Ground observations and remote sensing data for integrated modelisation of water budget in the Merguellil catchment, Tunisia

NASA Astrophysics Data System (ADS)

Mougenot, Bernard

2016-04-01

The Mediterranean region is affected by water scarcity. Some countries as Tunisia reached the limit of 550 m3/year/capita due overexploitation of low water resources for irrigation, domestic uses and industry. A lot of programs aim to evaluate strategies to improve water consumption at regional level. In central Tunisia, on the Merguellil catchment, we develop integrated water resources modelisations based on social investigations, ground observations and remote sensing data. The main objective is to close the water budget at regional level and to estimate irrigation and water pumping to test scenarios with endusers. Our works benefit from French, bilateral and European projects (ANR, MISTRALS/SICMed, FP6, FP7…), GMES/GEOLAND-ESA) and also network projects as JECAM and AERONET, where the Merguellil site is a reference. This site has specific characteristics associating irrigated and rainfed crops mixing cereals, market gardening and orchards and will be proposed as a new environmental observing system connected to the OMERE, TENSIFT and OSR systems respectively in Tunisia, Morocco and France. We show here an original and large set of ground and remote sensing data mainly acquired from 2008 to present to be used for calibration/validation of water budget processes and integrated models for present and scenarios: - Ground data: meteorological stations, water budget at local scale: fluxes tower, soil fluxes, soil and surface temperature, soil moisture, drainage, flow, water level in lakes, aquifer, vegetation parameters on selected fieds/month (LAI, height, biomass, yield), land cover: 3 times/year, bare soil roughness, irrigation and pumping estimations, soil texture. - Remote sensing data: remote sensing products from multi-platform (MODIS, SPOT, LANDSAT, ASTER, PLEIADES, ASAR, COSMO-SkyMed, TerraSAR X…), multi-wavelength (solar, micro-wave and thermal) and multi-resolution (0.5 meters to 1 km). Ground observations are used (1) to calibrate soil-vegetation-atmosphere models at field scale on different compartment and irrigated and rainfed land during a limited time (seasons or set of dry and wet years), (2) to calibrate and validate particularly evapotranspiration derived from multi-wavelength satellite data at watershed level in relationships with the aquifer conditions: pumping and recharge rate. We will point out some examples.

[Small unmanned aerial vehicles for low-altitude remote sensing and its application progress in ecology.

PubMed

Sun, Zhong Yu; Chen, Yan Qiao; Yang, Long; Tang, Guang Liang; Yuan, Shao Xiong; Lin, Zhi Wen

2017-02-01

Low-altitude unmanned aerial vehicles (UAV) remote sensing system overcomes the deficiencies of space and aerial remote sensing system in resolution, revisit period, cloud cover and cost, which provides a novel method for ecological research on mesoscale. This study introduced the composition of UAV remote sensing system, reviewed its applications in species, population, community and ecosystem ecology research. Challenges and opportunities of UAV ecology were identified to direct future research. The promising research area of UAV ecology includes the establishment of species morphology and spectral characteristic data base, species automatic identification, the revelation of relationship between spectral index and plant physiological processes, three-dimension monitoring of ecosystem, and the integration of remote sensing data from multi resources and multi scales. With the development of UAV platform, data transformation and sensors, UAV remote sensing technology will have wide application in ecology research.

Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion

PubMed Central

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G.; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Background Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. Methodology and Principal Findings A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Conclusion and Significance Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level. PMID:22235254

Mapping migratory bird prevalence using remote sensing data fusion.

PubMed

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.

[The progress in retrieving land surface temperature based on thermal infrared and microwave remote sensing technologies].

PubMed

Zhang, Jia-Hua; Li, Xin; Yao, Feng-Mei; Li, Xian-Hua

2009-08-01

Land surface temperature (LST) is an important parameter in the study on the exchange of substance and energy between land surface and air for the land surface physics process at regional and global scales. Many applications of satellites remotely sensed data must provide exact and quantificational LST, such as drought, high temperature, forest fire, earthquake, hydrology and the vegetation monitor, and the models of global circulation and regional climate also need LST as input parameter. Therefore, the retrieval of LST using remote sensing technology becomes one of the key tasks in quantificational remote sensing study. Normally, in the spectrum bands, the thermal infrared (TIR, 3-15 microm) and microwave bands (1 mm-1 m) are important for retrieval of the LST. In the present paper, firstly, several methods for estimating the LST on the basis of thermal infrared (TIR) remote sensing were synthetically reviewed, i. e., the LST measured with an ground-base infrared thermometer, the LST retrieval from mono-window algorithm (MWA), single-channel algorithm (SCA), split-window techniques (SWT) and multi-channels algorithm(MCA), single-channel & multi-angle algorithm and multi-channels algorithm & multi-angle algorithm, and retrieval method of land surface component temperature using thermal infrared remotely sensed satellite observation. Secondly, the study status of land surface emissivity (epsilon) was presented. Thirdly, in order to retrieve LST for all weather conditions, microwave remotely sensed data, instead of thermal infrared data, have been developed recently, and the LST retrieval method from passive microwave remotely sensed data was also introduced. Finally, the main merits and shortcomings of different kinds of LST retrieval methods were discussed, respectively.

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

NASA Astrophysics Data System (ADS)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure and increase the robustness of the proposed algorithm. The proposed algorithm is validated with a publicly available 10-class object detection dataset.

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

NASA Astrophysics Data System (ADS)

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.

Flood risks in urbanized areas - multi-sensoral approaches using remotely sensed data for risk assessment

NASA Astrophysics Data System (ADS)

Taubenböck, H.; Wurm, M.; Netzband, M.; Zwenzner, H.; Roth, A.; Rahman, A.; Dech, S.

2011-02-01

Estimating flood risks and managing disasters combines knowledge in climatology, meteorology, hydrology, hydraulic engineering, statistics, planning and geography - thus a complex multi-faceted problem. This study focuses on the capabilities of multi-source remote sensing data to support decision-making before, during and after a flood event. With our focus on urbanized areas, sample methods and applications show multi-scale products from the hazard and vulnerability perspective of the risk framework. From the hazard side, we present capabilities with which to assess flood-prone areas before an expected disaster. Then we map the spatial impact during or after a flood and finally, we analyze damage grades after a flood disaster. From the vulnerability side, we monitor urbanization over time on an urban footprint level, classify urban structures on an individual building level, assess building stability and quantify probably affected people. The results show a large database for sustainable development and for developing mitigation strategies, ad-hoc coordination of relief measures and organizing rehabilitation.

Objected-oriented remote sensing image classification method based on geographic ontology model

NASA Astrophysics Data System (ADS)

Chu, Z.; Liu, Z. J.; Gu, H. Y.

2016-11-01

Nowadays, with the development of high resolution remote sensing image and the wide application of laser point cloud data, proceeding objected-oriented remote sensing classification based on the characteristic knowledge of multi-source spatial data has been an important trend on the field of remote sensing image classification, which gradually replaced the traditional method through improving algorithm to optimize image classification results. For this purpose, the paper puts forward a remote sensing image classification method that uses the he characteristic knowledge of multi-source spatial data to build the geographic ontology semantic network model, and carries out the objected-oriented classification experiment to implement urban features classification, the experiment uses protégé software which is developed by Stanford University in the United States, and intelligent image analysis software—eCognition software as the experiment platform, uses hyperspectral image and Lidar data that is obtained through flight in DaFeng City of JiangSu as the main data source, first of all, the experiment uses hyperspectral image to obtain feature knowledge of remote sensing image and related special index, the second, the experiment uses Lidar data to generate nDSM(Normalized DSM, Normalized Digital Surface Model),obtaining elevation information, the last, the experiment bases image feature knowledge, special index and elevation information to build the geographic ontology semantic network model that implement urban features classification, the experiment results show that, this method is significantly higher than the traditional classification algorithm on classification accuracy, especially it performs more evidently on the respect of building classification. The method not only considers the advantage of multi-source spatial data, for example, remote sensing image, Lidar data and so on, but also realizes multi-source spatial data knowledge integration and application of the knowledge to the field of remote sensing image classification, which provides an effective way for objected-oriented remote sensing image classification in the future.

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE)

PubMed Central

Tian, Xin; Li, Zengyuan; Chen, Erxue; Liu, Qinhuo; Yan, Guangjian; Wang, Jindi; Niu, Zheng; Zhao, Shaojie; Li, Xin; Pang, Yong; Su, Zhongbo; van der Tol, Christiaan; Liu, Qingwang; Wu, Chaoyang; Xiao, Qing; Yang, Le; Mu, Xihan; Bo, Yanchen; Qu, Yonghua; Zhou, Hongmin; Gao, Shuai; Chai, Linna; Huang, Huaguo; Fan, Wenjie; Li, Shihua; Bai, Junhua; Jiang, Lingmei; Zhou, Ji

2015-01-01

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques. PMID:26332035

Operational considerations for the application of remotely sensed forest data from LANDSAT or other airborne platforms

NASA Technical Reports Server (NTRS)

Baker, G. R.; Fethe, T. P.

1975-01-01

Research in the application of remotely sensed data from LANDSAT or other airborne platforms to the efficient management of a large timber based forest industry was divided into three phases: (1) establishment of a photo/ground sample correlation, (2) investigation of techniques for multi-spectral digital analysis, and (3) development of a semi-automated multi-level sampling system. To properly verify results, three distinct test areas were selected: (1) Jacksonville Mill Region, Lower Coastal Plain, Flatwoods, (2) Pensacola Mill Region, Middle Coastal Plain, and (3) Mississippi Mill Region, Middle Coastal Plain. The following conclusions were reached: (1) the probability of establishing an information base suitable for management requirements through a photo/ground double sampling procedure, alleviating the ground sampling effort, is encouraging, (2) known classification techniques must be investigated to ascertain the level of precision possible in separating the many densities involved, and (3) the multi-level approach must be related to an information system that is executable and feasible.

Farm Management Support on Cloud Computing Platform: A System for Cropland Monitoring Using Multi-Source Remotely Sensed Data

NASA Astrophysics Data System (ADS)

Coburn, C. A.; Qin, Y.; Zhang, J.; Staenz, K.

2015-12-01

Food security is one of the most pressing issues facing humankind. Recent estimates predict that over one billion people don't have enough food to meet their basic nutritional needs. The ability of remote sensing tools to monitor and model crop production and predict crop yield is essential for providing governments and farmers with vital information to ensure food security. Google Earth Engine (GEE) is a cloud computing platform, which integrates storage and processing algorithms for massive remotely sensed imagery and vector data sets. By providing the capabilities of storing and analyzing the data sets, it provides an ideal platform for the development of advanced analytic tools for extracting key variables used in regional and national food security systems. With the high performance computing and storing capabilities of GEE, a cloud-computing based system for near real-time crop land monitoring was developed using multi-source remotely sensed data over large areas. The system is able to process and visualize the MODIS time series NDVI profile in conjunction with Landsat 8 image segmentation for crop monitoring. With multi-temporal Landsat 8 imagery, the crop fields are extracted using the image segmentation algorithm developed by Baatz et al.[1]. The MODIS time series NDVI data are modeled by TIMESAT [2], a software package developed for analyzing time series of satellite data. The seasonality of MODIS time series data, for example, the start date of the growing season, length of growing season, and NDVI peak at a field-level are obtained for evaluating the crop-growth conditions. The system fuses MODIS time series NDVI data and Landsat 8 imagery to provide information of near real-time crop-growth conditions through the visualization of MODIS NDVI time series and comparison of multi-year NDVI profiles. Stakeholders, i.e., farmers and government officers, are able to obtain crop-growth information at crop-field level online. This unique utilization of GEE in combination with advanced analytic and extraction techniques provides a vital remote sensing tool for decision makers and scientists with a high-degree of flexibility to adapt to different uses.

Analysing and correcting the differences between multi-source and multi-scale spatial remote sensing observations.

PubMed

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

2014-01-01

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

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

PubMed Central

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

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation. PMID:25405760

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

PubMed

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

2012-12-01

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

Impact of NO2 horizontal heterogeneity on tropospheric NO2 vertical columns retrieved from satellite, multi-axis differential optical absorption spectroscopy, and in situ measurements

NASA Astrophysics Data System (ADS)

Mendolia, D.; D'Souza, R. J. C.; Evans, G. J.; Brook, J.

2013-01-01

Tropospheric NO2 vertical column densities were retrieved for the first time in Toronto, Canada using three methods of differing spatial scales. Remotely-sensed NO2 vertical column densities, retrieved from multi-axis differential optical absorption spectroscopy and satellite remote sensing, were evaluated by comparison with in situ vertical column densities derived using a pair of chemiluminescence monitors situated 0.01 and 0.5 km above ground level. The chemiluminescence measurements were corrected for the influence of NOz, which reduced the NO2 concentrations at 0.01 and 0.5 km by 8 ± 1% and 12 ± 1%, respectively. The average absolute decrease in the chemiluminescence NO2 measurement as a result of this correction was less than 1 ppb. Good correlation was observed between the remotely sensed and in situ NO2 vertical column densities (Pearson R ranging from 0.68 to 0.79), but the in situ vertical column densities were 27% to 55% greater than the remotely-sensed columns. These results indicate that NO2 horizontal heterogeneity strongly impacted the magnitude of the remotely-sensed columns. The in situ columns reflected an urban environment with major traffic sources, while the remotely-sensed NO2 vertical column densities were representative of the region, which included spatial heterogeneity introduced by residential neighbourhoods and Lake Ontario. Despite the difference in absolute values, the reasonable correlation between the vertical column densities determined by three distinct methods increased confidence in the validity of the values provided by each of the methods.

PolarBRDF: A general purpose Python package for visualization and quantitative analysis of multi-angular remote sensing measurements

NASA Astrophysics Data System (ADS)

Singh, Manoj K.; Gautam, Ritesh; Gatebe, Charles K.; Poudyal, Rajesh

2016-11-01

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wildfire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

PolarBRDF: A general purpose Python package for visualization and quantitative analysis of multi-angular remote sensing measurements

NASA Astrophysics Data System (ADS)

Poudyal, R.; Singh, M.; Gautam, R.; Gatebe, C. K.

2016-12-01

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR)- http://car.gsfc.nasa.gov/. Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wildfire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

Polarbrdf: A General Purpose Python Package for Visualization Quantitative Analysis of Multi-Angular Remote Sensing Measurements

NASA Technical Reports Server (NTRS)

Singh, Manoj K.; Gautam, Ritesh; Gatebe, Charles K.; Poudyal, Rajesh

2016-01-01

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wild fire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

Disaster Emergency Rapid Assessment Based on Remote Sensing and Background Data

NASA Astrophysics Data System (ADS)

Han, X.; Wu, J.

2018-04-01

The period from starting to the stable conditions is an important stage of disaster development. In addition to collecting and reporting information on disaster situations, remote sensing images by satellites and drones and monitoring results from disaster-stricken areas should be obtained. Fusion of multi-source background data such as population, geography and topography, and remote sensing monitoring information can be used in geographic information system analysis to quickly and objectively assess the disaster information. According to the characteristics of different hazards, the models and methods driven by the rapid assessment of mission requirements are tested and screened. Based on remote sensing images, the features of exposures quickly determine disaster-affected areas and intensity levels, and extract key disaster information about affected hospitals and schools as well as cultivated land and crops, and make decisions after emergency response with visual assessment results.

Development of multi-mission satellite data systems at the German Remote Sensing Data Centre

NASA Astrophysics Data System (ADS)

Lotz-Iwen, H. J.; Markwitz, W.; Schreier, G.

1998-11-01

This paper focuses on conceptual aspects of the access to multi-mission remote sensing data by online catalogue and information systems. The system ISIS of the German Remote Sensing Data Centre is described as an example of a user interface to earth observation data. ISIS has been designed to support international scientific research as well as operational applications by offering online access to the database via public networks. It provides catalogue retrieval, visualisation and transfer of image data, and is integrated in international activities dedicated to catalogue and archive interoperability. Finally, an outlook is given on international projects dealing with access to remote sensing data in distributed archives.

Global hierarchical classification of deepwater and wetland environments from remote sensing products

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.; Aires, F.; Prigent, C.; McIntyre, P. B.

2017-12-01

Global surface water maps have improved in spatial and temporal resolutions through various remote sensing methods: open water extents with compiled Landsat archives and inundation with topographically downscaled multi-sensor retrievals. These time-series capture variations through time of open water and inundation without discriminating between hydrographic features (e.g. lakes, reservoirs, river channels and wetland types) as other databases have done as static representation. Available data sources present the opportunity to generate a comprehensive map and typology of aquatic environments (deepwater and wetlands) that improves on earlier digitized inventories and maps. The challenge of classifying surface waters globally is to distinguishing wetland types with meaningful characteristics or proxies (hydrology, water chemistry, soils, vegetation) while accommodating limitations of remote sensing data. We present a new wetland classification scheme designed for global application and produce a map of aquatic ecosystem types globally using state-of-the-art remote sensing products. Our classification scheme combines open water extent and expands it with downscaled multi-sensor inundation data to capture the maximal vegetated wetland extent. The hierarchical structure of the classification is modified from the Cowardin Systems (1979) developed for the USA. The first level classification is based on a combination of landscape positions and water source (e.g. lacustrine, riverine, palustrine, coastal and artificial) while the second level represents the hydrologic regime (e.g. perennial, seasonal, intermittent and waterlogged). Class-specific descriptors can further detail the wetland types with soils and vegetation cover. Our globally consistent nomenclature and top-down mapping allows for direct comparison across biogeographic regions, to upscale biogeochemical fluxes as well as other landscape level functions.

REMOTE SENSING IN OCEANOGRAPHY.

DTIC Science & Technology

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

Evapotranspiration estimates derived using multi-platform remote sensing in a semiarid region

USDA-ARS?s Scientific Manuscript database

Evapotranspiration (ET) is a key component of the water balance, especially in arid and semiarid regions. The current study takes advantage of spatially-distributed, near real-time information provided by satellite remote sensing to develop a regional scale ET product derived from remotely-sensed ob...

Development of the Metropolitan Water Availability Index (MWAI) and Short-term Assessment with Multi-scale Remote Sensing Technologies

EPA Science Inventory

Global climate change will change environmental conditions including temperature, precipitation, surface radiation, humidity, soil moisture, and sea level, and impact significantly the regional-scale hydrologic processes such as evapotranspiration (ET), runoff, groundwater levels...

Assessing Wetland Hydroperiod and Soil Moisture With Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

NASA Technical Reports Server (NTRS)

Brooks, Colin; Bourgeau-Chavez, Laura; Endres, Sarah; Battaglia, Michael; Shuchman, Robert

2015-01-01

Primary Goal: Assist with the evaluation and measuring of wetlands hydroperiod at the PlumBrook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: 1) Show the relative length of hydroperiod using available remote sensing datasets 2) Date linked table of wetlands extent over time for all feasible non-forested wetlands 3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables 4) A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment 5) A MTRI style report summarizing year 2 results. This report serves as a descriptive summary of our completion of these our deliverables. Additionally, two formal meetings were held with Larry Liou and Amanda Sprinzl to provide project updates and receive direction on outputs. These were held on 2/26/15 and 9/17/15 at the Plum Brook Station. Principal Component Analysis (PCA) is a multivariate statistical technique used to identify dominant spatial and temporal backscatter signatures. PCA reduces the information contained in the temporal dataset to the first few new Principal Component (PC) images. Some advantages of PCA include the ability to filter out temporal autocorrelation and reduce speckle to the higher order PC images. A PCA was performed using ERDAS Imagine on a time series of PALSAR dates. Hydroperiod maps were created by separating the PALSAR dates into two date ranges, 2006-2008 and 2010, and performing an unsupervised classification on the PCAs.

Remote Sensing Data Visualization, Fusion and Analysis via Giovanni

NASA Technical Reports Server (NTRS)

Leptoukh, G.; Zubko, V.; Gopalan, A.; Khayat, M.

2007-01-01

We describe Giovanni, the NASA Goddard developed online visualization and analysis tool that allows users explore various phenomena without learning remote sensing data formats and downloading voluminous data. Using MODIS aerosol data as an example, we formulate an approach to the data fusion for Giovanni to further enrich online multi-sensor remote sensing data comparison and analysis.

Remote sensing of ecosystem health: opportunities, challenges, and future perspectives.

PubMed

Li, Zhaoqin; Xu, Dandan; Guo, Xulin

2014-11-07

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

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

NASA Astrophysics Data System (ADS)

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.

Future of Land Remote Sensing: What is Needed

NASA Technical Reports Server (NTRS)

Goward, Samuel N.

2007-01-01

A viewgraph presentation describing the future of land remote sensing and the new technologies needed for clear views of the Earth is shown. The contents include: 1) Viewing the Earth; 2) Multi-Imagery; 3) May Missions and Sensors; 4) What is Needed; 5) Things to Think About; 6) Global Land Remote Sensing in Landsat 7 Era; 7) Seasonality; 8) Cloud Contamination; 9) NRC Decadal Study; 10) Atmospheric Attenuation; 11) Geo-Registration; 12) Orthorectification Required; 13) Band Registration with OLI; and 14) Things to Do. A viewgraph presentation describing the future of land remote sensing and the new technologies needed for clear views of the Earth is shown. The contents include: 1) Viewing the Earth; 2) Multi-Imagery; 3) May Missions and Sensors; 4) What is Needed; 5) Things to Think About; 6) Global Land Remote Sensing in Landsat 7 Era; 7) Seasonality; 8) Cloud Contamination; 9) NRC Decadal Study; 10) Atmospheric Attenuation; 11) Geo-Registration; 12) Orthorectification Required; 13) Band Registration with OLI; and 14) Things to Do.

Remote Sensing based modelling of Annual Surface Mass Balances of Chhota Shigiri Glacier, Western Himalayas, India

NASA Astrophysics Data System (ADS)

Chandrasekharan, Anita; Ramsankaran, Raaj

2017-04-01

The current study aims at modelling glacier mass balances over Chhota Shigiri glacier (32.28o N; 77.58° E) in Himachal Pradesh, India using the Equilibrium Line Altitude (ELA) gradient approach proposed by Rabatel et al. (2005). The model requires yearly ELA, average mass balance and mass balance gradient to estimate annual mass balance of a glacier which can be obtained either through field measurements or remote sensing observations. However, in view of the general scenario of lack of field data for Himalayan glaciers, in this study the model has been applied only using the inputs derived through multi-temporal satellite remote sensing observations thus eliminating the need for any field measurements. Preliminary analysis show that the obtained results are comparable with the observed field mass balance. The results also demonstrate that this approach with remote sensing inputs has potential to be used for glacier mass balance estimations provided good quality multi-temporal remote sensing dataset are available.

Multi-scale remote sensing of coral reefs

USGS Publications Warehouse

Andréfouët, Serge; Hochberg, E.J.; Chevillon, Christophe; Muller-Karger, Frank E.; Brock, John C.; Hu, Chuanmin

2005-01-01

In this chapter we present how both direct and indirect remote sensing can be integrated to address two major coral reef applications - coral bleaching and assessment of biodiversity. This approach reflects the current non-linear integration of remote sensing for environmental assessment of coral reefs, resulting from a rapid increase in available sensors, processing methods and interdisciplinary collaborations (Andréfouët and Riegl, 2004). Moreover, this approach has greatly benefited from recent collaborations of once independent investigations (e.g., benthic ecology, remote sensing, and numerical modeling).

Data and techniques for studying the urban heat island effect in Johannesburg

NASA Astrophysics Data System (ADS)

Hardy, C. H.; Nel, A. L.

2015-04-01

The city of Johannesburg contains over 10 million trees and is often referred to as an urban forest. The intra-urban spatial variability of the levels of vegetation across Johannesburg's residential regions has an influence on the urban heat island effect within the city. Residential areas with high levels of vegetation benefit from cooling due to evapo-transpirative processes and thus exhibit weaker heat island effects; while their impoverished counterparts are not so fortunate. The urban heat island effect describes a phenomenon where some urban areas exhibit temperatures that are warmer than that of surrounding areas. The factors influencing the urban heat island effect include the high density of people and buildings and low levels of vegetative cover within populated urban areas. This paper describes the remote sensing data sets and the processing techniques employed to study the heat island effect within Johannesburg. In particular we consider the use of multi-sensorial multi-temporal remote sensing data towards a predictive model, based on the analysis of influencing factors.

Comparison of tropospheric NO2 vertical columns in an urban environment using satellite, multi-axis differential optical absorption spectroscopy, and in situ measurements

NASA Astrophysics Data System (ADS)

Mendolia, D.; D'Souza, R. J. C.; Evans, G. J.; Brook, J.

2013-10-01

Tropospheric NO2 vertical column densities have been retrieved and compared for the first time in Toronto, Canada, using three methods of differing spatial scales. Remotely sensed NO2 vertical column densities, retrieved from multi-axis differential optical absorption spectroscopy and satellite remote sensing, were evaluated by comparison with in situ vertical column densities estimated using a pair of chemiluminescence monitors situated 0.01 and 0.5 km a.g.l. (above ground level). The chemiluminescence measurements were corrected for the influence of NOz, which reduced the NO2 concentrations at 0.01 and 0.5 km by an average of 8 ± 1% and 12 ± 1%, respectively. The average absolute decrease in the chemiluminescence NO2 measurement as a result of this correction was less than 1 ppb. The monthly averaged ratio of the NO2 concentration at 0.5 to 0.01 km varied seasonally, and exhibited a negative linear dependence on the monthly average temperature, with Pearson's R = 0.83. During the coldest month, February, this ratio was 0.52 ± 0.04, while during the warmest month, July, this ratio was 0.34 ± 0.04, illustrating that NO2 is not well mixed within 0.5 km above ground level. Good correlation was observed between the remotely sensed and in situ NO2 vertical column densities (Pearson's R value ranging from 0.72 to 0.81), but the in situ vertical column densities were 52 to 58% greater than the remotely sensed columns. These results indicate that NO2 horizontal heterogeneity strongly impacted the magnitude of the remotely sensed columns. The in situ columns reflected an urban environment with major traffic sources, while the remotely sensed NO2 vertical column densities were representative of the region, which included spatial heterogeneity introduced by residential neighbourhoods and Lake Ontario. Despite the difference in absolute values, the reasonable correlation between the vertical column densities determined by three distinct methods increased confidence in the validity of the values provided by each measurement technique.

Surveillance of Arthropod Vector-Borne Infectious Diseases Using Remote Sensing Techniques: A Review

PubMed Central

Kalluri, Satya; Gilruth, Peter; Rogers, David; Szczur, Martha

2007-01-01

Epidemiologists are adopting new remote sensing techniques to study a variety of vector-borne diseases. Associations between satellite-derived environmental variables such as temperature, humidity, and land cover type and vector density are used to identify and characterize vector habitats. The convergence of factors such as the availability of multi-temporal satellite data and georeferenced epidemiological data, collaboration between remote sensing scientists and biologists, and the availability of sophisticated, statistical geographic information system and image processing algorithms in a desktop environment creates a fertile research environment. The use of remote sensing techniques to map vector-borne diseases has evolved significantly over the past 25 years. In this paper, we review the status of remote sensing studies of arthropod vector-borne diseases due to mosquitoes, ticks, blackflies, tsetse flies, and sandflies, which are responsible for the majority of vector-borne diseases in the world. Examples of simple image classification techniques that associate land use and land cover types with vector habitats, as well as complex statistical models that link satellite-derived multi-temporal meteorological observations with vector biology and abundance, are discussed here. Future improvements in remote sensing applications in epidemiology are also discussed. PMID:17967056

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

NASA Astrophysics Data System (ADS)

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.

Application of remote sensing to water resources problems

NASA Technical Reports Server (NTRS)

Clapp, J. L.

1972-01-01

The following conclusions were reached concerning the applications of remote sensing to water resources problems: (1) Remote sensing methods provide the most practical method of obtaining data for many water resources problems; (2) the multi-disciplinary approach is essential to the effective application of remote sensing to water resource problems; (3) there is a correlation between the amount of suspended solids in an effluent discharged into a water body and reflected energy; (4) remote sensing provides for more effective and accurate monitoring, discovery and characterization of the mixing zone of effluent discharged into a receiving water body; and (5) it is possible to differentiate between blue and blue-green algae.

Remote Sensing Information Gateway

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

NASA Astrophysics Data System (ADS)

Li, Jing; Xie, Weixin; Pei, Jihong

2018-03-01

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

Sea-land segmentation for infrared remote sensing images based on superpixels and multi-scale features

NASA Astrophysics Data System (ADS)

Lei, Sen; Zou, Zhengxia; Liu, Dunge; Xia, Zhenghuan; Shi, Zhenwei

2018-06-01

Sea-land segmentation is a key step for the information processing of ocean remote sensing images. Traditional sea-land segmentation algorithms ignore the local similarity prior of sea and land, and thus fail in complex scenarios. In this paper, we propose a new sea-land segmentation method for infrared remote sensing images to tackle the problem based on superpixels and multi-scale features. Considering the connectivity and local similarity of sea or land, we interpret the sea-land segmentation task in view of superpixels rather than pixels, where similar pixels are clustered and the local similarity are explored. Moreover, the multi-scale features are elaborately designed, comprising of gray histogram and multi-scale total variation. Experimental results on infrared bands of Landsat-8 satellite images demonstrate that the proposed method can obtain more accurate and more robust sea-land segmentation results than the traditional algorithms.

Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives

PubMed Central

Li, Zhaoqin; Xu, Dandan; Guo, Xulin

2014-01-01

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges. PMID:25386759

Multi-resource data-based research on remote sensing monitoring over the green tide in the Yellow Sea

NASA Astrophysics Data System (ADS)

Gao, Zhiqiang; Xu, Fuxiang; Song, Debin; Zheng, Xiangyu; Chen, Maosi

2017-09-01

This paper conducted dynamic monitoring over the green tide (large green alga—Ulva prolifera) occurred in the Yellow Sea in 2014 to 2016 by the use of multi-source remote sensing data, including GF-1 WFV, HJ-1A/1B CCD, CBERS-04 WFI, Landsat-7 ETM+ and Landsta-8 OLI, and by the combination of VB-FAH (index of Virtual-Baseline Floating macroAlgae Height) with manual assisted interpretation based on remote sensing and geographic information system technologies. The result shows that unmanned aerial vehicle (UAV) and shipborne platform could accurately monitor the distribution of Ulva prolifera in small spaces, and therefore provide validation data for the result of remote sensing monitoring over Ulva prolifera. The result of this research can provide effective information support for the prevention and control of Ulva prolifera.

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

NASA Astrophysics Data System (ADS)

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.

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

PubMed

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

2016-01-01

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

Unsupervised Change Detection for Geological and Ecological Monitoring via Remote Sensing: Application on a Volcanic Area

NASA Astrophysics Data System (ADS)

Falco, N.; Pedersen, G. B. M.; Vilmunandardóttir, O. K.; Belart, J. M. M. C.; Sigurmundsson, F. S.; Benediktsson, J. A.

2016-12-01

The project "Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS)" aims at providing fast and reliable mapping and monitoring techniques on a big spatial scale with a high temporal resolution of the Icelandic landscape. Such mapping and monitoring will be crucial to both mitigate and understand the scale of processes and their often complex interlinked feedback mechanisms.In the EMMIRS project, the Hekla volcano area is one of the main sites under study, where the volcanic eruptions, extreme weather and human activities had an extensive impact on the landscape degradation. The development of innovative remote sensing approaches to compute earth observation variables as automatically as possible is one of the main tasks of the EMMIRS project. Furthermore, a temporal remote sensing archive is created and composed by images acquired by different sensors (Landsat, RapidEye, ASTER and SPOT5). Moreover, historical aerial stereo photos allowed decadal reconstruction of the landscape by reconstruction of digital elevation models. Here, we propose a novel architecture for automatic unsupervised change detection analysis able to ingest multi-source data in order to detect landscape changes in the Hekla area. The change detection analysis is based on multi-scale analysis, which allows the identification of changes at different level of abstraction, from pixel-level to region-level. For this purpose, operators defined in mathematical morphology framework are implemented to model the contextual information, represented by the neighbour system of a pixel, allowing the identification of changes related to both geometrical and spectral domains. Automatic radiometric normalization strategy is also implemented as pre-processing step, aiming at minimizing the effect of different acquisition conditions. The proposed architecture is tested on multi-temporal data sets acquired over different time periods coinciding with the last three eruptions (1980-1981, 1991, 2000) occurred on Hekla volcano. The results reveal emplacement of new lava flows and the initial vegetation succession, providing insightful information on the evolving of vegetation in such environment. Shadow and snow patch changes are resolved in post-processing by exploiting the available spectral information.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Urban structure analysis of mega city Mexico City using multisensoral remote sensing data

NASA Astrophysics Data System (ADS)

Taubenböck, H.; Esch, T.; Wurm, M.; Thiel, M.; Ullmann, T.; Roth, A.; Schmidt, M.; Mehl, H.; Dech, S.

2008-10-01

Mega city Mexico City is ranked the third largest urban agglomeration to date around the globe. The large extension as well as dynamic urban transformation and sprawl processes lead to a lack of up-to-date and area-wide data and information to measure, monitor, and understand the urban situation. This paper focuses on the capabilities of multisensoral remotely sensed data to provide a broad range of products derived from one scientific field - remote sensing - to support urban managing and planning. Therefore optical data sets from the Landsat and Quickbird sensors as well as radar data from the Shuttle Radar Topography Mission (SRTM) and the TerraSAR-X sensor are utilised. Using the multi-sensoral data sets the analysis are scale-dependent. On the one hand change detection on city level utilising the derived urban footprints enables to monitor and to assess spatiotemporal urban transformation, areal dimension of urban sprawl, its direction, and the built-up density distribution over time. On the other hand, structural characteristics of an urban landscape - the alignment and types of buildings, streets and open spaces - provide insight in the very detailed physical pattern of urban morphology on higher scale. The results show high accuracies of the derived multi-scale products. The multi-scale analysis allows quantifying urban processes and thus leading to an assessment and interpretation of urban trends.

Multi-decadal Arctic sea ice roughness.

NASA Astrophysics Data System (ADS)

Tsamados, M.; Stroeve, J.; Kharbouche, S.; Muller, J. P., , Prof; Nolin, A. W.; Petty, A.; Haas, C.; Girard-Ardhuin, F.; Landy, J.

2017-12-01

The transformation of Arctic sea ice from mainly perennial, multi-year ice to a seasonal, first-year ice is believed to have been accompanied by a reduction of the roughness of the ice cover surface. This smoothening effect has been shown to (i) modify the momentum and heat transfer between the atmosphere and ocean, (ii) to alter the ice thickness distribution which in turn controls the snow and melt pond repartition over the ice cover, and (iii) to bias airborne and satellite remote sensing measurements that depend on the scattering and reflective characteristics over the sea ice surface topography. We will review existing and novel remote sensing methodologies proposed to estimate sea ice roughness, ranging from airborne LIDAR measurement (ie Operation IceBridge), to backscatter coefficients from scatterometers (ASCAT, QUICKSCAT), to multi angle maging spectroradiometer (MISR), and to laser (Icesat) and radar altimeters (Envisat, Cryosat, Altika, Sentinel-3). We will show that by comparing and cross-calibrating these different products we can offer a consistent multi-mission, multi-decadal view of the declining sea ice roughness. Implications for sea ice physics, climate and remote sensing will also be discussed.

Instructional image processing on a university mainframe: The Kansas system

NASA Technical Reports Server (NTRS)

Williams, T. H. L.; Siebert, J.; Gunn, C.

1981-01-01

An interactive digital image processing program package was developed that runs on the University of Kansas central computer, a Honeywell Level 66 multi-processor system. The module form of the package allows easy and rapid upgrades and extensions of the system and is used in remote sensing courses in the Department of Geography, in regional five-day short courses for academics and professionals, and also in remote sensing projects and research. The package comprises three self-contained modules of processing functions: Subimage extraction and rectification; image enhancement, preprocessing and data reduction; and classification. Its use in a typical course setting is described. Availability and costs are considered.

Remote Sensing of Multi-Level Wind Fields with High-Energy Airborne Scanning Coherent Doppler Lidar

NASA Technical Reports Server (NTRS)

Rothermel, Jeffry; Olivier, Lisa D.; Banta, Robert M.; Hardesty, R. Michael; Howell, James N.; Cutten, Dean R.; Johnson, Steven C.; Menzies, Robert T.; Tratt, David M.

1997-01-01

The atmospheric lidar remote sensing groups of NOAA Environmental Technology Laboratory, NASA Marshall Space Flight Center, and Jet Propulsion Laboratory have developed and flown a scanning, 1 Joule per pulse, CO2 coherent Doppler lidar capable of mapping a three-dimensional volume of atmospheric winds and aerosol backscatter in the troposphere and lower stratosphere. Applications include the study of severe and non-severe atmospheric flows, intercomparisons with other sensors, and the simulation of prospective satellite Doppler lidar wind profilers. Examples of wind measurements are given for the marine boundary layer and near the coastline of the western United States.

Remote sensing of multi-level wind fields with high-energy airborne scanning coherent Doppler lidar.

PubMed

Rothermel, J; Olivier, L; Banta, R; Hardesty, R M; Howell, J; Cutten, D; Johnson, S; Menzies, R; Tratt, D M

1998-01-19

The atmospheric lidar remote sensing groups of NOAA Environmental Technology Laboratory, NASA Marshall Space Flight Center, and Jet Propulsion Laboratory have developed and flown a scanning, 1 Joule per pulse, CO2 coherent Doppler lidar capable of mapping a three-dimensional volume of atmospheric winds and aerosol backscatter in the planetary boundary layer, free troposphere, and lower stratosphere. Applications include the study of severe and non-severe atmospheric flows, intercomparisons with other sensors, and the simulation of prospective satellite Doppler lidar wind profilers. Examples of wind measurements are given for the marine boundary layer and near the coastline of the western United States.

A Prototype Hydrologic Observatory for the Neuse River Basin Using Remote Sensing Data as a Part of the CUAHSI-HIS Effort

NASA Astrophysics Data System (ADS)

Kanwar, R.; Narayan, U.; Lakshmi, V.

2005-12-01

Remote sensing has the potential to immensely advance the science and application of hydrology as it provides multi-scale and multi-temporal measurements of several hydrologic parameters. There is a wide variety of remote sensing data sources available to a hydrologist with a myriad of data formats, access techniques, data quality issues and temporal and spatial extents. It is very important to make data availability and its usage as convenient as possible for potential users. The CUAHSI Hydrologic Information System (HIS) initiative addresses this issue of better data access and management for hydrologists with a focus on in-situ data, that is point measurements of water and energy fluxes which make up the 'more conventional' sources of hydrologic data. This paper explores various sources of remotely sensed hydrologic data available, their data formats and volumes, current modes of data acquisition by end users, metadata associated with data itself, and requirements from potential data models that would allow a seamless integration of remotely sensed hydrologic observations into the Hydrologic Information System. Further, a prototype hydrologic observatory (HO) for the Neuse River Basin is developed using surface temperature, vegetation indices and soil moisture estimates available from remote sensing. The prototype (HO) uses the CUAHSI digital library system (DLS) on the back (server) end. On the front (client) end, a rich visual environment has been developed in order to provide better decision making tools in order to make an optimal choice in the selection of remote sensing data for a particular application. An easy point and click interface to the remote sensing data is also implemented for common users who are just interested in location based query of hydrologic variable values.

A Multi-Temporal Remote Sensing Approach to Freshwater Turtle Conservation

NASA Astrophysics Data System (ADS)

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, though accurate estimates across human-altered landscapes is challenging. Overall, this body of work supports the use of remote sensing imagery in species distribution models to strengthen the precision, and power of predictive models, and also draws attention to the need to consider a multi-temporal examination of species habitat requirements.

Analysis of multispectral signatures and investigation of multi-aspect remote sensing techniques

NASA Technical Reports Server (NTRS)

Malila, W. A.; Hieber, R. H.; Sarno, J. E.

1974-01-01

Two major aspects of remote sensing with multispectral scanners (MSS) are investigated. The first, multispectral signature analysis, includes the effects on classification performance of systematic variations found in the average signals received from various ground covers as well as the prediction of these variations with theoretical models of physical processes. The foremost effects studied are those associated with the time of day airborne MSS data are collected. Six data collection runs made over the same flight line in a period of five hours are analyzed, it is found that the time span significantly affects classification performance. Variations associated with scan angle also are studied. The second major topic of discussion is multi-aspect remote sensing, a new concept in remote sensing with scanners. Here, data are collected on multiple passes by a scanner that can be tilted to scan forward of the aircraft at different angles on different passes. The use of such spatially registered data to achieve improved classification of agricultural scenes is investigated and found promising. Also considered are the possibilities of extracting from multi-aspect data, information on the condition of corn canopies and the stand characteristics of forests.

Random-Forest Classification of High-Resolution Remote Sensing Images and Ndsm Over Urban Areas

NASA Astrophysics Data System (ADS)

Sun, X. F.; Lin, X. G.

2017-09-01

As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample's category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.

Satellite remote sensing of landscape freeze/thaw state dynamics for complex Topography and Fire Disturbance Areas Using multi-sensor radar and SRTM digital elevation models

NASA Technical Reports Server (NTRS)

Podest, Erika; McDonald, Kyle; Kimball, John; Randerson, James

2003-01-01

We characterize differences in radar-derived freeze/thaw state, examining transitions over complex terrain and landscape disturbance regimes. In areas of complex terrain, we explore freezekhaw dynamics related to elevation, slope aspect and varying landcover. In the burned regions, we explore the timing of seasonal freeze/thaw transition as related to the recovering landscape, relative to that of a nearby control site. We apply in situ biophysical measurements, including flux tower measurements to validate and interpret the remotely sensed parameters. A multi-scale analysis is performed relating high-resolution SAR backscatter and moderate resolution scatterometer measurements to assess trade-offs in spatial and temporal resolution in the remotely sensed fields.

Diverse Planning for UAV Control and Remote Sensing

PubMed Central

Tožička, Jan; Komenda, Antonín

2016-01-01

Unmanned aerial vehicles (UAVs) are suited to various remote sensing missions, such as measuring air quality. The conventional method of UAV control is by human operators. Such an approach is limited by the ability of cooperation among the operators controlling larger fleets of UAVs in a shared area. The remedy for this is to increase autonomy of the UAVs in planning their trajectories by considering other UAVs and their plans. To provide such improvement in autonomy, we need better algorithms for generating alternative trajectory variants that the UAV coordination algorithms can utilize. In this article, we define a novel family of multi-UAV sensing problems, solving task allocation of huge number of tasks (tens of thousands) to a group of configurable UAVs with non-zero weight of equipped sensors (comprising the air quality measurement as well) together with two base-line solvers. To solve the problem efficiently, we use an algorithm for diverse trajectory generation and integrate it with a solver for the multi-UAV coordination problem. Finally, we experimentally evaluate the multi-UAV sensing problem solver. The evaluation is done on synthetic and real-world-inspired benchmarks in a multi-UAV simulator. Results show that diverse planning is a valuable method for remote sensing applications containing multiple UAVs. PMID:28009831

Diverse Planning for UAV Control and Remote Sensing.

PubMed

Tožička, Jan; Komenda, Antonín

2016-12-21

Unmanned aerial vehicles (UAVs) are suited to various remote sensing missions, such as measuring air quality. The conventional method of UAV control is by human operators. Such an approach is limited by the ability of cooperation among the operators controlling larger fleets of UAVs in a shared area. The remedy for this is to increase autonomy of the UAVs in planning their trajectories by considering other UAVs and their plans. To provide such improvement in autonomy, we need better algorithms for generating alternative trajectory variants that the UAV coordination algorithms can utilize. In this article, we define a novel family of multi-UAV sensing problems, solving task allocation of huge number of tasks (tens of thousands) to a group of configurable UAVs with non-zero weight of equipped sensors (comprising the air quality measurement as well) together with two base-line solvers. To solve the problem efficiently, we use an algorithm for diverse trajectory generation and integrate it with a solver for the multi-UAV coordination problem. Finally, we experimentally evaluate the multi-UAV sensing problem solver. The evaluation is done on synthetic and real-world-inspired benchmarks in a multi-UAV simulator. Results show that diverse planning is a valuable method for remote sensing applications containing multiple UAVs.

Fast Detection of Airports on Remote Sensing Images with Single Shot MultiBox Detector

NASA Astrophysics Data System (ADS)

Xia, Fei; Li, HuiZhou

2018-01-01

This paper introduces a method for fast airport detection on remote sensing images (RSIs) using Single Shot MultiBox Detector (SSD). To our knowledge, this could be the first study which introduces an end-to-end detection model into airport detection on RSIs. Based on the common low-level features between natural images and RSIs, a convolution neural network trained on large amounts of natural images was transferred to tackle the airport detection problem with limited annotated data. To deal with the specific characteristics of RSIs, some related parameters in the SSD, such as the scales and layers, were modified for more accurate and rapider detection. The experiments show that the proposed method could achieve 83.5% Average Recall at 8 FPS on RSIs with the size of 1024*1024. In contrast to Faster R-CNN, an improvement on AP and speed could be obtained.

Design and Verification of Remote Sensing Image Data Center Storage Architecture Based on Hadoop

NASA Astrophysics Data System (ADS)

Tang, D.; Zhou, X.; Jing, Y.; Cong, W.; Li, C.

2018-04-01

The data center is a new concept of data processing and application proposed in recent years. It is a new method of processing technologies based on data, parallel computing, and compatibility with different hardware clusters. While optimizing the data storage management structure, it fully utilizes cluster resource computing nodes and improves the efficiency of data parallel application. This paper used mature Hadoop technology to build a large-scale distributed image management architecture for remote sensing imagery. Using MapReduce parallel processing technology, it called many computing nodes to process image storage blocks and pyramids in the background to improve the efficiency of image reading and application and sovled the need for concurrent multi-user high-speed access to remotely sensed data. It verified the rationality, reliability and superiority of the system design by testing the storage efficiency of different image data and multi-users and analyzing the distributed storage architecture to improve the application efficiency of remote sensing images through building an actual Hadoop service system.

ESTIMATING GROUND LEVEL PM 2.5 IN THE EASTERN UNITED STATES USING SATELLITE REMOTE SENSING

EPA Science Inventory

An empirical model based on the regression between daily average final particle (PM2.5) concentrations and aerosol optical thickness (AOT) measurements from the Multi-angle Imaging SpectroRadiometer (MISR) was developed and tested using data from the eastern United States during ...

Monitoring of "all-weather" evapotranspiration using optical and passive microwave remote sensing imagery over the River Source Region in Southwest China

NASA Astrophysics Data System (ADS)

Ma, Y.; Liu, S.

2017-12-01

Accurate estimation of surface evapotranspiration (ET) with high quality is one of the biggest obstacles for routine applications of remote sensing in eco-hydrological studies and water resource management at basin scale. However, many aspects urgently need to deeply research, such as the applicability of the ET models, the parameterization schemes optimization at the regional scale, the temporal upscaling, the selecting and developing of the spatiotemporal data fusion method and ground-based validation over heterogeneous land surfaces. This project is based on the theoretically robust surface energy balance system (SEBS) model, which the model mechanism need further investigation, including the applicability and the influencing factors, such as local environment, and heterogeneity of the landscape, for improving estimation accuracy. Due to technical and budget limitations, so far, optical remote sensing data is missing due to frequent cloud contamination and other poor atmospheric conditions in Southwest China. Here, a multi-source remote sensing data fusion method (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) method will be proposed through blending multi-source remote sensing data acquired by optical, and passive microwave remote sensors on board polar satellite platforms. The accurate "all-weather" ET estimation will be carried out for daily ET of the River Source Region in Southwest China, and then the remotely sensed ET results are overlapped with the footprint-weighted images of EC (eddy correlation) for ground-based validation.

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

PubMed Central

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

2018-01-01

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

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

PubMed

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

2018-02-07

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

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

PubMed

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.

Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

NASA Astrophysics Data System (ADS)

Yao, Y.; Liang, H.; Li, X.; Zhang, J.; He, J.

2017-09-01

With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

Remote Sensing Systems to Detect and Analyze Oil Spills on the U.S. Outer Continental Shelf - A State of the Art Assessment

DTIC Science & Technology

2016-08-18

multi- sensor remote sensing approach to describe the distribution of oil from the DWH spill. They used airborne and satellite , multi- and hyperspectral...Experimental Sensors e.g., Acoustic and Nuclear Magnetic Resonance (NMR) (Fingas and Brown, 2012; Puestow et al., 2013). These are further...ship, aerial - aircraft, aerostat or UAV, or satellite ), among other classification criteria. A comprehensive review of sensor categories employed

Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: a quasi-analytical approach

USDA-ARS?s Scientific Manuscript database

In this research, we present a novel technique to monitor cyanobacterial algal bloom using remote sensing measurements. We have used a multi-band quasi analytical algorithm that determines phytoplankton absorption coefficients, aF('), from above-surface remote sensing reflectance, Rrs('). In situ da...

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

NASA Astrophysics Data System (ADS)

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 water environmental parameters and further improved the inversion model. The results indicate that our proposed water environment inversion model can be a good inversion for alpine water environmental parameters, and can improve the monitoring and warning ability for the alpine river water environment in the future.

Canadian SAR remote sensing for the Terrestrial Wetland Global Change Research Network (TWGCRN)

USGS Publications Warehouse

Kaya, Shannon; Brisco, Brian; Cull, Andrew; Gallant, Alisa L.; Sadinski, Walter J.; Thompson, Dean

2010-01-01

The Canada Centre for Remote Sensing (CCRS) has more than 30 years of experience investigating the use of SAR remote sensing for many applications related to terrestrial water resources. Recently, CCRS scientists began contributing to the Terrestrial Wetland Global Change Research Network (TWGCRN), a bi-national research network dedicated to assessing impacts of global change on interconnected wetland-upland landscapes across a vital portion of North America. CCRS scientists are applying SAR remote sensing to characterize wetland components of these landscapes in three ways. First, they are using a comprehensive set of RADARSAT-2 SAR data collected during April to September 2009 to extract multi-temporal surface water information for key TWGCRN study landscapes in North America. Second, they are analyzing polarimetric RADARSAT-2 data to determine areas where double-bounce represents the primary scattering mechanism and is indicative of flooded vegetation in these landscapes. Third, they are testing advanced interferometric SAR techniques to estimate water levels with RADARSAT-2 Fine Quad polarimetric image pairs. The combined information from these three SAR analysis activities will provide TWGCRN scientists with an integrated view and monitoring capability for these dynamic wetland-upland landscapes. These data are being used in conjunction with other remote sensing and field data to study interactions between landscape and animal (birds and amphibians) responses to climate/global change.

Combining Hydrological Modeling and Remote Sensing Observations to Enable Data-Driven Decision Making for Devils Lake Flood Mitigation in a Changing Climate

NASA Technical Reports Server (NTRS)

Zhang, Xiaodong; Kirilenko, Andrei; Lim, Howe; Teng, Williams

2010-01-01

This slide presentation reviews work to combine the hydrological models and remote sensing observations to monitor Devils Lake in North Dakota, to assist in flood damage mitigation. This reports on the use of a distributed rainfall-runoff model, HEC-HMS, to simulate the hydro-dynamics of the lake watershed, and used NASA's remote sensing data, including the TRMM Multi-Satellite Precipitation Analysis (TMPA) and AIRS surface air temperature, to drive the model.

Multispectral, hyperspectral, and LiDAR remote sensing and geographic information fusion for improved earthquake response

NASA Astrophysics Data System (ADS)

Kruse, F. A.; Kim, A. M.; Runyon, S. C.; Carlisle, Sarah C.; Clasen, C. C.; Esterline, C. H.; Jalobeanu, A.; Metcalf, J. P.; Basgall, P. L.; Trask, D. M.; Olsen, R. C.

2014-06-01

The Naval Postgraduate School (NPS) Remote Sensing Center (RSC) and research partners have completed a remote sensing pilot project in support of California post-earthquake-event emergency response. The project goals were to dovetail emergency management requirements with remote sensing capabilities to develop prototype map products for improved earthquake response. NPS coordinated with emergency management services and first responders to compile information about essential elements of information (EEI) requirements. A wide variety of remote sensing datasets including multispectral imagery (MSI), hyperspectral imagery (HSI), and LiDAR were assembled by NPS for the purpose of building imagery baseline data; and to demonstrate the use of remote sensing to derive ground surface information for use in planning, conducting, and monitoring post-earthquake emergency response. Worldview-2 data were converted to reflectance, orthorectified, and mosaicked for most of Monterey County; CA. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired at two spatial resolutions were atmospherically corrected and analyzed in conjunction with the MSI data. LiDAR data at point densities from 1.4 pts/m2 to over 40 points/ m2 were analyzed to determine digital surface models. The multimodal data were then used to develop change detection approaches and products and other supporting information. Analysis results from these data along with other geographic information were used to identify and generate multi-tiered products tied to the level of post-event communications infrastructure (internet access + cell, cell only, no internet/cell). Technology transfer of these capabilities to local and state emergency response organizations gives emergency responders new tools in support of post-disaster operational scenarios.

Detection of geothermal anomalies in Tengchong, Yunnan Province, China from MODIS multi-temporal night LST imagery

NASA Astrophysics Data System (ADS)

Li, H.; Kusky, T. M.; Peng, S.; Zhu, M.

2012-12-01

Thermal infrared (TIR) remote sensing is an important technique in the exploration of geothermal resources. In this study, a geothermal survey is conducted in Tengchong area of Yunnan province in China using multi-temporal MODIS LST (Land Surface Temperature). The monthly night MODIS LST data from Mar. 2000 to Mar. 2011 of the study area were collected and analyzed. The 132 month average LST map was derived and three geothermal anomalies were identified. The findings of this study agree well with the results from relative geothermal gradient measurements. Finally, we conclude that TIR remote sensing is a cost-effective technique to detect geothermal anomalies. Combining TIR remote sensing with geological analysis and the understanding of geothermal mechanism is an accurate and efficient approach to geothermal area detection.

DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping

NASA Astrophysics Data System (ADS)

D'Addabbo, Annarita; Refice, Alberto; Lovergine, Francesco P.; Pasquariello, Guido

2018-03-01

High-resolution, remotely sensed images of the Earth surface have been proven to be of help in producing detailed flood maps, thanks to their synoptic overview of the flooded area and frequent revisits. However, flood scenarios can be complex situations, requiring the integration of different data in order to provide accurate and robust flood information. Several processing approaches have been recently proposed to efficiently combine and integrate heterogeneous information sources. In this paper, we introduce DAFNE, a Matlab®-based, open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of following the evolution of an event through multi-temporal output flood maps. Each DAFNE module can be easily modified or upgraded to meet different user needs. The DAFNE suite is presented together with an example of its application.

A light and faster regional convolutional neural network for object detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Ding, Peng; Zhang, Ye; Deng, Wei-Jian; Jia, Ping; Kuijper, Arjan

2018-07-01

Detection of objects from satellite optical remote sensing images is very important for many commercial and governmental applications. With the development of deep convolutional neural networks (deep CNNs), the field of object detection has seen tremendous advances. Currently, objects in satellite remote sensing images can be detected using deep CNNs. In general, optical remote sensing images contain many dense and small objects, and the use of the original Faster Regional CNN framework does not yield a suitably high precision. Therefore, after careful analysis we adopt dense convoluted networks, a multi-scale representation and various combinations of improvement schemes to enhance the structure of the base VGG16-Net for improving the precision. We propose an approach to reduce the test-time (detection time) and memory requirements. To validate the effectiveness of our approach, we perform experiments using satellite remote sensing image datasets of aircraft and automobiles. The results show that the improved network structure can detect objects in satellite optical remote sensing images more accurately and efficiently.

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

PubMed Central

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

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

PubMed

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.

Dual-core optical fiber based strain sensor for remote sensing in hard-to-reach areas

NASA Astrophysics Data System (ADS)

MÄ kowska, Anna; Szostkiewicz, Łukasz; Kołakowska, Agnieszka; Budnicki, Dawid; Bieńkowska, Beata; Ostrowski, Łukasz; Murawski, Michał; Napierała, Marek; Mergo, Paweł; Nasiłowski, Tomasz

2017-10-01

We present research on optical fiber sensors based on microstructured multi-core fiber. Elaborated sensor can be advantageously used in hard-to-reach areas by taking advantage of the fact, that optical fibers can play both the role of sensing elements and they can realize signal delivery. By using the sensor, it is possible to increase the level of the safety in the explosive endangered areas, e.g. in mine-like objects. As a base for the strain remote sensor we use dual-core fibers. The multi-core fibers possess a characteristic parameter called crosstalk, which is a measure of the amount of signal which can pass to the adjacent core. The strain-sensitive area is made by creating the tapered section, in which the level of crosstalk is changed. Due to this fact, we present broadened conception of fiber optic sensor designing. Strain measurement is realized thanks to the fact, that depending on the strain applied, the power distribution between the cores of dual-core fibers changes. Principle of operation allows realization of measurements both in wavelength and power domain.

