01

Based on the spectrum characteristics analysis of uranium mineralization alteration rocks and minerals, and using satellite multi-spectral remote sensing image data as the main information sources, multiple remote sensing data processing techniques and methods such as color compound, band ratio, principal component analysis and image color segmentation, are synthetically applied to extract uranium mineralization and alteration information from the remote sensing image. The results of this study provided basic data for analysis of uranium ore-formation conditions in the area. (authors)

Mars analog minerals' spectral reflectance characteristics under Martian surface conditions

Science.gov (United States)

Poitras, J. T.; Cloutis, E. A.; Salvatore, M. R.; Mertzman, S. A.; Applin, D. M.; Mann, P.

2018-05-01

We investigated the spectral reflectance properties of minerals under a simulated Martian environment. Twenty-eight different hydrated or hydroxylated phases of carbonates, sulfates, and silica minerals were selected based on past detection on Mars through spectral remote sensing data. Samples were ground and dry sieved to <45 μm grain size and characterized by XRD before and after 133 days inside a simulated Martian surface environment (pressure 5 Torr and CO2 fed). Reflectance spectra from 0.35 to 4 μm were taken periodically through a sapphire (0.35-2.5 μm) and zinc selenide (2.5-4 μm) window over a 133-day period. Mineral stability on the Martian surface was assessed through changes in spectral characteristics. Results indicate that the hydrated carbonates studied would be stable on the surface of Mars, only losing adsorbed H2O while maintaining their diagnostic spectral features. Sulfates were less stable, often with shifts in the band position of the SO, Fe, and OH absorption features. Silicas displayed spectral shifts related to SiOH and hydration state of the mineral surface, while diagnostic bands for quartz were stable. Previous detection of carbonate minerals based on 2.3-2.5 μm and 3.4-3.9 μm features appears to be consistent with our results. Sulfate mineral detection is more questionable since there can be shifts in band position related to SO4. The loss of the 0.43 μm Fe3+ band in many of the sulfates indicate that there are fewer potential candidates for Fe3+ sulfates to permanently exist on the Martian surface based on this band. The gypsum sample changed phase to basanite during desiccation as demonstrated by both reflectance and XRD. Silica on Mars has been detected using band depth ratio at 1.91 and 1.96 μm and band minimum position of the 1.4 μm feature, and the properties are also used to determine their age. This technique continues to be useful for positive silica identifications, however, silica age appears to be less consistent

High-speed Vibrational Imaging and Spectral Analysis of Lipid Bodies by Compound Raman Microscopy

OpenAIRE

Slipchenko, Mikhail N.; Le, Thuc T.; Chen, Hongtao; Cheng, Ji-Xin

2009-01-01

Cells store excess energy in the form of cytoplasmic lipid droplets. At present, it is unclear how different types of fatty acids contribute to the formation of lipid-droplets. We describe a compound Raman microscope capable of both high-speed chemical imaging and quantitative spectral analysis on the same platform. We use a picosecond laser source to perform coherent Raman scattering imaging of a biological sample and confocal Raman spectral analysis at points of interest. The potential of t...

Remote sensing estimation of vegetation moisture for the prediction of fire hazard

NARCIS (Netherlands)

Maffei, C.; Menenti, M.

2013-01-01

Various factors contribute to forest fire hazard, and among them vegetation moisture is the one that dictates susceptibility to fire ignition and propagation. The scientific community has developed a number of spectral indexes based on remote sensing measurements in the optical domain for the

Performance characterization of a pressure-tuned wide-angle Michelson interferometric spectral filter for high spectral resolution lidar

Science.gov (United States)

Seaman, Shane T.; Cook, Anthony L.; Scola, Salvatore J.; Hostetler, Chris A.; Miller, Ian; Welch, Wayne

2015-09-01

High Spectral Resolution Lidar (HSRL) is typically realized using an absorption filter to separate molecular returns from particulate returns. NASA Langley Research Center (LaRC) has designed and built a Pressure-Tuned Wide-Angle Michelson Interferometer (PTWAMI) as an alternate means to separate the two types of atmospheric returns. While absorption filters only work at certain wavelengths and suffer from low photon efficiency due to light absorption, an interferometric spectral filter can be designed for any wavelength and transmits nearly all incident photons. The interferometers developed at LaRC employ an air spacer in one arm, and a solid glass spacer in the other. Field widening is achieved by specific design and selection of the lengths and refractive indices of these two arms. The principal challenge in using such an interferometer as a spectral filter for HSRL aboard aircraft is that variations in glass temperature and air pressure cause changes in the interferometer's optical path difference. Therefore, a tuning mechanism is needed to actively accommodate for these changes. The pressure-tuning mechanism employed here relies on changing the pressure in an enclosed, air-filled arm of the interferometer to change the arm's optical path length. However, tuning using pressure will not adjust for tilt, mirror warpage, or thermally induced wavefront error, so the structural, thermal, and optical behavior of the device must be well understood and optimized in the design and manufacturing process. The PTWAMI has been characterized for particulate transmission ratio, wavefront error, and tilt, and shows acceptable performance for use in an HSRL instrument.

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

Directory of Open Access Journals (Sweden)

ZHANG Zhiqiang

2018-01-01

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

Voltage magnitude and margin controller for remote industrial microgrid with high wind penetration

DEFF Research Database (Denmark)

Cai, Yu; Lin, Jin; Song, Yonghua

2013-01-01

It is well known that the remote industrial microgrid is located at the periphery of the grid, which is weakly connected to the main grid. In order to enhance the voltage stability and ensure a good power quality for industries, a voltage magnitude and margin controller based on wind turbines...... is proposed in this paper. This controller includes two parts to improve voltage stability in different time scales by using local measurements. Case studies conducted for a remote microgrid with high wind penetration have proved the effectiveness of the proposed control scheme....

THE APPLICATION OF REMOTE SENSING AND AERO-GEOPHYSICS DATA FUSION ON METALLOGENIC PROGNOSIS IN QIMANTAGE OF EAST KUNLUN MONTAIN AREA

OpenAIRE

Jia, W.; Zhang, H.; Lin, J.; Zhao, H.

2013-01-01

Based on west of Qimantage of East Kunlun mountain area, takes advantage of ASTER data, according to the altered mineral spectral characteristics, remote sensing alteration information is extracted. Incorporation the anomaly extraction results with high-precision aero geophysical data processing results, a multiple resource information fusion model is proposed. The fusion model of two totally different type of data which is a special attention in geospatial academia now, which can im...

Hurricane coastal flood analysis using multispectral spectral images

Science.gov (United States)

Ogashawara, I.; Ferreira, C.; Curtarelli, M. P.

2013-12-01

Flooding is one of the main hazards caused by extreme events such as hurricanes and tropical storms. Therefore, flood maps are a crucial tool to support policy makers, environmental managers and other government agencies for emergency management, disaster recovery and risk reduction planning. However traditional flood mapping methods rely heavily on the interpolation of hydrodynamic models results, and most recently, the extensive collection of field data. These methods are time-consuming, labor intensive, and costly. Efficient and fast response alternative methods should be developed in order to improve flood mapping, and remote sensing has been proved as a valuable tool for this application. Our goal in this paper is to introduce a novel technique based on spectral analysis in order to aggregate knowledge and information to map coastal flood areas. For this purpose we used the Normalized Diference Water Index (NDWI) which was derived from two the medium resolution LANDSAT/TM 5 surface reflectance product from the LANDSAT climate data record (CDR). This product is generated from specialized software called Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). We used the surface reflectance products acquired before and after the passage of Hurricane Ike for East Texas in September of 2008. We used as end member a classification of estimated flooded area based on the United States Geological Survey (USGS) mobile storm surge network that was deployed for Hurricane Ike. We used a dataset which consisted of 59 water levels recording stations. The estimated flooded area was delineated interpolating the maximum surge in each location using a spline with barriers method with high tension and a 30 meter Digital Elevation Model (DEM) from the National Elevation Dataset (NED). Our results showed that, in the flooded area, the NDWI values decreased after the hurricane landfall on average from 0.38 to 0.18 and the median value decreased from 0.36 to 0.2. However

Depth Estimation of Submerged Aquatic Vegetation in Clear Water Streams Using Low-Altitude Optical Remote Sensing.

