REMOTE SENSING IN OCEANOGRAPHY.

Science.gov (United States)

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

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

Science.gov (United States)

Hong, Liang

2013-10-01

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

Ground-Based Correction of Remote-Sensing Spectral Imagery

Science.gov (United States)

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

2007-01-01

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

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

Science.gov (United States)

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

1996-01-01

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

Using Open Access Satellite Data Alongside Ground Based Remote Sensing: An Assessment, with Case Studies from Egypt’s Delta

Directory of Open Access Journals (Sweden)

Sarah Parcak

2017-09-01

Full Text Available This paper will assess the most recently available open access high-resolution optical satellite data (0.3 m–0.6 m and its detection of buried ancient features versus ground based remote sensing tools. It also discusses the importance of CORONA satellite data to evaluate landscape changes over the past 50 years surrounding sites. The study concentrates on Egypt’s Nile Delta, which is threatened by rising sea and water tables and urbanization. Many ancient coastal sites will be lost in the next few decades, thus this paper emphasizes the need to map them before they disappear. It shows that high resolution satellites can sometimes provide the same general picture on ancient sites in the Egyptian Nile Delta as ground based remote sensing, with relatively sandier sedimentary and degrading tell environments, during periods of rainfall, and higher groundwater conditions. Research results also suggest potential solutions for rapid mapping of threatened Delta sites, and urge a collaborative global effort to maps them before they disappear.

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

Science.gov (United States)

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

2012-12-01

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

A comparative study of LaBr3(Ce(3+)) and CeBr3 based gamma-ray spectrometers for planetary remote sensing applications.

Science.gov (United States)

Kozyrev, A; Mitrofanov, I; Owens, A; Quarati, F; Benkhoff, J; Bakhtin, B; Fedosov, F; Golovin, D; Litvak, M; Malakhov, A; Mokrousov, M; Nuzhdin, I; Sanin, A; Tretyakov, V; Vostrukhin, A; Timoshenko, G; Shvetsov, V; Granja, C; Slavicek, T; Pospisil, S

2016-08-01

The recent availability of large volume cerium bromide crystals raises the possibility of substantially improving gamma-ray spectrometer limiting flux sensitivities over current systems based on the lanthanum tri-halides, e.g., lanthanum bromide and lanthanum chloride, especially for remote sensing, low-level counting applications or any type of measurement characterized by poor signal to noise ratios. The Russian Space Research Institute has developed and manufactured a highly sensitive gamma-ray spectrometer for remote sensing observations of the planet Mercury from the Mercury Polar Orbiter (MPO), which forms part of ESA's BepiColombo mission. The Flight Model (FM) gamma-ray spectrometer is based on a 3-in. single crystal of LaBr3(Ce(3+)) produced in a separate crystal development programme specifically for this mission. During the spectrometers development, manufacturing, and qualification phases, large crystals of CeBr3 became available in a subsequent phase of the same crystal development programme. Consequently, the Flight Spare Model (FSM) gamma-ray spectrometer was retrofitted with a 3-in. CeBr3 crystal and qualified for space. Except for the crystals, the two systems are essentially identical. In this paper, we report on a comparative assessment of the two systems, in terms of their respective spectral properties, as well as their suitability for use in planetary mission with respect to radiation tolerance and their propensity for activation. We also contrast their performance with a Ge detector representative of that flown on MESSENGER and show that: (a) both LaBr3(Ce(3+)) and CeBr3 provide superior detection systems over HPGe in the context of minimally resourced spacecraft and (b) CeBr3 is a more attractive system than LaBr3(Ce(3+)) in terms of sensitivities at lower gamma fluxes. Based on the tests, the FM has now been replaced by the FSM on the BepiColombo spacecraft. Thus, CeBr3 now forms the central gamma-ray detection element on the MPO spacecraft.

Noninvasive Remote Sensing Techniques for Infrastructures Diagnostics

Directory of Open Access Journals (Sweden)

Angelo Palombo

2011-01-01

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

Ground-based remote sensing of volcanic CO2 and correlated SO2, HF, HCl, and BrO, in safe-distance from the crater

Science.gov (United States)

Butz, Andre; Solvejg Dinger, Anna; Bobrowski, Nicole; Kostinek, Julian; Fieber, Lukas; Fischerkeller, Constanze; Giuffrida, Giovanni Bruno; Hase, Frank; Klappenbach, Friedrich; Kuhn, Jonas; Lübcke, Peter; Tirpitz, Lukas; Tu, Qiansi

2017-04-01

Remote sensing of CO2 enhancements in volcanic plumes can be a tool to estimate volcanic CO2 emissions and thereby, to gain insight into the geological carbon cycle and into volcano interior processes. However, remote sensing of the volcanic CO2 is challenged by the large atmospheric background concentrations masking the minute volcanic signal. Here, we report on a demonstrator study conducted in September 2015 at Mt. Etna on Sicily, where we deployed an EM27/SUN Fourier Transform Spectrometer together with a UV spectrometer on a mobile remote sensing platform. The spectrometers were operated in direct-sun viewing geometry collecting cross-sectional scans of solar absorption spectra through the volcanic plume by operating the platform in stop-and-go patterns in 5 to 10 kilometers distance from the crater region. We successfully detected correlated intra-plume enhancements of CO2 and volcanic SO2, HF, HCl, and BrO. The path-integrated volcanic CO2 enhancements amounted to about 0.5 ppm (on top of the ˜400 ppm background). Key to successful detection of volcanic CO2 was A) the simultaneous observation of the O2 total column which allowed for correcting changes in the CO2 column caused by changes in observer altitude and B) the simultaneous measurement of volcanic species co-emitted with CO2 which allowed for discriminating intra-plume and extra-plume observations. The latter were used for subtracting the atmospheric CO2 background. The field study suggests that our remote sensing observatory is a candidate technique for volcano monitoring in safe distance from the crater region.

A comparative study of LaBr{sub 3}(Ce{sup 3+}) and CeBr{sub 3} based gamma-ray spectrometers for planetary remote sensing applications

Energy Technology Data Exchange (ETDEWEB)

Kozyrev, A., E-mail: kozyrev@mx.iki.rssi.ru; Mitrofanov, I.; Bakhtin, B.; Fedosov, F.; Golovin, D.; Litvak, M.; Malakhov, A.; Mokrousov, M.; Nuzhdin, I.; Sanin, A.; Tretyakov, V.; Vostrukhin, A. [Space Research Institute of the Russian Academy of Sciences (IKI), 84/32 Profsoyuznaya St., Moscow 117997 (Russian Federation); Owens, A.; Benkhoff, J. [European Space Agency, ESTEC, Keplerlaan, 2200 AG Noordwijk (Netherlands); Quarati, F. [AP, RST, FAME, Delft University of Technology, Mekelweg 15, 2629 JB Delft (Netherlands); Gonitec BV, J. Bildersstraat 60, 2596 EJ Den Haag (Netherlands); Timoshenko, G.; Shvetsov, V. [Joint Institute for Nuclear Research, Joliot-Curie 6, Dubna, Moscow Region 141980 (Russian Federation); Granja, C.; Slavicek, T.; Pospisil, S. [Institute of Experimental and Applied Physics, Czech Technical University in Prague, Horska 3a/22, 12800 Prague 2 (Czech Republic)

2016-08-15

The recent availability of large volume cerium bromide crystals raises the possibility of substantially improving gamma-ray spectrometer limiting flux sensitivities over current systems based on the lanthanum tri-halides, e.g., lanthanum bromide and lanthanum chloride, especially for remote sensing, low-level counting applications or any type of measurement characterized by poor signal to noise ratios. The Russian Space Research Institute has developed and manufactured a highly sensitive gamma-ray spectrometer for remote sensing observations of the planet Mercury from the Mercury Polar Orbiter (MPO), which forms part of ESA’s BepiColombo mission. The Flight Model (FM) gamma-ray spectrometer is based on a 3-in. single crystal of LaBr{sub 3}(Ce{sup 3+}) produced in a separate crystal development programme specifically for this mission. During the spectrometers development, manufacturing, and qualification phases, large crystals of CeBr{sub 3} became available in a subsequent phase of the same crystal development programme. Consequently, the Flight Spare Model (FSM) gamma-ray spectrometer was retrofitted with a 3-in. CeBr{sub 3} crystal and qualified for space. Except for the crystals, the two systems are essentially identical. In this paper, we report on a comparative assessment of the two systems, in terms of their respective spectral properties, as well as their suitability for use in planetary mission with respect to radiation tolerance and their propensity for activation. We also contrast their performance with a Ge detector representative of that flown on MESSENGER and show that: (a) both LaBr{sub 3}(Ce{sup 3+}) and CeBr{sub 3} provide superior detection systems over HPGe in the context of minimally resourced spacecraft and (b) CeBr{sub 3} is a more attractive system than LaBr{sub 3}(Ce{sup 3+}) in terms of sensitivities at lower gamma fluxes. Based on the tests, the FM has now been replaced by the FSM on the BepiColombo spacecraft. Thus, CeBr{sub 3} now forms

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

Science.gov (United States)

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

2016-01-01

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

Remote RemoteRemoteRemote sensing potential for sensing ...

African Journals Online (AJOL)

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

Non-Topographic Space-Based Laser Remote Sensing

Science.gov (United States)

Yu, Anthony W.; Abshire, James B.; Riris, Haris; Purucker, Michael; Janches, Diego; Getty, Stephanie; Krainak, Michael A.; Stephen, Mark A.; Chen, Jeffrey R.; Li, Steve X.;

Ground-based grasslands data to support remote sensing and ecosystem modeling of terrestrial primary production

Science.gov (United States)

Olson, R. J.; Scurlock, J. M. O.; Turner, R. S.; Jennings, S. V.

1995-01-01

Estimating terrestrial net primary production (NPP) using remote-sensing tools and ecosystem models requires adequate ground-based measurements for calibration, parameterization, and validation. These data needs were strongly endorsed at a recent meeting of ecosystem modelers organized by the International Geosphere-Biosphere Program's (IGBP's) Data and Information System (DIS) and its Global Analysis, Interpretation, and Modelling (GAIM) Task Force. To meet these needs, a multinational, multiagency project is being coordinated by the IGBP DIS to compile existing NPP data from field sites and to regionalize NPP point estimates to various-sized grid cells. Progress at Oak Ridge National Laboratory (ORNL) on compiling NPP data for grasslands as part of the IGBP DIS data initiative is described. Site data and associated documentation from diverse field studies are being acquired for selected grasslands and are being reviewed for completeness, consistency, and adequacy of documentation, including a description of sampling methods. Data are being compiled in a database with spatial, temporal, and thematic characteristics relevant to remote sensing and global modeling. NPP data are available from the ORNL Distributed Active Archive Center (DAAC) for biogeochemical dynamics. The ORNL DAAC is part of the Earth Observing System Data and Information System, of the US National Aeronautics and Space Administration.

Ground-based grasslands data to support remote sensing and ecosystem modeling of terrestrial primary production

Energy Technology Data Exchange (ETDEWEB)

Olson, R.J.; Turner, R.S. [Oak Ridge National Lab., TN (United States); Scurlock, J.M.O. [King`s College London, (England); Jennings, S.V. [Tennessee Univ., Knoxville, TN (United States)

1995-12-31

Estimating terrestrial net primary production (NPP) using remote- sensing tools and ecosystem models requires adequate ground-based measurements for calibration, parameterization, and validation. These data needs were strongly endorsed at a recent meeting of ecosystem modelers organized by the International Geosphere-Biosphere Programme`s (IGBP`s) Data and Information System (DIS) and its Global Analysis, Interpretation, and Modelling (GAIM) Task Force. To meet these needs, a multinational, multiagency project is being coordinated by the IGBP DIS to compile existing NPP data from field sites and to regionalize NPP point estimates to various-sized grid cells. Progress at Oak Ridge National Laboratory (ORNL) on compiling NPP data for grasslands as part of the IGBP DIS data initiative is described. Site data and associated documentation from diverse field studies are being acquired for selected grasslands and are being reviewed for completeness, consistency, and adequacy of documentation, including a description of sampling methods. Data are being compiled in a database with spatial, temporal, and thematic characteristics relevant to remote sensing and global modeling. NPP data are available from the ORNL Distributed Active Archive Center (DAAC) for biogeochemical dynamics. The ORNL DAAC is part of the Earth Observing System Data and Information System, of the US National Aeronautics and Space Administration.

A NDVI assisted remote sensing image adaptive scale segmentation method

Science.gov (United States)

Zhang, Hong; Shen, Jinxiang; Ma, Yanmei

2018-03-01

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

Integrating remote sensing techniques at Cuprite, Nevada: AVIRIS, Thematic Mapper, and field spectroscopy

Science.gov (United States)

Hill, Bradley; Nash, Greg; Ridd, Merrill; Hauff, Phoebe L.; Ebel, Phil

1992-01-01

The Cuprite mining district in southwestern Nevada has become a test site for remote sensing studies with numerous airborne scanners and ground sensor data sets collected over the past fifteen years. Structurally, the Cuprite region can be divided into two areas with slightly different alteration and mineralogy. These zones lie on either side of a postulated low-angle structural discontinuity that strikes nearly parallel to US Route 95. Hydrothermal alternation at Cuprite was classified into three major zones: silicified, opalized, and argillized. These alteration types form a bulls-eye pattern east of the highway and are more linear on the west side of the highway making a striking contrast from the air and the imagery. Cuprite is therefore an ideal location for remote sensing research as it exhibits easily identified hydrothermal zoning, is relatively devoid of vegetation, and contains a distinctive spectrally diagnostic mineral suite including the ammonium feldspar buddingtonite, several types of alunite, different jarosites, illite, kaolinite, smectite, dickite, and opal. This present study brings a new dimension to these previous remote sensing and ground data sets compiled for Cuprite. The development of a higher resolution field spectrometer now provides the capability to combine extensive in-situ mineralogical data with a new geologic field survey and detailed Airborne Visible/Infrared Imaging Spectrometers (AVIRIS) images. The various data collection methods and the refinement of the integrated techniques are discussed.

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards

Science.gov (United States)

Wright, Daniel B.; Mantilla, Ricardo; Peters-Lidard, Christa D.

2018-01-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions. PMID:29657544

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

Science.gov (United States)

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

2018-04-01

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

Project THEMIS: A Center for Remote Sensing.

Science.gov (United States)

This report summarizes the technical work accomplished under Project THEMIS, A Center for Remote Sensing at the University of Kansas during the...period 16 September 1967 through 15 September 1973. The highlights of the four major areas forming the remote sensing system are presented. A detailed description of the latest radar spectrometer results is presented.

Confronting remote sensing product with ground base measurements across time and scale

Science.gov (United States)

Pourmokhtarian, A.; Dietze, M.

2015-12-01

Ecosystem models are essential tools in forecasting ecosystem responses to global climate change. One of the most challenging issues in ecosystem modeling is scaling while preserving landscape characteristics and minimizing loss of information, when moving from point observation to regional scale. There is a keen interest in providing accurate inputs for ecosystem models which represent ecosystem initial state conditions. Remote sensing land cover products, such as Landsat NLCD and MODIS MCD12Q1, provide extensive spatio-temporal coverage but do not capture forest composition and structure. Lidar and hyperspectral have the potential to meet this need but lack sufficient spatial and historical coverage. Forest inventory measurements provide detailed information on the landscape but in a very small footprint. Combining inventory and land cover could improve estimates of ecosystem state and characteristic across time and space. This study focuses on the challenges associated with fusing and scaling the US Forest Service FIA database and NLCD across regional scales to quantify ecosystem characteristics and reduce associated uncertainties. Across Southeast of U.S. 400 stratified random samples of 10x10 km2 landscapes were selected. Data on plant density, species, age, and DBH of trees in FIA plots within each site were extracted. Using allometry equations, the canopy cover of different plant functional types (PFTs) was estimated using a PPA-style canopy model and used to assign each inventory plot to a land cover class. Inventory and land cover were fused in a Bayesian model that adjusts the fractional coverage of inventory plots while accounting for multiple sources of uncertainty. Results were compared to estimates derived from inventory alone, land cover alone, and model spin-up alone. Our findings create a framework of data assimilation to better interpret remote sensing data using ground-based measurements.

Hyperspectral remote sensing application for monitoring and preservation of plant ecosystems

Science.gov (United States)

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

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

Miniature, Low-Power, Waveguide Based Infrared Fourier Transform Spectrometer for Spacecraft Remote Sensing

Science.gov (United States)

Hewagama, TIlak; Aslam, Shahid; Talabac, Stephen; Allen, John E., Jr.; Annen, John N.; Jennings, Donald E.

2011-01-01

Fourier transform spectrometers have a venerable heritage as flight instruments. However, obtaining an accurate spectrum exacts a penalty in instrument mass and power requirements. Recent advances in a broad class of non-scanning Fourier transform spectrometer (FTS) devices, generally called spatial heterodyne spectrometers, offer distinct advantages as flight optimized systems. We are developing a miniaturized system that employs photonics lightwave circuit principles and functions as an FTS operating in the 7-14 micrometer spectral region. The inteferogram is constructed from an ensemble of Mach-Zehnder interferometers with path length differences calibrated to mimic scan mirror sample positions of a classic Michelson type FTS. One potential long-term application of this technology in low cost planetary missions is the concept of a self-contained sensor system. We are developing a systems architecture concept for wide area in situ and remote monitoring of characteristic properties that are of scientific interest. The system will be based on wavelength- and resolution-independent spectroscopic sensors for studying atmospheric and surface chemistry, physics, and mineralogy. The self-contained sensor network is based on our concept of an Addressable Photonics Cube (APC) which has real-time flexibility and broad science applications. It is envisaged that a spatially distributed autonomous sensor web concept that integrates multiple APCs will be reactive and dynamically driven. The network is designed to respond in an event- or model-driven manner or reconfigured as needed.

Coupling Fine-Scale Root and Canopy Structure Using Ground-Based Remote Sensing

Directory of Open Access Journals (Sweden)

Brady S. Hardiman

2017-02-01

Full Text Available Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above- and belowground structure at fine spatial resolution in functionally meaningful ways. To test this possibility, we employed ground-based portable canopy LiDAR (PCL and ground penetrating radar (GPR along co-located transects in forested sites spanning multiple stages of ecosystem development and, consequently, of structural complexity. We examined canopy and root structural data for coherence (i.e., correlation in the frequency of spatial variation at multiple spatial scales ≤10 m within each site using wavelet analysis. Forest sites varied substantially in vertical canopy and root structure, with leaf area index and root mass more becoming even vertically as forests aged. In all sites, above- and belowground structure, characterized as mean maximum canopy height and root mass, exhibited significant coherence at a scale of 3.5–4 m, and results suggest that the scale of coherence may increase with stand age. Our findings demonstrate that canopy and root structure are linked at characteristic spatial scales, which provides the basis to optimize scales of observation. Our study highlights the potential, and limitations, for fusing LiDAR and radar technologies to quantitatively couple above- and belowground ecosystem structure.

Recent advances in ground-based ultraviolet remote sensing of volcanic SO2 fluxes

Directory of Open Access Journals (Sweden)

Euripides P. Kantzas

2011-06-01

Full Text Available Measurements of volcanic SO2 emission rates have been the mainstay of remote-sensing volcanic gas geochemistry for almost four decades, and they have contributed significantly to our understanding of volcanic systems and their impact upon the atmosphere. The last ten years have brought step-change improvements in the instrumentation applied to these observations, which began with the application of miniature ultraviolet spectrometers that were deployed in scanning and traverse configurations, with differential optical absorption spectroscopy evaluation routines. This study catalogs the more recent empirical developments, including: ultraviolet cameras; wide-angle field-of-view differential optical absorption spectroscopy systems; advances in scanning operations, including tomography; and improved understanding of errors, in particular concerning radiative transfer. Furthermore, the outcomes of field deployments of sensors during the last decade are documented, with respect to improving our understanding of volcanic dynamics and degassing into the atmosphere.

Remote sensing of GHG over Paris megacity and Orléans forest using ground-based QualAir FTS and TCCON-Orléans

Science.gov (United States)

Te, Y.; Jeseck, P.; Da Costa, J.; Deutscher, N. M.; Warneke, T.; Notholt, J.

2012-04-01

In a growing world with more than 7 billion inhabitants and big emerging countries such as China, Brazil and India, emissions of anthropogenic pollutants are increasing continuously. Monitoring and control of atmospheric pollutants in megacities have become a major challenge for scientists and public health authorities in environmental research area. The QualAir platform at University Pierre et Marie Curie (UPMC), is an innovating experimental research platform dedicated to survey greenhouse gases (GHGs) and urban air quality. As one of the major instruments of the QualAir platform, the ground-based Fourier transform spectrometer (QualAir FTS, IFS 125HR model) analyses the composition of the urban atmosphere of Paris, which is the third European megacity. The continuous monitoring of atmospheric pollutants is essential to improve the understanding of urban air pollution processes. Associated with a sun-tracker, the QualAir remote sensing FTS operates in solar infrared absorption and enables to monitor many trace gases, and to follow up their variability in the Ile-de-France region. A description of the QualAir FTS will be given. Concentrations of atmospheric GHG, especially CO2 and CH4, are retrieved by the radiative transfer model PROFFIT. Located in the centre of Paris, the QualAir FTS can provide new and complementary urban measurements as compared to unpolluted ground-based stations of existing networks (NDACC and TCCON). The work made by LPMAA to join the TCCON network will also be presented. TCCON-Orléans is a ground-based FTS of the TCCON network located in the forest of Orléans (100 km south of Paris). Preliminary comparisons of GHGs measurements from both sites will be shown. Such ground-based information will help to better characterize regional GHGs, especially regarding anthropogenic emissions and trends.

Aerosol optical properties over the Svalbard region of Arctic: ground-based measurements and satellite remote sensing

Science.gov (United States)

Gogoi, Mukunda M.; Babu, S. Suresh

2016-05-01

In view of the increasing anthropogenic presence and influence of aerosols in the northern polar regions, long-term continuous measurements of aerosol optical parameters have been investigated over the Svalbard region of Norwegian Arctic (Ny-Ålesund, 79°N, 12°E, 8 m ASL). This study has shown a consistent enhancement in the aerosol scattering and absorption coefficients during spring. The relative dominance of absorbing aerosols is more near the surface (lower single scattering albedo), compared to that at the higher altitude. This is indicative of the presence of local anthropogenic activities. In addition, long-range transported biomass burning aerosols (inferred from the spectral variation of absorption coefficient) also contribute significantly to the higher aerosol absorption in the Arctic spring. Aerosol optical depth (AOD) estimates from ground based Microtop sun-photometer measurements reveals that the columnar abundance of aerosols reaches the peak during spring season. Comparison of AODs between ground based and satellite remote sensing indicates that deep blue algorithm of Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals over Arctic snow surfaces overestimate the columnar AOD.

Optical/Infrared Signatures for Space-Based Remote Sensing

National Research Council Canada - National Science Library

Picard, R. H; Dewan, E. M; Winick, J. R; O'Neil, R. R

2007-01-01

This report describes work carried out under the Air Force Research Laboratory's basic research task in optical remote-sensing signatures, entitled Optical / Infrared Signatures for Space-Based Remote Sensing...

Remote Sensing Image Registration Using Multiple Image Features

Directory of Open Access Journals (Sweden)

Kun Yang

2017-06-01

Full Text Available Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform distance which is endowed with the intensity information is used to measure the scale space extrema. (iii To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

Remote Sensing of Landslides—A Review

Directory of Open Access Journals (Sweden)

Chaoying Zhao

2018-02-01

Full Text Available Triggered by earthquakes, rainfall, or anthropogenic activities, landslides represent widespread and problematic geohazards worldwide. In recent years, multiple remote sensing techniques, including synthetic aperture radar, optical, and light detection and ranging measurements from spaceborne, airborne, and ground-based platforms, have been widely applied for the analysis of landslide processes. Current techniques include landslide detection, inventory mapping, surface deformation monitoring, trigger factor analysis and mechanism inversion. In addition, landslide susceptibility modelling, hazard assessment, and risk evaluation can be further analyzed using a synergic fusion of multiple remote sensing data and other factors affecting landslides. We summarize the 19 articles collected in this special issue of Remote Sensing of Landslide, in the terms of data, methods and applications used in the papers.

Simulation of submillimetre atmospheric spectra for characterising potential ground-based remote sensing observations

Directory of Open Access Journals (Sweden)

E. C. Turner

2016-11-01

Full Text Available The submillimetre is an understudied region of the Earth's atmospheric electromagnetic spectrum. Prior technological gaps and relatively high opacity due to the prevalence of rotational water vapour lines at these wavelengths have slowed progress from a ground-based remote sensing perspective; however, emerging superconducting detector technologies in the fields of astronomy offer the potential to address key atmospheric science challenges with new instrumental methods. A site study, with a focus on the polar regions, is performed to assess theoretical feasibility by simulating the downwelling (zenith angle = 0° clear-sky submillimetre spectrum from 30 mm (10 GHz to 150 µm (2000 GHz at six locations under annual mean, summer, winter, daytime, night-time and low-humidity conditions. Vertical profiles of temperature, pressure and 28 atmospheric gases are constructed by combining radiosonde, meteorological reanalysis and atmospheric chemistry model data. The sensitivity of the simulated spectra to the choice of water vapour continuum model and spectroscopic line database is explored. For the atmospheric trace species hypobromous acid (HOBr, hydrogen bromide (HBr, perhydroxyl radical (HO2 and nitrous oxide (N2O the emission lines producing the largest change in brightness temperature are identified. Signal strengths, centre frequencies, bandwidths, estimated minimum integration times and maximum receiver noise temperatures are determined for all cases. HOBr, HBr and HO2 produce brightness temperature peaks in the mK to µK range, whereas the N2O peaks are in the K range. The optimal submillimetre remote sensing lines for the four species are shown to vary significantly between location and scenario, strengthening the case for future hyperspectral instruments that measure over a broad wavelength range. The techniques presented here provide a framework that can be applied to additional species of interest and taken forward to simulate

Polarimetric Remote Sensing of Atmospheric Particulate Pollutants

Science.gov (United States)

Li, Z.; Zhang, Y.; Hong, J.

2018-04-01

Atmospheric particulate pollutants not only reduce atmospheric visibility, change the energy balance of the troposphere, but also affect human and vegetation health. For monitoring the particulate pollutants, we establish and develop a series of inversion algorithms based on polarimetric remote sensing technology which has unique advantages in dealing with atmospheric particulates. A solution is pointed out to estimate the near surface PM2.5 mass concentrations from full remote sensing measurements including polarimetric, active and infrared remote sensing technologies. It is found that the mean relative error of PM2.5 retrieved by full remote sensing measurements is 35.5 % in the case of October 5th 2013, improved to a certain degree compared to previous studies. A systematic comparison with the ground-based observations further indicates the effectiveness of the inversion algorithm and reliability of results. A new generation of polarized sensors (DPC and PCF), whose observation can support these algorithms, will be onboard GF series satellites and launched by China in the near future.

Supporting a Diverse Community of Undergraduate Researchers in Satellite and Ground-Based Remote Sensing

Science.gov (United States)

Blake, R.; Liou-Mark, J.