A multi-characteristic based algorithm for classifying vegetation in a plateau area: Qinghai Lake watershed, northwestern China

NASA Astrophysics Data System (ADS)

Ma, Weiwei; Gong, Cailan; Hu, Yong; Li, Long; Meng, Peng

2015-10-01

Remote sensing technology has been broadly recognized for its convenience and efficiency in mapping vegetation, particularly in high-altitude and inaccessible areas where there are lack of in-situ observations. In this study, Landsat Thematic Mapper (TM) images and Chinese environmental mitigation satellite CCD sensor (HJ-1 CCD) images, both of which are at 30m spatial resolution were employed for identifying and monitoring of vegetation types in a area of Western China——Qinghai Lake Watershed(QHLW). A decision classification tree (DCT) algorithm using multi-characteristic including seasonal TM/HJ-1 CCD time series data combined with digital elevation models (DEMs) dataset, and a supervised maximum likelihood classification (MLC) algorithm with single-data TM image were applied vegetation classification. Accuracy of the two algorithms was assessed using field observation data. Based on produced vegetation classification maps, it was found that the DCT using multi-season data and geomorphologic parameters was superior to the MLC algorithm using single-data image, improving the overall accuracy by 11.86% at second class level and significantly reducing the "salt and pepper" noise. The DCT algorithm applied to TM /HJ-1 CCD time series data geomorphologic parameters appeared as a valuable and reliable tool for monitoring vegetation at first class level (5 vegetation classes) and second class level(8 vegetation subclasses). The DCT algorithm using multi-characteristic might provide a theoretical basis and general approach to automatic extraction of vegetation types from remote sensing imagery over plateau areas.

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

Towards Remotely Sensed Composite Global Drought Risk Modelling

NASA Astrophysics Data System (ADS)

Dercas, Nicholas; Dalezios, Nicolas

2015-04-01

Drought is a multi-faceted issue and requires a multi-faceted assessment. Droughts may have the origin on precipitation deficits, which sequentially and by considering different time and space scales may impact soil moisture, plant wilting, stream flow, wildfire, ground water levels, famine and social impacts. There is a need to monitor drought even at a global scale. Key variables for monitoring drought include climate data, soil moisture, stream flow, ground water, reservoir and lake levels, snow pack, short-medium-long range forecasts, vegetation health and fire danger. However, there is no single definition of drought and there are different drought indicators and indices even for each drought type. There are already four operational global drought risk monitoring systems, namely the U.S. Drought Monitor, the European Drought Observatory (EDO), the African and the Australian systems, respectively. These systems require further research to improve the level of accuracy, the time and space scales, to consider all types of drought and to achieve operational efficiency, eventually. This paper attempts to contribute to the above mentioned objectives. Based on a similar general methodology, the multi-indicator approach is considered. This has resulted from previous research in the Mediterranean region, an agriculturally vulnerable region, using several drought indices separately, namely RDI and VHI. The proposed scheme attempts to consider different space scaling based on agroclimatic zoning through remotely sensed techniques and several indices. Needless to say, the agroclimatic potential of agricultural areas has to be assessed in order to achieve sustainable and efficient use of natural resources in combination with production maximization. Similarly, the time scale is also considered by addressing drought-related impacts affected by precipitation deficits on time scales ranging from a few days to a few months, such as non-irrigated agriculture, topsoil moisture, wildfire danger, range and pasture conditions and unregulated stream flows. Keywords Remote sensing; Composite Drought Indicators; Global Drought Risk Monitoring.

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2014-06-12

interferometry and polarimetry . In the paper, the model was used to simulate SAR data for Mangrove (tropical) and Nezer (temperate) forests for P-band and...Scattering Model Applied to Radiometry, Interferometry, and Polarimetry at P- and L-Band. IEEE Transactions on Geoscience and Remote Sensing 44(4): 849

Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion

USDA-ARS?s Scientific Manuscript database

A continuous monitoring of daily evapotranspiration (ET) at field scale can be achieved by combining thermal infrared remote sensing data information from multiple satellite platforms. Here, an integrated approach to field scale ET mapping is described, combining multi-scale surface energy balance e...

An algorithm for retrieving rock-desertification from multispectral remote sensing images

NASA Astrophysics Data System (ADS)

Xia, Xueqi; Tian, Qingjiu; Liao, Yan

2009-06-01

Rock-desertification is a typical environmental and ecological problem in Southwest China. As remote sensing is an important means of monitoring spatial variation of rock-desertification, a method is developed for measurement and information retrieval of rock-desertification from multi-spectral high-resolution remote sensing images. MNF transform is applied to 4-band IKONOS multi-spectral remotely sensed data to reduce the number of spectral dimensions to three. In the 3-demension endmembers are extracted and analyzed. It is found that various vegetations group into a line defined as "vegetation line", in which "dark vegetations", such as coniferous forest and broadleaf forest, continuously change to "bright vegetations", such as grasses. It is presumed that is caused by deferent proportion of shadow mixed in leaves or branches in various types of vegetation. Normalized distance between the endmember of rocks and the vegetation line is defined as Geometric Rock-desertification Index (GRI), which was used to scale rock-desertification. The case study with ground truth validation in Puding, Guizhou province showed successes and the advantages of this method.

Toward irrigation retrieval by combining multi-sensor remote sensing data into a land surface model over a semi-arid region

NASA Astrophysics Data System (ADS)

Malbéteau, Y.; Lopez, O.; Houborg, R.; McCabe, M.

2017-12-01

Agriculture places considerable pressure on water resources, with the relationship between water availability and food production being critical for sustaining population growth. Monitoring water resources is particularly important in arid and semi-arid regions, where irrigation can represent up to 80% of the consumptive uses of water. In this context, it is necessary to optimize on-farm irrigation management by adjusting irrigation to crop water requirements throughout the growing season. However, in situ point measurements are not routinely available over extended areas and may not be representative at the field scale. Remote sensing approaches present as a cost-effective technique for mapping and monitoring broad areas. By taking advantage of multi-sensor remote sensing methodologies, such as those provided by MODIS, Landsat, Sentinel and Cubesats, we propose a new method to estimate irrigation input at pivot-scale. Here we explore the development of crop-water use estimates via these remote sensing data and integrate them into a land surface modeling framework, using a farm in Saudi Arabia as a demonstration of what can be achieved at larger scales.

A parallel method of atmospheric correction for multispectral high spatial resolution remote sensing images

NASA Astrophysics Data System (ADS)

Zhao, Shaoshuai; Ni, Chen; Cao, Jing; Li, Zhengqiang; Chen, Xingfeng; Ma, Yan; Yang, Leiku; Hou, Weizhen; Qie, Lili; Ge, Bangyu; Liu, Li; Xing, Jin

2018-03-01

The remote sensing image is usually polluted by atmosphere components especially like aerosol particles. For the quantitative remote sensing applications, the radiative transfer model based atmospheric correction is used to get the reflectance with decoupling the atmosphere and surface by consuming a long computational time. The parallel computing is a solution method for the temporal acceleration. The parallel strategy which uses multi-CPU to work simultaneously is designed to do atmospheric correction for a multispectral remote sensing image. The parallel framework's flow and the main parallel body of atmospheric correction are described. Then, the multispectral remote sensing image of the Chinese Gaofen-2 satellite is used to test the acceleration efficiency. When the CPU number is increasing from 1 to 8, the computational speed is also increasing. The biggest acceleration rate is 6.5. Under the 8 CPU working mode, the whole image atmospheric correction costs 4 minutes.

A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images

NASA Astrophysics Data System (ADS)

Liu, Likun

2018-01-01

In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.

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

NASA Astrophysics Data System (ADS)

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 landscape level ecosystem structure and phenological dynamics at multiple temporal scales. Overall, this study has furthered our knowledge of how tundra ecosystems in the Arctic change seasonally and how such change could impact remote sensing studies conducted from multiple platforms and across multiple spatial scales. Additionally, this study also highlights the urgent need for research into the validation of satellite products in order to better understand the causes and consequences of the changing Arctic and its potential effects on global processes. This study focused on sites located in northern Alaska and was formed in collaboration with Florida International University (FIU) and Grand Valley State University (GVSU) as a contribution to the US Arctic Observing Network (AON). All efforts were supported through the National Science Foundation (NSF), the Cyber-ShARE Center of Excellence, and the International Tundra Experiment (ITEX).

Thirty years of use and improvement of remote sensing, applied to epidemiology: from early promises to lasting frustration.

PubMed

Herbreteau, Vincent; Salem, Gérard; Souris, Marc; Hugot, Jean-Pierre; Gonzalez, Jean-Paul

2007-06-01

Remote sensing, referring to the remote study of objects, was originally developed for Earth observation, through the use of sensors on board planes or satellites. Improvements in the use and accessibility of multi-temporal satellite-derived environmental data have, for 30 years, contributed to a growing use in epidemiology. Despite the potential of remote-sensed images and processing techniques for a better knowledge of disease dynamics, an exhaustive analysis of the bibliography shows a generalized use of pre-processed spatial data and low-cost images, resulting in a limited adaptability when addressing biological questions.

Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

USGS Publications Warehouse

Corcoran, Jennifer M.; Knight, Joseph F.; Gallant, Alisa L.

2013-01-01

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

Radar and optical remote sensing in offshore domain to detect, characterize, and quantify ocean surface oil slicks

NASA Astrophysics Data System (ADS)

Angelliaume, S.; Ceamanos, X.; Viallefont-Robinet, F.; Baqué, R.; Déliot, Ph.; Miegebielle, V.

2017-10-01

Radar and optical sensors are operationally used by authorities or petroleum companies for detecting and characterizing maritime pollution. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as the oil real fraction, which is critical for both exploration purposes and efficient cleanup operations. Today state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI, the airborne system developed by ONERA, during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this data set lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the electromagnetic spectrum. Specific processing techniques have been developed in order to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows to estimate slick surface properties such as the spatial abundance of oil and the relative concentration of hydrocarbons on the sea surface.

Methods of training the graduate level and professional geologist in remote sensing technology

NASA Technical Reports Server (NTRS)

Kolm, K. E.

1981-01-01

Requirements for a basic course in remote sensing to accommodate the needs of the graduate level and professional geologist are described. The course should stress the general topics of basic remote sensing theory, the theory and data types relating to different remote sensing systems, an introduction to the basic concepts of computer image processing and analysis, the characteristics of different data types, the development of methods for geological interpretations, the integration of all scales and data types of remote sensing in a given study, the integration of other data bases (geophysical and geochemical) into a remote sensing study, and geological remote sensing applications. The laboratories should stress hands on experience to reinforce the concepts and procedures presented in the lecture. The geologist should then be encouraged to pursue a second course in computer image processing and analysis of remotely sensed data.

A multi-sensor remote sensing approach for measuring primary production from space

NASA Technical Reports Server (NTRS)

Gautier, Catherine

1989-01-01

It is proposed to develop a multi-sensor remote sensing method for computing marine primary productivity from space, based on the capability to measure the primary ocean variables which regulate photosynthesis. The three variables and the sensors which measure them are: (1) downwelling photosynthetically available irradiance, measured by the VISSR sensor on the GOES satellite, (2) sea-surface temperature from AVHRR on NOAA series satellites, and (3) chlorophyll-like pigment concentration from the Nimbus-7/CZCS sensor. These and other measured variables would be combined within empirical or analytical models to compute primary productivity. With this proposed capability of mapping primary productivity on a regional scale, we could begin realizing a more precise and accurate global assessment of its magnitude and variability. Applications would include supplementation and expansion on the horizontal scale of ship-acquired biological data, which is more accurate and which supplies the vertical components of the field, monitoring oceanic response to increased atmospheric carbon dioxide levels, correlation with observed sedimentation patterns and processes, and fisheries management.

Improved resource use decisions and actions through remote sensing

NASA Technical Reports Server (NTRS)

Hill-Rowley, R.; Boylan, M.; Enslin, W.; Vlasin, R. D.

1975-01-01

Operational uses of remote sensing for improving management decisions and actions concerning resource uses are considered in terms of first generation, or direct-action; and second generation or indirect, delayed-action applications. From among applications completed during 1974-75, seven case studies are offered in illustration of the many contrasts which can be drawn between first and second generation application studies. These include: (1) multi-agency river basin planning; (2) corridor assessment and route location for highway location together with improvement of county-level planning decisions; (3) improving timber management practices; (4) enforcement of new state statutes; (5) county-wide open space preservation; (6) land value reappraisal relative to property tax equalization; and (7) optimizing agri-business processing plant locations.

Compositing multitemporal remote sensing data sets

USGS Publications Warehouse

Qi, J.; Huete, A.R.; Hood, J.; Kerr, Y.

1993-01-01

To eliminate cloud and atmosphere-affected pixels, the compositing of multi temporal remote sensing data sets is done by selecting the maximum vale of the normalized different vegetation index (NDVI) within a compositing period. The NDVI classifier, however, is strongly affected by surface type and anisotropic properties, sensor viewing geometries, and atmospheric conditions. Consequently, the composited, multi temporal, remote sensing data contain substantial noise from these external conditions. Consequently, the composited, multi temporal, remote sensing data contain substantial noise from these external effects. To improve the accuracy of compositing products, two key approaches can be taken: one is to refine the compositing classifier (NDVI) and the other is to improve existing compositing algorithms. In this project, an alternative classifier was developed and an alternative pixel selection criterion was proposed for compositing. The new classifier and the alternative compositing algorithm were applied to an advanced very high resolution radiometer data set of different biome types in the United States. The results were compared with the maximum value compositing and the best index slope extraction algorithms. The new approaches greatly reduced the high frequency noises related to the external factors and repainted more reliable data. The results suggest that the geometric-optical canopy properties of specific biomes may be needed in compositing. Limitations of the new approaches include the dependency of pixel selection on the length of the composite period and data discontinuity.

Analysis of flood inundation in ungauged basins based on multi-source remote sensing data.

PubMed

Gao, Wei; Shen, Qiu; Zhou, Yuehua; Li, Xin

2018-02-09

Floods are among the most expensive natural hazards experienced in many places of the world and can result in heavy losses of life and economic damages. The objective of this study is to analyze flood inundation in ungauged basins by performing near-real-time detection with flood extent and depth based on multi-source remote sensing data. Via spatial distribution analysis of flood extent and depth in a time series, the inundation condition and the characteristics of flood disaster can be reflected. The results show that the multi-source remote sensing data can make up the lack of hydrological data in ungauged basins, which is helpful to reconstruct hydrological sequence; the combination of MODIS (moderate-resolution imaging spectroradiometer) surface reflectance productions and the DFO (Dartmouth Flood Observatory) flood database can achieve the macro-dynamic monitoring of the flood inundation in ungauged basins, and then the differential technique of high-resolution optical and microwave images before and after floods can be used to calculate flood extent to reflect spatial changes of inundation; the monitoring algorithm for the flood depth combining RS and GIS is simple and easy and can quickly calculate the depth with a known flood extent that is obtained from remote sensing images in ungauged basins. Relevant results can provide effective help for the disaster relief work performed by government departments.

Regional Drought Monitoring Based on Multi-Sensor Remote Sensing

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Im, Jungho; Park, Seonyoung

2014-05-01

Drought originates from the deficit of precipitation and impacts environment including agriculture and hydrological resources as it persists. The assessment and monitoring of drought has traditionally been performed using a variety of drought indices based on meteorological data, and recently the use of remote sensing data is gaining much attention due to its vast spatial coverage and cost-effectiveness. Drought information has been successfully derived from remotely sensed data related to some biophysical and meteorological variables and drought monitoring is advancing with the development of remote sensing-based indices such as the Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Normalized Difference Water Index (NDWI) to name a few. The Scaled Drought Condition Index (SDCI) has also been proposed to be used for humid regions proving the performance of multi-sensor data for agricultural drought monitoring. In this study, remote sensing-based hydro-meteorological variables related to drought including precipitation, temperature, evapotranspiration, and soil moisture were examined and the SDCI was improved by providing multiple blends of the multi-sensor indices for different types of drought. Multiple indices were examined together since the coupling and feedback between variables are intertwined and it is not appropriate to investigate only limited variables to monitor each type of drought. The purpose of this study is to verify the significance of each variable to monitor each type of drought and to examine the combination of multi-sensor indices for more accurate and timely drought monitoring. The weights for the blends of multiple indicators were obtained from the importance of variables calculated by non-linear optimization using a Machine Learning technique called Random Forest. The case study was performed in the Republic of Korea, which has four distinct seasons over the course of the year and contains complex topography with a variety of land cover types. Remote sensing data from the Tropical Rainfall Measuring Mission satellite (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) sensors were obtained for the period from 2000 to 2012, and observation data from 99 weather stations, 441 streamflow gauges, as well as the gridded observation data from Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE) were obtained for validation. The objective blends of multiple indicators helped better assessment of various types of drought, and can be useful for drought early warning system. Since the improved SDCI is based on remotely sensed data, it can be easily applied to regions with limited or no observation data for drought assessment and monitoring.

Improving simulations of snow water equivalent and total water storage changes over the Upper Yangtze River basin using multi-source remote sensing data

NASA Astrophysics Data System (ADS)

Han, P.; Long, D.

2017-12-01

Snow water equivalent (SWE) and total water storage (TWS) changes are important hydrological state variables over cryospheric regions, such as China's Upper Yangtze River (UYR) basin. Accurate simulation of these two state variables plays a critical role in understanding hydrological processes over this region and, in turn, benefits water resource management, hydropower development, and ecological integrity over the lower reaches of the Yangtze River, one of the largest rivers globally. In this study, an improved CREST model coupled with a snow and glacier melting module was used to simulate SWE and TWS changes over the UYR, and to quantify contributions of snow and glacier meltwater to the total runoff. Forcing, calibration, and validation data are mainly from multi-source remote sensing observations, including satellite-based precipitation estimates, passive microwave remote sensing-based SWE, and GRACE-derived TWS changes, along with streamflow measurements at the Zhimenda gauging station. Results show that multi-source remote sensing information can be extremely valuable in model forcing, calibration, and validation over the poorly gauged region. The simulated SWE and TWS changes and the observed counterparts are highly consistent, showing NSE coefficients higher than 0.8. The results also show that the contributions of snow and glacier meltwater to the total runoff are 8% and 6%, respectively, during the period 2003‒2014, which is an important source of runoff. Moreover, from this study, the TWS is found to increase at a rate of 5 mm/a ( 0.72 Gt/a) for the period 2003‒2014. The snow melting module may overestimate SWE for high precipitation events and was improved in this study. Key words: CREST model; Remote Sensing; Melting model; Source Region of the Yangtze River

Buildings Change Detection Based on Shape Matching for Multi-Resolution Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Abdessetar, M.; Zhong, Y.

2017-09-01

Buildings change detection has the ability to quantify the temporal effect, on urban area, for urban evolution study or damage assessment in disaster cases. In this context, changes analysis might involve the utilization of the available satellite images with different resolutions for quick responses. In this paper, to avoid using traditional method with image resampling outcomes and salt-pepper effect, building change detection based on shape matching is proposed for multi-resolution remote sensing images. Since the object's shape can be extracted from remote sensing imagery and the shapes of corresponding objects in multi-scale images are similar, it is practical for detecting buildings changes in multi-scale imagery using shape analysis. Therefore, the proposed methodology can deal with different pixel size for identifying new and demolished buildings in urban area using geometric properties of objects of interest. After rectifying the desired multi-dates and multi-resolutions images, by image to image registration with optimal RMS value, objects based image classification is performed to extract buildings shape from the images. Next, Centroid-Coincident Matching is conducted, on the extracted building shapes, based on the Euclidean distance measurement between shapes centroid (from shape T0 to shape T1 and vice versa), in order to define corresponding building objects. Then, New and Demolished buildings are identified based on the obtained distances those are greater than RMS value (No match in the same location).

Studies and Application of Remote Sensing Retrieval Method of Soil Moisture Content in Land Parcel Units in Irrigation Area

NASA Astrophysics Data System (ADS)

Zhu, H.; Zhao, H. L.; Jiang, Y. Z.; Zang, W. B.

2018-05-01

Soil moisture is one of the important hydrological elements. Obtaining soil moisture accurately and effectively is of great significance for water resource management in irrigation area. During the process of soil moisture content retrieval with multiremote sensing data, multi- remote sensing data always brings multi-spatial scale problems which results in inconformity of soil moisture content retrieved by remote sensing in different spatial scale. In addition, agricultural water use management has suitable spatial scale of soil moisture information so as to satisfy the demands of dynamic management of water use and water demand in certain unit. We have proposed to use land parcel unit as the minimum unit to do soil moisture content research in agricultural water using area, according to soil characteristics, vegetation coverage characteristics in underlying layer, and hydrological characteristic into the basis of study unit division. We have proposed division method of land parcel units. Based on multi thermal infrared and near infrared remote sensing data, we calculate the ndvi and tvdi index and make a statistical model between the tvdi index and soil moisture of ground monitoring station. Then we move forward to study soil moisture remote sensing retrieval method on land parcel unit scale. And the method has been applied in Hetao irrigation area. Results show that compared with pixel scale the soil moisture content in land parcel unit scale has displayed stronger correlation with true value. Hence, remote sensing retrieval method of soil moisture content in land parcel unit scale has shown good applicability in Hetao irrigation area. We converted the research unit into the scale of land parcel unit. Using the land parcel units with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data caused by the research units of pixels, and doesn't involve compromises in the spatial scale and simulating precision like the grid simulation. When the application needs are met, the production efficiency of products can also be improved at a certain degree.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

NASA Astrophysics Data System (ADS)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

Multi-source and multi-angle remote sensing image data collection, application and sharing of Beichuan National Earthquake Ruins Museum

NASA Astrophysics Data System (ADS)

Lin, Yueguan; Wang, Wei; Wen, Qi; Huang, He; Lin, Jingli; Zhang, Wei

2015-12-01

Ms8.0 Wenchuan earthquake that occurred on May 12, 2008 brought huge casualties and property losses to the Chinese people, and Beichuan County was destroyed in the earthquake. In order to leave a site for commemorate of the people, and for science propaganda and research of earthquake science, Beichuan National Earthquake Ruins Museum has been built on the ruins of Beichuan county. Based on the demand for digital preservation of the earthquake ruins park and collection of earthquake damage assessment of research and data needs, we set up a data set of Beichuan National Earthquake Ruins Museum, including satellite remote sensing image, airborne remote sensing image, ground photogrammetry data and ground acquisition data. At the same time, in order to make a better service for earthquake science research, we design the sharing ideas and schemes for this scientific data set.

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

NASA Astrophysics Data System (ADS)

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-induced forest phenology changes at unprecedented temporal and spatial resolutions. This work provides the methodological approach monitor fire-induced forest changes in a spatially explicit manner across scales, with important implications for fire-related forest management and for constraining/benchmarking process models.

Resource analysis applications in Michigan. [NASA remote sensing

NASA Technical Reports Server (NTRS)

Schar, S. W.; Enslin, W. R.; Sattinger, I. J.; Robinson, J. G.; Hosford, K. R.; Fellows, R. S.; Raad, J. H.

1974-01-01

During the past two years, available NASA imagery has been applied to a broad spectrum of problems of concern to Michigan-based agencies. These demonstrations include the testing of remote sensing for the purposes of (1) highway corridor planning and impact assessments, (2) game management-area information bases, (3) multi-agency river basin planning, (4) timber resource management information systems, (5) agricultural land reservation policies, and (6) shoreline flooding damage assessment. In addition, cost accounting procedures have been developed for evaluating the relative costs of utilizing remote sensing in land cover and land use analysis data collection procedures.

Remote Sensing of Aerosol and their Radiative Forcing of Climate

NASA Technical Reports Server (NTRS)

Kaufman, Yoram J.; Tanre, Didier; Remer, Lorraine A.

1999-01-01

Remote sensing of aerosol and aerosol radiative forcing of climate is going through a major transformation. The launch in next few years of new satellites designed specifically for remote sensing of aerosol is expected to further revolutionized aerosol measurements: until five years ago satellites were not designed for remote sensing of aerosol. Aerosol optical thickness was derived as a by product, only over the oceans using one AVHRR channel with errors of approx. 50%. However it already revealed a very important first global picture of the distribution and sources of aerosol. In the last 5 years we saw the introduction of polarization and multi-view observations (POLDER and ATSR) for satellite remote sensing of aerosol over land and ocean. Better products are derived from AVHRR using its two channels. The new TOMS aerosol index shows the location and transport of aerosol over land and ocean. Now we anticipate the launch of EOS-Terra with MODIS, MISR and CERES on board for multi-view, multi-spectral remote sensing of aerosol and its radiative forcing. This will allow application of new techniques, e.g. using a wide spectral range (0.55-2.2 microns) to derive precise optical thickness, particle size and mass loading. Aerosol is transparent in the 2.2 microns channel, therefore this channel can be used to detect surface features that in turn are used to derive the aerosol optical thickness in the visible part of the spectrum. New techniques are developed to derive the aerosol single scattering albedo, a measure of absorption of sunlight, and techniques to derive directly the aerosol forcing at the top of the atmosphere. In the last 5 years a global network of sun/sky radiometers was formed, designed to communicate in real time the spectral optical thickness from 50-80 locations every day, every 15 minutes. The sky angular and spectral information is also measured and used to retrieve the aerosol size distribution, refractive index, single scattering albedo and the spectral flux reaching the surface. Effort to introduce remote sensing from lidars will literally additional dimension to aerosol remote sensing. The vertical dimension is a critical link between the global satellite observations and modeling of aerosol transport. Lidars are also critical to study aerosol impact on cloud microphysics and reflectance. Both lidar ground networks and satellite systems are in development. This new capability is expected to put remote sensing in the forefront of aerosol and climate studies. Together with field experiments, chemical analysis and chemical transport models we anticipate, in the next decade, to be able to resolve some of the outstanding questions regarding the role of aerosol in climate, in atmospheric chemistry and its influence on human health and life on this planet.

Tasseled cap transformation for HJ multispectral remote sensing data

NASA Astrophysics Data System (ADS)

Han, Ling; Han, Xiaoyong

2015-12-01

The tasseled cap transformation of remote sensing data has been widely used in environment, agriculture, forest and ecology. Tasseled cap transformation coefficients matrix of HJ multi-spectrum data has been established through Givens rotation matrix to rotate principal component transform vector to whiteness, greenness and blueness direction of ground object basing on 24 scenes year-round HJ multispectral remote sensing data. The whiteness component enhances the brightness difference of ground object, and the greenness component preserves more detailed information of vegetation change while enhances the vegetation characteristic, and the blueness component significantly enhances factory with blue plastic house roof around the town and also can enhance brightness of water. Tasseled cap transformation coefficients matrix of HJ will enhance the application effect of HJ multispectral remote sensing data in their application fields.

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

NASA Astrophysics Data System (ADS)

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

2012-12-01

Wetland ecosystems are prevalent in the Amazon basin, especially in northern Peru. Of specific interest are palm swamp wetlands because they are characterized by constant surface inundation and moderate seasonal water level variation. This combination of constantly saturated soils and warm temperatures year-round can lead to considerable methane release to the atmosphere. Because of the widespread occurrence and expected sensitivity of these ecosystems to climate change, it is critical to develop methods to quantify their spatial extent and inundation state in order to assess their carbon dynamics. Spatio-temporal information on palm swamps is difficult to gather because of their remoteness and difficult accessibility. Spaceborne microwave remote sensing is an effective tool for characterizing these ecosystems since it is sensitive to surface water and vegetation structure and allows monitoring large inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination. We developed a remote sensing methodology using multi-sensor remote sensing data from the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR), Shuttle Radar Topography Mission (SRTM) DEM, and Landsat to derive maps at 100 meter resolution of palm swamp extent and inundation based on ground data collections; and combined active and passive microwave data from AMSR-E and QuikSCAT to derive inundation extent at 25 kilometer resolution on a weekly basis. We then compared information content and accuracy of the coarse resolution products relative to the high-resolution datasets. The synergistic combination of high and low resolution datasets allowed for characterization of palm swamps and assessment of their flooding status. This work has been undertaken partly within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data have been provided by JAXA. Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

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

Treesearch

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...

Growth pattern research on the modern deposition of Ganjiang delta in Poyang lake basin by spatio-temporal remote sensing images

NASA Astrophysics Data System (ADS)

Zhou, Hongying; Yuan, Xuanjun; Zhang, Youyan; Dong, Wentong; Liu, Song

2016-11-01

It is of great importance for petroleum exploration to study the sedimentary features and the growth pattern of shoal water deltas in lake basins. Taking spatio-temporal remote sensing images as the principal data source, combined with field sedimentation survey, a quantitative research on the modern deposition of Ganjiang delta in the Poyang Lake Basin is described in this paper. Using 76 multi-temporal and multi-type remote sensing images acquired from 1973 to 2015, combined with field sedimentation survey, remote sensing interpretation analysis was conducted on the sedimentary facies of the Ganjiang delta. It is found that that the current Poyang Lake mainly consists of three types of sand body deposits including deltaic deposit, overflow channel deposit, and aeolian deposit, and the distribution of sand bodies was affected by the above three types of depositions jointly. The mid-branch channels of the Ganjiang delta increased on an exponential growth rhythm. The main growth pattern of the Ganjiang delta is dendritic and reticular, and the distributary channel mostly arborizes at lake inlet and was reworked to be reticulatus at late stage.

Multi-Sensor Remote Sensing of Forest Dynamics in Central Siberia

NASA Technical Reports Server (NTRS)

Ransom, K. J.; Sun, G.; Kharuk, V. I.; Howl, J.

2011-01-01

The forested regions of Siberia, Russia are vast and contain about a quarter of the world's forests that have not experienced harvesting. However, many Siberian forests are facing twin pressures of rapidly changing climate and increasing timber harvest activity. Monitoring the dynamics and mapping the structural parameters of the forest is important for understanding the causes and consequences of changes observed in these areas. Because of the inaccessibility and large extent of this forest, remote sensing data can play an important role for observing forest state and change. In Central Siberia, multi-sensor remote sensing data have been used to monitor forest disturbances and to map above-ground biomass from the Sayan Mountains in the south to the taiga-tundra boundaries in the north. Radar images from the Shuttle Imaging Radar-C (SIR-C)/XSAR mission were used for forest biomass estimation in the Sayan Mountains. Radar images from the Japanese Earth Resources Satellite-1 (JERS-1), European Remote Sensing Satellite-1 (ERS-1) and Canada's RADARSAT-1, and data from ETM+ on-board Landsat-7 were used to characterize forest disturbances from logging, fire, and insect damage in Boguchany and Priangare areas.

Development of a generalized multi-pixel and multi-parameter satellite remote sensing algorithm for aerosol properties

NASA Astrophysics Data System (ADS)

Hashimoto, M.; Nakajima, T.; Takenaka, H.; Higurashi, A.

2013-12-01

We develop a new satellite remote sensing algorithm to retrieve the properties of aerosol particles in the atmosphere. In late years, high resolution and multi-wavelength, and multiple-angle observation data have been obtained by grand-based spectral radiometers and imaging sensors on board the satellite. With this development, optimized multi-parameter remote sensing methods based on the Bayesian theory have become popularly used (Turchin and Nozik, 1969; Rodgers, 2000; Dubovik et al., 2000). Additionally, a direct use of radiation transfer calculation has been employed for non-linear remote sensing problems taking place of look up table methods supported by the progress of computing technology (Dubovik et al., 2011; Yoshida et al., 2011). We are developing a flexible multi-pixel and multi-parameter remote sensing algorithm for aerosol optical properties. In this algorithm, the inversion method is a combination of the MAP method (Maximum a posteriori method, Rodgers, 2000) and the Phillips-Twomey method (Phillips, 1962; Twomey, 1963) as a smoothing constraint for the state vector. Furthermore, we include a radiation transfer calculation code, Rstar (Nakajima and Tanaka, 1986, 1988), numerically solved each time in iteration for solution search. The Rstar-code has been directly used in the AERONET operational processing system (Dubovik and King, 2000). Retrieved parameters in our algorithm are aerosol optical properties, such as aerosol optical thickness (AOT) of fine mode, sea salt, and dust particles, a volume soot fraction in fine mode particles, and ground surface albedo of each observed wavelength. We simultaneously retrieve all the parameters that characterize pixels in each of horizontal sub-domains consisting the target area. Then we successively apply the retrieval method to all the sub-domains in the target area. We conducted numerical tests for the retrieval of aerosol properties and ground surface albedo for GOSAT/CAI imager data to test the algorithm for the land area. In this test, we simulated satellite-observed radiances for a sub-domain consisting of 5 by 5 pixels by the Rstar code assuming wavelengths of 380, 674, 870 and 1600 [nm], atmospheric condition of the US standard atmosphere, and the several aerosol and ground surface conditions. The result of the experiment showed that AOTs of fine mode and dust particles, soot fraction and ground surface albedo at the wavelength of 674 [nm] are retrieved within absolute value differences of 0.04, 0.01, 0.06 and 0.006 from the true value, respectively, for the case of dark surface, and also, for the case of blight surface, 0.06, 0.03, 0.04 and 0.10 from the true value, respectively. We will conduct more tests to study the information contents of parameters needed for aerosol and land surface remote sensing with different boundary conditions among sub-domains.

[On-Orbit Multispectral Sensor Characterization Based on Spectral Tarps].

PubMed

Li, Xin; Zhang, Li-ming; Chen, Hong-yao; Xu, Wei-wei

2016-03-01

The multispectral remote sensing technology has been a primary means in the research of biomass monitoring, climate change, disaster prediction and etc. The spectral sensitivity is essential in the quantitative analysis of remote sensing data. When the sensor is running in the space, it will be influenced by cosmic radiation, severe change of temperature, chemical molecular contamination, cosmic dust and etc. As a result, the spectral sensitivity will degrade by time, which has great implication on the accuracy and consistency of the physical measurements. This paper presents a characterization method of the degradation based on man-made spectral targets. Firstly, a degradation model is established in the paper. Then, combined with equivalent reflectance of spectral targets measured and inverted from image, the degradation characterization can be achieved. The simulation and on orbit experiment results showed that, using the proposed method, the change of center wavelength and band width can be monotored. The method proposed in the paper has great significance for improving the accuracy of long time series remote sensing data product and comprehensive utilization level of multi sensor data products.

Ground-based remote sensing of tropospheric water vapour isotopologues within the project MUSICA

NASA Astrophysics Data System (ADS)

Schneider, M.; Barthlott, S.; Hase, F.; González, Y.; Yoshimura, K.; García, O. E.; Sepúlveda, E.; Gomez-Pelaez, A.; Gisi, M.; Kohlhepp, R.; Dohe, S.; Blumenstock, T.; Wiegele, A.; Christner, E.; Strong, K.; Weaver, D.; Palm, M.; Deutscher, N. M.; Warneke, T.; Notholt, J.; Lejeune, B.; Demoulin, P.; Jones, N.; Griffith, D. W. T.; Smale, D.; Robinson, J.

2012-12-01

Within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water), long-term tropospheric water vapour isotopologue data records are provided for ten globally distributed ground-based mid-infrared remote sensing stations of the NDACC (Network for the Detection of Atmospheric Composition Change). We present a new method allowing for an extensive and straightforward characterisation of the complex nature of such isotopologue remote sensing datasets. We demonstrate that the MUSICA humidity profiles are representative for most of the troposphere with a vertical resolution ranging from about 2 km (in the lower troposphere) to 8 km (in the upper troposphere) and with an estimated precision of better than 10%. We find that the sensitivity with respect to the isotopologue composition is limited to the lower and middle troposphere, whereby we estimate a precision of about 30‰ for the ratio between the two isotopologues HD16O and H216O. The measurement noise, the applied atmospheric temperature profiles, the uncertainty in the spectral baseline, and the cross-dependence on humidity are the leading error sources. We introduce an a posteriori correction method of the cross-dependence on humidity, and we recommend applying it to isotopologue ratio remote sensing datasets in general. In addition, we present mid-infrared CO2 retrievals and use them for demonstrating the MUSICA network-wide data consistency. In order to indicate the potential of long-term isotopologue remote sensing data if provided with a well-documented quality, we present a climatology and compare it to simulations of an isotope incorporated AGCM (Atmospheric General Circulation Model). We identify differences in the multi-year mean and seasonal cycles that significantly exceed the estimated errors, thereby indicating deficits in the modeled atmospheric water cycle.

Ground-based remote sensing of tropospheric water vapour isotopologues within the project MUSICA

NASA Astrophysics Data System (ADS)

Schneider, M.; Barthlott, S.; Hase, F.; González, Y.; Yoshimura, K.; García, O. E.; Sepúlveda, E.; Gomez-Pelaez, A.; Gisi, M.; Kohlhepp, R.; Dohe, S.; Blumenstock, T.; Strong, K.; Weaver, D.; Palm, M.; Deutscher, N. M.; Warneke, T.; Notholt, J.; Lejeune, B.; Demoulin, P.; Jones, N.; Griffith, D. W. T.; Smale, D.; Robinson, J.

2012-08-01

Within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water), long-term tropospheric water vapour isotopologues data records are provided for ten globally distributed ground-based mid-infrared remote sensing stations of the NDACC (Network for the Detection of Atmospheric Composition Change). We present a new method allowing for an extensive and straightforward characterisation of the complex nature of such isotopologue remote sensing datasets. We demonstrate that the MUSICA humidity profiles are representative for most of the troposphere with a vertical resolution ranging from about 2 km (in the lower troposphere) to 8 km (in the upper troposphere) and with an estimated precision of better than 10%. We find that the sensitivity with respect to the isotopologue composition is limited to the lower and middle troposphere, whereby we estimate a precision of about 30‰ for the ratio between the two isotopologues HD16O and H216O. The measurement noise, the applied atmospheric temperature profiles, the uncertainty in the spectral baseline, and interferences from humidity are the leading error sources. We introduce an a posteriori correction method of the humidity interference error and we recommend applying it for isotopologue ratio remote sensing datasets in general. In addition, we present mid-infrared CO2 retrievals and use them for demonstrating the MUSICA network-wide data consistency. In order to indicate the potential of long-term isotopologue remote sensing data if provided with a well-documented quality, we present a climatology and compare it to simulations of an isotope incorporated AGCM (Atmospheric General Circulation Model). We identify differences in the multi-year mean and seasonal cycles that significantly exceed the estimated errors, thereby indicating deficits in the modeled atmospheric water cycle.

Effects of bathymetric lidar errors on flow properties predicted with a multi-dimensional hydraulic model

Treesearch

J. McKean; D. Tonina; C. Bohn; C. W. Wright

2014-01-01

New remote sensing technologies and improved computer performance now allow numerical flow modeling over large stream domains. However, there has been limited testing of whether channel topography can be remotely mapped with accuracy necessary for such modeling. We assessed the ability of the Experimental Advanced Airborne Research Lidar, to support a multi-dimensional...

Monitoring landscape level processes using remote sensing of large plots

Treesearch

Raymond L. Czaplewski

1991-01-01

Global and regional assessaents require timely information on landscape level status (e.g., areal extent of different ecosystems) and processes (e.g., changes in land use and land cover). To measure and understand these processes at the regional level, and model their impacts, remote sensing is often necessary. However, processing massive volumes of remotely sensing...

Accommodating Student Diversity in Remote Sensing Instruction.

ERIC Educational Resources Information Center

Hammen, John L., III.

1992-01-01

Discusses the difficulty of teaching computer-based remote sensing to students of varying levels of computer literacy. Suggests an instructional method that accommodates all levels of technical expertise through the use of microcomputers. Presents a curriculum that includes an introduction to remote sensing, digital image processing, and…

A multi-scale framework to link remotely sensed metrics with socioeconomic data

NASA Astrophysics Data System (ADS)

Watmough, Gary; Svenning, Jens-Christian; Palm, Cheryl; Sullivan, Clare; Danylo, Olha; McCallum, Ian

2017-04-01

There is increasing interest in the use of remotely sensed satellite data for estimating human poverty as it can bridge data gaps that prevent fine scale monitoring of development goals across large areas. The ways in which metrics derived from satellite imagery are linked with socioeconomic data are crucial for accurate estimation of poverty. Yet, to date, approaches in the literature linking satellite metrics with socioeconomic data are poorly characterized. Typically, approaches use a GIS approach such as circular buffer zones around a village or household or an administrative boundary such as a district or census enumeration area. These polygons are then used to extract environmental data from satellite imagery and related to the socioeconomic data in statistical analyses. The use of a single polygon to link environment and socioeconomic data is inappropriate in coupled human-natural systems as processes operate over multiple scales. Human interactions with the environment occur at multiple levels from individual (household) access to agricultural plots adjacent to homes, to communal access to common pool resources (CPR) such as forests at the village level. Here, we present a multi-scale framework that explicitly considers how people use the landscape. The framework is presented along with a case study example in Kenya. The multi-scale approach could enhance the modelling of human-environment interactions which will have important consequences for monitoring the sustainable development goals for human livelihoods and biodiversity conservation.

Development of a fusion approach selection tool

NASA Astrophysics Data System (ADS)

Pohl, C.; Zeng, Y.

2015-06-01

During the last decades number and quality of available remote sensing satellite sensors for Earth observation has grown significantly. The amount of available multi-sensor images along with their increased spatial and spectral resolution provides new challenges to Earth scientists. With a Fusion Approach Selection Tool (FAST) the remote sensing community would obtain access to an optimized and improved image processing technology. Remote sensing image fusion is a mean to produce images containing information that is not inherent in the single image alone. In the meantime the user has access to sophisticated commercialized image fusion techniques plus the option to tune the parameters of each individual technique to match the anticipated application. This leaves the operator with an uncountable number of options to combine remote sensing images, not talking about the selection of the appropriate images, resolution and bands. Image fusion can be a machine and time-consuming endeavour. In addition it requires knowledge about remote sensing, image fusion, digital image processing and the application. FAST shall provide the user with a quick overview of processing flows to choose from to reach the target. FAST will ask for available images, application parameters and desired information to process this input to come out with a workflow to quickly obtain the best results. It will optimize data and image fusion techniques. It provides an overview on the possible results from which the user can choose the best. FAST will enable even inexperienced users to use advanced processing methods to maximize the benefit of multi-sensor image exploitation.

A fast and fully automatic registration approach based on point features for multi-source remote-sensing images

NASA Astrophysics Data System (ADS)

Yu, Le; Zhang, Dengrong; Holden, Eun-Jung

2008-07-01

Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration.

SMERGE: A multi-decadal root-zone soil moisture product for CONUS

NASA Astrophysics Data System (ADS)

Crow, W. T.; Dong, J.; Tobin, K. J.; Torres, R.

2017-12-01

Multi-decadal root-zone soil moisture products are of value for a range of water resource and climate applications. The NASA-funded root-zone soil moisture merging project (SMERGE) seeks to develop such products through the optimal merging of land surface model predictions with surface soil moisture retrievals acquired from multi-sensor remote sensing products. This presentation will describe the creation and validation of a daily, multi-decadal (1979-2015), vertically-integrated (both surface to 40 cm and surface to 100 cm), 0.125-degree root-zone product over the contiguous United States (CONUS). The modeling backbone of the system is based on hourly root-zone soil moisture simulations generated by the Noah model (v3.2) operating within the North American Land Data Assimilation System (NLDAS-2). Remotely-sensed surface soil moisture retrievals are taken from the multi-sensor European Space Agency Climate Change Initiative soil moisture data set (ESA CCI SM). In particular, the talk will detail: 1) the exponential smoothing approach used to convert surface ESA CCI SM retrievals into root-zone soil moisture estimates, 2) the averaging technique applied to merge (temporally-sporadic) remotely-sensed with (continuous) NLDAS-2 land surface model estimates of root-zone soil moisture into the unified SMERGE product, and 3) the validation of the SMERGE product using long-term, ground-based soil moisture datasets available within CONUS.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

The Physics of Imaging with Remote Sensors : Photon State Space & Radiative Transfer

NASA Technical Reports Server (NTRS)

Davis, Anthony B.

2012-01-01

Standard (mono-pixel/steady-source) retrieval methodology is reaching its fundamental limit with access to multi-angle/multi-spectral photo- polarimetry. Next... Two emerging new classes of retrieval algorithm worth nurturing: multi-pixel time-domain Wave-radiometry transition regimes, and more... Cross-fertilization with bio-medical imaging. Physics-based remote sensing: - What is "photon state space?" - What is "radiative transfer?" - Is "the end" in sight? Two wide-open frontiers! center dot Examples (with variations.

Multi-Source Image Analysis.

DTIC Science & Technology

1979-12-01

vegetation shows on the imagery but emphasis has been placed on the detection of wooded and scrub areas and the differentiation between deciduous and...S. A., 1974b, Phenology and remote sensing, phenology and seasonality modeling: in Helmut Lieth, H. (ed.), Ecological Studies-Analysis and Synthesis...Remote Sensing of Ecology , University of d-eorgia Press, Athens, Georgia, p. 63-94. Phillipson, W. R. and T. Liang, 1975, Airphoto analysis in the

Exploring NASA and ESA Atmospheric Data Using GIOVANNI, the Online Visualization and Analysis Tool

NASA Technical Reports Server (NTRS)

Leptoukh, Gregory

2007-01-01

Giovanni, the NASA Goddard online visualization and analysis tool (http://giovanni.gsfc.nasa.gov) allows users explore various atmospheric phenomena without learning remote sensing data formats and downloading voluminous data. Using NASA MODIS (Terra and Aqua) and ESA MERIS (ENVISAT) aerosol data as an example, we demonstrate Giovanni usage for online multi-sensor remote sensing data comparison and analysis.

Re-sampling remotely sensed data to improve national and regional mapping of forest conditions with confidential field data

Treesearch

Raymond L. Czaplewski

2005-01-01

Forest Service Research and Development (R&D) and State and Private Forestry Deputy Areas, in partnership with the National Forest System Remote Sensing Applications Center (RSAC), built a 250-m resolution (6.25-ha pixel) dataset for the entire USA. It assembles multi-seasonal hyperspectral MODIS data and derivatives, Landsat derivatives (i.e., summary statistics...

Practical applications of remote sensing technology

NASA Technical Reports Server (NTRS)

Whitmore, Roy A., Jr.

1990-01-01

Land managers increasingly are becoming dependent upon remote sensing and automated analysis techniques for information gathering and synthesis. Remote sensing and geographic information system (GIS) techniques provide quick and economical information gathering for large areas. The outputs of remote sensing classification and analysis are most effective when combined with a total natural resources data base within the capabilities of a computerized GIS. Some examples are presented of the successes, as well as the problems, in integrating remote sensing and geographic information systems. The need to exploit remotely sensed data and the potential that geographic information systems offer for managing and analyzing such data continues to grow. New microcomputers with vastly enlarged memory, multi-fold increases in operating speed and storage capacity that was previously available only on mainframe computers are a reality. Improved raster GIS software systems have been developed for these high performance microcomputers. Vector GIS systems previously reserved for mini and mainframe systems are available to operate on these enhanced microcomputers. One of the more exciting areas that is beginning to emerge is the integration of both raster and vector formats on a single computer screen. This technology will allow satellite imagery or digital aerial photography to be presented as a background to a vector display.

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

NASA Astrophysics Data System (ADS)

Cheng, Gong; Han, Junwei; Zhou, Peicheng; Guo, Lei

2014-12-01

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.

Multisource geological data mining and its utilization of uranium resources exploration

NASA Astrophysics Data System (ADS)

Zhang, Jie-lin

2009-10-01

Nuclear energy as one of clear energy sources takes important role in economic development in CHINA, and according to the national long term development strategy, many more nuclear powers will be built in next few years, so it is a great challenge for uranium resources exploration. Research and practice on mineral exploration demonstrates that utilizing the modern Earth Observe System (EOS) technology and developing new multi-source geological data mining methods are effective approaches to uranium deposits prospecting. Based on data mining and knowledge discovery technology, this paper uses multi-source geological data to character electromagnetic spectral, geophysical and spatial information of uranium mineralization factors, and provides the technical support for uranium prospecting integrating with field remote sensing geological survey. Multi-source geological data used in this paper include satellite hyperspectral image (Hyperion), high spatial resolution remote sensing data, uranium geological information, airborne radiometric data, aeromagnetic and gravity data, and related data mining methods have been developed, such as data fusion of optical data and Radarsat image, information integration of remote sensing and geophysical data, and so on. Based on above approaches, the multi-geoscience information of uranium mineralization factors including complex polystage rock mass, mineralization controlling faults and hydrothermal alterations have been identified, the metallogenic potential of uranium has been evaluated, and some predicting areas have been located.

Remote Sensing of Drought: Progress and Opportunities for Improving Drought Monitoring and Prediction

NASA Astrophysics Data System (ADS)

AghaKouchak, A.; Huning, L. S.; Love, C. A.; Farahmand, A.

2017-12-01

This presentation surveys current and emerging drought monitoring approaches using satellite remote sensing observations from climatological and ecosystem perspectives. Satellite observations that are not currently used for operational drought monitoring, such as near-surface air relative humidity and water vapor, provide opportunities to improve early drought warning. Current and future satellite missions offer opportunities to develop composite and multi-indicator drought models. This presentation describes how different satellite observations can be combined for overall drought development and impact assessment. Finally, we provide an overview of the research gaps and challenges that are facing us ahead in the remote sensing of drought.

Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems

USGS Publications Warehouse

Stow, Douglas A.; Hope, Allen; McGuire, David; Verbyla, David; Gamon, John A.; Huemmrich, Fred; Houston, Stan; Racine, Charles H.; Sturm, Matthew; Tape, Ken D.; Hinzman, Larry D.; Yoshikawa, Kenji; Tweedie, Craig E.; Noyle, Brian; Silapaswan, Cherie; Douglas, David C.; Griffith, Brad; Jia, Gensuo; Howard E. Epstein,; Walker, Donald A.; Daeschner, Scott; Petersen, Aaron; Zhou, Liming; Myneni, Ranga B.

2004-01-01

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land–Air–Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms and wide-swath imaging sensors on polar orbiting satellites are particularly useful for capturing optical remote sensing data at sufficient frequency to study tundra vegetation dynamics and changes for the cloud prone Arctic. Less frequent imaging with high spatial resolution instruments on aircraft and lower orbiting satellites enable more detailed analyses of land cover change and calibration/validation of coarser resolution observations.The strongest signals of ecosystem change detected thus far appear to correspond to expansion of tundra shrubs and changes in the amount and extent of thaw lakes and ponds. Changes in shrub cover and extent have been documented by modern repeat imaging that matches archived historical aerial photography. NOAA Advanced Very High Resolution Radiometer (AVHRR) time series provide a 20-year record for determining changes in greenness that relates to photosynthetic activity, net primary production, and growing season length. The strong contrast between land materials and surface waters enables changes in lake and pond extent to be readily measured and monitored.

Integration of environmental simulation models with satellite remote sensing and geographic information systems technologies: case studies

USGS Publications Warehouse

Steyaert, Louis T.; Loveland, Thomas R.; Brown, Jesslyn F.; Reed, Bradley C.

1993-01-01

Environmental modelers are testing and evaluating a prototype land cover characteristics database for the conterminous United States developed by the EROS Data Center of the U.S. Geological Survey and the University of Nebraska Center for Advanced Land Management Information Technologies. This database was developed from multi temporal, 1-kilometer advanced very high resolution radiometer (AVHRR) data for 1990 and various ancillary data sets such as elevation, ecological regions, and selected climatic normals. Several case studies using this database were analyzed to illustrate the integration of satellite remote sensing and geographic information systems technologies with land-atmosphere interactions models at a variety of spatial and temporal scales. The case studies are representative of contemporary environmental simulation modeling at local to regional levels in global change research, land and water resource management, and environmental simulation modeling at local to regional levels in global change research, land and water resource management and environmental risk assessment. The case studies feature land surface parameterizations for atmospheric mesoscale and global climate models; biogenic-hydrocarbons emissions models; distributed parameter watershed and other hydrological models; and various ecological models such as ecosystem, dynamics, biogeochemical cycles, ecotone variability, and equilibrium vegetation models. The case studies demonstrate the important of multi temporal AVHRR data to develop to develop and maintain a flexible, near-realtime land cover characteristics database. Moreover, such a flexible database is needed to derive various vegetation classification schemes, to aggregate data for nested models, to develop remote sensing algorithms, and to provide data on dynamic landscape characteristics. The case studies illustrate how such a database supports research on spatial heterogeneity, land use, sensitivity analysis, and scaling issues involving regional extrapolations and parameterizations of dynamic land processes within simulation models.

Bush Encroachment Mapping for Africa - Multi-Scale Analysis with Remote Sensing and GIS

NASA Astrophysics Data System (ADS)

Graw, V. A. M.; Oldenburg, C.; Dubovyk, O.

2015-12-01

Bush encroachment describes a global problem which is especially facing the savanna ecosystem in Africa. Livestock is directly affected by decreasing grasslands and inedible invasive species which defines the process of bush encroachment. For many small scale farmers in developing countries livestock represents a type of insurance in times of crop failure or drought. Among that bush encroachment is also a problem for crop production. Studies on the mapping of bush encroachment so far focus on small scales using high-resolution data and rarely provide information beyond the national level. Therefore a process chain was developed using a multi-scale approach to detect bush encroachment for whole Africa. The bush encroachment map is calibrated with ground truth data provided by experts in Southern, Eastern and Western Africa. By up-scaling location specific information on different levels of remote sensing imagery - 30m with Landsat images and 250m with MODIS data - a map is created showing potential and actual areas of bush encroachment on the African continent and thereby provides an innovative approach to map bush encroachment on the regional scale. A classification approach links location data based on GPS information from experts to the respective pixel in the remote sensing imagery. Supervised classification is used while actual bush encroachment information represents the training samples for the up-scaling. The classification technique is based on Random Forests and regression trees, a machine learning classification approach. Working on multiple scales and with the help of field data an innovative approach can be presented showing areas affected by bush encroachment on the African continent. This information can help to prevent further grassland decrease and identify those regions where land management strategies are of high importance to sustain livestock keeping and thereby also secure livelihoods in rural areas.

Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks

NASA Astrophysics Data System (ADS)

Yang, Xue; Sun, Hao; Fu, Kun; Yang, Jirui; Sun, Xian; Yan, Menglong; Guo, Zhi

2018-01-01

Ship detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection and the redundancy of detection region. In order to solve such problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ship in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving the problem resulted from the narrow width of the ship. Compared with previous multi-scale detectors such as Feature Pyramid Network (FPN), DFPN builds the high-level semantic feature-maps for all scales by means of dense connections, through which enhances the feature propagation and encourages the feature reuse. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multi-scale ROI Align for the purpose of maintaining the completeness of semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has a state-of-the-art performance.

Reconstructing time series water volumes of drying lakes in Central Asia with ZY-3 stereo remote sensing data

NASA Astrophysics Data System (ADS)

Li, J.; Warner, T.; Bao, A.

2017-12-01

Central Asia is one of the world most vulnerable areas responding to global change. Lakes in arid regions of Central Asia remain sensitive to climatic change and fluctuate with temperature and precipitation variations. Study showed that some central asian inland lakes in showed a trend of area shrinkage or extinct in the last decades. Quantitative analysis of lake volume changes in spatio-temporal processes will improve our understanding water resource utilization in arid regions and their responses to regional climate change. However, due to the lack of lake bathmetry or observation data, the volumes of these lakes remain unknown. In this paper, three lakes, such as Chaiwopu lake, Alik Lake and Selectyteniz Lake in Central Asia are used to reconstruct lake volume changes. Firstly, stereo mapping technologies derived from ZY-3 high resolution data are used to map the high-precision 3-D lake bathmetry, so as to create "Area-Level-Volume" based on contours of lake bathmetry. Secondly, time series lake areas in the last 50 years are mapped with multi-source and multi-temporal remote sensing images. Based on lake storage curves and time series lake areas, lake volumes in the last 5 decades can be reconstructed, and the spatio-temporal characteristics of lake volume changes and their mechanisms are also analyzed. The results showed that the high-precision lake hydrological elements are reconstructed on arid drying lakes through the application of stereo mapping technology in remote sensing.

The Role of Combination Techniques in Maximizing the Utility of Precipitation Estimates from Several Multi-Purpose Remote-Sensing Systems

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.; Curtis, Scott; Einaudi, Franco (Technical Monitor)

2001-01-01

Multi-purpose remote-sensing products from various satellites have proved crucial in developing global estimates of precipitation. Examples of these products include low-earth-orbit and geosynchronous-orbit infrared (leo- and geo-IR), Outgoing Longwave Radiation (OLR), Television Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) data, and passive microwave data such as that from the Special Sensor Microwave/ Imager (SSM/I). Each of these datasets has served as the basis for at least one useful quasi-global precipitation estimation algorithm; however, the quality of estimates varies tremendously among the algorithms for the different climatic regions around the globe.

Evaluation of AirMSPI photopolarimetric retrievals of smoke properties with in-situ observations collected during the ImPACT-PM field campaign

NASA Astrophysics Data System (ADS)

Kalashnikova, O. V.; Garay, M. J.; Xu, F.; Seidel, F.; Diner, D. J.; Seinfeld, J.; Bates, K. H.; Kong, W.; Kenseth, C.; Cappa, C. D.