Science.gov (United States)

Visser, Fleur; Buis, Kerst; Verschoren, Veerle; Meire, Patrick

2015-09-30

UAVs and other low-altitude remote sensing platforms are proving very useful tools for remote sensing of river systems. Currently consumer grade cameras are still the most commonly used sensors for this purpose. In particular, progress is being made to obtain river bathymetry from the optical image data collected with such cameras, using the strong attenuation of light in water. No studies have yet applied this method to map submergence depth of aquatic vegetation, which has rather different reflectance characteristics from river bed substrate. This study therefore looked at the possibilities to use the optical image data to map submerged aquatic vegetation (SAV) depth in shallow clear water streams. We first applied the Optimal Band Ratio Analysis method (OBRA) of Legleiter et al. (2009) to a dataset of spectral signatures from three macrophyte species in a clear water stream. The results showed that for each species the ratio of certain wavelengths were strongly associated with depth. A combined assessment of all species resulted in equally strong associations, indicating that the effect of spectral variation in vegetation is subsidiary to spectral variation due to depth changes. Strongest associations (R²-values ranging from 0.67 to 0.90 for different species) were found for combinations including one band in the near infrared (NIR) region between 825 and 925 nm and one band in the visible light region. Currently data of both high spatial and spectral resolution is not commonly available to apply the OBRA results directly to image data for SAV depth mapping. Instead a novel, low-cost data acquisition method was used to obtain six-band high spatial resolution image composites using a NIR sensitive DSLR camera. A field dataset of SAV submergence depths was used to develop regression models for the mapping of submergence depth from image pixel values. Band (combinations) providing the best performing models (R²-values up to 0.77) corresponded with the OBRA

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

Directory of Open Access Journals (Sweden)

S. Lin

2018-04-01

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

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

Directory of Open Access Journals (Sweden)

Weixun Zhou

2017-05-01

Full Text Available Learning powerful feature representations for image retrieval has always been a challenging task in the field of remote sensing. Traditional methods focus on extracting low-level hand-crafted features which are not only time-consuming but also tend to achieve unsatisfactory performance due to the complexity of remote sensing images. In this paper, we investigate how to extract deep feature representations based on convolutional neural networks (CNNs for high-resolution remote sensing image retrieval (HRRSIR. To this end, several effective schemes are proposed to generate powerful feature representations for HRRSIR. In the first scheme, a CNN pre-trained on a different problem is treated as a feature extractor since there are no sufficiently-sized remote sensing datasets to train a CNN from scratch. In the second scheme, we investigate learning features that are specific to our problem by first fine-tuning the pre-trained CNN on a remote sensing dataset and then proposing a novel CNN architecture based on convolutional layers and a three-layer perceptron. The novel CNN has fewer parameters than the pre-trained and fine-tuned CNNs and can learn low dimensional features from limited labelled images. The schemes are evaluated on several challenging, publicly available datasets. The results indicate that the proposed schemes, particularly the novel CNN, achieve state-of-the-art performance.

Research on visible and near infrared spectral-polarimetric properties of soil polluted by crude oil

Science.gov (United States)

Shen, Hui-yan; Zhou, Pu-cheng; Pan, Bang-long

2017-10-01

Hydrocarbon contaminated soil can impose detrimental effects on forest health and quality of agricultural products. To manage such consequences, oil leak indicators should be detected quickly by monitoring systems. Remote sensing is one of the most suitable techniques for monitoring systems, especially for areas which are uninhabitable and difficulty to access. The most available physical quantities in optical remote sensing domain are the intensity and spectral information obtained by visible or infrared sensors. However, besides the intensity and wavelength, polarization is another primary physical quantity associated with an optical field. During the course of reflecting light-wave, the surface of soil polluted by crude oil will cause polarimetric properties which are related to the nature of itself. Thus, detection of the spectralpolarimetric properties for soil polluted by crude oil has become a new remote sensing monitoring method. In this paper, the multi-angle spectral-polarimetric instrument was used to obtain multi-angle visible and near infrared spectralpolarimetric characteristic data of soil polluted by crude oil. And then, the change rule between polarimetric properties with different affecting factors, such as viewing zenith angle, incidence zenith angle of the light source, relative azimuth angle, waveband of the detector as well as different grain size of soil were discussed, so as to provide a scientific basis for the research on polarization remote sensing for soil polluted by crude oil.

Spectroscopic Remote Sensing of Non-Structural Carbohydrates in Forest Canopies

Directory of Open Access Journals (Sweden)

Gregory P. Asner

2015-03-01

Full Text Available Non-structural carbohydrates (NSC are products of photosynthesis, and leaf NSC concentration may be a prognostic indicator of climate-change tolerance in woody plants. However, measurement of leaf NSC is prohibitively labor intensive, especially in tropical forests, where foliage is difficult to access and where NSC concentrations vary enormously by species and across environments. Imaging spectroscopy may allow quantitative mapping of leaf NSC, but this possibility remains unproven. We tested the accuracy of NSC remote sensing at leaf, canopy and stand levels using visible-to-shortwave infrared (VSWIR spectroscopy with partial least squares regression (PLSR techniques. Leaf-level analyses demonstrated the high precision (R2 = 0.69–0.73 and accuracy (%RMSE = 13%–14% of NSC estimates in 6136 live samples taken from 4222 forest canopy species worldwide. The leaf spectral data were combined with a radiative transfer model to simulate the role of canopy structural variability, which led to a reduction in the precision and accuracy of leaf NSC estimation (R2 = 0.56; %RMSE = 16%. Application of the approach to 79 one-hectare plots in Amazonia using the Carnegie Airborne Observatory VSWIR spectrometer indicated the good precision and accuracy of leaf NSC estimates at the forest stand level (R2 = 0.49; %RMSE = 9.1%. Spectral analyses indicated strong contributions of the shortwave-IR (1300–2500 nm region to leaf NSC determination at all scales. We conclude that leaf NSC can be remotely sensed, opening doors to monitoring forest canopy physiological responses to environmental stress and climate change.

USGS Digital Spectral Library splib06a

Science.gov (United States)

Clark, Roger N.; Swayze, Gregg A.; Wise, Richard A.; Livo, K. Eric; Hoefen, Todd M.; Kokaly, Raymond F.; Sutley, Stephen J.