2012-12-01

The U.S. remains in grave danger of losing its global competitive edge in STEM. To find solutions to this problem, the Obama Administration proposed two new national initiatives: the Educate to Innovate Initiative and the $100 million government/private industry initiative to train 100,000 STEM teachers and graduate 1 million additional STEM students over the next decade. To assist in ameliorating the national STEM plight, the New York City College of Technology has designed its NSF Research Experience for Undergraduate (REU) program in satellite and ground-based remote sensing to target underrepresented minority students. Since the inception of the program in 2008, a total of 45 undergraduate students of which 38 (84%) are considered underrepresented minorities in STEM have finished or are continuing with their research or are pursuing their STEM endeavors. The program is comprised of the three primary components. The first component, Structured Learning Environments: Preparation and Mentorship, provides the REU Scholars with the skill sets necessary for proficiency in satellite and ground-based remote sensing research. The students are offered mini-courses in Geographic Information Systems, MATLAB, and Remote Sensing. They also participate in workshops on the Ethics of Research. Each REU student is a member of a team that consists of faculty mentors, post doctorate/graduate students, and high school students. The second component, Student Support and Safety Nets, provides undergraduates a learning environment that supports them in becoming successful researchers. Special networking and Brown Bag sessions, and an annual picnic with research scientists are organized so that REU Scholars are provided with opportunities to expand their professional community. Graduate school support is provided by offering free Graduate Record Examination preparation courses and workshops on the graduate school application process. Additionally, students are supported by college

Supervised Gaussian mixture model based remote sensing image ...

African Journals Online (AJOL)

Using the supervised classification technique, both simulated and empirical satellite remote sensing data are used to train and test the Gaussian mixture model algorithm. For the purpose of validating the experiment, the resulting classified satellite image is compared with the ground truth data. For the simulated modelling, ...

Digital methods and remote sensing in archaeology archaeology in the age of sensing

CERN Document Server

Campana, Stefano

2016-01-01

This volume debuts the new scope of Remote Sensing, which was first defined as the analysis of data collected by sensors that were not in physical contact with the objects under investigation (using cameras, scanners, and radar systems operating from spaceborne or airborne platforms). A wider characterization is now possible: Remote Sensing can be any non-destructive approach to viewing the buried and nominally invisible evidence of past activity. Spaceborne and airborne sensors, now supplemented by laser scanning, are united using ground-based geophysical instruments and undersea remote sensing, as well as other non-invasive techniques such as surface collection or field-walking survey. Now, any method that enables observation of evidence on or beneath the surface of the earth, without impact on the surviving stratigraphy, is legitimately within the realm of Remote Sensing. The new interfaces and senses engaged in Remote Sensing appear throughout the book. On a philosophical level, this is about the landscap...

Analysis of 2015 Winter In-Flight Icing Case Studies with Ground-Based Remote Sensing Systems Compared to In-Situ SLW Sondes

Science.gov (United States)

Serke, David J.; King, Michael Christopher; Hansen, Reid; Reehorst, Andrew L.

2016-01-01

National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) have developed an icing remote sensing technology that has demonstrated skill at detecting and classifying icing hazards in a vertical column above an instrumented ground station. This technology has recently been extended to provide volumetric coverage surrounding an airport. Building on the existing vertical pointing system, the new method for providing volumetric coverage utilizes a vertical pointing cloud radar, a multi-frequency microwave radiometer with azimuth and elevation pointing, and a NEXRAD radar. The new terminal area icing remote sensing system processes the data streams from these instruments to derive temperature, liquid water content, and cloud droplet size for each examined point in space. These data are then combined to ultimately provide icing hazard classification along defined approach paths into an airport. To date, statistical comparisons of the vertical profiling technology have been made to Pilot Reports and Icing Forecast Products. With the extension into relatively large area coverage and the output of microphysical properties in addition to icing severity, the use of these comparators is not appropriate and a more rigorous assessment is required. NASA conducted a field campaign during the early months of 2015 to develop a database to enable the assessment of the new terminal area icing remote sensing system and further refinement of terminal area icing weather information technologies in general. In addition to the ground-based remote sensors listed earlier, in-situ icing environment measurements by weather balloons were performed to produce a comprehensive comparison database. Balloon data gathered consisted of temperature, humidity, pressure, super-cooled liquid water content, and 3-D position with time. Comparison data plots of weather balloon and remote measurements, weather balloon flight paths, bulk comparisons of

Sea Ice Thickness Measurement by Ground Penetrating Radar for Ground Truth of Microwave Remote Sensing Data

Science.gov (United States)

Matsumoto, M.; Yoshimura, M.; Naoki, K.; Cho, K.; Wakabayashi, H.

2018-04-01

Observation of sea ice thickness is one of key issues to understand regional effect of global warming. One of approaches to monitor sea ice in large area is microwave remote sensing data analysis. However, ground truth must be necessary to discuss the effectivity of this kind of approach. The conventional method to acquire ground truth of ice thickness is drilling ice layer and directly measuring the thickness by a ruler. However, this method is destructive, time-consuming and limited spatial resolution. Although there are several methods to acquire ice thickness in non-destructive way, ground penetrating radar (GPR) can be effective solution because it can discriminate snow-ice and ice-sea water interface. In this paper, we carried out GPR measurement in Lake Saroma for relatively large area (200 m by 300 m, approximately) aiming to obtain grand truth for remote sensing data. GPR survey was conducted at 5 locations in the area. The direct measurement was also conducted simultaneously in order to calibrate GPR data for thickness estimation and to validate the result. Although GPR Bscan image obtained from 600MHz contains the reflection which may come from a structure under snow, the origin of the reflection is not obvious. Therefore, further analysis and interpretation of the GPR image, such as numerical simulation, additional signal processing and use of 200 MHz antenna, are required to move on thickness estimation.

Evaluating statistical cloud schemes: What can we gain from ground-based remote sensing?

Science.gov (United States)

Grützun, V.; Quaas, J.; Morcrette, C. J.; Ament, F.

2013-09-01

Statistical cloud schemes with prognostic probability distribution functions have become more important in atmospheric modeling, especially since they are in principle scale adaptive and capture cloud physics in more detail. While in theory the schemes have a great potential, their accuracy is still questionable. High-resolution three-dimensional observational data of water vapor and cloud water, which could be used for testing them, are missing. We explore the potential of ground-based remote sensing such as lidar, microwave, and radar to evaluate prognostic distribution moments using the "perfect model approach." This means that we employ a high-resolution weather model as virtual reality and retrieve full three-dimensional atmospheric quantities and virtual ground-based observations. We then use statistics from the virtual observation to validate the modeled 3-D statistics. Since the data are entirely consistent, any discrepancy occurring is due to the method. Focusing on total water mixing ratio, we find that the mean ratio can be evaluated decently but that it strongly depends on the meteorological conditions as to whether the variance and skewness are reliable. Using some simple schematic description of different synoptic conditions, we show how statistics obtained from point or line measurements can be poor at representing the full three-dimensional distribution of water in the atmosphere. We argue that a careful analysis of measurement data and detailed knowledge of the meteorological situation is necessary to judge whether we can use the data for an evaluation of higher moments of the humidity distribution used by a statistical cloud scheme.

POLARIMETRIC REMOTE SENSING OF ATMOSPHERIC PARTICULATE POLLUTANTS

Directory of Open Access Journals (Sweden)

Z. Li

2018-04-01

Full Text Available Atmospheric particulate pollutants not only reduce atmospheric visibility, change the energy balance of the troposphere, but also affect human and vegetation health. For monitoring the particulate pollutants, we establish and develop a series of inversion algorithms based on polarimetric remote sensing technology which has unique advantages in dealing with atmospheric particulates. A solution is pointed out to estimate the near surface PM2.5 mass concentrations from full remote sensing measurements including polarimetric, active and infrared remote sensing technologies. It is found that the mean relative error of PM2.5 retrieved by full remote sensing measurements is 35.5 % in the case of October 5th 2013, improved to a certain degree compared to previous studies. A systematic comparison with the ground-based observations further indicates the effectiveness of the inversion algorithm and reliability of results. A new generation of polarized sensors (DPC and PCF, whose observation can support these algorithms, will be onboard GF series satellites and launched by China in the near future.

Prototype simulates remote sensing spectral measurements on fruits and vegetables

Science.gov (United States)

Hahn, Federico

1998-09-01

A prototype was designed to simulate spectral packinghouse measurements in order to simplify fruit and vegetable damage assessment. A computerized spectrometer is used together with lenses and an externally controlled illumination in order to have a remote sensing simulator. A laser is introduced between the spectrometer and the lenses in order to mark the zone where the measurement is being taken. This facilitates further correlation work and can assure that the physical and remote sensing measurements are taken in the same place. Tomato ripening and mango anthracnose spectral signatures are shown.

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

Science.gov (United States)

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

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

Science.gov (United States)

Elhag, Mohamed; Bahrawi, Jarbou A.

2017-03-01

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

Hyperspectral remote sensing for light pollution monitoring

Directory of Open Access Journals (Sweden)

P. Marcoionni

2006-06-01

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

Blowing snow detection in Antarctica, from space borne and ground-based remote sensing

Science.gov (United States)

Gossart, A.; Souverijns, N.; Lhermitte, S.; Lenaerts, J.; Gorodetskaya, I.; Schween, J. H.; Van Lipzig, N. P. M.

2017-12-01

Surface mass balance (SMB) strongly controls spatial and temporal variations in the Antarctic Ice Sheet (AIS) mass balance and its contribution to sea level rise. Currently, the scarcity of observational data and the challenges of climate modelling over the ice sheet limit our understanding of the processes controlling AIS SMB. Particularly, the impact of blowing snow on local SMB is not yet constrained and is subject to large uncertainties. To assess the impact of blowing snow on local SMB, we investigate the attenuated backscatter profiles from ceilometers at two East Antarctic locations in Dronning Maud Land. Ceilometers are robust ground-based remote sensing instruments that yield information on cloud base height and vertical structure, but also provide information on the particles present in the boundary layer. We developed a new algorithm to detect blowing snow (snow particles lifted by the wind from the surface to substantial height) from the ceilometer attenuated backscatter. The algorithm successfully allows to detect strong blowing snow signal from layers thicker than 15 m at the Princess Elisabeth (PE, (72°S, 23°E)) and Neumayer (70°S, 8° W) stations. Applying the algorithm to PE, we retrieve the frequency and annual cycle of blowing snow as well as discriminate between clear sky and overcast conditions during blowing snow. We further apply the blowing snow algorithm at PE to evaluate the blowing snow events detection by satellite imagery (Palm et al., 2011): the near-surface blowing snow layers are apparent in lidar backscatter profiles and enable snowdrift events detection (spatial and temporal frequency, height and optical depth). These data are processed from CALIPSO, at a high resolution (1x1 km digital elevation model). However, the remote sensing detection of blowing snow events by satellite is limited to layers of a minimal thickness of 20-30 m. In addition, thick clouds, mostly occurring during winter storms, can impede drifting snow

Remote sensing of natural resources. Quarterly literature review, October-December 1980

International Nuclear Information System (INIS)

Gonzales, R.W.; Inglis, M.H.

1981-02-01

This review covers literature pertaining to documented data and data gathering techniques that are performed or obtained remotely from space, aircraft, or ground-based stations. All of the documentation is related to remote sensing sensors or the remote sensing of the natural resources. Section headings are: general; geology; environmental quality; hydrology; vegetation; oceanography; regional planning and land use; data manipulation; and instrumentation and technology

Autonomous Coral Reef Survey in Support of Remote Sensing

Directory of Open Access Journals (Sweden)

Steven G. Ackleson

2017-10-01

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

Remote sensing of the lightning heating effect duration with ground-based microwave radiometer

Science.gov (United States)

Jiang, Sulin; Pan, Yun; Lei, Lianfa; Ma, Lina; Li, Qing; Wang, Zhenhui

2018-06-01

Artificially triggered lightning events from May 26, 2017 to July 16, 2017 in Guangzhou Field Experiment Site for Lightning Research and Test (GFESL) were intentionally remotely sensed with a ground-based microwave radiometer for the first time in order to obtain the features of lightning heating effect. The microwave radiometer antenna was adjusted to point at a certain elevation angle towards the expected artificially triggered lightning discharging path. Eight of the 16 successfully artificially triggered lightning events were captured and the brightness temperature data at four frequencies in K and V bands were obtained. The results from data time series analysis show that artificially triggered lightning can make the radiometer generate brightness temperature pulses, and the amplitudes of these pulses are in the range of 2.0 K to 73.8 K. The brightness temperature pulses associated with 7 events can be used to estimate the duration of lightning heating effect through accounting the number of the pulses in the continuous pulse sequence and the sampling interval between four frequencies. The maximum duration of the lightning heating effect is 1.13 s, the minimum is 0.172 s, and the average is 0.63 s.

Integration of Remote Sensing Products with Ground-Based Measurements to Understand the Dynamics of Nepal's Forests and Plantation Sites

Science.gov (United States)

Gilani, H.; Jain, A. K.

2016-12-01

This study assembles information from three sources - remote sensing, terrestrial photography and ground-based inventory data, to understand the dynamics of Nepal's tropical and sub-tropical forests and plantation sites for the period 1990-2015. Our study focuses on following three specific district areas, which have conserved forests through social and agroforestry management practices: 1. Dolakha district: This site has been selected to study the impact of community-based forest management on land cover change using repeat photography and satellite imagery, in combination with interviews with community members. The study time period is during the period 1990-2010. We determined that satellite data with ground photographs can provide transparency for long term monitoring. The initial results also suggests that community-based forest management program in the mid-hills of Nepal was successful. 2. Chitwan district: Here we use high resolution remote sensing data and optimized community field inventories to evaluate potential application and operational feasibility of community level REDD+ measuring, reporting and verification (MRV) systems. The study uses temporal dynamics of land cover transitions, tree canopy size classes and biomass over a Kayar khola watershed REDD+ study area with community forest to evaluate satellite Image segmentation for land cover, linear regression model for above ground biomass (AGB), and estimation and monitoring field data for tree crowns and AGB. We study three specific years 2002, 2009, 2012. Using integration of WorldView-2 and airborne LiDAR data for tree species level. 3. Nuwakot district: This district was selected to study the impact of establishment of tree plantation on total barren/fallow. Over the last 40 year, this area has went through a drastic changes, from barren land to forest area with tree species consisting of Dalbergia sissoo, Leucaena leucocephala, Michelia champaca, etc. In 1994, this district area was registered

SEA ICE THICKNESS MEASUREMENT BY GROUND PENETRATING RADAR FOR GROUND TRUTH OF MICROWAVE REMOTE SENSING DATA

Directory of Open Access Journals (Sweden)

M. Matsumoto

2018-04-01

Full Text Available Observation of sea ice thickness is one of key issues to understand regional effect of global warming. One of approaches to monitor sea ice in large area is microwave remote sensing data analysis. However, ground truth must be necessary to discuss the effectivity of this kind of approach. The conventional method to acquire ground truth of ice thickness is drilling ice layer and directly measuring the thickness by a ruler. However, this method is destructive, time-consuming and limited spatial resolution. Although there are several methods to acquire ice thickness in non-destructive way, ground penetrating radar (GPR can be effective solution because it can discriminate snow-ice and ice-sea water interface. In this paper, we carried out GPR measurement in Lake Saroma for relatively large area (200 m by 300 m, approximately aiming to obtain grand truth for remote sensing data. GPR survey was conducted at 5 locations in the area. The direct measurement was also conducted simultaneously in order to calibrate GPR data for thickness estimation and to validate the result. Although GPR Bscan image obtained from 600MHz contains the reflection which may come from a structure under snow, the origin of the reflection is not obvious. Therefore, further analysis and interpretation of the GPR image, such as numerical simulation, additional signal processing and use of 200 MHz antenna, are required to move on thickness estimation.

Regional Analysis of Remote Sensing Based Evapotranspiration Information

Science.gov (United States)

Geli, H. M. E.; Hain, C.; Anderson, M. C.; Senay, G. B.

2017-12-01

Recent research findings on modeling actual evapotranspiration (ET) using remote sensing data and methods have proven the ability of these methods to address wide range of hydrological and water resources issues including river basin water balance for improved water resources management, drought monitoring, drought impact and socioeconomic responses, agricultural water management, optimization of land-use for water conservations, water allocation agreement among others. However, there is still a critical need to identify appropriate type of ET information that can address each of these issues. The current trend of increasing demand for water due to population growth coupled with variable and limited water supply due to drought especially in arid and semiarid regions with limited water supply have highlighted the need for such information. To properly address these issues different spatial and temporal resolutions of ET information will need to be used. For example, agricultural water management applications require ET information at field (30-m) and daily time scales while for river basin hydrologic analysis relatively coarser spatial and temporal scales can be adequate for such regional applications. The objective of this analysis is to evaluate the potential of using an integrated ET information that can be used to address some of these issues collectively. This analysis will highlight efforts to address some of the issues that are applicable to New Mexico including assessment of statewide water budget as well as drought impact and socioeconomic responses which all require ET information but at different spatial and temporal scales. This analysis will provide an evaluation of four remote sensing based ET models including ALEXI, DisALEXI, SSEBop, and SEBAL3.0. The models will be compared with ground-based observations from eddy covariance towers and water balance calculations. Remote sensing data from Landsat, MODIS, and VIIRS sensors will be used to provide ET

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

Indian Academy of Sciences (India)

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

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

Science.gov (United States)

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

2018-04-01

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

CAPABILITIES OF REMOTE SENSING HYPERSPECTRAL IMAGES FOR THE DETECTION OF LEAD CONTAMINATION: A REVIEW

Directory of Open Access Journals (Sweden)

A. A. Maliki

2012-07-01

Full Text Available Advances in remote sensing technologies are increasingly becoming more useful for resource, ecosystem and agricultural management applications to the extent that these techniques can now also be applied for monitoring of soil contamination and human health risk assessment. While, extensive previous studies have shown that Visible and Near Infrared Spectroscopy (VNIRS in the spectral range 400–2500 nm can be used to quantify various soil constituents simultaneously, the direct determination of metal concentrations by remote sensing and reflectance spectroscopy is not as well examined as other soil parameters. The application of VNIRS, including laboratory hyperpectral measurements, field spectrometer measurements or image spectroscopy, generally achieves a good prediction of metal concentrations when compared to traditional wet chemical methods and has the advantage of being relatively less expensive and faster, allowing chemical assessment of contamination in close to real time. Furthermore, imaging spectroscopy can potentially provide significantly more samples over a larger spatial extent than traditional ground sampling methods. Thus the development of remote sensing techniques (field based and either airborne or satellite hyperspectral imaging can support the monitoring and efficient mapping of metal contamination (in dust and soil for environmental and health impact assessment. This review is concerned with the application of remote sensing and reflectance spectroscopy to the detection of heavy metals and discusses how current methods could be applied for the quantification of Pb contaminated soil surrounding mines and smelters.

The study of active tectonic based on hyperspectral remote sensing

Science.gov (United States)

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

2017-12-01

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

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

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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

Offshore winds mapped from satellite remote sensing

DEFF Research Database (Denmark)

Hasager, Charlotte Bay

2014-01-01

the uncertainty on the model results on the offshore wind resource, it is necessary to compare model results with observations. Observations from ground-based wind lidar and satellite remote sensing are the two main technologies that can provide new types of offshore wind data at relatively low cost....... The advantages of microwave satellite remote sensing are 1) horizontal spatial coverage, 2) long data archives and 3) high spatial detail both in the coastal zone and of far-field wind farm wake. Passive microwave ocean wind speed data are available since 1987 with up to 6 observations per day with near...

Ontology-based classification of remote sensing images using spectral rules

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Andrés, Samuel; Arvor, Damien; Mougenot, Isabelle; Libourel, Thérèse; Durieux, Laurent

2017-05-01

Earth Observation data is of great interest for a wide spectrum of scientific domain applications. An enhanced access to remote sensing images for "domain" experts thus represents a great advance since it allows users to interpret remote sensing images based on their domain expert knowledge. However, such an advantage can also turn into a major limitation if this knowledge is not formalized, and thus is difficult for it to be shared with and understood by other users. In this context, knowledge representation techniques such as ontologies should play a major role in the future of remote sensing applications. We implemented an ontology-based prototype to automatically classify Landsat images based on explicit spectral rules. The ontology is designed in a very modular way in order to achieve a generic and versatile representation of concepts we think of utmost importance in remote sensing. The prototype was tested on four subsets of Landsat images and the results confirmed the potential of ontologies to formalize expert knowledge and classify remote sensing images.

Ground-based remote sensing observation of the complex behaviour of the Marseille boundary layer during ESCOMPTE

Science.gov (United States)

Delbarre, H.; Augustin, P.; Saïd, F.; Campistron, B.; Bénech, B.; Lohou, F.; Puygrenier, V.; Moppert, C.; Cousin, F.; Fréville, P.; Fréjafon, E.

2005-03-01

Ground-based remote sensing systems have been used during the ESCOMPTE campaign, to continuously characterize the boundary-layer behaviour through many atmospheric parameters (wind, extinction and ozone concentration distribution, reflectivity, turbulence). This analysis is focused on the comparison of the atmospheric stratification retrieved from a UV angular ozone lidar, an Ultra High Frequency wind profiler and a sodar, above the area of Marseille, on June 26th 2001 (Intensive Observation Period 2b). The atmospheric stratification is shown to be very complex including two superimposed sea breezes, with an important contribution of advection. The temporal and spatial evolution of the stratification observed by the UV lidar and by the UHF radar are in good agreement although the origin of the echoes of these systems is quite different. The complexity of the dynamic situation has only partially been retrieved by a non-hydrostatic mesoscale model used with a 3 km resolution.

Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status

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Schmidt, Johannes; Fassnacht, Fabian Ewald; Neff, Christophe; Lausch, Angela; Kleinschmit, Birgit; Förster, Michael; Schmidtlein, Sebastian

2017-08-01

Remote sensing can be a valuable tool for supporting nature conservation monitoring systems. However, for many areas of conservation interest, there is still a considerable gap between field-based operational monitoring guidelines and the current remote sensing-based approaches. This hampers application in practice of the latter. Here, we propose a remote sensing approach for mapping the conservation status of Calluna-dominated Natura 2000 dwarf shrub habitats that is closely related to field mapping schemes. We transferred the evaluation criteria of the field guidelines to three related variables that can be captured by remote sensing: (1) coverage of the key species, (2) stand structural diversity, and (3) co-occurring species. Continuous information on these variables was obtained by regressing ground reference data from field surveys and UAV flights against airborne hyperspectral imagery. Merging the three resulting quality layers in an RGB representation allowed for illustrating the habitat quality in a continuous way. User-defined thresholds can be applied to this stack of quality layers to derive an overall assessment of habitat quality in terms of nature conservation, i.e. the conservation status. In our study, we found good accordance of the remotely sensed data with field-based information for the three variables key species, stand structural diversity and co-occurring vegetation (R2 of 0.79, 0.69, and 0.71, respectively) and it was possible to derive meaningful habitat quality maps. The conservation status could be derived with an accuracy of 65%. In interpreting these results it should be considered that the remote sensing based layers are independent estimates of habitat quality in their own right and not a mere replacement of the criteria used in the field guidelines. The approach is thought to be transferable to similar regions with minor adaptions. Our results refer to Calluna heathland which we consider a comparably easy target for remote sensing

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

Science.gov (United States)

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.

Earth and atmospheric remote sensing; Proceedings of the Meeting, Orlando, FL, Apr. 2-4, 1991

Science.gov (United States)

Curran, Robert J. (Editor); Smith, James A. (Editor); Watson, Ken (Editor)

1991-01-01

The papers presented in this volume address the technical aspects of earth and atmospheric remote sensing. Topics discussed include spaceborne and ground-based applications of laser remote sensing, advanced applications of lasers in remote sensing, laser ranging applications, data analysis and systems for biospheric processes, measurements for biospheric processes, and remote sensing for geology and geophysics. Papers are presented on a space-qualified laser transmitter for lidar applications, solid state lasers for planetary exploration, automated band selection for multispectral meteorological applications, aerospace remote sensing of natural water organics, and remote sensing of volcanic ash hazards to aircraft.

Educational activities of remote sensing archaeology (Conference Presentation)

Science.gov (United States)

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

2016-10-01

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

Remote sensing for studying atmospheric aerosols in Malaysia

Science.gov (United States)

Kanniah, Kasturi D.; Kamarul Zaman, Nurul A. F.

2015-10-01

The aerosol system is Southeast Asia is complex and the high concentrations are due to population growth, rapid urbanization and development of SEA countries. Nevertheless, only a few studies have been carried out especially at large spatial extent and on a continuous basis to study atmospheric aerosols in Malaysia. In this review paper we report the use of remote sensing data to study atmospheric aerosols in Malaysia and document gaps and recommend further studies to bridge the gaps. Satellite data have been used to study the spatial and seasonal patterns of aerosol optical depth (AOD) in Malaysia. Satellite data combined with AERONET data were used to delineate different types and sizes of aerosols and to identify the sources of aerosols in Malaysia. Most of the aerosol studies performed in Malaysia was based on station-based PM10 data that have limited spatial coverage. Thus, satellite data have been used to extrapolate and retrieve PM10 data over large areas by correlating remotely sensed AOD with ground-based PM10. Realising the critical role of aerosols on radiative forcing numerous studies have been conducted worldwide to assess the aerosol radiative forcing (ARF). Such studies are yet to be conducted in Malaysia. Although the only source of aerosol data covering large region in Malaysia is remote sensing, satellite observations are limited by cloud cover, orbital gaps of satellite track, etc. In addition, relatively less understanding is achieved on how the atmospheric aerosol interacts with the regional climate system. These gaps can be bridged by conducting more studies using integrated approach of remote sensing, AERONET and ground based measurements.

Robust Initial Wetness Condition Framework of an Event-Based Rainfall–Runoff Model Using Remotely Sensed Soil Moisture

Directory of Open Access Journals (Sweden)

Wooyeon Sunwoo

2017-01-01

Full Text Available Runoff prediction in limited-data areas is vital for hydrological applications, such as the design of infrastructure and flood defenses, runoff forecasting, and water management. Rainfall–runoff models may be useful for simulation of runoff generation, particularly event-based models, which offer a practical modeling scheme because of their simplicity. However, there is a need to reduce the uncertainties related to the estimation of the initial wetness condition (IWC prior to a rainfall event. Soil moisture is one of the most important variables in rainfall–runoff modeling, and remotely sensed soil moisture is recognized as an effective way to improve the accuracy of runoff prediction. In this study, the IWC was evaluated based on remotely sensed soil moisture by using the Soil Conservation Service-Curve Number (SCS-CN method, which is one of the representative event-based models used for reducing the uncertainty of runoff prediction. Four proxy variables for the IWC were determined from the measurements of total rainfall depth (API5, ground-based soil moisture (SSMinsitu, remotely sensed surface soil moisture (SSM, and soil water index (SWI provided by the advanced scatterometer (ASCAT. To obtain a robust IWC framework, this study consists of two main parts: the validation of remotely sensed soil moisture, and the evaluation of runoff prediction using four proxy variables with a set of rainfall–runoff events in the East Asian monsoon region. The results showed an acceptable agreement between remotely sensed soil moisture (SSM and SWI and ground based soil moisture data (SSMinsitu. In the proxy variable analysis, the SWI indicated the optimal value among the proposed proxy variables. In the runoff prediction analysis considering various infiltration conditions, the SSM and SWI proxy variables significantly reduced the runoff prediction error as compared with API5 by 60% and 66%, respectively. Moreover, the proposed IWC framework with

Limiting values for radionuclide concentration in the soil from remote spectrometer measurements

International Nuclear Information System (INIS)

Stuart, T.P.