2017-12-01

We introduce and evaluate an approach for obtaining closure between in situ and polarimetric remote sensing observations of smoke properties obtained during the collocated CIRPAS Twin Otter and ER-2 aircraft measurements of the Lebec fire event on July 8, 2016. We investigate the utility of multi-angle, spectropolarimetric remote sensing imagery to evaluate the relative contribution of organics, non-organic and black carbon particles to smoke particulate composition. The remote sensing data were collected during the Imaging Polarimetric and Characterization of Tropospheric Particular Matter (ImPACT-PM) field campaign by the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), which flew on NASA's high-altitude ER-2 aircraft. The ImPACT-PM field campaign was a joint JPL/Caltech effort to combine measurements from the Terra Multi-angle Imaging SpectroRadiometer (MISR), AirMSPI, in situ airborne measurements, and a chemical transport model to validate remote sensing retrievals of different types of airborne particulate matter with a particular emphasis on carbonaceous aerosols. The in-situ aerosol data were collected with a suite of Caltech instruments on board the CIRPAS Twin Otter aircraft and included the Aerosol Mass Spectrometer (AMS), the Differential Mobility Analyzer (DMA), and the Single Particle Soot Photometer (SP-2). The CIRPAS Twin Otter aircraft was also equipped with the Particle Soot Absorption Photometer (PSAP), nephelometer, a particle counter, and meteorological sensors. We found that the multi-angle polarimetric observations are capable of fire particulate emission monitoring by particle type as inferred from the in-situ airborne measurements. Modeling of retrieval sensitivities show that the characterization of black carbon is the most challenging. The work aims at evaluating multi-angle, spectropolarimetric capabilities for particulate matter characterization in support of the Multi-Angle Imager for Aerosols (MAIA) satellite investigation, which is currently in development under NASA's third Earth Venture Instrument Program.

Remote Sensing-Based, 5-m, Vegetation Distributions, Kougarok Study Site, Seward Peninsula, Alaska, ca. 2009 - 2016

DOE Office of Scientific and Technical Information (OSTI.GOV)

Langford, Zachary; Kumar, Jitendra; Hoffman, Forrest

A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5~m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. This data was developed using the fusion of hyperspectral, multispectral, and terrain datasets. The current data is located in the Kougarok watershed but we plan to expand this over the Seward Peninsula.

Water Quality Variable Estimation using Partial Least Squares Regression and Multi-Scale Remote Sensing.

NASA Astrophysics Data System (ADS)

Peterson, K. T.; Wulamu, A.

2017-12-01

Water, essential to all living organisms, is one of the Earth's most precious resources. Remote sensing offers an ideal approach to monitor water quality over traditional in-situ techniques that are highly time and resource consuming. Utilizing a multi-scale approach, incorporating data from handheld spectroscopy, UAS based hyperspectal, and satellite multispectral images were collected in coordination with in-situ water quality samples for the two midwestern watersheds. The remote sensing data was modeled and correlated to the in-situ water quality variables including chlorophyll content (Chl), turbidity, and total dissolved solids (TDS) using Normalized Difference Spectral Indices (NDSI) and Partial Least Squares Regression (PLSR). The results of the study supported the original hypothesis that correlating water quality variables with remotely sensed data benefits greatly from the use of more complex modeling and regression techniques such as PLSR. The final results generated from the PLSR analysis resulted in much higher R2 values for all variables when compared to NDSI. The combination of NDSI and PLSR analysis also identified key wavelengths for identification that aligned with previous study's findings. This research displays the advantages and future for complex modeling and machine learning techniques to improve water quality variable estimation from spectral data.

High Data Rate Satellite Communications for Environmental Remote Sensing

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Remote Sensing: Analyzing Satellite Images to Create Higher Order Thinking Skills.

ERIC Educational Resources Information Center

Marks, Steven K.; And Others

1996-01-01

Presents a unit that uses remote-sensing images from satellites and other spacecraft to provide new perspectives of the earth and generate greater global awareness. Relates the levels of Bloom's hierarchy to different aspects of the remote sensing unit to confirm that the concepts and principles of remote sensing and related images belong in…

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

NASA Astrophysics Data System (ADS)

Xue, Yong; Chen, Ziqiang; Xu, Hui; Ai, Jianwen; Jiang, Shuzheng; Li, Yingjie; Wang, Ying; Guang, Jie; Mei, Linlu; Jiao, Xijuan; He, Xingwei; Hou, Tingting

2011-12-01

The quality and accuracy of remote sensing instruments have been improved significantly, however, rapid processing of large-scale remote sensing data becomes the bottleneck for remote sensing quantitative retrieval applications. The remote sensing quantitative retrieval is a data-intensive computation application, which is one of the research issues of high throughput computation. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modeling, reconstruction and implementation of large-scale complex applications of remote sensing science. In this paper, we intend to study middleware components of the remote sensing Grid - the dynamic Grid workflow based on the remote sensing quantitative retrieval application on Grid platform. We designed a novel architecture for the remote sensing Grid workflow. According to this architecture, we constructed the Remote Sensing Information Service Grid Node (RSSN) with Condor. We developed a graphic user interface (GUI) tools to compose remote sensing processing Grid workflows, and took the aerosol optical depth (AOD) retrieval as an example. The case study showed that significant improvement in the system performance could be achieved with this implementation. The results also give a perspective on the potential of applying Grid workflow practices to remote sensing quantitative retrieval problems using commodity class PCs.

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

NASA Astrophysics Data System (ADS)

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.

Atmospheric correction for remote sensing image based on multi-spectral information

NASA Astrophysics Data System (ADS)

Wang, Yu; He, Hongyan; Tan, Wei; Qi, Wenwen

2018-03-01

The light collected from remote sensors taken from space must transit through the Earth's atmosphere. All satellite images are affected at some level by lightwave scattering and absorption from aerosols, water vapor and particulates in the atmosphere. For generating high-quality scientific data, atmospheric correction is required to remove atmospheric effects and to convert digital number (DN) values to surface reflectance (SR). Every optical satellite in orbit observes the earth through the same atmosphere, but each satellite image is impacted differently because atmospheric conditions are constantly changing. A physics-based detailed radiative transfer model 6SV requires a lot of key ancillary information about the atmospheric conditions at the acquisition time. This paper investigates to achieve the simultaneous acquisition of atmospheric radiation parameters based on the multi-spectral information, in order to improve the estimates of surface reflectance through physics-based atmospheric correction. Ancillary information on the aerosol optical depth (AOD) and total water vapor (TWV) derived from the multi-spectral information based on specific spectral properties was used for the 6SV model. The experimentation was carried out on images of Sentinel-2, which carries a Multispectral Instrument (MSI), recording in 13 spectral bands, covering a wide range of wavelengths from 440 up to 2200 nm. The results suggest that per-pixel atmospheric correction through 6SV model, integrating AOD and TWV derived from multispectral information, is better suited for accurate analysis of satellite images and quantitative remote sensing application.

HPT: A High Spatial Resolution Multispectral Sensor for Microsatellite Remote Sensing

PubMed Central

Takahashi, Yukihiro; Sakamoto, Yuji; Kuwahara, Toshinori

2018-01-01

Although nano/microsatellites have great potential as remote sensing platforms, the spatial and spectral resolutions of an optical payload instrument are limited. In this study, a high spatial resolution multispectral sensor, the High-Precision Telescope (HPT), was developed for the RISING-2 microsatellite. The HPT has four image sensors: three in the visible region of the spectrum used for the composition of true color images, and a fourth in the near-infrared region, which employs liquid crystal tunable filter (LCTF) technology for wavelength scanning. Band-to-band image registration methods have also been developed for the HPT and implemented in the image processing procedure. The processed images were compared with other satellite images, and proven to be useful in various remote sensing applications. Thus, LCTF technology can be considered an innovative tool that is suitable for future multi/hyperspectral remote sensing by nano/microsatellites. PMID:29463022

Geometric registration of remotely sensed data with SAMIR

NASA Astrophysics Data System (ADS)

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.

[Effect of different snow depth and area on the snow cover retrieval using remote sensing data].

PubMed

Jiang, Hong-bo; Qin, Qi-ming; Zhang, Ning; Dong, Heng; Chen, Chao

2011-12-01

For the needs of snow cover monitoring using multi-source remote sensing data, in the present article, based on the spectrum analysis of different depth and area of snow, the effect of snow depth on the results of snow cover retrieval using normalized difference snow index (NDSI) is discussed. Meanwhile, taking the HJ-1B and MODIS remote sensing data as an example, the snow area effect on the snow cover monitoring is also studied. The results show that: the difference of snow depth does not contribute to the retrieval results, while the snow area affects the results of retrieval to some extents because of the constraints of spatial resolution.

Improvements in agricultural water decision support using remote sensing

NASA Astrophysics Data System (ADS)

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 these tools into two new decision support systems: FEWSNET Early Warning Explorer (http://earlywarning.usgs.gov/fews/ewxindex.php) and the NASA Terrestrial Observation and Prediction System (http://ecocast.arc.nasa.gov/) for the first and second project respectively.

Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications

NASA Astrophysics Data System (ADS)

Liu, Jia; Liu, Longli; Xue, Yong; Dong, Jing; Hu, Yingcui; Hill, Richard; Guang, Jie; Li, Chi

2017-01-01

Workflow for remote sensing quantitative retrieval is the ;bridge; between Grid services and Grid-enabled application of remote sensing quantitative retrieval. Workflow averts low-level implementation details of the Grid and hence enables users to focus on higher levels of application. The workflow for remote sensing quantitative retrieval plays an important role in remote sensing Grid and Cloud computing services, which can support the modelling, construction and implementation of large-scale complicated applications of remote sensing science. The validation of workflow is important in order to support the large-scale sophisticated scientific computation processes with enhanced performance and to minimize potential waste of time and resources. To research the semantic correctness of user-defined workflows, in this paper, we propose a workflow validation method based on tacit knowledge research in the remote sensing domain. We first discuss the remote sensing model and metadata. Through detailed analysis, we then discuss the method of extracting the domain tacit knowledge and expressing the knowledge with ontology. Additionally, we construct the domain ontology with Protégé. Through our experimental study, we verify the validity of this method in two ways, namely data source consistency error validation and parameters matching error validation.

Hyperspectral and Radar Airborne Imagery over Controlled Release of Oil at Sea.

PubMed

Angelliaume, Sébastien; Ceamanos, Xavier; Viallefont-Robinet, Françoise; Baqué, Rémi; Déliot, Philippe; Miegebielle, Véronique

2017-08-02

Remote sensing techniques are commonly used by Oil and Gas companies to monitor hydrocarbon on the ocean surface. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as thickness and composition of the detected oil, which is critical for both exploration purposes and efficient cleanup operations. Today, state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI (Système Expérimental de Télédection Hyperfréquence Imageur), the airborne system developed by ONERA (the French Aerospace Lab), during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this dataset lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the EM spectrum. Specific processing techniques have been developed to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows estimating slick surface properties such as the age of the emulsion released at sea, the spatial abundance of oil and the relative concentration of hydrocarbons remaining on the sea surface.

Hyperspectral and Radar Airborne Imagery over Controlled Release of Oil at Sea

PubMed Central

Angelliaume, Sébastien; Ceamanos, Xavier; Viallefont-Robinet, Françoise; Baqué, Rémi; Déliot, Philippe

2017-01-01

Remote sensing techniques are commonly used by Oil and Gas companies to monitor hydrocarbon on the ocean surface. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as thickness and composition of the detected oil, which is critical for both exploration purposes and efficient cleanup operations. Today, state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time. Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI (Système Expérimental de Télédection Hyperfréquence Imageur), the airborne system developed by ONERA (the French Aerospace Lab), during an oil spill cleanup exercise carried out in 2015 in the North Sea, Europe. The uniqueness of this dataset lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the EM spectrum. Specific processing techniques have been developed to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows estimating slick surface properties such as the age of the emulsion released at sea, the spatial abundance of oil and the relative concentration of hydrocarbons remaining on the sea surface. PMID:28767059

Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection

NASA Astrophysics Data System (ADS)

Wang, Jinjin; Ma, Yi; Zhang, Jingyu

2018-03-01

Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out. The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2% (1.9%) 3.4% (1.8%), and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.

Space-Based Remote Sensing of Atmospheric Aerosols: The Multi-Angle Spectro-Polarimetric Frontier

NASA Technical Reports Server (NTRS)

Kokhanovsky, A. A.; Davis, A. B.; Cairns, B.; Dubovik, O.; Hasekamp, O. P.; Sano, I.; Mukai, S.; Rozanov, V. V.; Litvinov, P.; Lapyonok, T.;

Multi-angle Imaging Spectro Radiometer (MISR) Design Issues Influened by Performance Requirements

NASA Technical Reports Server (NTRS)

Bruegge, C. J.; White, M. L.; Chrien, N. C. L.; Villegas, E. B.; Raouf, N.

1993-01-01

The design of an Earth Remote Sensing Sensor, such as the Multi-angle Imaging SpectroRadiometer (MISR), begins with a set of science requirements and is quickly followed by a set of instrument specifications.

Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks

PubMed Central

Su, Jin-He; Piao, Ying-Chao; Luo, Ze; Yan, Bao-Ping

2018-01-01

Simple Summary The understanding of the spatio-temporal distribution of the species habitats would facilitate wildlife resource management and conservation efforts. Existing methods have poor performance due to the limited availability of training samples. More recently, location-aware sensors have been widely used to track animal movements. The aim of the study was to generate suitability maps of bar-head geese using movement data coupled with environmental parameters, such as remote sensing images and temperature data. Therefore, we modified a deep convolutional neural network for the multi-scale inputs. The results indicate that the proposed method can identify the areas with the dense goose species around Qinghai Lake. In addition, this approach might also be interesting for implementation in other species with different niche factors or in areas where biological survey data are scarce. Abstract With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction. PMID:29701686

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

Analysis of Coastal Dunes: A Remote Sensing and Statistical Approach.

ERIC Educational Resources Information Center

Jones, J. Richard

1985-01-01

Remote sensing analysis and statistical methods were used to analyze the coastal dunes of Plum Island, Massachusetts. The research methodology used provides an example of a student project for remote sensing, geomorphology, or spatial analysis courses at the university level. (RM)

Effects of land use/cover change and harvests on forest carbon dynamics in northern states of the United States from remote sensing and inventory data: 1992-2001

Treesearch

Daolan Zheng; Linda S. Heath; Mark J. Ducey; James E. Smith

2011-01-01

We examined spatial patterns of changes in forest area and nonsoil carbon (C) dynamics affected by land use/cover change (LUC) and harvests in 24 northern states of the United States using an integrated methodology combining remote sensing and ground inventory data between 1992 and 2001. We used the Retrofit Change Product from the Multi-Resolution Land Characteristics...

Applications of multi-season hyperspectral remote sensing for acid mine water characterization and mapping of secondary iron minerals associated with acid mine drainage

NASA Astrophysics Data System (ADS)

Davies, Gwendolyn E.

Acid mine drainage (AMD) resulting from the oxidation of sulfides in mine waste is a major environmental issue facing the mining industry today. Open pit mines, tailings ponds, ore stockpiles, and waste rock dumps can all be significant sources of pollution, primarily heavy metals. These large mining-induced footprints are often located across vast geographic expanses and are difficult to access. With the continuing advancement of imaging satellites, remote sensing may provide a useful monitoring tool for pit lake water quality and the rapid assessment of abandoned mine sites. This study explored the applications of laboratory spectroscopy and multi-season hyperspectral remote sensing for environmental monitoring of mine waste environments. Laboratory spectral experiments were first performed on acid mine waters and synthetic ferric iron solutions to identify and isolate the unique spectral properties of mine waters. These spectral characterizations were then applied to airborne hyperspectral imagery for identification of poor water quality in AMD ponds at the Leviathan Mine Superfund site, CA. Finally, imagery varying in temporal and spatial resolutions were used to identify changes in mineralogy over weathering overburden piles and on dry AMD pond liner surfaces at the Leviathan Mine. Results show the utility of hyperspectral remote sensing for monitoring a diverse range of surfaces associated with AMD.

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

NASA Astrophysics Data System (ADS)

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.

Identification of saline soils with multi-year remote sensing of crop yields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lobell, D; Ortiz-Monasterio, I; Gurrola, F C

2006-10-17

Soil salinity is an important constraint to agricultural sustainability, but accurate information on its variation across agricultural regions or its impact on regional crop productivity remains sparse. We evaluated the relationships between remotely sensed wheat yields and salinity in an irrigation district in the Colorado River Delta Region. The goals of this study were to (1) document the relative importance of salinity as a constraint to regional wheat production and (2) develop techniques to accurately identify saline fields. Estimates of wheat yield from six years of Landsat data agreed well with ground-based records on individual fields (R{sup 2} = 0.65).more » Salinity measurements on 122 randomly selected fields revealed that average 0-60 cm salinity levels > 4 dS m{sup -1} reduced wheat yields, but the relative scarcity of such fields resulted in less than 1% regional yield loss attributable to salinity. Moreover, low yield was not a reliable indicator of high salinity, because many other factors contributed to yield variability in individual years. However, temporal analysis of yield images showed a significant fraction of fields exhibited consistently low yields over the six year period. A subsequent survey of 60 additional fields, half of which were consistently low yielding, revealed that this targeted subset had significantly higher salinity at 30-60 cm depth than the control group (p = 0.02). These results suggest that high subsurface salinity is associated with consistently low yields in this region, and that multi-year yield maps derived from remote sensing therefore provide an opportunity to map salinity across agricultural regions.« less

Assessment and prediction of land ecological environment quality change based on remote sensing-a case study of the Dongting lake area in China

NASA Astrophysics Data System (ADS)

Hu, Wenmin; Wang, Zhongcheng; Li, Chunhua; Zhao, Jin; Li, Yi

2018-02-01

Multi-source remote sensing data is rarely used for the comprehensive assessment of land ecologic environment quality. In this study, a digital environmental model was proposed with the inversion algorithm of land and environmental factors based on the multi-source remote sensing data, and a comprehensive index (Ecoindex) was applied to reconstruct and predict the land environment quality of the Dongting Lake Area to assess the effect of human activities on the environment. The main finding was that with the decrease of Grade I and Grade II quality had a decreasing tendency in the lake area, mostly in suburbs and wetlands. Atmospheric water vapour, land use intensity, surface temperature, vegetation coverage, and soil water content were the main driving factors. The cause of degradation was the interference of multi-factor combinations, which led to positive and negative environmental agglomeration effects. Positive agglomeration, such as increased rainfall and vegetation coverage and reduced land use intensity, could increase environmental quality, while negative agglomeration resulted in the opposite. Therefore, reasonable ecological restoration measures should be beneficial to limit the negative effects and decreasing tendency, improve the land ecological environment quality and provide references for macroscopic planning by the government.

Rice Crop Monitoring Using Microwave and Optical Remotely Sensed Image Data

NASA Astrophysics Data System (ADS)

Suga, Y.; Konishi, T.; Takeuchi, S.; Kitano, Y.; Ito, S.

Hiroshima Institute of Technology HIT is operating the direct down-links of microwave and optical satellite data in Japan This study focuses on the validation for rice crop monitoring using microwave and optical remotely sensed image data acquired by satellites referring to ground truth data such as height of crop ratio of crop vegetation cover and leaf area index in the test sites of Japan ENVISAT-1 ASAR data has a capability to capture regularly and to monitor during the rice growing cycle by alternating cross polarization mode images However ASAR data is influenced by several parameters such as landcover structure direction and alignment of rice crop fields in the test sites In this study the validation was carried out combined with microwave and optical satellite image data and ground truth data regarding rice crop fields to investigate the above parameters Multi-temporal multi-direction descending and ascending and multi-angle ASAR alternating cross polarization mode images were used to investigate rice crop growing cycle LANDSAT data were used to detect landcover structure direction and alignment of rice crop fields corresponding to the backscatter of ASAR As the result of this study it was indicated that rice crop growth can be precisely monitored using multiple remotely sensed data and ground truth data considering with spatial spectral temporal and radiometric resolutions

Airplane detection in remote sensing images using convolutional neural networks

NASA Astrophysics Data System (ADS)

Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei

2018-03-01

Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

An airborne remote sensing platform of the Helsinki University of Technology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nikulainen, M.; Hallikainen, M.; Kemppinen, M.

1996-10-01

In 1994 Helsinki University of Technology acquired a Short SC7 Skyvan turboprop aircraft to be modified to carry remote sensing instruments. As the aircraft is originally designed to carry heavy and space consuming cargo, a modification program was implemented to make the aircraft feasible for remote sensing operations. The twelve-month long modification program had three design objectives: flexibility, accessibility and cost efficiency. The aircraft interior and electrical system were modified. Furthermore, the aircraft is equipped with DGPS-navigation system, multi-channel radiometer system and side looking airborne radar. Future projects include installation of local area network, attitude GPS system, imaging spectrometer andmore » 1.4 GHz radiometer. 6 refs., 5 figs., 1 tab.« less

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

USGS Publications Warehouse

Maxwell, S.K.; Meliker, J.R.; Goovaerts, P.

2010-01-01

In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.

Study on paddy rice yield estimation based on multisource data and the Grey system theory

NASA Astrophysics Data System (ADS)

Deng, Wensheng; Wang, Wei; Liu, Hai; Li, Chen; Ge, Yimin; Zheng, Xianghua

2009-10-01

The paddy rice is our important crops. In study of the paddy rice yield estimation, compared with the scholars who usually only take the remote sensing data or meteorology as the influence factors, we combine the remote sensing and the meteorological data to make the monitoring result closer reality. Although the gray system theory has used in many aspects, it is applied very little in paddy rice yield estimation. This study introduces it to the paddy rice yield estimation, and makes the yield estimation model. This can resolve small data sets problem that can not be solved by deterministic model. It selects some regions in Jianghan plain for the study area. The data includes multi-temporal remote sensing image, meteorological and statistic data. The remote sensing data is the 16-day composite images (250-m spatial resolution) of MODIS. The meteorological data includes monthly average temperature, sunshine duration and rain fall amount. The statistical data is the long-term paddy rice yield of the study area. Firstly, it extracts the paddy rice planting area from the multi-temporal MODIS images with the help of GIS and RS. Then taking the paddy rice yield as the reference sequence, MODIS data and meteorological data as the comparative sequence, computing the gray correlative coefficient, it selects the yield estimation factor based on the grey system theory. Finally, using the factors, it establishes the yield estimation model and does the result test. The result indicated that the method is feasible and the conclusion is credible. It can provide the scientific method and reference value to carry on the region paddy rice remote sensing estimation.

Long-range monostatic remote sensing of geomaterial structure weak vibrations

NASA Astrophysics Data System (ADS)

Heifetz, Alexander; Bakhtiari, Sasan; Gopalsami, Nachappa; Elmer, Thomas W.; Mukherjee, Souvik

2018-04-01

We study analytically and numerically signal sensitivity in remote sensing measurements of weak mechanical vibration of structures made of typical construction geomaterials, such as concrete. The analysis includes considerations of electromagnetic beam atmospheric absorption, reflection, scattering, diffraction and losses. Comparison is made between electromagnetic frequencies of 35GHz (Ka-band), 94GHz (W-band) and 260GHz (WR-3 waveguide band), corresponding to atmospheric transparency windows of the electromagnetic spectrum. Numerical simulations indicate that 94GHz frequency is optimal in terms of signal sensitivity and specificity for long-distance (>1.5km) sensing of weak multi-mode vibrations.

Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks.

PubMed

Su, Jin-He; Piao, Ying-Chao; Luo, Ze; Yan, Bao-Ping

2018-04-26

With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction.

[Study on artificial neural network combined with multispectral remote sensing imagery for forest site evaluation].

PubMed

Gong, Yin-Xi; He, Cheng; Yan, Fei; Feng, Zhong-Ke; Cao, Meng-Lei; Gao, Yuan; Miao, Jie; Zhao, Jin-Long

2013-10-01

Multispectral remote sensing data containing rich site information are not fully used by the classic site quality evaluation system, as it merely adopts artificial ground survey data. In order to establish a more effective site quality evaluation system, a neural network model which combined remote sensing spectra factors with site factors and site index relations was established and used to study the sublot site quality evaluation in the Wangyedian Forest Farm in Inner Mongolia Province, Chifeng City. Based on the improved back propagation artificial neural network (BPANN), this model combined multispectral remote sensing data with sublot survey data, and took larch as example, Through training data set sensitivity analysis weak or irrelevant factor was excluded, the size of neural network was simplified, and the efficiency of network training was improved. This optimal site index prediction model had an accuracy up to 95.36%, which was 9.83% higher than that of the neural network model based on classic sublot survey data, and this shows that using multi-spectral remote sensing and small class survey data to determine the status of larch index prediction model has the highest predictive accuracy. The results fully indicate the effectiveness and superiority of this method.

Multi-sensor Cloud Retrieval Simulator and Remote Sensing from Model Parameters . Pt. 1; Synthetic Sensor Radiance Formulation; [Synthetic Sensor Radiance Formulation

NASA Technical Reports Server (NTRS)

Wind, G.; DaSilva, A. M.; Norris, P. M.; Platnick, S.

2013-01-01

In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies.We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.

Forest Fires and Post - Fire Regeneration in Algeria Analysis with Satellite Data

NASA Astrophysics Data System (ADS)

Zegrar, Ahmed

2016-07-01

The Algerian forests are characterized by a particularly flammable material and fuel. The wind, the relief and the slope facilitates the propagation of fire. The use of remote sensing data multi-­dates, combined with other types of data of various kinds on the environment and forest burned, opens up interesting perspectives for the management of post-­fire regeneration. In this study the use of multi-­temporal remote sensing image Alsat-­1 and Landsat combined with other types of data concerning both background and burned down forest appears to be promising in evaluating and spatial and temporal effects of post fire regeneration. A spatial analysis taking into consideration the characteristics of the burned down site in the North West of Algeria, allowed to better account new factors to explain the regeneration and its temporal and spatial variation. We intended to show the potential use of remote sensing data from satellite ALSAT-­1, of spatial resolution of 32 m. . This approach allows showing the contribution of the data of Algerian satellite ALSAT in the detection and the well attended some forest fires in Algeria.

Identification of pests and diseases of Dalbergia hainanensis based on EVI time series and classification of decision tree

NASA Astrophysics Data System (ADS)

Luo, Qiu; Xin, Wu; Qiming, Xiong

2017-06-01

In the process of vegetation remote sensing information extraction, the problem of phenological features and low performance of remote sensing analysis algorithm is not considered. To solve this problem, the method of remote sensing vegetation information based on EVI time-series and the classification of decision-tree of multi-source branch similarity is promoted. Firstly, to improve the time-series stability of recognition accuracy, the seasonal feature of vegetation is extracted based on the fitting span range of time-series. Secondly, the decision-tree similarity is distinguished by adaptive selection path or probability parameter of component prediction. As an index, it is to evaluate the degree of task association, decide whether to perform migration of multi-source decision tree, and ensure the speed of migration. Finally, the accuracy of classification and recognition of pests and diseases can reach 87%--98% of commercial forest in Dalbergia hainanensis, which is significantly better than that of MODIS coverage accuracy of 80%--96% in this area. Therefore, the validity of the proposed method can be verified.

A remote sensing-assisted risk rating study to predict oak decline and recovery in the Missouri Ozark Highlands, USA

Treesearch

Cuizhen Wang; Hong S. He; John M. Kabrick

2008-01-01

Forests in the Ozark Highlands underwent widespread oak decline affected by severe droughts in 1999-2000. In this study, the differential normalized difference water index was calculated to detect crown dieback. A multi-factor risk rating system was built to map risk levels of stands. As a quick response to drought, decline in 2000 mostly occurred in stands at low to...

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

NASA Technical Reports Server (NTRS)

Chirayath, Ved

2018-01-01

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

Future Applications of Remote Sensing to Archeological Research

NASA Technical Reports Server (NTRS)

Sever, Thomas L.

2003-01-01

Archeology was one of the first disciplines to use aerial photography in its investigations at the turn of the 20th century. However, the low resolution of satellite technology that became available in the 1970 s limited their application to regional studies. That has recently changed. The arrival of the high resolution, multi-spectral capabilities of the IKONOS and QUICKBIRD satellites and the scheduled launch of new satellites in the next few years provides an unlimited horizon for future archeological research. In addition, affordable aerial and ground-based remote sensing instrumentation are providing archeologists with information that is not available through traditional methodologies. Although many archeologists are not yet comfortable with remote sensing technology a new generation has embraced it and is accumulating a wealth of new evidence. They have discovered that through the use of remote sensing it is possible to gather information without disturbing the site and that those cultural resources can be monitored and protected for the future.

Allometric constraints to inversion of canopy structure from remote sensing

NASA Astrophysics Data System (ADS)

Wolf, A.; Berry, J. A.; Asner, G. P.

2008-12-01

Canopy radiative transfer models employ a large number of vegetation architectural and leaf biochemical attributes. Studies of leaf biochemistry show a wide array of chemical and spectral diversity that suggests that several leaf biochemical constituents can be independently retrieved from multi-spectral remotely sensed imagery. In contrast, attempts to exploit multi-angle imagery to retrieve canopy structure only succeed in finding two or three of the many unknown canopy arhitectural attributes. We examine a database of over 5000 destructive tree harvests from Eurasia to show that allometry - the covariation of plant form across a broad range of plant size and canopy density - restricts the architectural diversity of plant canopies into a single composite variable ranging from young canopies with many short trees with small crowns to older canopies with fewer trees and larger crowns. Moreover, these architectural attributes are closely linked to biomass via allometric constraints such as the "self-thinning law". We use the measured variance and covariance of plant canopy architecture in these stands to drive the radiative transfer model DISORD, which employs the Li-Strahler geometric optics model. This correlations introduced in the Monte Carlo study are used to determine which attributes of canopy architecture lead to important variation that can be observed by multi-angle or multi-spectral satellite observations, using the sun-view geometry characteristic of MODIS observations in different biomes located at different latitude bands. We conclude that although multi-angle/multi-spectral remote sensing is only sensitive to some of the many unknown canopy attributes that ecologists would wish to know, the strong allometric covariation between these attributes and others permits a large number of inferrences, such as forest biomass, that will be meaningful next-generation vegetation products useful for data assimilation.

Adjoint Methods for Adjusting Three-Dimensional Atmosphere and Surface Properties to Fit Multi-Angle Multi-Pixel Polarimetric Measurements

NASA Technical Reports Server (NTRS)

Martin, William G.; Cairns, Brian; Bal, Guillaume

2014-01-01

This paper derives an efficient procedure for using the three-dimensional (3D) vector radiative transfer equation (VRTE) to adjust atmosphere and surface properties and improve their fit with multi-angle/multi-pixel radiometric and polarimetric measurements of scattered sunlight. The proposed adjoint method uses the 3D VRTE to compute the measurement misfit function and the adjoint 3D VRTE to compute its gradient with respect to all unknown parameters. In the remote sensing problems of interest, the scalar-valued misfit function quantifies agreement with data as a function of atmosphere and surface properties, and its gradient guides the search through this parameter space. Remote sensing of the atmosphere and surface in a three-dimensional region may require thousands of unknown parameters and millions of data points. Many approaches would require calls to the 3D VRTE solver in proportion to the number of unknown parameters or measurements. To avoid this issue of scale, we focus on computing the gradient of the misfit function as an alternative to the Jacobian of the measurement operator. The resulting adjoint method provides a way to adjust 3D atmosphere and surface properties with only two calls to the 3D VRTE solver for each spectral channel, regardless of the number of retrieval parameters, measurement view angles or pixels. This gives a procedure for adjusting atmosphere and surface parameters that will scale to the large problems of 3D remote sensing. For certain types of multi-angle/multi-pixel polarimetric measurements, this encourages the development of a new class of three-dimensional retrieval algorithms with more flexible parametrizations of spatial heterogeneity, less reliance on data screening procedures, and improved coverage in terms of the resolved physical processes in the Earth?s atmosphere.

Evaluation of MuSyQ land surface albedo based on LAnd surface Parameters VAlidation System (LAPVAS)

NASA Astrophysics Data System (ADS)

Dou, B.; Wen, J.; Xinwen, L.; Zhiming, F.; Wu, S.; Zhang, Y.

2016-12-01

satellite derived Land surface albedo is an essential climate variable which controls the earth energy budget and it can be used in applications such as climate change, hydrology, and numerical weather prediction. However, the accuracy and uncertainty of surface albedo products should be evaluated with a reliable reference truth data prior to applications. A new comprehensive and systemic project of china, called the Remote Sensing Application Network (CRSAN), has been launched recent years. Two subjects of this project is developing a Multi-source data Synergized Quantitative Remote Sensin g Production System ( MuSyQ ) and a Web-based validation system named LAnd surface remote sensing Product VAlidation System (LAPVAS) , which aims to generate a quantitative remote sensing product for ecosystem and environmental monitoring and validate them with a reference validation data and a standard validation system, respectively. Land surface BRDF/albedo is one of product datasets of MuSyQ which has a pentad period with 1km spatial resolution and is derived by Multi-sensor Combined BRDF Inversion ( MCBI ) Model. In this MuSyQ albedo evaluation, a multi-validation strategy is implemented by LAPVAS, including directly and multi-scale validation with field measured albedo and cross validation with MODIS albedo product with different land cover. The results reveal that MuSyQ albedo data with a 5-day temporal resolution is in higher sensibility and accuracy during land cover change period, e.g. snowing. But results without regard to snow or changed land cover, MuSyQ albedo generally is in similar accuracy with MODIS albedo and meet the climate modeling requirement of an absolute accuracy of 0.05.

Discharge estimation for the Upper Brahmaputra River in the Tibetan Plateau using multi-source remote sensing data

NASA Astrophysics Data System (ADS)

Huang, Q.; Long, D.; Du, M.; Hong, Y.

2017-12-01

River discharge is among the most important hydrological variables of hydrologists' concern, as it links drinking water supply, irrigation, and flood forecast together. Despite its importance, there are extremely limited gauging stations across most of alpine regions such as the Tibetan Plateau (TP) known as Asia's water towers. Use of remote sensing combined with partial in situ discharge measurements is a promising way of retrieving river discharge over ungauged or poorly gauged basins. Successful discharge estimation depends largely on accurate water width (area) and water level, but it is challenging to obtain these variables for alpine regions from a single satellite platform due to narrow river channels, complex terrain, and limited observations. Here, we used high-spatial-resolution images from Landsat series to derive water area, and satellite altimetry (Jason 2) to derive water level for the Upper Brahmaputra River (UBR) in the TP with narrow river width (less than 400 m in most occasions). We performed waveform retracking using a 50% Threshold and Ice-1 Combined algorithm (TIC) developed in this study to obtain accurate water level measurements. The discharge was estimated well using a range of derived formulas including the power function between water level and discharge, and that between water area and discharge suitable for the triangular cross-section around the Nuxia gauging station in the UBR. Results showed that the power function using Jason 2-derived water levels after performing waveform retracking performed best, showing an overall NSE value of 0.92. The proposed approach for remotely sensed river discharge is effective in the UBR and possibly other alpine rivers globally.

Identification of Terrestrial Reflectance From Remote Sensing

NASA Technical Reports Server (NTRS)

Alter-Gartenberg, Rachel; Nolf, Scott R.; Stacy, Kathryn (Technical Monitor)

2000-01-01

Correcting for atmospheric effects is an essential part of surface-reflectance recovery from radiance measurements. Model-based atmospheric correction techniques enable an accurate identification and classification of terrestrial reflectances from multi-spectral imagery. Successful and efficient removal of atmospheric effects from remote-sensing data is a key factor in the success of Earth observation missions. This report assesses the performance, robustness and sensitivity of two atmospheric-correction and reflectance-recovery techniques as part of an end-to-end simulation of hyper-spectral acquisition, identification and classification.

THE IDEA IS TO USEMODIS IN CONJUNCTION WITH THE CURRENT LIMITED LANDSAT CAPABILITY, COMMERCIAL SATELLITES, ANDUNMANNED AERIAL VEHICLES (UAV), IN A MULTI-STAGE APPROACH TO MEET EPA INFORMATION NEEDS.REMOTE SENSING OVERVIEW: EPA CAPABILITIES, PRIORITY AGENCY APPLICATIONS, SENSOR/AIRCRAFT CAPABILITIES, COST CONSIDERATIONS, SPECTRAL AND SPATIAL RESOLUTIONS, AND TEMPORAL CONSIDERATIONS

EPA Science Inventory

EPA remote sensing capabilities include applied research for priority applications and technology support for operational assistance to clients across the Agency. The idea is to use MODIS in conjunction with the current limited Landsat capability, commercial satellites, and Unma...

Remote sensing imagery classification using multi-objective gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2016-10-01

Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

Satellites, Remote Sensing, and Classroom Geography for Canadian Teachers.

ERIC Educational Resources Information Center

Kirman, Joseph M.

1998-01-01

Argues that remote sensing images are a powerful tool for teaching geography. Discusses the use of remote sensing images in the classroom and provides a number of sources for them, some free, many on the World Wide Web. Reviews each source's usefulness for different grade levels and geographic topics. (DSK)

Modelling submerged coastal environments: Remote sensing technologies, techniques, and comparative analysis

NASA Astrophysics Data System (ADS)

Dillon, Chris

Built upon remote sensing and GIS littoral zone characterization methodologies of the past decade, a series of loosely coupled models aimed to test, compare and synthesize multi-beam SONAR (MBES), Airborne LiDAR Bathymetry (ALB), and satellite based optical data sets in the Gulf of St. Lawrence, Canada, eco-region. Bathymetry and relative intensity metrics for the MBES and ALB data sets were run through a quantitative and qualitative comparison, which included outputs from the Benthic Terrain Modeller (BTM) tool. Substrate classification based on relative intensities of respective data sets and textural indices generated using grey level co-occurrence matrices (GLCM) were investigated. A spatial modelling framework built in ArcGIS(TM) for the derivation of bathymetric data sets from optical satellite imagery was also tested for proof of concept and validation. Where possible, efficiencies and semi-automation for repeatable testing was achieved using ArcGIS(TM) ModelBuilder. The findings from this study could assist future decision makers in the field of coastal management and hydrographic studies. Keywords: Seafloor terrain characterization, Benthic Terrain Modeller (BTM), Multi-beam SONAR, Airborne LiDAR Bathymetry, Satellite Derived Bathymetry, ArcGISTM ModelBuilder, Textural analysis, Substrate classification.

Exploring multi-scale forest above ground biomass estimation with optical remote sensing imageries

NASA Astrophysics Data System (ADS)

Koju, U.; Zhang, J.; Gilani, H.

2017-02-01

Forest shares 80% of total exchange of carbon between the atmosphere and the terrestrial ecosystem. Due to this monitoring of forest above ground biomass (as carbon can be calculated as 0.47 part of total biomass) has become very important. Forest above ground biomass as being the major portion of total forest biomass should be given a very careful consideration in its estimation. It is hoped to be useful in addressing the ongoing problems of deforestation and degradation and to gain carbon mitigation benefits through mechanisms like Reducing Emissions from Deforestation and Forest Degradation (REDD+). Many methods of above ground biomass estimation are in used ranging from use of optical remote sensing imageries of very high to very low resolution to SAR data and LIDAR. This paper describes a multi-scale approach for assessing forest above ground biomass, and ultimately carbon stocks, using very high imageries, open source medium resolution and medium resolution satellite datasets with a very limited number of field plots. We found this method is one of the most promising method for forest above ground biomass estimation with higher accuracy and low cost budget. Pilot study was conducted in Chitwan district of Nepal on the estimation of biomass using this technique. The GeoEye-1 (0.5m), Landsat (30m) and Google Earth (GE) images were used remote sensing imageries. Object-based image analysis (OBIA) classification technique was done on Geo-eye imagery for the tree crown delineation at the watershed level. After then, crown projection area (CPA) vs. biomass model was developed and validated at the watershed level. Open source GE imageries were used to calculate the CPA and biomass from virtual plots at district level. Using data mining technique, different parameters from Landsat imageries along with the virtual sample biomass were used for upscaling biomass estimation at district level. We found, this approach can considerably reduce field data requirements for estimation of biomass and carbon in comparison with inventory methods based on enumeration of all trees in a plot. The proposed methodology is very cost effective and can be replicated with limited resources and time.

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Advances in multi-sensor data fusion: algorithms and applications.

PubMed

Dong, Jiang; Zhuang, Dafang; Huang, Yaohuan; Fu, Jingying

2009-01-01

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme.

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

NASA Astrophysics Data System (ADS)

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.

Multi-scale functional mapping of tidal marsh vegetation for restoration monitoring

NASA Astrophysics Data System (ADS)

Tuxen Bettman, Karin

2007-12-01

Nearly half of the world's natural wetlands have been destroyed or degraded, and in recent years, there have been significant endeavors to restore wetland habitat throughout the world. Detailed mapping of restoring wetlands can offer valuable information about changes in vegetation and geomorphology, which can inform the restoration process and ultimately help to improve chances of restoration success. I studied six tidal marshes in the San Francisco Estuary, CA, US, between 2003 and 2004 in order to develop techniques for mapping tidal marshes at multiple scales by incorporating specific restoration objectives for improved longer term monitoring. I explored a "pixel-based" remote sensing image analysis method for mapping vegetation in restored and natural tidal marshes, describing the benefits and limitations of this type of approach (Chapter 2). I also performed a multi-scale analysis of vegetation pattern metrics for a recently restored tidal marsh in order to target the metrics that are consistent across scales and will be robust measures of marsh vegetation change (Chapter 3). Finally, I performed an "object-based" image analysis using the same remotely sensed imagery, which maps vegetation type and specific wetland functions at multiple scales (Chapter 4). The combined results of my work highlight important trends and management implications for monitoring wetland restoration using remote sensing, and will better enable restoration ecologists to use remote sensing for tidal marsh monitoring. Several findings important for tidal marsh restoration monitoring were made. Overall results showed that pixel-based methods are effective at quantifying landscape changes in composition and diversity in recently restored marshes, but are limited in their use for quantifying smaller, more fine-scale changes. While pattern metrics can highlight small but important changes in vegetation composition and configuration across years, scientists should exercise caution when using metrics in their studies or to validate restoration management decisions, and multi-scale analyses should be performed before metrics are used in restoration science for important management decisions. Lastly, restoration objectives, ecosystem function, and scale can each be integrated into monitoring techniques using remote sensing for improved restoration monitoring.

Mapping multi-scale vascular plant richness in a forest landscape with integrated LiDAR and hyperspectral remote-sensing.

PubMed

Hakkenberg, C R; Zhu, K; Peet, R K; Song, C

2018-02-01

The central role of floristic diversity in maintaining habitat integrity and ecosystem function has propelled efforts to map and monitor its distribution across forest landscapes. While biodiversity studies have traditionally relied largely on ground-based observations, the immensity of the task of generating accurate, repeatable, and spatially-continuous data on biodiversity patterns at large scales has stimulated the development of remote-sensing methods for scaling up from field plot measurements. One such approach is through integrated LiDAR and hyperspectral remote-sensing. However, despite their efficiencies in cost and effort, LiDAR-hyperspectral sensors are still highly constrained in structurally- and taxonomically-heterogeneous forests - especially when species' cover is smaller than the image resolution, intertwined with neighboring taxa, or otherwise obscured by overlapping canopy strata. In light of these challenges, this study goes beyond the remote characterization of upper canopy diversity to instead model total vascular plant species richness in a continuous-cover North Carolina Piedmont forest landscape. We focus on two related, but parallel, tasks. First, we demonstrate an application of predictive biodiversity mapping, using nonparametric models trained with spatially-nested field plots and aerial LiDAR-hyperspectral data, to predict spatially-explicit landscape patterns in floristic diversity across seven spatial scales between 0.01-900 m 2 . Second, we employ bivariate parametric models to test the significance of individual, remotely-sensed predictors of plant richness to determine how parameter estimates vary with scale. Cross-validated results indicate that predictive models were able to account for 15-70% of variance in plant richness, with LiDAR-derived estimates of topography and forest structural complexity, as well as spectral variance in hyperspectral imagery explaining the largest portion of variance in diversity levels. Importantly, bivariate tests provide evidence of scale-dependence among predictors, such that remotely-sensed variables significantly predict plant richness only at spatial scales that sufficiently subsume geolocational imprecision between remotely-sensed and field data, and best align with stand components including plant size and density, as well as canopy gaps and understory growth patterns. Beyond their insights into the scale-dependent patterns and drivers of plant diversity in Piedmont forests, these results highlight the potential of remotely-sensible essential biodiversity variables for mapping and monitoring landscape floristic diversity from air- and space-borne platforms. © 2017 by the Ecological Society of America.

Accomplishments of the MUSICA project to provide accurate, long-term, global and high-resolution observations of tropospheric {H2O,δD} pairs - a review

NASA Astrophysics Data System (ADS)

Schneider, Matthias; Wiegele, Andreas; Barthlott, Sabine; González, Yenny; Christner, Emanuel; Dyroff, Christoph; García, Omaira E.; Hase, Frank; Blumenstock, Thomas; Sepúlveda, Eliezer; Mengistu Tsidu, Gizaw; Takele Kenea, Samuel; Rodríguez, Sergio; Andrey, Javier

2016-07-01

In the lower/middle troposphere, {H2O,δD} pairs are good proxies for moisture pathways; however, their observation, in particular when using remote sensing techniques, is challenging. The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) addresses this challenge by integrating the remote sensing with in situ measurement techniques. The aim is to retrieve calibrated tropospheric {H2O,δD} pairs from the middle infrared spectra measured from ground by FTIR (Fourier transform infrared) spectrometers of the NDACC (Network for the Detection of Atmospheric Composition Change) and the thermal nadir spectra measured by IASI (Infrared Atmospheric Sounding Interferometer) aboard the MetOp satellites. In this paper, we present the final MUSICA products, and discuss the characteristics and potential of the NDACC/FTIR and MetOp/IASI {H2O,δD} data pairs. First, we briefly resume the particularities of an {H2O,δD} pair retrieval. Second, we show that the remote sensing data of the final product version are absolutely calibrated with respect to H2O and δD in situ profile references measured in the subtropics, between 0 and 7 km. Third, we reveal that the {H2O,δD} pair distributions obtained from the different remote sensors are consistent and allow distinct lower/middle tropospheric moisture pathways to be identified in agreement with multi-year in situ references. Fourth, we document the possibilities of the NDACC/FTIR instruments for climatological studies (due to long-term monitoring) and of the MetOp/IASI sensors for observing diurnal signals on a quasi-global scale and with high horizontal resolution. Fifth, we discuss the risk of misinterpreting {H2O,δD} pair distributions due to incomplete processing of the remote sensing products.

Reliability analysis of airship remote sensing system

NASA Astrophysics Data System (ADS)

Qin, Jun

1998-08-01

Airship Remote Sensing System (ARSS) for obtain the dynamic or real time images in the remote sensing of the catastrophe and the environment, is a mixed complex system. Its sensor platform is a remote control airship. The achievement of a remote sensing mission depends on a series of factors. For this reason, it is very important for us to analyze reliability of ARSS. In first place, the system model was simplified form multi-stage system to two-state system on the basis of the result of the failure mode and effect analysis and the failure tree failure mode effect and criticality analysis. The failure tree was created after analyzing all factors and their interrelations. This failure tree includes four branches, e.g. engine subsystem, remote control subsystem, airship construction subsystem, flying metrology and climate subsystem. By way of failure tree analysis and basic-events classing, the weak links were discovered. The result of test running shown no difference in comparison with theory analysis. In accordance with the above conclusions, a plan of the reliability growth and reliability maintenance were posed. System's reliability are raised from 89 percent to 92 percent with the reformation of the man-machine interactive interface, the augmentation of the secondary better-groupie and the secondary remote control equipment.

An empirical study on the utility of BRDF model parameters and topographic parameters for mapping vegetation in a semi-arid region with MISR imagery

USDA-ARS?s Scientific Manuscript database

Multi-angle remote sensing has been proved useful for mapping vegetation community types in desert regions. Based on Multi-angle Imaging Spectro-Radiometer (MISR) multi-angular images, this study compares roles played by Bidirectional Reflectance Distribution Function (BRDF) model parameters with th...

Multi- and hyperspectral remote sensing of tropical marine benthic habitats

NASA Astrophysics Data System (ADS)

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 consistently more accurate (84%) including finer definition of geomorphological features than the satellite sensors. IKONOS (81%) and QuickBird (81%) sensors showed similar accuracy to AISA, however, such similarity was only reached at the coarse classification levels of 5 and 6 habitats. These results confirm the potential of an effective combination of high spectral and spatial resolution sensor, for accurate benthic habitat mapping.

Teaching Geologic/Earth Science Remote Sensing at the Collegiate and the Secondary School Level

ERIC Educational Resources Information Center

Fisher, John J.

1977-01-01

Describes util satellite photography, satellite remote sensing, and high altitude aircraft photography for teaching environmental and ecological aspects of earth science at the secondary or college levels. (SL)

Intercomparison of in-situ and remote sensing δD signals in tropospheric water vapour

NASA Astrophysics Data System (ADS)

Schneider, Matthias; González, Yenny; Dyroff, Christoph; Christner, Emanuel; García, Omaira; Wiegele, Andreas; Andrey, Javier; Barthlott, Sabine; Blumenstock, Thomas; Guirado, Carmen; Hase, Frank; Ramos, Ramon; Rodríguez, Sergio; Sepúveda, Eliezer

2014-05-01

The main mission of the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) is the generation of a quasi-global tropospheric water vapour isototopologue dataset of a good and well-documented quality. We present a first empirical validation of MUSICA's remote sensing δD products (ground-based FTIR within NDACC, Network for the Detection of Atmospheric Composition Change, and space-based with IASI, Infrared Atmospheric Sounding Interferometer, flown on METOP). As reference we use in-situ measurements made on the island of Tenerife at two different altitudes (2370 and 3550 m a.s.l., using two Picarro L2120-i water isotopologue analyzers) and aboard an aircraft (between 200 and 6800 m a.s.l., using the homemade ISOWAT instrument).

Multi-crop area estimation and mapping on a microprocessor/mainframe network

NASA Technical Reports Server (NTRS)

Sheffner, E.

1985-01-01

The data processing system is outlined for a 1985 test aimed at determining the performance characteristics of area estimation and mapping procedures connected with the California Cooperative Remote Sensing Project. The project is a joint effort of the USDA Statistical Reporting Service-Remote Sensing Branch, the California Department of Water Resources, NASA-Ames Research Center, and the University of California Remote Sensing Research Program. One objective of the program was to study performance when data processing is done on a microprocessor/mainframe network under operational conditions. The 1985 test covered the hardware, software, and network specifications and the integration of these three components. Plans for the year - including planned completion of PEDITOR software, testing of software on MIDAS, and accomplishment of data processing on the MIDAS-VAX-CRAY network - are discussed briefly.

Assessing regional environmental quality by integrated use of remote sensing, GIS, and spatial multi-criteria evaluation for prioritization of environmental restoration.

PubMed

Rahman, Md Rejaur; Shi, Z H; Chongfa, Cai

2014-11-01

This study was an attempt to analyse the regional environmental quality with the application of remote sensing, geographical information system, and spatial multiple criteria decision analysis and, to project a quantitative method applicable to identify the status of the regional environment of the study area. Using spatial multi-criteria evaluation (SMCE) approach with expert knowledge in this study, an integrated regional environmental quality index (REQI) was computed and classified into five levels of regional environment quality viz. worse, poor, moderate, good, and very good. During the process, a set of spatial criteria were selected (here, 15 criterions) together with the degree of importance of criteria in sustainability of the regional environment. Integrated remote sensing and GIS technique and models were applied to generate the necessary factors (criterions) maps for the SMCE approach. The ranking, along with expected value method, was used to standardize the factors and on the other hand, an analytical hierarchy process (AHP) was applied for calculating factor weights. The entire process was executed in the integrated land and water information system (ILWIS) software tool that supports SMCE. The analysis showed that the overall regional environmental quality of the area was at moderate level and was partly determined by elevation. Areas under worse and poor quality of environment indicated that the regional environmental status showed decline in these parts of the county. The study also revealed that the human activities, vegetation condition, soil erosion, topography, climate, and soil conditions have serious influence on the regional environment condition of the area. Considering the regional characteristics of environmental quality, priority, and practical needs for environmental restoration, the study area was further regionalized into four priority areas which may serve as base areas of decision making for the recovery, rebuilding, and protection of the environment.

Remote sensing based crop type mapping and evapotranspiration estimates at the farm level in arid regions of the globe

NASA Astrophysics Data System (ADS)

Ozdogan, M.; Serrat-Capdevila, A.; Anderson, M. C.

2017-12-01

Despite increasing scarcity of freshwater resources, there is dearth of spatially explicit information on irrigation water consumption through evapotranspiration, particularly in semi-arid and arid geographies. Remote sensing, either alone or in combination with ground surveys, is increasingly being used for irrigation water management by quantifying evaporative losses at the farm level. Increased availability of observations, sophisticated algorithms, and access to cloud-based computing is also helping this effort. This presentation will focus on crop-specific evapotranspiration estimates at the farm level derived from remote sensing in a number of water-scarce regions of the world. The work is part of a larger effort to quantify irrigation water use and improve use efficiencies associated with several World Bank projects. Examples will be drawn from India, where groundwater based irrigation withdrawals are monitored with the help of crop type mapping and evapotranspiration estimates from remote sensing. Another example will be provided from a northern irrigation district in Mexico, where remote sensing is used for detailed water accounting at the farm level. These locations exemplify the success stories in irrigation water management with the help of remote sensing with the hope that spatially disaggregated information on evapotranspiration can be used as inputs for various water management decisions as well as for better water allocation strategies in many other water scarce regions.

a Rough Set Decision Tree Based Mlp-Cnn for Very High Resolution Remotely Sensed Image Classification

NASA Astrophysics Data System (ADS)

Zhang, C.; Pan, X.; Zhang, S. Q.; Li, H. P.; Atkinson, P. M.

2017-09-01

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.

Multi Source Remote Sensing for Monitoring Light-Absorbing Impurities on Snow and Ice in the European Alps

NASA Astrophysics Data System (ADS)

Colombo, R.; Baccolo, G.; Garzonio, R.; Massabò, D.; Julitta, T.; Rossini, M.; Ferrero, L.; Delmonte, B.; Maggi, V.; Mattavelli, M.; Panigada, C.; Cogliati, S.; Cremonese, E.; Di Mauro, B.

2016-12-01

The European Alps are located close to one of the most industrialized areas of the planet and they are 3.000 km from the largest desert of the Earth. Light-absorbing impurities (LAI) emitted from these sources can reach the Alpine chain and deposit on snow covered areas and mountain glaciers. Although several studies show that LAI have important impacts on the optical properties of snow and ice, reducing the albedo and promoting the melt, this impact has been poorly characterized in the Alps. In this contribution, we present the results of a multisource remote sensing approach aimed to study the LAI impact on snow and ice properties in the Alpine area. This process has been observed by means of remote and proximal sensing methods, using satellite (Landsat 8, Hyperion and MODIS data), field spectroscopy (ASD measurements), Automatic Weather Stations, aerial surveys (Unmanned Aerial Vehicle), radiative transfer modeling (SNICAR and TARTES) and laboratory analysis (hyperspectral imaging system). Furthermore, particle size (Coulter Counter), geochemical (Instrumental Neutron Activation Analysis, INAA) and optical (Multi-Wavelength Absorbance Analyzer, MWAA) analyses have been applied to determine the nature and radiative properties of particulate material deposited on snow and ice or aggregated into cryoconite holes. Our results demonstrate that LAI can be monitored from remote sensing at different scale. LAI showed to have a strong impact on the Alpine cryosphere, paving the way for the assessment of their role in melting processes.