2007-01-01

Introduction We have assembled a digital reflectance spectral library that covers the wavelength range from the ultraviolet to far infrared along with sample documentation. The library includes samples of minerals, rocks, soils, physically constructed as well as mathematically computed mixtures, plants, vegetation communities, microorganisms, and man-made materials. The samples and spectra collected were assembled for the purpose of using spectral features for the remote detection of these and similar materials. Analysis of spectroscopic data from laboratory, aircraft, and spacecraft instrumentation requires a knowledge base. The spectral library discussed here forms a knowledge base for the spectroscopy of minerals and related materials of importance to a variety of research programs being conducted at the U.S. Geological Survey. Much of this library grew out of the need for spectra to support imaging spectroscopy studies of the Earth and planets. Imaging spectrometers, such as the National Aeronautics and Space Administration (NASA) Airborne Visible/Infra Red Imaging Spectrometer (AVIRIS) or the NASA Cassini Visual and Infrared Mapping Spectrometer (VIMS) which is currently orbiting Saturn, have narrow bandwidths in many contiguous spectral channels that permit accurate definition of absorption features in spectra from a variety of materials. Identification of materials from such data requires a comprehensive spectral library of minerals, vegetation, man-made materials, and other subjects in the scene. Our research involves the use of the spectral library to identify the components in a spectrum of an unknown. Therefore, the quality of the library must be very good. However, the quality required in a spectral library to successfully perform an investigation depends on the scientific questions to be answered and the type of algorithms to be used. For example, to map a mineral using imaging spectroscopy and the mapping algorithm of Clark and others (1990a, 2003b

Spectral unmixing of urban land cover using a generic library approach

Science.gov (United States)

Degerickx, Jeroen; Lordache, Marian-Daniel; Okujeni, Akpona; Hermy, Martin; van der Linden, Sebastian; Somers, Ben

2016-10-01

Remote sensing based land cover classification in urban areas generally requires the use of subpixel classification algorithms to take into account the high spatial heterogeneity. These spectral unmixing techniques often rely on spectral libraries, i.e. collections of pure material spectra (endmembers, EM), which ideally cover the large EM variability typically present in urban scenes. Despite the advent of several (semi-) automated EM detection algorithms, the collection of such image-specific libraries remains a tedious and time-consuming task. As an alternative, we suggest the use of a generic urban EM library, containing material spectra under varying conditions, acquired from different locations and sensors. This approach requires an efficient EM selection technique, capable of only selecting those spectra relevant for a specific image. In this paper, we evaluate and compare the potential of different existing library pruning algorithms (Iterative Endmember Selection and MUSIC) using simulated hyperspectral (APEX) data of the Brussels metropolitan area. In addition, we develop a new hybrid EM selection method which is shown to be highly efficient in dealing with both imagespecific and generic libraries, subsequently yielding more robust land cover classification results compared to existing methods. Future research will include further optimization of the proposed algorithm and additional tests on both simulated and real hyperspectral data.

THERMAL AND VISIBLE SATELLITE IMAGE FUSION USING WAVELET IN REMOTE SENSING AND SATELLITE IMAGE PROCESSING

Directory of Open Access Journals (Sweden)

A. H. Ahrari

2017-09-01

Full Text Available Multimodal remote sensing approach is based on merging different data in different portions of electromagnetic radiation that improves the accuracy in satellite image processing and interpretations. Remote Sensing Visible and thermal infrared bands independently contain valuable spatial and spectral information. Visible bands make enough information spatially and thermal makes more different radiometric and spectral information than visible. However low spatial resolution is the most important limitation in thermal infrared bands. Using satellite image fusion, it is possible to merge them as a single thermal image that contains high spectral and spatial information at the same time. The aim of this study is a performance assessment of thermal and visible image fusion quantitatively and qualitatively with wavelet transform and different filters. In this research, wavelet algorithm (Haar and different decomposition filters (mean.linear,ma,min and rand for thermal and panchromatic bands of Landast8 Satellite were applied as shortwave and longwave fusion method . Finally, quality assessment has been done with quantitative and qualitative approaches. Quantitative parameters such as Entropy, Standard Deviation, Cross Correlation, Q Factor and Mutual Information were used. For thermal and visible image fusion accuracy assessment, all parameters (quantitative and qualitative must be analysed with respect to each other. Among all relevant statistical factors, correlation has the most meaningful result and similarity to the qualitative assessment. Results showed that mean and linear filters make better fused images against the other filters in Haar algorithm. Linear and mean filters have same performance and there is not any difference between their qualitative and quantitative results.

Breast density estimation from high spectral and spatial resolution MRI

Science.gov (United States)

Li, Hui; Weiss, William A.; Medved, Milica; Abe, Hiroyuki; Newstead, Gillian M.; Karczmar, Gregory S.; Giger, Maryellen L.

2016-01-01

Abstract. A three-dimensional breast density estimation method is presented for high spectral and spatial resolution (HiSS) MR imaging. Twenty-two patients were recruited (under an Institutional Review Board--approved Health Insurance Portability and Accountability Act-compliant protocol) for high-risk breast cancer screening. Each patient received standard-of-care clinical digital x-ray mammograms and MR scans, as well as HiSS scans. The algorithm for breast density estimation includes breast mask generating, breast skin removal, and breast percentage density calculation. The inter- and intra-user variabilities of the HiSS-based density estimation were determined using correlation analysis and limits of agreement. Correlation analysis was also performed between the HiSS-based density estimation and radiologists’ breast imaging-reporting and data system (BI-RADS) density ratings. A correlation coefficient of 0.91 (pdensity estimations. An interclass correlation coefficient of 0.99 (pdensity estimations. A moderate correlation coefficient of 0.55 (p=0.0076) was observed between HiSS-based breast density estimations and radiologists’ BI-RADS. In summary, an objective density estimation method using HiSS spectral data from breast MRI was developed. The high reproducibility with low inter- and low intra-user variabilities shown in this preliminary study suggest that such a HiSS-based density metric may be potentially beneficial in programs requiring breast density such as in breast cancer risk assessment and monitoring effects of therapy. PMID:28042590

Wide-field Spatio-Spectral Interferometry: Bringing High Resolution to the Far- Infrared

Science.gov (United States)

Leisawitx, David

Wide-field spatio-spectral interferometry combines spatial and spectral interferometric data to provide integral field spectroscopic information over a wide field of view. This technology breaks through a mission cost barrier that stands in the way of resolving spatially and measuring spectroscopically at far-infrared wavelengths objects that will lead to a deep understanding of planetary system and galaxy formation processes. A space-based far-IR interferometer will combine Spitzer s superb sensitivity with a two order of magnitude gain in angular resolution, and with spectral resolution in the thousands. With the possible exception of detector technology, which is advancing with support from other research programs, the greatest challenge for far-IR interferometry is to demonstrate that the interferometer will actually produce the images and spectra needed to satisfy mission science requirements. With past APRA support, our team has already developed the highly specialized hardware testbed, image projector, computational model, and image construction software required for the proposed effort, and we have access to an ideal test facility.

Underwater welding using remote controlled robots. Development of remote underwater welding technology with a high power YAG laser

International Nuclear Information System (INIS)

Miwa, Yasuhiro; Sato, Syuuichi; Kojima, Toshio; Owaki, Katsura; Hirose, Naoya

2002-01-01

As components in nuclear power plant have been periodically carried out their inspection and repair to keep their integrity, on radioactive liquid wastes storage facility, because of difficulty on their inspection by human beings, some are remained without inspection, and even when capable of inspection, conversion from human works to remote operations is desired from a viewpoint of their operation efficiency upgrading. For response to these needs, some developments on a technology capable of carrying out inspection of their inside at underwater environment and repairing welding with YAG laser by means of remote operation, have been performed. Remote underwater inspection and repair technology is a combination technology of already applied underwater mobile technique (underwater inspection robot) with underwater YAG laser welding technique which is recently at actual using level. Therefore, this technology is composed of an inspection robot and a repair welding robot. And, testing results using the underwater inspection robot and welding test results using the underwater repair welding robot, were enough preferable to obtain forecasting applicable to actual apparatuses. This technology is especially effective for inspection and repair of inside of nuclear fuel cycle apparatuses and relatively high dose apparatuses, and can be thought to be applicable also to large capacity tanks, tanks dealing with harmful matters, underwater structures, and so on, in general industries. (G.K.)

Human-factors-based implementation of the remote characterization system high-level control station

International Nuclear Information System (INIS)

Noakes, M.W.; Richardson, B.S.; Rowe, J.C.; Draper, J.V.; Sandness, G.R.