1977-08-01

Spectrometers that remotely sense γ-rays in the soil are usually oriented with the normal to a planar surface perpendicular to the air-soil interface. When this is the case, and when the thickness of the detector is not greater than the linear dimensions that determine the aforementioned surface area, simple assumptions can be made to calculate high and low limits for factors that convert from photopeak count rates in the spectrometer to soil concentrations. An H.P. 65 calculator program is developed to calculate these two conversion factors as a function of detector altitude, counting rates from a single measurement with a point calibration source, shielding on the surface of the detector, and depth of activity in the soil. The assumption of an exponential decrease with depth allows the previously reported results of Beck et al to be applied to convert from soil concentration to dose rate at 1 m above the ground

Remote sensing image segmentation based on Hadoop cloud platform

Science.gov (United States)

Li, Jie; Zhu, Lingling; Cao, Fubin

2018-01-01

To solve the problem that the remote sensing image segmentation speed is slow and the real-time performance is poor, this paper studies the method of remote sensing image segmentation based on Hadoop platform. On the basis of analyzing the structural characteristics of Hadoop cloud platform and its component MapReduce programming, this paper proposes a method of image segmentation based on the combination of OpenCV and Hadoop cloud platform. Firstly, the MapReduce image processing model of Hadoop cloud platform is designed, the input and output of image are customized and the segmentation method of the data file is rewritten. Then the Mean Shift image segmentation algorithm is implemented. Finally, this paper makes a segmentation experiment on remote sensing image, and uses MATLAB to realize the Mean Shift image segmentation algorithm to compare the same image segmentation experiment. The experimental results show that under the premise of ensuring good effect, the segmentation rate of remote sensing image segmentation based on Hadoop cloud Platform has been greatly improved compared with the single MATLAB image segmentation, and there is a great improvement in the effectiveness of image segmentation.

Assimilating Merged Remote Sensing and Ground based Snowpack Information for Runoff Simulation and Forecasting using Hydrological Models

Science.gov (United States)

Infante Corona, J. A.; Lakhankar, T.; Khanbilvardi, R.; Pradhanang, S. M.

2013-12-01

Stream flow estimation and flood prediction influenced by snow melting processes have been studied for the past couple of decades because of their destruction potential, money losses and demises. It has been observed that snow, that was very stationary during its seasons, now is variable in shorter time-scales (daily and hourly) and rapid snowmelt can contribute or been the cause of floods. Therefore, good estimates of snowpack properties on ground are necessary in order to have an accurate prediction of these destructive events. The snow thermal model (SNTHERM) is a 1-dimensional model that analyzes the snowpack properties given the climatological conditions of a particular area. Gridded data from both, in-situ meteorological observations and remote sensing data will be produced using interpolation methods; thus, snow water equivalent (SWE) and snowmelt estimations can be obtained. The soil and water assessment tool (SWAT) is a hydrological model capable of predicting runoff quantity and quality of a watershed given its main physical and hydrological properties. The results from SNTHERM will be used as an input for SWAT in order to have simulated runoff under snowmelt conditions. This project attempts to improve the river discharge estimation considering both, excess rainfall runoff and the snow melting process. Obtaining a better estimation of the snowpack properties and evolution is expected. A coupled use of SNTHERM and SWAT based on meteorological in situ and remote sensed data will improve the temporal and spatial resolution of the snowpack characterization and river discharge estimations, and thus flood prediction.

First observations of tropospheric δD data observed by ground- and space-based remote sensing and surface in-situ measurement techniques at MUSICA's principle reference station (Izaña Observatory, Spain)

Science.gov (United States)

González, Yenny; Schneider, Matthias; Christner, Emanuel; Rodríguez, Omaira E.; Sepúlveda, Eliezer; Dyroff, Christoph; Wiegele, Andreas

2013-04-01

The main goal of the project MUSICA (Multiplatform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) is the generation of a quasi global tropospheric water vapor isototopologue dataset of a good and well-documented quality. Therefore, new ground- and space-based remote sensing observations (NDACC-FTIR and IASI/METOP) are combined with in-situ measurements. This work presents the first comparison between in-situ and remote sensing observations made at the Izaña Atmospheric Research Centre (Tenerife, Canary Islands, Spain). The in-situ measurements are made by a Picarro L2120-i water vapor isotopologue analyzer. At Izaña the in-situ data are affected by local small-scale mixing processes: during daylight, the thermally buoyant upslope flow prompts the mixing between the Marine Boundary Layer (MBL) and the low Free Troposphere (FT). However, the remote sensors detect δD values averaged over altitudes that are more representative for the free troposphere. This difference has to be considered for the comparison. In general, a good agreement between the MUSICA remote sensing and the in situ H2O-versus-δD plots is found, which demonstrates that the MUSICA δD remote sensing products add scientifically valuable information to the H2O data.

In situ Volcanic Plume Monitoring with small Unmanned Aerial Systems for Cal/Val of Satellite Remote Sensing Data: CARTA-UAV 2013 Mission (Invited)

Science.gov (United States)

Diaz, J. A.; Pieri, D. C.; Bland, G.; Fladeland, M. M.

2013-12-01

The development of small unmanned aerial systems (sUAS) with a variety of sensor packages, enables in situ and proximal remote sensing measurements of volcanic plumes. Using Costa Rican volcanoes as a Natural Laboratory, the University of Costa Rica as host institution, in collaboration with four NASA centers, have started an initiative to develop low-cost, field-deployable airborne platforms to perform volcanic gas & ash plume research, and in-situ volcanic monitoring in general, in conjunction with orbital assets and state-of-the-art models of plume transport and composition. Several gas sensors have been deployed into the active plume of Turrialba Volcano including a miniature mass spectrometer, and an electrochemical SO2 sensor system with temperature, pressure, relative humidity, and GPS sensors. Several different airborne platforms such as manned research aircraft, unmanned aerial vehicles, tethered balloons, as well as man-portable in-situ ground truth systems are being used for this research. Remote sensing data is also collected from the ASTER and OMI spaceborne instruments and compared with in situ data. The CARTA-UAV 2013 Mission deployment and follow up measurements successfully demonstrated a path to study and visualize gaseous volcanic emissions using mass spectrometer and gas sensor based instrumentation in harsh environment conditions to correlate in situ ground/airborne data with remote sensing satellite data for calibration and validation purposes. The deployment of such technology improves on our current capabilities to detect, analyze, monitor, model, and predict hazards presented to aircraft by volcanogenic ash clouds from active and impending volcanic eruptions.

Airborne in situ vertical profiling of HDO / H216O in the subtropical troposphere during the MUSICA remote sensing validation campaign

Science.gov (United States)

Dyroff, C.; Sanati, S.; Christner, E.; Zahn, A.; Balzer, M.; Bouquet, H.; McManus, J. B.; Gonzalez-Ramos, Y.; Schneider, M.

2015-05-01

Vertical profiles of water vapor (H2O) and its isotope ratio D / H expressed as δD(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(δD) ≈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 δD(H2O) as a means to validate the remote sensing humidity and δD(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 δD(H2O) correlations we were able to identify different layers of air masses with specific isotopic signatures. The results are discussed.

CO Seasonal Variability and Trend over Paris Megacity Using Ground-Based QualAir FTS and Satellite IASI-MetOp Measurements

Science.gov (United States)

Te, Yao; Jeseck, Pascal; Hadji-Lazaro, Juliette

2012-11-01

In a growing world with more than 7 billion inhabitants and big emerging countries such as China, Brazil and India, emissions of anthropogenic pollutants are increasing continuously. Monitoring and control of atmospheric pollutants in megacities have become a major challenge for scientists and public health authorities in environmental research area. The QualAir platform at University Pierre et Marie Curie (UPMC), is an innovating experimental research platform dedicated to survey urban atmospheric pollution and air quality. A Bruker Optics IFS 125HR Fourier transform spectrometer belonged to the Laboratoire de Physique Moléculaire pour l'Atmosphère et l'Astrophysique (LPMAA), was adapted for ground-based atmospheric measurements. As one of the major instruments of the QualAir platform, this ground-based Fourier transform spectrometer (QualAir FTS) analyses the composition of the urban atmosphere of Paris, which is the third largest European megacity. The continuous monitoring of atmospheric pollutants is essential to improve the understanding of urban air pollution processes. Associated with a sun-tracker, the QualAir remote sensing FTS operates in solar infrared absorption and enables to monitor many trace gases, and to follow up their variability in the Ile-de-France region. Concentrations of atmospheric pollutants are retrieved by the radiative transfer model PROFFIT. These ground-based remote sensing measurements are compared to ground in-situ measurements and to satellite data from IASI-MetOp (Infrared Atmospheric Sounding Interferometer). The remote sensing total column of the carbon monoxide (CO) obtained from January 2009 to June 2012, has a seasonal variability with a maximum in April and a minimum in October. While, after 2008, the mean CO level is quite stable (no significant decrease as before 2008).

Remote Sensing

CERN Document Server

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

2012-01-01

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

Remote sensing and water resources

CERN Document Server

Champollion, N; Benveniste, J; Chen, J

2016-01-01

This book is a collection of overview articles showing how space-based observations, combined with hydrological modeling, have considerably improved our knowledge of the continental water cycle and its sensitivity to climate change. Two main issues are highlighted: (1) the use in combination of space observations for monitoring water storage changes in river basins worldwide, and (2) the use of space data in hydrological modeling either through data assimilation or as external constraints. The water resources aspect is also addressed, as well as the impacts of direct anthropogenic forcing on land hydrology (e.g. ground water depletion, dam building on rivers, crop irrigation, changes in land use and agricultural practices, etc.). Remote sensing observations offer important new information on this important topic as well, which is highly useful for achieving water management objectives. Over the past 15 years, remote sensing techniques have increasingly demonstrated their capability to monitor components of th...

Integrationof Remote Sensing and Geographic information system in Ground Water Quality Assessment and Management

Science.gov (United States)

Shakak, N.

2015-04-01

Spatial variations in ground water quality in the Khartoum state, Sudan, have been studied using geographic information system (GIS) and remote sensing technique. Gegraphical informtion system a tool which is used for storing, analyzing and displaying spatial data is also used for investigating ground water quality information. Khartoum landsat mosac image aquired in 2013was used, Arc/Gis software applied to extract the boundary of the study area, the image was classified to create land use/land cover map. The land use map,geological and soil map are used for correlation between land use , geological formations, and soil types to understand the source of natural pollution that can lower the ground water quality. For this study, the global positioning system (GPS), used in the field to identify the borehole location in a three dimentional coordinate (Latitude, longitude, and altitude), water samples were collected from 156 borehole wells, and analyzed for physico-chemical parameters like electrical conductivity, Total dissolved solid,Chloride, Nitrate, Sodium, Magnisium, Calcium,and Flouride, using standard techniques in the laboratory and compared with the standards.The ground water quality maps of the entire study area have been prepared using spatial interpolation technique for all the above parameters.then the created maps used to visualize, analyze, and understand the relationship among the measured points. Mapping was coded for potable zones, non-potable zones in the study area, in terms of water quality sutability for drinking water and sutability for irrigation. In general satellite remote sensing in conjunction with geographical information system (GIS) offers great potential for water resource development and management.

Use of microwave remote sensing in salinity estimation

International Nuclear Information System (INIS)

Singh, R.P.; Kumar, V.; Srivastav, S.K.

1990-01-01

Soil-moisture interaction and the consequent liberation of ions causes the salinity of waters. The salinity of river, lake, ocean and ground water changes due to seepage and surface runoff. We have studied the feasibility of using microwave remote sensing for the estimation of salinity by carrying out numerical calculations to study the microwave remote sensing responses of various models representative of river, lake and ocean water. The results show the dependence of microwave remote sensing responses on the salinity and surface temperature of water. The results presented in this paper will be useful in the selection of microwave sensor parameters and in the accurate estimation of salinity from microwave remote sensing data

Remote Sensing Image Classification Based on Stacked Denoising Autoencoder

Directory of Open Access Journals (Sweden)

Peng Liang

2017-12-01

Full Text Available Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised Greedy layer-wise training algorithm is used to train each layer in turn for more robust expressing, characteristics are obtained in supervised learning by Back Propagation (BP neural network, and the whole network is optimized by error back propagation. Finally, Gaofen-1 satellite (GF-1 remote sensing data are used for evaluation, and the total accuracy and kappa accuracy reach 95.7% and 0.955, respectively, which are higher than that of the Support Vector Machine and Back Propagation neural network. The experiment results show that the proposed method can effectively improve the accuracy of remote sensing image classification.

Radiometric modeling and calibration of the Geostationary Imaging Fourier Transform Spectrometer (GIFTS) ground based measurement experiment

Science.gov (United States)

Tian, Jialin; Smith, William L.; Gazarik, Michael J.

2008-12-01

The ultimate remote sensing benefits of the high resolution Infrared radiance spectrometers will be realized with their geostationary satellite implementation in the form of imaging spectrometers. This will enable dynamic features of the atmosphere's thermodynamic fields and pollutant and greenhouse gas constituents to be observed for revolutionary improvements in weather forecasts and more accurate air quality and climate predictions. As an important step toward realizing this application objective, the Geostationary Imaging Fourier Transform Spectrometer (GIFTS) Engineering Demonstration Unit (EDU) was successfully developed under the NASA New Millennium Program, 2000-2006. The GIFTS-EDU instrument employs three focal plane arrays (FPAs), which gather measurements across the long-wave IR (LWIR), short/mid-wave IR (SMWIR), and visible spectral bands. The GIFTS calibration is achieved using internal blackbody calibration references at ambient (260 K) and hot (286 K) temperatures. In this paper, we introduce a refined calibration technique that utilizes Principle Component (PC) analysis to compensate for instrument distortions and artifacts, therefore, enhancing the absolute calibration accuracy. This method is applied to data collected during the GIFTS Ground Based Measurement (GBM) experiment, together with simultaneous observations by the accurately calibrated AERI (Atmospheric Emitted Radiance Interferometer), both simultaneously zenith viewing the sky through the same external scene mirror at ten-minute intervals throughout a cloudless day at Logan Utah on September 13, 2006. The accurately calibrated GIFTS radiances are produced using the first four PC scores in the GIFTS-AERI regression model. Temperature and moisture profiles retrieved from the PC-calibrated GIFTS radiances are verified against radiosonde measurements collected throughout the GIFTS sky measurement period. Using the GIFTS GBM calibration model, we compute the calibrated radiances from data

Combining remote sensing and water-balance evapotranspiration estimates for the conterminous United States

Science.gov (United States)

Reitz, Meredith; Senay, Gabriel; Sanford, Ward E.

2017-01-01

Evapotranspiration (ET) is a key component of the hydrologic cycle, accounting for ~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to significant advances in the frequency and spatial resolution of ET estimates, derived from energy balance principles with variables such as temperature used to estimate surface latent heat flux. Although remote sensing methods excel at depicting spatial and temporal variability, estimation of ET independently of other water budget components can lead to inconsistency with other budget terms. Methods that rely on ground-based data better constrain long-term ET, but are unable to provide the same temporal resolution. Here we combine long-term ET estimates from a water-balance approach with the SSEBop (operational Simplified Surface Energy Balance) remote sensing-based ET product for 2000–2015. We test the new combined method, the original SSEBop product, and another remote sensing ET product (MOD16) against monthly measurements from 119 flux towers. The new product showed advantages especially in non-irrigated areas where the new method showed a coefficient of determination R2 of 0.44, compared to 0.41 for SSEBop or 0.35 for MOD16. The resulting monthly data set will be a useful, unique contribution to ET estimation, due to its combination of remote sensing-based variability and ground-based long-term water balance constraints.

Remote Sensing of the Heliospheric Solar Wind using Radio ...

Indian Academy of Sciences (India)

tribpo

Astr. (2000) 21, 439–444. Remote Sensing of the Heliospheric Solar Wind using Radio. Astronomy Methods and Numerical Simulations. S. Ananthakrishnan, National Center for Radio Astrophysics, Tata Institute of. Fundamental Research, Pune, India. Abstract. The ground-based radio astronomy method of interplanetary.

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

Science.gov (United States)

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

2016-05-01

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

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

OpenAIRE

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

2017-01-01

Spectral radiance measurements collected in nadir and sideward viewing directions by two airborne passive solar remote sensing instruments, the Spectral Modular Airborne Radiation measurement sysTem (SMART) and the Differential Optical Absorption Spectrometer (mini-DOAS), are used to compare the remote sensing results of cirrus optical thickness τ. The comparison is based on a sensitivity study using radiative transfer simulations (RTS) and on data obtained during three airb...

Remote sensing of stratospheric O3 and NO2 using a portable and compact DOAS spectrometer

International Nuclear Information System (INIS)

Raponi, M M; Wolfram, E; Quel, E J; Jimenez, R; Tocho, J O

2011-01-01

The use of passive and active remote sensing systems has largely contributed to advance our understanding of important atmospheric phenomena. Here we present a compact and portable passive DOAS (Differential Optical Absorption Spectroscopy) system, developed for measuring the vertical column density (VCD) of multiple atmospheric trace gases. We highlight the main characteristics of the system components: a mini-spectrometer (HR4000, Ocean Optics), two optical fibers (400 μm of core, 6 m and 25 cm of longitude), an external shutter and the control/data processing software. Nitrogen dioxide (NO 2 ) and ozone (O 3 ) VCDs are derived from solar spectra acquired during twilights (87 0 - 91 0 zenithal angles) using the DOAS technique. The analysis is carried out by solving the Beer-Lambert-Bouger (BLB) law for the main atmospheric absorbers at selected wavelength ranges. The algorithm minimizes the fitting residuals to the BLB law, having as unknown the slant column density (SCD) of the species to determine. We present measurements carried out at the Marambio Antarctic Base (64 0 14' 25'' S; 56 0 37' 21'' W, 197 m asl) during January - February 2008. In addition, we compare our results with co-located measurements performed with EVA, a visible absorption spectrometer of Instituto Nacional de Tecnica Aeroespacial (INTA, Spain), a Dobson spectrophotometer of Servicio Meteorologico Nacional (SMN, Argentine) and the Ozone Monitoring Instrument (OMI), on board AURA satellite.

Synergistic Use of Citizen Science and Remote Sensing for Continental-Scale Measurements of Forest Tree Phenology

Directory of Open Access Journals (Sweden)

Andrew J. Elmore

2016-06-01

Full Text Available There is great potential value in linking geographically dispersed multitemporal observations collected by lay volunteers (or “citizen scientists” with remotely-sensed observations of plant phenology, which are recognized as useful indicators of climate change. However, challenges include a large mismatch in spatial scale and diverse sources of uncertainty in the two measurement types. These challenges must be overcome if the data from each source are to be compared and jointly used to understand spatial and temporal variation in phenology, or if remote observations are to be used to predict ground-based observations. We investigated the correlation between land surface phenology derived from Moderate Resolution Imaging Spectrometer (MODIS data and citizen scientists’ phenology observations from the USA National Phenology Network (NPN. The volunteer observations spanned 2004 to 2013 and represented 25 plant species and nine phenophases. We developed quality control procedures that removed observations outside of an a priori determined acceptable period and observations that were made more than 10 days after a preceding observation. We found that these two quality control steps improved the correlation between ground- and remote-observations, but the largest improvement was achieved when the analysis was restricted to forested MODIS pixels. These results demonstrate a high degree of correlation between the phenology of individual trees (particularly dominant forest trees such as quaking aspen, white oak, and American beech and the phenology of the surrounding forested landscape. These results provide helpful guidelines for the joint use of citizen scientists’ observations and remote sensing phenology in work aimed at understanding continental scale variation and temporal trends.

Remote sensing of sagebrush canopy nitrogen

Science.gov (United States)

Mitchell, Jessica J.; Glenn, Nancy F.; Sankey, Temuulen T.; Derryberry, DeWayne R.; Germino, Matthew J.

2012-01-01

This paper presents a combination of techniques suitable for remotely sensing foliar Nitrogen (N) in semiarid shrublands – a capability that would significantly improve our limited understanding of vegetation functionality in dryland ecosystems. The ability to estimate foliar N distributions across arid and semi-arid environments could help answer process-driven questions related to topics such as controls on canopy photosynthesis, the influence of N on carbon cycling behavior, nutrient pulse dynamics, and post-fire recovery. Our study determined that further exploration into estimating sagebrush canopy N concentrations from an airborne platform is warranted, despite remote sensing challenges inherent to open canopy systems. Hyperspectral data transformed using standard derivative analysis were capable of quantifying sagebrush canopy N concentrations using partial least squares (PLS) regression with an R2 value of 0.72 and an R2 predicted value of 0.42 (n = 35). Subsetting the dataset to minimize the influence of bare ground (n = 19) increased R2 to 0.95 (R2 predicted = 0.56). Ground-based estimates of canopy N using leaf mass per unit area measurements (LMA) yielded consistently better model fits than ground-based estimates of canopy N using cover and height measurements. The LMA approach is likely a method that could be extended to other semiarid shrublands. Overall, the results of this study are encouraging for future landscape scale N estimates and represent an important step in addressing the confounding influence of bare ground, which we found to be a major influence on predictions of sagebrush canopy N from an airborne platform.

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

DEFF Research Database (Denmark)

Guzinski, Radoslaw

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

PhotoSpec - Ground-based Remote Sensing of Solar-Induced Chlorophyll Fluorescence: First Results

Science.gov (United States)

Grossmann, K.; Magney, T. S.; Frankenberg, C.; Seibt, U.; Pivovaroff, A. L.; Hurlock, S. C.; Stutz, J.

2016-12-01

Solar-Induced Chlorophyll Fluorescence (SIF) emitted from vegetation can be used as a proxy for photosynthetic activity and is observable on a global scale from space. However, many issues on a leaf-to-canopy scale remain poorly understood, such as influences on the SIF signal from environmental conditions, water stress, or radiation. We have developed a novel ground-based spectrometer system for measuring SIF from natural ecosystems. The instrumental set-up, requirements, and measurement technique are based on decades of experience using Differential Optical Absorption Spectroscopy (DOAS), an established method to measure atmospheric trace gases. The instrument consists of three thermally stabilized commercial spectrometers that are linked to a 2D scanning telescope unit via optical fiber bundles, and also includes a commercial photosynthetic active radiation (PAR) sensor. The spectrometers cover a SIF retrieval wavelength range at high spectral resolution (670 - 780 nm, 0.1 nm FWHM), and also provide moderate resolution spectra (400 - 800 nm, 1.5 nm FWHM) to retrieve vegetation indices and the photochemical reflectance index (PRI). We report on results of the first continuous field measurements of this novel system at Stunt Ranch Santa Monica Mountains UC Reserve, where the PhotoSpec instrument was monitoring SIF of four native Californian shrubland species with different adaptations to seasonal summer drought. We report on the correlation with CO2 fluxes over both the growing season and the hot summer period in 2016. We also show detailed measurements of the diurnal cycle of the SIF signal of single broad leaves, as well as dark-light transitions, under controlled experimental conditions. In addition to demonstrating the instrumental set-up, retrieval algorithm, and instrument performance, our results illustrate that SIF measurements at the leaf to ecosystem scale are needed to understand and interpret the SIF signals retrieved at larger scales.

A stereo remote sensing feature selection method based on artificial bee colony algorithm

Science.gov (United States)

Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

2014-05-01

To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

Remote sensing to monitor uranium tailing sites

International Nuclear Information System (INIS)

1992-02-01

This report concerns the feasibility of using remotely-sensed data for long-term monitoring of uranium tailings. Decommissioning of uranium mine tailings sites may require long-term monitoring to confirm that no unanticipated release of contaminants occurs. Traditional ground-based monitoring of specific criteria of concern would be a significant expense depending on the nature and frequency of the monitoring. The objective of this study was to evaluate whether available remote-sensing data and techniques were applicable to the long-term monitoring of tailings sites. This objective was met by evaluating to what extent the data and techniques could be used to identify and discriminate information useful for monitoring tailings sites. The cost associated with obtaining and interpreting this information was also evaluated. Satellite and aircraft remote-sensing-based activities were evaluated. A monitoring programme based on annual coverage of Landsat Thematic Mapper data is recommended. Immediately prior to and for several years after decommissioning of the tailings sites, airborne multispectral and thermal infrared surveys combined with field verification data are required in order to establish a baseline for the long-term satellite-based monitoring programme. More frequent airborne surveys may be required if rapidly changing phenomena require monitoring. The use of a geographic information system is recommended for the effective storage and manipulation of data accumulated over a number of years

The potential of remote sensing technology for the detection and ...

African Journals Online (AJOL)

Internationally, a number of studies have successfully used remote sensing technology to monitor forest damage. Remote sensing technology allows for instantaneous methods of assessments whereby ground assessments would be impossible on a regular basis. This paper provides an overview of how advances in ...

Remote sensing of Sonoran Desert vegetation structure and phenology with ground-based LiDAR

Science.gov (United States)

Sankey, Joel B.; Munson, Seth M.; Webb, Robert H.; Wallace, Cynthia S.A.; Duran, Cesar M.

2015-01-01

Long-term vegetation monitoring efforts have become increasingly important for understanding ecosystem response to global change. Many traditional methods for monitoring can be infrequent and limited in scope. Ground-based LiDAR is one remote sensing method that offers a clear advancement to monitor vegetation dynamics at high spatial and temporal resolution. We determined the effectiveness of LiDAR to detect intra-annual variability in vegetation structure at a long-term Sonoran Desert monitoring plot dominated by cacti, deciduous and evergreen shrubs. Monthly repeat LiDAR scans of perennial plant canopies over the course of one year had high precision. LiDAR measurements of canopy height and area were accurate with respect to total station survey measurements of individual plants. We found an increase in the number of LiDAR vegetation returns following the wet North American Monsoon season. This intra-annual variability in vegetation structure detected by LiDAR was attributable to a drought deciduous shrub Ambrosia deltoidea, whereas the evergreen shrub Larrea tridentata and cactus Opuntia engelmannii had low variability. Benefits of using LiDAR over traditional methods to census desert plants are more rapid, consistent, and cost-effective data acquisition in a high-resolution, 3-dimensional context. We conclude that repeat LiDAR measurements can be an effective method for documenting ecosystem response to desert climatology and drought over short time intervals and at detailed-local spatial scale.

Review of research on remote sensing with digital map. Remote sensing to suchi chizu no ketsugo ni yoru kenkyu no shokai

Energy Technology Data Exchange (ETDEWEB)

Tanaka, S; Sugimura, T [Remote Sensing Technology Center of Japan, Tokyo (Japan)

1990-12-05

This paper describes the relationship between remote sensing and digital map. The relation between remote sensing and digital map is roughly classified into two kinds. One of them is utilization of remote sensing and digital map in combination to analyze phenomena, and the other is normalization of remote sensing data by use of digital map. For examples of utilizing remote sensing and digital map, there are the creation of a perspective image of ground scene from Landsat MSS data by use of a mesh type digital map of the orthogonal co-ordinates, and the creation of an image of the enviromental research along roads from satilite data by use of a vector type digital map. Furthermore, this paper introduces a procedure of correcting geographical strains by use of a digital map and converting a radar image to corrected plane image, and the use of a digital map in the global scale for the analysis of floods and other purposes. 20 refs., 5 figs., 1 tab.

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

International Nuclear Information System (INIS)

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

2014-01-01

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

Remote Sensing and Special Surveys Program annual report, January--December 1993

International Nuclear Information System (INIS)

Conder, S.R.; Doll, W.E.; Gabrielsen, C.A.; King, A.D.; Durfee, R.C.; Parr, P.D.