Civil Engineering Applications of Ground Penetrating Radar Recent Advances @ the ELEDIA Research Center

NASA Astrophysics Data System (ADS)

Salucci, Marco; Tenuti, Lorenza; Nardin, Cristina; Oliveri, Giacomo; Viani, Federico; Rocca, Paolo; Massa, Andrea

2014-05-01

The application of non-destructive testing and evaluation (NDT/NDE) methodologies in civil engineering has raised a growing interest during the last years because of its potential impact in several different scenarios. As a consequence, Ground Penetrating Radar (GPR) technologies have been widely adopted as an instrument for the inspection of the structural stability of buildings and for the detection of cracks and voids. In this framework, the development and validation of GPR algorithms and methodologies represents one of the most active research areas within the ELEDIA Research Center of the University of Trento. More in detail, great efforts have been devoted towards the development of inversion techniques based on the integration of deterministic and stochastic search algorithms with multi-focusing strategies. These approaches proved to be effective in mitigating the effects of both nonlinearity and ill-posedness of microwave imaging problems, which represent the well-known issues arising in GPR inverse scattering formulations. More in detail, a regularized multi-resolution approach based on the Inexact Newton Method (INM) has been recently applied to subsurface prospecting, showing a remarkable advantage over a single-resolution implementation [1]. Moreover, the use of multi-frequency or frequency-hopping strategies to exploit the information coming from GPR data collected in time domain and transformed into its frequency components has been proposed as well. In this framework, the effectiveness of the multi-resolution multi-frequency techniques has been proven on synthetic data generated with numerical models such as GprMax [2]. The application of inversion algorithms based on Bayesian Compressive Sampling (BCS) [3][4] to GPR is currently under investigation, as well, in order to exploit their capability to provide satisfactory reconstructions in presence of single and multiple sparse scatterers [3][4]. Furthermore, multi-scaling approaches exploiting level-set-based optimization have been developed for the qualitative reconstruction of multiple and disconnected homogeneous scatterers [5]. Finally, the real-time detection and classification of subsurface scatterers has been investigated by means of learning-by-examples (LBE) techniques, such as Support Vector Machines (SVM) [6]. Acknowledgment - This work was partially supported by COST Action TU1208 'Civil Engineering Applications of Ground Penetrating Radar' References [1] M. Salucci, D. Sartori, N. Anselmi, A. Randazzo, G. Oliveri, and A. Massa, 'Imaging Buried Objects within the Second-Order Born Approximation through a Multiresolution Regularized Inexact-Newton Method', 2013 International Symposium on Electromagnetic Theory (EMTS), (Hiroshima, Japan), May 20-24 2013 (invited). [2] A. Giannopoulos, 'Modelling ground penetrating radar by GprMax', Construct. Build. Mater., vol. 19, no. 10, pp.755 -762 2005 [3] L. Poli, G. Oliveri, P. Rocca, and A. Massa, "Bayesian compressive sensing approaches for the reconstruction of two-dimensional sparse scatterers under TE illumination," IEEE Trans. Geosci. Remote Sensing, vol. 51, no. 5, pp. 2920-2936, May. 2013. [4] L. Poli, G. Oliveri, and A. Massa, "Imaging sparse metallic cylinders through a Local Shape Function Bayesian Compressive Sensing approach," Journal of Optical Society of America A, vol. 30, no. 6, pp. 1261-1272, 2013. [5] M. Benedetti, D. Lesselier, M. Lambert, and A. Massa, "Multiple shapes reconstruction by means of multi-region level sets," IEEE Trans. Geosci. Remote Sensing, vol. 48, no. 5, pp. 2330-2342, May 2010. [6] L. Lizzi, F. Viani, P. Rocca, G. Oliveri, M. Benedetti and A. Massa, "Three-dimensional real-time localization of subsurface objects - From theory to experimental validation," 2009 IEEE International Geoscience and Remote Sensing Symposium, vol. 2, pp. II-121-II-124, 12-17 July 2009.

Sea ice-atmosphere interaction: Application of multispectral satellite data in polar surface energy flux estimates

NASA Technical Reports Server (NTRS)

Steffen, K.; Schweiger, A.; Maslanik, J.; Key, J.; Weaver, R.; Barry, R.

1990-01-01

The application of multi-spectral satellite data to estimate polar surface energy fluxes is addressed. To what accuracy and over which geographic areas large scale energy budgets can be estimated are investigated based upon a combination of available remote sensing and climatological data sets. The general approach was to: (1) formulate parameterization schemes for the appropriate sea ice energy budget terms based upon the remotely sensed and/or in-situ data sets; (2) conduct sensitivity analyses using as input both natural variability (observed data in regional case studies) and theoretical variability based upon energy flux model concepts; (3) assess the applicability of these parameterization schemes to both regional and basin wide energy balance estimates using remote sensing data sets; and (4) assemble multi-spectral, multi-sensor data sets for at least two regions of the Arctic Basin and possibly one region of the Antarctic. The type of data needed for a basin-wide assessment is described and the temporal coverage of these data sets are determined by data availability and need as defined by parameterization scheme. The titles of the subjects are as follows: (1) Heat flux calculations from SSM/I and LANDSAT data in the Bering Sea; (2) Energy flux estimation using passive microwave data; (3) Fetch and stability sensitivity estimates of turbulent heat flux; and (4) Surface temperature algorithm.

Bridging the Scales from Field to Region with Practical Tools to Couple Time- and Space-Synchronized Data from Flux Towers and Networks with Proximal and Remote Sensing Data

NASA Astrophysics Data System (ADS)

Burba, G. G.; Avenson, T.; Burkart, A.; Gamon, J. A.; Guan, K.; Julitta, T.; Pastorello, G.; Sakowska, K.

2017-12-01

Many hundreds of flux towers are presently operational as standalone projects and as parts of regional networks. However, the vast majority of these towers do not allow straightforward coupling with remote sensing (drone, aircraft, satellite, etc.) data, and even fewer have optical sensors for validation of remote sensing products, and upscaling from field to regional levels. In 2016-2017, new tools to collect, process, and share time-synchronized flux data from multiple towers were developed and deployed globally. Originally designed to automate site and data management, and to streamline flux data analysis, these tools allow relatively easy matching of tower data with remote sensing data: GPS-driven PTP time protocol synchronizes instrumentation within the station, different stations with each other, and all of these to remote sensing data to precisely align remote sensing and flux data in time Footprint size and coordinates computed and stored with flux data help correctly align tower flux footprints and drone, aircraft or satellite motion to precisely align optical and flux data in space Full snapshot of the remote sensing pixel can then be constructed, including leaf-level, ground optical sensor, and flux tower measurements from the same footprint area, closely coupled with the remote sensing measurements to help interpret remote sensing data, validate models, and improve upscaling Additionally, current flux towers can be augmented with advanced ground optical sensors and can use standard routines to deliver continuous products (e.g. SIF, PRI, NDVI, etc.) based on automated field spectrometers (e.g., FloX and RoX, etc.) and other optical systems. Several dozens of new towers already operational globally can be readily used for the proposed workflow. Over 500 active traditional flux towers can be updated to synchronize their data with remote sensing measurements. This presentation will show how the new tools are used by major networks, and describe how this approach can be utilized for matching remote sensing and tower data to aid in ground truthing, improve scientific interactions, and promote joint grant writing and other forms of collaboration between the flux and remote sensing communities.

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

NASA Astrophysics Data System (ADS)

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).

Optical sampling of the flux tower footprint

NASA Astrophysics Data System (ADS)

Gamon, J. A.

2015-03-01

The purpose of this review is to address the reasons and methods for conducting optical remote sensing within the flux tower footprint. Fundamental principles and conclusions gleaned from over two decades of proximal remote sensing at flux tower sites are reviewed. An organizing framework is the light-use efficiency (LUE) model, both because it is widely used, and because it provides a useful theoretical construct for integrating optical remote sensing with flux measurements. Multiple ways of driving this model, ranging from meteorological measurements to remote sensing, have emerged in recent years, making it a convenient conceptual framework for comparative experimental studies. New interpretations of established optical sampling methods, including the Photochemical Reflectance Index (PRI) and Solar-Induced Fluorescence (SIF), are discussed within the context of the LUE model. Multi-scale analysis across temporal and spatial axes is a central theme, because such scaling can provide links between ecophysiological mechanisms detectable at the level of individual organisms and broad patterns emerging at larger scales, enabling evaluation of emergent properties and extrapolation to the flux footprint and beyond. Proper analysis of sampling scale requires an awareness of sampling context that is often essential to the proper interpretation of optical signals. Additionally, the concept of optical types, vegetation exhibiting contrasting optical behavior in time and space, is explored as a way to frame our understanding of the controls on surface-atmosphere fluxes. Complementary NDVI and PRI patterns across ecosystems are offered as an example of this hypothesis, with the LUE model and light-response curve providing an integrating framework. We conclude that experimental approaches allowing systematic exploration of plant optical behavior in the context of the flux tower network provides a unique way to improve our understanding of environmental constraints and ecophysiological function. In addition to an enhanced mechanistic understanding of ecosystem processes, this integration of remote sensing with flux measurements offers many rich opportunities for upscaling, satellite validation, and informing practical management objectives ranging form assessing ecosystem health and productivity to quantifying biospheric carbon sequestration.

Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA's-AVHRR.

PubMed

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha(-1). Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly.

Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR

PubMed Central

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha−1. Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly. PMID:23967112

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

PubMed

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.

Real-Time and Post-Processed Georeferencing for Hyperpspectral Drone Remote Sensing

NASA Astrophysics Data System (ADS)

Oliveira, R. A.; Khoramshahi, E.; Suomalainen, J.; Hakala, T.; Viljanen, N.; Honkavaara, E.

2018-05-01

The use of drones and photogrammetric technologies are increasing rapidly in different applications. Currently, drone processing workflow is in most cases based on sequential image acquisition and post-processing, but there are great interests towards real-time solutions. Fast and reliable real-time drone data processing can benefit, for instance, environmental monitoring tasks in precision agriculture and in forest. Recent developments in miniaturized and low-cost inertial measurement systems and GNSS sensors, and Real-time kinematic (RTK) position data are offering new perspectives for the comprehensive remote sensing applications. The combination of these sensors and light-weight and low-cost multi- or hyperspectral frame sensors in drones provides the opportunity of creating near real-time or real-time remote sensing data of target object. We have developed a system with direct georeferencing onboard drone to be used combined with hyperspectral frame cameras in real-time remote sensing applications. The objective of this study is to evaluate the real-time georeferencing comparing with post-processing solutions. Experimental data sets were captured in agricultural and forested test sites using the system. The accuracy of onboard georeferencing data were better than 0.5 m. The results showed that the real-time remote sensing is promising and feasible in both test sites.

Radar remote sensing in biology

USGS Publications Warehouse

Moore, Richard K.; Simonett, David S.

1967-01-01

The present status of research on discrimination of natural and cultivated vegetation using radar imaging systems is sketched. The value of multiple polarization radar in improved discrimination of vegetation types over monoscopic radars is also documented. Possible future use of multi-frequency, multi-polarization radar systems for all weather agricultural survey is noted.

Tracking diurnal changes of photosynthesis and evapotranspiration using fluorescence, gas exchange and hyperspectral remote sensing measurements

NASA Astrophysics Data System (ADS)

Wang, S.; Zhang, L.; Guanter, L.; Huang, C.

2017-12-01

Photosynthesis and evapotranspiration (ET) are the two most important activities of vegetation and make a great contribution to carbon, water and energy exchanges. Remote sensing provides opportunities for monitoring these processes across time and space. This study focuses on tracking diurnal changes of photosynthesis and evapotranspiration over soybean using multiple measurement techniques. Diurnal changes of both remote sensing-based indicators, including active and passive chlorophyll fluorescence and biophysical-related parameters, including photosynthesis rate (photo) and leaf stomatal conductance (cond), were observed. Results showed that both leaf-level steady-state fluorescence (Fs) and canopy-level solar-induced chlorophyll fluorescence were linearly correlated to photosynthetically active radiation (PAR) during the daytime. A double-peak diurnal change curve was observed for leaf-level photo and cond but not for Fs or SIF. Photo and cond showed a strong nonlinear (second-order) correlation, indicating that photosynthesis, which might be remotely sensed by SIF, has the opportunity to track short-term changes of ET. Results presented in this report will be helpful for better understanding the relationship between remote-sensing-based indices and vegetation's biophysical processes.

Active Remote Sensing of Natural Resources: Course Notes. Science Series No. 5. Final Technical Report.

ERIC Educational Resources Information Center

Maxwell, Eugene L.

Presented is a portion of a research project which developed materials for teaching remote sensing of natural resources on an interdisciplinary basis at the graduate level. This volume contains notes developed for a course in active remote sensing. It is concerned with those methods or systems which generate the electromagnetic energy…

Remote sensing of wetlands, marshes, and shorelines in Michigan including St. John's Marsh

NASA Technical Reports Server (NTRS)

Lowe, D. S.

1976-01-01

Remote sensing data are used to show the strategic relationship of the endangered marsh to population centers of SE Michigan. The potential ecological consequences and the impact of past development and changing lake levels are discussed. Applications of remote sensing are presented showing its usefulness for preparing statewide infrared wetland and forest mapping.

Remote sensing of land degradation: experiences from Latin America and the Caribbean.

PubMed

Metternicht, G; Zinck, J A; Blanco, P D; del Valle, H F

2010-01-01

Land degradation caused by deforestation, overgrazing, and inappropriate irrigation practices affects about 16% of Latin America and the Caribbean (LAC). This paper addresses issues related to the application of remote sensing technologies for the identification and mapping of land degradation features, with special attention to the LAC region. The contribution of remote sensing to mapping land degradation is analyzed from the compilation of a large set of research papers published between the 1980s and 2009, dealing with water and wind erosion, salinization, and changes of vegetation cover. The analysis undertaken found that Landsat series (MSS, TM, ETM+) are the most commonly used data source (49% of the papers report their use), followed by aerial photographs (39%), and microwave sensing (ERS, JERS-1, Radarsat) (27%). About 43% of the works analyzed use multi-scale, multi-sensor, multi-spectral approaches for mapping degraded areas, with a combination of visual interpretation and advanced image processing techniques. The use of more expensive hyperspectral and/or very high spatial resolution sensors like AVIRIS, Hyperion, SPOT-5, and IKONOS tends to be limited to small surface areas. The key issue of indicators that can directly or indirectly help recognize land degradation features in the visible, infrared, and microwave regions of the electromagnetic spectrum are discussed. Factors considered when selecting indicators for establishing land degradation baselines include, among others, the mapping scale, the spectral characteristics of the sensors, and the time of image acquisition. The validation methods used to assess the accuracy of maps produced with satellite data are discussed as well.

Plant trait detection with multi-scale spectrometry

NASA Astrophysics Data System (ADS)

Gamon, J. A.; Wang, R.

2017-12-01

Proximal and remote sensing using imaging spectrometry offers new opportunities for detecting plant traits, with benefits for phenotyping, productivity estimation, stress detection, and biodiversity studies. Using proximal and airborne spectrometry, we evaluated variation in plant optical properties at various spatial and spectral scales with the goal of identifying optimal scales for distinguishing plant traits related to photosynthetic function. Using directed approaches based on physiological vegetation indices, and statistical approaches based on spectral information content, we explored alternate ways of distinguishing plant traits with imaging spectrometry. With both leaf traits and canopy structure contributing to the signals, results exhibit a strong scale dependence. Our results demonstrate the benefits of multi-scale experimental approaches within a clear conceptual framework when applying remote sensing methods to plant trait detection for phenotyping, productivity, and biodiversity studies.

Multi- and hyperspectral geologic remote sensing: A review

NASA Astrophysics Data System (ADS)

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 workflows should be multidisciplinary and remote sensing data should be integrated with field observations and subsurface geophysical data to monitor and understand geologic processes.

Measuring and Monitoring Long Term Disaster Recovery Using Remote Sensing: A Case Study of Post Katrina New Orleans

NASA Astrophysics Data System (ADS)

Archer, Reginald S.

This research focuses on measuring and monitoring long term recovery progress from the impacts of Hurricane Katrina on New Orleans, LA. Remote sensing has frequently been used for emergency response and damage assessment after natural disasters. However, techniques for analysis of long term disaster recovery using remote sensing have not been widely explored. With increased availability and lower costs, remote sensing offers an objective perspective, systematic and repeatable analysis, and provides a substitute to multiple site visits. In addition, remote sensing allows access to large geographical areas and areas where ground access may be disrupted, restricted or denied. This dissertation addressed the primary difficulties involved in the development of change detection methods capable of detecting changes experienced by disaster recovery indicators. Maximum likelihood classification and post-classification change detection were applied to multi-temporal high resolution aerial images to quantitatively measure the progress of recovery. Images were classified to automatically identify disaster recovery indicators and exploit the indicators that are visible within each image. The spectral analysis demonstrated that employing maximum likelihood classification to high resolution true color aerial images performed adequately and provided a good indication of spectral pattern recognition, despite the limited spectral information. Applying the change detection to the classified images was effective for determining the temporal trajectory of indicators categorized as blue tarps, FEMA trailers, houses, vegetation, bare earth and pavement. The results of the post classification change detection revealed a dominant change trajectory from bluetarp to house, as damaged houses became permanently repaired. Specifically, the level of activity of blue tarps, housing, vegetation, FEMA trailers (temporary housing) pavement and bare earth were derived from aerial image processing to measure and monitor the progress of recovery. Trajectories of recovery for each individual indicator were examined to provide a better understanding of activity during reconstruction. A collection of spatial metrics was explored in order to identify spatial patterns and characterize classes in terms of patches of pixels. One of the key findings of the spatial analysis is that patch shapes were more complex in the presence of debris and damaged or destroyed buildings. The combination of spectral, temporal, and spatial analysis provided a satisfactory, though limited, solution to the question of whether remote sensing alone, can be used to quantitatively assess and monitor the progress of long term recovery following a major disaster. The research described in this dissertation provided a detailed illustration of the level of activity experienced by different recovery indicators during the long term recovery process. It also addressed the primary difficulties involved in the development of change detection methods capable of detecting changes experienced by disaster recovery indicators identified from classified high resolution true color aerial imagery. The results produced in this research demonstrate that the observed trajectories for actual indicators of recovery indicate different levels of recovery activity even within the same community. The level of activity of the long term reconstruction phase observed in the Kates model is not consistent with the level of activity of key recovery indicators in the Lower 9th Ward during the same period. Used in the proper context, these methods and results provide decision making information for determining resources. KEYWORDS: Change detection, classification, Katrina, New Orleans, remote sensing, disaster recovery, spatial metrics

Model for mapping settlements

DOEpatents

Vatsavai, Ranga Raju; Graesser, Jordan B.; Bhaduri, Budhendra L.

2016-07-05

A programmable media includes a graphical processing unit in communication with a memory element. The graphical processing unit is configured to detect one or more settlement regions from a high resolution remote sensed image based on the execution of programming code. The graphical processing unit identifies one or more settlements through the execution of the programming code that executes a multi-instance learning algorithm that models portions of the high resolution remote sensed image. The identification is based on spectral bands transmitted by a satellite and on selected designations of the image patches.

Modeling multi-layer effects in passive microwave remote sensing of dry snow using Dense Media Radiative Transfer Theory (DMRT) based on quasicrystalline approximation

USGS Publications Warehouse

Liang, D.; Xu, X.; Tsang, L.; Andreadis, K.M.; Josberger, E.G.

2008-01-01

The Dense Media Radiative Transfer theory (DMRT) of Quasicrystalline Approximation of Mie scattering by sticky particles is used to study the multiple scattering effects in layered snow in microwave remote sensing. Results are illustrated for various snow profile characteristics. Polarization differences and frequency dependences of multilayer snow model are significantly different from that of the single-layer snow model. Comparisons are also made with CLPX data using snow parameters as given by the VIC model. ?? 2007 IEEE.

Information recovery through image sequence fusion under wavelet transformation

NASA Astrophysics Data System (ADS)

He, Qiang

2010-04-01

Remote sensing is widely applied to provide information of areas with limited ground access with applications such as to assess the destruction from natural disasters and to plan relief and recovery operations. However, the data collection of aerial digital images is constrained by bad weather, atmospheric conditions, and unstable camera or camcorder. Therefore, how to recover the information from the low-quality remote sensing images and how to enhance the image quality becomes very important for many visual understanding tasks, such like feature detection, object segmentation, and object recognition. The quality of remote sensing imagery can be improved through meaningful combination of the employed images captured from different sensors or from different conditions through information fusion. Here we particularly address information fusion to remote sensing images under multi-resolution analysis in the employed image sequences. The image fusion is to recover complete information by integrating multiple images captured from the same scene. Through image fusion, a new image with high-resolution or more perceptive for human and machine is created from a time series of low-quality images based on image registration between different video frames.

Integrated Decision Tools for Sustainable Watershed/Ground Water and Crop Health using Predictive Weather, Remote Sensing, and Irrigation Decision Tools

NASA Astrophysics Data System (ADS)

Jones, A. S.; Andales, A.; McGovern, C.; Smith, G. E. B.; David, O.; Fletcher, S. J.

2017-12-01

US agricultural and Govt. lands have a unique co-dependent relationship, particularly in the Western US. More than 30% of all irrigated US agricultural output comes from lands sustained by the Ogallala Aquifer in the western Great Plains. Six US Forest Service National Grasslands reside within the aquifer region, consisting of over 375,000 ha (3,759 km2) of USFS managed lands. Likewise, National Forest lands are the headwaters to many intensive agricultural regions. Our Ogallala Aquifer team is enhancing crop irrigation decision tools with predictive weather and remote sensing data to better manage water for irrigated crops within these regions. An integrated multi-model software framework is used to link irrigation decision tools, resulting in positive management benefits on natural water resources. Teams and teams-of-teams can build upon these multi-disciplinary multi-faceted modeling capabilities. For example, the CSU Catalyst for Innovative Partnerships program has formed a new multidisciplinary team that will address "Rural Wealth Creation" focusing on the many integrated links between economic, agricultural production and management, natural resource availabilities, and key social aspects of govt. policy recommendations. By enhancing tools like these with predictive weather and other related data (like in situ measurements, hydrologic models, remotely sensed data sets, and (in the near future) linking to agro-economic and life cycle assessment models) this work demonstrates an integrated data-driven future vision of inter-meshed dynamic systems that can address challenging multi-system problems. We will present the present state of the work and opportunities for future involvement.

A review of future remote sensing satellite capabilities

NASA Technical Reports Server (NTRS)

Calabrese, M. A.

1980-01-01

Existing, planned and future NASA capabilities in the field of remote sensing satellites are reviewed in relation to the use of remote sensing techniques for the identification of irrigated lands. The status of the currently operational Landsat 2 and 3 satellites is indicated, and it is noted that Landsat D is scheduled to be in operation in two years. The orbital configuration and instrumentation of Landsat D are discussed, with particular attention given to the thematic mapper, which is expected to improve capabilities for small field identification and crop discrimination and classification. Future possibilities are then considered, including a multi-spectral resource sampler supplying high spatial and temporal resolution data possibly based on push-broom scanning, Shuttle-maintained Landsat follow-on missions, a satellite to obtain high-resolution stereoscopic data, further satellites providing all-weather radar capability and the Large Format Camera.

Remote Sensing by Satellite for Environmental Education: A Survey and a Proposal for Teaching at Upper Secondary and University Level.

ERIC Educational Resources Information Center

Bosler, Ulrich

Knowledge of the environment has grown to such an extent that information technology (IT) is essential to make sense of the available data. An example of this is remote sensing by satellite. In recent years this field has grown in importance and remote sensing is used for a range of uses including the automatic survey of wheat yields in North…

Detection and Monitoring of Small-Scale Mining Operations in the Eastern Democratic Republic of the Congo (DRC) Using Multi-Temporal, Multi-Sensor Remote Sensing Data

NASA Astrophysics Data System (ADS)

Walther, Christian; Frei, Michaela

2017-04-01

Mining of so-called "conflict minerals" is often related with small-scale mining activities. The here discussed activities are located in forested areas in the eastern DRC, which are often remote, difficult to access and insecure for traditional geological field inspection. In order to accelerate their CTC (Certified Trading Chain)-certification process, remote sensing data are used for detection and monitoring of these small-scale mining operations. This requires a high image acquisition frequency due to mining site relocations and for compensation of year-round high cloud coverage, especially for optical data evaluation. Freely available medium resolution optical data of Sentinel-2 and Landsat-8 as well as SAR data of Sentinel-1 are used for detecting small mining targets with a minimum size of approximately 0.5 km2. The developed method enables a robust multi-temporal detection of mining sites, monitoring of mining site spatio-temporal relocations and environmental changes. Since qualitative and quantitative comparable results are generated, the followed change detection approach is objective and transparent and may push the certification process forward.

A Survey of Ethics Content in College-Level Remote Sensing Courses in the United States

ERIC Educational Resources Information Center

Wetherholt, William A.; Rundquist, Bradley C.

2010-01-01

Easier access to submeter imagery has fueled debates over ethical uses of remote sensing. Some have called for ethics instruction to counter undesired uses of the technology. Here, this article reports the results of a survey examining attitudes related to teaching ethics in remote sensing. It was found that 52 percent of respondents teaching…

Validating Remotely Sensed Land Surface Evapotranspiration Based on Multi-scale Field Measurements

NASA Astrophysics Data System (ADS)

Jia, Z.; Liu, S.; Ziwei, X.; Liang, S.

2012-12-01

The land surface evapotranspiration plays an important role in the surface energy balance and the water cycle. There have been significant technical and theoretical advances in our knowledge of evapotranspiration over the past two decades. Acquisition of the temporally and spatially continuous distribution of evapotranspiration using remote sensing technology has attracted the widespread attention of researchers and managers. However, remote sensing technology still has many uncertainties coming from model mechanism, model inputs, parameterization schemes, and scaling issue in the regional estimation. Achieving remotely sensed evapotranspiration (RS_ET) with confident certainty is required but difficult. As a result, it is indispensable to develop the validation methods to quantitatively assess the accuracy and error sources of the regional RS_ET estimations. This study proposes an innovative validation method based on multi-scale evapotranspiration acquired from field measurements, with the validation results including the accuracy assessment, error source analysis, and uncertainty analysis of the validation process. It is a potentially useful approach to evaluate the accuracy and analyze the spatio-temporal properties of RS_ET at both the basin and local scales, and is appropriate to validate RS_ET in diverse resolutions at different time-scales. An independent RS_ET validation using this method was presented over the Hai River Basin, China in 2002-2009 as a case study. Validation at the basin scale showed good agreements between the 1 km annual RS_ET and the validation data such as the water balanced evapotranspiration, MODIS evapotranspiration products, precipitation, and landuse types. Validation at the local scale also had good results for monthly, daily RS_ET at 30 m and 1 km resolutions, comparing to the multi-scale evapotranspiration measurements from the EC and LAS, respectively, with the footprint model over three typical landscapes. Although some validation experiments demonstrated that the models yield accurate estimates at flux measurement sites, the question remains whether they are performing well over the broader landscape. Moreover, a large number of RS_ET products have been released in recent years. Thus, we also pay attention to the cross-validation method of RS_ET derived from multi-source models. "The Multi-scale Observation Experiment on Evapotranspiration over Heterogeneous Land Surfaces: Flux Observation Matrix" campaign is carried out at the middle reaches of the Heihe River Basin, China in 2012. Flux measurements from an observation matrix composed of 22 EC and 4 LAS are acquired to investigate the cross-validation of multi-source models over different landscapes. In this case, six remote sensing models, including the empirical statistical model, the one-source and two-source models, the Penman-Monteith equation based model, the Priestley-Taylor equation based model, and the complementary relationship based model, are used to perform an intercomparison. All the results from the two cases of RS_ET validation showed that the proposed validation methods are reasonable and feasible.

An investigation of satellite sounding products for the remote sensing of the surface energy balance and soil moisture

NASA Technical Reports Server (NTRS)

Diak, George R.

1989-01-01

Improved techniques for the remote sensing of the land surface energy balance (SEB) and soil moisture would greatly improve prediction of climate and weather as well as be of benefit to agriculture, hydrology and many associated fields. Most of the satellite remote sensing methods which were researched to date rely upon satellite-measured infrared surface temperatures or their time changes as a remote sensing signal. Optimistically, only four or five levels of information (wet to dry) in surface heating/evaporation are discernable by surface temperature methods and a good understanding of atmospheric conditions is necessary to bring them to this accuracy level. Skin temperature methods were researched as well as begun work on several new methods for the remote sensing of the SEB, some elements of which are applicable to current and retrospective data sources and some which will rely on instrumentation from the Earth Observing System (EOS) program in the 1990s.

Multi-Spectral Stereo Atmospheric Remote Sensing (STARS) for Retrieval of Cloud Properties and Cloud-Motion Vectors

NASA Astrophysics Data System (ADS)

Kelly, M. A.; Boldt, J.; Wilson, J. P.; Yee, J. H.; Stoffler, R.

2017-12-01

The multi-spectral STereo Atmospheric Remote Sensing (STARS) concept has the objective to provide high-spatial and -temporal-resolution observations of 3D cloud structures related to hurricane development and other severe weather events. The rapid evolution of severe weather demonstrates a critical need for mesoscale observations of severe weather dynamics, but such observations are rare, particularly over the ocean where extratropical and tropical cyclones can undergo explosive development. Coincident space-based measurements of wind velocity and cloud properties at the mesoscale remain a great challenge, but are critically needed to improve the understanding and prediction of severe weather and cyclogenesis. STARS employs a mature stereoscopic imaging technique on two satellites (e.g. two CubeSats, two hosted payloads) to simultaneously retrieve cloud motion vectors (CMVs), cloud-top temperatures (CTTs), and cloud geometric heights (CGHs) from multi-angle, multi-spectral observations of cloud features. STARS is a pushbroom system based on separate wide-field-of-view co-boresighted multi-spectral cameras in the visible, midwave infrared (MWIR), and longwave infrared (LWIR) with high spatial resolution (better than 1 km). The visible system is based on a pan-chromatic, low-light imager to resolve cloud structures under nighttime illumination down to ¼ moon. The MWIR instrument, which is being developed as a NASA ESTO Instrument Incubator Program (IIP) project, is based on recent advances in MWIR detector technology that requires only modest cooling. The STARS payload provides flexible options for spaceflight due to its low size, weight, power (SWaP) and very modest cooling requirements. STARS also meets AF operational requirements for cloud characterization and theater weather imagery. In this paper, an overview of the STARS concept, including the high-level sensor design, the concept of operations, and measurement capability will be presented.

Use of Openly Available Satellite Images for Remote Sensing Education

NASA Astrophysics Data System (ADS)

Wang, C.-K.

2011-09-01

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

A preliminary study of the statistical analyses and sampling strategies associated with the integration of remote sensing capabilities into the current agricultural crop forecasting system

NASA Technical Reports Server (NTRS)

Sand, F.; Christie, R.

1975-01-01

Extending the crop survey application of remote sensing from small experimental regions to state and national levels requires that a sample of agricultural fields be chosen for remote sensing of crop acreage, and that a statistical estimate be formulated with measurable characteristics. The critical requirements for the success of the application are reviewed in this report. The problem of sampling in the presence of cloud cover is discussed. Integration of remotely sensed information about crops into current agricultural crop forecasting systems is treated on the basis of the USDA multiple frame survey concepts, with an assumed addition of a new frame derived from remote sensing. Evolution of a crop forecasting system which utilizes LANDSAT and future remote sensing systems is projected for the 1975-1990 time frame.

Portable Laser Spectrometer for Airborne and Ground-Based Remote Sensing of Geological CO2 Emissions

NASA Technical Reports Server (NTRS)

Queisser, Manuel; Burton, Mike; Allan, Graham R.; Chiarugi, Antonio

2017-01-01

A 24 kilogram, suitcase-sized, CW (Continuous Wave) Laser Remote Sensing Spectrometer (LARSS) with an approximately 2-kilometer range has been developed. It has demonstrated its flexibility in measuring both atmospheric CO2 from an airborne platform and terrestrial emission of CO2 from a remote mud volcano, Bledug Kuwu, Indonesia, from a ground-based sight. This system scans the CO2 absorption line with 20 discrete wavelengths, as opposed to the typical two-wavelength online-offline instrument. This multi-wavelength approach offers an effective quality control, bias control, and confidence estimate of measured CO2 concentrations via spectral fitting. The simplicity, ruggedness, and flexibility in the design allow for easy transportation and use on different platforms with a quick setup in some of the most challenging climatic conditions. While more refinement is needed, the results represent a stepping stone towards widespread use of active one-sided gas remote sensing in the earth sciences.

Portable laser spectrometer for airborne and ground-based remote sensing of geological CO₂ emissions.

PubMed

Queisser, Manuel; Burton, Mike; Allan, Graham R; Chiarugi, Antonio

2017-07-15

A 24 kg, suitcase sized, CW laser remote sensing spectrometer (LARSS) with a ~2 km range has been developed. It has demonstrated its flexibility in measuring both atmospheric CO₂ from an airborne platform and terrestrial emission of CO₂ from a remote mud volcano, Bledug Kuwu, Indonesia, from a ground-based sight. This system scans the CO₂ absorption line with 20 discrete wavelengths, as opposed to the typical two-wavelength online offline instrument. This multi-wavelength approach offers an effective quality control, bias control, and confidence estimate of measured CO₂ concentrations via spectral fitting. The simplicity, ruggedness, and flexibility in the design allow for easy transportation and use on different platforms with a quick setup in some of the most challenging climatic conditions. While more refinement is needed, the results represent a stepping stone towards widespread use of active one-sided gas remote sensing in the earth sciences.

Merged data models for multi-parameterized querying: Spectral data base meets GIS-based map archive

NASA Astrophysics Data System (ADS)

Naß, A.; D'Amore, M.; Helbert, J.

2017-09-01

Current and upcoming planetary missions deliver a huge amount of different data (remote sensing data, in-situ data, and derived products). Within this contribution present how different data (bases) can be managed and merged, to enable multi-parameterized querying based on the constant spatial context.

Numerically stable algorithm for combining census and sample estimates with the multivariate composite estimator

Treesearch

R. L. Czaplewski

2009-01-01

The minimum variance multivariate composite estimator is a relatively simple sequential estimator for complex sampling designs (Czaplewski 2009). Such designs combine a probability sample of expensive field data with multiple censuses and/or samples of relatively inexpensive multi-sensor, multi-resolution remotely sensed data. Unfortunately, the multivariate composite...

Remote sensing techniques for conservation and management of natural vegetation ecosystems

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Verdesio, J. J.; Dossantos, J. R.

1981-01-01

The importance of using remote sensing techniques, in the visible and near-infrared ranges, for mapping, inventory, conservation and management of natural ecosystems is discussed. Some examples realized in Brazil or other countries are given to evaluate the products from orbital platform (MSS and RBV imagery of LANDSAT) and aerial level (photography) for ecosystems study. The maximum quantitative and qualitative information which can be obtained from each sensor, at different level, are discussed. Based on the developed experiments it is concluded that the remote sensing technique is a useful tool in mapping vegetation units, estimating biomass, forecasting and evaluation of fire damage, disease detection, deforestation mapping and change detection in land-use. In addition, remote sensing techniques can be used in controling implantation and planning natural/artificial regeneration.

The Feasibility Evaluation of Land Use Change Detection Using GAOFEN-3 Data

NASA Astrophysics Data System (ADS)

Huang, G.; Sun, Y.; Zhao, Z.

2018-04-01

GaoFen-3 (GF-3) satellite, is the first C band and multi-polarimetric Synthetic Aperture Radar (SAR) satellite in China. In order to explore the feasibility of GF-3 satellite in remote sensing interpretation and land-use remote sensing change detection, taking Guangzhou, China as a study area, the full polarimetric image of GF-3 satellite with 8 m resolution of two temporal as the data source. Firstly, the image is pre-processed by orthorectification, image registration and mosaic, and the land-use remote sensing digital orthophoto map (DOM) in 2017 is made according to the each county. Then the classification analysis and judgment of ground objects on the image are carried out by means of ArcGIS combining with the auxiliary data and using artificial visual interpretation, to determine the area of changes and the category of change objects. According to the unified change information extraction principle to extract change areas. Finally, the change detection results are compared with 3 m resolution TerraSAR-X data and 2 m resolution multi-spectral image, and the accuracy is evaluated. Experimental results show that the accuracy of the GF-3 data is over 75 % in detecting the change of ground objects, and the detection capability of new filling soil is better than that of TerraSAR-X data, verify the detection and monitoring capability of GF-3 data to the change information extraction, also, it shows that GF-3 can provide effective data support for the remote sensing detection of land resources.

A Comparative Distributed Evaluation of the NWS-RDHM using Shape Matching and Traditional Measures with In Situ and Remotely Sensed Information

NASA Astrophysics Data System (ADS)

KIM, J.; Bastidas, L. A.

2011-12-01

We evaluate, calibrate and diagnose the performance of National Weather Service RDHM distributed model over the Durango River Basin in Colorado using simultaneously in situ and remotely sensed information from different discharge gaging stations (USGS), information about snow cover (SCV) and snow water equivalent (SWE) in situ from several SNOTEL sites and snow information distributed over the catchment from remotely sensed information (NOAA-NASA). In the process of evaluation we attempt to establish the optimal degree of parameter distribution over the catchment by calibration. A multi-criteria approach based on traditional measures (RMSE) and similarity based pattern comparisons using the Hausdorff and Earth Movers Distance approaches is used for the overall evaluation of the model performance. These pattern based approaches (shape matching) are found to be extremely relevant to account for the relatively large degree of inaccuracy in the remotely sensed SWE (judged inaccurate in terms of the value but reliable in terms of the distribution pattern) and the high reliability of the SCV (yes/no situation) while at the same time allow for an evaluation that quantifies the accuracy of the model over the entire catchment considering the different types of observations. The Hausdorff norm, due to its intrinsically multi-dimensional nature, allows for the incorporation of variables such as the terrain elevation as one of the variables for evaluation. The EMD, because of its extremely high computational overburden, requires the mapping of the set of evaluation variables into a two dimensional matrix for computation.

Troubleshooting RSIG

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

A WebGIS system on the base of satellite data processing system for marine application

NASA Astrophysics Data System (ADS)

Gong, Fang; Wang, Difeng; Huang, Haiqing; Chen, Jianyu

2007-10-01

From 2002 to 2004, a satellite data processing system for marine application had been built up in State Key Laboratory of Satellite Ocean Environment Dynamics (Second Institute of Oceanography, State Oceanic Administration). The system received satellite data from TERRA, AQUA, NOAA-12/15/16/17/18, FY-1D and automatically generated Level3 products and Level4 products(products of single orbit and merged multi-orbits products) deriving from Level0 data, which is controlled by an operational control sub-system. Currently, the products created by this system play an important role in the marine environment monitoring, disaster monitoring and researches. Now a distribution platform has been developed on this foundation, namely WebGIS system for querying and browsing of oceanic remote sensing data. This system is based upon large database system-Oracle. We made use of the space database engine of ArcSDE and other middleware to perform database operation in addition. J2EE frame was adopted as development model, and Oracle 9.2 DBMS as database background and server. Simply using standard browsers(such as IE6.0), users can visit and browse the public service information that provided by system, including browsing for oceanic remote sensing data, and enlarge, contract, move, renew, traveling, further data inquiry, attribution search and data download etc. The system is still under test now. Founding of such a system will become an important distribution platform of Chinese satellite oceanic environment products of special topic and category (including Sea surface temperature, Concentration of chlorophyll, and so on), for the exaltation of satellite products' utilization and promoting the data share and the research of the oceanic remote sensing platform.

A Integrated Service Platform for Remote Sensing Image 3D Interpretation and Draughting based on HTML5

NASA Astrophysics Data System (ADS)

LIU, Yiping; XU, Qing; ZhANG, Heng; LV, Liang; LU, Wanjie; WANG, Dandi

2016-11-01

The purpose of this paper is to solve the problems of the traditional single system for interpretation and draughting such as inconsistent standards, single function, dependence on plug-ins, closed system and low integration level. On the basis of the comprehensive analysis of the target elements composition, map representation and similar system features, a 3D interpretation and draughting integrated service platform for multi-source, multi-scale and multi-resolution geospatial objects is established based on HTML5 and WebGL, which not only integrates object recognition, access, retrieval, three-dimensional display and test evaluation but also achieves collection, transfer, storage, refreshing and maintenance of data about Geospatial Objects and shows value in certain prospects and potential for growth.

a Cloud Boundary Detection Scheme Combined with Aslic and Cnn Using ZY-3, GF-1/2 Satellite Imagery

NASA Astrophysics Data System (ADS)

Guo, Z.; Li, C.; Wang, Z.; Kwok, E.; Wei, X.

2018-04-01

Remote sensing optical image cloud detection is one of the most important problems in remote sensing data processing. Aiming at the information loss caused by cloud cover, a cloud detection method based on convolution neural network (CNN) is presented in this paper. Firstly, a deep CNN network is used to extract the multi-level feature generation model of cloud from the training samples. Secondly, the adaptive simple linear iterative clustering (ASLIC) method is used to divide the detected images into superpixels. Finally, the probability of each superpixel belonging to the cloud region is predicted by the trained network model, thereby generating a cloud probability map. The typical region of GF-1/2 and ZY-3 were selected to carry out the cloud detection test, and compared with the traditional SLIC method. The experiment results show that the average accuracy of cloud detection is increased by more than 5 %, and it can detected thin-thick cloud and the whole cloud boundary well on different imaging platforms.

Assessing Structure and Condition of Temperate And Tropical Forests: Fusion of Terrestrial Lidar and Airborne Multi-Angle and Lidar Remote Sensing

NASA Astrophysics Data System (ADS)

Saenz, Edward J.

Forests provide vital ecosystem functions and services that maintain the integrity of our natural and human environment. Understanding the structural components of forests (extent, tree density, heights of multi-story canopies, biomass, etc.) provides necessary information to preserve ecosystem services. Increasingly, remote sensing resources have been used to map and monitor forests globally. However, traditional satellite and airborne multi-angle imagery only provide information about the top of the canopy and little about the forest structure and understory. In this research, we investigative the use of rapidly evolving lidar technology, and how the fusion of aerial and terrestrial lidar data can be utilized to better characterize forest stand information. We further apply a novel terrestrial lidar methodology to characterize a Hemlock Woolly Adelgid infestation in Harvard Forest, Massachusetts, and adapt a dynamic terrestrial lidar sampling scheme to identify key structural vegetation profiles of tropical rainforests in La Selva, Costa Rica.

Empirical validation and proof of added value of MUSICA's tropospheric δD remote sensing products

NASA Astrophysics Data System (ADS)

Schneider, M.; González, Y.; Dyroff, C.; Christner, E.; Wiegele, A.; Barthlott, S.; García, O. E.; Sepúlveda, E.; Hase, F.; Andrey, J.; Blumenstock, T.; Guirado, C.; Ramos, R.; Rodríguez, S.

2015-01-01

The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) integrates tropospheric water vapour isotopologue remote sensing and in situ observations. This paper presents a first empirical validation of MUSICA's H2O and δD remote sensing products, generated from ground-based FTIR (Fourier transform infrared), spectrometer and space-based IASI (infrared atmospheric sounding interferometer) observation. The study is made in the area of the Canary Islands in the subtropical northern Atlantic. As reference we use well calibrated in situ measurements made aboard an aircraft (between 200 and 6800 m a.s.l.) by the dedicated ISOWAT instrument and on the island of Tenerife at two different altitudes (at Izaña, 2370 m a.s.l., and at Teide, 3550 m a.s.l.) by two commercial Picarro L2120-i water isotopologue analysers. The comparison to the ISOWAT profile measurements shows that the remote sensors can well capture the variations in the water vapour isotopologues, and the scatter with respect to the in situ references suggests a δD random uncertainty for the FTIR product of much better than 45‰ in the lower troposphere and of about 15‰ for the middle troposphere. For the middle tropospheric IASI δD product the study suggests a respective uncertainty of about 15‰. In both remote sensing data sets we find a positive δD bias of 30-70‰. Complementing H2O observations with δD data allows moisture transport studies that are not possible with H2O observations alone. We are able to qualitatively demonstrate the added value of the MUSICA δD remote sensing data. We document that the δD-H2O curves obtained from the different in situ and remote sensing data sets (ISOWAT, Picarro at Izaña and Teide, FTIR, and IASI) consistently identify two different moisture transport pathways to the subtropical north eastern Atlantic free troposphere.

Multi-stage robust scheme for citrus identification from high resolution airborne images

NASA Astrophysics Data System (ADS)

Amorós-López, Julia; Izquierdo Verdiguier, Emma; Gómez-Chova, Luis; Muñoz-Marí, Jordi; Zoilo Rodríguez-Barreiro, Jorge; Camps-Valls, Gustavo; Calpe-Maravilla, Javier

2008-10-01

Identification of land cover types is one of the most critical activities in remote sensing. Nowadays, managing land resources by using remote sensing techniques is becoming a common procedure to speed up the process while reducing costs. However, data analysis procedures should satisfy the accuracy figures demanded by institutions and governments for further administrative actions. This paper presents a methodological scheme to update the citrus Geographical Information Systems (GIS) of the Comunidad Valenciana autonomous region, Spain). The proposed approach introduces a multi-stage automatic scheme to reduce visual photointerpretation and ground validation tasks. First, an object-oriented feature extraction process is carried out for each cadastral parcel from very high spatial resolution (VHR) images (0.5m) acquired in the visible and near infrared. Next, several automatic classifiers (decision trees, multilayer perceptron, and support vector machines) are trained and combined to improve the final accuracy of the results. The proposed strategy fulfills the high accuracy demanded by policy makers by means of combining automatic classification methods with visual photointerpretation available resources. A level of confidence based on the agreement between classifiers allows us an effective management by fixing the quantity of parcels to be reviewed. The proposed methodology can be applied to similar problems and applications.

Microwave remote sensing from space for earth resource surveys

NASA Technical Reports Server (NTRS)

1977-01-01

The concepts of radar remote sensing and microwave radiometry are discussed and their utility in earth resource sensing is examined. The direct relationship between the character of the remotely sensed data and the level of decision making for which the data are appropriate is considered. Applications of active and a passive microwave sensing covered include hydrology, land use, mapping, vegetation classification, environmental monitoring, coastal features and processes, geology, and ice and snow. Approved and proposed microwave sensors are described and the use of space shuttle as a development platform is evaluated.

Prediction of health levels by remote sensing

NASA Technical Reports Server (NTRS)

Rush, M.; Vernon, S.

1975-01-01

Measures of the environment derived from remote sensing were compared to census population/housing measures in their ability to discriminate among health status areas in two urban communities. Three hypotheses were developed to explore the relationships between environmental and health data. Univariate and multiple step-wise linear regression analyses were performed on data from two sample areas in Houston and Galveston, Texas. Environmental data gathered by remote sensing were found to equal or surpass census data in predicting rates of health outcomes. Remote sensing offers the advantages of data collection for any chosen area or time interval, flexibilities not allowed by the decennial census.

Recent observations of carbon and sulfur gas emissions from Tavurvur, Bagana and Ulawun (Papua New Guinea) with a combination of ground- and air-borne direct and remote sensing techniques

NASA Astrophysics Data System (ADS)

Arellano, Santiago; Galle, Bo; Mulina, Kila; Wallius, Julia; McCormick, Brendan; Salem, Lois; D'aleo, Roberto; Itikarai, Ima; Tirpitz, Lukas; Bobrowski, Nicole; Aiuppa, Alessandro

2017-04-01

Satellite observations reveal that volcanoes from Papua New Guinea contributed with ca. 15{%} of the global emission of volcanic sulfur dioxide (SO2) during the period 2005-2014. Relatively little is known about their carbon dioxide (CO2) outputs and more recent levels and dynamics of degassing activity. During September 2016 we conducted measurements of the CO2/SO2 ratio and the SO2 flux from Tavurvur, Bagana and Ulawun volcanoes using a combination of remote sensing and direct sampling techniques. Tavurvur exhibits low-level passive degassing from a modestly active vent and few other intra-crater fumaroles, which made access possible for direct measurements of the CO2/SO2 ratio with a compact Multi-GAS instrument. A wide-field of view pointing DOAS monitor was deployed for longer term monitoring of the SO2 flux from a distance of about 2 km. Bagana degasses continuously with occasional emissions of ash, and its SO2 flux, plume velocity and height was constrained by simultaneous scanning and dual-beam DOAS measurements. Molar ratios in the plume of Bagana were measured by the compact Multi-GAS aboard a multi-rotor UAV, up to a height of 1.6 km above ground. Ulawun showed continuous passive degassing and measurements with the UAV, up to an altitude of ca. 1.8 km, and mobile-DOAS traverses from a car were used to constrain its gas emission. Here we present an overview of the challenging conditions, measurement strategies and results of this campaign that forms part of the ongoing international effort DECADE aiming to better quantify the global gas emission of carbon- and sulfur containing species from volcanoes.

RSIG Data Inventory

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RSIG Video Demonstrations

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

How RSIG Regrids Data

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

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

PubMed

Li, Linyi; Xu, Tingbao; Chen, Yun

2017-01-01

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.

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

PubMed Central

Xu, Tingbao; Chen, Yun

2017-01-01

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. PMID:28761440

Impact of 3D Canopy Structure on Remote Sensing Vegetation Index and Solar Induced Chlorophyll Fluorescence

NASA Astrophysics Data System (ADS)

Zeng, Y.; Berry, J. A.; Jing, L.; Qinhuo, L.

2017-12-01

Terrestrial ecosystem plays a critical role in removing CO2 from atmosphere by photosynthesis. Remote sensing provides a possible way to monitor the Gross Primary Production (GPP) at the global scale. Vegetation Indices (VI), e.g., NDVI and NIRv, and Solar Induced Fluorescence (SIF) have been widely used as a proxy for GPP, while the impact of 3D canopy structure on VI and SIF has not be comprehensively studied yet. In this research, firstly, a unified radiative transfer model for visible/near-infrared reflectance and solar induced chlorophyll fluorescence has been developed based on recollision probability and directional escape probability. Then, the impact of view angles, solar angles, weather conditions, leaf area index, and multi-layer leaf angle distribution (LAD) on VI and SIF has been studied. Results suggest that canopy structure plays a critical role in distorting pixel-scale remote sensing signal from leaf-scale scattering. In thin canopy, LAD affects both of the remote sensing estimated GPP and real GPP, while in dense canopy, SIF variations are mainly due to canopy structure, instead of just due to physiology. At the microscale, leaf angle reflects the plant strategy to light on the photosynthesis efficiency, and at the macroscale, a priori knowledge of leaf angle distribution for specific species can improve the global GPP estimation by remote sensing.

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost

PubMed Central

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited. PMID:26528811

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost.

PubMed

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited.

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

PubMed

Zou, Zhengxia; Shi, Zhenwei

2018-03-01

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

Remote Sensing based multi-temporal observation of North Korea mining activities : A case study of Rakyeon mine

NASA Astrophysics Data System (ADS)

Lim, J. H.; Yu, J.; Koh, S. M.; Lee, G.

2017-12-01

Mining is a major industrial business of North Korea accounting for significant portion of an export for North Korean economy. However, due to its veiled political system, details of mining activities of North Korea is rarely known. This study investigated mining activities of Rakyeon Au-Ag mine, North Korea based on remote sensing based multi-temporal observation. To monitor the mining activities, CORONA data acquired in 1960s and 1970s, SPOT and Landsat data acquired in 1980s and 1990s and KOMPSAT-2 data acquired in 2010s are utilized. The results show that mining activities of Rakyeon mine continuously carried out for the observation period expanding tailing areas of the mine. However, its expanding rate varies between the period related to North Korea's economic and political situations.

Incorporating a constrained optimization algorithm into remote sensing/precision agriculture methodology

NASA Astrophysics Data System (ADS)

Moreenthaler, George W.; Khatib, Nader; Kim, Byoungsoo

2003-08-01

For two decades now, the use of Remote Sensing/Precision Agriculture to improve farm yields while reducing the use of polluting chemicals and the limited water supply has been a major goal. With world population growing exponentially, arable land being consumed by urbanization, and an unfavorable farm economy, farm efficiency must increase to meet future food requirements and to make farming a sustainable, profitable occupation. "Precision Agriculture" refers to a farming methodology that applies nutrients and moisture only where and when they are needed in the field. The real goal is to increase farm profitability by identifying the additional treatments of chemicals and water that increase revenues more than they increase costs and do no exceed pollution standards (constrained optimization). Even though the economic and environmental benefits appear to be great, Remote Sensing/Precision Agriculture has not grown as rapidly as early advocates envisioned. Technology for a successful Remote Sensing/Precision Agriculture system is now in place, but other needed factors have been missing. Commercial satellite systems can now image the Earth (multi-spectrally) with a resolution as fine as 2.5 m. Precision variable dispensing systems using GPS are now available and affordable. Crop models that predict yield as a function of soil, chemical, and irrigation parameter levels have been developed. Personal computers and internet access are now in place in most farm homes and can provide a mechanism for periodically disseminating advice on what quantities of water and chemicals are needed in specific regions of each field. Several processes have been selected that fuse the disparate sources of information on the current and historic states of the crop and soil, and the remaining resource levels available, with the critical decisions that farmers are required to make. These are done in a way that is easy for the farmer to understand and profitable to implement. A "Constrained Optimization Algorithm" to further improve these processes will be presented. The objective function of the model will used to maximize the farmer's profit via increasing yields while decreasing environmental damage and decreasing applications of costly treatments. This model will incorporate information from Remote Sensing, from in-situ weather sources, from soil history, and from tacit farmer knowledge of the relative productivity of selected "Management Zones" of the farm, to provide incremental advice throughout the growing season on the optimum usage of water and chemical treatments.

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling.

PubMed

Stoy, Paul C; Quaife, Tristan

2015-01-01

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling

PubMed Central

Stoy, Paul C.; Quaife, Tristan

2015-01-01

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes. PMID:26067835

The Real-Time Monitoring Service Platform for Land Supervision Based on Cloud Integration

NASA Astrophysics Data System (ADS)

Sun, J.; Mao, M.; Xiang, H.; Wang, G.; Liang, Y.

2018-04-01

Remote sensing monitoring has become the important means for land and resources departments to strengthen supervision. Aiming at the problems of low monitoring frequency and poor data currency in current remote sensing monitoring, this paper researched and developed the cloud-integrated real-time monitoring service platform for land supervision which enhanced the monitoring frequency by acquiring the domestic satellite image data overall and accelerated the remote sensing image data processing efficiency by exploiting the intelligent dynamic processing technology of multi-source images. Through the pilot application in Jinan Bureau of State Land Supervision, it has been proved that the real-time monitoring technical method for land supervision is feasible. In addition, the functions of real-time monitoring and early warning are carried out on illegal land use, permanent basic farmland protection and boundary breakthrough in urban development. The application has achieved remarkable results.

Geometric representation methods for multi-type self-defining remote sensing data sets

NASA Technical Reports Server (NTRS)

Anuta, P. E.

1980-01-01

Efficient and convenient representation of remote sensing data is highly important for an effective utilization. The task of merging different data types is currently dealt with by treating each case as an individual problem. A description is provided of work which is carried out to standardize the multidata merging process. The basic concept of the new approach is that of the self-defining data set (SDDS). The creation of a standard is proposed. This standard would be such that data which may be of interest in a large number of earth resources remote sensing applications would be in a format which allows convenient and automatic merging. Attention is given to details regarding the multidata merging problem, a geometric description of multitype data sets, image reconstruction from track-type data, a data set generation system, and an example multitype data set.

ATHENA: Remote Sensing Science Center for Cultural Heritage in Cyprus

NASA Astrophysics Data System (ADS)

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

2016-04-01

The Cultural Heritage (CH) sector, especially those of monuments and sites has always been facing a number of challenges from environmental pressure, pollution, human intervention from tourism to destruction by terrorism.Within this context, CH professionals are seeking to improve currently used methodologies, in order to better understand, protect and valorise the common European past and common identity. "ATHENA" H2020-TWINN-2015 project will seek to improve and expand the capabilities of the Cyprus University of Technology, involving professionals dealing with remote sensing technologies for supporting CH sector from the National Research Center of Italy (CNR) and German Aerospace Centre (DLR). The ATHENA centre will be devoted to the development, introduction and systematic use of advanced remote sensing science and technologies in the field of archaeology, built cultural heritage, their multi-temporal analysis and interpretation and the distant monitoring of their natural and anthropogenic environment in the area of Eastern Mediterranean.

Application of remote sensing to estimating soil erosion potential

NASA Technical Reports Server (NTRS)

Morris-Jones, D. R.; Kiefer, R. W.

1980-01-01

A variety of remote sensing data sources and interpretation techniques has been tested in a 6136 hectare watershed with agricultural, forest and urban land cover to determine the relative utility of alternative aerial photographic data sources for gathering the desired land use/land cover data. The principal photographic data sources are high altitude 9 x 9 inch color infrared photos at 1:120,000 and 1:60,000 and multi-date medium altitude color and color infrared photos at 1:60,000. Principal data for estimating soil erosion potential include precipitation, soil, slope, crop, crop practice, and land use/land cover data derived from topographic maps, soil maps, and remote sensing. A computer-based geographic information system organized on a one-hectare grid cell basis is used to store and quantify the information collected using different data sources and interpretation techniques. Research results are compared with traditional Universal Soil Loss Equation field survey methods.

The depiction of Alboran Sea Gyre during Donde Va? using remote sensing and conventional data

NASA Technical Reports Server (NTRS)

Laviolette, P. E.