1993-01-01

The detection and characterization of buried objects and materials is an important first step in the restoration of the numerous US Department of Energy (DOE) and US Department of Defense waste disposal sites. DOE, through its Environmental Restoration and Waste Management Robotics and Technology Development Program, has developed the Remote Characterization System (RCS) to address the needs of remote subsurfacecharacterization. The RCS consists of a low-metal-content (low-metallic-signature) remotely piloted vehicle, a high-level control station (HLCS) where operators can remotely control the vehicle and analyze real-time data from sensors, and an array of sensors that can be chosen to meet the survey task at hand. Communication between the vehicle and the base station is handled by a radio link. Site mapping is made possible through the use of geopositioning satellite data. The primary mode of vehicle operation is teleoperation, but provision has been made for semiautonomous or supervisory control that allows for automated sitesurvey on simple sites. Data analysis and display is supported for both real-time observation and postprocessing of data. The particular emphasis of this paper documents the human-factors-based design influences on the HLCS and describes the design in detail

Examination of Spectral Transformations on Spectral Mixture Analysis

Science.gov (United States)

Deng, Y.; Wu, C.

2018-04-01

While many spectral transformation techniques have been applied on spectral mixture analysis (SMA), few study examined their necessity and applicability. This paper focused on exploring the difference between spectrally transformed schemes and untransformed scheme to find out which transformed scheme performed better in SMA. In particular, nine spectrally transformed schemes as well as untransformed scheme were examined in two study areas. Each transformed scheme was tested 100 times using different endmember classes' spectra under the endmember model of vegetation- high albedo impervious surface area-low albedo impervious surface area-soil (V-ISAh-ISAl-S). Performance of each scheme was assessed based on mean absolute error (MAE). Statistical analysis technique, Paired-Samples T test, was applied to test the significance of mean MAEs' difference between transformed and untransformed schemes. Results demonstrated that only NSMA could exceed the untransformed scheme in all study areas. Some transformed schemes showed unstable performance since they outperformed the untransformed scheme in one area but weakened the SMA result in another region.

Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

Science.gov (United States)

Liu, Da; Li, Jianxun

2016-12-16

Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

Remote handling maintenance of ITER

International Nuclear Information System (INIS)

Haange, R.

1999-01-01

The remote maintenance strategy and the associated component design of the International Thermonuclear Experimental Reactor (ITER) have reached a high degree of completeness, especially with respect to those components that are expected to require frequent or occasional remote maintenance. Large-scale test stands, to demonstrate the principle feasibility of the remote maintenance procedures and to develop the required equipment and tools, were operational at the end of the Engineering Design Activities (EDA) phase. The initial results are highly encouraging: major remote equipment deployment and component replacement operations have been successfully demonstrated. (author)

GOSAT TIR spectral validation with High/Low temperature target using Aircraft base-FTS S-HIS

Science.gov (United States)

Kataoka, F.; Knuteson, R.; Taylor, J. K.; Kuze, A.; Shiomi, K.; Suto, H.; Yoshida, J.

2017-12-01

The Greenhouse gases Observing SATellite (GOSAT) was launched on January 2009. The GOSAT is equipped with TANSO-FTS (Fourier-Transform Spectrometer), which observe reflected solar radiation from the Earth's surface with shortwave infrared (SWIR) band and thermal emission from the Earth's surface and atmosphere with thermal infrared (TIR) band. The TIR band cover wide spectral range (650 - 1800 [cm-1]) with a high spectral resolution (0.2 [cm-1]). The TIR spectral information provide vertical distribution of CO2 and CH4. GOSAT has been operation more than eight years. In this long operation, GOSAT had experienced two big accidents; Rotation of one of the solar paddles stopped and sudden TANSO-FTS operation stop in May 2014 and cryocooler shutdown and restart in August - September 2015. These events affected the operation condition of the TIR photo-conductive (PC)-MCT detector. FTS technology using multiplex wide spectra needs wide dynamic range. PC detector has nonlinearity. Its correction needs accurate estimation of time-dependent offset. In current TIR Level 1B product version (V201), the non-photon level offset (Vdc_offset) estimated from on-orbit deep space calibration data and pre-launch background radiation model. But the background radiation and detector temperature have changed after cryocooler shutdown events. These changes are too small to detect from onboard temperature sensors. The next TIR Level 1B product uses cross calibration data together with deep space calibration data and instrument radiation model has been updated. This work describes the evaluation of new TIR Level 1B spectral quality with aircraft-based FTS; Scanning High-resolution Interferometer Sounder (S-HIS). The S-HIS mounted on the high-altitude ER-2 aircraft and flew at about 20km altitude. Because the observation geometry of GOSAT and S-HIS are quite different, we used the double difference method using atmospheric transfer model. GOSAT TIR band cover wide dynamic range, so we check

High resolution color imagery for orthomaps and remote sensing

Energy Technology Data Exchange (ETDEWEB)

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

2005-07-01

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

The Effects of High Frequency ULF Wave Activity on the Spectral Characteristics of Coherent HF Radar Returns

Science.gov (United States)

Wright, D. M.; Yeoman, T. K.; Woodfield, E. E.

2003-12-01

It is now a common practice to employ ground-based radars in order to distinguish between those regions of the Earth's upper atmosphere which are magnetically conjugate to open and closed field lines. Radar returns from ionospheric irregularities inside the polar cap and cusp regions generally exhibit large spectral widths in contrast to those which exist on closed field lines at lower latitudes. It has been suggested that the so-called Spectral Width Boundary (SWB) might act as a proxy for the open-closed field line boundary (OCFLB), which would then be an invaluable tool for investigating reconnection rates in the magnetosphere. The exact cause of the increased spectral widths observed at very high latitudes is still subject to considerable debate. Several mechanisms have been proposed. This paper compares a dusk-sector interval of coherent HF radar data with measurements made by an induction coil magnetometer located at Tromso, Norway (66° N geomagnetic). On this occasion, a series of transient regions of radar backscatter exhibiting large spectral widths are accompanied by increases in spectral power of ULF waves in the Pc1-2 frequency band. These observations would then, seem to support the possibility that high frequency magnetospheric wave activity at least contribute to the observed spectral characteristics and that such wave activity might play a significant role in the cusp and polar cap ionospheres.

Phylogenetic Structure of Foliar Spectral Traits in Tropical Forest Canopies

Directory of Open Access Journals (Sweden)

Kelly M. McManus

2016-02-01

Full Text Available The Spectranomics approach to tropical forest remote sensing has established a link between foliar reflectance spectra and the phylogenetic composition of tropical canopy tree communities vis-à-vis the taxonomic organization of biochemical trait variation. However, a direct relationship between phylogenetic affiliation and foliar reflectance spectra of species has not been established. We sought to develop this relationship by quantifying the extent to which underlying patterns of phylogenetic structure drive interspecific variation among foliar reflectance spectra within three Neotropical canopy tree communities with varying levels of soil fertility. We interpreted the resulting spectral patterns of phylogenetic signal in the context of foliar biochemical traits that may contribute to the spectral-phylogenetic link. We utilized a multi-model ensemble to elucidate trait-spectral relationships, and quantified phylogenetic signal for spectral wavelengths and traits using Pagel’s lambda statistic. Foliar reflectance spectra showed evidence of phylogenetic influence primarily within the visible and shortwave infrared spectral regions. These regions were also selected by the multi-model ensemble as those most important to the quantitative prediction of several foliar biochemical traits. Patterns of phylogenetic organization of spectra and traits varied across sites and with soil fertility, indicative of the complex interactions between the environmental and phylogenetic controls underlying patterns of biodiversity.

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

Directory of Open Access Journals (Sweden)

Yansheng Li

2016-08-01

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

Advanced and applied remote sensing of environmental conditions

Science.gov (United States)

Slonecker, E. Terrence; Fisher, Gary B.; Marr, David A.; Milheim, Lesley E.; Roig-Silva, Coral M.