1994-03-01

The Remote Sensing and Special Surveys Program has been established to provide environmental characterization data, change data, and trend data to various Environmental Restoration and Waste Management (ERWM) programs. The data are acquired through several different types of survey platforms. During the calendar year of 1993, a variety of surveys were conducted through the Remote Sensing and Special Surveys Program. The aerial surveys included geophysical, radiological, false color infrared (IR) photography, and natural color photography. Ground surveys were conducted to correlate data collected from the airborne platforms to data measured at ground level. Ground surveys were also conducted to determine the existence or absence of threatened and endangered plant species on the Oak Ridge Reservation. Some of the special surveys included laser induced fluorescence imaging, solar reflectance, and various remote sensing and ground control activities for the Strategic Environmental Research and Development Program (SERDP) initiative. Data analysis, management, and storage are also conducted by the Remote Sensing and Special Surveys Program to achieve the highest level of data useability possible. The data acquired through these surveys have provided and will continue to provide much needed information to ERWM programs

Progress in Analysis to Remote Sensed Thermal Abnormity with Fault Activity and Seismogenic Process

Directory of Open Access Journals (Sweden)

WU Lixin

2017-10-01

Full Text Available Research to the remote sensed thermal abnormity with fault activity and seismogenic process is a vital topic of the Earth observation and remote sensing application. It is presented that a systematic review on the international researches on the topic during the past 30 years, in the respects of remote sensing data applications, anomaly analysis methods, and mechanism understanding. Firstly, the outlines of remote sensing data applications are given including infrared brightness temperature, microwave brightness temperature, outgoing longwave radiation, and assimilated data from multiple earth observations. Secondly, three development phases are summarized as qualitative analysis based on visual interpretation, quantitative analysis based on image processing, and multi-parameter spatio-temporal correlation analysis. Thirdly, the theoretical hypotheses presented for the mechanism understanding are introduced including earth degassing, stress-induced heat, crustal rock battery conversion, latent heat release due to radon decay as well as multi-spheres coupling effect. Finally, three key directions of future research on this topic are proposed:anomaly recognizing by remote sensing monitoring and data analysis for typical tectonic activity areas; anomaly mechanism understanding based on earthquake-related earth system responses; spatio-temporal correlation analysis of air-based, space-based and ground-based stereoscopic observations.

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

African Journals Online (AJOL)

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

DARLA: Data Assimilation and Remote Sensing for Littoral Applications

Science.gov (United States)

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

2012-12-01

DARLA is 5-year collaborative project that couples state-of-the-art remote sensing and in situ measurements with advanced data assimilation (DA) modeling to (a) evaluate and improve remote sensing retrieval algorithms for environmental parameters, (b) determine the extent to which remote sensing data can be used in place of in situ data in models, and (c) infer bathymetry for littoral environments by combining remotely-sensed parameters and data assimilation models. The project uses microwave, electro-optical, and infrared techniques to characterize the littoral ocean with a focus on wave and current parameters required for DA modeling. In conjunction with the RIVET (River and Inlets) Project, extensive in situ measurements provide ground truth for both the remote sensing retrieval algorithms and the DA modeling. Our goal is to use remote sensing to constrain data assimilation models of wave and circulation dynamics in a tidal inlet and surrounding beaches. We seek to improve environmental parameter estimation via remote sensing fusion, determine the success of using remote sensing data to drive DA models, and produce a dynamically consistent representation of the wave, circulation, and bathymetry fields in complex environments. The objectives are to test the following three hypotheses: 1. Environmental parameter estimation using remote sensing techniques can be significantly improved by fusion of multiple sensor products. 2. Data assimilation models can be adequately constrained (i.e., forced or guided) with environmental parameters derived from remote sensing measurements. 3. Bathymetry on open beaches, river mouths, and at tidal inlets can be inferred from a combination of remotely-sensed parameters and data assimilation models. Our approach is to conduct a series of field experiments combining remote sensing and in situ measurements to investigate signature physics and to gather data for developing and testing DA models. A preliminary experiment conducted at

Remote sensing, hydrological modeling and in situ observations in snow cover research: A review

Science.gov (United States)

Dong, Chunyu

2018-06-01

Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models.

Prospecting for coal in China with remote sensing

Energy Technology Data Exchange (ETDEWEB)

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

2008-12-15

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

AN INTERACTIVE WEB-BASED ANALYSIS FRAMEWORK FOR REMOTE SENSING CLOUD COMPUTING

Directory of Open Access Journals (Sweden)

X. Z. Wang

2015-07-01

Full Text Available Spatiotemporal data, especially remote sensing data, are widely used in ecological, geographical, agriculture, and military research and applications. With the development of remote sensing technology, more and more remote sensing data are accumulated and stored in the cloud. An effective way for cloud users to access and analyse these massive spatiotemporal data in the web clients becomes an urgent issue. In this paper, we proposed a new scalable, interactive and web-based cloud computing solution for massive remote sensing data analysis. We build a spatiotemporal analysis platform to provide the end-user with a safe and convenient way to access massive remote sensing data stored in the cloud. The lightweight cloud storage system used to store public data and users’ private data is constructed based on open source distributed file system. In it, massive remote sensing data are stored as public data, while the intermediate and input data are stored as private data. The elastic, scalable, and flexible cloud computing environment is built using Docker, which is a technology of open-source lightweight cloud computing container in the Linux operating system. In the Docker container, open-source software such as IPython, NumPy, GDAL, and Grass GIS etc., are deployed. Users can write scripts in the IPython Notebook web page through the web browser to process data, and the scripts will be submitted to IPython kernel to be executed. By comparing the performance of remote sensing data analysis tasks executed in Docker container, KVM virtual machines and physical machines respectively, we can conclude that the cloud computing environment built by Docker makes the greatest use of the host system resources, and can handle more concurrent spatial-temporal computing tasks. Docker technology provides resource isolation mechanism in aspects of IO, CPU, and memory etc., which offers security guarantee when processing remote sensing data in the IPython Notebook

An Interactive Web-Based Analysis Framework for Remote Sensing Cloud Computing

Science.gov (United States)

Wang, X. Z.; Zhang, H. M.; Zhao, J. H.; Lin, Q. H.; Zhou, Y. C.; Li, J. H.

2015-07-01

Spatiotemporal data, especially remote sensing data, are widely used in ecological, geographical, agriculture, and military research and applications. With the development of remote sensing technology, more and more remote sensing data are accumulated and stored in the cloud. An effective way for cloud users to access and analyse these massive spatiotemporal data in the web clients becomes an urgent issue. In this paper, we proposed a new scalable, interactive and web-based cloud computing solution for massive remote sensing data analysis. We build a spatiotemporal analysis platform to provide the end-user with a safe and convenient way to access massive remote sensing data stored in the cloud. The lightweight cloud storage system used to store public data and users' private data is constructed based on open source distributed file system. In it, massive remote sensing data are stored as public data, while the intermediate and input data are stored as private data. The elastic, scalable, and flexible cloud computing environment is built using Docker, which is a technology of open-source lightweight cloud computing container in the Linux operating system. In the Docker container, open-source software such as IPython, NumPy, GDAL, and Grass GIS etc., are deployed. Users can write scripts in the IPython Notebook web page through the web browser to process data, and the scripts will be submitted to IPython kernel to be executed. By comparing the performance of remote sensing data analysis tasks executed in Docker container, KVM virtual machines and physical machines respectively, we can conclude that the cloud computing environment built by Docker makes the greatest use of the host system resources, and can handle more concurrent spatial-temporal computing tasks. Docker technology provides resource isolation mechanism in aspects of IO, CPU, and memory etc., which offers security guarantee when processing remote sensing data in the IPython Notebook. Users can write

Research on Remote Sensing Image Template Processing Based on Global Subdivision Theory

OpenAIRE

Xiong Delan; Du Genyuan

2013-01-01

Aiming at the questions of vast data, complex operation, and time consuming processing for remote sensing image, subdivision template was proposed based on global subdivision theory, which can set up high level of abstraction and generalization for remote sensing image. The paper emphatically discussed the model and structure of subdivision template, and put forward some new ideas for remote sensing image template processing, key technology and quickly applied demonstration. The research has ...

Development of airborne remote sensing data assimilation system

International Nuclear Information System (INIS)

Gudu, B R; Bi, H Y; Wang, H Y; Qin, S X; Ma, J W

2014-01-01

In this paper, an airborne remote sensing data assimilation system for China Airborne Remote Sensing System is introduced. This data assimilation system is composed of a land surface model, data assimilation algorithms, observation data and fundamental parameters forcing the land surface model. In this data assimilation system, Variable Infiltration Capacity hydrologic model is selected as the land surface model, which also serves as the main framework of the system. Three-dimensional variation algorithm, four-dimensional variation algorithms, ensemble Kalman filter and Particle filter algorithms are integrated in this system. Observation data includes ground observations and remotely sensed data. The fundamental forcing parameters include soil parameters, vegetation parameters and the meteorological data

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

Science.gov (United States)

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.

Development of a data driven process-based model for remote sensing of terrestrial ecosystem productivity, evapotranspiration, and above-ground biomass

Science.gov (United States)

El Masri, Bassil

2011-12-01

Modeling terrestrial ecosystem functions and structure has been a subject of increasing interest because of the importance of the terrestrial carbon cycle in global carbon budget and climate change. In this study, satellite data were used to estimate gross primary production (GPP), evapotranspiration (ET) for two deciduous forests: Morgan Monroe State forest (MMSF) in Indiana and Harvard forest in Massachusetts. Also, above-ground biomass (AGB) was estimated for the MMSF and the Howland forest (mixed forest) in Maine. Surface reflectance and temperature, vegetation indices, soil moisture, tree height and canopy area derived from the Moderate Resolution Imagining Spectroradiometer (MODIS), the Advanced Microwave Scanning Radiometer (AMRS-E), LIDAR, and aerial imagery respectively, were used for this purpose. These variables along with others derived from remotely sensed data were used as inputs variables to process-based models which estimated GPP and ET and to a regression model which estimated AGB. The process-based models were BIOME-BGC and the Penman-Monteith equation. Measured values for the carbon and water fluxes obtained from the Eddy covariance flux tower were compared to the modeled GPP and ET. The data driven methods produced good estimation of GPP and ET with an average root mean square error (RMSE) of 0.17 molC/m2 and 0.40 mm/day, respectively for the MMSF and the Harvard forest. In addition, allometric data for the MMSF were used to develop the regression model relating AGB with stem volume. The performance of the AGB regression model was compared to site measurements using remotely sensed data for the MMSF and the Howland forest where the model AGB RMSE ranged between 2.92--3.30 Kg C/m2. Sensitivity analysis revealed that improvement in maintenance respiration estimation and remotely sensed maximum photosynthetic activity as well as accurate estimate of canopy resistance will result in improved GPP and ET predictions. Moreover, AGB estimates were

A New Capability for Automated Target Selection and Sampling for use with Remote Sensing Instruments on the MER Rovers

Science.gov (United States)

Castano, R.; Estlin, T.; Anderson, R. C.; Gaines, D.; Bornstein, B.; de Granville, C.; Tang, B.; Thompson, D.; Judd, M.

2008-12-01

The Onboard Autonomous Science Investigation System (OASIS) evaluates geologic data gathered by a planetary rover. The system is designed to operate onboard a rover identifying and reacting to serendipitous science opportunities, such as rocks with novel properties. OASIS operates by analyzing data the rover gathers, and then using machine learning techniques, prioritizing the data based on criteria set by the science team. This prioritization can be used to organize data for transmission back to Earth and it can be used to search for specific targets it has been told to find by the science team. If one of these targets is found, it is identified as a new science opportunity and a "science alert" is sent to a planning and scheduling system. After reviewing the rover's current operational status to ensure that it has enough resources to complete its traverse and act on the new science opportunity, OASIS can change the command sequence of the rover in order to obtain additional science measurements. Currently, OASIS is being applied on a new front. OASIS is providing a new rover mission technology that enables targeted remote-sensing science in an automated fashion during or after rover traverses. Currently, targets for remote sensing instruments, especially narrow field-of-view instruments (such as the MER Mini- TES spectrometer or the 2009 MSL ChemCam spectrometer) must be selected manually based on imagery already on the ground with the operations team. OASIS will enable the rover flight software to analyze imagery onboard in order to autonomously select and sequence targeted remote-sensing observations in an opportunistic fashion. We are in the process of scheduling an onboard MER experiment to demonstrate the OASIS capability in early 2009.

Spaceborne Remote Sensing of Aerosol Type: Global Distribution, Model Evaluation and Translation into Chemical Speciation

Science.gov (United States)

Kacenelenbogen, M. S.; Tan, Q.; Johnson, M. S.; Burton, S. P.; Redemann, J.; Hasekamp, O. P.; Dawson, K. W.; Hair, J. W.; Ferrare, R. A.; Butler, C. F.; Holben, B. N.; Beyersdorf, A. J.; Ziemba, L. D.; Froyd, K. D.; Dibb, J. E.; Shingler, T.; Sorooshian, A.; Jimenez, J. L.; Campuzano Jost, P.; Jacob, D.; Kim, P. S.; Travis, K.; Lacagnina, C.

2016-12-01

It is essential to evaluate and refine aerosol classification methods applied to passive satellite remote sensing. We have developed an aerosol classification algorithm (called Specified Clustering and Mahalanobis Classification, SCMC) that assigns an aerosol type to multi-parameter retrievals by spaceborne, airborne or ground-based passive remote sensing instruments [1]. The aerosol types identified by our scheme are pure dust, polluted dust, urban-industrial/developed economy, urban-industrial/developing economy, dark biomass smoke, light biomass smoke and pure marine. We apply the SCMC method to inversions from the ground-based AErosol RObotic NETwork (AERONET [2]) and retrievals from the space-borne Polarization and Directionality of Earth's Reflectances instrument (POLDER, [3]). The POLDER retrievals that we use differ from the standard POLDER retrievals [4] as they make full use of multi-angle, multispectral polarimetric data [5]. We analyze agreement in the aerosol types inferred from both AERONET and POLDER and evaluate GEOS-Chem [6] simulations over the globe. Finally, we use in-situ observations from the SEAC4RS airborne field experiment to bridge the gap between remote sensing-inferred qualitative SCMC aerosol types and their corresponding quantitative chemical speciation. We apply the SCMC method to airborne in-situ observations from the NASA Langley Aerosol Research Group Experiment (LARGE, [7]) and the Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe (DASH-SP, [8]) instruments; we then relate each coarsely defined SCMC type to a sum of percentage of individual aerosol species, using in-situ observations from the Particle Analysis by Laser Mass Spectrometry (PALMS, [9]), the Soluble Acidic Gases and Aerosol (SAGA, [10]), and the High - Resolution Time - of - Flight Aerosol Mass Spectrometer (HR ToF AMS, [11]). [1] Russell P. B., et al., JGR, 119.16 (2014) [2] Holben B. N., et al., RSE, 66.1 (1998) [3] Tanré D., et al., AMT, 4.7 (2011

Practical Approach To Building A Mid-Wave Remote Sensing System

Energy Technology Data Exchange (ETDEWEB)

Pyke, Benjamin J. [Univ. of Arizona, Tucson, AZ (United States)

2017-01-01

The purpose of this project, Laser Active Transmitter & Receiver (LATR), was to build a mobile ground based remote sensing system that can detect, identify and quantify a specific gaseous species using Differential Absorption LIDAR (DIAL). This thesis project is concerned with the development and field testing of a mid-wave infrared active remote sensing system, capable of identifying and quantifying emissions in the 3.2 – 3.5 micron range. The goal is to give a brief description of what remote sensing is about and the specific technique used to analyze the collected data. The thesis will discuss the transmitter and the associated subsystems used to create the required wavelength, and the receiver used to collect the returns. And finally, the thesis will discuss the process of collecting the data and some of the results from field and lab collections.

Review of Remote Sensing Needs and Applications in Africa

Science.gov (United States)

Brown, Molly E.

2007-01-01

Remote sensing data has had an important role in identifying and responding to inter-annual variations in the African environment during the past three decades. As a largely agricultural region with diverse but generally limited government capacity to acquire and distribute ground observations of rainfall, temperature and other parameters, remote sensing is sometimes the only reliable measure of crop growing conditions in Africa. Thus, developing and maintaining the technical and scientific capacity to analyze and utilize satellite remote sensing data in Africa is critical to augmenting the continent's local weather/climate observation networks as well as its agricultural and natural resource development and management. The report Review of Remote Sensing Needs and Applications in Africa' has as its central goal to recommend to the US Agency for International Development an appropriate approach to support sustainable remote sensing applications at African regional remote sensing centers. The report focuses on "RS applications" to refer to the acquisition, maintenance and archiving, dissemination, distribution, analysis, and interpretation of remote sensing data, as well as the integration of interpreted data with other spatial data products. The report focuses on three primary remote sensing centers: (1) The AGRHYMET Regional Center in Niamey, Niger, created in 1974, is a specialized institute of the Permanent Interstate Committee for Drought Control in the Sahel (CILSS), with particular specialization in science and techniques applied to agricultural development, rural development, and natural resource management. (2) The Regional Centre for Maiming of Resources for Development (RCMRD) in Nairobi, Kenya, established in 1975 under the auspices of the United Nations Economic Commission for Africa and the Organization of African Unity (now the African Union), is an intergovernmental organization, with 15 member states from eastern and southern Africa. (3) The

Monitoring water quality by remote sensing

Science.gov (United States)

Brown, R. L. (Principal Investigator)

1977-01-01

The author has identified the following significant results. A limited study was conducted to determine the applicability of remote sensing for evaluating water quality conditions in the San Francisco Bay and delta. Considerable supporting data were available for the study area from other than overflight sources, but short-term temporal and spatial variability precluded their use. The study results were not sufficient to shed much light on the subject, but it did appear that, with the present state of the art in image analysis and the large amount of ground truth needed, remote sensing has only limited application in monitoring water quality.

Remote sensing of water and nitrogen stress in broccoli

Science.gov (United States)

Elsheikha, Diael-Deen Mohamed

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

Advanced and applied remote sensing of environmental conditions

Science.gov (United States)

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

2013-01-01

"Remote sensing” is a general term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth’s surface. Remotely sensed data are fundamental to geographic science. The U.S. Geological Survey’s (USGS) Eastern Geographic Science Center (EGSC) is currently conducting and promoting the research and development of several different aspects of remote sensing science in both the laboratory and from overhead instruments. Spectroscopy is the science of recording interactions of energy and matter and is the bench science for all remote sensing. Visible and infrared analysis in the laboratory with special instruments called spectrometers enables the transfer of this research from the laboratory to multispectral (5–15 broad bands) and hyperspectral (50–300 narrow contiguous bands) analyses from aircraft and satellite sensors. In addition, mid-wave (3–5 micrometers, µm) and long-wave (8–14 µm) infrared data analysis, such as attenuated total reflectance (ATR) spectral analysis, are also conducted. ATR is a special form of vibrational infrared spectroscopy that has many applications in chemistry and biology but has recently been shown to be especially diagnostic for vegetation analysis.

REMOTE-SENSING-BASED BIOPHYSICAL MODELS FOR ESTIMATING LAI OF IRRIGATED CROPS IN MURRY DARLING BASIN

Directory of Open Access Journals (Sweden)

I. Wittamperuma

2012-07-01

Full Text Available Remote sensing is a rapid and reliable method for estimating crop growth data from individual plant to crops in irrigated agriculture ecosystem. The LAI is one of the important biophysical parameter for determining vegetation health, biomass, photosynthesis and evapotranspiration (ET for the modelling of crop yield and water productivity. Ground measurement of this parameter is tedious and time-consuming due to heterogeneity across the landscape over time and space. This study deals with the development of remote-sensing based empirical relationships for the estimation of ground-based LAI (LAIG using NDVI, modelled with and without atmospheric correction models for three irrigated crops (corn, wheat and rice grown in irrigated farms within Coleambally Irrigation Area (CIA which is located in southern Murray Darling basin, NSW in Australia. Extensive ground truthing campaigns were carried out to measure crop growth and to collect field samples of LAI using LAI- 2000 Plant Canopy Analyser and reflectance using CROPSCAN Multi Spectral Radiometer at several farms within the CIA. A Set of 12 cloud free Landsat 5 TM satellite images for the period of 2010-11 were downloaded and regression analysis was carried out to analyse the co-relationships between satellite and ground measured reflectance and to check the reliability of data sets for the crops. Among all the developed regression relationships between LAI and NDVI, the atmospheric correction process has significantly improved the relationship between LAI and NDVI for Landsat 5 TM images. The regression analysis also shows strong correlations for corn and wheat but weak correlations for rice which is currently being investigated.

Remote Sensing of Volcanic ASH at the Met Office

Directory of Open Access Journals (Sweden)

Marenco F.

2016-01-01

Full Text Available The eruption of Eyjafjallajökull in 2010 has triggered the rapid development of volcanic ash remote sensing activities at the Met Office. Volcanic ash qualitative and quantitative mapping have been achieved using lidar on board the Facility for Airborne Atmospheric Measurements (FAAM research aircraft, and using improved satellite retrieval algorithms. After the eruption, a new aircraft facility, the Met Office Civil Contingencies Aircraft (MOCCA, has been set up to enable a rapid response, and a network of ground-based remote sensing sites with lidars and sunphotometers is currently being developed. Thanks to these efforts, the United Kingdom (UK will be much better equipped to deal with such a crisis, should it happen in the future.

Preliminary Results of BTDF Calibration of Transmissive Solar Diffusers for Remote Sensing

Science.gov (United States)

Georgiev, Georgi T.; Butler, James J.; Thome, Kurt; Cooksey, Catherine; Ding, Leibo

2016-01-01

Satellite instruments operating in the reflected solar wavelength region require accurate and precise determination of the optical properties of their diffusers used in pre-flight and post-flight calibrations. The majority of recent and current space instruments use reflective diffusers. As a result, numerous Bidirectional Reflectance Distribution Function (BRDF) calibration comparisons have been conducted between the National Institute of Standards and Technology (NIST) and other industry and university-based metrology laboratories. However, based on literature searches and communications with NIST and other laboratories, no Bidirectional Transmittance Distribution Function (BTDF) measurement comparisons have been conducted between National Measurement Laboratories (NMLs) and other metrology laboratories. On the other hand, there is a growing interest in the use of transmissive diffusers in the calibration of satellite, air-borne, and ground-based remote sensing instruments. Current remote sensing instruments employing transmissive diffusers include the Ozone Mapping and Profiler Suite instrument (OMPS) Limb instrument on the Suomi-National Polar-orbiting Partnership (S-NPP) platform,, the Geostationary Ocean Color Imager (GOCI) on the Korea Aerospace Research Institute's (KARI) Communication, Ocean, and Meteorological Satellite (COMS), the Ozone Monitoring Instrument (OMI) on NASA's Earth Observing System (EOS) Aura platform, the Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument and the Geostationary Environmental Monitoring Spectrometer (GEMS).. This ensemble of instruments requires validated BTDF measurements of their on-board transmissive diffusers from the ultraviolet through the near infrared. This paper presents the preliminary results of a BTDF comparison between the NASA Diffuser Calibration Laboratory (DCL) and NIST on quartz and thin Spectralon samples.

Preliminary results of BTDF calibration of transmissive solar diffusers for remote sensing

Science.gov (United States)

Georgiev, Georgi T.; Butler, James J.; Thome, Kurt; Cooksey, Catherine; Ding, Leibo

2016-09-01

Satellite instruments operating in the reflected solar wavelength region require accurate and precise determination of the optical properties of their diffusers used in pre-flight and post-flight calibrations. The majority of recent and current space instruments use reflective diffusers. As a result, numerous Bidirectional Reflectance Distribution Function (BRDF) calibration comparisons have been conducted between the National Institute of Standards and Technology (NIST) and other industry and university-based metrology laboratories. However, based on literature searches and communications with NIST and other laboratories, no Bidirectional Transmittance Distribution Function (BTDF) measurement comparisons have been conducted between National Measurement Laboratories (NMLs) and other metrology laboratories. On the other hand, there is a growing interest in the use of transmissive diffusers in the calibration of satellite, air-borne, and ground-based remote sensing instruments. Current remote sensing instruments employing transmissive diffusers include the Ozone Mapping and Profiler Suite instrument (OMPS) Limb instrument on the Suomi-National Polar-orbiting Partnership (S-NPP) platform,, the Geostationary Ocean Color Imager (GOCI) on the Korea Aerospace Research Institute's (KARI) Communication, Ocean, and Meteorological Satellite (COMS), the Ozone Monitoring Instrument (OMI) on NASA's Earth Observing System (EOS) Aura platform, the Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument and the Geostationary Environmental Monitoring Spectrometer (GEMS).. This ensemble of instruments requires validated BTDF measurements of their onboard transmissive diffusers from the ultraviolet through the near infrared. This paper presents the preliminary results of a BTDF comparison between the NASA Diffuser Calibration Laboratory (DCL) and NIST on quartz and thin Spectralon samples.

The laser absorption spectrometer - A new remote sensing instrument for atmospheric pollution monitoring

Science.gov (United States)

Shumate, M. S.

1974-01-01

An instrument capable of remotely monitoring trace atmospheric constituents is described. The instrument, called a laser absorption spectrometer, can be operated from an aircraft or spacecraft to measure the concentration of selected gases in three dimensions. This device will be particularly useful for rapid determination of pollutant levels in urban areas.

Remote Sensing and Reflectance Profiling in Entomology.

Science.gov (United States)

Nansen, Christian; Elliott, Norman

2016-01-01

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

MetaSensing's FastGBSAR: ground based radar for deformation monitoring

Science.gov (United States)

Rödelsperger, Sabine; Meta, Adriano

2014-10-01

The continuous monitoring of ground deformation and structural movement has become an important task in engineering. MetaSensing introduces a novel sensor system, the Fast Ground Based Synthetic Aperture Radar (FastGBSAR), based on innovative technologies that have already been successfully applied to airborne SAR applications. The FastGBSAR allows the remote sensing of deformations of a slope or infrastructure from up to a distance of 4 km. The FastGBSAR can be setup in two different configurations: in Real Aperture Radar (RAR) mode it is capable of accurately measuring displacements along a linear range profile, ideal for monitoring vibrations of structures like bridges and towers (displacement accuracy up to 0.01 mm). Modal parameters can be determined within half an hour. Alternatively, in Synthetic Aperture Radar (SAR) configuration it produces two-dimensional displacement images with an acquisition time of less than 5 seconds, ideal for monitoring areal structures like dams, landslides and open pit mines (displacement accuracy up to 0.1 mm). The MetaSensing FastGBSAR is the first ground based SAR instrument on the market able to produce two-dimensional deformation maps with this high acquisition rate. By that, deformation time series with a high temporal and spatial resolution can be generated, giving detailed information useful to determine the deformation mechanisms involved and eventually to predict an incoming failure. The system is fully portable and can be quickly installed on bedrock or a basement. The data acquisition and processing can be fully automated leading to a low effort in instrument operation and maintenance. Due to the short acquisition time of FastGBSAR, the coherence between two acquisitions is very high and the phase unwrapping is simplified enormously. This yields a high density of resolution cells with good quality and high reliability of the acquired deformations. The deformation maps can directly be used as input into an Early

POLARIZATION REMOTE SENSING PHYSICAL MECHANISM, KEY METHODS AND APPLICATION

Directory of Open Access Journals (Sweden)

B. Yang

2017-09-01

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

Time-sensitive remote sensing

CERN Document Server

Lippitt, Christopher; Coulter, Lloyd

2015-01-01

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

Remote sensing: best practice

Energy Technology Data Exchange (ETDEWEB)

Brown, Gareth [Sgurr Energy (Canada)

2011-07-01

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

Urban land use: Remote sensing of ground-basin permeability

Science.gov (United States)

Tinney, L. R.; Jensen, J. R.; Estes, J. E.

1975-01-01

A remote sensing analysis of the amount and type of permeable and impermeable surfaces overlying an urban recharge basin is discussed. An effective methodology for accurately generating this data as input to a safe yield study is detailed and compared to more conventional alternative approaches. The amount of area inventoried, approximately 10 sq. miles, should provide a reliable base against which automatic pattern recognition algorithms, currently under investigation for this task, can be evaluated. If successful, such approaches can significantly reduce the time and effort involved in obtaining permeability data, an important aspect of urban hydrology dynamics.