1984-01-01

Experienced oceanographic investigators have come to realize that remote sensing techniques are most successful when applied as part of programs of integrated measurements aimed at solving specific oceanographic problems. A good example of such integration occurred during the multi-platform international experiment, Donde Va? in the Alboran Sea during the period June through October, 1982. The objective of Donde Va? was to derive the interrelationship of the Atlantic waters entering the Mediterranean Sea and the Alboran Sea Gyre. The experimental plan conceived solely with this objective in mind consisted of a variety of remote sensing and conventional platforms: three ships, three aircraft, five current moorings, two satellites and a specialized beach radar (CODAR). Integrated analyses of these multiple-data sets are still being conducted. However, the initial results show detailed structure of the incoming Atlantic jet and Alboran Sea Gyre that would not have been possible by conventional means.

a Data Field Method for Urban Remotely Sensed Imagery Classification Considering Spatial Correlation

NASA Astrophysics Data System (ADS)

Zhang, Y.; Qin, K.; Zeng, C.; Zhang, E. B.; Yue, M. X.; Tong, X.

2016-06-01

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

Monitoring Ecosystem Dynamics Ecosystem Using Hyperspectral Reflectance and a Robotic Tram System in Barrow Alaska

NASA Astrophysics Data System (ADS)

Goswami, S.; Gamon, J. A.; Tweedie, C. E.

2012-12-01

Understanding the future state of the earth system requires improved knowledge of ecosystem dynamics and long term observations of how ecosystem structures and functions are being impacted by global change. Improving remote sensing methods is essential for such advancement because satellite remote sensing is the only means by which landscape to continental-scale change can be observed. The Arctic appears to be impacted by climate change more than any other region on Earth. Arctic terrestrial ecosystems comprise only 6% of the land surface area on Earth yet contain an estimated 25% of global soil organic carbon, most of which is stored in permafrost. If projected increases in plant productivity do not offset forecast losses of soil carbon to the atmosphere as greenhouse gases, regional to global greenhouse warming could be enhanced. Soil moisture is an important control of land-atmosphere carbon exchange in arctic terrestrial ecosystems. However, few studies to date have examined using remote sensing, or developed remote sensing methods for observing the complex interplay between soil moisture and plant phenology and productivity in arctic landscapes. This study was motivated by this knowledge gap and addressed the following questions as a contribution to a large scale, multi investigator flooding and draining experiment funded by the National Science Foundation near Barrow, Alaska from 2005 - 2009. 1. How can optical remote sensing be used to monitor the surface hydrology of arctic landscapes? 2. What are the spatio-temporal dynamics of land-surface phenology (NDVI) in the study area and do hydrological treatment has any effect on inter-annual patterns? A new spectral index, the normalized difference surface water index (NDSWI) was developed and tested at multiple spatial and temporal scales. NDSWI uses the 460nm (blue) and 1000nm (IR) bands and was developed to capture surface hydrological dynamics in the study area using the robotic tram system. When applied to high spatial resolution satellite imagery, NDSWI was also able to capture changes in surface hydrology at the landscape scale. Interannual patterns of landsurface phenology (measured with the normalized difference vegetation index - NDVI) unexpectedly lacked marked differences under experimental conditions. Measurement of NDVI was, however, compromised when WTD was above ground level. NDVI and NDSWI were negatively correlated when WTD was above ground level, which held when scaled to MODIS imagery collected from satellite, suggesting that published findings showing a 'greening of the Arctic' may be related to a 'drying of the Arctic' in landscapes dominated by vegetated landscapes where WTD is close to ground level.

Applying remote sensing and GIS techniques in solving rural county information needs

NASA Technical Reports Server (NTRS)

Johannsen, Chris J.; Fernandez, R. Norberto; Lozano-Garcia, D. Fabian

1992-01-01

The project designed was to acquaint county government officials and their clientele with remote sensing and GIS products that contain information about land conditions and land use. Other users determined through the course of this project were federal agencies working at the county level, agricultural businesses and others in need of spatial information. The specific project objectives were: (1) to investigate the feasibility of using remotely sensed data to identify and quantify specific land cover categories and conditions for purposes of tax assessment, cropland area measurements and land use evaluation; (2) to investigate the use of satellite remote sensing data as an aid in assessing soil management practices; and (3) to evaluate the use of remotely sensed data to assess soil resources and conditions which affect productivity.

Advances in Spatial Data Infrastructure, Acquisition, Analysis, Archiving and Dissemination

NASA Technical Reports Server (NTRS)

Ramapriyan, Hampapuran K.; Rochon, Gilbert L.; Duerr, Ruth; Rank, Robert; Nativi, Stefano; Stocker, Erich Franz

2010-01-01

The authors review recent contributions to the state-of-thescience and benign proliferation of satellite remote sensing, spatial data infrastructure, near-real-time data acquisition, analysis on high performance computing platforms, sapient archiving, multi-modal dissemination and utilization for a wide array of scientific applications. The authors also address advances in Geoinformatics and its growing ubiquity, as evidenced by its inclusion as a focus area within the American Geophysical Union (AGU), European Geosciences Union (EGU), as well as by the evolution of the IEEE Geoscience and Remote Sensing Society's (GRSS) Data Archiving and Distribution Technical Committee (DAD TC).

Supporting Remote Sensing Research with Small Unmanned Aerial Systems

NASA Astrophysics Data System (ADS)

Anderson, R. C.; Shanks, P. C.; Kritis, L. A.; Trani, M. G.

2014-11-01

We describe several remote sensing research projects supported with small Unmanned Aerial Systems (sUAS) operated by the NGA Basic and Applied Research Office. These sUAS collections provide data supporting Small Business Innovative Research (SBIR), NGA University Research Initiative (NURI), and Cooperative Research And Development Agreements (CRADA) efforts in addition to inhouse research. Some preliminary results related to 3D electro-optical point clouds are presented, and some research goals discussed. Additional details related to the autonomous operational mode of both our multi-rotor and fixed wing small Unmanned Aerial System (sUAS) platforms are presented.

Introduction to Remote Sensing Image Registration

NASA Technical Reports Server (NTRS)

Le Moigne, Jacqueline

2017-01-01

For many applications, accurate and fast image registration of large amounts of multi-source data is the first necessary step before subsequent processing and integration. Image registration is defined by several steps and each step can be approached by various methods which all present diverse advantages and drawbacks depending on the type of data, the type of applications, the a prior information known about the data and the type of accuracy that is required. This paper will first present a general overview of remote sensing image registration and then will go over a few specific methods and their applications

Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations

DOE PAGES

Gonçalves, Fabio; Treuhaft, Robert; Law, Beverly; ...

2017-01-07

Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertaintymore » that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Lastly, our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches.« less

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

NASA Astrophysics Data System (ADS)

Kittel, Cecile; Bauer-Gottwein, Peter; Nielsen, Karina; Tøttrup, Christian

2017-04-01

Knowledge on hydrological regimes of river basins is crucial for water management. However, data requirements often limit the applicability of hydrological models in basins with scarce in-situ data. Remote sensing provides a unique possibility to acquire information on hydrological variables in these basins. This study explores how multi-mission remote sensing data can inform a hydrological model. The Ogooué basin in Gabon is used as study area. No previous modelling efforts have been conducted for the basin and only historical flow and precipitation observations are available. Publicly available remote sensing observations are used to parametrize, force, calibrate and validate a hydrological model of the Ogooué. The modelling framework used in the study, is a lumped conceptual rainfall-runoff model based on the Budyko framework coupled to a Muskingum routing scheme. Precipitation is a crucial driver of the land-surface water balance, therefore two satellite-based rainfall estimates, Tropical Rainfall Measuring Mission (TRMM) product 3B42 version 7 and Famine Early Warning System - Rainfall Estimate (FEWS-RFE), are compared. The comparison shows good seasonal and spatial agreement between the products; however, TRMM consistently predicts significantly more precipitation: 1726 mm on average per year against 1556 mm for FEWS-RFE. Best modeling results are obtained with the TRMM precipitation forcing. Model calibration combines historical in-situ flow observations and GRACE total water storage observations using the Jet Propulsion Laboratory (JPL) mascon solution in a multi-objective approach. The two models are calibrated using flow duration curves and climatology benchmarks to overcome the lack of simultaneity between simulated and observed discharge. The objectives are aggregated into a global objective function, and the models are calibrated using the Shuffled Complex Evolution Algorithm. Water height observations from drifting orbit altimetry missions are extracted along the river line, using a detailed water mask based on Sentinel-1 SAR imagery. 1399 single CryoSat-2 altimetry observations and 48 ICESat observations are acquired. Additionally, water heights have been measured by the repeat-orbit satellite missions Envisat and Jason-2 at 12 virtual stations along the river. The four missions show generally good agreement in terms of mean annual water height amplitudes. The altimetry observations are used to validate the hydrological model of the Ogooué River. By combining hydrological modelling and remote sensing, new information on an otherwise unstudied basin is obtained. The study shows the potential of using remote sensing observations to parameterize, force, calibrate and validate models of poorly gauged river basins. Specifically, the study shows how Sentinel-1 SAR imagery supports the extraction of satellite altimetry data over rivers. The model can be used to assess climate change scenarios, evaluate hydraulic infrastructure development projects and predict the impact of irrigation diversions.

Serving Satellite Remote Sensing Data to User Community through the OGC Interoperability Protocols

NASA Astrophysics Data System (ADS)

di, L.; Yang, W.; Bai, Y.

2005-12-01

Remote sensing is one of the major methods for collecting geospatial data. Hugh amount of remote sensing data has been collected by space agencies and private companies around the world. For example, NASA's Earth Observing System (EOS) is generating more than 3 Tb of remote sensing data per day. The data collected by EOS are processed, distributed, archived, and managed by the EOS Data and Information System (EOSDIS). Currently, EOSDIS is managing several petabytes of data. All of those data are not only valuable for global change research, but also useful for local and regional application and decision makings. How to make the data easily accessible to and usable by the user community is one of key issues for realizing the full potential of these valuable datasets. In the past several years, the Open Geospatial Consortium (OGC) has developed several interoperability protocols aiming at making geospatial data easily accessible to and usable by the user community through Internet. The protocols particularly relevant to the discovery, access, and integration of multi-source satellite remote sensing data are the Catalog Service for Web (CS/W) and Web Coverage Services (WCS) Specifications. The OGC CS/W specifies the interfaces, HTTP protocol bindings, and a framework for defining application profiles required to publish and access digital catalogues of metadata for geographic data, services, and related resource information. The OGC WCS specification defines the interfaces between web-based clients and servers for accessing on-line multi-dimensional, multi-temporal geospatial coverage in an interoperable way. Based on definitions by OGC and ISO 19123, coverage data include all remote sensing images as well as gridded model outputs. The Laboratory for Advanced Information Technology and Standards (LAITS), George Mason University, has been working on developing and implementing OGC specifications for better serving NASA Earth science data to the user community for many years. We have developed the NWGISS software package that implements multiple OGC specifications, including OGC WMS, WCS, CS/W, and WFS. As a part of NASA REASON GeoBrain project, the NWGISS WCS and CS/W servers have been extended to provide operational access to NASA EOS data at data pools through OGC protocols and to make both services chainable in the web-service chaining. The extensions in the WCS server include the implementation of WCS 1.0.0 and WCS 1.0.2, and the development of WSDL description of the WCS services. In order to find the on-line EOS data resources, the CS/W server is extended at the backend to search metadata in NASA ECHO. This presentation reports those extensions and discuss lessons-learned on the implementation. It also discusses the advantage, disadvantages, and future improvement of OGC specifications, particularly the WCS.

Photogrammetry - Remote Sensing and Geoinformation

NASA Astrophysics Data System (ADS)

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

2012-07-01

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

Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure

Treesearch

T. Ryan McCarley; Crystal A. Kolden; Nicole M. Vaillant; Andrew T. Hudak; Alistair M. S. Smith; Brian M. Wing; Bryce S. Kellogg; Jason Kreitler

2017-01-01

Measuring post-fire effects at landscape scales is critical to an ecological understanding of wildfire effects. Predominantly this is accomplished with either multi-spectral remote sensing data or through ground-based field sampling plots.While these methods are important, field data is usually limited to opportunistic post-fire observations, and spectral data often...

Remote Sensing and the Kyoto Protocol: A Workshop Summary

NASA Technical Reports Server (NTRS)

Rosenqvist, Ake; Imhoff, Marc; Milne, Anthony; Dobson, Craig

2000-01-01

The Kyoto Protocol to the United Nations Framework Convention on Climate Change contains quantified, legally binding commitments to limit or reduce greenhouse gas emissions to 1990 levels and allows carbon emissions to be balanced by carbon sinks represented by vegetation. The issue of using vegetation cover as an emission offset raises a debate about the adequacy of current remote sensing systems and data archives to both assess carbon stocks/sinks at 1990 levels, and monitor the current and future global status of those stocks. These concerns and the potential ratification of the Protocol among participating countries is stimulating policy debates and underscoring a need for the exchange of information between the international legal community and the remote sensing community. On October 20-22 1999, two working groups of the International Society for Photogrammetry and Remote Sensing (ISPRS) joined with the University of Michigan (Michigan, USA) to convene discussions on how remote sensing technology could contribute to the information requirements raised by implementation of, and compliance with, the Kyoto Protocol. The meeting originated as a joint effort between the Global Monitoring Working Group and the Radar Applications Working Group in Commission VII of the ISPRS, co-sponsored by the University of Michigan. Tile meeting was attended by representatives from national government agencies and international organizations and academic institutions. Some of the key themes addressed were: (1) legal aspects of transnational remote sensing in the context of the Kyoto Protocol; (2) a review of the current and future and remote sensing technologies that could be applied to the Kyoto Protocol; (3) identification of areas where additional research is needed in order to advance and align remote sensing technology with the requirements and expectations of the Protocol; and 94) the bureaucratic and research management approaches needed to align the remote sensing community with both the science and policy communities.

Web Access to RSIG Data

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

Tips for Running RSIG2D

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

Ambiguity of Quality in Remote Sensing Data

NASA Technical Reports Server (NTRS)

Lynnes, Christopher; Leptoukh, Greg

2010-01-01

This slide presentation reviews some of the issues in quality of remote sensing data. Data "quality" is used in several different contexts in remote sensing data, with quite different meanings. At the pixel level, quality typically refers to a quality control process exercised by the processing algorithm, not an explicit declaration of accuracy or precision. File level quality is usually a statistical summary of the pixel-level quality but is of doubtful use for scenes covering large areal extents. Quality at the dataset or product level, on the other hand, usually refers to how accurately the dataset is believed to represent the physical quantities it purports to measure. This assessment often bears but an indirect relationship at best to pixel level quality. In addition to ambiguity at different levels of granularity, ambiguity is endemic within levels. Pixel-level quality terms vary widely, as do recommendations for use of these flags. At the dataset/product level, quality for low-resolution gridded products is often extrapolated from validation campaigns using high spatial resolution swath data, a suspect practice at best. Making use of quality at all levels is complicated by the dependence on application needs. We will present examples of the various meanings of quality in remote sensing data and possible ways forward toward a more unified and usable quality framework.

Calibration of a distributed hydrologic model for six European catchments using remote sensing data

NASA Astrophysics Data System (ADS)

Stisen, S.; Demirel, M. C.; Mendiguren González, G.; Kumar, R.; Rakovec, O.; Samaniego, L. E.

2017-12-01

While observed streamflow has been the single reference for most conventional hydrologic model calibration exercises, the availability of spatially distributed remote sensing observations provide new possibilities for multi-variable calibration assessing both spatial and temporal variability of different hydrologic processes. In this study, we first identify the key transfer parameters of the mesoscale Hydrologic Model (mHM) controlling both the discharge and the spatial distribution of actual evapotranspiration (AET) across six central European catchments (Elbe, Main, Meuse, Moselle, Neckar and Vienne). These catchments are selected based on their limited topographical and climatic variability which enables to evaluate the effect of spatial parameterization on the simulated evapotranspiration patterns. We develop a European scale remote sensing based actual evapotranspiration dataset at a 1 km grid scale driven primarily by land surface temperature observations from MODIS using the TSEB approach. Using the observed AET maps we analyze the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mHM model. This model allows calibrating one-basin-at-a-time or all-basins-together using its unique structure and multi-parameter regionalization approach. Results will indicate any tradeoffs between spatial pattern and discharge simulation during model calibration and through validation against independent internal discharge locations. Moreover, added value on internal water balances will be analyzed.

Research and Application of Remote Sensing Monitoring Method for Desertification Land Under Time and Space Constraints

NASA Astrophysics Data System (ADS)

Zhang, Nannnan; Wang, Rongbao; Zhang, Feng

2018-04-01

Serious land desertification and sandified threaten the urban ecological security and the sustainable economic and social development. In recent years, a large number of mobile sand dunes in Horqin sandy land flow into the northwest of Liaoning Province under the monsoon, make local agriculture suffer serious harm. According to the characteristics of desertification land in northwestern Liaoning, based on the First National Geographical Survey data, the Second National Land Survey data and the 1984-2014 Landsat satellite long time sequence data and other multi-source data, we constructed a remote sensing monitoring index system of desertification land in Northwest Liaoning. Through the analysis of space-time-spectral characteristics of desertification land, a method for multi-spectral remote sensing image recognition of desertification land under time-space constraints is proposed. This method was used to identify and extract the distribution and classification of desertification land of Chaoyang City (a typical citie of desertification in northwestern Liaoning) in 2008 and 2014, and monitored the changes and transfers of desertification land from 2008 to 2014. Sandification information was added to the analysis of traditional landscape changes, improved the analysis model of desertification land landscape index, and the characteristics and laws of landscape dynamics and landscape pattern change of desertification land from 2008 to 2014 were analyzed and revealed.

Multi-sensor data processing method for improved satellite retrievals

NASA Astrophysics Data System (ADS)

Fan, Xingwang

2017-04-01

Satellite remote sensing has provided massive data that improve the overall accuracy and extend the time series of environmental studies. In reflective solar bands, satellite data are related to land surface properties via radiative transfer (RT) equations. These equations generally include sensor-related (calibration coefficients), atmosphere-related (aerosol optical thickness) and surface-related (surface reflectance) parameters. It is an ill-posed problem to solve three parameters with only one RT equation. Even if there are two RT equations (dual-sensor data), the problem is still unsolvable. However, a robust solution can be obtained when any two parameters are known. If surface and atmosphere are known, sensor intercalibration can be performed. For example, the Advanced Very High Resolution Radiometer (AVHRR) was calibrated to the MODerate-resolution Imaging Spectroradiometer (MODIS) in Fan and Liu (2014) [Fan, X., and Liu, Y. (2014). Quantifying the relationship between intersensor images in solar reflective bands: Implications for intercalibration. IEEE Transactions on Geoscience and Remote Sensing, 52(12), 7727-7737.]. If sensor and surface are known, atmospheric data can be retrieved. For example, aerosol data were retrieved using tandem TERRA and AQUA MODIS images in Fan and Liu (2016a) [Fan, X., and Liu, Y. (2016a). Exploiting TERRA-AQUA MODIS relationship in the reflective solar bands for aerosol retrieval. Remote Sensing, 8(12), 996.]. If sensor and atmosphere are known, data consistency can be obtained. For example, Normalized Difference Vegetation Index (NDVI) data were intercalibrated among coarse-resolution sensors in Fan and Liu (2016b) [Fan, X., and Liu, Y. (2016b). A global study of NDVI difference among moderate-resolution satellite sensors. ISPRS Journal of Photogrammetry and Remote Sensing, 121, 177-191.], and among fine-resolution sensors in Fan and Liu (2017) [Fan, X., and Liu, Y. (2017). A generalized model for intersensor NDVI calibration and its comparison with regression approaches. IEEE Transactions on Geoscience and Remote Sensing, 55(3), doi: 10.1109/TGRS.2016.2635802.]. These studies demonstrate the success of multi-sensor data and novel methods in the research domain of geoscience. These data will benefit remote sensing of terrestrial parameters in decadal timescales, such as soil salinity content in Fan et al. (2016) [Fan, X., Weng, Y., and Tao, J. (2016). Towards decadal soil salinity mapping using Landsat time series data. International Journal of Applied Earth Observation and Geoinformation, 52, 32-41.].

Integration of remote sensing and hydrologic modeling through multi-disciplinary semiarid field campaigns: Moonsoon 1990, Walnut Gulch 1992, and SALSA-MEX

NASA Technical Reports Server (NTRS)

Moran, M. S.; Goodrich, D. C.; Kustas, W. P.

1994-01-01

A research and modeling strategy is presented for development of distributed hydrologic models given by a combination of remotely sensed and ground based data. In support of this strategy, two experiments Moonsoon'90 and Walnut Gulch'92 were conducted in a semiarid rangeland southeast of Tucson, Arizona, (U.S.) and a third experiment, the SALSA-MEX (Semi Arid Land Surface Atmospheric Mountain Experiment) was proposed. Results from the Moonsoon'90 experiment substantially advanced the understanding of the hydrologic and atmospheric fluxes in an arid environment and provided insight into the use of remote sensing data for hydrologic modeling. The Walnut Gulch'92 experiment addressed the seasonal hydrologic dynamics of the region and the potential of combined optical microwave remote sensing for hydrologic applications. SALSA-MEX will combine measurements and modeling to study hydrologic processes influenced by surrounding mountains, such as enhanced precipitation, snowmelt and recharge to ground water aquifers. The results from these experiments, along with the extensive experimental data bases, should aid the research community in large scale modeling of mass and energy exchanges across the soil-plant-atmosphere interface.

Tracking plant physiological properties from multi-angular tower-based remote sensing.

PubMed

Hilker, Thomas; Gitelson, Anatoly; Coops, Nicholas C; Hall, Forrest G; Black, T Andrew

2011-04-01

Imaging spectroscopy is a powerful technique for monitoring the biochemical constituents of vegetation and is critical for understanding the fluxes of carbon and water between the land surface and the atmosphere. However, spectral observations are subject to the sun-observer geometry and canopy structure which impose confounding effects on spectral estimates of leaf pigments. For instance, the sun-observer geometry influences the spectral brightness measured by the sensor. Likewise, when considering pigment distribution at the stand level scale, the pigment content observed from single view angles may not necessarily be representative of stand-level conditions as some constituents vary as a function of the degree of leaf illumination and are therefore not isotropic. As an alternative to mono-angle observations, multi-angular remote sensing can describe the anisotropy of surface reflectance and yield accurate information on canopy structure. These observations can also be used to describe the bi-directional reflectance distribution which then allows the modeling of reflectance independently of the observation geometry. In this paper, we demonstrate a method for estimating pigment contents of chlorophyll and carotenoids continuously over a year from tower-based, multi-angular spectro-radiometer observations. Estimates of chlorophyll and carotenoid content were derived at two flux-tower sites in western Canada. Pigment contents derived from inversion of a CR model (PROSAIL) compared well to those estimated using a semi-analytical approach (r(2) = 0.90 and r(2) = 0.69, P < 0.05 for both sites, respectively). Analysis of the seasonal dynamics indicated that net ecosystem productivity was strongly related to total canopy chlorophyll content at the deciduous site (r(2) = 0.70, P < 0.001), but not at the coniferous site. Similarly, spectral estimates of photosynthetic light-use efficiency showed strong seasonal patterns in the deciduous stand, but not in conifers. We conclude that multi-angular, spectral observations can play a key role in explaining seasonal dynamics of fluxes of carbon and water and provide a valuable addition to flux-tower-based networks.

Spectral data analysis of rock and mineral in Hatu Western Junggar Region, Xinjiang

NASA Astrophysics Data System (ADS)

Wang, Shanshan; Zhou, Kefa; Zhang, Nannan; Wang, Jinlin

2014-11-01

Mineral resources are important material basis for the survival and development of human society. The development of hyperspectral remote sensing technology, which has made direct identification of minerals or mineral aggregates become possible, paves a new way for the application of remote sensing geology. The West Junggar region is located Xinjiang west verge of Junggar, with ore-forming geological conditions be richly endowed by nature and huge prospecting potentiality. The area has very good outcrop exposure with almost no vegetation cover, which is a natural test new method of remote sensing geological exploration. The characteristic of rock and mineral spectrum is not only the physical base of geological remote sensing technical application but also the base of the quantificational analysis of geological remote sensing, and the study of reflectance spectrum is the main content in the basic research of remote sensing. In this study, we collected the outdoor and indoor reflectance spectrum of rocks and minerals by using a spectroradiometer (ASD FieldSpec FR, ASD, USA), which band extent varied from 350 to 2,500 nm. Basin on a great deal of spectral data for different kinds of rocks and minerals, we have analyzed the spectrum characteristics and change of seven typical mineral rocks. According to the actual conditions, we analyzed the data noise characteristic of the spectrum and got rid of the noise. Meanwhile, continuum removed technology was used to remove the environmental background influence. Finally, in order to take full advantage of multi-spectrum data, ground information is absolutely necessary, and it is important to build a representative spectral library. We build the spectral library of rocks and minerals in Hatu, which can be used for features investigation, mineral classification, mineral mapping and geological prospecting in Hatu Western Junggar region by remote sensing. The result of this research will be significant to the research of accelerating Western Junggar mineral exploration.

The Increasing Use of Remote Sensing Data in Studying the Climatological Impacts on Public Health

NASA Astrophysics Data System (ADS)

Kempler, S.; Benedict, K. K.; Ceccato, P.; Golden, M.; Maxwell, S.; Morain, S.; Soebiyanto, R.; Tong, D.

2011-12-01

One of the most fortunate outcomes of the capture and transformation of remote sensing data into applied information is their usefulness and impacts to better understanding climatological impacts on public health. Today, with petabytes of remote sensing data providing global coverage of climatological parameters, public health research and policy decision makers have an unprecedented (and growing) data record that relates the effects of climatic parameters, such as rainfall, heat, soil moisture, etc. to incidences and spread of disease, as well as predictive modeling. In addition, tools and services that specifically serve public health researchers and respondents have grown in response to the needs of the these information users. This presentation provides: A perspective of the use of remote sensing data in public health research; NASA funded systems developed to facilitate specific public health decision and public support services, and: Insights on remote sensing data and information services that are available for public health studies and decision making. After providing a review of the use of remote sensing data, the following specific services will be discussed: - Rainfall, Vegetation and Water Bodies Monitoring for Malaria Surveillance - Heat Evaluation and Assessment - Multi-resolution Nested Dust Forecast - Socioeconomic Data and Application Center (SEDAC) Health Related Data and Services - Goddard Earth Sciences Data and Information Services Center (GES DISC) Health Related Data and Services The purpose of this presentation is to provide a (strong) flavor of the data and information services available to public health research and decision making, to invoke new ways of thinking about how public health work can be accomplished, and stimulate new ideas on how information services can be further utilized.

The research of road and vehicle information extraction algorithm based on high resolution remote sensing image

NASA Astrophysics Data System (ADS)

Zhou, Tingting; Gu, Lingjia; Ren, Ruizhi; Cao, Qiong

2016-09-01

With the rapid development of remote sensing technology, the spatial resolution and temporal resolution of satellite imagery also have a huge increase. Meanwhile, High-spatial-resolution images are becoming increasingly popular for commercial applications. The remote sensing image technology has broad application prospects in intelligent traffic. Compared with traditional traffic information collection methods, vehicle information extraction using high-resolution remote sensing image has the advantages of high resolution and wide coverage. This has great guiding significance to urban planning, transportation management, travel route choice and so on. Firstly, this paper preprocessed the acquired high-resolution multi-spectral and panchromatic remote sensing images. After that, on the one hand, in order to get the optimal thresholding for image segmentation, histogram equalization and linear enhancement technologies were applied into the preprocessing results. On the other hand, considering distribution characteristics of road, the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used to suppress water and vegetation information of preprocessing results. Then, the above two processing result were combined. Finally, the geometric characteristics were used to completed road information extraction. The road vector extracted was used to limit the target vehicle area. Target vehicle extraction was divided into bright vehicles extraction and dark vehicles extraction. Eventually, the extraction results of the two kinds of vehicles were combined to get the final results. The experiment results demonstrated that the proposed algorithm has a high precision for the vehicle information extraction for different high resolution remote sensing images. Among these results, the average fault detection rate was about 5.36%, the average residual rate was about 13.60% and the average accuracy was approximately 91.26%.

Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing.

PubMed

Hakkenberg, C R; Peet, R K; Urban, D L; Song, C

2018-01-01

In light of the need to operationalize the mapping of forest composition at landscape scales, this study uses multi-scale nested vegetation sampling in conjunction with LiDAR-hyperspectral remotely sensed data from the G-LiHT airborne sensor to map vascular plant compositional turnover in a compositionally and structurally complex North Carolina Piedmont forest. Reflecting a shift in emphasis from remotely sensing individual crowns to detecting aggregate optical-structural properties of forest stands, predictive maps reflect the composition of entire vascular plant communities, inclusive of those species smaller than the resolution of the remotely sensed imagery, intertwined with proximate taxa, or otherwise obscured from optical sensors by dense upper canopies. Stand-scale vascular plant composition is modeled as community continua: where discrete community-unit classes at different compositional resolutions provide interpretable context for continuous gradient maps that depict n-dimensional compositional complexity as a single, consistent RGB color combination. In total, derived remotely sensed predictors explain 71%, 54%, and 48% of the variation in the first three components of vascular plant composition, respectively. Among all remotely sensed environmental gradients, topography derived from LiDAR ground returns, forest structure estimated from LiDAR all returns, and morphological-biochemical traits determined from hyperspectral imagery each significantly correspond to the three primary axes of floristic composition in the study site. Results confirm the complementarity of LiDAR and hyperspectral sensors for modeling the environmental gradients constraining landscape turnover in vascular plant composition and hold promise for predictive mapping applications spanning local land management to global ecosystem modeling. © 2017 by the Ecological Society of America.

Sources of support for remote sensing education

NASA Technical Reports Server (NTRS)

Estes, J. E.

1981-01-01

Past financial support for educational programs in remote sensing came largely in the form of short courses funded by the National Science Foundation. Later NASA began to fund such courses for local and state government and for some university participants in its regional programs. The greater impact came from the funding by a variety of federal agencies for remote sensing research projects at educational institutions throughout the country. Probably the best and most significant example of these programs, from the university standpoint is, and should continue to be, the NASA university affairs programs, which with its long term step funding of a number of institutions has probably done more for remote sensing education than any other federal program in this country. An incomplete listing of federal agencies that support remote sensing research at the university level is presented.

A mission-oriented orbit design method of remote sensing satellite for region monitoring mission based on evolutionary algorithm

NASA Astrophysics Data System (ADS)

Shen, Xin; Zhang, Jing; Yao, Huang

2015-12-01

Remote sensing satellites play an increasingly prominent role in environmental monitoring and disaster rescue. Taking advantage of almost the same sunshine condition to same place and global coverage, most of these satellites are operated on the sun-synchronous orbit. However, it brings some problems inevitably, the most significant one is that the temporal resolution of sun-synchronous orbit satellite can't satisfy the demand of specific region monitoring mission. To overcome the disadvantages, two methods are exploited: the first one is to build satellite constellation which contains multiple sunsynchronous satellites, just like the CHARTER mechanism has done; the second is to design non-predetermined orbit based on the concrete mission demand. An effective method for remote sensing satellite orbit design based on multiobjective evolution algorithm is presented in this paper. Orbit design problem is converted into a multi-objective optimization problem, and a fast and elitist multi-objective genetic algorithm is utilized to solve this problem. Firstly, the demand of the mission is transformed into multiple objective functions, and the six orbit elements of the satellite are taken as genes in design space, then a simulate evolution process is performed. An optimal resolution can be obtained after specified generation via evolution operation (selection, crossover, and mutation). To examine validity of the proposed method, a case study is introduced: Orbit design of an optical satellite for regional disaster monitoring, the mission demand include both minimizing the average revisit time internal of two objectives. The simulation result shows that the solution for this mission obtained by our method meet the demand the users' demand. We can draw a conclusion that the method presented in this paper is efficient for remote sensing orbit design.

AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data

NASA Astrophysics Data System (ADS)

Laura, Jason; Rodriguez, Kelvin; Paquette, Adam C.; Dunn, Evin

2018-01-01

In this work we describe the AutoCNet library, written in Python, to support the application of computer vision techniques for n-image correspondence identification in remotely sensed planetary images and subsequent bundle adjustment. The library is designed to support exploratory data analysis, algorithm and processing pipeline development, and application at scale in High Performance Computing (HPC) environments for processing large data sets and generating foundational data products. We also present a brief case study illustrating high level usage for the Apollo 15 Metric camera.

Multi-sun-synchronous (MSS) orbits for earth observation

NASA Astrophysics Data System (ADS)

Ulivieri, Carlo; Anselmo, Luciano

1992-08-01

A case study is outlined for a remote-sensing mission at low and middle latitudes based on multi-sun-synchronous (MSS) orbits. The scenario involves the use of small payloads in low-earth posigrade orbits that would overfly the Mediterranean region. A 600-kg spacecraft is considered in an orbit that is 571 km in altitude and at an inclination of 42.5 deg. The orbit is analyzed in terms of mission characteristics, and two years of operation is shown to be feasible with a fuel-consumption rate of less than three kg/yr of hydrazine. The mission could be based on the use of a Scout solid-propellant rockets into an MSS orbit, and only a limited number of ground stations are required for good data collection. A remote-sensing mission at low/middle latitudes is shown to be efficient in terms of both revisit frequency, fuel consumption, and data acquisition.

A Web-GIS Procedure Based on Satellite Multi-Spectral and Airborne LIDAR Data to Map the Road blockage Due to seismic Damages of Built-Up Urban Areas

NASA Astrophysics Data System (ADS)

Costanzo, Antonio; Montuori, Antonio; Silva, Juan Pablo; Silvestri, Malvina; Musacchio, Massimo; Buongiorno, Maria Fabrizia; Stramondo, Salvatore

2016-08-01

In this work, a web-GIS procedure to map the risk of road blockage in urban environments through the combined use of space-borne and airborne remote sensing sensors is presented. The methodology concerns (1) the provision of a geo-database through the integration of space-borne multispectral images and airborne LiDAR data products; (2) the modeling of building vulnerability, based on the corresponding 3D geometry and construction time information; (3) the GIS-based mapping of road closure due to seismic- related building collapses based on the building characteristic height and the width of the road. Experimental results, gathered for the Cosenza urban area, allow demonstrating the benefits of both the proposed approach and the GIS-based integration of multi-platforms remote sensing sensors and techniques for seismic road assessment purposes.

Sample project: establishing a global forest monitoring capability using multi-resolution and multi-temporal remotely sensed data sets

USGS Publications Warehouse

Hansen, Matt; Stehman, Steve; Loveland, Tom; Vogelmann, Jim; Cochrane, Mark

2009-01-01

Quantifying rates of forest-cover change is important for improved carbon accounting and climate change modeling, management of forestry and agricultural resources, and biodiversity monitoring. A practical solution to examining trends in forest cover change at global scale is to employ remotely sensed data. Satellite-based monitoring of forest cover can be implemented consistently across large regions at annual and inter-annual intervals. This research extends previous research on global forest-cover dynamics and land-cover change estimation to establish a robust, operational forest monitoring and assessment system. The approach integrates both MODIS and Landsat data to provide timely biome-scale forest change estimation. This is achieved by using annual MODIS change indicator maps to stratify biomes into low, medium and high change categories. Landsat image pairs can then be sampled within these strata and analyzed for estimating area of forest cleared.

Remote sensing of forest dynamics and land use in Amazonia

NASA Astrophysics Data System (ADS)

Toomey, Michael Paul

The rich, vast Amazonian ecosystem is directly and indirectly threatened by human activities; remote sensing serves as an essential tool for monitoring, understanding and mitigating these threats. A multi-faceted body of work is described here, addressing three major issues that employ and advance remote sensing techniques for the study of Amazonia and other tropical rainforest regions. In Chapter 2, canopy reflectance modeling and satellite observations were used to quantify the effect of epiphylls on remote sensing of humid forests. Modeling simulations demonstrated sensitivity of canopy-level near infrared and green reflectance to epiphylls on leaves. Time series of Moderate Resolution Imaging Spectrometer (MODIS) data corroborated the modeling results, suggesting a degree of coupling between epiphyll cover and vegetation indices which must be accounted for when using optical remote sensing in humid forests. In Chapter 4, 11 years (2000--2010) of MODIS land surface temperature (LST) data covering the entire Amazon basin were used to ascertain the role of heat stress during droughts in 2005 and 2010. Preliminary accuracy assessments showed that LST data provided reasonably accurate estimates of daytime air temperatures (RMSE = 1.45°C; Chapter 3). There were moderate to strong correlations between LST-based air temperature estimates and tower measurements (mean r = 0.64), illustrating a sensitivity to temporal variability. During both droughts, MODIS LST data detected anomalously high daytime and nighttime canopy temperatures throughout drought-affected regions. Multivariate linear models of LST and precipitation anomalies explained 65.1% of the variability in forest biomass losses, as determined from a wide network of forest inventory plots. These results suggest that models should incorporate both heat and moisture to predict drought effects on tropical forests. In Chapter 5, I performed high spatial and temporal resolution modeling of carbon stocks and fluxes in the state of Rondonia, Brazil for the period 1985--2009. Based on this analysis, Rondonia contributed ˜4% of pan-tropical humid forest deforestation emissions while carbon uptake by secondary forest was negligible due to limited spatial extent and high turnover rates. Spatial analysis of land cover change demonstrated the necessity for fine resolution carbon monitoring in tropical regions dominated by non-mechanized, smallholder land uses.

Reviews and Syntheses: optical sampling of the flux tower footprint

NASA Astrophysics Data System (ADS)

Gamon, J. A.

2015-07-01

The purpose of this review is to address the reasons and methods for conducting optical remote sensing within the flux tower footprint. Fundamental principles and conclusions gleaned from over 2 decades of proximal remote sensing at flux tower sites are reviewed. The organizing framework used here is the light-use efficiency (LUE) model, both because it is widely used, and because it provides a useful theoretical construct for integrating optical remote sensing with flux measurements. Multiple ways of driving this model, ranging from meteorological measurements to remote sensing, have emerged in recent years, making it a convenient conceptual framework for comparative experimental studies. New interpretations of established optical sampling methods, including the photochemical reflectance index (PRI) and solar-induced chlorophyll fluorescence (SIF), are discussed within the context of the LUE model. Multi-scale analysis across temporal and spatial axes is a central theme because such scaling can provide links between ecophysiological mechanisms detectable at the level of individual organisms and broad patterns emerging at larger scales, enabling evaluation of emergent properties and extrapolation to the flux footprint and beyond. Proper analysis of the sampling scale requires an awareness of sampling context that is often essential to the proper interpretation of optical signals. Additionally, the concept of optical types, vegetation exhibiting contrasting optical behavior in time and space, is explored as a way to frame our understanding of the controls on surface-atmosphere fluxes. Complementary normalized difference vegetation index (NDVI) and PRI patterns across ecosystems are offered as an example of this hypothesis, with the LUE model and light-response curve providing an integrating framework. I conclude that experimental approaches allowing systematic exploration of plant optical behavior in the context of the flux tower network provides a unique way to improve our understanding of environmental constraints and ecophysiological function. In addition to an enhanced mechanistic understanding of ecosystem processes, this integration of remote sensing with flux measurements offers many rich opportunities for upscaling, satellite validation, and informing practical management objectives ranging from assessing ecosystem health and productivity to quantifying biospheric carbon sequestration.

Scalability Issues for Remote Sensing Infrastructure: A Case Study.

PubMed

Liu, Yang; Picard, Sean; Williamson, Carey

2017-04-29

For the past decade, a team of University of Calgary researchers has operated a large "sensor Web" to collect, analyze, and share scientific data from remote measurement instruments across northern Canada. This sensor Web receives real-time data streams from over a thousand Internet-connected sensors, with a particular emphasis on environmental data (e.g., space weather, auroral phenomena, atmospheric imaging). Through research collaborations, we had the opportunity to evaluate the performance and scalability of their remote sensing infrastructure. This article reports the lessons learned from our study, which considered both data collection and data dissemination aspects of their system. On the data collection front, we used benchmarking techniques to identify and fix a performance bottleneck in the system's memory management for TCP data streams, while also improving system efficiency on multi-core architectures. On the data dissemination front, we used passive and active network traffic measurements to identify and reduce excessive network traffic from the Web robots and JavaScript techniques used for data sharing. While our results are from one specific sensor Web system, the lessons learned may apply to other scientific Web sites with remote sensing infrastructure.

Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery

Treesearch

Arjan J. H. Meddens; Jeffrey A. Hicke; Lee A. Vierling; Andrew T. Hudak

2013-01-01

Bark beetles cause significant tree mortality in coniferous forests across North America. Mapping beetle-caused tree mortality is therefore important for gauging impacts to forest ecosystems and assessing trends. Remote sensing offers the potential for accurate, repeatable estimates of tree mortality in outbreak areas. With the advancement of multi-temporal disturbance...

Status of the Multi-Angle SpectroRadiometer Instrument for EOS- AM1 and Its Application to Remote Sensing of Aerosols

NASA Technical Reports Server (NTRS)

Diner, D. J.; Abdou, W. A.; Bruegge, C. J.; Conel, J. E.; Kahn, R. A.; Martonchik, J. V.; Paradise, S. R.; West, R. A.

1995-01-01

The Multi-Angle Imaging SpectroRadiometer (MISR) is being developed at JPL for the AM1 spacecraft in the Earth Observing System (EOS) series. This paper reports on the progress of instrument fabrication and testing, and it discusses the strategy to use the instrument for studying tropospheric aerosols.

The MUSICA MetOp/IASI H2O and δD products: characterisation and long-term comparison to NDACC/FTIR data

NASA Astrophysics Data System (ADS)

Wiegele, A.; Schneider, M.; Hase, F.; Barthlott, S.; García, O. E.; Sepúlveda, E.; González, Y.; Blumenstock, T.; Raffalski, U.; Gisi, M.; Kohlhepp, R.

2014-04-01

Within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) ground- and space-based remote sensing as well as in-situ datasets of tropospheric water vapour isotopologues are provided. The space-based remote-sensing dataset is produced from spectra measured by the IASI (Infrared Atmospheric Sounding Interferometer) sensor and is potentially available on a global scale. Here, we present the MUSICA IASI data for three different geophysical locations (subtropics, mid-latitudes, and arctic) and we provide a comprehensive characterisation of the complex nature of such space-based isotopologue remote sensing products. The quality assessment study is complemented by a comparison to MUSICA's ground-based FTIR (Fourier-Transform InfraRed) remote sensing data retrieved from the spectra recorded at three different locations within the framework of NDACC (Network for the Detection of Atmospheric Composition Change). We confirm that IASI is able to measure tropospheric H2O profiles with a vertical resolution of about 4 km and a random error of about 10%. In addition IASI can observe middle tropospheric δD that adds complementary value to IASI's middle tropospheric H2O observations. Our study is both, a theoretical and an empirical proof that IASI has the capability for a global observation of middle tropospheric water vapour isotopologues on a daily timescale and at a quality that is sufficiently high for water cycle research purposes.

The MUSICA MetOp/IASI H2O and δD products: characterisation and long-term comparison to NDACC/FTIR data

NASA Astrophysics Data System (ADS)

Wiegele, A.; Schneider, M.; Hase, F.; Barthlott, S.; García, O. E.; Sepúlveda, E.; González, Y.; Blumenstock, T.; Raffalski, U.; Gisi, M.; Kohlhepp, R.

2014-08-01

Within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) ground- and space-based remote sensing as well as in situ data sets of tropospheric water vapour isotopologues are provided. The space-based remote-sensing data set is produced from spectra measured by the IASI (Infrared Atmospheric Sounding Interferometer) sensor and is potentially available on a global scale. Here, we present the MUSICA IASI data for three different geophysical locations (subtropics, midlatitudes, and Arctic), and we provide a comprehensive characterisation of the complex nature of such space-based isotopologue remote-sensing products. The quality assessment study is complemented by a comparison to MUSICA's ground-based FTIR (Fourier Transform InfraRed) remote-sensing data retrieved from the spectra recorded at three different locations within the framework of NDACC (Network for the Detection of Atmospheric Composition Change). We confirm that IASI is able to measure tropospheric H2O profiles with a vertical resolution of about 4 km and a random error of about 10%. In addition IASI can observe middle tropospheric δD that adds complementary value to IASI's middle tropospheric H2O observations. Our study presents theoretical and empirical proof that IASI has the capability for a global observation of middle tropospheric water vapour isotopologues on a daily timescale and at a quality that is sufficiently high for water cycle research purposes.

Mapping CDOM Concentration in Waters Influenced by the Mississippi River Plume

NASA Technical Reports Server (NTRS)

Miller, Richard L.; DelCastillo, Carlos E.; Powell, Rodney T.; DSa, Eurico; Spiering, Bruce

2002-01-01

Colored dissolved organic matter (CDOM) is often an important component of the organic carbon pool in river-dominated coastal margins. CDOM directly influences remote sensing applications through its strong absorption in the UV and blue regions of the spectrum. This effect can complicate the use of chlorophyll a retrieval algorithms and phytoplankton production models that are based on remotely sensed ocean color. As freshwater input is the principle source of CDOM in coastal margins, CDOM distribution can often be described by conservative mixing with open ocean waters and may serve as an optical tracer of riverine water. Hence, there is considerable interest in the ability to accurately measure and map CDOM concentrations as well as understand the processes that govern the optical properties and distribution of CDOM in coastal environments. We are examining CDOM dynamics in the waters influenced by the Mississippi River plume. Our program incorporates discrete samples, flow-through measurements, and remote sensing. CDOM absorption spectra of discrete samples are measured at sea using a portable, multiple pathlength waveguide system. A SAFire multi-spectral fluorescence meter provides spectral characterization of CDOM (fluorescence and absorption) using a ship flow-through system for continuous surface mapping. In situ reflectance spectra are obtained by a hand held spectroradiometer. Remotely sensed images are obtained from the SeaWiFS and CRIS (Coastal Research Imaging Spectrometer) instruments. We describe here the instruments used, sampling protocols employed, and the relationships derived between in situ measurements and remotely sensed data for this optically complex environment.

Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability

NASA Astrophysics Data System (ADS)

Herman, Matthew R.; Nejadhashemi, A. Pouyan; Abouali, Mohammad; Hernandez-Suarez, Juan Sebastian; Daneshvar, Fariborz; Zhang, Zhen; Anderson, Martha C.; Sadeghi, Ali M.; Hain, Christopher R.; Sharifi, Amirreza

2018-01-01

As the global demands for the use of freshwater resources continues to rise, it has become increasingly important to insure the sustainability of this resources. This is accomplished through the use of management strategies that often utilize monitoring and the use of hydrological models. However, monitoring at large scales is not feasible and therefore model applications are becoming challenging, especially when spatially distributed datasets, such as evapotranspiration, are needed to understand the model performances. Due to these limitations, most of the hydrological models are only calibrated for data obtained from site/point observations, such as streamflow. Therefore, the main focus of this paper is to examine whether the incorporation of remotely sensed and spatially distributed datasets can improve the overall performance of the model. In this study, actual evapotranspiration (ETa) data was obtained from the two different sets of satellite based remote sensing data. One dataset estimates ETa based on the Simplified Surface Energy Balance (SSEBop) model while the other one estimates ETa based on the Atmosphere-Land Exchange Inverse (ALEXI) model. The hydrological model used in this study is the Soil and Water Assessment Tool (SWAT), which was calibrated against spatially distributed ETa and single point streamflow records for the Honeyoey Creek-Pine Creek Watershed, located in Michigan, USA. Two different techniques, multi-variable and genetic algorithm, were used to calibrate the SWAT model. Using the aforementioned datasets, the performance of the hydrological model in estimating ETa was improved using both calibration techniques by achieving Nash-Sutcliffe efficiency (NSE) values >0.5 (0.73-0.85), percent bias (PBIAS) values within ±25% (±21.73%), and root mean squared error - observations standard deviation ratio (RSR) values <0.7 (0.39-0.52). However, the genetic algorithm technique was more effective with the ETa calibration while significantly reducing the model performance for estimating the streamflow (NSE: 0.32-0.52, PBIAS: ±32.73%, and RSR: 0.63-0.82). Meanwhile, using the multi-variable technique, the model performance for estimating the streamflow was maintained with a high level of accuracy (NSE: 0.59-0.61, PBIAS: ±13.70%, and RSR: 0.63-0.64) while the evapotranspiration estimations were improved. Results from this assessment shows that incorporation of remotely sensed and spatially distributed data can improve the hydrological model performance if it is coupled with a right calibration technique.

NASA Remote Sensing Research as Applied to Archaeology

NASA Technical Reports Server (NTRS)

Giardino, Marco J.; Thomas, Michael R.

2002-01-01

The use of remotely sensed images is not new to archaeology. Ever since balloons and airplanes first flew cameras over archaeological sites, researchers have taken advantage of the elevated observation platforms to understand sites better. When viewed from above, crop marks, soil anomalies and buried features revealed new information that was not readily visible from ground level. Since 1974 and initially under the leadership of Dr. Tom Sever, NASA's Stennis Space Center, located on the Mississippi Gulf Coast, pioneered and expanded the application of remote sensing to archaeological topics, including cultural resource management. Building on remote sensing activities initiated by the National Park Service, archaeologists increasingly used this technology to study the past in greater depth. By the early 1980s, there were sufficient accomplishments in the application of remote sensing to anthropology and archaeology that a chapter on the subject was included in fundamental remote sensing references. Remote sensing technology and image analysis are currently undergoing a profound shift in emphasis from broad classification to detection, identification and condition of specific materials, both organic and inorganic. In the last few years, remote sensing platforms have grown increasingly capable and sophisticated. Sensors currently in use, or nearing deployment, offer significantly finer spatial and spectral resolutions than were previously available. Paired with new techniques of image analysis, this technology may make the direct detection of archaeological sites a realistic goal.

Remote Sensing of Precipitation from Space

NASA Technical Reports Server (NTRS)

Stocker, Erich Franz

2010-01-01

This slide presentation reviews the use of remote sensing of precipitation from satellite observations. The purpose of the presentation is to introduce the three prime instrument types for measuring precipitation from space, give an overview of the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission, provides examples of how measurements from space can be used, and provides simple, high level scenarios for how remote sensed precipitation data can be used by planners and managers.

Progress and needs in agricultural research, development, and applications programs

NASA Technical Reports Server (NTRS)

Moore, D. G.; Myers, V. I.

1977-01-01

The dynamic nature of agriculture requires repetitive resource assessments such as those from remote sensing. Until recently, the use of remote sensing in agriculture has been limited primarily to site specific investigations without large-scale evaluations. Examples of successful applications at various user levels are provided. The stage of development for applying remote sensing to many agricultural problems is assessed, and goals for planning future data characteristics for increased use in agriculture are suggested.

Regional landslide susceptibility assessment using multi-stage remote sensing data along the coastal range highway in northeastern Taiwan

NASA Astrophysics Data System (ADS)

Lee, Ching-Fang; Huang, Wei-Kai; Chang, Yu-Lin; Chi, Shu-Yeong; Liao, Wu-Chang

2018-01-01

Typhoons Megi (2010) and Saola (2012) brought torrential rainfall which triggered regional landslides and flooding hazards along Provincial Highway No. 9 in northeastern Taiwan. To reduce property loss and saving lives, this study combines multi-hazard susceptibility assessment with environmental geology map a rock mass rating system (RMR), remote sensing analysis, and micro-topography interpretation to develop an integrated landslide hazard assessment approach and reflect the intrinsic state of slopeland from the past toward the future. First, the degree of hazard as indicated by historical landslides was used to determine many landslide regions in the past. Secondly, geo-mechanical classification of rock outcroppings was performed by in-situ investigation along the vulnerable road sections. Finally, a high-resolution digital elevation model was extracted from airborne LiDAR and multi-temporal remote sensing images which was analyzed to discover possible catastrophic landslide hotspot shortly. The results of the analysis showed that 37% of the road sections in the study area were highly susceptible to landslide hazards. The spatial distribution of the road sections revealed that those characterized by high susceptibility were located near the boundaries of fault zones and in areas of lithologic dissimilarity. Headward erosion of gullies and concave-shaped topographic features had an adverse effect and was the dominant factor triggering landslides. Regional landslide reactivation on this coastal highway are almost related to the past landslide region based on hazard statistics. The final results of field validation demonstrated that an accuracy of 91% could be achieved for forecasting geohazard followed by intense rainfall events and typhoons.

Assessing the Tundra-taiga Boundary with Multi-Sensor Satellite Data

NASA Technical Reports Server (NTRS)

Ranson, K. J.; Sun, G.; Kharuk, V. I.; Kovacs, K.

2004-01-01

Monitoring the dynamics of the circumpolar boreal forest (taiga) and Arctic tundra boundary is important for understanding the causes and consequences of changes observed in these areas. This ecotone, the world's largest, stretches for over 13,400 km and marks the transition between the northern limits of forests and the southern margin of the tundra. Because of the inaccessibility and large extent of this zone, remote sensing data can play an important role for mapping the characteristics and monitoring the dynamics. Basic understanding of the capabilities of existing space borne instruments for these purposes is required. In this study we examined the use of several remote sensing techniques for identifying the existing tundra- taiga ecotone. These include Landsat-7, MISR, MODIS and RADARSAT data. Historical cover maps, recent forest stand measurements and high-resolution IKONOS images were used for local ground truth. It was found that a tundra-taiga transitional area can be characterized using multi- spectral Landsat ETM+ summer images, multi-angle MISR red band reflectance images, RADARSAT images with larger incidence angle, or multi-temporal and multi-spectral MODIS data. Because of different resolutions and spectral regions covered, the transition zone maps derived from different data types were not identical, but the general patterns were consistent.

Public health applications of remote sensing of the environment, an evaluation

NASA Technical Reports Server (NTRS)

1972-01-01

The available techniques were examined in the field of remote sensing (including aerial photography, infrared detection, radar, etc.) and applications to a number of problems in the wide field of public health determined. The specific areas of public health examined included: air pollution, water pollution, communicable disease, and the combined problems of urban growth and the effect of disasters on human communities. The assessment of the possible applications of remote sensing to these problems was made primarily by examination of the available literature in each field, and by interviews with health authorities, physicists, biologists, and other interested workers. Three types of programs employing remote sensors were outlined in the air pollution field: (1) proving ability of sensors to monitor pollutants at three levels of interest - point source, ambient levels in cities, and global patterns; (2) detection of effects of pollutants on the environment at local and global levels; and (3) routine monitoring.

Elicitation of State and Local User Needs for Future Moderate Resolution Earth Observations: The AmericaView Contribution

NASA Astrophysics Data System (ADS)

French, N. H. F.; Lawrence, R. L.

2017-12-01

AmericaView is a nationwide partnership of remote sensing scientists who support the use of Landsat and other public domain remotely sensed data through applied remote sensing research, K-12 and higher STEM education, workforce development, and technology transfer. The national AmericaView program currently has active university-lead members in 39 states, each of which has a "stateview" consortium consisting of some combination of university, agency, non-profit, and other members. This "consortium of consortia" has resulted in a strong and unique nationwide network of remote sensing practitioners. AmericaView has used this network to contribute to the USGS Requirements Capabilities & Analysis for Earth Observations. Participating states have conducted interviews of key remote sensing end users across the country to provide key input at the state and local level for the design and implementation of future U.S. moderate resolution Earth observations.

Use of remote sensing technology for inventorying and planning utilization of land resources in South Dakota

NASA Technical Reports Server (NTRS)

1974-01-01

A comprehensive land use planning process model is being developed in Meade County, South Dakota, using remote sensing technology. The proper role of remote sensing in the land use planning process is being determined by interaction of remote sensing specialists with local land use planners. The data that were collected by remote sensing techniques are as follows: (1) level I land use data interpreted at a scale of 1:250,000 from false color enlargement prints of ERTS-1 color composite transparencies; (2) detailed land use data interpreted at a scale of 1:24,000 from enlargement color prints of high altitude RB-57 photography; and (3) general soils map interpreted at a scale of 1:250,000 from false color enlargement prints of ERTS-1 color composite transparencies. In addition to use of imagery as an interpretation aid, the utility of using photographs as base maps was demonstrated.

Modeling the impacts of phenological and inter-annual changes in landscape metrics on local biodiversity of agricultural lands of Eastern Ontario using multi-spatial and multi-temporal remote sensing data

NASA Astrophysics Data System (ADS)

Alavi-Shoushtari, N.; King, D.

2017-12-01

Agricultural landscapes are highly variable ecosystems and are home to many local farmland species. Seasonal, phenological and inter-annual agricultural landscape dynamics have potential to affect the richness and abundance of farmland species. Remote sensing provides data and techniques which enable monitoring landscape changes in multiple temporal and spatial scales. MODIS high temporal resolution remote sensing images enable detection of seasonal and phenological trends, while Landsat higher spatial resolution images, with its long term archive enables inter-annual trend analysis over several decades. The objective of this study to use multi-spatial and multi-temporal remote sensing data to model the response of farmland species to landscape metrics. The study area is the predominantly agricultural region of eastern Ontario. 92 sample landscapes were selected within this region using a protocol designed to maximize variance in composition and configuration heterogeneity while controlling for amount of forest and spatial autocorrelation. Two sample landscape extents (1×1km and 3×3km) were selected to analyze the impacts of spatial scale on biodiversity response. Gamma diversity index data for four taxa groups (birds, butterflies, plants, and beetles) were collected during the summers of 2011 and 2012 within the cropped area of each landscape. To extract the seasonal and phenological metrics a 2000-2012 MODIS NDVI time-series was used, while a 1985-2012 Landsat time-series was used to model the inter-annual trends of change in the sample landscapes. The results of statistical modeling showed significant relationships between farmland biodiversity for several taxa and the phenological and inter-annual variables. The following general results were obtained: 1) Among the taxa groups, plant and beetles diversity was most significantly correlated with the phenological variables; 2) Those phenological variables which are associated with the variability in the start of season date across the sample landscapes and the variability in the corresponding NDVI values at that date showed the strongest correlation with the biodiversity indices; 3) The significance of the models improved when using 3×3km site extent both for MODIS and Landsat based models due most likely to the larger sample size over 3x3km.