2013-01-01

"Remote sensing” is a general 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 U.S. Geological Survey’s (USGS) Eastern Geographic Science Center (EGSC) is currently conducting and promoting the research and development of several different aspects of remote sensing science in both the laboratory and from overhead instruments. Spectroscopy is the science of recording interactions of energy and matter and is the bench science for all remote sensing. Visible and infrared analysis in the laboratory with special instruments called spectrometers enables the transfer of this research from the laboratory to multispectral (5–15 broad bands) and hyperspectral (50–300 narrow contiguous bands) analyses from aircraft and satellite sensors. In addition, mid-wave (3–5 micrometers, µm) and long-wave (8–14 µm) infrared data analysis, such as attenuated total reflectance (ATR) spectral analysis, are also conducted. ATR is a special form of vibrational infrared spectroscopy that has many applications in chemistry and biology but has recently been shown to be especially diagnostic for vegetation analysis.

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.

Spectral evolution of soft x-ray emission from optically thin, high electron temperature platinum plasmas

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Hiroyuki Hara

2017-08-01

Full Text Available The soft x-ray spectra of heavy element plasmas are frequently dominated by unresolved transition array (UTA emission. We describe the spectral evolution of an intense UTA under optically thin conditions in platinum plasmas. The UTA was observed to have a peak wavelength around 4.6 nm at line-of-sight averaged electron temperatures less than 1.4 keV at electron densities of (2.5–7.5 × 1013 cm−3. The UTA spectral structure was due to emission from 4d–4f transitions in highly charged ions with average charge states of q = 20–40. A numerical simulation successfully reproduced the observed spectral behavior.

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

REMOTE DIAGNOSTICS OF TURNOUTS STATE ON TIMING AND SPECTRAL COMPOSITION IN CURRENT CURVE

Directory of Open Access Journals (Sweden)

S. Yu. Buryak

2015-03-01

Full Text Available Purpose. Development and implementation the points system diagnostics that would allow determining remotely the current state of turnout with all possible faults, gradual and sudden failures, damages, and in real time to report about their appearance. Methodology. State diagnostics on the values analysis of turnout main parameters is proposed to carry out with the help of a computer and analog-to-digital converter (ADC. Connecting measurements performance is advisable to produce to a shunt ammeter, installed in the working circuit of the point feed panel. ADC converts the analog signal of lost volts at the shunt into digital form and transmits it to a computer which stores the received data on its own recording medium for their further processing and storage. There is special software that is capable to reconnect signal and construct its temporal characteristic as well as decompose it on the spectral components. Using it one can analyze the obtained data, which allows diagnosing state of points upon change the nature, values and composition of the current curve. Findings. The computer diagnosis method was confirmed in practice for possible indications of problems that are associated with both the mechanical part of the turnout and the electrical part of it, while controlling parameters such as the amount of current normal transition, when working on frictions, the duration of the transition, properly adjusted headset and attachment points, the state of the motor. Originality. The use of computer technology in the diagnosis of the state of turnouts during their operation to monitor the current values of technical indicators, analysis and storage for all types of electric switches with different types of engines both DC and AC occurs through digitization and recording signal from measuring shunt of point feeder panel. Practical value. The proposed method enables timely, still in the early stages of defect parts, damages or failures of nodes

Developing the remote sensing-based water environmental model for monitoring alpine river water environment over Plateau cold zone

Science.gov (United States)

You, Y.; Wang, S.; Yang, Q.; Shen, M.; Chen, G.

2017-12-01

Alpine river water environment on the Plateau (such as Tibetan Plateau, China) is a key indicator for water security and environmental security in China. Due to the complex terrain and various surface eco-environment, it is a very difficult to monitor the water environment over the complex land surface of the plateau. The increasing availability of remote sensing techniques with appropriate spatiotemporal resolutions, broad coverage and low costs allows for effective monitoring river water environment on the Plateau, particularly in remote and inaccessible areas where are lack of in situ observations. In this study, we propose a remote sense-based monitoring model by using multi-platform remote sensing data for monitoring alpine river environment. In this study some parameterization methodologies based on satellite remote sensing data and field observations have been proposed for monitoring the water environmental parameters (including chlorophyll-a concentration (Chl-a), water turbidity (WT) or water clarity (SD), total nitrogen (TN), total phosphorus (TP), and total organic carbon (TOC)) over the china's southwest highland rivers, such as the Brahmaputra. First, because most sensors do not collect multiple observations of a target in a single pass, data from multiple orbits or acquisition times may be used, and varying atmospheric and irradiance effects must be reconciled. So based on various types of satellite data, at first we developed the techniques of multi-sensor data correction, atmospheric correction. Second, we also built the inversion spectral database derived from long-term remote sensing data and field sampling data. Then we have studied and developed a high-precision inversion model over the southwest highland river backed by inversion spectral database through using the techniques of multi-sensor remote sensing information optimization and collaboration. Third, take the middle reaches of the Brahmaputra river as the study area, we validated the key

Spectral reflectance characteristics of soils in northeastern Brazil as influenced by salinity levels.

Science.gov (United States)

Pessoa, Luiz Guilherme Medeiros; Freire, Maria Betânia Galvão Dos Santos; Wilcox, Bradford Paul; Green, Colleen Heather Machado; De Araújo, Rômulo José Tolêdo; De Araújo Filho, José Coelho

2016-11-01

In northeastern Brazil, large swaths of once-productive soils have been severely degraded by soil salinization, but the true extent of the damage has not been assessed. Emerging remote sensing technology based on hyperspectral analysis offers one possibility for large-scale assessment, but it has been unclear to what extent the spectral properties of soils are related to salinity characteristics. The purpose of this study was to characterize the spectral properties of degraded (saline) and non-degraded agricultural soils in northeastern Brazil and determine the extent to which these properties correspond to soil salinity. We took soil samples from 78 locations within a 45,000-km 2 site in Pernambuco State. We used cluster analysis to group the soil samples on the basis of similarities in salinity and sodicity levels, and then obtained spectral data for each group. The physical properties analysis indicated a predominance of the coarse sand fraction in almost all the soil groups, and total porosity was similar for all the groups. The chemical analysis revealed different levels of degradation among the groups, ranging from non-degraded to strongly degraded conditions, as defined by the degree of salinity and sodicity. The soil properties showing the highest correlation with spectral reflectance were the exchangeable sodium percentage followed by fine sand. Differences in the reflectance curves for the various soil groups were relatively small and were not significant. These results suggest that, where soil crusts are not present, significant challenges remain for using hyperspectral remote sensing to assess soil salinity in northeastern Brazil.

Remote Sensing Information Science Research

Science.gov (United States)

Clarke, Keith C.; Scepan, Joseph; Hemphill, Jeffrey; Herold, Martin; Husak, Gregory; Kline, Karen; Knight, Kevin

2002-01-01

This document is the final report summarizing research conducted by the Remote Sensing Research Unit, Department of Geography, University of California, Santa Barbara under National Aeronautics and Space Administration Research Grant NAG5-10457. This document describes work performed during the period of 1 March 2001 thorough 30 September 2002. This report includes a survey of research proposed and performed within RSRU and the UCSB Geography Department during the past 25 years. A broad suite of RSRU research conducted under NAG5-10457 is also described under themes of Applied Research Activities and Information Science Research. This research includes: 1. NASA ESA Research Grant Performance Metrics Reporting. 2. Global Data Set Thematic Accuracy Analysis. 3. ISCGM/Global Map Project Support. 4. Cooperative International Activities. 5. User Model Study of Global Environmental Data Sets. 6. Global Spatial Data Infrastructure. 7. CIESIN Collaboration. 8. On the Value of Coordinating Landsat Operations. 10. The California Marine Protected Areas Database: Compilation and Accuracy Issues. 11. Assessing Landslide Hazard Over a 130-Year Period for La Conchita, California Remote Sensing and Spatial Metrics for Applied Urban Area Analysis, including: (1) IKONOS Data Processing for Urban Analysis. (2) Image Segmentation and Object Oriented Classification. (3) Spectral Properties of Urban Materials. (4) Spatial Scale in Urban Mapping. (5) Variable Scale Spatial and Temporal Urban Growth Signatures. (6) Interpretation and Verification of SLEUTH Modeling Results. (7) Spatial Land Cover Pattern Analysis for Representing Urban Land Use and Socioeconomic Structures. 12. Colorado River Flood Plain Remote Sensing Study Support. 13. African Rainfall Modeling and Assessment. 14. Remote Sensing and GIS Integration.