Remote Sensing of Water Pollution

Science.gov (United States)

White, P. G.

1971-01-01

Remote sensing, as a tool to aid in the control of water pollution, offers a means of making rapid, economical surveys of areas that are relatively inaccessible on the ground. At the same time, it offers the only practical means of mapping pollution patterns that cover large areas. Detection of oil slicks, thermal pollution, sewage, and algae are discussed.

Remote Sensing of Vegetation Species Diversity: The Utility of Integrated Airborne Hyperspectral and Lidar Data

Science.gov (United States)

Krause, Keith Stuart

The change, reduction, or extinction of species is a major issue currently facing the Earth. Efforts are underway to measure, monitor, and protect habitats that contain high species diversity. Remote sensing technology shows extreme value for monitoring species diversity by mapping ecosystems and using those land cover maps or other derived data as proxies to species number and distribution. The National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) consists of remote sensing instruments such as an imaging spectrometer, a full-waveform lidar, and a high-resolution color camera. AOP collected data over the Ordway-Swisher Biological Station (OSBS) in May 2014. A majority of the OSBS site is covered by the Sandhill ecosystem, which contains a very high diversity of vegetation species and is a native habitat for several threatened fauna species. The research presented here investigates ways to analyze the AOP data to map ecosystems at the OSBS site. The research attempts to leverage the high spatial resolution data and study the variability of the data within a ground plot scale along with integrating data from the different sensors. Mathematical features are derived from the data and brought into a decision tree classification algorithm (rpart), in order to create an ecosystem map for the site. The hyperspectral and lidar features serve as proxies for chemical, functional, and structural differences in the vegetation types for each of the ecosystems. K-folds cross validation shows a training accuracy of 91%, a validation accuracy of 78%, and a 66% accuracy using independent ground validation. The results presented here represent an important contribution to utilizing integrated hyperspectral and lidar remote sensing data for ecosystem mapping, by relating the spatial variability of the data within a ground plot scale to a collection of vegetation types that make up a given ecosystem.

Ground-based hyperspectral remote sensing to discriminate biotic stress in cotton crop

Science.gov (United States)

Nigam, Rahul; Kot, Rajsi; Sandhu, Sandeep S.; Bhattacharya, Bimal K.; Chandi, Ravinder S.; Singh, Manjeet; Singh, Jagdish; Manjunath, K. R.

2016-05-01

A large gap exists between the potential yield and the yield realized at the agricultural field. Among the factors contributing towards this yield gap are the biotic stresses that affect the crops growth and development. Severity of infestation of the pests and diseases differs between agroclimatic region, individual crops and seasons within a region. Information about the timing of start of infestation of these diseases and pests with their gradual progress in advance could enable plan necessary pesticide schedule for the season, region on the particular crop against the specific menace expected. This could be enabled by development of region, crop and pest-specific prediction models to forewarn these menaces. In India most (70%) of the land-holding size of farmers average 0.39 ha (some even 20 m x 20 m) and only 1% crop growers holdproblems in its proper assessment and management. Thus, such exercise could be highly time-consuming and labour-intensive for the seventh largest country with difficult terrain, 66% gross cropped area under food crops, lacking in number of skilled manpower and shrinking resources. Remote sensing overcomes such limitations with ability to access all parts of the country and can often achieve a high spatial, temporal and spectral resolution and thus leading to an accurate estimation of area affected. Due to pest and disease stress plants showed different behavior in terms of physiological and morphological changes lead to symptoms such as wilting, curling of leaf, stunned growth, reduction in leaf area due to severe defoliation or chlorosis or necrosis of photosynthetically active parts (Prabhakar et al., 2011; Booteet al., 1983; Aggarwal et al., 2006). Damage evaluation of diseases has been largely done by visual inspections and quantification but visual quantification of plant pest and diseases with accuracy and precision is a tough task. Utilization of remote sensing techniques are based on the assumption that plant pest and disease

Optical Remote Sensing Potentials for Looting Detection

Directory of Open Access Journals (Sweden)

Athos Agapiou

2017-10-01

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

Best practices in Remote Sensing for REDD+

DEFF Research Database (Denmark)

Dons, Klaus; Grogan, Kenneth

2012-01-01

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

Portable, remotely operated, computer-controlled, quadrupole mass spectrometer for field use

International Nuclear Information System (INIS)

Friesen, R.D.; Newton, J.C.; Smith, C.F.

1982-04-01

A portable, remote-controlled mass spectrometer was required at the Nevada Test Site to analyze prompt post-event gas from the nuclear cavity in support of the underground testing program. A Balzers QMG-511 quadrupole was chosen for its ability to be interfaced to a DEC LSI-11 computer and to withstand the ground movement caused by this field environment. The inlet system valves, the pumps, the pressure and temperature transducers, and the quadrupole mass spectrometer are controlled by a read-only-memory-based DEC LSI-11/2 with a high-speed microwave link to the control point which is typically 30 miles away. The computer at the control point is a DEC LSI-11/23 running the RSX-11 operating system. The instrument was automated as much as possible because the system is run by inexperienced operators at times. The mass spectrometer has been used on an initial field event with excellent performance. The gas analysis system is described, including automation by a novel computer control method which reduces operator errors and allows dynamic access to the system parameters

Satellite remote sensing in epidemiological studies.

Science.gov (United States)

Sorek-Hamer, Meytar; Just, Allan C; Kloog, Itai

2016-04-01

Particulate matter air pollution is a ubiquitous exposure linked with multiple adverse health outcomes for children and across the life course. The recent development of satellite-based remote-sensing models for air pollution enables the quantification of these risks and addresses many limitations of previous air pollution research strategies. We review the recent literature on the applications of satellite remote sensing in air quality research, with a focus on their use in epidemiological studies. Aerosol optical depth (AOD) is a focus of this review and a significant number of studies show that ground-level particulate matter can be estimated from columnar AOD. Satellite measurements have been found to be an important source of data for particulate matter model-based exposure estimates, and recently have been used in health studies to increase the spatial breadth and temporal resolution of these estimates. It is suggested that satellite-based models improve our understanding of the spatial characteristics of air quality. Although the adoption of satellite-based measures of air quality in health studies is in its infancy, it is rapidly growing. Nevertheless, further investigation is still needed in order to have a better understanding of the AOD contribution to these prediction models in order to use them with higher accuracy in epidemiological studies.

Uncertainty of Forest Biomass Estimates in North Temperate Forests Due to Allometry: Implications for Remote Sensing

Directory of Open Access Journals (Sweden)

Razi Ahmed

2013-06-01

Full Text Available Estimates of above ground biomass density in forests are crucial for refining global climate models and understanding climate change. Although data from field studies can be aggregated to estimate carbon stocks on global scales, the sparsity of such field data, temporal heterogeneity and methodological variations introduce large errors. Remote sensing measurements from spaceborne sensors are a realistic alternative for global carbon accounting; however, the uncertainty of such measurements is not well known and remains an active area of research. This article describes an effort to collect field data at the Harvard and Howland Forest sites, set in the temperate forests of the Northeastern United States in an attempt to establish ground truth forest biomass for calibration of remote sensing measurements. We present an assessment of the quality of ground truth biomass estimates derived from three different sets of diameter-based allometric equations over the Harvard and Howland Forests to establish the contribution of errors in ground truth data to the error in biomass estimates from remote sensing measurements.

Uniform competency-based local feature extraction for remote sensing images

Science.gov (United States)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

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

UAS-Borne Photogrammetry for Surface Topographic Characterization: A Ground-Truth Baseline for Future Change Detection and Refinement of Scaled Remotely-Sensed Datasets

Science.gov (United States)

Coppersmith, R.; Schultz-Fellenz, E. S.; Sussman, A. J.; Vigil, S.; Dzur, R.; Norskog, K.; Kelley, R.; Miller, L.

2015-12-01

While long-term objectives of monitoring and verification regimes include remote characterization and discrimination of surficial geologic and topographic features at sites of interest, ground truth data is required to advance development of remote sensing techniques. Increasingly, it is desirable for these ground-based or ground-proximal characterization methodologies to be as nimble, efficient, non-invasive, and non-destructive as their higher-altitude airborne counterparts while ideally providing superior resolution. For this study, the area of interest is an alluvial site at the Nevada National Security Site intended for use in the Source Physics Experiment's (Snelson et al., 2013) second phase. Ground-truth surface topographic characterization was performed using a DJI Inspire 1 unmanned aerial system (UAS), at very low altitude (clouds. Within the area of interest, careful installation of surveyed ground control fiducial markers supplied necessary targets for field collection, and information for model georectification. The resulting model includes a Digital Elevation Model derived from 2D imagery. It is anticipated that this flexible and versatile characterization process will provide point cloud data resolution equivalent to a purely ground-based LiDAR scanning deployment (e.g., 1-2cm horizontal and vertical resolution; e.g., Sussman et al., 2012; Schultz-Fellenz et al., 2013). In addition to drastically increasing time efficiency in the field, the UAS method also allows for more complete coverage of the study area when compared to ground-based LiDAR. Comparison and integration of these data with conventionally-acquired airborne LiDAR data from a higher-altitude (~ 450m) platform will aid significantly in the refinement of technologies and detection capabilities of remote optical systems to identify and detect surface geologic and topographic signatures of interest. This work includes a preliminary comparison of surface signatures detected from varying

Predictive modeling of hazardous waste landfill total above-ground biomass using passive optical and LIDAR remotely sensed data

Science.gov (United States)

Hadley, Brian Christopher

This dissertation assessed remotely sensed data and geospatial modeling technique(s) to map the spatial distribution of total above-ground biomass present on the surface of the Savannah River National Laboratory's (SRNL) Mixed Waste Management Facility (MWMF) hazardous waste landfill. Ordinary least squares (OLS) regression, regression kriging, and tree-structured regression were employed to model the empirical relationship between in-situ measured Bahia (Paspalum notatum Flugge) and Centipede [Eremochloa ophiuroides (Munro) Hack.] grass biomass against an assortment of explanatory variables extracted from fine spatial resolution passive optical and LIDAR remotely sensed data. Explanatory variables included: (1) discrete channels of visible, near-infrared (NIR), and short-wave infrared (SWIR) reflectance, (2) spectral vegetation indices (SVI), (3) spectral mixture analysis (SMA) modeled fractions, (4) narrow-band derivative-based vegetation indices, and (5) LIDAR derived topographic variables (i.e. elevation, slope, and aspect). Results showed that a linear combination of the first- (1DZ_DGVI), second- (2DZ_DGVI), and third-derivative of green vegetation indices (3DZ_DGVI) calculated from hyperspectral data recorded over the 400--960 nm wavelengths of the electromagnetic spectrum explained the largest percentage of statistical variation (R2 = 0.5184) in the total above-ground biomass measurements. In general, the topographic variables did not correlate well with the MWMF biomass data, accounting for less than five percent of the statistical variation. It was concluded that tree-structured regression represented the optimum geospatial modeling technique due to a combination of model performance and efficiency/flexibility factors.

Real-Time and Seamless Monitoring of Ground-Level PM2.5 Using Satellite Remote Sensing

Science.gov (United States)

Li, Tongwen; Zhang, Chengyue; Shen, Huanfeng; Yuan, Qiangqiang; Zhang, Liangpei

2018-04-01

Satellite remote sensing has been reported to be a promising approach for the monitoring of atmospheric PM2.5. However, the satellite-based monitoring of ground-level PM2.5 is still challenging. First, the previously used polar-orbiting satellite observations, which can be usually acquired only once per day, are hard to monitor PM2.5 in real time. Second, many data gaps exist in satellitederived PM2.5 due to the cloud contamination. In this paper, the hourly geostationary satellite (i.e., Harawari-8) observations were adopted for the real-time monitoring of PM2.5 in a deep learning architecture. On this basis, the satellite-derived PM2.5 in conjunction with ground PM2.5 measurements are incorporated into a spatio-temporal fusion model to fill the data gaps. Using Wuhan Urban Agglomeration as an example, we have successfully derived the real-time and seamless PM2.5 distributions. The results demonstrate that Harawari-8 satellite-based deep learning model achieves a satisfactory performance (out-of-sample cross-validation R2 = 0.80, RMSE = 17.49 μg/m3) for the estimation of PM2.5. The missing data in satellite-derive PM2.5 are accurately recovered, with R2 between recoveries and ground measurements of 0.75. Overall, this study has inherently provided an effective strategy for the realtime and seamless monitoring of ground-level PM2.5.

Remote Sensing Terminology in a Global and Knowledge-Based World

Science.gov (United States)

Kancheva, Rumiana

The paper is devoted to terminology issues related to all aspects of remote sensing research and applications. Terminology is the basis for a better understanding among people. It is crucial to keep up with the latest developments and novelties of the terminology in advanced technology fields such as aerospace science and industry. This is especially true in remote sensing and geoinformatics which develop rapidly and have ever extending applications in various domains of science and human activities. Remote sensing terminology issues are directly relevant to the contemporary worldwide policies on information accessibility, dissemination and utilization of research results in support of solutions to global environmental challenges and sustainable development goals. Remote sensing and spatial information technologies are an integral part of the international strategies for cooperation in scientific, research and application areas with a particular accent on environmental monitoring, ecological problems natural resources management, climate modeling, weather forecasts, disaster mitigation and many others to which remote sensing data can be put. Remote sensing researchers, professionals, students and decision makers of different counties and nationalities should fully understand, interpret and translate into their native language any term, definition or acronym found in papers, books, proceedings, specifications, documentation, and etc. The importance of the correct use, precise definition and unification of remote sensing terms refers not only to people working in this field but also to experts in a variety of disciplines who handle remote sensing data and information products. In this paper, we draw the attention on the specifics, peculiarities and recent needs of compiling specialized dictionaries in the area of remote sensing focusing on Earth observations and the integration of remote sensing with other geoinformation technologies such as photogrammetry, geodesy

Remote sensing in the coming decade: the vision and the reality

Science.gov (United States)

Gail, William B.

2006-08-01

Investment in understanding the Earth pays off twice. It enables pursuit of scientific questions that rank among the most interesting and profound of our time. It also serves society's practical need for increased prosperity and security. Over the last half-century, we have built a sophisticated network of satellites, aircraft, and ground-based remote sensing systems to provide the raw information from which we derive Earth knowledge. This network has served us well in the development of science and the provision of operational services. In the next decade, the demand for such information will grow dramatically. New remote sensing capabilities will emerge. Rapid evolution of Internet geospatial and location-based services will make communication and sharing of Earth knowledge much easier. Governments, businesses, and consumers will all benefit. But this exciting future is threatened from many directions. Risks range from technology and market uncertainties in the private sector to budget cuts and project setbacks in the public sector. The coming decade will see a dramatic confrontation between the vision of what needs to be accomplished in Earth remote sensing and the reality of our resources and commitment. The outcome will have long-term implications for both the remote sensing community and society as a whole.

Remote Sensing of Irrigated Agriculture: Opportunities and Challenges

Directory of Open Access Journals (Sweden)

Chelsea Cervantes

2010-09-01

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

A comparision between satellite based and drone based remote sensing technology to achieve sustainable development: a review

Directory of Open Access Journals (Sweden)

Babankumar Bansod

2017-12-01

Full Text Available Precision agriculture is a way to manage the crop yield resources like water, fertilizers, soil, seeds in order to increase production, quality, gain and reduce squander products so that the existing system become eco-friendly. The main target of precision agriculture is to match resources and execution according to the crop and climate to ameliorate the effects of Praxis. Global Positioning System, Geographic Information System, Remote sensing technologies and various sensors are used in Precision farming for identifying the variability in field and using different methods to deal with them. Satellite based remote sensing is used to study the variability in crop and ground but suffer from various disadvantageous such as prohibited use, high price, less revisiting them, poor resolution due to great height, Unmanned Aerial Vehicle (UAV is other alternative option for application in precision farming. UAV overcomes the drawback of the ground based system, i.e. inaccessibility to muddy and very dense regions. Hovering at a peak of 500 meter - 1000 meter is good enough to offer various advantageous in image acquisition such as high spatial and temporal resolution, full flexibility, low cost. Recent studies of application of UAV in precision farming indicate advanced designing of UAV, enhancement in georeferencing and the mosaicking of image, analysis and extraction of information required for supplying a true end product to farmers. This paper also discusses the various platforms of UAV used in farming applications, its technical constraints, seclusion rites, reliability and safety.

Status of the undisturbed mangroves at Brunei Bay, East Malaysia: a preliminary assessment based on remote sensing and ground-truth observations

Directory of Open Access Journals (Sweden)

Behara Satyanarayana

2018-02-01

Full Text Available Brunei Bay, which receives freshwater discharge from four major rivers, namely Limbang, Sundar, Weston and Menumbok, hosts a luxuriant mangrove cover in East Malaysia. However, this relatively undisturbed mangrove forest has been less scientifically explored, especially in terms of vegetation structure, ecosystem services and functioning, and land-use/cover changes. In the present study, mangrove areal extent together with species composition and distribution at the four notified estuaries was evaluated through remote sensing (Advanced Land Observation Satellite—ALOS and ground-truth (Point-Centred Quarter Method—PCQM observations. As of 2010, the total mangrove cover was found to be ca. 35,183.74 ha, of which Weston and Menumbok occupied more than two-folds (58%, followed by Sundar (27% and Limbang (15%. The medium resolution ALOS data were efficient for mapping dominant mangrove species such as Nypa fruticans, Rhizophora apiculata, Sonneratia caseolaris, S. alba and Xylocarpus granatum in the vicinity (accuracy: 80%. The PCQM estimates found a higher basal area at Limbang and Menumbok—suggestive of more mature vegetation, compared to Sundar and Weston. Mangrove stand structural complexity (derived from the complexity index was also high in the order of Limbang > Menumbok > Sundar > Weston and supporting the perspective of less/undisturbed vegetation at two former locations. Both remote sensing and ground-truth observations have complementarily represented the distribution of Sonneratia spp. as pioneer vegetation at shallow river mouths, N. fruticans in the areas of strong freshwater discharge, R. apiculata in the areas of strong neritic incursion and X. granatum at interior/elevated grounds. The results from this study would be able to serve as strong baseline data for future mangrove investigations at Brunei Bay, including for monitoring and management purposes locally at present.

Status of the undisturbed mangroves at Brunei Bay, East Malaysia: a preliminary assessment based on remote sensing and ground-truth observations

Science.gov (United States)

Izzaty Horsali, Nurul Amira; Mat Zauki, Nurul Ashikin; Otero, Viviana; Nadzri, Muhammad Izuan; Ibrahim, Sulong; Husain, Mohd-Lokman; Dahdouh-Guebas, Farid

2018-01-01

Brunei Bay, which receives freshwater discharge from four major rivers, namely Limbang, Sundar, Weston and Menumbok, hosts a luxuriant mangrove cover in East Malaysia. However, this relatively undisturbed mangrove forest has been less scientifically explored, especially in terms of vegetation structure, ecosystem services and functioning, and land-use/cover changes. In the present study, mangrove areal extent together with species composition and distribution at the four notified estuaries was evaluated through remote sensing (Advanced Land Observation Satellite—ALOS) and ground-truth (Point-Centred Quarter Method—PCQM) observations. As of 2010, the total mangrove cover was found to be ca. 35,183.74 ha, of which Weston and Menumbok occupied more than two-folds (58%), followed by Sundar (27%) and Limbang (15%). The medium resolution ALOS data were efficient for mapping dominant mangrove species such as Nypa fruticans, Rhizophora apiculata, Sonneratia caseolaris, S. alba and Xylocarpus granatum in the vicinity (accuracy: 80%). The PCQM estimates found a higher basal area at Limbang and Menumbok—suggestive of more mature vegetation, compared to Sundar and Weston. Mangrove stand structural complexity (derived from the complexity index) was also high in the order of Limbang > Menumbok > Sundar > Weston and supporting the perspective of less/undisturbed vegetation at two former locations. Both remote sensing and ground-truth observations have complementarily represented the distribution of Sonneratia spp. as pioneer vegetation at shallow river mouths, N. fruticans in the areas of strong freshwater discharge, R. apiculata in the areas of strong neritic incursion and X. granatum at interior/elevated grounds. The results from this study would be able to serve as strong baseline data for future mangrove investigations at Brunei Bay, including for monitoring and management purposes locally at present. PMID:29479500

Status of the undisturbed mangroves at Brunei Bay, East Malaysia: a preliminary assessment based on remote sensing and ground-truth observations.

Science.gov (United States)

Satyanarayana, Behara; M Muslim, Aidy; Izzaty Horsali, Nurul Amira; Mat Zauki, Nurul Ashikin; Otero, Viviana; Nadzri, Muhammad Izuan; Ibrahim, Sulong; Husain, Mohd-Lokman; Dahdouh-Guebas, Farid

2018-01-01

Brunei Bay, which receives freshwater discharge from four major rivers, namely Limbang, Sundar, Weston and Menumbok, hosts a luxuriant mangrove cover in East Malaysia. However, this relatively undisturbed mangrove forest has been less scientifically explored, especially in terms of vegetation structure, ecosystem services and functioning, and land-use/cover changes. In the present study, mangrove areal extent together with species composition and distribution at the four notified estuaries was evaluated through remote sensing (Advanced Land Observation Satellite-ALOS) and ground-truth (Point-Centred Quarter Method-PCQM) observations. As of 2010, the total mangrove cover was found to be ca. 35,183.74 ha, of which Weston and Menumbok occupied more than two-folds (58%), followed by Sundar (27%) and Limbang (15%). The medium resolution ALOS data were efficient for mapping dominant mangrove species such as Nypa fruticans , Rhizophora apiculata , Sonneratia caseolaris , S. alba and Xylocarpus granatum in the vicinity (accuracy: 80%). The PCQM estimates found a higher basal area at Limbang and Menumbok-suggestive of more mature vegetation, compared to Sundar and Weston. Mangrove stand structural complexity (derived from the complexity index) was also high in the order of Limbang > Menumbok > Sundar > Weston and supporting the perspective of less/undisturbed vegetation at two former locations. Both remote sensing and ground-truth observations have complementarily represented the distribution of Sonneratia spp. as pioneer vegetation at shallow river mouths, N. fruticans in the areas of strong freshwater discharge, R. apiculata in the areas of strong neritic incursion and X. granatum at interior/elevated grounds. The results from this study would be able to serve as strong baseline data for future mangrove investigations at Brunei Bay, including for monitoring and management purposes locally at present.

ANALYSIS OF COMBINED UAV-BASED RGB AND THERMAL REMOTE SENSING DATA: A NEW APPROACH TO CROWD MONITORING

Directory of Open Access Journals (Sweden)

S. Schulte

2017-08-01

Full Text Available Collecting vast amount of data does not solely help to fulfil information needs related to crowd monitoring, it is rather important to collect data that is suitable to meet specific information requirements. In order to address this issue, a prototype is developed to facilitate the combination of UAV-based RGB and thermal remote sensing datasets. In an experimental approach, image sensors were mounted on a remotely piloted aircraft and captured two video datasets over a crowd. A group of volunteers performed diverse movements that depict real world scenarios. The prototype is deriving the movement on the ground and is programmed in MATLAB. This novel detection approach using combined data is afterwards evaluated against detection algorithms that only use a single data source. Our tests show that the combination of RGB and thermal remote sensing data is beneficial for the field of crowd monitoring regarding the detection of crowd movement.

Using optical remote sensing model to estimate oil slick thickness based on satellite image

International Nuclear Information System (INIS)

Lu, Y C; Tian, Q J; Lyu, C G; Fu, W X; Han, W C

2014-01-01

An optical remote sensing model has been established based on two-beam interference theory to estimate marine oil slick thickness. Extinction coefficient and normalized reflectance of oil are two important parts in this model. Extinction coefficient is an important inherent optical property and will not vary with the background reflectance changed. Normalized reflectance can be used to eliminate the background differences between in situ measured spectra and remotely sensing image. Therefore, marine oil slick thickness and area can be estimated and mapped based on optical remotely sensing image and extinction coefficient

Integration of remotely sensed data and hydrodynamic modeling into a GIS to assess the impacts of thermal effluent in an estuary

International Nuclear Information System (INIS)

Mustard, J.F.; Sen, A.; Swanson, C.; Mendelsohn, D.

1997-01-01

A project to assess the effects of thermal effluent from a electrical generating facility on an estuary is described. The project is designed to integrate remote sensing observations with in situ field data and a computer model system to provide continuous, estuary-wide predictions of the effluent plume location and configurations. Remote sensing data was acquired over the ebb tide cycle by an aircraft-mounted multi-channel imaging spectrometer. A ground truth field program measured water temperature on a series of transects during the overflights. Moored instruments continuously acquired data on currents, salinity, temperature and dissolved oxygen before, during and after the day of the overflight. A hydrodynamic model was used to predict the three-dimensional structure of the currents, temperature and salinity in the estuary. A Lagrangian particle-based trajectory model was used to predict the small scale surface features seen in the overflight images. Results indicate that such a system can provide useful data in support of analyses of thermal effects of the ecology of estuarine environments

[Use of Remote Sensing for Crop and Soil Analysis

Science.gov (United States)

Johannsen, Chris J.

1997-01-01

The primary agricultural objective of this research is to determine what soil and crop information can be verified from remotely sensed images during the growing season. Specifically: (1) Elements of crop stress due to drought, weeds, disease and nutrient deficiencies will be documented with ground truth over specific agricultural sites and (2) Use of remote sensing with GPS and GIS technology for providing a safe and environmentally friendly application of fertilizers and chemicals will be documented.

Remote Sensing of Sonoran Desert Vegetation Structure and Phenology with Ground-Based LiDAR

Directory of Open Access Journals (Sweden)

Joel B. Sankey

2014-12-01

Full Text Available Long-term vegetation monitoring efforts have become increasingly important for understanding ecosystem response to global change. Many traditional methods for monitoring can be infrequent and limited in scope. Ground-based LiDAR is one remote sensing method that offers a clear advancement to monitor vegetation dynamics at high spatial and temporal resolution. We determined the effectiveness of LiDAR to detect intra-annual variability in vegetation structure at a long-term Sonoran Desert monitoring plot dominated by cacti, deciduous and evergreen shrubs. Monthly repeat LiDAR scans of perennial plant canopies over the course of one year had high precision. LiDAR measurements of canopy height and area were accurate with respect to total station survey measurements of individual plants. We found an increase in the number of LiDAR vegetation returns following the wet North American Monsoon season. This intra-annual variability in vegetation structure detected by LiDAR was attributable to a drought deciduous shrub Ambrosia deltoidea, whereas the evergreen shrub Larrea tridentata and cactus Opuntia engelmannii had low variability. Benefits of using LiDAR over traditional methods to census desert plants are more rapid, consistent, and cost-effective data acquisition in a high-resolution, 3-dimensional context. We conclude that repeat LiDAR measurements can be an effective method for documenting ecosystem response to desert climatology and drought over short time intervals and at detailed-local spatial scale.

Gamma-ray remote sensing of soil properties in a forested area near Batlow, NSW

International Nuclear Information System (INIS)

Bierwirth, P.N.; Aspin, S.J.; Ryan, P.J.; McKenzie, N.J.