Flat Surface Damage Detection System (FSDDS)

NASA Technical Reports Server (NTRS)

Williams, Martha; Lewis, Mark; Gibson, Tracy; Lane, John; Medelius, Pedro; Snyder, Sarah; Ciarlariello, Dan; Parks, Steve; Carrejo, Danny; Rojdev, Kristina

2013-01-01

The Flat Surface Damage Detection system (FSDDS} is a sensory system that is capable of detecting impact damages to surfaces utilizing a novel sensor system. This system will provide the ability to monitor the integrity of an inflatable habitat during in situ system health monitoring. The system consists of three main custom designed subsystems: the multi-layer sensing panel, the embedded monitoring system, and the graphical user interface (GUI). The GUI LABVIEW software uses a custom developed damage detection algorithm to determine the damage location based on the sequence of broken sensing lines. It estimates the damage size, the maximum depth, and plots the damage location on a graph. Successfully demonstrated as a stand alone technology during 2011 D-RATS. Software modification also allowed for communication with HDU avionics crew display which was demonstrated remotely (KSC to JSC} during 2012 integration testing. Integrated FSDDS system and stand alone multi-panel systems were demonstrated remotely and at JSC, Mission Operations Test using Space Network Research Federation (SNRF} network in 2012. FY13, FSDDS multi-panel integration with JSC and SNRF network Technology can allow for integration with other complementary damage detection systems.

Multi-Sensor Approach for Assessing the Taiga-Tundra Boundary

NASA Technical Reports Server (NTRS)

Ranson, K. J.; Sun, G.; Kharuk, V. I.; Kovacs, K.

2003-01-01

Monitoring the dynamics of the tundra-taiga boundary is critical for our understanding of the causes and consequences of the changes in this area. Because of its inaccessibility, remote sensing data will play an important role. In this study we examined the use of several remote sensing techniques for identifying the existing tundra-taiga ecotone. These include Landsat, MISR and RADARSAT data. High-resolution IKONOS images were used for local ground truth. It was found that on Landsat ETM+ summer images, reflectance from tundra and taiga at band 4 (NIR) is similar, but different at other bands such as red, and MIR bands. When the incidence angle is small, C-band HH-pol backscattering coefficients from both tundra and taiga are relatively high. The backscattering from tundra targets decreases faster than taiga targets when the incidence angle increases, because the tundra targets look smoother than taiga. Because of the shading effect of the vegetation, the MISR data, both multi-spectral data at nadir looking and multi-angle data at red and NIR bands, clearly show the transition zone.

Empirical validation and proof of added value of MUSICA's tropospheric δD remote sensing products

NASA Astrophysics Data System (ADS)

Schneider, M.; González, Y.; Dyroff, C.; Christner, E.; Wiegele, A.; Barthlott, S.; García, O. E.; Sepúlveda, E.; Hase, F.; Andrey, J.; Blumenstock, T.; Guirado, C.; Ramos, R.; Rodríguez, S.

2014-07-01

The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) integrates tropospheric water vapour isototopologue remote sensing and in-situ observations. This paper presents a first empirical validation of MUSICA's H2O and δD remote sensing products (generated from ground-based FTIR, Fourier Transform InfraRed, spectrometer and space-based IASI, Infrared Atmospheric Sounding Interferometer, observation). As reference we use well calibrated in-situ measurements made aboard an aircraft (between 200 and 6800 m a.s.l.) by the dedicated ISOWAT instrument and on the island of Tenerife at two different altitudes (at Izaña, 2370 m a.s.l., and at Teide, 3550 m a.s.l.) by two commercial Picarro L2120-i water isotopologue analysers. The comparison to the ISOWAT profile measurements shows that the remote sensors can well capture the variations in the water vapour isotopologues and the scatter with respect to the in-situ references suggests a δD random uncertainty for the FTIR product of much better than 45‰ in the lower troposphere and of about 15‰ for the middle troposphere. For the middle tropospheric IASI δD product the study suggests a respective uncertainty of about 15‰. In addition, we find indications for a positive δD bias in the remote sensing products. The δD data are scientifically interesting only if they add information to the H2O observations. We are able to qualitatively demonstrate the added value of the MUSICA δD remote sensing data by comparing δD-vs.-H2O curves. First, we show that the added value of δD as seen in the Picarro data is similarly seen in FTIR data measured in coincidence. Second, we document that the δD-vs.-H2O curves obtained from the different in-situ and remote sensing data sets (ISOWAT, Picarro at Izaña and Teide, FTIR, and IASI) consistently identify two different moisture transport pathways to the subtropical north eastern Atlantic free troposphere.

Evaluation of Ice sheet evolution and coastline changes from 1960s in Amery Ice Shelf using multi-source remote sensing images

NASA Astrophysics Data System (ADS)

Qiao, G.; Ye, W.; Scaioni, M.; Liu, S.; Feng, T.; Liu, Y.; Tong, X.; Li, R.

2013-12-01

Global change is one of the major challenges that all the nations are commonly facing, and the Antarctica ice sheet changes have been playing a critical role in the global change research field during the past years. Long time-series of ice sheet observations in Antarctica would contribute to the quantitative evaluation and precise prediction of the effects on global change induced by the ice sheet, of which the remote sensing technology would make critical contributions. As the biggest ice shelf and one of the dominant drainage systems in East Antarctic, the Amery Ice Shelf has been making significant contributions to the mass balance of the Antarctic. Study of Amery Ice shelf changes would advance the understanding of Antarctic ice shelf evolution as well as the overall mass balance. At the same time, as one of the important indicators of Antarctica ice sheet characteristics, coastlines that can be detected from remote sensing imagery can help reveal the nature of the changes of ice sheet evolution. Most of the scientific research on Antarctica with satellite remote sensing dated from 1970s after LANDSAT satellite was brought into operation. It was the declassification of the cold war satellite reconnaissance photographs in 1995, known as Declassified Intelligence Satellite Photograph (DISP) that provided a direct overall view of the Antarctica ice-sheet's configuration in 1960s, greatly extending the time span of Antarctica surface observations. This paper will present the evaluation of ice-sheet evolution and coastline changes in Amery Ice Shelf from 1960s, by using multi-source remote sensing images including the DISP images and the modern optical satellite images. The DISP images scanned from negatives were first interior-oriented with the associated parameters, and then bundle block adjustment technology was employed based on the tie points and control points, to derive the mosaic image of the research region. Experimental results of coastlines generated from DISP images and that from ASTER images were analyzed, and the changes and evolution of Amery ice shelf were then evaluated, following by the discussion of the possible drives.

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

PubMed

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 statistical modeling techniques, demonstrated advantages for estimating the TP concentration in a large lake and had a strong potential for universal application for the TP concentration estimation in large lake waters worldwide. Copyright © 2014 Elsevier Ltd. All rights reserved.

Assessment of groundwater potential of the crystalline basement of Wadi-Fira (Eastern Chad) using a multi-criteria correlation analysis and Remote Sensing data

NASA Astrophysics Data System (ADS)

Brahim Mahamat, Hamza; Coz Mathieu, Le; Abderamane, Hamit; Razack, Moumtaz

2017-04-01

Access to water in the Wadi-Fira aquifer system is a crucial problem in Eastern Chad because of (i) the complexity of the hydrogeological context (fractured basement), (ii) large extent of the study area (50,000 km2); And (iii) hard-to-access field data (only 34 water points were available to determine physicochemical and hydrodynamic parameters) often associated with high uncertainty. This groundwater resource is paramount in this arid environment, to meet the water needs of an increasingly growing population (refugees from Darfur) with a predominant pastoral activity. In order to optimally exploit the available data, correlative analyzes are carried out by integrating the spatial dimension of the data with GIS tools. A three-step strategy is thus implemented, based on: (i) point field data with physicochemical and hydrodynamic parameters; (ii) maps interpolated from point data, to increase the number of ''comparable'' parameters for each site; and (iii) interpolated maps coupled to maps from Remote Sensing results describing the area's structural geomorphology (slopes, hydrographic network, faults). The first results show marked correlations between physico-chemical and hydrodynamical parameters. According to the correlation matrix, the static level correlates significantly with the dominant cations (Ca2+ ; R = 0.52) and anions (HCO3- ; R = 0.53). Correlations are lower between electrical conductivity and transmissivity, and electrical conductivity and measured static level. A negative correlation is observed between Fluorine and transmissivity (r = -0.65), and the altered horizon (r = -0.5). The most significant discharges are obtained in fissured horizons. The correlative analysis allowsto differentiate mapped sectors according to the productivity and chemical quality regarding groundwater resource. Keywords: Hydrodynamics, Hydrochemistry, Remote Sensing, SRTM, Basement aquifer, Alteration, Lineaments, Wadi-Fira, Tchad.

Digital spatial soil and land information for agriculture development

NASA Astrophysics Data System (ADS)

Sharma, R. K.; Laghathe, Pankaj; Meena, Ranglal; Barman, Alok Kumar; Das, Satyendra Nath

2006-12-01

Natural resource management calls for study of natural system prevailing in the country. In India floods and droughts visit regularly, causing extensive damages of natural wealth including agriculture that are crucial for sustenance of economic growth. The Indian Sub-continent drained by many major rivers and their tributaries where watershed, the hydrological unit forms a natural system that allows management and development of land resources following natural harmony. Acquisition of various kinds and levels of soil and land characteristics using both conventional and remote sensing techniques and subsequent development of digital spatial data base are essential to evolve strategy for planning watershed development programmes, their monitoring and impact evaluation. The multi-temporal capability of remote sensing sensors helps to update the existing data base which are of dynamic in nature. The paper outlines the concept of spatial data base development, generation using remote sensing techniques, designing of data structure, standardization and integration with watershed layers and various non spatial attribute data for various applications covering watershed development planning, alternate land use planning, soil and water conservation, diversified agriculture practices, generation of soil health card, soil and land reclamation, etc. The soil and land characteristics are vital to derive various interpretative groupings or master table that helps to generate the desired level of information of various clients using the GIS platform. The digital spatial data base on soils and watersheds generated by All India Soil and Land Use Survey will act as a sub-server of the main GIS based Web Server being hoisted by the planning commission for application of spatial data for planning purposes under G2G domain. It will facilitate e-governance for natural resource management using modern technology.

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

PubMed Central

Zheng, Guang; Moskal, L. Monika

2009-01-01

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels. PMID:22574042

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

PubMed

Zheng, Guang; Moskal, L Monika

2009-01-01

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

Weak Learner Method for Estimating River Discharges using Remotely Sensed Data: Central Congo River as a Testbed

NASA Astrophysics Data System (ADS)

Kim, D.; Lee, H.; Yu, H.; Beighley, E.; Durand, M. T.; Alsdorf, D. E.; Hwang, E.

2017-12-01

River discharge is a prerequisite for an understanding of flood hazard and water resource management, yet we have poor knowledge of it, especially over remote basins. Previous studies have successfully used a classic hydraulic geometry, at-many-stations hydraulic geometry (AMHG), and Manning's equation to estimate the river discharge. Theoretical bases of these empirical methods were introduced by Leopold and Maddock (1953) and Manning (1889), and those have been long used in the field of hydrology, water resources, and geomorphology. However, the methods to estimate the river discharge from remotely sensed data essentially require bathymetric information of the river or are not applicable to braided rivers. Furthermore, the methods used in the previous studies adopted assumptions of river conditions to be steady and uniform. Consequently, those methods have limitations in estimating the river discharge in complex and unsteady flow in nature. In this study, we developed a novel approach to estimating river discharges by applying the weak learner method (here termed WLQ), which is one of the ensemble methods using multiple classifiers, to the remotely sensed measurements of water levels from Envisat altimetry, effective river widths from PALSAR images, and multi-temporal surface water slopes over a part of the mainstem Congo. Compared with the methods used in the previous studies, the root mean square error (RMSE) decreased from 5,089 m3s-1 to 3,701 m3s-1, and the relative RMSE (RRMSE) improved from 12% to 8%. It is expected that our method can provide improved estimates of river discharges in complex and unsteady flow conditions based on the data-driven prediction model by machine learning (i.e. WLQ), even when the bathymetric data is not available or in case of the braided rivers. Moreover, it is also expected that the WLQ can be applied to the measurements of river levels, slopes and widths from the future Surface Water Ocean Topography (SWOT) mission to be launched in 2021.

Development of the Metropolitan Water Availability Index (MWAI) and short-term assessment with multi-scale remote sensing technologies.

PubMed

Chang, Ni-Bin; Yang, Y Jeffrey; Goodrich, James A; Daranpob, Ammarin

2010-06-01

Global climate change will influence environmental conditions including temperature, surface radiation, soil moisture, and sea level, and it will also significantly impact regional-scale hydrologic processes such as evapotranspiration (ET), precipitation, runoff, and snowmelt. The quantity and quality of water available for drinking and other domestic usage is also likely to be affected by changes in these processes. Consequently, it is necessary to assess and reflect upon the challenges ahead for water infrastructure and the general public in metropolitan regions. One approach to the problem is to use index-based assessment, forecasting and planning. The drought indices previously developed were not developed for domestic water supplies, and thus are insufficient for the purpose of such an assessment. This paper aims to propose and develop a "Metropolitan Water Availability Index (MWAI)" to assess the status of both the quantity and quality of available potable water sources diverted from the hydrologic cycle in a metropolitan region. In this approach, the accessible water may be expressed as volume per month or week (i.e., m(3)/month or m(3)/week) relative to a prescribed historical record, and such a trend analysis may result in final MWAI values ranging from -1 to +1 for regional water management decision making. The MWAI computation uses data and information from both historical point measurements and spatial remote-sensing based monitoring. Variables such as precipitation, river discharge, and water quality changes at drinking water plant intakes at specific locations are past "point" measurements in MWAI calculations. On the other hand, remote sensing provides information on both spatial and temporal distributions of key variables. Examples of remote-sensing images and sensor network technologies are in-situ sensor networks, ground-based radar, air-borne aircraft, and even space-borne satellites. A case study in Tampa Bay, Florida is described to demonstrate the short-term assessment of the MWAI concept at a practical level. It is anticipated that such a forecasting methodology may be extended for middle-term and long-term water supply assessment. (c) 2010 Elsevier Ltd. All rights reserved.

Object Manifold Alignment for Multi-Temporal High Resolution Remote Sensing Images Classification

NASA Astrophysics Data System (ADS)

Gao, G.; Zhang, M.; Gu, Y.

2017-05-01

Multi-temporal remote sensing images classification is very useful for monitoring the land cover changes. Traditional approaches in this field mainly face to limited labelled samples and spectral drift of image information. With spatial resolution improvement, "pepper and salt" appears and classification results will be effected when the pixelwise classification algorithms are applied to high-resolution satellite images, in which the spatial relationship among the pixels is ignored. For classifying the multi-temporal high resolution images with limited labelled samples, spectral drift and "pepper and salt" problem, an object-based manifold alignment method is proposed. Firstly, multi-temporal multispectral images are cut to superpixels by simple linear iterative clustering (SLIC) respectively. Secondly, some features obtained from superpixels are formed as vector. Thirdly, a majority voting manifold alignment method aiming at solving high resolution problem is proposed and mapping the vector data to alignment space. At last, all the data in the alignment space are classified by using KNN method. Multi-temporal images from different areas or the same area are both considered in this paper. In the experiments, 2 groups of multi-temporal HR images collected by China GF1 and GF2 satellites are used for performance evaluation. Experimental results indicate that the proposed method not only has significantly outperforms than traditional domain adaptation methods in classification accuracy, but also effectively overcome the problem of "pepper and salt".

Ionospheric Profiles from Ultraviolet Remote Sensing

DTIC Science & Technology

1998-01-01

remote sensing of the ionosphere from orbiting space platforms. Remote sensing of the nighttime ionosphere is a relatively straightforward process due to the absence of the complications brought about by daytime solar radiation. Further, during the nighttime hours, the O(+)-H(+) transition level in both the mid- and low-latitude ionospheres lies around 750 km, which is within the range of accuracy of the path matrix inversion. The intensity of the O(+)-e(-) recombination radiation as observed from orbiting space platforms can now be used to

Human and remote sensing data to investigate the frontiers of urbanization in the south of Mexico City.

PubMed

Rodriguez Lopez, Juan Miguel; Heider, Katharina; Scheffran, Jürgen

2017-04-01

The data presented here were originally collected for the article "Frontiers of Urbanization: Identifying and Explaining Urbanization Hot Spots in the South of Mexico City Using Human and Remote Sensing" (Rodriguez et al. 2017) [4]. They were divided into three databases (remote sensing, human sensing, and census information), using a multi-method approach with the goal of analyzing the impact of urbanization on protected areas in southern Mexico City. The remote sensing database was prepared as a result of a semi-automatic classification, dividing the land cover data into urban and non-urban classes. The second data set details an alternative view of the phenomena of urbanization by concentrating on illegal settlements in the conservation zone. It was based on voluntary complaints about environmental and land use offences filed at the Procuraduria Ambiental y del Ordenamiento Territorial del Distrito Federal (PAOT), which is a governmental entity responsible for reviewing and processing grievances on five basic topics: illegal land use, deterioration of green areas, waste, noise/vibrations, and animals. Anyone can file a PAOT complaint by phone, electronically, or in person. The complaint ends with a resolution, act of conciliation, or recommendation for action by other actors, such as the police or health office. The third data about unemployment was extracted from Mexico׳s National Census 2010 database available via public access.

Land cover's refined classification based on multi source of remote sensing information fusion: a case study of national geographic conditions census in China

NASA Astrophysics Data System (ADS)

Cheng, Tao; Zhang, Jialong; Zheng, Xinyan; Yuan, Rujin

2018-03-01

The project of The First National Geographic Conditions Census developed by Chinese government has designed the data acquisition content and indexes, and has built corresponding classification system mainly based on the natural property of material. However, the unified standard for land cover classification system has not been formed; the production always needs converting to meet the actual needs. Therefore, it proposed a refined classification method based on multi source of remote sensing information fusion. It takes the third-level classes of forest land and grassland for example, and has collected the thematic data of Vegetation Map of China (1:1,000,000), attempts to develop refined classification utilizing raster spatial analysis model. Study area is selected, and refined classification is achieved by using the proposed method. The results show that land cover within study area is divided principally among 20 classes, from subtropical broad-leaved forest (31131) to grass-forb community type of low coverage grassland (41192); what's more, after 30 years in the study area, climatic factors, developmental rhythm characteristics and vegetation ecological geographical characteristics have not changed fundamentally, only part of the original vegetation types have changed in spatial distribution range or land cover types. Research shows that refined classification for the third-level classes of forest land and grassland could make the results take on both the natural attributes of the original and plant community ecology characteristics, which could meet the needs of some industry application, and has certain practical significance for promoting the product of The First National Geographic Conditions Census.

Remote sensing for the sustainable management and conservation of forest environments biodiversity: the conservation managers perspective.

NASA Astrophysics Data System (ADS)

Aguilar-Amuchas, N.; Henebry, G. M.; Blanchard, J.; Sutter, R.

2008-12-01

The potential use of remote sensing for the design and implementation of sustainable management, conservation, and monitoring of forest biodiversity has been well documented in the scientific literature. However, when we look into how often remote sensing is actually being used in the decision making processes affecting biodiversity conservation and sustainable management, we find that, apart from specific study cases, its use is not as widespread as we know it should. There is an enormous gap between our scientific achievements and their use in the real world towards the preservation of a rapidly vanishing biodiversity. Conservation managers understand the potential remote sensing has. However, logistical constraints and high technical skills requirements render the use of remote sensing data difficult. Sound and easy approaches need to be developed and implemented. We present two study cases that illustrate 1st. How the interaction between tropical forest managers and remote sensing specialist allowed developing a simple method for the identification of priority areas for field surveys of tropical forests management ecological sustainability indicators and, 2nd. How remote sensing is being used by The Nature Conservancy as a first level approach towards the assessment of forest conservation strategies effectiveness in for areas located in 11 states, covering different forest types and a variety of conservation objectives.

Capturing the fugitive: Applying remote sensing to terrestrial animal distribution and diversity

NASA Astrophysics Data System (ADS)

Leyequien, Euridice; Verrelst, Jochem; Slot, Martijn; Schaepman-Strub, Gabriela; Heitkönig, Ignas M. A.; Skidmore, Andrew

2007-02-01

Amongst many ongoing initiatives to preserve biodiversity, the Millennium Ecosystem Assessment again shows the importance to slow down the loss of biological diversity. However, there is still a gap in the overview of global patterns of species distributions. This paper reviews how remote sensing has been used to assess terrestrial faunal diversity, with emphasis on proxies and methodologies, while exploring prospective challenges for the conservation and sustainable use of biodiversity. We grouped and discussed papers dealing with the faunal taxa mammals, birds, reptiles, amphibians, and invertebrates into five classes of surrogates of animal diversity: (1) habitat suitability, (2) photosynthetic productivity, (3) multi-temporal patterns, (4) structural properties of habitat, and (5) forage quality. It is concluded that the most promising approach for the assessment, monitoring, prediction, and conservation of faunal diversity appears to be the synergy of remote sensing products and auxiliary data with ecological biodiversity models, and a subsequent validation of the results using traditional observation techniques.

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

NASA Astrophysics Data System (ADS)

Kittel, Cecile M. M.; Nielsen, Karina; Tøttrup, Christian; Bauer-Gottwein, Peter

2018-02-01

Remote sensing provides a unique opportunity to inform and constrain a hydrological model and to increase its value as a decision-support tool. In this study, we applied a multi-mission approach to force, calibrate and validate a hydrological model of the ungauged Ogooué river basin in Africa with publicly available and free remote sensing observations. We used a rainfall-runoff model based on the Budyko framework coupled with a Muskingum routing approach. We parametrized the model using the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) and forced it using precipitation from two satellite-based rainfall estimates, FEWS-RFE (Famine Early Warning System rainfall estimate) and the Tropical Rainfall Measuring Mission (TRMM) 3B42 v.7, and temperature from ECMWF ERA-Interim. We combined three different datasets to calibrate the model using an aggregated objective function with contributions from (1) historical in situ discharge observations from the period 1953-1984 at six locations in the basin, (2) radar altimetry measurements of river stages by Envisat and Jason-2 at 12 locations in the basin and (3) GRACE (Gravity Recovery and Climate Experiment) total water storage change (TWSC). Additionally, we extracted CryoSat-2 observations throughout the basin using a Sentinel-1 SAR (synthetic aperture radar) imagery water mask and used the observations for validation of the model. The use of new satellite missions, including Sentinel-1 and CryoSat-2, increased the spatial characterization of river stage. Throughout the basin, we achieved good agreement between observed and simulated discharge and the river stage, with an RMSD between simulated and observed water amplitudes at virtual stations of 0.74 m for the TRMM-forced model and 0.87 m for the FEWS-RFE-forced model. The hydrological model also captures overall total water storage change patterns, although the amplitude of storage change is generally underestimated. By combining hydrological modeling with multi-mission remote sensing from 10 different satellite missions, we obtain new information on an otherwise unstudied basin. The proposed model is the best current baseline characterization of hydrological conditions in the Ogooué in light of the available observations.

Monitoring Crop Phenology and Growth Stages from Space: Opportunities and Challenges

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.; Mladenova, I. E.; Kustas, W. P.; Alfieri, J. G.

2014-12-01

Crop growth stages in concert with weather and soil moisture conditions can have a significant impact on crop yields. In the U.S., crop growth stages and conditions are reported by farmers at the county level. These reports are somewhat subjective and fluctuate between different reporters, locations and times. Remote sensing data provide an alternative approach to monitoring crop growth over large areas in a more consistent and quantitative way. In the recent years, remote sensing data have been used to detect vegetation phenology at 1-km spatial resolution globally. However, agricultural applications at field scale require finer spatial resolution remote sensing data. Landsat (30-m) data have been successfully used for agricultural applications. There are many medium resolution sensors available today or in near future. These include Landsat, SPOT, RapidEye, ASTER and future Sentinel-2 etc. Approaches have been developed in the past several years to integrate remote sensing data from different sensors which may have different sensor characteristics, and spatial and temporal resolutions. This allows us opportunities today to map crop growth stages and conditions using dense time-series remote sensing at field scales. However, remotely sensed phenology (or phenological metrics) is normally derived based on the mathematical functions of the time-series data. The phenological metrics are determined by either identifying inflection (curvature) points or some pre-defined thresholds in the remote sensing phenology algorithms. Furthermore, physiological crop growth stages may not be directly correlated to the remotely sensed phenology. The relationship between remotely sensed phenology and crop growth stages is likely to vary for specific crop types and varieties, growing stages, conditions and even locations. In this presentation, we will examine the relationship between remotely sensed phenology and crop growth stages using in-situ measurements from Fluxnet sites and crop progress reports from USDA NASS. We will present remote sensing approaches and focus on: 1) integrating multiple sources of remote sensing data; and 2) extracting crop phenology at field scales. An example in the U.S. Corn Belt area will be presented and analyzed. Future directions for mapping crop growth stages will be discussed.

Air pollution linked to Remote Sensing tools - Science training using a Master's Level e-Learning Tool

NASA Astrophysics Data System (ADS)

Ladstaetter-Weissenmayer, A.; Kanakidou, M.; Richter, A.; Wagner, T.; Borrell, P.; Law, R. J.; Burrows, J. P.

2009-09-01

As we know it today air pollution is a release into the atmosphere of any substances, chemicals or particles, which are harmful both to the human and animal health as well as the health of the wider environment. The use of satellite based instruments is a young and developing research field and excellent for studying air pollution events over large areas at high spatial-temporal resolutions, especially when ground measurements, which are limited in spatial-temporal coverage, are not available. Students on postgraduate level should be trained in using, and analysing remote sensing data from both ground and satellite based or in interpreting the high variety in remote sensing e.g satellite images or maps. As follows an e-learning online module has been devised and constructed to facilitate the teaching of Remote Sensing of Troposphere from Space to research students at a Master's level. The module, which is essentially an interactive on-line text book, is stand alone, although it could be encompassed within a standard course management system. The scientific content is presented as study pages under three headings: remote sensing from space, the basics of radiation transfer, and retrieval procedures for tropospheric satellite data.The student is encouraged to test his or her comprehension of the material through exercises on the scientific topics.

Air pollution linked to Remote Sensing tools - Science training using a Master's Level e-Learning Tool

NASA Astrophysics Data System (ADS)

Ladstätter-Weißenmayer, A.; Kanakidou, M.; Richter, A.; Wagner, T.; Borrell, P.; Law, R. J.; Burrows, J. P.

2009-04-01

As we know it today air pollution is a release into the atmosphere of any substances, chemicals or particles, which are harmful both to the human and animal health as well as the health of the wider environment. The use of satellite based instruments is a young and developing research field and excellent for studying air pollution events over large areas at high spatial-temporal resolutions, especially when ground measurements, which are limited in spatial-temporal coverage, are not available. Students on postgraduate level should be trained in using, and analysing remote sensing data from both ground and satellite based or in interpreting the high variety in remote sensing e.g satellite images or maps. As follows an e-learning online module has been devised and constructed to facilitate the teaching of Remote Sensing of Troposphere from Space to research students at a Master's level. The module, which is essentially an interactive on-line text book, is stand alone, although it could be encompassed within a standard course management system. The scientific content is presented as study pages under three headings: remote sensing from space, the basics of radiation transfer, and retrieval procedures for tropospheric satellite data.The student is encouraged to test his or her comprehension of the material through exercises on the scientific topics.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gonçalves, Fabio; Treuhaft, Robert; Law, Beverly

Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertaintymore » that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Lastly, our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches.« less

An evaluation of the use of remotely sensed parameters for prediction of incidence and risk associated with Vibrio parahaemolyticus in Gulf Coast oysters (Crassostrea virginica).

PubMed

Phillips, A M B; Depaola, A; Bowers, J; Ladner, S; Grimes, D J

2007-04-01

The U.S. Food and Drug Administration recently published a Vibrio parahaemolyticus risk assessment for consumption of raw oysters that predicts V. parahaemolyticus densities at harvest based on water temperature. We retrospectively compared archived remotely sensed measurements (sea surface temperature, chlorophyll, and turbidity) with previously published data from an environmental study of V. parahaemolyticus in Alabama oysters to assess the utility of the former data for predicting V. parahaemolyticus densities in oysters. Remotely sensed sea surface temperature correlated well with previous in situ measurements (R(2) = 0.86) of bottom water temperature, supporting the notion that remotely sensed sea surface temperature data are a sufficiently accurate substitute for direct measurement. Turbidity and chlorophyll levels were not determined in the previous study, but in comparison with the V. parahaemolyticus data, remotely sensed values for these parameters may explain some of the variation in V. parahaemolyticus levels. More accurate determination of these effects and the temporal and spatial variability of these parameters may further improve the accuracy of prediction models. To illustrate the utility of remotely sensed data as a basis for risk management, predictions based on the U.S. Food and Drug Administration V. parahaemolyticus risk assessment model were integrated with remotely sensed sea surface temperature data to display graphically variations in V. parahaemolyticus density in oysters associated with spatial variations in water temperature. We believe images such as these could be posted in near real time, and that the availability of such information in a user-friendly format could be the basis for timely and informed risk management decisions.

Multi-modal remote sensing system (MRSS) for transportation infrastructure inspection and monitoring.

DOT National Transportation Integrated Search

2013-12-01

In this final report draft, our research results during the project period are highlighted : and summarized. Detailed description of our research activities is documented in our : submitted quarterly reports. The results reported in this Final Report...

Current Research in Lidar Technology Used for the Remote Sensing of Atmospheric Aerosols

PubMed Central

Comerón, Adolfo; Muñoz-Porcar, Constantino; Rocadenbosch, Francesc; Rodríguez-Gómez, Alejandro; Sicard, Michaël

2017-01-01

Lidars are active optical remote sensing instruments with unique capabilities for atmospheric sounding. A manifold of atmospheric variables can be profiled using different types of lidar: concentration of species, wind speed, temperature, etc. Among them, measurement of the properties of aerosol particles, whose influence in many atmospheric processes is important but is still poorly stated, stands as one of the main fields of application of current lidar systems. This paper presents a review on fundamentals, technology, methodologies and state-of-the art of the lidar systems used to obtain aerosol information. Retrieval of structural (aerosol layers profiling), optical (backscatter and extinction coefficients) and microphysical (size, shape and type) properties requires however different levels of instrumental complexity; this general outlook is structured following a classification that attends these criteria. Thus, elastic systems (detection only of emitted frequencies), Raman systems (detection also of Raman frequency-shifted spectral lines), high spectral resolution lidars, systems with depolarization measurement capabilities and multi-wavelength instruments are described, and the fundamentals in which the retrieval of aerosol parameters is based is in each case detailed. PMID:28632170

A geospatial database model for the management of remote sensing datasets at multiple spectral, spatial, and temporal scales

NASA Astrophysics Data System (ADS)

Ifimov, Gabriela; Pigeau, Grace; Arroyo-Mora, J. Pablo; Soffer, Raymond; Leblanc, George

2017-10-01

In this study the development and implementation of a geospatial database model for the management of multiscale datasets encompassing airborne imagery and associated metadata is presented. To develop the multi-source geospatial database we have used a Relational Database Management System (RDBMS) on a Structure Query Language (SQL) server which was then integrated into ArcGIS and implemented as a geodatabase. The acquired datasets were compiled, standardized, and integrated into the RDBMS, where logical associations between different types of information were linked (e.g. location, date, and instrument). Airborne data, at different processing levels (digital numbers through geocorrected reflectance), were implemented in the geospatial database where the datasets are linked spatially and temporally. An example dataset consisting of airborne hyperspectral imagery, collected for inter and intra-annual vegetation characterization and detection of potential hydrocarbon seepage events over pipeline areas, is presented. Our work provides a model for the management of airborne imagery, which is a challenging aspect of data management in remote sensing, especially when large volumes of data are collected.

Multi-Scale Drought Analysis using Thermal Remote Sensing: A Case Study in Georgia’s Altamaha River Watershed

NASA Astrophysics Data System (ADS)

Jacobs, J. M.; Bhat, S.; Choi, M.; Mecikalski, J. R.; Anderson, M. C.

2009-12-01

The unprecedented recent droughts in the Southeast US caused reservoir levels to drop dangerously low, elevated wildfire hazard risks, reduced hydropower generation and caused severe economic hardships. Most drought indices are based on recent rainfall or changes in vegetation condition. However in heterogeneous landscapes, soils and vegetation (type and cover) combine to differentially stress regions even under similar weather conditions. This is particularly true for the heterogeneous landscapes and highly variable rainfall in the Southeastern United States. This research examines the spatiotemperal evolution of watershed scale drought using a remotely sensed stress index. Using thermal-infrared imagery, a fully automated inverse model of Atmosphere-Land Exchange (ALEXI), GIS datasets and analysis tools, modeled daily surface moisture stress is examined at a 10-km resolution grid covering central to southern Georgia. Regional results are presented for the 2000-2008 period. The ALEXI evaporative stress index (ESI) is compared to existing regional drought products and validated using local hydrologic measurements in Georgia’s Altamaha River watershed at scales from 10 to 10,000 km2.

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

PubMed

Zhou, Weiqi; Troy, Austin; Grove, Morgan

2008-05-01

This article investigates how remotely sensed lawn characteristics, such as parcel lawn area and parcel lawn greenness, combined with household characteristics, can be used to predict household lawn fertilization practices on private residential lands. This study involves two watersheds, Glyndon and Baisman's Run, in Baltimore County, Maryland, USA. Parcel lawn area and lawn greenness were derived from high-resolution aerial imagery using an object-oriented classification approach. Four indicators of household characteristics, including lot size, square footage of the house, housing value, and housing age were obtained from a property database. Residential lawn care survey data combined with remotely sensed parcel lawn area and greenness data were used to estimate two measures of household lawn fertilization practices, household annual fertilizer nitrogen application amount (N_yr) and household annual fertilizer nitrogen application rate (N_ha_yr). Using multiple regression with multi-model inferential procedures, we found that a combination of parcel lawn area and parcel lawn greenness best predicts N_yr, whereas a combination of parcel lawn greenness and lot size best predicts variation in N_ha_yr. Our analyses show that household fertilization practices can be effectively predicted by remotely sensed lawn indices and household characteristics. This has significant implications for urban watershed managers and modelers.

Characterizing environmental change in interior Alaska (1982-2012) using multi-temporal, multi-scale remote sensing data and field measurements

Treesearch

Hans-Erik Andersen; Robert. Pattison

2012-01-01

We investigate how vegetation in the Tanana Valley of interior Alaska (120,000 km2) has responded to a changing climate over the preceding three decades (1982-2012). Expected impacts include: 1) drying of wetlands and subsequent encroachment of woody vegetation into areas previously dominated by herbaceous and bryoid vegetation types, 2) changes...

Satellite remote sensing of surface energy and mass balance - Results from FIFE

NASA Technical Reports Server (NTRS)

Hall, F. G.; Markham, B. J.; Wang, J. R.; Huemmrich, F.; Sellers, P. J.; Strebel, D. E.; Kanemasu, E. T.; Kelly, Robert D.; Blad, Blaine L.

1991-01-01

Results obtained from the FIFE experiments conducted in 1987 and 1989 are summarized. Data analyses indicate that the hypotheses linking energy balance components to surface biology and remote sensing are reasonable at a point level, and that satellite remote sensing can potentially provide useful estimates of the surface energy budget. An investigation of atmospheric scattering and absorption effects on satellite remote sensing of surface radiance shows that the magnitude of atmospheric opacity variations within the FIFE site and with season can have a large effect on satellite measured values of surface radiances. Comparisons of atmospherically corrected TM radiances with surface measured radiances agreed to within about two percent at the visible and near-infrared wavelengths and to 6 percent in the midinfrared.

An information system design for watershed-wide modeling of water loss to the atmosphere using remote sensing techniques

NASA Technical Reports Server (NTRS)

Khorram, S.

1977-01-01

Results are presented of a study intended to develop a general location-specific remote-sensing procedure for watershed-wide estimation of water loss to the atmosphere by evaporation and transpiration. The general approach involves a stepwise sequence of required information definition (input data), appropriate sample design, mathematical modeling, and evaluation of results. More specifically, the remote sensing-aided system developed to evaluate evapotranspiration employs a basic two-stage two-phase sample of three information resolution levels. Based on the discussed design, documentation, and feasibility analysis to yield timely, relatively accurate, and cost-effective evapotranspiration estimates on a watershed or subwatershed basis, work is now proceeding to implement this remote sensing-aided system.

Multi-Sensor Registration of Earth Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Cole-Rhodes, Arlene; Eastman, Roger; Johnson, Kisha; Morisette, Jeffrey; Netanyahu, Nathan S.; Stone, Harold S.; Zavorin, Ilya; Zukor, Dorothy (Technical Monitor)

2001-01-01

Assuming that approximate registration is given within a few pixels by a systematic correction system, we develop automatic image registration methods for multi-sensor data with the goal of achieving sub-pixel accuracy. Automatic image registration is usually defined by three steps; feature extraction, feature matching, and data resampling or fusion. Our previous work focused on image correlation methods based on the use of different features. In this paper, we study different feature matching techniques and present five algorithms where the features are either original gray levels or wavelet-like features, and the feature matching is based on gradient descent optimization, statistical robust matching, and mutual information. These algorithms are tested and compared on several multi-sensor datasets covering one of the EOS Core Sites, the Konza Prairie in Kansas, from four different sensors: IKONOS (4m), Landsat-7/ETM+ (30m), MODIS (500m), and SeaWIFS (1000m).

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

NASA Astrophysics Data System (ADS)

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

2009-05-01

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

Investigation of Oil Fluorescence as a Technique for the Remote Sensing of Oil Spills

DOT National Transportation Integrated Search

1971-06-01

The flexibility of remote sensing of oil spills by laser-excited oil fluorescence is investigated. The required parameters are fed into a physical model to predict signal and background levels; and the predictions are verified by field experiments. A...

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

NASA Astrophysics Data System (ADS)

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.

The application of geologic remote sensing to vertebrate biostratigraphy - General results from the Wind River Basin, Wyoming

NASA Technical Reports Server (NTRS)

Stucky, Richard K.; Krishtalka, Leonard

1991-01-01

Since 1986, remote sensing images derived from satellite and aircraft-borne sensor data have been used to study the stratigraphy and sedimentology of the vertebrate-bearing Wind River and Wagon Bed formations in the Wind River Basin (Wyoming). Landsat 5 TM and aircraft Thermal Infrared Multispectral Scanner data were combined with conventional geologic analyses. The remote sensing data have contributed significantly to: (1) geologic mapping at the formation, member, and bed levels; (2) stratigraphic correlation; (3) reconstruction of ancient depositional environments; and (4) identification of structural complexity. This information is critical to vertebrate paleontology in providing the stratigraphic, sedimentologic, and structural framework required for evolutionary and paleoecologic studies. Of primary importance is the ability to map at minimal cost the geology of large areas (20,000 sq km or greater) at a high level of precision. Remote sensing data can be especially useful in geologically and paleontologically unexplored or poorly understood regions.

Remote sensing for industrial applications in the energy business: digital territorial data integration for planning of overhead power transmission lines (OHTLs)

NASA Astrophysics Data System (ADS)

Terrazzino, Alfonso; Volponi, Silvia; Borgogno Mondino, Enrico

2001-12-01

An investigation has been carried out, concerning remote sensing techniques, in order to assess their potential application to the energy system business: the most interesting results concern a new approach, based on digital data from remote sensing, to infrastructures with a large territorial distribution: in particular OverHead Transmission Lines, for the high voltage transmission and distribution of electricity on large distances. Remote sensing could in principle be applied to all the phases of the system lifetime, from planning to design, to construction, management, monitoring and maintenance. In this article, a remote sensing based approach is presented, targeted to the line planning: optimization of OHTLs path and layout, according to different parameters (technical, environmental and industrial). Planning new OHTLs is of particular interest in emerging markets, where typically the cartography is missing or available only on low accuracy scale (1:50.000 and lower), often not updated. Multi- spectral images can be used to generate thematic maps of the region of interest for the planning (soil coverage). Digital Elevation Models (DEMs), allow the planners to easily access the morphologic information of the surface. Other auxiliary information from local laws, environmental instances, international (IEC) standards can be integrated in order to perform an accurate optimized path choice and preliminary spotting of the OHTLs. This operation is carried out by an ABB proprietary optimization algorithm: the output is a preliminary path that bests fits the optimization parameters of the line in a life cycle approach.

Criteria for the optimal selection of remote sensing optical images to map event landslides

NASA Astrophysics Data System (ADS)

Fiorucci, Federica; Giordan, Daniele; Santangelo, Michele; Dutto, Furio; Rossi, Mauro; Guzzetti, Fausto

2018-01-01

Landslides leave discernible signs on the land surface, most of which can be captured in remote sensing images. Trained geomorphologists analyse remote sensing images and map landslides through heuristic interpretation of photographic and morphological characteristics. Despite a wide use of remote sensing images for landslide mapping, no attempt to evaluate how the image characteristics influence landslide identification and mapping exists. This paper presents an experiment to determine the effects of optical image characteristics, such as spatial resolution, spectral content and image type (monoscopic or stereoscopic), on landslide mapping. We considered eight maps of the same landslide in central Italy: (i) six maps obtained through expert heuristic visual interpretation of remote sensing images, (ii) one map through a reconnaissance field survey, and (iii) one map obtained through a real-time kinematic (RTK) differential global positioning system (dGPS) survey, which served as a benchmark. The eight maps were compared pairwise and to a benchmark. The mismatch between each map pair was quantified by the error index, E. Results show that the map closest to the benchmark delineation of the landslide was obtained using the higher resolution image, where the landslide signature was primarily photographical (in the landslide source and transport area). Conversely, where the landslide signature was mainly morphological (in the landslide deposit) the best mapping result was obtained using the stereoscopic images. Albeit conducted on a single landslide, the experiment results are general, and provide useful information to decide on the optimal imagery for the production of event, seasonal and multi-temporal landslide inventory maps.

Combining optical remote sensing, agricultural statistics and field observations for culture recognition over a peri-urban region

NASA Astrophysics Data System (ADS)

Delbart, Nicolas; Emmanuelle, Vaudour; Fabienne, Maignan; Catherine, Ottlé; Jean-Marc, Gilliot

2017-04-01

This study explores the potential of multi-temporal optical remote sensing, with high revisit frequency, to derive missing information on agricultural calendar and crop types over the agricultural lands in the Versailles plain in the western Paris suburbs. This study comes besides past and ongoing studies on the use of radar and high spatial resolution optical remote sensing to monitor agricultural practices in this study area (e.g. Vaudour et al. 2014). Agricultural statistics, such as the Land Parcel Identification System (LPIS) for France, permit to know the nature of annual crops for each digitized declared field of this land parcel registry. However, within each declared field several cropped plots and a diversity of practices may exist, being marked by agricultural rotations which vary both spatially and temporally within it and differ from one year to the other. Even though the new LPIS to be released in 2016 is expected to describe individual plots within declared fields, its attributes may not enable to discriminate between winter and spring crops. Here we evaluate the potential of high observation frequency remote sensing to differentiate seasonal crops based essentially on the seasonality of the spectral properties. In particular, we use the Landsat data to spatially disaggregate the LPIS statistical data, on the basis of the analysis of the remote sensing spectral seasonality measured on a number of selected ground-observed fields. This work is carried out in the framework of the CNES TOSCA-PLEIADES-CO of the French Space Agency.

Spatially Distributed Assimilation of Remotely Sensed Leaf Area Index and Potential Evapotranspiration for Hydrologic Modeling in Wetland Landscapes

NASA Astrophysics Data System (ADS)

Rajib, A.; Evenson, G. R.; Golden, H. E.; Lane, C.

2017-12-01

Evapotranspiration (ET), a highly dynamic flux in wetland landscapes, regulates the accuracy of surface/sub-surface runoff simulation in a hydrologic model. Accordingly, considerable uncertainty in simulating ET-related processes remains, including our limited ability to incorporate realistic ground conditions, particularly those involved with complex land-atmosphere feedbacks, vegetation growth, and energy balances. Uncertainty persists despite using high resolution topography and/or detailed land use data. Thus, a good hydrologic model can produce right answers for wrong reasons. In this study, we develop an efficient approach for multi-variable assimilation of remotely sensed earth observations (EOs) into a hydrologic model and apply it in the 1700 km2 Pipestem Creek watershed in the Prairie Pothole Region of North Dakota, USA. Our goal is to employ EOs, specifically Leaf Area Index (LAI) and Potential Evapotranspiration (PET), as surrogates for the aforementioned processes without overruling the model's built-in physical/semi-empirical process conceptualizations. To do this, we modified the source code of an already-improved version of the Soil and Water Assessment Tool (SWAT) for wetland hydrology (Evenson et al. 2016 HP 30(22):4168) to directly assimilate remotely-sensed LAI and PET (obtained from the 500 m and 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) gridded products, respectively) into each model Hydrologic Response Unit (HRU). Two configurations of the model, one with and one without EO assimilation, are calibrated against streamflow observations at the watershed outlet. Spatio-temporal changes in the HRU-level water balance, based on calibrated outputs, are evaluated using MODIS Actual Evapotranspiration (AET) as a reference. It is expected that the model configuration having remotely sensed LAI and PET, will simulate more realistic land-atmosphere feedbacks, vegetation growth and energy balance. As a result, this will decrease simulated water balance uncertainties compared to the default model configuration.

Studies and proposals on remote sensing programs for the evaluation and management of resources in Latin America (Columbia)

NASA Technical Reports Server (NTRS)

1982-01-01

A report is given on the situation in regard to diagnostic studies carried out on the use of remote sensing techniques at the regional level, and discussions held at international meetings on this topic. Studies and proposals presented by different organizations are discussed. It is concluded that a consultative body must be established at the regional level.

Sen2-Agri country level demonstration for Ukraine

NASA Astrophysics Data System (ADS)

Kussul, N.; Kolotii, A.; Shelestov, A.; Lavreniuk, M. S.

2016-12-01

Due to launch of Sentinel-2 mission European Space Agency (ESA) started Sentinel-2 for Agriculture (Sen2-Agri) project coordinated by Universite catholique de Louvain (UCL). Ukraine is selected as one of 3 country level demonstration sites for benchmarking Sentinel-2 data due to wide range of main crops (both winter and summer), big fields and high enough climate variability over the territory [1-2]. Within this county level demonstration main objectives are following: i) Sentinel's products quality assessment and their suitability estimation for the territory of Ukraine [2]; ii) demonstration in order to convince decision makers and state authorities; iii) assessment of the personnel and facilities required to run the Sen2-Agri system and creation of Sen-2 Agri products (crop type maps and such essential climatic variable as Leaf Area Index - LAI [3]). During this project ground data were collected for crop land mapping and crop type classification along the roads within main agro-climatic zones of Ukraine. For LAI estimation we used indirect non-destructive method which is based on DHP-images and VALERI protocol. Products created with use of Sen2-Agri system deployed during project execution and results of neural-network approach utilization will be compared. References Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (6), 2500-2508. Kussul, N., Skakun, S., Shelestov, A., Lavreniuk, M., Yailymov, B., & Kussul, O. (2015). Regional scale crop mapping using multi-temporal satellite imagery. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 45-52. Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., & Kostetsky, O. (2015, July). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1733-1736

Development of a multi-sensor based urban discharge forecasting system using remotely sensed data: A case study of extreme rainfall in South Korea

NASA Astrophysics Data System (ADS)

Yoon, Sunkwon; Jang, Sangmin; Park, Kyungwon

2017-04-01

Extreme weather due to changing climate is a main source of water-related disasters such as flooding and inundation and its damage will be accelerated somewhere in world wide. To prevent the water-related disasters and mitigate their damage in urban areas in future, we developed a multi-sensor based real-time discharge forecasting system using remotely sensed data such as radar and satellite. We used Communication, Ocean and Meteorological Satellite (COMS) and Korea Meteorological Agency (KMA) weather radar for quantitative precipitation estimation. The Automatic Weather System (AWS) and McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) were used for verification of rainfall accuracy. The optimal Z-R relation was applied the Tropical Z-R relationship (Z=32R1.65), it has been confirmed that the accuracy is improved in the extreme rainfall events. In addition, the performance of blended multi-sensor combining rainfall was improved in 60mm/h rainfall and more strong heavy rainfall events. Moreover, we adjusted to forecast the urban discharge using Storm Water Management Model (SWMM). Several statistical methods have been used for assessment of model simulation between observed and simulated discharge. In terms of the correlation coefficient and r-squared discharge between observed and forecasted were highly correlated. Based on this study, we captured a possibility of real-time urban discharge forecasting system using remotely sensed data and its utilization for real-time flood warning. Acknowledgement This research was supported by a grant (13AWMP-B066744-01) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport (MOLIT) of Korean government.

A novel linear physical model for remote sensing of snow wetness and snow density using the visible and infrared bands

NASA Astrophysics Data System (ADS)

Varade, D. M.; Dikshit, O.

2017-12-01

Modeling and forecasting of snowmelt runoff are significant for understanding the hydrological processes in the cryosphere which requires timely information regarding snow physical properties such as liquid water content and density of snow in the topmost layer of the snowpack. Both the seasonal runoffs and avalanche forecasting are vastly dependent on the inherent physical characteristics of the snowpack which are conventionally measured by field surveys in difficult terrains at larger impending costs and manpower. With advances in remote sensing technology and the increase in the availability of satellite data, the frequency and extent of these surveys could see a declining trend in future. In this study, we present a novel approach for estimating snow wetness and snow density using visible and infrared bands that are available with most multi-spectral sensors. We define a trapezoidal feature space based on the spectral reflectance in the near infrared band and the Normalized Differenced Snow Index (NDSI), referred to as NIR-NDSI space, where dry snow and wet snow are observed in the left diagonal upper and lower right corners, respectively. The corresponding pixels are extracted by approximating the dry and wet edges which are used to develop a linear physical model to estimate snow wetness. Snow density is then estimated using the modeled snow wetness. Although the proposed approach has used Sentinel-2 data, it can be extended to incorporate data from other multi-spectral sensors. The estimated values for snow wetness and snow density show a high correlation with respect to in-situ measurements. The proposed model opens a new avenue for remote sensing of snow physical properties using multi-spectral data, which were limited in the literature.

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase one, volume 1 : summary report.

DOT National Transportation Integrated Search

2009-12-01

The Integrated Remote Sensing and Visualization System (IRSV) is being designed to accommodate the needs of todays Bridge : Engineers at the state and local level from the following aspects: : Better understanding and enforcement of a complex ...

Moving forward on remote sensing of soil salinity at regional scale

USDA-ARS?s Scientific Manuscript database

Soil salinity undermines global agriculture by reducing crop yield and soil quality. Irrigation management can help control salinity levels within the root-zone. To best allocate water resources, accurate regional-scale inventories are needed. Two remote sensing approaches are currently used to moni...

Validation plays the role of a "bridge" in connecting remote sensing research and applications

NASA Astrophysics Data System (ADS)

Wang, Zhiqiang; Deng, Ying; Fan, Yida

2018-07-01

Remote sensing products contribute to improving earth observations over space and time. Uncertainties exist in products of different levels; thus, validation of these products before and during their applications is critical. This study discusses the meaning of validation in depth and proposes a new definition of reliability for use with such products. In this context, validation should include three aspects: a description of the relevant uncertainties, quantitative measurement results and a qualitative judgment that considers the needs of users. A literature overview is then presented evidencing improvements in the concepts associated with validation. It shows that the root mean squared error (RMSE) is widely used to express accuracy; increasing numbers of remote sensing products have been validated; research institutes contribute most validation efforts; and sufficient validation studies encourage the application of remote sensing products. Validation plays a connecting role in the distribution and application of remote sensing products. Validation connects simple remote sensing subjects with other disciplines, and it connects primary research with practical applications. Based on the above findings, it is suggested that validation efforts that include wider cooperation among research institutes and full consideration of the needs of users should be promoted.

Applications of Remote Sensing to Alien Invasive Plant Studies

PubMed Central

Huang, Cho-ying; Asner, Gregory P.

2009-01-01

Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions. Therefore, it is often important to systematically monitor the spread of species over a broad region. Remote sensing has been an important tool for large-scale ecological studies in the past three decades, but it was not commonly used to study alien invasive plants until the mid 1990s. We synthesize previous research efforts on remote sensing of invasive plants from spatial, temporal and spectral perspectives. We also highlight a recently developed state-of-the-art image fusion technique that integrates passive and active energies concurrently collected by an imaging spectrometer and a scanning-waveform light detection and ranging (LiDAR) system, respectively. This approach provides a means to detect the structure and functional properties of invasive plants of different canopy levels. Finally, we summarize regional studies of biological invasions using remote sensing, discuss the limitations of remote sensing approaches, and highlight current research needs and future directions. PMID:22408558

On multidisciplinary research on the application of remote sensing to water resources problems. [Wisconsin

NASA Technical Reports Server (NTRS)

Clapp, J. L.

1973-01-01

Research objectives during 1972-73 were to: (1) Ascertain the extent to which special aerial photography can be operationally used in monitoring water pollution parameters. (2) Ascertain the effectiveness of remote sensing in the investigation of nearshore mixing and coastal entrapment in large water bodies. (3) Develop an explicit relationship of the extent of the mixing zone in terms of the outfall, effluent and water body characteristics. (4) Develop and demonstrate the use of the remote sensing method as an effective legal implement through which administrative agencies and courts can not only investigate possible pollution sources but also legally prove the source of water pollution. (5) Evaluate the field potential of remote sensing techniques in monitoring algal blooms and aquatic macrophytes, and the use of these as indicators of lake eutrophication level. (6) Develop a remote sensing technique for the determination of the location and extent of hydrologically active source areas in a watershed.

Hydrological Relevant Parameters from Remote Sensing - Spatial Modelling Input and Validation Basis

NASA Astrophysics Data System (ADS)

Hochschild, V.

2012-12-01

This keynote paper will demonstrate how multisensoral remote sensing data is used as spatial input for mesoscale hydrological modeling as well as for sophisticated validation purposes. The tasks of Water Resources Management are subject as well as the role of remote sensing in regional catchment modeling. Parameters derived from remote sensing discussed in this presentation will be land cover, topographical information from digital elevation models, biophysical vegetation parameters, surface soil moisture, evapotranspiration estimations, lake level measurements, determination of snow covered area, lake ice cycles, soil erosion type, mass wasting monitoring, sealed area, flash flood estimation. The actual possibilities of recent satellite and airborne systems are discussed, as well as the data integration into GIS and hydrological modeling, scaling issues and quality assessment will be mentioned. The presentation will provide an overview of own research examples from Germany, Tibet and Africa (Ethiopia, South Africa) as well as other international research activities. Finally the paper gives an outlook on upcoming sensors and concludes the possibilities of remote sensing in hydrology.

MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Lin, Daoyu; Fu, Kun; Wang, Yang; Xu, Guangluan; Sun, Xian

2017-11-01

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model $G$ and a discriminative model $D$. We treat $D$ as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. $G$ can produce numerous images that are similar to the training data; therefore, $D$ can learn better representations of remotely sensed images using the training data provided by $G$. The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.

Tools and Data Services from the NASA Earth Satellite Observations for Remote Sensing Commercial Applications

NASA Technical Reports Server (NTRS)

Vicente, Gilberto

2005-01-01

Several commercial applications of remote sensing data, such as water resources management, environmental monitoring, climate prediction, agriculture, forestry, preparation for and migration of extreme weather events, require access to vast amounts of archived high quality data, software tools and services for data manipulation and information extraction. These on the other hand require gaining detailed understanding of the data's internal structure and physical implementation of data reduction, combination and data product production. The time-consuming task must be undertaken before the core investigation can begin and is an especially difficult challenge when science objectives require users to deal with large multi-sensor data sets of different formats, structures, and resolutions.