BIOME: An Ecosystem Remote Sensor Based on Imaging Interferometry

Science.gov (United States)

Peterson, David L.; Hammer, Philip; Smith, William H.; Lawless, James G. (Technical Monitor)

1994-01-01

Until recent times, optical remote sensing of ecosystem properties from space has been limited to broad band multispectral scanners such as Landsat and AVHRR. While these sensor data can be used to derive important information about ecosystem parameters, they are very limited for measuring key biogeochemical cycling parameters such as the chemical content of plant canopies. Such parameters, for example the lignin and nitrogen contents, are potentially amenable to measurements by very high spectral resolution instruments using a spectroscopic approach. Airborne sensors based on grating imaging spectrometers gave the first promise of such potential but the recent decision not to deploy the space version has left the community without many alternatives. In the past few years, advancements in high performance deep well digital sensor arrays coupled with a patented design for a two-beam interferometer has produced an entirely new design for acquiring imaging spectroscopic data at the signal to noise levels necessary for quantitatively estimating chemical composition (1000:1 at 2 microns). This design has been assembled as a laboratory instrument and the principles demonstrated for acquiring remote scenes. An airborne instrument is in production and spaceborne sensors being proposed. The instrument is extremely promising because of its low cost, lower power requirements, very low weight, simplicity (no moving parts), and high performance. For these reasons, we have called it the first instrument optimized for ecosystem studies as part of a Biological Imaging and Observation Mission to Earth (BIOME).

Unmanned Aerial Systems and Spectroscopy for Remote Sensing Applications in Archaeology

Science.gov (United States)

Themistocleous, K.; Agapiou, A.; Cuca, B.; Hadjimitsis, D. G.

2015-04-01

Remote sensing has open up new dimensions in archaeological research. Although there has been significant progress in increasing the resolution of space/aerial sensors and image processing, the detection of the crop (and soil marks) formations, which relate to buried archaeological remains, are difficult to detect since these marks may not be visible in the images if observed over different period or at different spatial/spectral resolution. In order to support the improvement of earth observation remote sensing technologies specifically targeting archaeological research, a better understanding of the crop/soil marks formation needs to be studied in detail. In this paper the contribution of both Unmanned Aerial Systems as well ground spectroradiometers is discussed in a variety of examples applied in the eastern Mediterranean region (Cyprus and Greece) as well in Central Europe (Hungary). In- situ spectroradiometric campaigns can be applied for the removal of atmospheric impact to simultaneous satellite overpass images. In addition, as shown in this paper, the systematic collection of ground truth data prior to the satellite/aerial acquisition can be used to detect the optimum temporal and spectral resolution for the detection of stress vegetation related to buried archaeological remains. Moreover, phenological studies of the crops from the area of interest can be simulated to the potential sensors based on their Relative Response Filters and therefore prepare better the satellite-aerial campaigns. Ground data and the use of Unmanned Aerial Systems (UAS) can provide an increased insight for studying the formation of crop and soil marks. New algorithms such as vegetation indices and linear orthogonal equations for the enhancement of crop marks can be developed based on the specific spectral characteristics of the area. As well, UAS can be used for remote sensing applications in order to document, survey and model cultural heritage and archaeological sites.

Evolutionary Computing Methods for Spectral Retrieval

Science.gov (United States)

Terrile, Richard; Fink, Wolfgang; Huntsberger, Terrance; Lee, Seugwon; Tisdale, Edwin; VonAllmen, Paul; Tinetti, Geivanna

2009-01-01

A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest. The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings. In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness.

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

Science.gov (United States)

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

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

Spectral unmixing of hyperspectral data to map bauxite deposits

Science.gov (United States)

Shanmugam, Sanjeevi; Abhishekh, P. V.

2006-12-01

This paper presents a study about the potential of remote sensing in bauxite exploration in the Kolli hills of Tamilnadu state, southern India. ASTER image (acquired in the VNIR and SWIR regions) has been used in conjunction with SRTM - DEM in this study. A new approach of spectral unmixing of ASTER image data delineated areas rich in alumina. Various geological and geomorphological parameters that control bauxite formation were also derived from the ASTER image. All these information, when integrated, showed that there are 16 cappings (including the existing mines) that satisfy most of the conditions favouring bauxitization in the Kolli Hills. The study concludes that spectral unmixing of hyperspectral satellite data in the VNIR and SWIR regions may be combined with the terrain parameters to get accurate information about bauxite deposits, including their quality.

Highly active vitrification plant remote handling operational experience and improvements

International Nuclear Information System (INIS)

Milgate, I.

1996-01-01

All the main process plant and equipment at the Sellafield Waste Vitrification Plant (WVP) is enclosed in heavily shielded concrete walled cells. There is a large quantity of relatively complex plant and equipment which must be remotely operated, maintained or replaced in-cell in a severe environment. The WVP has five in-cell polar cranes which are of modular construction to aid replacement of failed components. Each can be withdrawn into a shielded cell extension for decontamination and hands-on maintenance. The cells have a total of 80 through wall tube positions to receive Master Slave Manipulators (MSMs). The MSMs are used where possible for ''pick and place'' purposes but are often called upon to position substantial pieces of mechanical equipment and thus are subject to heavy loading and high failure rates. An inward flow of air is maintained in the active cells. The discharged air passes through a filter cell where remote damper operation filter changing and maintenance is carried out by means of a PAR3000 manipulator. A Nuclear Engineered Advanced Teleoperated Robot (Neater) swabs the vitrified product container to ensure cleanliness before storage. There is a significant arising of solid radioactive waste from replaced in-cell items which undergoes sorting and size reduction in a breakdown cell equipped with a large reciprocating saw and a hydraulic shear. Improvements to the remote handling facilities made in the light of operational experience are described. (UK)

Hyperspectral remote sensing exploration of carbonatite - an example from Epembe, Kunene region, Namibia

Science.gov (United States)

Zimmermann, Robert; Brandmeier, Melanie; Andreani, Louis; Gloaguen, Richard

2015-04-01

Remote sensing data can provide valuable information about ore deposits and their alteration zones at surface level. High spectral and spatial resolution of the data is essential for detailed mapping of mineral abundances and related structures. Carbonatites are well known for hosting economic enrichments in REE, Ta, Nb and P (Jones et al. 2013). These make them a preferential target for exploration for those critical elements. In this study we show how combining geomorphic, textural and spectral data improves classification result. We selected a site with a well-known occurrence in northern Namibia: the Epembe dyke. For analysis LANDSAT 8, SRTM and airborne hyperspectral (HyMap) data were chosen. The overlapping data allows a multi-scale and multi-resolution approach. Results from data analysis were validated during fieldwork in 2014. Data was corrected for atmospherical and geometrical effects. Image classification, mineral mapping and tectonic geomorphology allow a refinement of the geological map by lithological mapping in a second step. Detailed mineral abundance maps were computed using spectral unmixing techniques. These techniques are well suited to map abundances of carbonate minerals, but not to discriminate the carbonatite itself from surrounding rocks with similar spectral signatures. Thus, geometric indices were calculated using tectonic geomorphology and textures. For this purpose the TecDEM-toolbox (SHAHZAD & GLOAGUEN 2011) was applied to the SRTM-data for geomorphic analysis. Textural indices (e.g. uniformity, entropy, angular second moment) were derived from HyMap and SRTM by a grey-level co-occurrence matrix (CLAUSI 2002). The carbonatite in the study area is ridge-forming and shows a narrow linear feature in the textural bands. Spectral and geometric information were combined using kohonen Self-Organizing Maps (SOM) for unsupervised clustering. The resulting class spectra were visually compared and interpreted. Classes with similar signatures

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

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

Science.gov (United States)

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

2017-12-01

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

STANDARDIZING QUALITY ASSESSMENT OF FUSED REMOTELY SENSED IMAGES

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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.