1998-01-01

In forested and agricultural areas, reflective remote sensing methods are of limited utility for soil studies due to the variable effects of vegetation. Airborne gamma-ray remote sensing is presented here as a useful technique for soils. Short wavelength gamma-rays are detected from the upper 0.30-0.45 m of the soil . They are emitted from radioactive elements in the soil and largely pass through vegetation cover. In this paper, images of gamma parent elements (K, Th and U) are presented and element associations with soil properties and vegetation are analysed for a forested area near Batlow, NSW. Effects of vegetation are evident in gamma-ray data and in Landsat TM along powerlines and in clearings. A technique for removing this effect in the gamma-ray data is demonstrated. Detailed soil and rock chemistry together with ground gamma-spectrometer measurements were collected to support the interpretation and analysis of the image data. The work focuses mainly on the variation of soil properties within areas mapped as granodiorite lithology. Many areas of deep red soils are accurately mapped by the radiometric K data. The precise origin of these soils is not clear and their parent materials may include contributions from aeolian deposition, in situ weathering of granodiorite, and remnant basalt. . In areas of granodiorite, K patterns are interpreted to be a function of the degree of mineral weathering and can be related to soil depth and erosion status. This study demonstrates the effectiveness of gamma-ray remote sensing for directly mapping soil units and properties (authors). Copyright (1998) Remote Sensing and Photogrammetry Association of Australasia Ltd

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

Science.gov (United States)

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

2017-06-01

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

Testing the accuracy of remote sensing land use maps

Science.gov (United States)

Vangenderen, J. L.; Lock, B. F.; Vass, P. A.

1977-01-01

Some of the main aspects that need to be considered in a remote sensing sampling design are: (1) the frequency that any one land use type (on the ground) is erroneously attributed to another class by the interpreter; (2) the frequency that the wrong land use (as observed on the ground) is erroneously included in any one class by the remote sensing interpreter; (3) the proportion of all land (as determined in the field) that is mistakenly attributed by the interpreter; and (4) the determination of whether the mistakes are random (so that the overall proportions are approximately correct) or subject to a persistent bias. A sampling and statistical testing procedure is presented which allows an approximate answer to each of these aspects. The concept developed and described incorporates the probability of making incorrect interpretations at particular prescribed accuracy levels, for a certain number of errors, for a particular sample size. It is considered that this approach offers a meaningful explanation of the interpretation accuracy level of an entire remote sensing land use survey.

On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery.

Science.gov (United States)

Qi, Baogui; Shi, Hao; Zhuang, Yin; Chen, He; Chen, Liang

2018-04-25

With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited.

On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery

Science.gov (United States)

Qi, Baogui; Zhuang, Yin; Chen, He; Chen, Liang

2018-01-01

With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited. PMID:29693585

Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake

Directory of Open Access Journals (Sweden)

Shuai Xie

2016-09-01

Full Text Available Remote sensing (RS images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE, Bayes’ theorem and expectation-maximization (EM algorithm are applied to quantitatively estimate the individual error-rate and “ground truth” according to multiple participants’ assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring “ground truth” according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image.

Operational remote sensing of aerosols over land to account for directional effects

International Nuclear Information System (INIS)

Ramon, Didier; Santer, Richard

2001-01-01

The assumption that the ground is a Lambertian reflector is commonly adopted in operational atmospheric corrections of spaceborne sensors. Through a simple modeling of directional effects in radiative transfer following the second simulation of the satellite signal in the solar spectrum (6S) approach, we propose an operational method to account for the departure from Lambertian behavior of a reflector covered by a scattering medium. This method relies on the computation of coupling terms between the reflecting and the scattering media and is able to deal with a two-layer atmosphere. We focus on the difficult problem of aerosol remote sensing over land. One popular sensing method relies on observations over dense dark vegetation, for which the surface reflectance is low and quite well defined in the blue and in the red. Therefore a study was made for three cases: (1) dark vegetation covered by atmospheric aerosols, (2) atmospheric aerosols covered by molecules, and finally (3) dark vegetation covered by atmospheric aerosols covered by molecules. Comparisons of top-of-the-atmosphere reflectances computed with our modeling and reference computations made with the successive-order-of-scattering code show the robustness of the modeling in the blue and in the red for aerosol optical thicknesses as great as 0.6 and solar zenith angles as large as 60 deg. . The model begins to fail only in the blue for large solar zenith angles. The benefits expected for aerosol remote sensing over land are evaluated with an aerosol retrieval scheme developed for the Medium-Resolution Imaging Spectrometer. The main result is a better constraint on the aerosol model with inclusion of directional effects and a weaker effect on the optical thickness of the retrieval aerosol. The directional scheme is then applied to the aerosol remote-sensing problem in actual Indian Remote Sensing Satellite P3/Modular Optoelectronic Scanner images over land and shows significant improvement compared with a

Commercial future: making remote sensing a media event

Science.gov (United States)

Lurie, Ian

1999-12-01

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

Sensing our Environment: Remote sensing in a physics classroom

Science.gov (United States)

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

2017-04-01

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

Remote sensing of stratospheric O{sub 3} and NO{sub 2} using a portable and compact DOAS spectrometer

Energy Technology Data Exchange (ETDEWEB)

Raponi, M M; Wolfram, E; Quel, E J [Division LIDAR, Centro de Investigaciones en Laseres y Aplicaciones, CEILAP (CITEFA-CONICET), Juan B. de La Salle 4397 (B1603ALO), Villa Martelli, Buenos Aires (Argentina); Jimenez, R [Department of Chemical and Environmental Engineering, Universidad Nacional de Colombia, Bogota (Colombia); Tocho, J O, E-mail: mraponi@citefa.gov.ar [Centro de Investigaciones Opticas, CIOp (CONICET La Plata-CIC), Buenos Aires (Argentina)

2011-01-01

The use of passive and active remote sensing systems has largely contributed to advance our understanding of important atmospheric phenomena. Here we present a compact and portable passive DOAS (Differential Optical Absorption Spectroscopy) system, developed for measuring the vertical column density (VCD) of multiple atmospheric trace gases. We highlight the main characteristics of the system components: a mini-spectrometer (HR4000, Ocean Optics), two optical fibers (400 {mu}m of core, 6 m and 25 cm of longitude), an external shutter and the control/data processing software. Nitrogen dioxide (NO{sub 2}) and ozone (O{sub 3}) VCDs are derived from solar spectra acquired during twilights (87{sup 0} - 91{sup 0} zenithal angles) using the DOAS technique. The analysis is carried out by solving the Beer-Lambert-Bouger (BLB) law for the main atmospheric absorbers at selected wavelength ranges. The algorithm minimizes the fitting residuals to the BLB law, having as unknown the slant column density (SCD) of the species to determine. We present measurements carried out at the Marambio Antarctic Base (64{sup 0} 14' 25'' S; 56{sup 0} 37' 21'' W, 197 m asl) during January - February 2008. In addition, we compare our results with co-located measurements performed with EVA, a visible absorption spectrometer of Instituto Nacional de Tecnica Aeroespacial (INTA, Spain), a Dobson spectrophotometer of Servicio Meteorologico Nacional (SMN, Argentine) and the Ozone Monitoring Instrument (OMI), on board AURA satellite.

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

Directory of Open Access Journals (Sweden)

ZHANG Zhiqiang

2018-01-01

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

Prioritization of catchments based on soil erosion using remote sensing and GIS.

Science.gov (United States)

Khadse, Gajanan K; Vijay, Ritesh; Labhasetwar, Pawan K

2015-06-01

Water and soil are the most essential natural resources for socioeconomic development and sustenance of life. A study of soil and water dynamics at a watershed level facilitates a scientific approach towards their conservation and management. Remote sensing and Geographic Information System are tools that help to plan and manage natural resources on watershed basis. Studies were conducted for the formulation of catchment area treatment plan based on watershed prioritization with soil erosion studies using remote sensing techniques, corroborated with Geographic Information System (GIS), secondary data and ground truth information. Estimation of runoff and sediment yield is necessary in prioritization of catchment for the design of soil conservation structures and for identifying the critical erosion-prone areas of a catchment for implementation of best management plan with limited resources. The Universal Soil Loss Equation, Sediment Yield Determination and silt yield index methods are used for runoff and soil loss estimation for prioritization of the catchments. On the basis of soil erosion classes, the watersheds were grouped into very high, high, moderate and low priorities. High-priority watersheds need immediate attention for soil and water conservation, whereas low-priority watershed having good vegetative cover and low silt yield index may not need immediate attention for such treatments.

A Web-Based Airborne Remote Sensing Telemetry Server, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — A Web-based Airborne Remote Sensing Telemetry Server (WARSTS) is proposed to integrate UAV telemetry and web-technology into an innovative communication, command,...

Assessment of the use of remotely sensed rainfall products for runoff ...

African Journals Online (AJOL)

The main objective of this research is to compare the performance of SWAT model using rainfall input data from remotely sensed and ground measured data for Gilgel abbay catchment. Based on the results obtained, it can be said that SWAT model yields good results for the satellite rainfall input data when compared to in ...

Introduction to remote sensing

CERN Document Server

Cracknell, Arthur P

2007-01-01

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

The effect of spectroscopic parameter inaccuracies on ground-based millimeter wave remote sensing of the atmosphere

International Nuclear Information System (INIS)

Ryan, Niall J.; Walker, Kaley A.

2015-01-01

A sensitivity study was performed to assess the impact that uncertainties in the spectroscopic parameters of atmospheric species have on the retrieval of gas concentrations using the 265–280 GHz region of the electromagnetic spectrum. Errors in the retrieval of O 3 , N 2 O, HNO 3 , and ClO from spectra measured by ground-based radiometers were investigated. The goal of the study was to identify the spectroscopic parameters of these target species, and other interfering species, available in the JPL and HITRAN 2008 catalogues, which contribute the largest error to retrieved atmospheric concentration profiles in order to provide recommendations for new laboratory measurements. The parameters investigated were the line position, line strength, broadening coefficients and their temperature dependence, and pressure shift. Uncertainties in the air broadening coefficients of gases tend to contribute the largest error to retrieved atmospheric concentration profiles. For O 3 and N 2 O, gases with relatively strong spectral signatures, the retrieval is sensitive to uncertainties in the parameters of the main spectral line that is observed. For HNO 3 , the uncertainties in many closely spaced HNO 3 lines can cause large errors in the retrieved profile, and for ClO, the error in the profile is dominated by uncertainties in nearby, stronger O 3 lines. Fourteen spectroscopic parameters are identified, for which updated measurements would have the most impact on the accuracy of ground-based remote sensing of the target species at 265–280 GHz. - Highlights: • The sensitivity of retrievals to spectroscopic parameters is assessed. • Air broadening parameters contribute the most to the error budget. • O 3 and N 2 O retrievals are sensitive to parameters of the target spectral lines. • Many HNO 3 lines in close proximity can cause large errors in HNO 3 retrievals. • ClO retrievals are sensitive to uncertainties in parameters of nearby O 3 lines

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

Science.gov (United States)

1991-01-01

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

Development of a remote sensing-based rice yield forecasting model

Energy Technology Data Exchange (ETDEWEB)

Mosleh, M.K.; Hassan, Q.K.; Chowdhury, E.H.

2016-11-01

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh. (Author)

Optical remote sensing

CERN Document Server

Prasad, Saurabh; Chanussot, Jocelyn

2011-01-01

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

What is a picture worth? A history of remote sensing

Science.gov (United States)

Moore, Gerald K.

1979-01-01

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

Remote sensing image ship target detection method based on visual attention model

Science.gov (United States)

Sun, Yuejiao; Lei, Wuhu; Ren, Xiaodong

2017-11-01

The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.

High Data Rate Satellite Communications for Environmental Remote Sensing

Science.gov (United States)

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

2014-12-01

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

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

Science.gov (United States)

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.

A tool for NDVI time series extraction from wide-swath remotely sensed images

Science.gov (United States)

Li, Zhishan; Shi, Runhe; Zhou, Cong

2015-09-01

Normalized Difference Vegetation Index (NDVI) is one of the most widely used indicators for monitoring the vegetation coverage in land surface. The time series features of NDVI are capable of reflecting dynamic changes of various ecosystems. Calculating NDVI via Moderate Resolution Imaging Spectrometer (MODIS) and other wide-swath remotely sensed images provides an important way to monitor the spatial and temporal characteristics of large-scale NDVI. However, difficulties are still existed for ecologists to extract such information correctly and efficiently because of the problems in several professional processes on the original remote sensing images including radiometric calibration, geometric correction, multiple data composition and curve smoothing. In this study, we developed an efficient and convenient online toolbox for non-remote sensing professionals who want to extract NDVI time series with a friendly graphic user interface. It is based on Java Web and Web GIS technically. Moreover, Struts, Spring and Hibernate frameworks (SSH) are integrated in the system for the purpose of easy maintenance and expansion. Latitude, longitude and time period are the key inputs that users need to provide, and the NDVI time series are calculated automatically.

Overview of Ground Air Quality Measurements and Their Links to Airborne, Remote Sensing and Model Studies during the KORUS-AQ Campaign

Science.gov (United States)

Lee, G.; Ahn, J. Y.; Chang, L. S.; Kim, J.; Park, R.

2017-12-01

During the KORUS-AQ, extensive sets of chemical measurements for reactive gases and aerosol species were made at 3 major sites on upwind island (Baengyeong Island), urban (Olympic Park in Seoul) and downwind rural forest location (Taewha Forest). Also, intensive aerosol size and composition observations from 5 NIER super sites, 3 NIMR monitoring sites, and 5 other university sites were currently facilitated in the KORUS-AQ data set. In addition, air quality criteria species data from 264 nation-wide ground monitoring sites with 5 minute temporal resolution during the whole campaign period were supplemented to cover mostly in densely populated urban areas, but sparsely in rural areas. The specific objectives of these ground sites were to provide highly comprehensive data set to coordinate the close collaborations among other research platforms including airborne measurements, remote sensing, and model studies. The continuous measurements at ground sites were well compared with repetitive low-level aircraft observations of NASA's DC-8 over Olympic Park and Taewha Forest site. Similarly, many ground measurements enabled the validation of chemical transport models and the remote sensing observations from ground and NASA's King Air. The observed results from inter-comparison studies in many reactive gases and aerosol compositions between different measurement methods and platforms will be presented. Compiling data sets from ground sites, source-wise analysis for ozone and aerosol, their in-situ formations, and transport characteristics by local/regional circulation will be discussed, too.

Importance of Surface Texture to Infrared Remote Sensing Interpretations

Science.gov (United States)

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

2001-11-01

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

Rangeland monitoring using remote sensing: comparison of cover estimates from field measurements and image analysis

Directory of Open Access Journals (Sweden)

Ammon Boswell

2017-01-01

Full Text Available Rangeland monitoring is important for evaluating and assessing semi-arid plant communities. Remote sensing provides an effective tool for rapidly and accurately assessing rangeland vegetation and other surface attributes such as bare soil and rock. The purpose of this study was to evaluate the efficacy of remote sensing as a surrogate for field-based sampling techniques in detecting ground cover features (i.e., trees, shrubs, herbaceous cover, litter, surface, and comparing results with field-based measurements collected by the Utah Division of Wildlife Resources Range Trent Program. In the field, five 152 m long transects were used to sample plant, litter, rock, and bare-ground cover using the Daubenmire ocular estimate method. At the same location of each field plot, a 4-band (R,G,B,NIR, 25 cm pixel resolution, remotely sensed image was taken from a fixed-wing aircraft. Each image was spectrally classified producing 4 cover classes (tree, shrub, herbaceous, surface. No significant differences were detected between canopy cover collected remotely and in the field for tree (P = 0.652, shrub (P = 0.800, and herbaceous vegetation (P = 0.258. Surface cover was higher in field plots (P < 0.001, likely in response to the methods used to sample surface features by field crews. Accurately classifying vegetation and other features from remote sensed information can improve the efficiency of collecting vegetation and surface data. This information can also be used to improve data collection frequency for rangeland monitoring and to efficiently quantify ecological succession patterns.

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

Science.gov (United States)

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

Remote sensing of natural phenomena

Directory of Open Access Journals (Sweden)

Miodrag D. Regodić

2014-06-01

monitoring natural phenomena The images taken from Remote Sensing have helped men to use the environment and natural resources in a better way. It is expected that the developement of new technologies will spread the usage of satellite images for the welfare of mankind as well.  Besides monitoring the surface of the Earth, the satellite monitoring of  the processes inside the Earth itself is of great importance since these processes can  cause different catastrophes such as earthquakes, volcano eruptions, floods, etc. Usage of satellite images in monitoring atmospheric phenomena The launch of artificial earth satellites has opened new possibilities for monitoring and studying atmospheric phenomena. A large number of meteorological satellites have been launched by now (Nimbus, Meteor, SNS, ESSA, Meteosat, Terra, etc.. Since these images are primarily used for weather forecast, meteorologists use them to get information about the characteristics of clouds related to their temperature, the temperature of the cloud layer, the degree of cloudness, the profiles of humidity content, the wind parameters, etc. Meteosat satellites Meteosat is the first European geostationary satellite designed for meteorological research. The use of these satellites enabled the surveying in the visible and the near IR part of the spectrum as well as in the infrared thermal and water steam track. Based on these images, it was possible to obtain data such as:  height of clouds, cloud spreading and moving, sea surface temperature, speed of wind, distribution of the water steam, balance of radiation, etc. Usage of satellite images in monitoring floods Satellite images are an excellent background and an initial phase for preventing severe catastrophic events caused by floods. Due to satellite images, it is possible to manage overflown regions before, during and after floods. This enables prevention, forecasting, detection and elimination of consequences, i.e. demage. Satellite images are of great help

Satellite Remote Sensing: Aerosol Measurements

Science.gov (United States)

Kahn, Ralph A.

2013-01-01

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

Use of UAV-Borne Spectrometer for Land Cover Classification

Directory of Open Access Journals (Sweden)

Sowmya Natesan

2018-04-01

Full Text Available Unmanned aerial vehicles (UAV are being used for low altitude remote sensing for thematic land classification using visible light and multi-spectral sensors. The objective of this work was to investigate the use of UAV equipped with a compact spectrometer for land cover classification. The UAV platform used was a DJI Flamewheel F550 hexacopter equipped with GPS and Inertial Measurement Unit (IMU navigation sensors, and a Raspberry Pi processor and camera module. The spectrometer used was the FLAME-NIR, a near-infrared spectrometer for hyperspectral measurements. RGB images and spectrometer data were captured simultaneously. As spectrometer data do not provide continuous terrain coverage, the locations of their ground elliptical footprints were determined from the bundle adjustment solution of the captured images. For each of the spectrometer ground ellipses, the land cover signature at the footprint location was determined to enable the characterization, identification, and classification of land cover elements. To attain a continuous land cover classification map, spatial interpolation was carried out from the irregularly distributed labeled spectrometer points. The accuracy of the classification was assessed using spatial intersection with the object-based image classification performed using the RGB images. Results show that in homogeneous land cover, like water, the accuracy of classification is 78% and in mixed classes, like grass, trees and manmade features, the average accuracy is 50%, thus, indicating the contribution of hyperspectral measurements of low altitude UAV-borne spectrometers to improve land cover classification.

Unmanned Aerial Systems and Spectroscopy for Remote Sensing Applications in Archaeology

Science.gov (United States)

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

2015-04-01

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

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

Science.gov (United States)

Sofina, N.; Ehlers, M.

2012-08-01

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

Using ontological inference and hierarchical matchmaking to overcome semantic heterogeneity in remote sensing-based biodiversity monitoring

Science.gov (United States)

Nieland, Simon; Kleinschmit, Birgit; Förster, Michael

2015-05-01

Ontology-based applications hold promise in improving spatial data interoperability. In this work we use remote sensing-based biodiversity information and apply semantic formalisation and ontological inference to show improvements in data interoperability/comparability. The proposed methodology includes an observation-based, "bottom-up" engineering approach for remote sensing applications and gives a practical example of semantic mediation of geospatial products. We apply the methodology to three different nomenclatures used for remote sensing-based classification of two heathland nature conservation areas in Belgium and Germany. We analysed sensor nomenclatures with respect to their semantic formalisation and their bio-geographical differences. The results indicate that a hierarchical and transparent nomenclature is far more important for transferability than the sensor or study area. The inclusion of additional information, not necessarily belonging to a vegetation class description, is a key factor for the future success of using semantics for interoperability in remote sensing.

Potential for using remote sensing to estimate carbon fluxes across northern peatlands - A review.

Science.gov (United States)

Lees, K J; Quaife, T; Artz, R R E; Khomik, M; Clark, J M

2018-02-15

Peatlands store large amounts of terrestrial carbon and any changes to their carbon balance could cause large changes in the greenhouse gas (GHG) balance of the Earth's atmosphere. There is still much uncertainty about how the GHG dynamics of peatlands are affected by climate and land use change. Current field-based methods of estimating annual carbon exchange between peatlands and the atmosphere include flux chambers and eddy covariance towers. However, remote sensing has several advantages over these traditional approaches in terms of cost, spatial coverage and accessibility to remote locations. In this paper, we outline the basic principles of using remote sensing to estimate ecosystem carbon fluxes and explain the range of satellite data available for such estimations, considering the indices and models developed to make use of the data. Past studies, which have used remote sensing data in comparison with ground-based calculations of carbon fluxes over Northern peatland landscapes, are discussed, as well as the challenges of working with remote sensing on peatlands. Finally, we suggest areas in need of future work on this topic. We conclude that the application of remote sensing to models of carbon fluxes is a viable research method over Northern peatlands but further work is needed to develop more comprehensive carbon cycle models and to improve the long-term reliability of models, particularly on peatland sites undergoing restoration. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Remotely Sensed Active Layer Thickness (ReSALT at Barrow, Alaska Using Interferometric Synthetic Aperture Radar

Directory of Open Access Journals (Sweden)

Kevin Schaefer

2015-03-01

Full Text Available Active layer thickness (ALT is a critical parameter for monitoring the status of permafrost that is typically measured at specific locations using probing, in situ temperature sensors, or other ground-based observations. Here we evaluated the Remotely Sensed Active Layer Thickness (ReSALT product that uses the Interferometric Synthetic Aperture Radar technique to measure seasonal surface subsidence and infer ALT around Barrow, Alaska. We compared ReSALT with ground-based ALT obtained using probing and calibrated, 500 MHz Ground Penetrating Radar at multiple sites around Barrow. ReSALT accurately reproduced observed ALT within uncertainty of the GPR and probing data in ~76% of the study area. However, ReSALT was less than observed ALT in ~22% of the study area with well-drained soils and in ~1% of the area where soils contained gravel. ReSALT was greater than observed ALT in some drained thermokarst lake basins representing ~1% of the area. These results indicate remote sensing techniques based on InSAR could be an effective way to measure and monitor ALT over large areas on the Arctic coastal plain.

Apollo 16 landing site: Summary of earth based remote sensing data, part W

Science.gov (United States)

Zisk, S. H.; Masursky, H.; Milton, D. J.; Schaber, G. G.; Shorthill, R. W.; Thompson, T. W.

1972-01-01

Infrared and radar studies of the Apollo 16 landing site are summarized. Correlations and comparisons between earth based remote sensing data, IR observations, and other data are discussed in detail. Remote sensing studies were devoted to solving two problems: (1) determining the physical difference between Cayley and Descartes geologic units near the landing site; and (2) determining the nature of the bright unit of Descartes mountain material.

Assessing diversity of prairie plants using remote sensing

Science.gov (United States)

Gamon, J. A.; Wang, R.

2017-12-01

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

Laser long-range remote-sensing program experimental results

Science.gov (United States)

Highland, Ronald G.; Shilko, Michael L.; Fox, Marsha J.; Gonglewski, John D.; Czyzak, Stanley R.; Dowling, James A.; Kelly, Brian; Pierrottet, Diego F.; Ruffatto, Donald; Loando, Sharon; Matsuura, Chris; Senft, Daniel C.; Finkner, Lyle; Rae, Joe; Gallegos, Joe

1995-12-01

A laser long range remote sensing (LRS) program is being conducted by the United States Air Force Phillips Laboratory (AF/PL). As part of this program, AF/PL is testing the feasibility of developing a long path CO(subscript 2) laser-based DIAL system for remote sensing. In support of this program, the AF/PL has recently completed an experimental series using a 21 km slant- range path (3.05 km ASL transceiver height to 0.067 km ASL target height) at its Phillips Laboratory Air Force Maui Optical Station (AMOS) facility located on Maui, Hawaii. The dial system uses a 3-joule, (superscript 13)C isotope laser coupled into a 0.6 m diameter telescope. The atmospheric optical characterization incorporates information from an infrared scintillometer co-aligned to the laser path, atmospheric profiles from weather balloons launched from the target site, and meteorological data from ground stations at AMOS and the target site. In this paper, we report a description of the experiment configuration, a summary of the results, a summary of the atmospheric conditions and their implications to the LRS program. The capability of such a system for long-range, low-angle, slant-path remote sensing is discussed. System performance issues relating to both coherent and incoherent detection methods, atmospheric limitations, as well as, the development of advanced models to predict performance of long range scenarios are presented.

Advanced Remote Sensing Research

Science.gov (United States)

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

2008-01-01

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

Estimation of Hydraulic properties of a sandy soil using ground-based active and passive microwave remote sensing

KAUST Repository

Jonard, François

2015-06-01

In this paper, we experimentally analyzed the feasibility of estimating soil hydraulic properties from 1.4 GHz radiometer and 0.8-2.6 GHz ground-penetrating radar (GPR) data. Radiometer and GPR measurements were performed above a sand box, which was subjected to a series of vertical water content profiles in hydrostatic equilibrium with a water table located at different depths. A coherent radiative transfer model was used to simulate brightness temperatures measured with the radiometer. GPR data were modeled using full-wave layered medium Green\\'s functions and an intrinsic antenna representation. These forward models were inverted to optimally match the corresponding passive and active microwave data. This allowed us to reconstruct the water content profiles, and thereby estimate the sand water retention curve described using the van Genuchten model. Uncertainty of the estimated hydraulic parameters was quantified using the Bayesian-based DREAM algorithm. For both radiometer and GPR methods, the results were in close agreement with in situ time-domain reflectometry (TDR) estimates. Compared with radiometer and TDR, much smaller confidence intervals were obtained for GPR, which was attributed to its relatively large bandwidth of operation, including frequencies smaller than 1.4 GHz. These results offer valuable insights into future potential and emerging challenges in the development of joint analyses of passive and active remote sensing data to retrieve effective soil hydraulic properties.

Analysis of polarization characteristics of plant canopies using ground-based remote sensing measurements

International Nuclear Information System (INIS)

Sid’ko, A.F.; Botvich, I.Yu.; Pisman, T.I.; Shevyrnogov, A.P.

2014-01-01

The paper presents results and analysis of a study on polarized characteristics of the reflectance factor of different plant canopies under field conditions, using optical remote sensing techniques. Polarization characteristics were recorded from the elevated work platform at heights of 10–18 m in June and July. Measurements were performed using a double-beam spectrophotometer with a polarized light filter attachment, within the spectral range from 400 to 820 nm. The viewing zenith angle was below 20 degree. Birch (Betila pubescens), pine (Pinus sylvestris L.), wheat (Triticum acstivum) [L.] crops, corn (Zea mays L. ssp. mays) crops, and various grass canopies were used in this study. The following polarization characteristics were studied: the reflectance factor of the canopy with the polarizer adjusted to transmit the maximum and minimum amounts of light (R max and R min ), polarized component of the reflectance factor (R q ), and the degree of polarization (P). Wheat, corn, and grass canopies have higher R max and R min values than forest plants. The R q and P values are higher for the birch than for the pine within the wavelength range between 430 and 740 nm. The study shows that polarization characteristics of plant canopies may be used as an effective means of decoding remote sensing data. - Highlights: • The reflection and polarization properties of plant were studied. • The compiled electronic database of the spectrophotometric information of plant. • Polarization characteristics are a source of useful data on the state of plants

Hyperspectral remote sensing

CERN Document Server

Eismann, Michael

2012-01-01

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

Hydrologic Remote Sensing and Land Surface Data Assimilation.