Supervised Semantic Classification for Nuclear Proliferation Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vatsavai, Raju; Cheriyadat, Anil M; Gleason, Shaun Scott

2010-01-01

Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remote sensing imagery. In this paper we present a supervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 120 images collected under different spatial and temporal settings over the globe representing three major semantic categories: airports, nuclear, and coal power plants. Initial experimental results show a reasonable discrimination of these three categories even though coal and nuclear images share highly common and overlapping objects. This research also identified several research challenges associated with nuclear proliferationmore » monitoring using high resolution remote sensing images.« less

A computer software system for integration and analysis of grid-based remote sensing data with other natural resource data. Remote Sensing Project

NASA Technical Reports Server (NTRS)

Tilmann, S. E.; Enslin, W. R.; Hill-Rowley, R.

1977-01-01

A computer-based information system is described designed to assist in the integration of commonly available spatial data for regional planning and resource analysis. The Resource Analysis Program (RAP) provides a variety of analytical and mapping phases for single factor or multi-factor analyses. The unique analytical and graphic capabilities of RAP are demonstrated with a study conducted in Windsor Township, Eaton County, Michigan. Soil, land cover/use, topographic and geological maps were used as a data base to develope an eleven map portfolio. The major themes of the portfolio are land cover/use, non-point water pollution, waste disposal, and ground water recharge.

[A review on research of land surface water and heat fluxes].

PubMed

Sun, Rui; Liu, Changming

2003-03-01

Many field experiments were done, and soil-vegetation-atmosphere transfer(SVAT) models were stablished to estimate land surface heat fluxes. In this paper, the processes of experimental research on land surface water and heat fluxes are reviewed, and three kinds of SVAT model(single layer model, two layer model and multi-layer model) are analyzed. Remote sensing data are widely used to estimate land surface heat fluxes. Based on remote sensing and energy balance equation, different models such as simplified model, single layer model, extra resistance model, crop water stress index model and two source resistance model are developed to estimate land surface heat fluxes and evapotranspiration. These models are also analyzed in this paper.

Spatial Predictive Modeling and Remote Sensing of Land Use Change in the Chesapeake Bay Watershed

NASA Technical Reports Server (NTRS)

Goetz, Scott J.; Bockstael, Nancy E.; Jantz, Claire A.

2005-01-01

This project was focused on modeling the processes by which increasing demand for developed land uses, brought about by changes in the regional economy and the socio-demographics of the region, are translated into a changing spatial pattern of land use. Our study focused on a portion of the Chesapeake Bay Watershed where the spatial patterns of sprawl represent a set of conditions generally prevalent in much of the U.S. Working in the region permitted us access to (i) a time-series of multi-scale and multi-temporal (including historical) satellite imagery and (ii) an established network of collaborating partners and agencies willing to share resources and to utilize developed techniques and model results. In addition, a unique parcel-level tax assessment database and linked parcel boundary maps exists for two counties in the Maryland portion of this region that made it possible to establish a historical cross-section time-series database of parcel level development decisions. Scenario analyses of future land use dynamics provided critical quantitative insight into the impact of alternative land management and policy decisions. These also have been specifically aimed at addressing growth control policies aimed at curbing exurban (sprawl) development. Our initial technical approach included three components: (i) spatial econometric modeling of the development decision, (ii) remote sensing of suburban change and residential land use density, including comparisons of past change from Landsat analyses and more traditional sources, and (iii) linkages between the two through variable initialization and supplementation of parcel level data. To these we added a fourth component, (iv) cellular automata modeling of urbanization, which proved to be a valuable addition to the project. This project has generated both remote sensing and spatially explicit socio-economic data to estimate and calibrate the parameters for two different types of land use change models and has undertaken analyses of these models. One (the CA model) is driven largely by observations on past patterns of land use change, while the other (the EC model) is driven by mechanisms of the land use change decision at the parcel level. Our project may be the first serious attempt at developing both types of models for the same area, using as much common data as possible. We have identified the strengths and weaknesses of the two approaches and plan to continue to revise each model in the light of new data and new lessons learned through continued collaboration. Questions, approaches, findings, publication and presentation lists concerning the research are also presented.

Scalability Issues for Remote Sensing Infrastructure: A Case Study

PubMed Central

Liu, Yang; Picard, Sean; Williamson, Carey

2017-01-01

For the past decade, a team of University of Calgary researchers has operated a large “sensor Web” to collect, analyze, and share scientific data from remote measurement instruments across northern Canada. This sensor Web receives real-time data streams from over a thousand Internet-connected sensors, with a particular emphasis on environmental data (e.g., space weather, auroral phenomena, atmospheric imaging). Through research collaborations, we had the opportunity to evaluate the performance and scalability of their remote sensing infrastructure. This article reports the lessons learned from our study, which considered both data collection and data dissemination aspects of their system. On the data collection front, we used benchmarking techniques to identify and fix a performance bottleneck in the system’s memory management for TCP data streams, while also improving system efficiency on multi-core architectures. On the data dissemination front, we used passive and active network traffic measurements to identify and reduce excessive network traffic from the Web robots and JavaScript techniques used for data sharing. While our results are from one specific sensor Web system, the lessons learned may apply to other scientific Web sites with remote sensing infrastructure. PMID:28468262

Working Together for Better Student Learning: A Multi-University, Multi-Federal Partner Program for Asynchronous Learning Module Development for Radar-Based Remote Sensing Systems

ERIC Educational Resources Information Center

Yeary, M. B.; Yu, T.; Palmer, R. D.; Monroy, H.; Ruin, I.; Zhang, G.; Chilson, P. B.; Biggerstaff, M. I.; Weiss, C.; Mitchell, K. A.; Fink, L. D.

2010-01-01

Students are not exposed to enough real-life data. This paper describes how a community of scholars seeks to remedy this deficiency and gives the pedagogical details of an ongoing project that commenced in the Fall 2004 semester. Fostering deep learning, this multiyear project offers a new active-learning, hands-on interdisciplinary laboratory…

Analyzing soil erosion using a multi-temporal UAV data set after one year of active agriculture in Navarra, Spain

NASA Astrophysics Data System (ADS)

Anders, Niels; Keesstra, Saskia; Masselink, Rens

2014-05-01

Unmanned Aerial System (UAS) are becoming popular tools in the geosciences due to improving technology and processing/analysis techniques. They can potentially fill the gap between spaceborne or manned aircraft remote sensing and terrestrial remote sensing, both in terms of spatial and temporal resolution. In this study we analyze a multi-temporal data set that was acquired with a fixed-wing UAS in an agricultural catchment (2 sq. km) in Navarra, Spain. The goal of this study is to register soil erosion activity after one year of agricultural activity. The aircraft was equipped with a Panasonic GX1 16MP pocket camera with a 20 mm lens to capture normal JPEG RGB images. The data set consisted of two sets of imagery acquired in the end of February in 2013 and 2014 after harvesting. The raw images were processed using Agisoft Photoscan Pro which includes the structure-from-motion (SfM) and multi-view stereopsis (MVS) algorithms producing digital surface models and orthophotos of both data sets. A discussion is presented that is focused on the suitability of multi-temporal UAS data and SfM/MVS processing for quantifying soil loss, mapping the distribution of eroded materials and analyzing re-occurrences of rill patterns after plowing.

Neural networks for satellite remote sensing and robotic sensor interpretation

NASA Astrophysics Data System (ADS)

Martens, Siegfried

Remote sensing of forests and robotic sensor fusion can be viewed, in part, as supervised learning problems, mapping from sensory input to perceptual output. This dissertation develops ARTMAP neural networks for real-time category learning, pattern recognition, and prediction tailored to remote sensing and robotics applications. Three studies are presented. The first two use ARTMAP to create maps from remotely sensed data, while the third uses an ARTMAP system for sensor fusion on a mobile robot. The first study uses ARTMAP to predict vegetation mixtures in the Plumas National Forest based on spectral data from the Landsat Thematic Mapper satellite. While most previous ARTMAP systems have predicted discrete output classes, this project develops new capabilities for multi-valued prediction. On the mixture prediction task, the new network is shown to perform better than maximum likelihood and linear mixture models. The second remote sensing study uses an ARTMAP classification system to evaluate the relative importance of spectral and terrain data for map-making. This project has produced a large-scale map of remotely sensed vegetation in the Sierra National Forest. Network predictions are validated with ground truth data, and maps produced using the ARTMAP system are compared to a map produced by human experts. The ARTMAP Sierra map was generated in an afternoon, while the labor intensive expert method required nearly a year to perform the same task. The robotics research uses an ARTMAP system to integrate visual information and ultrasonic sensory information on a B14 mobile robot. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. ARTMAP effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.

Hyperspectral remote sensing tools for quantifying plant litter and invasive species in arid ecosystems

USGS Publications Warehouse

Nagler, Pamela L.; Sridhar, B.B. Maruthi; Olsson, Aaryn Dyami; Glenn, Edward P.; van Leeuwen, Willem J.D.; Thenkabail, Prasad S.; Huete, Alfredo; Lyon, John G.

2012-01-01

Green vegetation can be distinguished using visible and infrared multi-band and hyperspectral remote sensing methods. The problem has been in identifying and distinguishing the non-photosynthetically active radiation (PAR) landscape components, such as litter and soils, and from green vegetation. Additionally, distinguishing different species of green vegetation is challenging using the relatively few bands available on most satellite sensors. This chapter focuses on hyperspectral remote sensing characteristics that aim to distinguish between green vegetation, soil, and litter (or senescent vegetation). Quantifying litter by remote sensing methods is important in constructing carbon budgets of natural and agricultural ecosystems. Distinguishing between plant types is important in tracking the spread of invasive species. Green leaves of different species usually have similar spectra, making it difficult to distinguish between species. However, in this chapter we show that phenological differences between species can be used to detect some invasive species by their distinct patterns of greening and dormancy over an annual cycle based on hyperspectral data. Both applications require methods to quantify the non-green cellulosic fractions of plant tissues by remote sensing even in the presence of soil and green plant cover. We explore these methods and offer three case studies. The first concerns distinguishing surface litter from soil using the Cellulose Absorption Index (CAI), as applied to no-till farming practices where plant litter is left on the soil after harvest. The second involves using different band combinations to distinguish invasive saltcedar from agricultural and native riparian plants on the Lower Colorado River. The third illustrates the use of the CAI and NDVI in time-series analyses to distinguish between invasive buffelgrass and native plants in a desert environment in Arizona. Together the results show how hyperspectral imagery can be applied to solve problems that are not amendable to solution by the simple band combinations normally used in remote sensing.

Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing

NASA Astrophysics Data System (ADS)

Fan, Lei

Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each individual pixel in a hyperspectral image (HSI), a continuous spectrum is sampled as the spectral reflectance/radiance signature to facilitate identification of ground cover and surface material. The abundant spectrum knowledge allows all available information from the data to be mined. The superior qualities within hyperspectral imaging allow wide applications such as mineral exploration, agriculture monitoring, and ecological surveillance, etc. The processing of massive high-dimensional HSI datasets is a challenge since many data processing techniques have a computational complexity that grows exponentially with the dimension. Besides, a HSI dataset may contain a limited number of degrees of freedom due to the high correlations between data points and among the spectra. On the other hand, merely taking advantage of the sampled spectrum of individual HSI data point may produce inaccurate results due to the mixed nature of raw HSI data, such as mixed pixels, optical interferences and etc. Fusion strategies are widely adopted in data processing to achieve better performance, especially in the field of classification and clustering. There are mainly three types of fusion strategies, namely low-level data fusion, intermediate-level feature fusion, and high-level decision fusion. Low-level data fusion combines multi-source data that is expected to be complementary or cooperative. Intermediate-level feature fusion aims at selection and combination of features to remove redundant information. Decision level fusion exploits a set of classifiers to provide more accurate results. The fusion strategies have wide applications including HSI data processing. With the fast development of multiple remote sensing modalities, e.g. Very High Resolution (VHR) optical sensors, LiDAR, etc., fusion of multi-source data can in principal produce more detailed information than each single source. On the other hand, besides the abundant spectral information contained in HSI data, features such as texture and shape may be employed to represent data points from a spatial perspective. Furthermore, feature fusion also includes the strategy of removing redundant and noisy features in the dataset. One of the major problems in machine learning and pattern recognition is to develop appropriate representations for complex nonlinear data. In HSI processing, a particular data point is usually described as a vector with coordinates corresponding to the intensities measured in the spectral bands. This vector representation permits the application of linear and nonlinear transformations with linear algebra to find an alternative representation of the data. More generally, HSI is multi-dimensional in nature and the vector representation may lose the contextual correlations. Tensor representation provides a more sophisticated modeling technique and a higher-order generalization to linear subspace analysis. In graph theory, data points can be generalized as nodes with connectivities measured from the proximity of a local neighborhood. The graph-based framework efficiently characterizes the relationships among the data and allows for convenient mathematical manipulation in many applications, such as data clustering, feature extraction, feature selection and data alignment. In this thesis, graph-based approaches applied in the field of multi-source feature and data fusion in remote sensing area are explored. We will mainly investigate the fusion of spatial, spectral and LiDAR information with linear and multilinear algebra under graph-based framework for data clustering and classification problems.

PROGRAM ASPECT - FOR REMOTE SENSING OF AIRBORNE PLUMES

EPA Science Inventory

The SAFEGUARD program is a multi-sensor program for the detection and imaging of chemical plumes and vapors. The system is composed of an airborne sensor suite including an infrared line scanner and a high-speed fourier transform infrared spectrometer. Both systems are integrat...

MULTI-TEMPORAL REMOTE SENSING ANALYTICAL APPROACHES FOR CHARACTERIZING LANDSCAPE CHANGE

EPA Science Inventory

Changes in landscape composition and function result from both acute land-cover conversions and chronic landscape changes. Land-cover conversions are typically mediated by human land-use activities (e.g. conversion from forest to agriculture), while more subtle chronic l...

High-resolution computational ghost imaging and ghost diffraction through turbulence via a beam-shaping method

NASA Astrophysics Data System (ADS)

Luo, Chun-Ling; Zhuo, Ling-Qing

2017-01-01

Imaging through atmospheric turbulence is a topic with a long history and grand challenges still exist in the remote sensing and astro observation fields. In this letter, we try to propose a simple scheme to improve the resolution of imaging through turbulence based on the computational ghost imaging (CGI) and computational ghost diffraction (CGD) setup via the laser beam shaping techniques. A unified theory of CGI and CGD through turbulence with the multi-Gaussian shaped incoherent source is developed, and numerical examples are given to see clearly the effects of the system parameters to CGI and CGD. Our results show that the atmospheric effect to the CGI and CGD system is closely related to the propagation distance between the source and the object. In addition, by properly increasing the beam order of the multi-Gaussian source, we can improve the resolution of CGI and CGD through turbulence relative to the commonly used Gaussian source. Therefore our results may find applications in remote sensing and astro observation.

Combined Infrared Stereo and Laser Ranging Cloud Measurements from Shuttle Mission STS-85

NASA Technical Reports Server (NTRS)

Lancaster, Redgie S.; Spinhirne, James D.; OCStarr, David (Technical Monitor)

2001-01-01

Multi-angle remote sensing provides a wealth of information for earth and climate monitoring. And, as technology advances so do the options for developing instrumentation versatile enough to meet the demands associated with these types of measurements. In the current work, the multiangle measurement capability of the Infrared Spectral Imaging Radiometer is demonstrated. This instrument flew as part of mission STS-85 of the space shuttle Columbia in 1997 and was the first earth-observing radiometer to incorporate an uncooled microbolometer array detector as its image sensor. Specifically, a method for computing cloud-top height from the multi-spectral stereo measurements acquired during this flight has been developed and the results demonstrate that a vertical precision of 10.6 km was achieved. Further, the accuracy of these measurements is confirmed by comparison with coincident direct laser ranging measurements from the Shuttle Laser Altimeter. Mission STS-85 was the first space flight to combine laser ranging and thermal IR camera systems for cloud remote sensing.

A new method of Quickbird own image fusion

NASA Astrophysics Data System (ADS)

Han, Ying; Jiang, Hong; Zhang, Xiuying

2009-10-01

With the rapid development of remote sensing technology, the means of accessing to remote sensing data become increasingly abundant, thus the same area can form a large number of multi-temporal, different resolution image sequence. At present, the fusion methods are mainly: HPF, IHS transform method, PCA method, Brovey, Mallat algorithm and wavelet transform and so on. There exists a serious distortion of the spectrums in the IHS transform, Mallat algorithm omits low-frequency information of the high spatial resolution images, the integration results of which has obvious blocking effects. Wavelet multi-scale decomposition for different sizes, the directions, details and the edges can have achieved very good results, but different fusion rules and algorithms can achieve different effects. This article takes the Quickbird own image fusion as an example, basing on wavelet transform and HVS, wavelet transform and IHS integration. The result shows that the former better. This paper introduces the correlation coefficient, the relative average spectral error index and usual index to evaluate the quality of image.

Change Detection of Remote Sensing Images by Dt-Cwt and Mrf

NASA Astrophysics Data System (ADS)

Ouyang, S.; Fan, K.; Wang, H.; Wang, Z.

2017-05-01

Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.

Airborne and Ground-Based Optical Characterization of Legacy Underground Nuclear Test Sites

NASA Astrophysics Data System (ADS)

Vigil, S.; Craven, J.; Anderson, D.; Dzur, R.; Schultz-Fellenz, E. S.; Sussman, A. J.

2015-12-01

Detecting, locating, and characterizing suspected underground nuclear test sites is a U.S. security priority. Currently, global underground nuclear explosion monitoring relies on seismic and infrasound sensor networks to provide rapid initial detection of potential underground nuclear tests. While seismic and infrasound might be able to generally locate potential underground nuclear tests, additional sensing methods might be required to further pinpoint test site locations. Optical remote sensing is a robust approach for site location and characterization due to the ability it provides to search large areas relatively quickly, resolve surface features in fine detail, and perform these tasks non-intrusively. Optical remote sensing provides both cultural and surface geological information about a site, for example, operational infrastructure, surface fractures. Surface geological information, when combined with known or estimated subsurface geologic information, could provide clues concerning test parameters. We have characterized two legacy nuclear test sites on the Nevada National Security Site (NNSS), U20ak and U20az using helicopter-, ground- and unmanned aerial system-based RGB imagery and light detection and ranging (lidar) systems. The multi-faceted information garnered from these different sensing modalities has allowed us to build a knowledge base of how a nuclear test site might look when sensed remotely, and the standoff distances required to resolve important site characteristics.

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

NASA Astrophysics Data System (ADS)

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 hyperspectral images based on time sequence. The research results of this paper will help to break the traditional concept of remote sensing monitoring coastal wetlands by satellite and manned aerial vehicle, lead the trend of this monitoring technology, and put forward a new technical proposal for grasping the distribution of the coastal wetland and the changing trend and carrying out the protection and management of the coastal wetland.

Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy.

PubMed

Baldeck, Claire A; Asner, Gregory P; Martin, Robin E; Anderson, Christopher B; Knapp, David E; Kellner, James R; Wright, S Joseph

2015-01-01

Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods--binary support vector machine (SVM) and biased SVM--for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer's accuracies of 94-97% for the three focal species, and field validation of the predicted crown objects indicated that these had user's accuracies of 94-100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems.

Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy

PubMed Central

Baldeck, Claire A.; Asner, Gregory P.; Martin, Robin E.; Anderson, Christopher B.; Knapp, David E.; Kellner, James R.; Wright, S. Joseph

2015-01-01

Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods—binary support vector machine (SVM) and biased SVM—for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer’s accuracies of 94–97% for the three focal species, and field validation of the predicted crown objects indicated that these had user’s accuracies of 94–100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems. PMID:26153693

Identifying Communities of Vulnerability: Using NASA's Multiangle Imaging Spectroradiometer to Enhance Public Health Tracking of Particle Exposure in Los Angeles - An Empirical Approach to Examining L1 MISR Radiance Measurements and PM2.5 Relationships

NASA Astrophysics Data System (ADS)

Laygo, K.; Kontgis, C.; Hollins, A.

2011-12-01

Los Angeles is consistently ranked as one of the most polluted cities in the United States, exhibiting high levels of both ozone and particulate matter. Particulate matter with an aerodynamic diameter of 2.5 microns or less, or PM2.5, is of special concern for health professionals, since it is fine enough to be inhaled into the lungs. Additionally, studies show that it is associated with respiratory disease risks such as asthma. Remote sensing technologies have the potential to be useful in air pollution health studies, but have so far been sparsely implemented. Satellite-derived measurements would be especially useful in air pollution studies, since the concentrations of interest can change by orders of magnitude over small distances. However, with current remote sensing technologies, it is difficult to predict pollution levels within small areas. This study utilizes remote sensing information in combination with a ground-based network of data to create a more comprehensive approach to tracking public health concerns. According to the 2007 NRC Decadal Survey, there is a continued need for research that establishes the relationship between remotely sensed data and predicting public health risks related to environmental factors. For this study, we conducted linear regression models using Multi-Angle Imaging SpectroRadiometer (MISR) L1 radiance data and ground-based PM2.5 measurements from 13 EPA stations within the Los Angeles Metropolitan Statistical Area. MISR senses in 4 bands (visible blue, green, red and near infrared) and 9 separate angles, producing a total of 36 bands. Using all 36 bands, we generated models for each station individually and for all stations combined. Two time periods were assessed: June, July and August from 2000 - 2009, and all months from 2009. Summer months were looked at specifically, since pollution levels tend to be higher than other parts of the year due to strong inversion layers and low rainfall levels. Generally, the models performed well, suggesting that MISR radiances are able to accurately predict levels of PM2.5. For 2009 data, all models had R-squared values over 0.93. For summer month data, the model R-square values were markedly lower and more varied than for the 2009 data, ranging from 0.33 - 0.92. When looking at the 2009 data, non-summer month models performed better than did summer-month models. A brief analysis of temperature data indicates that temperature and deviation from the norm are not associated with model predictability. All 36 MISR channels were plotted against their weights for each model, but no band combination obviously weighed more than other bands. Further research needs to be conducted to understand why models were able to predict 2009 PM2.5 levels, but were unable to accurately fit summer data from 2000 - 2009.

Remote Sensing of Crystal Shapes in Ice Clouds

NASA Technical Reports Server (NTRS)

van Diedenhoven, Bastiaan

2017-01-01

Ice crystals in clouds exist in a virtually limitless variation of geometries. The most basic shapes of ice crystals are columnar or plate-like hexagonal prisms with aspect ratios determined by relative humidity and temperature. However, crystals in ice clouds generally display more complex structures owing to aggregation, riming and growth histories through varying temperature and humidity regimes. Crystal shape is relevant for cloud evolution as it affects microphysical properties such as fall speeds and aggregation efficiency. Furthermore, the scattering properties of ice crystals are affected by their general shape, as well as by microscopic features such as surface roughness, impurities and internal structure. To improve the representation of ice clouds in climate models, increased understanding of the global variation of crystal shape and how it relates to, e.g., location, cloud temperature and atmospheric state is crucial. Here, the remote sensing of ice crystal macroscale and microscale structure from airborne and space-based lidar depolarization observations and multi-directional measurements of total and polarized reflectances is reviewed. In addition, a brief overview is given of in situ and laboratory observations of ice crystal shape as well as the optical properties of ice crystals that serve as foundations for the remote sensing approaches. Lidar depolarization is generally found to increase with increasing cloud height and to vary with latitude. Although this variation is generally linked to the variation of ice crystal shape, the interpretation of the depolarization remains largely qualitative and more research is needed before quantitative conclusions about ice shape can be deduced. The angular variation of total and polarized reflectances of ice clouds has been analyzed by numerous studies in order to infer information about ice crystal shapes from them. From these studies it is apparent that pristine crystals with smooth surfaces are generally inconsistent with the data and thus crystal impurity, distortion or surface roughness is prevalent. However, conclusions about the dominating ice shapes are often inconclusive and contradictory and are highly dependent on the limited selection of shapes included in the investigations. Since ice crystal optical properties are mostly determined by the aspect ratios of the crystal components and their microscale structure, it is advised that remote sensing applications focus on the variation of these ice shape characteristics, rather than on the macroscale shape or habit. Recent studies use databases with nearly continuous ranges of crystal component aspect ratio and-or roughness levels to infer the variation of ice crystal shape from satellite and airborne remote sensing measurements. Here, the rationale and results of varying strategies for the remote sensing of ice crystal shape are reviewed. Observed systematic variations of ice crystal geometry with location, cloud height and atmospheric state suggested by the data are discussed. Finally, a prospective is given on the future of the remote sensing of ice cloud particle shapes.

Modelling Biophysical Parameters of Maize Using Landsat 8 Time Series

NASA Astrophysics Data System (ADS)

Dahms, Thorsten; Seissiger, Sylvia; Conrad, Christopher; Borg, Erik

2016-06-01

Open and free access to multi-frequent high-resolution data (e.g. Sentinel - 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9. Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model biophysical parameters.

Monitoring, analyzing and simulating of spatial-temporal changes of landscape pattern over mining area

NASA Astrophysics Data System (ADS)

Liu, Pei; Han, Ruimei; Wang, Shuangting

2014-11-01

According to the merits of remotely sensed data in depicting regional land cover and Land changes, multi- objective information processing is employed to remote sensing images to analyze and simulate land cover in mining areas. In this paper, multi-temporal remotely sensed data were selected to monitor the pattern, distri- bution and trend of LUCC and predict its impacts on ecological environment and human settlement in mining area. The monitor, analysis and simulation of LUCC in this coal mining areas are divided into five steps. The are information integration of optical and SAR data, LULC types extraction with SVM classifier, LULC trends simulation with CA Markov model, landscape temporal changes monitoring and analysis with confusion matrixes and landscape indices. The results demonstrate that the improved data fusion algorithm could make full use of information extracted from optical and SAR data; SVM classifier has an efficient and stable ability to obtain land cover maps, which could provide a good basis for both land cover change analysis and trend simulation; CA Markov model is able to predict LULC trends with good performance, and it is an effective way to integrate remotely sensed data with spatial-temporal model for analysis of land use / cover change and corresponding environmental impacts in mining area. Confusion matrixes are combined with landscape indices to evaluation and analysis show that, there was a sustained downward trend in agricultural land and bare land, but a continues growth trend tendency in water body, forest and other lands, and building area showing a wave like change, first increased and then decreased; mining landscape has undergone a from small to large and large to small process of fragmentation, agricultural land is the strongest influenced landscape type in this area, and human activities are the primary cause, so the problem should be pay more attentions by government and other organizations.

Land use/cover classification in the Brazilian Amazon using satellite images.

PubMed

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

2012-09-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Geographic techniques and recent applications of remote sensing to landscape-water quality studies

USGS Publications Warehouse

Griffith, J.A.

2002-01-01

This article overviews recent advances in studies of landscape-water quality relationships using remote sensing techniques. With the increasing feasibility of using remotely-sensed data, landscape-water quality studies can now be more easily performed on regional, multi-state scales. The traditional method of relating land use and land cover to water quality has been extended to include landscape pattern and other landscape information derived from satellite data. Three items are focused on in this article: 1) the increasing recognition of the importance of larger-scale studies of regional water quality that require a landscape perspective; 2) the increasing importance of remotely sensed data, such as the imagery-derived normalized difference vegetation index (NDVI) and vegetation phenological metrics derived from time-series NDVI data; and 3) landscape pattern. In some studies, using landscape pattern metrics explained some of the variation in water quality not explained by land use/cover. However, in some other studies, the NDVI metrics were even more highly correlated to certain water quality parameters than either landscape pattern metrics or land use/cover proportions. Although studies relating landscape pattern metrics to water quality have had mixed results, this recent body of work applying these landscape measures and satellite-derived metrics to water quality analysis has demonstrated their potential usefulness in monitoring watershed conditions across large regions.

Image Mining in Remote Sensing for Coastal Wetlands Mapping: from Pixel Based to Object Based Approach

NASA Astrophysics Data System (ADS)

Farda, N. M.; Danoedoro, P.; Hartono; Harjoko, A.

2016-11-01

The availably of remote sensing image data is numerous now, and with a large amount of data it makes “knowledge gap” in extraction of selected information, especially coastal wetlands. Coastal wetlands provide ecosystem services essential to people and the environment. The aim of this research is to extract coastal wetlands information from satellite data using pixel based and object based image mining approach. Landsat MSS, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images located in Segara Anakan lagoon are selected to represent data at various multi temporal images. The input for image mining are visible and near infrared bands, PCA band, invers PCA bands, mean shift segmentation bands, bare soil index, vegetation index, wetness index, elevation from SRTM and ASTER GDEM, and GLCM (Harralick) or variability texture. There is three methods were applied to extract coastal wetlands using image mining: pixel based - Decision Tree C4.5, pixel based - Back Propagation Neural Network, and object based - Mean Shift segmentation and Decision Tree C4.5. The results show that remote sensing image mining can be used to map coastal wetlands ecosystem. Decision Tree C4.5 can be mapped with highest accuracy (0.75 overall kappa). The availability of remote sensing image mining for mapping coastal wetlands is very important to provide better understanding about their spatiotemporal coastal wetlands dynamics distribution.

Land use/cover classification in the Brazilian Amazon using satellite images

PubMed Central

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira

2013-01-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353

[Crop geometry identification based on inversion of semiempirical BRDF models].

PubMed

Zhao, Chun-jiang; Huang, Wen-jiang; Mu, Xu-han; Wang, Jin-diz; Wang, Ji-hua

2009-09-01

With the rapid development of remote sensing technology, the application of remote sensing has extended from single view angle to multi-view angles. It was studied for the qualitative and quantitative effect of average leaf angle (ALA) on crop canopy reflected spectrum. Effect of ALA on canopy reflected spectrum can not be ignored with inversion of leaf area index (LAI) and monitoring of crop growth condition by remote sensing technology. Investigations of the effect of erective and horizontal varieties were conducted by bidirectional canopy reflected spectrum and semiempirical bidirectional reflectance distribution function (BRDF) models. The sensitive analysis was done based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso) at red band (680 nm) and near infrared band (800 nm). By combining the weights of the red and near-infrared bands, the semiempirical models can obtain structural information by retrieving biophysical parameters from the physical BRDF model and a number of bidirectional observations. So, it will allow an on-site and non-sampling mode of crop ALA identification, which is useful for using remote sensing for crop growth monitoring and for improving the LAI inversion accuracy, and it will help the farmers in guiding the fertilizer and irrigation management in the farmland without a priori knowledge.

Orbiting multi-beam microwave radiometer for soil moisture remote sensing

NASA Technical Reports Server (NTRS)

Shiue, J. C.; Lawrence, R. W.

1985-01-01

The effects of soil moisture and other factors on soil surface emissivity are reviewed and design concepts for a multibeam microwave radiometer with a 15 m antenna are described. Characteristic antenna gain and radiation patterns are shown and losses due to reflector roughness are estimated.

Spatial methods for deriving crop rotation history

USDA-ARS?s Scientific Manuscript database

Converting multi-year remote sensing classification data into crop rotations is beneficial by defining length of crop rotation cycles and the specific sequences of intervening crops grown between the final year of a grass seed stand and establishment of a new perennial ryegrass seed crop. Markov mod...

Magnitude and variability of land evaporation and its components at the global scale

USDA-ARS?s Scientific Manuscript database

A physics-based methodology is applied to estimate global land-surface evaporation from multi-satellite observations. GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) combines a wide range of remotely sensed observations within a Priestley and Taylor-based framework. Daily actual e...

Remote Sensing Classification of Grass Seed Cropping Practices in Western Oregon

USDA-ARS?s Scientific Manuscript database

Multiband Landsat images and multi-temporal MODIS 16-day composite NDVI were classified into 16 categories representing the primary crop rotation options and stand establishment conditions present in western Oregon grass seed fields. Mismatch in resolution between MODIS and Landsat data was resolved...

Integrated Use of Remote Sensing, GIS and GPS Technology for Monitoring the Environmental Problem of Shyamnagar

NASA Astrophysics Data System (ADS)

Akbar, M. S.; Sarker, M. H.; Sattar, M. A.; Sarwar, G. M.; Rahman, S. M. M.; Rahman, M. M.; Khan, Z. U.

2017-05-01

Cultivation of shrimp mostly in unplanned way has been considered as one of the major environmental disasters of Shamnagar. Villagers surrounding the rivers are mainly involved with fish (shrimp) cultivation. So, fertile agriculture land has been converted to shrimp cultivation. Conversion of agriculture land to other usage is a common but acute problem for land resources of the country like Bangladesh. Conventional methods for collecting this information are relatively costly and time consuming. Contrarily, Remote Sensing satellite observation with its unique capability to provide cost-effective support in compiling the latest information about the natural resource. Remote sensing, in conjunction with GIS, has been widely applied and been recognized as a powerful and effective tool in detecting land use and land cover changes. RapidEye, Landsat8 images were used to identify land use and land cover of the area during the period 2008 and 2015. Google images were used to identify the micro-level land use features of the same period. Multi-spectral classifications using unsupervised and supervised classification were done and results have been compared based on the field investigation. The study reveals that during the period 2008 to 2015 agricultural practice has been reduced from 35 % to 21 % and shrimp cultivation area increased from 38 % to 50 %. Due to the impact of high salinity and salt water intrusion caused by natural disaster, agricultural activities is reduced and farmers have been converted to other practices, as a result shrimp farming is gaining popularity in the area.

Enhancing the Teaching of Digital Processing of Remote Sensing Image Course through Geospatial Web Processing Services

NASA Astrophysics Data System (ADS)

di, L.; Deng, M.

2010-12-01

Remote sensing (RS) is an essential method to collect data for Earth science research. Huge amount of remote sensing data, most of them in the image form, have been acquired. Almost all geography departments in the world offer courses in digital processing of remote sensing images. Such courses place emphasis on how to digitally process large amount of multi-source images for solving real world problems. However, due to the diversity and complexity of RS images and the shortcomings of current data and processing infrastructure, obstacles for effectively teaching such courses still remain. The major obstacles include 1) difficulties in finding, accessing, integrating and using massive RS images by students and educators, and 2) inadequate processing functions and computing facilities for students to freely explore the massive data. Recent development in geospatial Web processing service systems, which make massive data, computing powers, and processing capabilities to average Internet users anywhere in the world, promises the removal of the obstacles. The GeoBrain system developed by CSISS is an example of such systems. All functions available in GRASS Open Source GIS have been implemented as Web services in GeoBrain. Petabytes of remote sensing images in NASA data centers, the USGS Landsat data archive, and NOAA CLASS are accessible transparently and processable through GeoBrain. The GeoBrain system is operated on a high performance cluster server with large disk storage and fast Internet connection. All GeoBrain capabilities can be accessed by any Internet-connected Web browser. Dozens of universities have used GeoBrain as an ideal platform to support data-intensive remote sensing education. This presentation gives a specific example of using GeoBrain geoprocessing services to enhance the teaching of GGS 588, Digital Remote Sensing taught at the Department of Geography and Geoinformation Science, George Mason University. The course uses the textbook "Introductory Digital Image Processing, A Remote Sensing Perspective" authored by John Jensen. The textbook is widely adopted in the geography departments around the world for training students on digital processing of remote sensing images. In the traditional teaching setting for the course, the instructor prepares a set of sample remote sensing images to be used for the course. Commercial desktop remote sensing software, such as ERDAS, is used for students to do the lab exercises. The students have to do the excurses in the lab and can only use the simple images. For this specific course at GMU, we developed GeoBrain-based lab excurses for the course. With GeoBrain, students now can explore petabytes of remote sensing images in the NASA, NOAA, and USGS data archives instead of dealing only with sample images. Students have a much more powerful computing facility available for their lab excurses. They can explore the data and do the excurses any time at any place they want as long as they can access the Internet through the Web Browser. The feedbacks from students are all very positive about the learning experience on the digital image processing with the help of GeoBrain web processing services. The teaching/lab materials and GeoBrain services are freely available to anyone at http://www.laits.gmu.edu.

Disease detection in sugar beet fields: a multi-temporal and multi-sensoral approach on different scales

NASA Astrophysics Data System (ADS)

Mahlein, Anne-Katrin; Hillnhütter, Christian; Mewes, Thorsten; Scholz, Christine; Steiner, Ulrike; Dehne, Heinz-Willhelm; Oerke, Erich-Christian

2009-09-01

Depending on environmental factors fungal diseases of crops are often distributed heterogeneously in fields. Precision agriculture in plant protection implies a targeted fungicide application adjusted these field heterogeneities. Therefore an understanding of the spatial and temporal occurrence of pathogens is elementary. As shown in previous studies, remote sensing techniques can be used to detect and observe spectral anomalies in the field. In 2008, a sugar beet field site was observed at different growth stages of the crop using different remote sensing techniques. The experimental field site consisted of two treatments. One plot was sprayed with a fungicide to avoid fungal infections. In order to obtain sugar beet plants infected with foliar diseases the other plot was not sprayed. Remote sensing data were acquired from the high-resolution airborne hyperspectral imaging ROSIS in July 2008 at sugar beet growth stage 39 and from the HyMap sensor systems in August 2008 at sugar beet growth stage 45, respectively. Additionally hyperspectral signatures of diseased and non-diseased sugar beet plants were measured with a non-imaging hand held spectroradiometer at growth stage 49 in September. Ground truth data, in particular disease severity were collected at 50 sampling points in the field. Changes of reflection rates were related to disease severity increasing with time. Erysiphe betae causing powdery mildew was the most frequent leaf pathogen. A classification of healthy and diseased sugar beets in the field was possible by using hyperspectral vegetation indices calculated from canopy reflectance.

Fusion of multisource and multiscale remote sensing data for water availability assessment in a metropolitan region

NASA Astrophysics Data System (ADS)

Chang, N. B.; Yang, Y. J.; Daranpob, A.

2009-09-01

Recent extreme hydroclimatic events in the United States alone include, but are not limited to, the droughts in Maryland and the Chesapeake Bay area in 2001 through September 2002; Lake Mead in Las Vegas in 2000 through 2004; the Peace River and Lake Okeechobee in South Florida in 2006; and Lake Lanier in Atlanta, Georgia in 2007 that affected the water resources distribution in three states - Alabama, Florida and Georgia. This paper provides evidence from previous work and elaborates on the future perspectives that will collectively employ remote sensing and in-situ observations to support the implementation of the water availability assessment in a metropolitan region. Within the hydrological cycle, precipitation, soil moisture, and evapotranspiration can be monitored by using WSR-88D/NEXRAD data, RADARSAT-1 images, and GEOS images collectively to address the spatiotemporal variations of quantitative availability of waters whereas the MODIS images may be used to track down the qualitative availability of waters in terms of turbidity, Chlorophyll-a and other constitutes of concern. Tampa Bay in Florida was selected as a study site in this analysis, where the water supply infrastructure covers groundwater, desalination plant, and surface water at the same time. Research findings show that through the proper fusion of multi-source and multi-scale remote sensing data for water availability assessment in metropolitan region, a new insight of water infrastructure assessment can be gained to support sustainable planning region wide.

Planning Tripoli Metro Network by the Use of Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Alhusain, O.; Engedy, Gy.; Milady, A.; Paulini, L.; Soos, G.

2012-08-01

Tripoli, the capital city of Libya is going through significant and integrated development process, this development is expected to continue in the next few decades. The Libyan authorities have put it as their goal to develop Tripoli to an important metropolis in North Africa. To achieve this goal, they identified goals for the city's future development in all human, economic, cultural, touristic, and nonetheless infrastructure levels. On the infrastructure development level, among other things, they have identified the development of public transportation as one of the important development priorities. At present, public transportation in Tripoli is carried out by a limited capacity bus network alongside of individual transportation. However, movement in the city is characterized mainly by individual transportation with all its disadvantages such as traffic jams, significant air pollution with both carbon monoxide and dust, and lack of parking space. The Libyan authorities wisely opted for an efficient, modern, and environment friendly solution for public transportation, this was to plan a complex Metro Network as the backbone of public transportation in the city, and to develop and integrate the bus network and other means of transportation to be in harmony with the planned Metro network. The Metro network is planned to provide convenient connections to Tripoli International Airport and to the planned Railway station. They plan to build a system of Park and Ride (P+R) facilities at suitable locations along the Metro lines. This paper will present in details the planned Metro Network, some of the applied technological solutions, the importance of applying remote sensing and GIS technologies in different planning phases, and problems and benefits associated with the use of multi-temporal-, multi-format spatial data in the whole network planning phase.

Comparison of Remote Sensing and Fixed-Site Monitoring Approaches for Examining Air Pollution and Health in a National Study Population

NASA Technical Reports Server (NTRS)

Prud'homme, Genevieve; Dobbin, Nina A.; Sun, Liu; Burnet, Richard T.; Martin, Randall V.; Davidson, Andrew; Cakmak, Sabit; Villeneuve, Paul J.; Lamsal, Lok N.; vanDonkelaar, Aaron;

Modeling Atmospheric CO2 Processes to Constrain the Missing Sink

NASA Technical Reports Server (NTRS)

Kawa, S. R.; Denning, A. S.; Erickson, D. J.; Collatz, J. C.; Pawson, S.

2005-01-01

We report on a NASA supported modeling effort to reduce uncertainty in carbon cycle processes that create the so-called missing sink of atmospheric CO2. Our overall objective is to improve characterization of CO2 source/sink processes globally with improved formulations for atmospheric transport, terrestrial uptake and release, biomass and fossil fuel burning, and observational data analysis. The motivation for this study follows from the perspective that progress in determining CO2 sources and sinks beyond the current state of the art will rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. The major components of this effort are: 1) Continued development of the chemistry and transport model using analyzed meteorological fields from the Goddard Global Modeling and Assimilation Office, with comparison to real time data in both forward and inverse modes; 2) An advanced biosphere model, constrained by remote sensing data, coupled to the global transport model to produce distributions of CO2 fluxes and concentrations that are consistent with actual meteorological variability; 3) Improved remote sensing estimates for biomass burning emission fluxes to better characterize interannual variability in the atmospheric CO2 budget and to better constrain the land use change source; 4) Evaluating the impact of temporally resolved fossil fuel emission distributions on atmospheric CO2 gradients and variability. 5) Testing the impact of existing and planned remote sensing data sources (e.g., AIRS, MODIS, OCO) on inference of CO2 sources and sinks, and use the model to help establish measurement requirements for future remote sensing instruments. The results will help to prepare for the use of OCO and other satellite data in a multi-disciplinary carbon data assimilation system for analysis and prediction of carbon cycle changes and carbodclimate interactions.

Supervised classification of aerial imagery and multi-source data fusion for flood assessment

NASA Astrophysics Data System (ADS)

Sava, E.; Harding, L.; Cervone, G.

2015-12-01

Floods are among the most devastating natural hazards and the ability to produce an accurate and timely flood assessment before, during, and after an event is critical for their mitigation and response. Remote sensing technologies have become the de-facto approach for observing the Earth and its environment. However, satellite remote sensing data are not always available. For these reasons, it is crucial to develop new techniques in order to produce flood assessments during and after an event. Recent advancements in data fusion techniques of remote sensing with near real time heterogeneous datasets have allowed emergency responders to more efficiently extract increasingly precise and relevant knowledge from the available information. This research presents a fusion technique using satellite remote sensing imagery coupled with non-authoritative data such as Civil Air Patrol (CAP) and tweets. A new computational methodology is proposed based on machine learning algorithms to automatically identify water pixels in CAP imagery. Specifically, wavelet transformations are paired with multiple classifiers, run in parallel, to build models discriminating water and non-water regions. The learned classification models are first tested against a set of control cases, and then used to automatically classify each image separately. A measure of uncertainty is computed for each pixel in an image proportional to the number of models classifying the pixel as water. Geo-tagged tweets are continuously harvested and stored on a MongoDB and queried in real time. They are fused with CAP classified data, and with satellite remote sensing derived flood extent results to produce comprehensive flood assessment maps. The final maps are then compared with FEMA generated flood extents to assess their accuracy. The proposed methodology is applied on two test cases, relative to the 2013 floods in Boulder CO, and the 2015 floods in Texas.

A Changing Number of Alternative States in the Boreal Biome: Reproducibility Risks of Replacing Remote Sensing Products.

PubMed

Xu, Chi; Holmgren, Milena; Van Nes, Egbert H; Hirota, Marina; Chapin, F Stuart; Scheffer, Marten

2015-01-01

Publicly available remote sensing products have boosted science in many ways. The openness of these data sources suggests high reproducibility. However, as we show here, results may be specific to versions of the data products that can become unavailable as new versions are posted. We focus on remotely-sensed tree cover. Recent studies have used this public resource to detect multi-modality in tree cover in the tropical and boreal biomes. Such patterns suggest alternative stable states separated by critical tipping points. This has important implications for the potential response of these ecosystems to global climate change. For the boreal region, four distinct ecosystem states (i.e., treeless, sparse and dense woodland, and boreal forest) were previously identified by using the Collection 3 data of MODIS Vegetation Continuous Fields (VCF). Since then, the MODIS VCF product has been updated to Collection 5; and a Landsat VCF product of global tree cover at a fine spatial resolution of 30 meters has been developed. Here we compare these different remote-sensing products of tree cover to show that identification of alternative stable states in the boreal biome partly depends on the data source used. The updated MODIS data and the newer Landsat data consistently demonstrate three distinct modes around similar tree-cover values. Our analysis suggests that the boreal region has three modes: one sparsely vegetated state (treeless), one distinct 'savanna-like' state and one forest state, which could be alternative stable states. Our analysis illustrates that qualitative outcomes of studies may change fundamentally as new versions of remote sensing products are used. Scientific reproducibility thus requires that old versions remain publicly available.

Modeling the Hydrological Regime of Turkana Lake (Kenya, Ethiopia) by Combining Spatially Distributed Hydrological Modeling and Remote Sensing Datasets

NASA Astrophysics Data System (ADS)

Anghileri, D.; Kaelin, A.; Peleg, N.; Fatichi, S.; Molnar, P.; Roques, C.; Longuevergne, L.; Burlando, P.

2017-12-01

Hydrological modeling in poorly gauged basins can benefit from the use of remote sensing datasets although there are challenges associated with the mismatch in spatial and temporal scales between catchment scale hydrological models and remote sensing products. We model the hydrological processes and long-term water budget of the Lake Turkana catchment, a transboundary basin between Kenya and Ethiopia, by integrating several remote sensing products into a spatially distributed and physically explicit model, Topkapi-ETH. Lake Turkana is the world largest desert lake draining a catchment of 145'500 km2. It has three main contributing rivers: the Omo river, which contributes most of the annual lake inflow, the Turkwel river, and the Kerio rivers, which contribute the remaining part. The lake levels have shown great variations in the last decades due to long-term climate fluctuations and the regulation of three reservoirs, Gibe I, II, and III, which significantly alter the hydrological seasonality. Another large reservoir is planned and may be built in the next decade, generating concerns about the fate of Lake Turkana in the long run because of this additional anthropogenic pressure and increasing evaporation driven by climate change. We consider different remote sensing datasets, i.e., TRMM-V7 for precipitation, MERRA-2 for temperature, as inputs to the spatially distributed hydrological model. We validate the simulation results with other remote sensing datasets, i.e., GRACE for total water storage anomalies, GLDAS-NOAH for soil moisture, ERA-Interim/Land for surface runoff, and TOPEX/Poseidon for satellite altimetry data. Results highlight how different remote sensing products can be integrated into a hydrological modeling framework accounting for their relative uncertainties. We also carried out simulations with the artificial reservoirs planned in the north part of the catchment and without any reservoirs, to assess their impacts on the catchment hydrological regime and the Lake Turkana level variability.

Earth view: A business guide to orbital remote sensing

NASA Technical Reports Server (NTRS)

Bishop, Peter C.

1990-01-01

The following subject areas are covered: Earth view - a guide to orbital remote sensing; current orbital remote sensing systems (LANDSAT, SPOT image, MOS-1, Soviet remote sensing systems); remote sensing satellite; and remote sensing organizations.

Borehole sounding device with sealed depth and water level sensors

DOEpatents

Skalski, Joseph C.; Henke, Michael D.

2005-08-02

A borehole device having proximal and distal ends comprises an enclosure at the proximal end for accepting an aircraft cable containing a plurality of insulated conductors from a remote position. A water sensing enclosure is sealingly attached to the enclosure and contains means for detecting water, and sending a signal on the cable to the remote position indicating water has been detected. A bottom sensing enclosure is sealingly attached to the water sensing enclosure for determining when the borehole device encounters borehole bottom and sends a signal on the cable to the remote position indicating that borehole bottom has been encountered.

Reconstruction of 3D Shapes of Opaque Cumulus Clouds from Airborne Multiangle Imaging: A Proof-of-Concept

NASA Astrophysics Data System (ADS)

Davis, A. B.; Bal, G.; Chen, J.

2015-12-01

Operational remote sensing of microphysical and optical cloud properties is invariably predicated on the assumption of plane-parallel slab geometry for the targeted cloud. The sole benefit of this often-questionable assumption about the cloud is that it leads to one-dimensional (1D) radiative transfer (RT)---a textbook, computationally tractable model. We present new results as evidence that, thanks to converging advances in 3D RT, inverse problem theory, algorithm implementation, and computer hardware, we are at the dawn of a new era in cloud remote sensing where we can finally go beyond the plane-parallel paradigm. Granted, the plane-parallel/1D RT assumption is reasonable for spatially extended stratiform cloud layers, as well as the smoothly distributed background aerosol layers. However, these 1D RT-friendly scenarios exclude cases that are critically important for climate physics. 1D RT---whence operational cloud remote sensing---fails catastrophically for cumuliform clouds that have fully 3D outer shapes and internal structures driven by shallow or deep convection. For these situations, the first order of business in a robust characterization by remote sensing is to abandon the slab geometry framework and determine the 3D geometry of the cloud, as a first step toward bone fide 3D cloud tomography. With this specific goal in mind, we deliver a proof-of-concept for an entirely new kind of remote sensing applicable to 3D clouds. It is based on highly simplified 3D RT and exploits multi-angular suites of cloud images at high spatial resolution. Airborne sensors like AirMSPI readily acquire such data. The key element of the reconstruction algorithm is a sophisticated solution of the nonlinear inverse problem via linearization of the forward model and an iteration scheme supported, where necessary, by adaptive regularization. Currently, the demo uses a 2D setting to show how either vertical profiles or horizontal slices of the cloud can be accurately reconstructed. Extension to 3D volumes is straightforward but the next challenge is to accommodate images at lower spatial resolution, e.g., from MISR/Terra. G. Bal, J. Chen, and A.B. Davis (2015). Reconstruction of cloud geometry from multi-angle images, Inverse Problems in Imaging (submitted).

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany

NASA Astrophysics Data System (ADS)

Waldhoff, Guido; Lussem, Ulrike; Bareth, Georg

2017-09-01

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008-2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase two, volume 1 : outreach and commercialization of IRSV prototype.

DOT National Transportation Integrated Search

2012-03-01

The Integrated Remote Sensing and Visualization System (IRSV) was developed in Phase One of this project in order to : accommodate the needs of todays Bridge Engineers at the state and local level. Overall goals of this project are: : Better u...

Assimilating Leaf Area Index Estimates from Remote Sensing into the Simulations of a Cropping Systems Model

USDA-ARS?s Scientific Manuscript database

Spatial extrapolation of cropping systems models for regional crop growth and water use assessment and farm-level precision management has been limited by the vast model input requirements and the model sensitivity to parameter uncertainty. Remote sensing has been proposed as a viable source of spat...

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase one, volume 2 : knowledge modeling and database development.

DOT National Transportation Integrated Search

2009-12-01

The Integrated Remote Sensing and Visualization System (IRSV) is being designed to accommodate the needs of todays Bridge Engineers at the : state and local level from several aspects that were documented in Volume One, Summary Report. The followi...

A component-based system for agricultural drought monitoring by remote sensing.

PubMed

Dong, Heng; Li, Jun; Yuan, Yanbin; You, Lin; Chen, Chao

2017-01-01

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China's Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring.

A component-based system for agricultural drought monitoring by remote sensing

PubMed Central

Yuan, Yanbin; You, Lin; Chen, Chao

2017-01-01

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China’s Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring. PMID:29236700

The potential of volunteered geographic information to investigate peri-urbanization in the conservation zone of Mexico City.

PubMed

Heider, Katharina; Lopez, Juan Miguel Rodriguez; Scheffran, Jürgen

2018-03-14

Due to the availability of Web 2.0 technologies, volunteered geographic information (VGI) is on the rise. This new type of data is available on many topics and on different scales. Thus, it has become interesting for research. This article deals with the collective potential of VGI and remote sensing to detect peri-urbanization in the conservation zone of Mexico City. On the one hand, remote sensing identifies horizontal urban expansion, and on the other hand, VGI of ecological complaints provides data about informal settlements. This enables the combination of top-down approaches (remote sensing) and bottom-up approaches (ecological complaints). Within the analysis, we identify areas of high urbanization as well as complaint densities and bring them together in a multi-scale analysis using Geographic Information Systems (GIS). Furthermore, we investigate the influence of settlement patterns and main roads on the peri-urbanization process in Mexico City using OpenStreetMap. Peri-urbanization is detected especially in the transition zone between the urban and rural (conservation) area and near main roads as well as settlements.

Risk assessment of storm surge disaster based on numerical models and remote sensing

NASA Astrophysics Data System (ADS)

Liu, Qingrong; Ruan, Chengqing; Zhong, Shan; Li, Jian; Yin, Zhonghui; Lian, Xihu

2018-06-01

Storm surge is one of the most serious ocean disasters in the world. Risk assessment of storm surge disaster for coastal areas has important implications for planning economic development and reducing disaster losses. Based on risk assessment theory, this paper uses coastal hydrological observations, a numerical storm surge model and multi-source remote sensing data, proposes methods for valuing hazard and vulnerability for storm surge and builds a storm surge risk assessment model. Storm surges in different recurrence periods are simulated in numerical models and the flooding areas and depth are calculated, which are used for assessing the hazard of storm surge; remote sensing data and GIS technology are used for extraction of coastal key objects and classification of coastal land use are identified, which is used for vulnerability assessment of storm surge disaster. The storm surge risk assessment model is applied for a typical coastal city, and the result shows the reliability and validity of the risk assessment model. The building and application of storm surge risk assessment model provides some basis reference for the city development plan and strengthens disaster prevention and mitigation.

A review of spatial downscaling of satellite remotely sensed soil moisture

NASA Astrophysics Data System (ADS)

Peng, Jian; Loew, Alexander; Merlin, Olivier; Verhoest, Niko E. C.

2017-06-01

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.

The application of remote sensing to resource management and environmental quality programs in Kansas

NASA Technical Reports Server (NTRS)

Barr, B. G.; Martinko, E. A. (Principal Investigator)

1983-01-01

The activities of the Kansas Applied Remote Sensing (KARS) Program during the period April 1, 1982 through Marsh 31, 1983 are described. The most important work revolved around the Kansas Interagency Task Force on Applied Remote Sensing and its efforts to establish an operational service oriented remote sensing program in Kansas state government. Concomitant with this work was the upgrading of KARS capabilities to process data for state agencies through the vehicle of a low cost digital data processing system. The KARS Program continued to take an active role in irrigation mapping. KARS is now integrating data acquired through analysis of LANDSAT into geographic information systems designed for evaluating groundwater resources. KARS also continues to work at the national level on the national inventory of state natural resources information systems.

Real time forest fire warning and forest fire risk zoning: a Vietnamese case study

NASA Astrophysics Data System (ADS)

Chu, T.; Pham, D.; Phung, T.; Ha, A.; Paschke, M.

2016-12-01

Forest fire occurs seriously in Vietnam and has been considered as one of the major causes of forest lost and degradation. Several studies of forest fire risk warning were conducted using Modified Nesterov Index (MNI) but remaining shortcomings and inaccurate predictions that needs to be urgently improved. In our study, several important topographic and social factors such as aspect, slope, elevation, distance to residential areas and road system were considered as "permanent" factors while meteorological data were updated hourly using near-real-time (NRT) remotely sensed data (i.e. MODIS Terra/Aqua and TRMM) for the prediction and warning of fire. Due to the limited number of weather stations in Vietnam, data from all active stations (i.e. 178) were used with the satellite data to calibrate and upscale meteorological variables. These data with finer resolution were then used to generate MNI. The only significant "permanent" factors were selected as input variables based on the correlation coefficients that computed from multi-variable regression among true fire-burning (collected from 1/2007) and its spatial characteristics. These coefficients also used to suggest appropriate weight for computing forest fire risk (FR) model. Forest fire risk model was calculated from the MNI and the selected factors using fuzzy regression models (FRMs) and GIS based multi-criteria analysis. By this approach, the FR was slightly modified from MNI by the integrated use of various factors in our fire warning and prediction model. Multifactor-based maps of forest fire risk zone were generated from classifying FR into three potential danger levels. Fire risk maps were displayed using webgis technology that is easy for managing data and extracting reports. Reported fire-burnings thereafter have been used as true values for validating the forest fire risk. Fire probability has strong relationship with potential danger levels (varied from 5.3% to 53.8%) indicating that the higher potential risk, the more chance of fire happen. By adding spatial factors to continuous daily updated remote sensing based meteo-data, results are valuable for both mapping forest fire risk zones in short and long-term and real time fire warning in Vietnam. Key words: Near-real-time, forest fire warning, fuzzy regression model, remote sensing.