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

Directory of Open Access Journals (Sweden)

Chunyan Wang

2018-05-01

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

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.

A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping

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Rodrigo A. Garcia

2015-10-01

Full Text Available Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remote sensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth, sensor and environmental noise, spectral resolution of the sensor and spectral variability of the benthic classes. In this paper, we present a simple hierarchical clustering method coupled with a shallow water forward model to generate water-column specific spectral libraries. This technique requires no prior decision on the number of classes to output: the resultant classes are optically separable above the spectral noise introduced by the sensor, image based radiometric corrections, the benthos’ natural spectral variability and the attenuating properties of a variable water column at depth. The modeling reveals the effect reducing the spectral resolution has on the number and type of classes that are optically distinct. We illustrate the potential of this clustering algorithm in an analysis of the conditions, including clustering accuracy, sensor spectral resolution and water column optical properties and depth that enabled the spectral distinction of the seagrass Amphibolis antartica from benthic algae.

High-definition television evaluation for remote handling task performance

International Nuclear Information System (INIS)

Fujita, Y.; Omori, E.; Hayashi, S.; Draper, J.V.; Herndon, J.N.

1986-01-01

This paper describes experiments designed to evaluate the impact of HDTV on the performance of typical remote tasks. The experiments described in this paper compared the performance of four operators using HDTV with their performance while using other television systems. The experiments included four television systems: (1) high-definition color television, (2) high-definition monochromatic television, (3) standard-resolution monochromatic television, and (4) standard-resolution stereoscopic monochromatic television. The stereo system accomplished stereoscopy by displaying two cross-polarized images, one reflected by a half-silvered mirror and one seen through the mirror. Observers wore a pair of glasses with cross-polarized lenses so that the left eye received only the view from the left camera and the right eye received only the view from the right camera

Review of oil spill remote sensing.

Science.gov (United States)

Fingas, Merv; Brown, Carl

2014-06-15

Remote-sensing for oil spills is reviewed. The use of visible techniques is ubiquitous, however it gives only the same results as visual monitoring. Oil has no particular spectral features that would allow for identification among the many possible background interferences. Cameras are only useful to provide documentation. In daytime oil absorbs light and remits this as thermal energy at temperatures 3-8K above ambient, this is detectable by infrared (IR) cameras. Laser fluorosensors are useful instruments because of their unique capability to identify oil on backgrounds that include water, soil, weeds, ice and snow. They are the only sensor that can positively discriminate oil on most backgrounds. Radar detects oil on water by the fact that oil will dampen water-surface capillary waves under low to moderate wave/wind conditions. Radar offers the only potential for large area searches, day/night and foul weather remote sensing. Copyright © 2014 Elsevier Ltd. All rights reserved.

Monitoring and predicting crop growth and analysing agricultural ecosystems by remote sensing

Directory of Open Access Journals (Sweden)

Tsuyoshi Akiyama

1996-05-01

Full Text Available LANDSAT/TM data, which are characterized by high spectral/spatial resolutions, are able to contribute to practical agricultural management. In the first part of the paper, the authors review some recent applications of satellite remote sensing in agriculture. Techniques for crop discrimination and mapping have made such rapid progress that we can classify crop types with more than 80% accuracy. The estimation of crop biomass using satellite data, including leaf area, dry and fresh weights, and the prediction of grain yield, has been attempted using various spectral vegetation indices. Plant stresses caused by nutrient deficiency and water deficit have also been analysed successfully. Such information may be useful for farm management. In the latter half of the paper, we introduce the Arctic Science Project, which was carried out under the Science and Technology Agency of Japan collaborating with Finnish scientists. In this project, monitoring of the boreal forest was carried out using LANDSAT data. Changes in the phenology of subarctic ground vegetation, based on spectral properties, were measured by a boom-mounted, four-band spectroradiometer. The turning point dates of the seasonal near-infrared (NIR and red (R reflectance factors might indicate the end of growth and the beginning of autumnal tints, respectively.

Remote Monitoring of Forest Insect Defoliation -A Review-

Directory of Open Access Journals (Sweden)

C.D. Rullan-Silva

2013-12-01

Full Text Available Aim of study: This paper reviews the global research during the last 6 years (2007-2012 on the state, trends and potential of remote sensing for detecting, mapping and monitoring forest defoliation caused by insects.Area of study: The review covers research carried out within different countries in Europe and America.Main results: A nation or region wide monitoring system should be scaled in two levels, one using time-series with moderate to coarse resolutions, and the other with fine or high resolution. Thus, MODIS data is increasingly used for early warning detection, whereas Landsat data is predominant in defoliation damage research. Furthermore, ALS data currently stands as the more promising option for operative detection of defoliation.Vegetation indices based on infrared-medium/near-infrared ratios and on moisture content indicators are of great potential for mapping insect pest defoliation, although NDVI is the most widely used and tested.Research highlights: Among most promising methods for insect defoliation monitoring are Spectral Mixture Analysis, best suited for detection due to its sub-pixel recognition enhancing multispectral data, and use of logistic models as function of vegetation index change between two dates, recommended for predicting defoliation.Key words: vegetation damage; pest outbreak; spectral change detection.

Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: Implications for onshore exploration and monitoring

Science.gov (United States)

Scafutto, Rebecca Del'Papa Moreira; de Souza Filho, Carlos Roberto; de Oliveira, Wilson José

2017-06-01

Remote detection and mapping of hydrocarbons (PHCs) in situ in continental areas is still an operational challenge due to the small scale of the occurrences and the mix of spectral signatures of PHCs and mineral substrates in imagery pixels. Despite the increasing development of new technologies, the use of hyperspectral remote sensing data as a complementary tool for both oil exploration and environmental monitoring is not standard in the oil industry, despite its potential. The high spectral resolution of hyperspectral images allows the direct identification of PHCs on the surface and provides valuable information regarding the location and spread of oil spills that can assist in containment and cleanup operations. Combining the spectral information with statistical techniques also offers the potential to improve exploration programs focused on the discovery of new exploration fields through the qualitative and quantitative characterization of oil occurrences in onshore areas. In this scenario, the aim of this work was to develop methods that can assist the detection of continental areas affected by natural oil seeps or leaks (crude oils and fuels). A field experiment was designed by impregnating several mineral substrates with crude oils and fuels in varying concentrations. Simultaneous measurements of soil-PHC combinations were taken using both a hand-held spectrometer and an airborne hyperspectral imager. Classification algorithms were used to directly map the PHCs on the surface. Spectral information was submitted to a PLS (partial least square regression) to create a prediction model for the estimation of the concentrations of PHCs in soils. The developed model was able to detect three impregnation levels (low, intermediate, high), predicting values close to the concentrations used in the experiment. Given the quality of the results in controlled experiments, the methods developed in this research show the potential to support the oil industry in the

[Vegetation index estimation by chlorophyll content of grassland based on spectral analysis].