Science.gov (United States)

Moradkhani, Hamid

2008-05-06

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear

VME-based remote instrument control without ground loops

CERN Document Server

Belleman, J; González, J L

1997-01-01

New electronics has been developed for the remote control of the pick-up electrodes at the CERN Proton Synchrotron (PS). Communication between VME-based control computers and remote equipment is via full duplex point-to-point digital data links. Data are sent and received in serial format over simple twisted pairs at a rate of 1 Mbit/s, for distances of up to 300 m. Coupling transformers are used to avoid ground loops. The link hardware consists of a general-purpose VME-module, the 'TRX' (transceiver), containing four FIFO-buffered communication channels, and a dedicated control card for each remote station. Remote transceiver electronics is simple enough not to require micro-controllers or processors. Currently, some sixty pick-up stations of various types, all over the PS Complex (accelerators and associated beam transfer lines) are equipped with the new system. Even though the TRX was designed primarily for communication with pick-up electronics, it could also be used for other purposes, for example to for...

The Potential and Uptake of Remote Sensing in Insurance: A Review

Directory of Open Access Journals (Sweden)

Jan de Leeuw

2014-11-01

Full Text Available Global insurance markets are vast and diverse, and may offer many opportunities for remote sensing. To date, however, few operational applications of remote sensing for insurance exist. Papers claiming potential application of remote sensing typically stress the technical possibilities, without considering its contribution to customer value for the insured or to the profitability of the insurance industry. Based on a systematic search of available literature, this review investigates the potential and actual support of remote sensing to the insurance industry. The review reveals that research on remote sensing in classical claim-based insurance described in the literature revolve around crop damage and flood and fire risk assessment. Surprisingly, the use of remote sensing in claim-based insurance appears to be instigated by government rather than the insurance industry. In contrast, insurance companies are offering various index insurance products that are based on remote sensing. For example, remotely sensed index insurance for rangelands and livestock are operational, while various applications in crop index insurance are being considered or under development. The paper discusses these differences and concludes that there is particular scope for application of remote sensing by the insurance industry in index insurance because (1 indices can be constructed that correlate well with what is insured; (2 these indices can be delivered at low cost; and (3 it opens up new markets that are not served by claim-based insurance. The paper finally suggests that limited adoption of remote sensing in insurance results from a lack of mutual understanding and calls for greater cooperation between the insurance industry and the remote sensing community.

A patch-based convolutional neural network for remote sensing image classification.

Science.gov (United States)

Sharma, Atharva; Liu, Xiuwen; Yang, Xiaojun; Shi, Di

2017-11-01

Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas. Copyright © 2017 Elsevier Ltd. All rights reserved.

Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing.

Science.gov (United States)

Liu, Jiayin; Tang, Zhenmin; Cui, Ying; Wu, Guoxing

2017-06-12

Remote sensing technologies have been widely applied in urban environments' monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the "salt and pepper" phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.

Ground and satellite-based remote sensing of mineral dust using AERI spectra and MODIS thermal infrared window brightness temperatures

Science.gov (United States)

Hansell, Richard Allen, Jr.

The radiative effects of dust aerosol on our climate system have yet to be fully understood and remain a topic of contemporary research. To investigate these effects, detection/retrieval methods for dust events over major dust outbreak and transport areas have been developed using satellite and ground-based approaches. To this end, both the shortwave and longwave surface radiative forcing of dust aerosol were investigated. The ground-based remote sensing approach uses the Atmospheric Emitted Radiance Interferometer brightness temperature spectra to detect mineral dust events and to retrieve their properties. Taking advantage of the high spectral resolution of the AERI instrument, absorptive differences in prescribed thermal IR window sub-band channels were exploited to differentiate dust from cirrus clouds. AERI data collected during the UAE2 at Al-Ain UAE was employed for dust retrieval. Assuming a specified dust composition model a priori and using the light scattering programs of T-matrix and the finite difference time domain methods for oblate spheroids and hexagonal plates, respectively, dust optical depths have been retrieved and compared to those inferred from a collocated and coincident AERONET sun-photometer dataset. The retrieved optical depths were then used to determine the dust longwave surface forcing during the UAE2. Likewise, dust shortwave surface forcing is investigated employing a differential technique from previous field studies. The satellite-based approach uses MODIS thermal infrared brightness temperature window data for the simultaneous detection/separation of mineral dust and cirrus clouds. Based on the spectral variability of dust emissivity at the 3.75, 8.6, 11 and 12 mum wavelengths, the D*-parameter, BTD-slope and BTD3-11 tests are combined to identify dust and cirrus. MODIS data for the three dust-laden scenes have been analyzed to demonstrate the effectiveness of this detection/separation method. Detected daytime dust and cloud

[An operational remote sensing algorithm of land surface evapotranspiration based on NOAA PAL dataset].

Science.gov (United States)

Hou, Ying-Yu; He, Yan-Bo; Wang, Jian-Lin; Tian, Guo-Liang

2009-10-01

Based on the time series 10-day composite NOAA Pathfinder AVHRR Land (PAL) dataset (8 km x 8 km), and by using land surface energy balance equation and "VI-Ts" (vegetation index-land surface temperature) method, a new algorithm of land surface evapotranspiration (ET) was constructed. This new algorithm did not need the support from meteorological observation data, and all of its parameters and variables were directly inversed or derived from remote sensing data. A widely accepted ET model of remote sensing, i. e., SEBS model, was chosen to validate the new algorithm. The validation test showed that both the ET and its seasonal variation trend estimated by SEBS model and our new algorithm accorded well, suggesting that the ET estimated from the new algorithm was reliable, being able to reflect the actual land surface ET. The new ET algorithm of remote sensing was practical and operational, which offered a new approach to study the spatiotemporal variation of ET in continental scale and global scale based on the long-term time series satellite remote sensing images.

Multi-source remote sensing data management system

International Nuclear Information System (INIS)

Qin Kai; Zhao Yingjun; Lu Donghua; Zhang Donghui; Wu Wenhuan

2014-01-01

In this thesis, the author explored multi-source management problems of remote sensing data. The main idea is to use the mosaic dataset model, and the ways of an integreted display of image and its interpretation. Based on ArcGIS and IMINT feature knowledge platform, the author used the C# and other programming tools for development work, so as to design and implement multi-source remote sensing data management system function module which is able to simply, conveniently and efficiently manage multi-source remote sensing data. (authors)

Statistical inference for remote sensing-based estimates of net deforestation

Science.gov (United States)

Ronald E. McRoberts; Brian F. Walters

2012-01-01

Statistical inference requires expression of an estimate in probabilistic terms, usually in the form of a confidence interval. An approach to constructing confidence intervals for remote sensing-based estimates of net deforestation is illustrated. The approach is based on post-classification methods using two independent forest/non-forest classifications because...

Levee Health Monitoring With Radar Remote Sensing

Science.gov (United States)

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

2012-12-01

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

Remote Sensing Image Enhancement Based on Non-subsampled Shearlet Transform and Parameterized Logarithmic Image Processing Model

Directory of Open Access Journals (Sweden)

TAO Feixiang

2015-08-01

Full Text Available Aiming at parts of remote sensing images with dark brightness and low contrast, a remote sensing image enhancement method based on non-subsampled Shearlet transform and parameterized logarithmic image processing model is proposed in this paper to improve the visual effects and interpretability of remote sensing images. Firstly, a remote sensing image is decomposed into a low-frequency component and high frequency components by non-subsampled Shearlet transform.Then the low frequency component is enhanced according to PLIP (parameterized logarithmic image processing model, which can improve the contrast of image, while the improved fuzzy enhancement method is used to enhance the high frequency components in order to highlight the information of edges and details. A large number of experimental results show that, compared with five kinds of image enhancement methods such as bidirectional histogram equalization method, the method based on stationary wavelet transform and the method based on non-subsampled contourlet transform, the proposed method has advantages in both subjective visual effects and objective quantitative evaluation indexes such as contrast and definition, which can more effectively improve the contrast of remote sensing image and enhance edges and texture details with better visual effects.

Wageningen UR Unmanned Aerial Remote Sensing Facility - Overview of activities

Science.gov (United States)

Bartholomeus, Harm; Keesstra, Saskia; Kooistra, Lammert; Suomalainen, Juha; Mucher, Sander; Kramer, Henk; Franke, Jappe

2016-04-01

To support environmental management there is an increasing need for timely, accurate and detailed information on our land. Unmanned Aerial Systems (UAS) are increasingly used to monitor agricultural crop development, habitat quality or urban heat efficiency. An important reason is that UAS technology is maturing quickly while the flexible capabilities of UAS fill a gap between satellite based and ground based geo-sensing systems. In 2012, different groups within Wageningen University and Research Centre have established an Unmanned Airborne Remote Sensing Facility. The objective of this facility is threefold: a) To develop innovation in the field of remote sensing science by providing a platform for dedicated and high-quality experiments; b) To support high quality UAS services by providing calibration facilities and disseminating processing procedures to the UAS user community; and c) To promote and test the use of UAS in a broad range of application fields like habitat monitoring, precision agriculture and land degradation assessment. The facility is hosted by the Laboratory of Geo-Information Science and Remote Sensing (GRS) and the Department of Soil Physics and Land Management (SLM) of Wageningen University together with the team Earth Informatics (EI) of Alterra. The added value of the Unmanned Aerial Remote Sensing Facility is that compared to for example satellite based remote sensing more dedicated science experiments can be prepared. This includes for example higher frequent observations in time (e.g., diurnal observations), observations of an object under different observation angles for characterization of BRDF and flexibility in use of camera's and sensors types. In this way, laboratory type of set ups can be tested in a field situation and effects of up-scaling can be tested. In the last years we developed and implemented different camera systems (e.g. a hyperspectral pushbroom system, and multispectral frame cameras) which we operated in projects all

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

Science.gov (United States)

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.

Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

Science.gov (United States)

Guo, Yiqing; Jia, Xiuping; Paull, David

2018-06-01

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

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

Directory of Open Access Journals (Sweden)

H. Gao

2018-04-01

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

Local evaluation of air pollution by remote sensing

Energy Technology Data Exchange (ETDEWEB)

1975-02-01

Air pollution in Kanagawa Prefecture was studied by examining the relationship between tree vitality (on the ground) and the density distribution of trees as remotely measured with an aerial multiband camera. There was a close relationship between tree vitality and air pollution; a positive significant correlation existed between the density determination of trees obtained by remote sensing and the vitality of trees. The best time for photographing the trees by multiband camera was August. 4 figures, 24 tables.

Development of a Remote-Sensing Based Framework for Mapping Drought over North America

Science.gov (United States)

Hain, C.; Anderson, M. C.; Zhan, X.; Gao, F.; Svoboda, M.; Wardlow, B.; Mladenova, I. E.

2012-12-01

This presentation will address the development of a multi-scale drought monitoring tool for North America based on remotely sensed estimates of evapotranspiration. The North American continent represents a broad range in vegetation and climate conditions, from the boreal forests in Canada to the arid deserts in Mexico. This domain also encompasses a range in constraints limiting vegetation growth, with a gradient from radiation/energy limitation in the north to moisture limits in the south. This feasibility study over NA will provide a valuable test bed for future implementation world-wide in support of proposed global drought monitoring and early warning efforts. The Evaporative Stress Index (ESI) represents anomalies in the ratio of actual-to-potential ET (fPET), generated with the thermal remote sensing based Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance model and associated disaggregation algorithm, DisALEXI demonstrated that ESI maps over the continental US (CONUS) show good correspondence with standard drought metrics and with patterns of antecedent precipitation, but can be generated at significantly higher spatial resolution due to a limited reliance on ground observations. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall, for example in areas where drought impacts are being mitigated by intense irrigation or shallow water tables. As such, the ESI is a measure of actual stress rather than potential for stress, and has physical relevance to projected crop development. Because precipitation is not used in construction of the ESI, this index provides an independent assessment of drought conditions and will have particular utility for real-time monitoring in regions with sparse rainfall data or significant delays in meteorological reporting. The North American ESI product will be quantitatively compared with spatiotemporal patterns in the NADM, and with

New advance in the research of post-remote sensing application technology. Series of 'proposition and consideration of post-remote sensing application technology'

International Nuclear Information System (INIS)

Liu Dechang; Ye Fawang

2005-01-01

Based on deep consideration in post-remote sensing application technology, this article pays more attention to its technological meaning. The application idea of post-remote sensing application technology to uranium exploration is also discussed. The proposition and research on new concept of post-remote sensing application technology is an important search and of important theoretical and practical significance to uranium exploration. (authors)

[Object-oriented stand type classification based on the combination of multi-source remote sen-sing data].

Science.gov (United States)

Mao, Xue Gang; Wei, Jing Yu

2017-11-01

The recognition of forest type is one of the key problems in forest resource monitoring. The Radarsat-2 data and QuickBird remote sensing image were used for object-based classification to study the object-based forest type classification and recognition based on the combination of multi-source remote sensing data. In the process of object-based classification, three segmentation schemes (segmentation with QuickBird remote sensing image only, segmentation with Radarsat-2 data only, segmentation with combination of QuickBird and Radarsat-2) were adopted. For the three segmentation schemes, ten segmentation scale parameters were adopted (25-250, step 25), and modified Euclidean distance 3 index was further used to evaluate the segmented results to determine the optimal segmentation scheme and segmentation scale. Based on the optimal segmented result, three forest types of Chinese fir, Masson pine and broad-leaved forest were classified and recognized using Support Vector Machine (SVM) classifier with Radial Basis Foundation (RBF) kernel according to different feature combinations of topography, height, spectrum and common features. The results showed that the combination of Radarsat-2 data and QuickBird remote sensing image had its advantages of object-based forest type classification over using Radarsat-2 data or QuickBird remote sensing image only. The optimal scale parameter for QuickBirdRadarsat-2 segmentation was 100, and at the optimal scale, the accuracy of object-based forest type classification was the highest (OA=86%, Kappa=0.86), when using all features which were extracted from two kinds of data resources. This study could not only provide a reference for forest type recognition using multi-source remote sensing data, but also had a practical significance for forest resource investigation and monitoring.

Physics teaching by infrared remote sensing of vegetation

Science.gov (United States)

Schüttler, Tobias; Maman, Shimrit; Girwidz, Raimund

2018-05-01

Context- and project-based teaching has proven to foster different affective and cognitive aspects of learning. As a versatile and multidisciplinary scientific research area with diverse applications for everyday life, satellite remote sensing is an interesting context for physics education. In this paper we give a brief overview of satellite remote sensing of vegetation and how to obtain your own, individual infrared remote sensing data with affordable converted digital cameras. This novel technique provides the opportunity to conduct individual remote sensing measurement projects with students in their respective environment. The data can be compared to real satellite data and is of sufficient accuracy for educational purposes.

Some problems on remote sensing geology for uranium prospecting

International Nuclear Information System (INIS)

Yang Tinghuai.

1988-01-01

Remote sensing is a kind of very effective method which can be used in all stages of geological prospecting. Geological prospecting with remote sensing method must be based on different genetic models of ore deposits, characteristics of geology-landscape and comprehensive analysis for geophysical and geochemical data, that is, by way of conceptual model prospecting. The prospecting results based on remote sensing geology should be assessed from three aspects such as direct, indirect and potential ones

Watermarking-based protection of remote sensing images: requirements and possible solutions

Science.gov (United States)

Barni, Mauro; Bartolini, Franco; Cappellini, Vito; Magli, Enrico; Olmo, Gabriella

2001-12-01

Earth observation missions have recently attracted ag rowing interest form the scientific and industrial communities, mainly due to the large number of possible applications capable to exploit remotely sensed data and images. Along with the increase of market potential, the need arises for the protection of the image products from non-authorized use. Such a need is a very crucial one even because the Internet and other public/private networks have become preferred means of data exchange. A crucial issue arising when dealing with digital image distribution is copyright protection. Such a problem has been largely addressed by resorting to watermarking technology. A question that obviously arises is whether the requirements imposed by remote sensing imagery are compatible with existing watermarking techniques. On the basis of these motivations, the contribution of this work is twofold: i) assessment of the requirements imposed by the characteristics of remotely sensed images on watermark-based copyright protection ii) analysis of the state-of-the-art, and performance evaluation of existing algorithms in terms of the requirements at the previous point.

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

Science.gov (United States)

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

2016-04-01

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

Active Ground Optical Remote Sensing for Improved Monitoring of Seedling Stress in Nurseries

Directory of Open Access Journals (Sweden)

Jan U. H. Eitel

2010-03-01

Full Text Available Active ground optical remote sensing (AGORS devices mounted on overhead irrigation booms could help to improve seedling quality by autonomously monitoring seedling stress. In contrast to traditionally used passive optical sensors, AGORS devices operate independently of ambient light conditions and do not require spectral reference readings. Besides measuring red (590–670 nm and near-infrared (>760 nm reflectance AGORS devices have recently become available that also measure red-edge (730 nm reflectance. We tested the hypothesis that the additional availability of red-edge reflectance information would improve AGORS of plant stress induced chlorophyll breakdown in Scots pine (Pinus sylvestris. Our results showed that the availability of red-edge reflectance information improved AGORS estimates of stress induced variation in chlorophyll concentration (r2 > 0.73, RMSE < 1.69 when compared to those without (r2 = 0.57, RMSE = 2.11.

Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing

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Jiayin Liu

2017-06-01

Full Text Available Remote sensing technologies have been widely applied in urban environments’ monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the “salt and pepper” phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC, which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF, which is estimated by Kernel Density Estimation (KDE with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.

Kingfisher: a system for remote sensing image database management

Science.gov (United States)

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

2003-04-01

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

Lidar remote sensing of above-ground biomass in three biomes.

Science.gov (United States)

Michael A. Lefsky; Warren B. Cohen; David J. Harding; Geoffrey G. Parkers; Steven A. Acker; S. Thomas. Gower

2002-01-01

Estimation of the amount of carbon stored in forests is a key challenge for understanding the global carbon cycle, one which remote sensing is expected to help address. However, estimation of carbon storage in moderate to high biomass forests is difficult for conventional optical and radar sensors. Lidar (light detection and ranging) instruments measure the vertical...

Freeware for GIS and Remote Sensing

OpenAIRE

Lena Halounová

2007-01-01

Education in remote sensing and GIS is based on software utilization. The software needs to be installed in computer rooms with a certain number of licenses. The commercial software equipment is therefore financially demanding and not only for universities, but especially for students. Internet research brings a long list of free software of various capabilities. The paper shows a present state of GIS, image processing and remote sensing free software.

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

Directory of Open Access Journals (Sweden)

Elahe Akbari

2017-12-01

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

Ten ways remote sensing can contribute to conservation

Science.gov (United States)

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

2014-01-01

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

Ten ways remote sensing can contribute to conservation.

Science.gov (United States)

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

2015-04-01

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

Nasa's Land Remote Sensing Plans for the 1980's

Science.gov (United States)

Higg, H. C.; Butera, K. M.; Settle, M.

1985-01-01

Research since the launch of LANDSAT-1 has been primarily directed to the development of analysis techniques and to the conduct of applications studies designed to address resource information needs in the United States and in many other countries. The current measurement capabilities represented by MSS, TM, and SIR-A and B, coupled with the present level of remote sensing understanding and the state of knowledge in the discipline earth sciences, form the foundation for NASA's Land Processes Program. Science issues to be systematically addressed include: energy balance, hydrologic cycle, biogeochemical cycles, biological productivity, rock cycle, landscape development, geological and botanical associations, and land surface inventory, monitoring, and modeling. A global perspective is required for using remote sensing technology for problem solving or applications context. A successful model for this kind of activity involves joint research with a user entity where the user provides a test site and ground truth and NASA provides the remote sensing techniques to be tested.

China national space remote sensing infrastructure and its application

Science.gov (United States)

Li, Ming

2016-07-01

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

TANGOO: A ground-based tilting-filter spectrometer for deriving the temperature in the mesopause region

Science.gov (United States)

Wildner, S.; Bittner, M.

2009-04-01

TANGOO (Tilting-filter spectrometer for Atmospheric Nocturnal Ground-based Oxygen & hydrOxyl emission measurements) is a passive, ground-based optical instrument for the purpose of a simultanously automatic long-term monitoring of OH(6-2) and O2 atm. Band (0-1) emissions (called "airglow"), yielding rotational temperatures in about 87 and 95 km, respectively. TANGOO, being a transportable and comparatively easy-to-use instrument, is the enhancement of the Argentine Airglow Spectrometer (Scheer, 1987) and shows significant improvements in the temporal resolution and throughput. It will be located on the German Enviromental Research Station "Schneefernerhaus", Zugspitze (47°,4 N, 11° E) and will start measurements in 2009. Objectives of TANGOO cover the analysis of dynamical processes such as gravity waves as well as the identification of climate signals. The observation method will be presented.

Suitability Evaluation for Products Generation from Multisource Remote Sensing Data

Directory of Open Access Journals (Sweden)

Jining Yan

2016-12-01

Full Text Available With the arrival of the big data era in Earth observation, the remote sensing communities have accumulated a large amount of invaluable and irreplaceable data for global monitoring. These massive remote sensing data have enabled large-area and long-term series Earth observation, and have, in particular, made standard, automated product generation more popular. However, there is more than one type of data selection for producing a certain remote sensing product; no single remote sensor can cover such a large area at one time. Therefore, we should automatically select the best data source from redundant multisource remote sensing data, or select substitute data if data is lacking, during the generation of remote sensing products. However, the current data selection strategy mainly adopts the empirical model, and has a lack of theoretical support and quantitative analysis. Hence, comprehensively considering the spectral characteristics of ground objects and spectra differences of each remote sensor, by means of spectrum simulation and correlation analysis, we propose a suitability evaluation model for product generation. The model will enable us to obtain the Production Suitability Index (PSI of each remote sensing data. In order to validate the proposed model, two typical value-added information products, NDVI and NDWI, and two similar or complementary remote sensors, Landsat-OLI and HJ1A-CCD1, were chosen, and the verification experiments were performed. Through qualitative and quantitative analysis, the experimental results were consistent with our model calculation results, and strongly proved the validity of the suitability evaluation model. The proposed production suitability evaluation model could assist with standard, automated, serialized product generation. It will play an important role in one-station, value-added information services during the big data era of Earth observation.

Determining spatio-temporal distribution of bee forage species of Al-Baha region based on ground inventorying supported with GIS applications and Remote Sensed Satellite Image analysis

Directory of Open Access Journals (Sweden)

Nuru Adgaba

2017-07-01

Full Text Available In arid zones, the shortage of bee forage is critical and usually compels beekeepers to move their colonies in search of better forages. Identifying and mapping the spatiotemporal distribution of the bee forages over given area is important for better management of bee colonies. In this study honey bee plants in the target areas were inventoried following, ground inventory work supported with GIS applications. The study was conducted on 85 large plots of 50 × 50 m each. At each plot, data on species name, height, base diameter, crown height, crown diameter has been taken for each plant with their respective geographical positions. The data were stored, and processed using Trimble GPS supported with ArcGIS10 software program. The data were used to estimate the relative frequency, density, abundance and species diversity, species important value index and apicultural value of the species. In addition, Remotely Sensed Satellite Image of the area was obtained and processed using Hopfield Artificial Neural Network techniques. During the study, 182 species from 49 plant families were identified as bee forages of the target area. From the total number of species; shrubs, herbs and trees were accounting for 61%, 27.67%, and 11.53% respectively. Of which Ziziphus spina-christi, Acacia tortilis, Acacia origina, Acacia asak, Lavandula dentata, and Hypoestes forskaolii were the major nectar source plants of the area in their degree of importance. The average vegetation cover values of the study areas were low (<30% with low Shannon’s species diversity indices (H′ of 0.5–1.52 for different sites. Based on the eco-climatological factors and the variations in their flowering period, these major bee forage species were found to form eight distinct spatiotemporal categories which allow beekeepers to migrate their colonies to exploit the resources at different seasons and place. The Remote Sensed Satellite Image analysis confirmed the spatial

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

Science.gov (United States)

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).

Classification of permafrost active layer depth from remotely sensed and topographic evidence

International Nuclear Information System (INIS)

Peddle, D.R.; Franklin, S.E.

1993-01-01

The remote detection of permafrost (perennially frozen ground) has important implications to environmental resource development, engineering studies, natural hazard prediction, and climate change research. In this study, the authors present results from two experiments into the classification of permafrost active layer depth within the zone of discontinuous permafrost in northern Canada. A new software system based on evidential reasoning was implemented to permit the integrated classification of multisource data consisting of landcover, terrain aspect, and equivalent latitude, each of which possessed different formats, data types, or statistical properties that could not be handled by conventional classification algorithms available to this study. In the first experiment, four active layer depth classes were classified using ground based measurements of the three variables with an accuracy of 83% compared to in situ soil probe determination of permafrost active layer depth at over 500 field sites. This confirmed the environmental significance of the variables selected, and provided a baseline result to which a remote sensing classification could be compared. In the second experiment, evidence for each input variable was obtained from image processing of digital SPOT imagery and a photogrammetric digital elevation model, and used to classify active layer depth with an accuracy of 79%. These results suggest the classification of evidence from remotely sensed measures of spectral response and topography may provide suitable indicators of permafrost active layer depth

Assessing forest resources in Denmark using wall-to-wall remote sensing data

DEFF Research Database (Denmark)

Schumacher, Johannes

then be applied to estimate resources on both small and large scales. Numerous studies have investigated the possibilities of using remote sensing data for forest monitoring at plot or single tree levels. However, experience of estimating these properties for larger areas, for example regional or country...... assessments, is lacking. In this thesis wall-to-wall remote sensing data (from aerial images, airborne LiDAR, and space-borne SAR) were combined with ground reference data (from NFI plots and tree species experiments) to build and evaluate models estimating properties such as basal area, timber volume......, the thesis extends the application of remote sensing methods to estimate important variables with relevance to water catchment management....

Ground based remote sensing and physiological measurements provide novel insights into canopy photosynthetic optimization in arctic shrubs

Science.gov (United States)

Magney, T. S.; Griffin, K. L.; Boelman, N.; Eitel, J.; Greaves, H.; Prager, C.; Logan, B.; Oliver, R.; Fortin, L.; Vierling, L. A.

2014-12-01

Because changes in vegetation structure and function in the Arctic are rapid and highly dynamic phenomena, efforts to understand the C balance of the tundra require repeatable, objective, and accurate remote sensing methods for estimating aboveground C pools and fluxes over large areas. A key challenge addressing the modelling of aboveground C is to utilize process-level information from fine-scale studies. Utilizing information obtained from high resolution remote sensing systems could help to better understand the C source/sink strength of the tundra, which will in part depend on changes in photosynthesis resulting from the partitioning of photosynthetic machinery within and among deciduous shrub canopies. Terrestrial LiDAR and passive hyperspectral remote sensing measurements offer an effective, repeatable, and scalable method to understand photosynthetic performance and partitioning at the canopy scale previously unexplored in arctic systems. Using a 3-D shrub canopy model derived from LiDAR, we quantified the light regime of leaves within shrub canopies to gain a better understanding of how light interception varies in response to the Arctic's complex radiation regime. This information was then coupled with pigment sampling (i.e., xanthophylls, and Chl a/b) to evaluate the optimization of foliage photosynthetic capacity within shrub canopies due to light availability. In addition, a lab experiment was performed to validate evidence of canopy level optimization via gradients of light intensity and leaf light environment. For this, hyperspectral reflectance (photochemical reflectance index (PRI)), and solar induced fluorescence (SIF)) was collected in conjunction with destructive pigment samples (xanthophylls) and chlorophyll fluorescence measurements in both sunlit and shaded canopy positions.

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

Science.gov (United States)

Goetz, Alexander F. H.