Analysis and modeling of atmospheric turbulence on the high-resolution space optical systems

NASA Astrophysics Data System (ADS)

Lili, Jiang; Chen, Xiaomei; Ni, Guoqiang

2016-09-01

Modeling and simulation of optical remote sensing system plays an unslightable role in remote sensing mission predictions, imaging system design, image quality assessment. It has already become a hot research topic at home and abroad. Atmospheric turbulence influence on optical systems is attached more and more importance to as technologies of remote sensing are developed. In order to study the influence of atmospheric turbulence on earth observation system, the atmospheric structure parameter was calculated by using the weak atmospheric turbulence model; and the relationship of the atmospheric coherence length and high resolution remote sensing optical system was established; then the influence of atmospheric turbulence on the coefficient r0h of optical remote sensing system of ground resolution was derived; finally different orbit height of high resolution optical system imaging quality affected by atmospheric turbulence was analyzed. Results show that the influence of atmospheric turbulence on the high resolution remote sensing optical system, the resolution of which has reached sub meter level meter or even the 0.5m, 0.35m and even 0.15m ultra in recent years, image quality will be quite serious. In the above situation, the influence of the atmospheric turbulence must be corrected. Simulation algorithms of PSF are presented based on the above results. Experiment and analytical results are posted.

GMES Initial Operations - Network for Earth Observation Research Training (GIONET)

NASA Astrophysics Data System (ADS)

Nicolas-Perea, V.; Balzter, H.

2012-12-01

GMES Initial Operations - Network for Earth Observation Research Training (GIONET) is a Marie Curie funded project that aims to establish the first of a kind European Centre of Excellence for Earth Observation Research Training. GIONET is a partnership of leading Universities, research institutes and private companies from across Europe aiming to cultivate a community of early stage researchers in the areas of optical and radar remote sensing skilled for the emerging GMES land monitoring services during the GMES Initial Operations period (2011-2013) and beyond. GIONET is expected to satisfy the demand for highly skilled researchers and provide personnel for operational phase of the GMES and monitoring and emergency services. It will achieve this by: -Providing postgraduate training in Earth Observation Science that exposes students to different research disciplines and complementary skills, providing work experiences in the private and academic sectors, and leading to a recognized qualification (Doctorate). -Enabling access to first class training in both fundamental and applied research skills to early-stage researchers at world-class academic centers and market leaders in the private sector. -Building on the experience from previous GMES research and development projects in the land monitoring and emergency information services. The training program through supervised research focuses on 14 research topics (each carried out by an Early Stage Researchers based in one of the partner organization) divided in 5 main areas: Forest monitoring: Global biomass information systems Forest Monitoring of the Congo Basin using Synthetic Aperture radar (SAR) Multi-concept Earth Observation Capabilities for Biomass Mapping and Change Detection: Synergy of Multi-temporal and Multi-frequency Interferometric Radar and Optical Satellite Data Land cover and change: Multi-scale Remote Sensing Synergy for Land Process Studies: from field Spectrometry to Airborne Hyperspectral and Lidar Campaigns to Radar-Optical Satellite Data Multi-temporal, multi-frequency SAR for landscape dynamics Coastal zone and freshwater monitoring: Optical and SAR-based EO in support of Integrated Coastal Zone Management Dynamics and conservation ecology of emergent and submerged macrophytes in Lake Balaton using airborne remote sensing Satellite remote sensing of water quality (chlorophyll and suspended sediment) using MODIS and ship-mounted LIDAR Geohazards and emergency response: Methods for detection and monitoring of small scale land surface feature changes in complex crisis situations Monitoring landslide displacements with Radar Interferometry DINSAR/PSI hybrid methodologies for ground-motion monitoring Climate adaptation and emergency response: Earth Observation based analysis of regional impact of climate change induced water stress patterns fuelling human crisis and conflict situations in semi dry climate regimes Satellite Derived Information for Drought Detection and Estimation of the Water Balance GIONET will also cover methodologies including (i) modelling fundamental radiative processes determining the satellite signal, (ii) atmospheric correction and calibration, (iii) processing higher-order data products, (iii) developing information products from satellite data to meet user requirements, and (iv) statistical methods for assessing the quality and accuracy of data products.

Hybrid networking sensing system for structural health monitoring of a concrete cable-stayed bridge

NASA Astrophysics Data System (ADS)

Torbol, Marco; Kim, Sehwan; Chien, Ting-Chou; Shinozuka, Masanobu

2013-04-01

The purpose of this study is the remote structural health monitoring to identify the torsional natural frequencies and mode shapes of a concrete cable-stayed bridge using a hybrid networking sensing system. The system consists of one data aggregation unit, which is daisy-chained to one or more sensing nodes. A wireless interface is used between the data aggregation units, whereas a wired interface is used between a data aggregation unit and the sensing nodes. Each sensing node is equipped with high-precision MEMS accelerometers with adjustable sampling frequency from 0.2 Hz to 1.2 kHz. The entire system was installed inside the reinforced concrete box-girder deck of Hwamyung Bridge, which is a cable stayed bridge in Busan, South Korea, to protect the system from the harsh environmental conditions. This deployment makes wireless communication a challenge due to the signal losses and the high levels of attenuation. To address these issues, the concept of hybrid networking system is introduced with the efficient local power distribution technique. The theoretical communication range of Wi-Fi is 100m. However, inside the concrete girder, the peer to peer wireless communication cannot exceed about 20m. The distance is further reduced by the line of sight between the antennas. However, the wired daisy-chained connection between sensing nodes is useful because the data aggregation unit can be placed in the optimal location for transmission. To overcome the limitation of the wireless communication range, we adopt a high-gain antenna that extends the wireless communication distance to 50m. Additional help is given by the multi-hopping data communication protocol. The 4G modem, which allows remote access to the system, is the only component exposed to the external environment.

Eco-hydrological Wireless Sensor Network and upscaling method research in the Heihe River Basin, China

NASA Astrophysics Data System (ADS)

Jin, Rui; kang, Jian

2017-04-01

Wireless Sensor Networks are recognized as one of most important near-surface components of GEOSS (Global Earth Observation System of Systems), with flourish development of low-cost, robust and integrated data loggers and sensors. A nested eco-hydrological wireless sensor network (EHWSN) was installed in the up- and middle-reaches of the Heihe River Basin, operated to obtain multi-scale observation of soil moisture, soil temperature and land surface temperature from 2012 till now. The spatial distribution of EHWSN was optimally designed based on the geo-statistical theory, with the aim to capture the spatial variations and temporal dynamics of soil moisture and soil temperature, and to produce ground truth at grid scale for validating the related remote sensing products and model simulation in the heterogeneous land surface. In terms of upscaling research, we have developed a set of method to aggregate multi-point WSN observations to grid scale ( 1km), including regression kriging estimation to utilize multi-resource remote sensing auxiliary information, block kriging with homogeneous measurement errors, and bayesian-based upscaling algorithm that utilizes MODIS-derived apparent thermal inertia. All the EHWSN observation are organized as datasets to be freely published at http://westdc.westgis.ac.cn/hiwater. EHWSN integrates distributed observation nodes to achieve an automated, intelligent and remote-controllable network that provides superior integrated, standardized and automated observation capabilities for hydrological and ecological processes research at the basin scale.

Capturing Micro-topography of an Arctic Tundra Landscape through Digital Elevation Models (DEMs) Acquired from Various Remote Sensing Platforms

NASA Astrophysics Data System (ADS)

Vargas, S. A., Jr.; Tweedie, C. E.; Oberbauer, S. F.

2013-12-01

The need to improve the spatial and temporal scaling and extrapolation of plot level measurements of ecosystem structure and function to the landscape level has been identified as a persistent research challenge in the arctic terrestrial sciences. Although there has been a range of advances in remote sensing capabilities on satellite, fixed wing, helicopter and unmanned aerial vehicle platforms over the past decade, these present costly, logistically challenging (especially in the Arctic), technically demanding solutions for applications in an arctic environment. Here, we present a relatively low cost alternative to these platforms that uses kite aerial photography (KAP). Specifically, we demonstrate how digital elevation models (DEMs) were derived from this system for a coastal arctic landscape near Barrow, Alaska. DEMs of this area acquired from other remote sensing platforms such as Terrestrial Laser Scanning (TLS), Airborne Laser Scanning, and satellite imagery were also used in this study to determine accuracy and validity of results. DEMs interpolated using the KAP system were comparable to DEMs derived from the other platforms. For remotely sensing acre to kilometer square areas of interest, KAP has proven to be a low cost solution from which derived products that interface ground and satellite platforms can be developed by users with access to low-tech solutions and a limited knowledge of remote sensing.

A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States.

PubMed

Beckerman, Bernardo S; Jerrett, Michael; Serre, Marc; Martin, Randall V; Lee, Seung-Jae; van Donkelaar, Aaron; Ross, Zev; Su, Jason; Burnett, Richard T

2013-07-02

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R(2) values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R(2) were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.

International Commercial Remote Sensing Practices and Policies: A Comparative Analysis

NASA Astrophysics Data System (ADS)

Stryker, Timothy

In recent years, there has been much discussion about U.S. commercial remoteUnder the Act, the Secretary of Commerce sensing policies and how effectively theylicenses the operations of private U.S. address U.S. national security, foreignremote sensing satellite systems, in policy, commercial, and public interests.consultation with the Secretaries of Defense, This paper will provide an overview of U.S.State, and Interior. PDD-23 provided further commercial remote sensing laws,details concerning the operation of advanced regulations, and policies, and describe recentsystems, as well as criteria for the export of NOAA initiatives. It will also addressturnkey systems and/or components. In July related foreign practices, and the overall2000, pursuant to the authority delegated to legal context for trade and investment in thisit by the Secretary of Commerce, NOAA critical industry.iss ued new regulations for the industry. Licensing and Regulationsatellite systems. NOAA's program is The 1992 Land Remote Sensing Policy Act ("the Act"), and the 1994 policy on Foreign Access to Remote Sensing Space Capabilities (known as Presidential Decision Directive-23, or PDD-23) put into place an ambitious legal and policy framework for the U.S. Government's licensing of privately-owned, high-resolution satellite systems. Previously, capabilities afforded national security and observes the international obligations of the United States; maintain positive control of spacecraft operations; maintain a tasking record in conjunction with other record-keeping requirements; provide U.S. Government access to and use of data when required for national security or foreign policy purposes; provide for U.S. Government review of all significant foreign agreements; obtain U.S. Government approval for any encryption devices used; make available unenhanced data to a "sensed state" as soon as such data are available and on reasonable cost terms and conditions; make available unenhanced data as requested by the U.S. Government Archive; and, obtain a priori U.S. Government approval of all plans and procedures to deal with safe disposition of the satellite. Further information on NOAA's regulations and NOAA's licensing program is available at www.licensing.noaa.gov. Monitoring and Enforcement NOAA's enforcement mission is focused on the legislative mandate which states that the Secretary of Commerce has a continuing obligation to ensure that licensed imaging systems are operated lawfully to preserve the national security and foreign policies of the United States. NOAA has constructed an end-to-end monitoring and compliance program to review the activities of licensed companies. This program includes a pre- launch review, an operational baseline audit, and an annual comprehensive national security audit. If at any time there is suspicion or concern that a system is being operated unlawfully, a no-notice inspection may be initiated. setbacks, three U.S. companies are now operational, with more firms expected to become so in the future. While NOAA does not disclose specific systems capabilities for proprietary reasons, its current licensing resolution thresholds for general commercial availability are as follows: 0.5 meter Ground Sample Distance (GSD) for panchromatic systems, 2 meter GSD for multi-spectral systems, 3 meter Impulse Response (IPR) for Synthetic Aperture Radar systems, and 20 meter GSD for hyperspectral systems (with certain 8-meter hyperspectral derived products also licensed for commercial distribution). These thresholds are subject to change based upon foreign availability and other considerations. It should also be noted that license applications are reviewed and granted on a case-by-case basis, pursuant to each system's technology and concept of operations. In 2001, NOAA, along with the Department of Commerce's International Trade Administration, commissioned a study by the RAND Corporation to assess the risks faced by the U.S. commercial remote sensing satellite industry. In commissioning this study, NOAA's goal was to better understand the role that U.S. Government policies and regulations have in shaping the prospects for emerging commercial remote sensing satellite firms. The study assessed the risks against broader trends in the larger U.S. remote sensing industry and geospatial technology and effective policy implementation. The Department of Commerce is working with NOAA licensees to identify foreign actions which could restrict market access by U.S. firms, and seeking to provide a "level playing field" for U.S. service providers. The Department of Commerce has dedicated new resources to its licensing activities. In Fiscal Year 2002, the Department obtained 1.2 million in funding to support the NOAA program, through staff, equipment, technical support, constituent outreach, and market and policy studies. To better understand the market and make more well-informed licensing decisions, NOAA is participating in a broad-based market study effort under the direction of the American Society for Photogrammetry and Remote Sensing (ASPRS) and NASA's Commercial Remote Sensing Program. This study is providing long-term analysis of the commercial remote sensing industry. It is being supported by interviews with industry and government experts, a web-based survey, and a thorough review and analysis of related literature. The project should more clearly determine future remote sensing needs and requirements, and maximize the industry's baselines, standards, and socio-economic potential. NOAA, through its participation in this study, has gained important new insights into the status and future trends of this industry. The study's initial findings estimate 2001 industry revenue at 2 billion, growing at 13% per year, to an approximate level of 6 billion in 2010 (in constant, calendar year 2000 dollars). Currently, across all sectors, the most active market segments are in nati onal /glo bal security, mapping/geography, civil government, and have provided for appropriate measures for monitoring and compliance. This approach provides a valuable framework for companies, investors, customers, and foreign partners. The clearly-defined ground rules are designed to facilitate full private sector competition, innovation, and domestic and international market development. International market development remains a key issue for the U.S. Government and for U.S. industry in general. NOAA has learned of some interest by foreign governments in promulgating new laws and regulations to address this growing industry. However, to date, most governments have yet to publicize new commercial remote sensing laws or regulations. In some instances, data policies for commercial remote sensing have been developed, but only in the context of government-owned and operated systems, or private systems in which a government is the controlling shareholder. Other than some initial consultations and limited agreements between supplier nations, there has to date been little overall international coordination of commercial remote sensing policies and practices. The result has been an uncertain and non- uniform international business environment, which can cause difficulties for all commercial remote sensing operators. Related international market distortions inhibit the maturation of the industry and the normalization of business practices. This situation may make it more difficult for key stakeholders to make decisions on investments, purchases, regulatory affairs, and international partnerships. To put this growing industry on a more level footing, there should be further coordination

Unmanned aerial systems for photogrammetry and remote sensing: A review

NASA Astrophysics Data System (ADS)

Colomina, I.; Molina, P.

2014-06-01

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

Investigating trends in water use over the Choptank River watershed using a multi-satellite data fusion approach

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing technologies have been widely used to map spatiotemporal variability in consumptive water use (or evapotranspiration; ET) for agricultural water management applications. However, current satellite-based sensors with the high spatial resolution required to map ET at sub-field...

Investigating water use over the Choptank River Watershed using a multi-satellite data fusion approach

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing technologies have been widely used to map spatiotemporal variability in consumptive water use (or evapotranspiration; ET) for agricultural water management applications. However, current satellite-based sensors with the high spatial resolution required to map ET at sub-field...

MULTI-SCALE REMOTE SENSING MAPPING OF ANTHROPOGENIC IMPERVIOUS SURFACES: SPATIAL AND TEMPORAL SCALING ISSUES RELATED TO ECOLOGICAL AND HYDROLOGICAL LANDSCAPE ANALYSES

EPA Science Inventory

Anthropogenic impervious surfaces are leading contributors to non-point-source water pollution in urban watersheds. These human-created surfaces include such features as roads, parking lots, rooftops, sideways, and driveways. Aerial photography provides a historical vehicle for...

Exploring the use of multi-sensor data fusion for daily evapotranspiration mapping at field scale

USDA-ARS?s Scientific Manuscript database

Modern practices of water management in agriculture can significantly benefit from accurate mapping of crop water consumption at field scale. Assuming that actual evapotranspiration (ET) is the main water loss in land hydrological balance, remote sensing data represent an invaluable tool for water u...

Estimating the future agriculture freight transportation network needs due to climate change using remote sensing and regional climate models.

DOT National Transportation Integrated Search

2016-12-01

A reoccurring challenge with increasing fuel prices is optimization of multi- and inter-modal freight transport to move products most efficiently. Projections for the future of agriculture in the United States (U.S.) combined with regional climate mo...

Testing of two source energy balance model under irrigated and dryland conditions using high resolution airborne imagery

USDA-ARS?s Scientific Manuscript database

Two Source Model (TSM) calculates the heat and water exchange and interaction between soil-atmosphere and vegetation-atmosphere separately. This is achieved through decomposition of radiometric surface temperature to soil and vegetation component temperatures either from multi-angular remotely sense...

The Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX)-a synopsis

USDA-ARS?s Scientific Manuscript database

Considering California’s recent multi-year drought as well as the severe droughts recently in Italy and South Africa, there is a critical need for accurate and timely evapotranspiration (ET) and crop stress information to ensure long-term sustainability of high-value value perennial crops (vineyards...

A multi-scale analysis of landscape statistics

Treesearch

Douglas H. Cain; Kurt H. Riitters; Kenneth Orvis

1997-01-01

It is now feasible to monitor some aspects of landscape ecological condition nationwide using remotely- sensed imagery and indicators of land cover pattern. Previous research showed redundancies among many reported pattern indicators and identified six unique dimensions of land cover pattern. This study tested the stability of those dimensions and representative...

Effect of spatial image support in detecting long-term vegetation change from satellite time-series

USDA-ARS?s Scientific Manuscript database

Context Arid rangelands have been severely degraded over the past century. Multi-temporal remote sensing techniques are ideally suited to detect significant changes in ecosystem state; however, considerable uncertainty exists regarding the effects of changing image resolution on their ability to de...

Multi-scale soil salinity mapping and monitoring with proximal and remote sensing

USDA-ARS?s Scientific Manuscript database

This talk is part of a technical short course on “Soil mapping and process modelling at diverse scales”. In the talk, guidelines, special considerations, protocols, and strengths and limitations are presented for characterizing spatial and temporal variation in soil salinity at several spatial scale...

Daily monitoring of vegetation conditions and evapotranspiration at field scale by fusing multi-satellite images

USDA-ARS?s Scientific Manuscript database

Vegetation monitoring requires frequent remote sensing observations. While imagery from coarse resolution sensors such as MODIS/VIIRS can provide daily observations, they lack spatial detail to capture surface features for vegetation monitoring. The medium spatial resolution (10-100m) sensors are su...

Analysis of remote sensing data collected for detection and mapping of oil spills: Reduction and analysis of multi-sensor airborne data of the NASA Wallops oil spill exercise of November 1978

NASA Technical Reports Server (NTRS)

1982-01-01

Airborne, remotely sensed data of the NASA Wallops controlled oil spill were corrected, reduced and analysed. Sensor performance comparisons were made by registering data sets from different sensors, which were near-coincident in time and location. Multispectral scanner images were, in turn, overlayed with profiles of correlation between airborne and laboratory-acquired fluorosensor spectra of oil; oil-thickness contours derived (by NASA) from a scanning fluorosensor and also from a two-channel scanning microwave radiometer; and synthetic aperture radar X-HH images. Microwave scatterometer data were correlated with dual-channel (UV and TIR) line scanner images of the oil slick.

A Multi-Disciplinary Approach to Remote Sensing through Low-Cost UAVs.

PubMed

Calvario, Gabriela; Sierra, Basilio; Alarcón, Teresa E; Hernandez, Carmen; Dalmau, Oscar

2017-06-16

The use of Unmanned Aerial Vehicles (UAVs) based on remote sensing has generated low cost monitoring, since the data can be acquired quickly and easily. This paper reports the experience related to agave crop analysis with a low cost UAV. The data were processed by traditional photogrammetric flow and data extraction techniques were applied to extract new layers and separate the agave plants from weeds and other elements of the environment. Our proposal combines elements of photogrammetry, computer vision, data mining, geomatics and computer science. This fusion leads to very interesting results in agave control. This paper aims to demonstrate the potential of UAV monitoring in agave crops and the importance of information processing with reliable data flow.

A Multi-Disciplinary Approach to Remote Sensing through Low-Cost UAVs

PubMed Central

Calvario, Gabriela; Sierra, Basilio; Alarcón, Teresa E.; Hernandez, Carmen; Dalmau, Oscar

2017-01-01

The use of Unmanned Aerial Vehicles (UAVs) based on remote sensing has generated low cost monitoring, since the data can be acquired quickly and easily. This paper reports the experience related to agave crop analysis with a low cost UAV. The data were processed by traditional photogrammetric flow and data extraction techniques were applied to extract new layers and separate the agave plants from weeds and other elements of the environment. Our proposal combines elements of photogrammetry, computer vision, data mining, geomatics and computer science. This fusion leads to very interesting results in agave control. This paper aims to demonstrate the potential of UAV monitoring in agave crops and the importance of information processing with reliable data flow. PMID:28621740

Airborne multicamera system for geo-spatial applications

NASA Astrophysics Data System (ADS)

Bachnak, Rafic; Kulkarni, Rahul R.; Lyle, Stacey; Steidley, Carl W.

2003-08-01

Airborne remote sensing has many applications that include vegetation detection, oceanography, marine biology, geographical information systems, and environmental coastal science analysis. Remotely sensed images, for example, can be used to study the aftermath of episodic events such as the hurricanes and floods that occur year round in the coastal bend area of Corpus Christi. This paper describes an Airborne Multi-Spectral Imaging System that uses digital cameras to provide high resolution at very high rates. The software is based on Delphi 5.0 and IC Imaging Control's ActiveX controls. Both time and the GPS coordinates are recorded. Three successful test flights have been conducted so far. The paper present flight test results and discusses the issues being addressed to fully develop the system.

Technology study of quantum remote sensing imaging

NASA Astrophysics Data System (ADS)

Bi, Siwen; Lin, Xuling; Yang, Song; Wu, Zhiqiang

2016-02-01

According to remote sensing science and technology development and application requirements, quantum remote sensing is proposed. First on the background of quantum remote sensing, quantum remote sensing theory, information mechanism, imaging experiments and prototype principle prototype research situation, related research at home and abroad are briefly introduced. Then we expounds compress operator of the quantum remote sensing radiation field and the basic principles of single-mode compression operator, quantum quantum light field of remote sensing image compression experiment preparation and optical imaging, the quantum remote sensing imaging principle prototype, Quantum remote sensing spaceborne active imaging technology is brought forward, mainly including quantum remote sensing spaceborne active imaging system composition and working principle, preparation and injection compression light active imaging device and quantum noise amplification device. Finally, the summary of quantum remote sensing research in the past 15 years work and future development are introduced.

Numerical Model of Multiple Scattering and Emission from Layering Snowpack for Microwave Remote Sensing

NASA Astrophysics Data System (ADS)

Jin, Y.; Liang, Z.

2002-12-01

The vector radiative transfer (VRT) equation is an integral-deferential equation to describe multiple scattering, absorption and transmission of four Stokes parameters in random scatter media. From the integral formal solution of VRT equation, the lower order solutions, such as the first-order scattering for a layer medium or the second order scattering for a half space, can be obtained. The lower order solutions are usually good at low frequency when high-order scattering is negligible. It won't be feasible to continue iteration for obtaining high order scattering solution because too many folds integration would be involved. In the space-borne microwave remote sensing, for example, the DMSP (Defense Meterological Satellite Program) SSM/I (Special Sensor Microwave/Imager) employed seven channels of 19, 22, 37 and 85GHz. Multiple scattering from the terrain surfaces such as snowpack cannot be neglected at these channels. The discrete ordinate and eigen-analysis method has been studied to take into account for multiple scattering and applied to remote sensing of atmospheric precipitation, snowpack etc. Snowpack was modeled as a layer of dense spherical particles, and the VRT for a layer of uniformly dense spherical particles has been numerically studied by the discrete ordinate method. However, due to surface melting and refrozen crusts, the snowpack undergoes stratifying to form inhomegeneous profiles of the ice grain size, fractional volume and physical temperature etc. It becomes necessary to study multiple scattering and emission from stratified snowpack of dense ice grains. But, the discrete ordinate and eigen-analysis method cannot be simply applied to multi-layers model, because numerically solving a set of multi-equations of VRT is difficult. Stratifying the inhomogeneous media into multi-slabs and employing the first order Mueller matrix of each thin slab, this paper developed an iterative method to derive high orders scattering solutions of whole scatter media. High order scattering and emission from inhomogeneous stratifying media of dense spherical particles are numerically obtained. The brightness temperature at low frequency such as 5.3 GHz without high order scattering and at SSM/I channels with high order scattering are obtained. This approach is also compared with the conventional discrete ordinate method for an uniform layer model. Numerical simulation for inhomogeneous snowpack is also compared with the measurements of microwave remote sensing.

Topographic mapping on large-scale tidal flats with an iterative approach on the waterline method

NASA Astrophysics Data System (ADS)

Kang, Yanyan; Ding, Xianrong; Xu, Fan; Zhang, Changkuan; Ge, Xiaoping

2017-05-01

Tidal flats, which are both a natural ecosystem and a type of landscape, are of significant importance to ecosystem function and land resource potential. Morphologic monitoring of tidal flats has become increasingly important with respect to achieving sustainable development targets. Remote sensing is an established technique for the measurement of topography over tidal flats; of the available methods, the waterline method is particularly effective for constructing a digital elevation model (DEM) of intertidal areas. However, application of the waterline method is more limited in large-scale, shifting tidal flats areas, where the tides are not synchronized and the waterline is not a quasi-contour line. For this study, a topographical map of the intertidal regions within the Radial Sand Ridges (RSR) along the Jiangsu Coast, China, was generated using an iterative approach on the waterline method. A series of 21 multi-temporal satellite images (18 HJ-1A/B CCD and three Landsat TM/OLI) of the RSR area collected at different water levels within a five month period (31 December 2013-28 May 2014) was used to extract waterlines based on feature extraction techniques and artificial further modification. These 'remotely-sensed waterlines' were combined with the corresponding water levels from the 'model waterlines' simulated by a hydrodynamic model with an initial generalized DEM of exposed tidal flats. Based on the 21 heighted 'remotely-sensed waterlines', a DEM was constructed using the ANUDEM interpolation method. Using this new DEM as the input data, it was re-entered into the hydrodynamic model, and a new round of water level assignment of waterlines was performed. A third and final output DEM was generated covering an area of approximately 1900 km2 of tidal flats in the RSR. The water level simulation accuracy of the hydrodynamic model was within 0.15 m based on five real-time tide stations, and the height accuracy (root mean square error) of the final DEM was 0.182 m based on six transects of measured data. This study aimed at construction of an accurate DEM for a large-scale, high-variable zone within a short timespan based on an iterative way of the waterline method.

Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data.

PubMed

Cui, Tianxiang; Wang, Yujie; Sun, Rui; Qiao, Chen; Fan, Wenjie; Jiang, Guoqing; Hao, Lvyuan; Zhang, Lei

2016-01-01

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m(-2) d(-1) and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m(-2) d(-1) and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution.

Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data

PubMed Central

Cui, Tianxiang; Wang, Yujie; Sun, Rui; Qiao, Chen; Fan, Wenjie; Jiang, Guoqing; Hao, Lvyuan; Zhang, Lei

2016-01-01

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m-2 d-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m-2 d-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution. PMID:27088356

Scalable Algorithms for Clustering Large Geospatiotemporal Data Sets on Manycore Architectures

NASA Astrophysics Data System (ADS)

Mills, R. T.; Hoffman, F. M.; Kumar, J.; Sreepathi, S.; Sripathi, V.

2016-12-01

The increasing availability of high-resolution geospatiotemporal data sets from sources such as observatory networks, remote sensing platforms, and computational Earth system models has opened new possibilities for knowledge discovery using data sets fused from disparate sources. Traditional algorithms and computing platforms are impractical for the analysis and synthesis of data sets of this size; however, new algorithmic approaches that can effectively utilize the complex memory hierarchies and the extremely high levels of available parallelism in state-of-the-art high-performance computing platforms can enable such analysis. We describe a massively parallel implementation of accelerated k-means clustering and some optimizations to boost computational intensity and utilization of wide SIMD lanes on state-of-the art multi- and manycore processors, including the second-generation Intel Xeon Phi ("Knights Landing") processor based on the Intel Many Integrated Core (MIC) architecture, which includes several new features, including an on-package high-bandwidth memory. We also analyze the code in the context of a few practical applications to the analysis of climatic and remotely-sensed vegetation phenology data sets, and speculate on some of the new applications that such scalable analysis methods may enable.

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

NASA Astrophysics Data System (ADS)

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 were merged according to geological context. The major conclusions are: 1. Carbonate minerals can be mapped using spectral unmixing techniques. 2. Carbonatites are associated with specific geometric pattern 3. The combination of spectral and geometric information improves classification result and reduces misclassification. References Clausi, D. A. (2002): An analysis of co-occurrence texture statistics as a function of grey-level quantization. - Canadian Journal of Remote Sensing, 28 (1), 45-62 Jones, A. P., Genge, M. and Carmody, L (2013): Carbonate Melts and Carbonatites. - Reviews in Mineralogy & Geochemistry, 75, 289-322 Shahzad, F. & Gloaguen, R. (2011): TecDEM: A MATLAB based toolbox for tectonic geomorphology, Part 2: Surface dynamics and basin analysis. - Computers and Geosciences, 37 (2), 261-271

BOREAS Level-0 AOCI Imagery: Digital Counts in BIL Format

NASA Technical Reports Server (NTRS)

Newcomer, Jeffrey A.; Dominquez, Roseanne; Hall, Forrest G. (Editor)

2000-01-01

The level-0 AOCI imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas. The AOCI was the only remote sensing instrument flown with wavelength bands specific to the investigation of various aquatic parameters such as chlorophyll content and turbidity. Only one flight of the AOCI instrument was made onboard the ER-2 aircraft on 21-Jul-1994 over the SSA.

Retrieving the properties of ice-phase precipitation with multi-frequency radar measurements

NASA Astrophysics Data System (ADS)

Mace, G. G.; Gergely, M.; Mascio, J.

2017-12-01

The objective of most retrieval algorithms applied to remote sensing measurements is the microphysical properties that a model might predict such as condensed water content, particle number, or effective size. However, because ice crystals grow and aggregate into complex non spherical shapes, the microphysical properties of interest are very much dependent on the physical characteristics of the precipitation such as how mass and crystal area are distributed as a function of particle size. Such physical properties also have a strong influence on how microwave electromagnetic energy scatters from ice crystals causing significant ambiguity in retrieval algorithms. In fact, passive and active microwave remote sensing measurements are typically nearly as sensitive to the ice crystal physical properties as they are to the microphysical characteristics that are typically the aim of the retrieval algorithm. There has, however, been active development of multi frequency algorithms recently that attempt to ameliorate and even exploit this sensitivity. In this paper, we will review these approaches and present practical applications of retrieving ice crystal properties such as mass- and area dimensional relationships from single and dual frequency radar measurements of precipitating ice using data collected aboard ship in the Southern Ocean and from remote sensors in the Rocky Mountains of the Western U.S.

A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers

Treesearch

John A. Gamon; K. Fred Huemmrich; Christopher Y. S. Wong; Ingo Ensminger; Steven Garrity; David Y. Hollinger; Asko Noormets; Josep Peñuelas

2016-01-01

In evergreen conifers, where the foliage amount changes little with season, accurate detection of the underlying “photosynthetic phenology” from satellite remote sensing has been difficult, presenting challenges for global models of ecosystem carbon uptake. Here, we report a close correspondence between seasonally changing foliar pigment levels, expressed as...

Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories

Treesearch

Steen Magnussen; Ronald E. McRoberts; Erkki O. Tomppo

2009-01-01

New model-based estimators of the uncertainty of pixel-level and areal k-nearest neighbour (knn) predictions of attribute Y from remotely-sensed ancillary data X are presented. Non-parametric functions predict Y from scalar 'Single Index Model' transformations of X. Variance functions generated...

An evaluation of a UAV guidance system with consumer grade GPS receivers

NASA Astrophysics Data System (ADS)

Rosenberg, Abigail Stella

Remote sensing has been demonstrated an important tool in agricultural and natural resource management and research applications, however there are limitations that exist with traditional platforms (i.e., hand held sensors, linear moves, vehicle mounted, airplanes, remotely piloted vehicles (RPVs), unmanned aerial vehicles (UAVs) and satellites). Rapid technological advances in electronics, computers, software applications, and the aerospace industry have dramatically reduced the cost and increased the availability of remote sensing technologies. Remote sensing imagery vary in spectral, spatial, and temporal resolutions and are available from numerous providers. Appendix A presented results of a test project that acquired high-resolution aerial photography with a RPV to map the boundary of a 0.42 km2 fire area. The project mapped the boundaries of the fire area from a mosaic of the aerial images collected and compared this with ground-based measurements. The project achieved a 92.4% correlation between the aerial assessment and the ground truth data. Appendix B used multi-objective analysis to quantitatively assess the tradeoffs between different sensor platform attributes to identify the best overall technology. Experts were surveyed to identify the best overall technology at three different pixel sizes. Appendix C evaluated the positional accuracy of a relatively low cost UAV designed for high resolution remote sensing of small areas in order to determine the positional accuracy of sensor readings. The study evaluated the accuracy and uncertainty of a UAV flight route with respect to the programmed waypoints and of the UAV's GPS position, respectively. In addition, the potential displacement of sensor data was evaluated based on (1) GPS measurements on board the aircraft and (2) the autopilot's circuit board with 3-axis gyros and accelerometers (i.e., roll, pitch, and yaw). The accuracies were estimated based on a 95% confidence interval or similar methods. The accuracy achieved in the second and third manuscripts demonstrates that reasonably priced, high resolution remote sensing via RPVs and UAVs is practical for agriculture and natural resource professionals.

A tree canopy height delineation method based on Morphological Reconstruction—Open Crown Decomposition

NASA Astrophysics Data System (ADS)

Liu, Q.; Jing, L.; Li, Y.; Tang, Y.; Li, H.; Lin, Q.

2016-04-01

For the purpose of forest management, high resolution LIDAR and optical remote sensing imageries are used for treetop detection, tree crown delineation, and classification. The purpose of this study is to develop a self-adjusted dominant scales calculation method and a new crown horizontal cutting method of tree canopy height model (CHM) to detect and delineate tree crowns from LIDAR, under the hypothesis that a treetop is radiometric or altitudinal maximum and tree crowns consist of multi-scale branches. The major concept of the method is to develop an automatic selecting strategy of feature scale on CHM, and a multi-scale morphological reconstruction-open crown decomposition (MRCD) to get morphological multi-scale features of CHM by: cutting CHM from treetop to the ground; analysing and refining the dominant multiple scales with differential horizontal profiles to get treetops; segmenting LiDAR CHM using watershed a segmentation approach marked with MRCD treetops. This method has solved the problems of false detection of CHM side-surface extracted by the traditional morphological opening canopy segment (MOCS) method. The novel MRCD delineates more accurate and quantitative multi-scale features of CHM, and enables more accurate detection and segmentation of treetops and crown. Besides, the MRCD method can also be extended to high optical remote sensing tree crown extraction. In an experiment on aerial LiDAR CHM of a forest of multi-scale tree crowns, the proposed method yielded high-quality tree crown maps.

American Society for Photogrammetry and Remote Sensing and ACSM, Fall Convention, Reno, NV, Oct. 4-9, 1987, ASPRS Technical Papers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1987-01-01

Recent advances in remote-sensing technology and applications are examined in reviews and reports. Topics addressed include the use of Landsat TM data to assess suspended-sediment dispersion in a coastal lagoon, the use of sun incidence angle and IR reflectance levels in mapping old-growth coniferous forests, information-management systems, Large-Format-Camera soil mapping, and the economic potential of Landsat TM winter-wheat crop-condition assessment. Consideration is given to measurement of ephemeral gully erosion by airborne laser ranging, the creation of a multipurpose cadaster, high-resolution remote sensing and the news media, the role of vegetation in the global carbon cycle, PC applications in analytical photogrammetry,more » multispectral geological remote sensing of a suspected impact crater, fractional calculus in digital terrain modeling, and automated mapping using GP-based survey data.« less

Fusion of multi-source remote sensing data for agriculture monitoring tasks

NASA Astrophysics Data System (ADS)

Skakun, S.; Franch, B.; Vermote, E.; Roger, J. C.; Becker Reshef, I.; Justice, C. O.; Masek, J. G.; Murphy, E.

2016-12-01

Remote sensing data is essential source of information for enabling monitoring and quantification of crop state at global and regional scales. Crop mapping, state assessment, area estimation and yield forecasting are the main tasks that are being addressed within GEO-GLAM. Efficiency of agriculture monitoring can be improved when heterogeneous multi-source remote sensing datasets are integrated. Here, we present several case studies of utilizing MODIS, Landsat-8 and Sentinel-2 data along with meteorological data (growing degree days - GDD) for winter wheat yield forecasting, mapping and area estimation. Archived coarse spatial resolution data, such as MODIS, VIIRS and AVHRR, can provide daily global observations that coupled with statistical data on crop yield can enable the development of empirical models for timely yield forecasting at national level. With the availability of high-temporal and high spatial resolution Landsat-8 and Sentinel-2A imagery, course resolution empirical yield models can be downscaled to provide yield estimates at regional and field scale. In particular, we present the case study of downscaling the MODIS CMG based generalized winter wheat yield forecasting model to high spatial resolution data sets, namely harmonized Landsat-8 - Sentinel-2A surface reflectance product (HLS). Since the yield model requires corresponding in season crop masks, we propose an automatic approach to extract winter crop maps from MODIS NDVI and MERRA2 derived GDD using Gaussian mixture model (GMM). Validation for the state of Kansas (US) and Ukraine showed that the approach can yield accuracies > 90% without using reference (ground truth) data sets. Another application of yearly derived winter crop maps is their use for stratification purposes within area frame sampling for crop area estimation. In particular, one can simulate the dependence of error (coefficient of variation) on the number of samples and strata size. This approach was used for estimating the area of winter crops in Ukraine for 2013-2016. The GMM-GDD approach is further extended for HLS data to provide automatic winter crop mapping at 30 m resolution for crop yield model and area estimation. In case of persistent cloudiness, addition of Sentinel-1A synthetic aperture radar (SAR) images is explored for automatic winter crop mapping.

Comparative analysis of single-walled and multi-walled carbon nanotubes for electrochemical sensing of glucose on gold printed circuit boards.

PubMed

Alhans, Ruby; Singh, Anukriti; Singhal, Chaitali; Narang, Jagriti; Wadhwa, Shikha; Mathur, Ashish

2018-09-01

In the present work, a comparative study was performed between single-walled carbon nanotubes and multi-walled carbon nanotubes coated gold printed circuit board electrodes for glucose detection. Various characterization techniques were demonstrated in order to compare the modified electrodes viz. cyclic voltammetry, electrochemical impedance spectroscopy and chrono-amperometry. Results revealed that single-walled carbon nanotubes outperformed multi-walled carbon nanotubes and proved to be a better sensing interface for glucose detection. The single-walled carbon nanotubes coated gold printed circuit board electrodes showed a wide linear sensing range (1 mM to 100 mM) with detection limit of 0.1 mM with response time of 5 s while multi-walled carbon nanotubes coated printed circuit board gold electrodes showed linear sensing range (1 mM to 100 mM) with detection limit of 0.1 mM with response time of 5 s. This work provided low cost sensors with enhanced sensitivity, fast response time and reliable results for glucose detection which increased the affordability of such tests in remote areas. In addition, the comparative results confirmed that single-walled carbon nanotubes modified electrodes can be exploited for better amplification signal as compared to multi-walled carbon nanotubes. Copyright © 2018. Published by Elsevier B.V.

Immersive telepresence system using high-resolution omnidirectional movies and a locomotion interface

NASA Astrophysics Data System (ADS)

Ikeda, Sei; Sato, Tomokazu; Kanbara, Masayuki; Yokoya, Naokazu

2004-05-01

Technology that enables users to experience a remote site virtually is called telepresence. A telepresence system using real environment images is expected to be used in the field of entertainment, medicine, education and so on. This paper describes a novel telepresence system which enables users to walk through a photorealistic virtualized environment by actual walking. To realize such a system, a wide-angle high-resolution movie is projected on an immersive multi-screen display to present users the virtualized environments and a treadmill is controlled according to detected user's locomotion. In this study, we use an omnidirectional multi-camera system to acquire images real outdoor scene. The proposed system provides users with rich sense of walking in a remote site.

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

NASA Technical Reports Server (NTRS)

2002-01-01

Contents include the following: Monitoring the Ancient Countryside: Remote Sensing and GIS at the Chora of Chersonesos (Crimea, Ukraine). Integration of Remote Sensing and GIS for Management Decision Support in the Pendjari Biosphere Reserve (Republic of Benin). Monitoring of deforestation invasion in natural reserves of northern Madagascar based on space imagery. Cartography of Kahuzi-Biega National Park. Cartography and Land Use Change of World Heritage Areas and the Benefits of Remote Sensing and GIS for Conservation. Assessing and Monitoring Vegetation in Nabq Protected Area, South Sinai, Egypt, using combine approach of Satellite Imagery and Land Surveys. Evaluation of forage resources in semi-arid savannah environments with satellite imagery: contribution to the management of a protected area (Nakuru National Park) in Kenya. SOGHA, the Surveillance of Gorilla Habitat in World Heritage sites using Space Technologies. Application of Remote Sensing to monitor the Mont-Saint-Michel Bay (France). Application of Remote Sensing & GIS for the Conservation of Natural and Cultural Heritage Sites of the Southern Province of Sri Lanka. Social and Environmental monitoring of a UNESCO Biosphere Reserve: Case Study over the Vosges du Nord and Pfalzerwald Parks using Corona and Spot Imagery. Satellite Remote Sensing as tool to Monitor Indian Reservation in the Brazilian Amazonia. Remote Sensing and GIS Technology for Monitoring UNESCO World Heritage Sites - A Pilot Project. Urban Green Spaces: Modern Heritage. Monitoring of the technical condition of the St. Sophia Cathedral and related monastic buildings in Kiev with Space Applications, geo-positioning systems and GIS tools. The Murghab delta palaeochannel Reconstruction on the Basis of Remote Sensing from Space. Acquisition, Registration and Application of IKONOS Space Imagery for the cultural World Heritage site at Mew, Turkmenistan. Remote Sensing and VR applications for the reconstruction of archaeological landscapes. Archaeology through Space: Experience in Indian Subcontinent. The creation of a GIS Archaeological Site Location Catalogue in Yucatan: A Tool to preserve its Cultural Heritage. Mapping the Ancient Anasazi Roads of Southeast Utah. Remote Sensing and GIS Technology for Identification of Conservation and Heritage sites in Urban Planning. Mapping Angkor: For a new appraisal of the Angkor region. Angkor and radar imaging: seeing a vast pre-industrial low-density, dispersed urban complex. Technical and methodological aspects of archaeological CRM integrating high resolution satellite imagery. The contribution of satellite imagery to archaeological survey: an example from western Syria. The use of satellite images, digital elevation models and ground truth for the monitoring of land degradation in the "Cinque Terre" National park. Remote Sensing and GIS Applications for Protection and Conservation of World Heritage Site on the coast - Case Study of Tamil Nadu Coast, India. Multispectral high resolution satellite imagery in combination with "traditional" remote sensing and ground survey methods to the study of archaeological landscapes. The case study of Tuscany. Use of Remotely-Sensed Imagery in Cultural Landscape. Characterisation at Fort Hood, Texas. Heritage Learning and Data Collection: Biodiversity & Heritage Conservation through Collaborative Monitoring & Research. A collaborative project by UNESCO's WHC (World Heritage Center) & The GLOBE Program (Global Learning and Observations to Benefit the Environment). Practical Remote Sensing Activities in an Interdisciplinary Master-Level Space Course.

[Fractional vegetation cover of invasive Spartina alterniflora in coastal wetland using unmanned aerial vehicle (UAV)remote sensing].

PubMed

Zhou, Zai Ming; Yang, Yan Ming; Chen, Ben Qing

2016-12-01

The effective management and utilization of resources and ecological environment of coastal wetland require investigation and analysis in high precision of the fractional vegetation cover of invasive species Spartina alterniflora. In this study, Sansha Bay was selected as the experimental region, and visible and multi-spectral images obtained by low-altitude UAV in the region were used to monitor the fractional vegetation cover of S. alterniflora. Fractional vegetation cover parameters in the multi-spectral images were then estimated by NDVI index model, and the accuracy was tested against visible images as references. Results showed that vegetation covers of S. alterniflora in the image area were mainly at medium high level (40%-60%) and high level (60%-80%). Root mean square error (RMSE) between the NDVI model estimation values and true values was 0.06, while the determination coefficient R 2 was 0.92, indicating a good consistency between the estimation value and the true value.

Satellite Imagery Analysis for Automated Global Food Security Forecasting

NASA Astrophysics Data System (ADS)

Moody, D.; Brumby, S. P.; Chartrand, R.; Keisler, R.; Mathis, M.; Beneke, C. M.; Nicholaeff, D.; Skillman, S.; Warren, M. S.; Poehnelt, J.

2017-12-01

The recent computing performance revolution has driven improvements in sensor, communication, and storage technology. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. Cloud computing and storage, combined with recent advances in machine learning, are enabling understanding of the world at a scale and at a level of detail never before feasible. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and that can scale with the high-rate and dimensionality of imagery being collected. We focus on the problem of monitoring food crop productivity across the Middle East and North Africa, and show how an analysis-ready, multi-sensor data platform enables quick prototyping of satellite imagery analysis algorithms, from land use/land cover classification and natural resource mapping, to yearly and monthly vegetative health change trends at the structural field level.

Applications of Remote Sensing to Emergency Management.

DTIC Science & Technology

1980-02-15

Contents: Foundations of Remote Sensing : Data Acquisition and Interpretation; Availability of Remote Sensing Technology for Disaster Response...Imaging Systems, Current and Near Future Satellite and Aircraft Remote Sensing Systems; Utilization of Remote Sensing in Disaster Response: Categories of...Disasters, Phases of Monitoring Activities; Recommendations for Utilization of Remote Sensing Technology in Disaster Response; Selected Reading List.

Remote real-time monitoring of subsurface landfill gas migration.

PubMed

Fay, Cormac; Doherty, Aiden R; Beirne, Stephen; Collins, Fiachra; Foley, Colum; Healy, John; Kiernan, Breda M; Lee, Hyowon; Maher, Damien; Orpen, Dylan; Phelan, Thomas; Qiu, Zhengwei; Zhang, Kirk; Gurrin, Cathal; Corcoran, Brian; O'Connor, Noel E; Smeaton, Alan F; Diamond, Dermot

2011-01-01

The cost of monitoring greenhouse gas emissions from landfill sites is of major concern for regulatory authorities. The current monitoring procedure is recognised as labour intensive, requiring agency inspectors to physically travel to perimeter borehole wells in rough terrain and manually measure gas concentration levels with expensive hand-held instrumentation. In this article we present a cost-effective and efficient system for remotely monitoring landfill subsurface migration of methane and carbon dioxide concentration levels. Based purely on an autonomous sensing architecture, the proposed sensing platform was capable of performing complex analytical measurements in situ and successfully communicating the data remotely to a cloud database. A web tool was developed to present the sensed data to relevant stakeholders. We report our experiences in deploying such an approach in the field over a period of approximately 16 months.

Remote Real-Time Monitoring of Subsurface Landfill Gas Migration

PubMed Central

Fay, Cormac; Doherty, Aiden R.; Beirne, Stephen; Collins, Fiachra; Foley, Colum; Healy, John; Kiernan, Breda M.; Lee, Hyowon; Maher, Damien; Orpen, Dylan; Phelan, Thomas; Qiu, Zhengwei; Zhang, Kirk; Gurrin, Cathal; Corcoran, Brian; O’Connor, Noel E.; Smeaton, Alan F.; Diamond, Dermot

2011-01-01

The cost of monitoring greenhouse gas emissions from landfill sites is of major concern for regulatory authorities. The current monitoring procedure is recognised as labour intensive, requiring agency inspectors to physically travel to perimeter borehole wells in rough terrain and manually measure gas concentration levels with expensive hand-held instrumentation. In this article we present a cost-effective and efficient system for remotely monitoring landfill subsurface migration of methane and carbon dioxide concentration levels. Based purely on an autonomous sensing architecture, the proposed sensing platform was capable of performing complex analytical measurements in situ and successfully communicating the data remotely to a cloud database. A web tool was developed to present the sensed data to relevant stakeholders. We report our experiences in deploying such an approach in the field over a period of approximately 16 months. PMID:22163975

Verification of retail food outlet location data from a local health department using ground-truthing and remote-sensing technology: assessing differences by neighborhood characteristics.

PubMed

Rossen, Lauren M; Pollack, Keshia M; Curriero, Frank C

2012-09-01

Obtaining valid and accurate data on community food environments is critical for research evaluating associations between the food environment and health outcomes. This study utilized ground-truthing and remote-sensing technology to validate a food outlet retail list obtained from an urban local health department in Baltimore, Maryland in 2009. Ten percent of outlets (n=169) were assessed, and differences in accuracy were explored by neighborhood characteristics (96 census tracts) to determine if discrepancies were differential or non-differential. Inaccuracies were largely unrelated to a variety of neighborhood-level variables, with the exception of number of vacant housing units. Although remote-sensing technologies are a promising low-cost alternative to direct observation, this study demonstrated only moderate levels of agreement with ground-truthing. Published by Elsevier Ltd.

Remote sensing for rural development planning in Africa

NASA Technical Reports Server (NTRS)

Dunford, C.; Mouat, D. A.; Norton-Griffiths, M.; Slaymaker, D. M.

1983-01-01

Multilevel remote-sensing techniques were combined to provide land resource and land-use information for rural development planning in Arusha Region, Tanzania. Enhanced Landsat imagery, supplemented by low-level aerial survey data, slope angle data from topographic sheets, and existing reports on vegetation and soil conditions, was used jointly by image analysts and district-level land-management officials to divide the region's six districts into land-planning units. District-planning officials selected a number of these land-planning units for priority planning and development activities. For the priority areas, natural color aerial photographs provided detailed information for land-use planning discussions between district officials and villagers. Consideration of the efficiency of this remote sensing approach leads to general recommendations for similar applications. The technology and timing of data collection and interpretation activities should allow maximum participation by intended users of the information.

View angle effects on relationships between leaf area index in wheat and vegetation indices

NASA Astrophysics Data System (ADS)

Chen, H.; Li, W.; Huang, W.; Niu, Z.

2016-12-01

The effects of plant types and view angles on the canopy-reflected spectrum can not be ignored in the estimation of leaf area index (LAI) using remote sensing vegetation indices. While vegetation indices derived from nadir-viewing remote sensors are insufficient in leaf area index (LAI) estimation because of its misinterpretation of structural characteristecs, vegetation indices derived from multi-angular remote sensors have potential to improve detection of LAI. However, view angle effects on relationships between these indices and LAI for low standing crops (i.e. wheat) has not been fully evaluated and thus limits them to applied for consistent and accurate monitoring of vegetation. View angles effects of two types of winter wheat (wheat 411, erectophile; and wheat 9507, planophile) on relationship between LAI and spectral reflectance are assessed and compared in this study. An evaluation is conducted with in-situ measurements of LAI and bidirectional reflectance in the principal plane from -60° (back-scattering direction ) ot 60° (forward scattering direction) in the growth cycle of winter wheat. A variety of vegetation indices (VIs) published are calculated by BRDF. Additionally, all combinations of the bands are used in order to calculate Normalized difference Spectral Indices (NDSI) and Simple Subtraction Indices (SSI). The performance of the above indices along with raw reflectance and reflectance derivatives on LAI estimation are examined based on a linearity comparison. The results will be helpful in further developing multi-angle remote sensing models for accurate LAI evaluation.

Agricultural Land Use mapping by multi-sensor approach for hydrological water quality monitoring

NASA Astrophysics Data System (ADS)

Brodsky, Lukas; Kodesova, Radka; Kodes, Vit

2010-05-01

The main objective of this study is to demonstrate potential of operational use of the high and medium resolution remote sensing data for hydrological water quality monitoring by mapping agriculture intensity and crop structures. In particular use of remote sensing mapping for optimization of pesticide monitoring. The agricultural mapping task is tackled by means of medium spatial and high temporal resolution ESA Envisat MERIS FR images together with single high spatial resolution IRS AWiFS image covering the whole area of interest (the Czech Republic). High resolution data (e.g. SPOT, ALOS, Landsat) are often used for agricultural land use classification, but usually only at regional or local level due to data availability and financial constraints. AWiFS data (nominal spatial resolution 56 m) due to the wide satellite swath seems to be more suitable for use at national level. Nevertheless, one of the critical issues for such a classification is to have sufficient image acquisitions over the whole vegetation period to describe crop development in appropriate way. ESA MERIS middle-resolution data were used in several studies for crop classification. The high temporal and also spectral resolution of MERIS data has indisputable advantage for crop classification. However, spatial resolution of 300 m results in mixture signal in a single pixel. AWiFS-MERIS data synergy brings new perspectives in agricultural Land Use mapping. Also, the developed methodology procedure is fully compatible with future use of ESA (GMES) Sentinel satellite images. The applied methodology of hybrid multi-sensor approach consists of these main stages: a/ parcel segmentation and spectral pre-classification of high resolution image (AWiFS); b/ ingestion of middle resolution (MERIS) vegetation spectro-temporal features; c/ vegetation signatures unmixing; and d/ semantic object-oriented classification of vegetation classes into final classification scheme. These crop groups were selected to be classified: winter crops, spring crops, oilseed rape, legumes, summer and other crops. This study highlights operational potentials of high temporal full resolution MERIS images in agricultural land use monitoring. Practical application of this methodology is foreseen, among others, in the water quality monitoring. Effective pesticide monitoring relies also on spatial distribution of applied pesticides, which can be derived from crop - plant protection product relationship. Knowledge of areas with predominant occurrence of specific crop based on remote sensing data described above can be used for a forecast of probable plant protection product application, thus cost-effective pesticide monitoring. The remote sensing data used on a continuous basis can be used in other long-term water management issues and provide valuable data for decision makers. Acknowledgement: Authors acknowledge the financial support of the Ministry of Education, Youth and Sports of the Czech Republic (grants No. 2B06095 and No. MSM 6046070901). The study was also supported by ESA CAT-1 (ref. 4358) and SOSI projects (Spatial Observation Services and Infrastructure; ref. GSTP-RTDA-EOPG-SW-08-0004).

The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhou, Liqing

2015-12-01

With the development of satellite remote sensing technology and the remote sensing image data, traditional remote sensing image segmentation technology cannot meet the massive remote sensing image processing and storage requirements. This article put cloud computing and parallel computing technology in remote sensing image segmentation process, and build a cheap and efficient computer cluster system that uses parallel processing to achieve MeanShift algorithm of remote sensing image segmentation based on the MapReduce model, not only to ensure the quality of remote sensing image segmentation, improved split speed, and better meet the real-time requirements. The remote sensing image segmentation MeanShift algorithm parallel processing algorithm based on MapReduce shows certain significance and a realization of value.

Airborne in situ vertical profiling of HDO/H216O in the subtropical troposphere during the MUSICA remote sensing validation campaign

NASA Astrophysics Data System (ADS)

Dyroff, C.; Sanati, S.; Christner, E.; Zahn, A.; Balzer, M.; Bouquet, H.; McManus, J. B.; González-Ramos, Y.; Schneider, M.

2015-01-01

Vertical profiles of water vapor (H2O) and its isotope ratio D / H expressed as δ D(H2O were measured in situ by the ISOWAT II diode-laser spectrometer during the MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) airborne campaign. We present recent modifications of the instrument design. The instrument calibration on the ground as well as in flight is described. Based on the calibration measurements, the humidity-dependent uncertainty of our airborne data is determined. For the majority of the airborne data we achieved an accuracy (uncertainty of the mean) of Δ(δ D) ≈ 10‰. Vertical profiles between 150 and ~7000 m were obtained during 7 days in July and August 2013 over the subtropical North Atlantic Ocean near Tenerife. The flights were coordinated with ground-based (Network for the Detection of Atmospheric Composition Change, NDACC) and space-based (Infrared Atmospheric Sounding Interferometer, IASI) FTIR remote-sensing measurements of δ D(H2O) as a means to validate the remote sensing humidity and δ D(H2O) data products. The results of the validation are presented in detail in a separate paper (Schneider et al., 2014). The profiles were obtained with a high vertical resolution of around 3 m. By analyzing humidity and δ D(H2O) correlations we were able to identify different layers of airmasses with specific isotopic signatures. The results are discussed.

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

NASA Astrophysics Data System (ADS)

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

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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