Science.gov (United States)

Xiao, Han; Chen, Xiu-Wan; Yang, Zhen-Yu; Li, Huai-Yu; Zhu, Han

2014-11-01

Comparing the methods of existing remote sensing research on the estimation of chlorophyll content, the present paper confirms that the vegetation index is one of the most practical and popular research methods. In recent years, the increasingly serious problem of grassland degradation. This paper, firstly, analyzes the measured reflectance spectral curve and its first derivative curve in the grasslands of Songpan, Sichuan and Gongger, Inner Mongolia, conducts correlation analysis between these two spectral curves and chlorophyll content, and finds out the regulation between REP (red edge position) and grassland chlorophyll content, that is, the higher the chlorophyll content is, the higher the REIP (red-edge inflection point) value would be. Then, this paper constructs GCI (grassland chlorophyll index) and selects the most suitable band for retrieval. Finally, this paper calculates the GCI by the use of satellite hyperspectral image, conducts the verification and accuracy analysis of the calculation results compared with chlorophyll content data collected from field of twice experiments. The result shows that for grassland chlorophyll content, GCI has stronger sensitivity than other indices of chlorophyll, and has higher estimation accuracy. GCI is the first proposed to estimate the grassland chlorophyll content, and has wide application potential for the remote sensing retrieval of grassland chlorophyll content. In addition, the grassland chlorophyll content estimation method based on remote sensing retrieval in this paper provides new research ideas for other vegetation biochemical parameters' estimation, vegetation growth status' evaluation and grassland ecological environment change's monitoring.

Small-angle scattering of polychromatic X-rays: effects of bandwidth, spectral shape and high harmonics

Energy Technology Data Exchange (ETDEWEB)

Chen, Sen; Luo, Sheng-Nian

2018-02-16

Polychromatic X-ray sources can be useful for photon-starved small-angle X-ray scattering given their high spectral fluxes. Their bandwidths, however, are 10–100 times larger than those using monochromators. To explore the feasibility, ideal scattering curves of homogeneous spherical particles for polychromatic X-rays are calculated and analyzed using the Guinier approach, maximum entropy and regularization methods. Monodisperse and polydisperse systems are explored. The influence of bandwidth and asymmetric spectra shape are exploredviaGaussian and half-Gaussian spectra. Synchrotron undulator spectra represented by two undulator sources of the Advanced Photon Source are examined as an example, as regards the influence of asymmetric harmonic shape, fundamental harmonic bandwidth and high harmonics. The effects of bandwidth, spectral shape and high harmonics on particle size determination are evaluated quantitatively.

Small-angle scattering of polychromatic X-rays: effects of bandwidth, spectral shape and high harmonics.

Science.gov (United States)

Chen, Sen; Luo, Sheng Nian

2018-03-01

Polychromatic X-ray sources can be useful for photon-starved small-angle X-ray scattering given their high spectral fluxes. Their bandwidths, however, are 10-100 times larger than those using monochromators. To explore the feasibility, ideal scattering curves of homogeneous spherical particles for polychromatic X-rays are calculated and analyzed using the Guinier approach, maximum entropy and regularization methods. Monodisperse and polydisperse systems are explored. The influence of bandwidth and asymmetric spectra shape are explored via Gaussian and half-Gaussian spectra. Synchrotron undulator spectra represented by two undulator sources of the Advanced Photon Source are examined as an example, as regards the influence of asymmetric harmonic shape, fundamental harmonic bandwidth and high harmonics. The effects of bandwidth, spectral shape and high harmonics on particle size determination are evaluated quantitatively.

Spectral evolution of galaxies

International Nuclear Information System (INIS)

Rocca-Volmerange, B.

1989-01-01

A recent striking event in Observational Cosmology is the discovery of a large population of galaxies at extreme cosmological distances (extended from spectral redshifts ≅ 1 to ≥ 3) corresponding to a lookback time of 80% of the Universe's age. However when galaxies are observed at such remote epochs, their appearances are affected by at least two simultaneous effects which are respectively a cosmological effect and the intrinsic evolution of their stellar populations which appear younger than in our nearby galaxies. The fundamental problem is first to disentangle the respective contributions of these two effects to apparent magnitudes and colors of distant galaxies. Other effects which are likely to modify the appearance of galaxies are amplification by gravitational lensing and interaction with environment will also be considered. (author)

Collection and spectral control of high-order harmonics generated with a 50 W high-repetition rate Ytterbium femtosecond laser system

International Nuclear Information System (INIS)

Cabasse, A; Hazera, Ch; Quintard, L; Cormier, E; Petit, S; Constant, E

2016-01-01

We generate high-order harmonics with a 50 W, Yb femtosecond fiber laser system operating at 100 kHz in a tight focusing configuration. We achieve a high photon flux even with pulses longer than 500 fs. We collect the diverging extreme ultraviolet (XUV) harmonic beam in a 35 mrad wide solid angle by using a spectrometer designed to handle the high thermal load under vacuum and refocus the XUV beam onto a detector where the beam is characterised or can alternatively be used for experiments. This setup is designed for a 50 eV XUV bandwidth and offers the possibility to perform XUV-IR pump probe experiments with both temporal and spectral resolution. The high-order harmonics were generated and optimized at 100 kHz by using several gas target geometries (a gas jet and a semi-infinite gas cell) and several gases (argon, krypton, xenon) that provide XUV beams with different characteristics. After the spectrometer and for high-order harmonic generation (HHG) in xenon, we detect more than 4 × 10 10 photons per second over four harmonics, that is a useful XUV power on target of 0.1 μW. This corresponds to the emission of more than 1 μW per harmonic at the source and we achieved a similar flux with both the semi-infinite cell and the jet. In addition, we observe a strong spectral selectivity when generating harmonics in a semi-infinite gas cell as few harmonics clearly dominate the neighbouring harmonics. We attribute this spectral selectivity to phase matching effects. (paper)

Applicability of spectral indices on thickness identification of oil slick

Science.gov (United States)

Niu, Yanfei; Shen, Yonglin; Chen, Qihao; Liu, Xiuguo

2016-10-01

Hyperspectral remote sensing technology has played a vital role in the identification and monitoring of oil spill events, and amount of spectral indices have been developed. In this paper, the applicability of six frequently-used indices is analyzed, and a combination of spectral indices in aids of support vector machine (SVM) algorithm is used to identify the oil slicks and corresponding thickness. The six spectral indices are spectral rotation (SR), spectral absorption depth (HI), band ratio of blue and green (BG), band ratio of BG and shortwave infrared index (BGN), 555nm and 645nm normalized by the blue band index (NB) and spectral slope (ND). The experimental study is conducted in the Gulf of Mexico oil spill zone, with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery captured in May 17, 2010. The results show that SR index is the best in all six indices, which can effectively distinguish the thickness of the oil slick and identify it from seawater; HI index and ND index can obviously distinguish oil slick thickness; BG, BGN and NB are more suitable to identify oil slick from seawater. With the comparison among different kernel functions of SVM, the classify accuracy show that the polynomial and RBF kernel functions have the best effect on the separation of oil slick thickness and the relatively pure seawater. The applicability of spectral indices of oil slick and the method of oil film thickness identification will in aids of oil/gas exploration and oil spill monitoring.

Subsurface water parameters: optimization approach to their determination from remotely sensed water color data.

Science.gov (United States)

Jain, S C; Miller, J R

1976-04-01

A method, using an optimization scheme, has been developed for the interpretation of spectral albedo (or spectral reflectance) curves obtained from remotely sensed water color data. This method used a two-flow model of the radiation flow and solves for the albedo. Optimization fitting of predicted to observed reflectance data is performed by a quadratic interpolation method for the variables chlorophyll concentration and scattering coefficient. The technique is applied to airborne water color data obtained from Kawartha Lakes, Sargasso Sea, and Nova Scotia coast. The modeled spectral albedo curves are compared to those obtained experimentally, and the computed optimum water parameters are compared to ground truth values. It is shown that the backscattered spectral signal contains information that can be interpreted to give quantitative estimates of the chlorophyll concentration and turbidity in the waters studied.

Support Vector Machines for Hyperspectral Remote Sensing Classification