1986-01-01

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

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

Science.gov (United States)

Chirayath, Ved

2018-01-01

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

Integrated Monitoring and Modeling of Carbon Dioxide Leakage Risk Using Remote Sensing, Ground-Based Monitoring, Atmospheric Models and Risk-Indexing Tools

Science.gov (United States)

Burton, E. A.; Pickles, W. L.; Gouveia, F. J.; Bogen, K. T.; Rau, G. H.; Friedmann, J.

2006-12-01

Correct assessment of the potential for CO2 leakage to the atmosphere or near surface is key to managing the risk associated with CO2 storage. Catastrophic, point-source leaks, diffuse seepage, and low leakage rates all merit assessment. Smaller leaks may be early warnings of catastrophic failures, and may be sufficient to damage natural vegetation or crops. Small leaks also may lead to cumulative build-up of lethal levels of CO2 in enclosed spaces, such as basements, groundwater-well head spaces, and caverns. Working with our ZERT partners, we are integrating a variety of monitoring and modeling approaches to understand how to assess potential health, property and environmental risks across this spectrum of leakage types. Remote sensing offers a rapid technique to monitor large areas for adverse environmental effects. If it can be deployed prior to the onset of storage operations, remote sensing also can document baseline conditions against which future claims of environmental damage can be compared. LLNL has been using hyperspectral imaging to detect plant stress associated with CO2 gas leakage, and has begun investigating use of NASA's new satellite or airborne instrumentation that directly measures gas compositions in the atmosphere. While remote sensing techniques have been criticized as lacking the necessary resolution to address environmental problems, new instruments and data processing techniques are demonstrated to resolve environmental changes at the scale associated with gas-leakage scenarios. During the shallow low-flow- CO2 release field experiments planned by ZERT, for the first time, we will have the opportunity to ground- truth hyperspectral data by simultaneous measurement of changes in hyperspectral readings, soil and root zone microbiology, ambient air, soil and aquifer CO2 concentrations. When monitoring data appear to indicate a CO2 leakage event, risk assessment and mitigation of that event requires a robust and nearly real-time method for

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

International Nuclear Information System (INIS)

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

2012-01-01

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

Freeware for GIS and Remote Sensing

Directory of Open Access Journals (Sweden)

Lena Halounová

2007-12-01

Full Text Available Education in remote sensing and GIS is based on software utilization. The software needs to be installed in computer rooms with a certain number of licenses. The commercial software equipment is therefore financially demanding and not only for universities, but especially for students. Internet research brings a long list of free software of various capabilities. The paper shows a present state of GIS, image processing and remote sensing free software.

EVALUATION OF A FORMER LANDFILL SITE IN FORT COLLINS, COLORADO USING GROUND-BASED OPTICAL REMOTE SENSING TECHNOLOGY

Science.gov (United States)

This report details a measurement campaign conducted using the Radial Plume Mapping (RPM) method and optical remote sensing technologies to characterize fugitive emissions. This work was funded by EPA′s Monitoring and Measurement for the 21st Century Initiative, or 21M2. The si...

Measurement of Oil and Natural Gas Well Pad Enclosed Combustor Emissions Using Optical Remote Sensing Technologies

Science.gov (United States)

The U.S. Environmental Protection Agency (EPA), Office of Research and Development (ORD) and EPA Region 8 are collaborating under the EPA’s Regional Applied Research Effort (RARE) program to evaluate ground-based remote sensing technologies that could be used to characterize emis...

Remotely Sensed Based Lake/Reservoir Routing in Congo River Basin

Science.gov (United States)

Raoufi, R.; Beighley, E.; Lee, H.

2017-12-01

Lake and reservoir dynamics can influence local to regional water cycles but are often not well represented in hydrologic models. One challenge that limits their inclusion in models is the need for detailed storage-discharge behavior that can be further complicated in reservoirs where specific operation rules are employed. Here, the Hillslope River Routing (HRR) model is combined with a remotely sensed based Reservoir Routing (RR) method and applied to the Congo River Basin. Given that topographic data are often continuous over the entire terrestrial surface (i.e., does not differentiate between land and open water), the HRR-RR model integrates topographic derived river networks and catchment boundaries (e.g., HydroSHEDs) with water boundary extents (e.g., Global Lakes and Wetlands Database) to develop the computational framework. The catchments bordering lakes and reservoirs are partitioned into water and land portions, where representative flowpath characteristics are determined and vertical water balance and lateral routings is performed separately on each partition based on applicable process models (e.g., open water evaporation vs. evapotranspiration). To enable reservoir routing, remotely sensed water surface elevations and extents are combined to determine the storage change time series. Based on the available time series, representative storage change patterns are determined. Lake/reservoir routing is performed by combining inflows from the HRR-RR model and the representative storage change patterns to determine outflows. In this study, a suite of storage change patterns derived from remotely sensed measurements are determined representative patterns for wet, dry and average conditions. The HRR-RR model dynamically selects and uses the optimal storage change pattern for the routing process based on these hydrologic conditions. The HRR-RR model results are presented to highlight the importance of lake attenuation/routing in the Congo Basin.

Validation of MOPITT carbon monoxide using ground-based Fourier transform infrared spectrometer data from NDACC

Science.gov (United States)

Buchholz, Rebecca R.; Deeter, Merritt N.; Worden, Helen M.; Gille, John; Edwards, David P.; Hannigan, James W.; Jones, Nicholas B.; Paton-Walsh, Clare; Griffith, David W. T.; Smale, Dan; Robinson, John; Strong, Kimberly; Conway, Stephanie; Sussmann, Ralf; Hase, Frank; Blumenstock, Thomas; Mahieu, Emmanuel; Langerock, Bavo

2017-06-01

The Measurements of Pollution in the Troposphere (MOPITT) satellite instrument provides the longest continuous dataset of carbon monoxide (CO) from space. We perform the first validation of MOPITT version 6 retrievals using total column CO measurements from ground-based remote-sensing Fourier transform infrared spectrometers (FTSs). Validation uses data recorded at 14 stations, that span a wide range of latitudes (80° N to 78° S), in the Network for the Detection of Atmospheric Composition Change (NDACC). MOPITT measurements are spatially co-located with each station, and different vertical sensitivities between instruments are accounted for by using MOPITT averaging kernels (AKs). All three MOPITT retrieval types are analyzed: thermal infrared (TIR-only), joint thermal and near infrared (TIR-NIR), and near infrared (NIR-only). Generally, MOPITT measurements overestimate CO relative to FTS measurements, but the bias is typically less than 10 %. Mean bias is 2.4 % for TIR-only, 5.1 % for TIR-NIR, and 6.5 % for NIR-only. The TIR-NIR and NIR-only products consistently produce a larger bias and lower correlation than the TIR-only. Validation performance of MOPITT for TIR-only and TIR-NIR retrievals over land or water scenes is equivalent. The four MOPITT detector element pixels are validated separately to account for their different uncertainty characteristics. Pixel 1 produces the highest standard deviation and lowest correlation for all three MOPITT products. However, for TIR-only and TIR-NIR, the error-weighted average that includes all four pixels often provides the best correlation, indicating compensating pixel biases and well-captured error characteristics. We find that MOPITT bias does not depend on latitude but rather is influenced by the proximity to rapidly changing atmospheric CO. MOPITT bias drift has been bound geographically to within ±0.5 % yr-1 or lower at almost all locations.

Assessing recharge using remotely sensed data in the Guarani Aquifer System outcrop zone

Science.gov (United States)

Lucas, M. C.; Oliveira, P. T. S.; Melo, D. D.; Wendland, E.

2014-12-01

Groundwater recharge is an essential hydrology component for sustainable water withdrawal from an aquifer. The Guarani Aquifer System (GAS) is the largest (~1.2 million km2) transboundary groundwater reservoir in South America, supplying freshwater to four countries: Brazil, Argentina, Paraguay and Uruguay. However, recharge in the GAS outcrop zones is one of the least known hydrological variables, in part because studies from hydrological data are scarce or nonexistent. We assess recharge using the water-budget as the difference of precipitation (P) and evapotranspiration (ET). Data is derived from remotely sensed estimates of P (TRMM 3B42 V7) and ET (MOD16) in the Onça Creek watershed over the 2004­-12 period. This is an upland-flat watershed (slope steepness < 1%) dominated by sand soils and representative of the GAS outcrop zones. We compared the remote sensing approach against Water Table Fluctuation (WTF) method and another water-budget using ground-based measurements. Uncertainty propagation analysis were also performed. On monthly basis, TRMM P exhibited a great agreement with ground-based P data (R2 = 0.86 and RMSE = 41 mm). Historical (2004-12) mean(±sd) satellite-based recharge (Rsat) was 537(±224) mm y-1, while ground-based recharge using water-budget (Rgr) and WTF (Rwtf) method was 469 mm y-1 and 311(±150) mm y-1, respectively. We found that ~440 mm y-1 is a reasonable historical mean (between Rsat, Rgr and Rwtf) recharge for the study area over 2004-2012 period. The latter mean recharge estimate is about 29% of the mean historical P (1,514 mm y-1). Our results provide the first insight about an intercomparison of water budget from remote sensing and measured data to estimate recharge in the GAS outcrop zone. These results should be useful for future studies on assessing recharge in the GAS outcrop zones. Since accurate and precise recharge estimation still is a gap, our recharge satellite-based is considered acceptable for the Onça Creek

METHOD OF GROUP OBJECTS FORMING FOR SPACE-BASED REMOTE SENSING OF THE EARTH

Directory of Open Access Journals (Sweden)

A. N. Grigoriev

2015-07-01

Full Text Available Subject of Research. Research findings of the specific application of space-based optical-electronic and radar means for the Earth remote sensing are considered. The subject matter of the study is the current planning of objects survey on the underlying surface in order to increase the effectiveness of sensing system due to the rational use of its resources. Method. New concept of a group object, stochastic swath and stochastic length of the route is introduced. The overview of models for single, group objects and their parameters is given. The criterion for the existence of the group object based on two single objects is formulated. The method for group objects formation while current survey planning has been developed and its description is presented. The method comprises several processing stages for data about objects with the calculation of new parameters, the stochastic characteristics of space means and validates the spatial size of the object value of the stochastic swath and stochastic length of the route. The strict mathematical description of techniques for model creation of a group object based on data about a single object and onboard special complex facilities in difficult conditions of registration of spatial data is given. Main Results. The developed method is implemented on the basis of modern geographic information system in the form of a software tool layout with advanced tools of processing and analysis of spatial data in vector format. Experimental studies of the forming method for the group of objects were carried out on a different real object environment using the parameters of modern national systems of the Earth remote sensing detailed observation Canopus-B and Resurs-P. Practical Relevance. The proposed models and method are focused on practical implementation using vector spatial data models and modern geoinformation technologies. Practical value lies in the reduction in the amount of consumable resources by means of

Ground-based transmission line conductor motion sensor

International Nuclear Information System (INIS)

Jacobs, M.L.; Milano, U.

1988-01-01

A ground-based-conductor motion-sensing apparatus is provided for remotely sensing movement of electric-power transmission lines, particularly as would occur during the wind-induced condition known as galloping. The apparatus is comprised of a motion sensor and signal-generating means which are placed underneath a transmission line and will sense changes in the electric field around the line due to excessive line motion. The detector then signals a remote station when a conditioning of galloping is sensed. The apparatus of the present invention is advantageous over the line-mounted sensors of the prior art in that it is easier and less hazardous to install. The system can also be modified so that a signal will only be given when particular conditions, such as specific temperature range, large-amplitude line motion, or excessive duration of the line motion, are occurring

The Powell Volcano Remote Sensing Working Group Overview

Science.gov (United States)

Reath, K.; Pritchard, M. E.; Poland, M. P.; Wessels, R. L.; Biggs, J.; Carn, S. A.; Griswold, J. P.; Ogburn, S. E.; Wright, R.; Lundgren, P.; Andrews, B. J.; Wauthier, C.; Lopez, T.; Vaughan, R. G.; Rumpf, M. E.; Webley, P. W.; Loughlin, S.; Meyer, F. J.; Pavolonis, M. J.

2017-12-01

Hazards from volcanic eruptions pose risks to the lives and livelihood of local populations, with potential global impacts to businesses, agriculture, and air travel. The 2015 Global Assessment of Risk report notes that 800 million people are estimated to live within 100 km of 1400 subaerial volcanoes identified as having eruption potential. However, only 55% of these volcanoes have any type of ground-based monitoring. The only methods currently available to monitor these unmonitored volcanoes are space-based systems that provide a global view. However, with the explosion of data techniques and sensors currently available, taking full advantage of these resources can be challenging. The USGS Powell Center Volcano Remote Sensing Working Group is working with many partners to optimize satellite resources for global detection of volcanic unrest and assessment of potential eruption hazards. In this presentation we will describe our efforts to: 1) work with space agencies to target acquisitions from the international constellation of satellites to collect the right types of data at volcanoes with forecasting potential; 2) collaborate with the scientific community to develop databases of remotely acquired observations of volcanic thermal, degassing, and deformation signals to facilitate change detection and assess how these changes are (or are not) related to eruption; and 3) improve usage of satellite observations by end users at volcano observatories that report to their respective governments. Currently, the group has developed time series plots for 48 Latin American volcanoes that incorporate variations in thermal, degassing, and deformation readings over time. These are compared against eruption timing and ground-based data provided by the Smithsonian Institute Global Volcanism Program. Distinct patterns in unrest and eruption are observed at different volcanoes, illustrating the difficulty in developing generalizations, but highlighting the power of remote sensing

Remote Sensing of Landscapes with Spectral Images

Science.gov (United States)

Adams, John B.; Gillespie, Alan R.

2006-05-01

Remote Sensing of Landscapes with Spectral Images describes how to process and interpret spectral images using physical models to bridge the gap between the engineering and theoretical sides of remote-sensing and the world that we encounter when we venture outdoors. The emphasis is on the practical use of images rather than on theory and mathematical derivations. Examples are drawn from a variety of landscapes and interpretations are tested against the reality seen on the ground. The reader is led through analysis of real images (using figures and explanations); the examples are chosen to illustrate important aspects of the analytic framework. This textbook will form a valuable reference for graduate students and professionals in a variety of disciplines including ecology, forestry, geology, geography, urban planning, archeology and civil engineering. It is supplemented by a web-site hosting digital color versions of figures in the book as well as ancillary images (www.cambridge.org/9780521662214). Presents a coherent view of practical remote sensing, leading from imaging and field work to the generation of useful thematic maps Explains how to apply physical models to help interpret spectral images Supplemented by a website hosting digital colour versions of figures in the book, as well as additional colour figures

NASA programs in technology transfer and their relation to remote sensing education

Science.gov (United States)

Weinstein, R. H.

1980-01-01

Technology transfer to users is a central feature of NASA programs. In each major area of responsibility, a variety of mechanisms was established to provide for this transfer of operational capability to the proper end user, be it a Federal agency, industry, or other public sector users. In addition, the Technology Utilization program was established to cut across all program areas and to make available a wealth of 'spinoff' technology (i.e., secondary applications of space technology to ground-based use). The transfer of remote sensing technology, particularly to state and local users, presents some real challenges in application and education for NASA and the university community. The agency's approach to the transfer of remote sensing technology and the current and potential role of universities in the process are considered.

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

Science.gov (United States)

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

2018-04-01

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

Economic optimization and evolutionary programming when using remote sensing data

OpenAIRE

Shamin Roman; Alberto Gabriel Enrike; Uryngaliyeva Ayzhana; Semenov Aleksandr

2018-01-01

The article considers the issues of optimizing the use of remote sensing data. Built a mathematical model to describe the economic effect of the use of remote sensing data. It is shown that this model is incorrect optimisation task. Given a numerical method of solving this problem. Also discusses how to optimize organizational structure by using genetic algorithm based on remote sensing. The methods considered allow the use of remote sensing data in an optimal way. The proposed mathematical m...

Remote Sensing for Wind Energy

DEFF Research Database (Denmark)

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

Remote Sensing of Aboveground Biomass in Tropical Secondary Forests: A Review

Directory of Open Access Journals (Sweden)

J. M. Barbosa

2014-01-01

Full Text Available Tropical landscapes are, in general, a mosaic of pasture, agriculture, and forest undergoing various stages of succession. Forest succession is comprised of continuous structural changes over time and results in increases in aboveground biomass (AGB. New remote sensing methods, including sensors, image processing, statistical methods, and uncertainty evaluations, are constantly being developed to estimate biophysical forest changes. We review 318 peer-reviewed studies related to the use of remotely sensed AGB estimations in tropical forest succession studies and summarize their geographic distribution, sensors and methods used, and their most frequent ecological inferences. Remotely sensed AGB is broadly used in forest management studies, conservation status evaluations, carbon source and sink investigations, and for studies of the relationships between environmental conditions and forest structure. Uncertainties in AGB estimations were found to be heterogeneous with biases related to sensor type, processing methodology, ground truthing availability, and forest characteristics. Remotely sensed AGB of successional forests is more reliable for the study of spatial patterns of forest succession and over large time scales than that of individual stands. Remote sensing of temporal patterns in biomass requires further study, in particular, as it is critical for understanding forest regrowth at scales useful for regional or global analyses.

Remote Sensing-based estimates of herbaceous aboveground biomass on the Mongolian Plateau

Science.gov (United States)

John, R.; Chen, J.; Kim, Y.; Ouyang, Z.; Park, H.; Shao, C.

2015-12-01

Grasslands comprise most of the land area on the Mongolian Plateau, which includes Mongolia (MG), and the province of Inner Mongolia (IM). Substantial land cover/use change in the recent past, driven by a combination of post-liberalization, socio-economic changes as well as extreme climatic events has resulted in degradation of grasslands in structure and function, for e.g., their carbon sequestration ability. Hence there is a need for precise estimation of above-ground biomass (AGB). In this study, we collected surface reflectance spectra from field radiometry and quadrats and line transects, which include percentage of ground cover, vegetation height, above ground biomass, and species richness, during the growing season, between the periods, 2006-2011 in IM and 2011-2015 in MG. The field sampling was stratified by the dominant vegetation types on the plateau, including the meadow steppe, typical steppe, and the desert steppe. These sampling data were used as training and validation data for developing and testing predictive models for total herbaceous vegetation, and AGB, using Landsat and MODIS-surface reflectance bands and derived vegetation indices optimized for low cover conditions. Our results show that the independent ground sampling data were significantly correlated with remotely sensed estimates. In addition to providing measures of carbon sequestration to the community, these predictive models offer decision makers and rangeland managers the ability to accurately monitor grassland dynamics, control livestock stocking rates in these remote and extensive grasslands.

Integrated remotely sensed datasets for disaster management

Science.gov (United States)

McCarthy, Timothy; Farrell, Ronan; Curtis, Andrew; Fotheringham, A. Stewart

2008-10-01

Video imagery can be acquired from aerial, terrestrial and marine based platforms and has been exploited for a range of remote sensing applications over the past two decades. Examples include coastal surveys using aerial video, routecorridor infrastructures surveys using vehicle mounted video cameras, aerial surveys over forestry and agriculture, underwater habitat mapping and disaster management. Many of these video systems are based on interlaced, television standards such as North America's NTSC and European SECAM and PAL television systems that are then recorded using various video formats. This technology has recently being employed as a front-line, remote sensing technology for damage assessment post-disaster. This paper traces the development of spatial video as a remote sensing tool from the early 1980s to the present day. The background to a new spatial-video research initiative based at National University of Ireland, Maynooth, (NUIM) is described. New improvements are proposed and include; low-cost encoders, easy to use software decoders, timing issues and interoperability. These developments will enable specialists and non-specialists collect, process and integrate these datasets within minimal support. This integrated approach will enable decision makers to access relevant remotely sensed datasets quickly and so, carry out rapid damage assessment during and post-disaster.

Tunnel-Site Selection by Remote Sensing Techniques

Science.gov (United States)

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

Remote Sensing Best Paper Award 2013

OpenAIRE

Prasad Thenkabail

2013-01-01

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

Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data

Directory of Open Access Journals (Sweden)

Jingyi Zhang

2018-06-01

Full Text Available This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF method to estimate ground PM2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM2.5 analysis and prediction.

Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data.

Science.gov (United States)

Zhang, Jingyi; Li, Bin; Chen, Yumin; Chen, Meijie; Fang, Tao; Liu, Yongfeng

2018-06-11

This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM 2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM 2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM 2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM 2.5 analysis and prediction.

Municipality Level Simulations of Dengue Fever Incidence in Puerto Rico Using Ground Based and Remotely Sensed Climate Data

Science.gov (United States)

Quattrochi, Dale A.; Morin, Cory

2015-01-01

Dengue fever (DF) is caused by a virus transmitted between humans and Aedes genus mosquitoes through blood feeding. In recent decades incidence of the disease has drastically increased in the tropical Americas, culminating with the Pan American outbreak in 2010 which resulted in 1.7 million reported cases. In Puerto Rico dengue is endemic, however, there is significant inter-annual, intraannual, and spatial variability in case loads. Variability in climate and the environment, herd immunity and virus genetics, and demographic characteristics may all contribute to differing patterns of transmission both spatially and temporally. Knowledge of climate influences on dengue incidence could facilitate development of early warning systems allowing public health workers to implement appropriate transmission intervention strategies. In this study, we simulate dengue incidence in several municipalities in Puerto Rico using population and meteorological data derived from ground based stations and remote sensing instruments. This data was used to drive a process based model of vector population development and virus transmission. Model parameter values for container composition, vector characteristics, and incubation period were chosen by employing a Monte Carlo approach. Multiple simulations were performed for each municipality and the results were compared with reported dengue cases. The best performing simulations were retained and their parameter values and meteorological input were compared between years and municipalities. Parameter values varied by municipality and year illustrating the complexity and sensitivity of the disease system. Local characteristics including the natural and built environment impact transmission dynamics and produce varying responses to meteorological conditions.

Data Fusion for Earth Science Remote Sensing

Science.gov (United States)

Braverman, Amy

2007-01-01

Beginning in 2004, NASA has supported the development of an international network of ground-based remote sensing installations for the measurement of greenhouse gas columns. This collaboration has been successful and is currently used in both carbon cycle investigations and in the efforts to validate the GOSAT space-based column observations of CO2 and CH4. With the support of a grant, this research group has established a network of ground-based column observations that provide an essential link between the satellite observations of CO2, CO, and CH4 and the extensive global in situ surface network. The Total Carbon Column Observing Network (TCCON) was established in 2004. At the time of this report seven sites, employing modern instrumentation, were operational or were expected to be shortly. TCCON is expected to expand. In addition to providing the most direct means of tying the in situ and remote sensing data sets together, TCCON provides a means of testing the retrieval algorithms of SCIAMACHY and GOSAT over the broadest variation in atmospheric state. TCCON provides a critically maintained and long timescale record for identification of temporal drift and spatial bias in the calibration of the space-based sensors. Finally, the global observations from TCCON are improving our understanding of how to use column observations to provide robust estimates of surface exchange of C02 and CH4 in advance of the launch of OCO and GOSAT. TCCON data are being used to better understand the impact of both regional fluxes and long-range transport on gradients in the C02 column. Such knowledge is essential for identifying the tools required to best use the space-based observations. The technical approach and methodology of retrieving greenhouse gas columns from near-IR solar spectra, data quality and process control are described. Additionally, the impact of and relevance to NASA of TCCON and satellite validation and carbon science are addressed.

Information Extraction of Tourist Geological Resources Based on 3d Visualization Remote Sensing Image

Science.gov (United States)

Wang, X.

2018-04-01

Tourism geological resources are of high value in admiration, scientific research and universal education, which need to be protected and rationally utilized. In the past, most of the remote sensing investigations of tourism geological resources used two-dimensional remote sensing interpretation method, which made it difficult for some geological heritages to be interpreted and led to the omission of some information. This aim of this paper is to assess the value of a method using the three-dimensional visual remote sensing image to extract information of geological heritages. skyline software system is applied to fuse the 0.36 m aerial images and 5m interval DEM to establish the digital earth model. Based on the three-dimensional shape, color tone, shadow, texture and other image features, the distribution of tourism geological resources in Shandong Province and the location of geological heritage sites were obtained, such as geological structure, DaiGu landform, granite landform, Volcanic landform, sandy landform, Waterscapes, etc. The results show that using this method for remote sensing interpretation is highly recognizable, making the interpretation more accurate and comprehensive.

A component-based system for agricultural drought monitoring by remote sensing.

Science.gov (United States)

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.

The use of remote sensing for landslide studies in Europe

Science.gov (United States)

Tofani, Veronica; Agostini, Andrea; Segoni, Samuele; Catani, Filippo; Casagli, Nicola

2013-04-01

results can be obtained combining remote sensing with ground based networks data and in field observations, as this can allow defining the deformation patterns of a landslide and its relationship with the triggering conditions . According to the research and working experience of the compilers, remote sensing is generally considered to have a medium effectiveness/reliability for landslide studies. Moreover this depends also on how remote sensing is used: an increase in the number of used remote sensing data type (aerial photos, satellite optical, satellite radar etc.), corresponds to a growth of the degree of effectiveness/reliability. In general the number of parameters detectable through remote sensing is linked to the number of techniques employed: an increase in the number of measured parameters is related to an increase in the number of the techniques used, both for monitoring and for detection/mapping. Many answers reported the possibility of detecting more than one parameters by only using radar technologies: this could be considered as an indicator of a better efficiency of radar with respect to optical techniques. The results of the questionnaire thus contribute to draw a sketch of the use of remote sensing in current landslide studies and show that remote sensing can be considered a powerful and well established instrument for landslides mapping, monitoring and hazard analysis and highlight that a wide range of available techniques and source data can be approached depending on the size and velocity of the investigated phenomena

Hydrologic Remote Sensing and Land Surface Data Assimilation

Directory of Open Access Journals (Sweden)

Hamid Moradkhani

2008-05-01

Full Text Available Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface–atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF and Particle filter (PF, for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law and could be a strong alternative to EnKF which is subject to some

Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production

Directory of Open Access Journals (Sweden)

Marston Héracles Domingues Franceschini

2017-06-01

Full Text Available Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate spectra with crop traits. Although monitoring frameworks using multiple sensors can be more flexible, they may result in higher inaccuracy due to differences related to the sensors characteristics, which can affect information sampling. Also organic production systems can benefit from continuous monitoring focusing on crop management and stress detection, but few studies have evaluated applications with this objective. In this study, ground-based and UAV spectrometers were compared in the context of organic potato cultivation. Relatively accurate estimates were obtained for leaf chlorophyll (RMSE = 6.07 µg·cm−2, leaf area index (RMSE = 0.67 m2·m−2, canopy chlorophyll (RMSE = 0.24 g·m−2 and ground cover (RMSE = 5.5% using five UAV-based data acquisitions, from 43 to 99 days after planting. These retrievals are slightly better than those derived from ground-based measurements (RMSE = 7.25 µg·cm−2, 0.85 m2·m−2, 0.28 g·m−2 and 6.8%, respectively, for the same period. Excluding observations corresponding to the first acquisition increased retrieval accuracy and made outputs more comparable between sensors, due to relatively low vegetation cover on this date. Intercomparison of vegetation indices indicated that indices based on the contrast between spectral bands in the visible and near-infrared, like OSAVI, MCARI2 and CIg provided, at certain extent, robust outputs that could be transferred between sensors. Information sampling at plot level by both sensing solutions resulted in comparable discriminative potential concerning advanced stages of late blight incidence. These results indicate that optical

Pattern Recognition in Optical Remote Sensing Data Processing

Science.gov (United States)

Kozoderov, Vladimir; Kondranin, Timofei; Dmitriev, Egor; Kamentsev, Vladimir

produced in Russia enables to conclude that the forest classes on a test area are separated with high accuracy. The proposed approach is recommended to account for the needed set of ground-based measurements during field campaigns for the validation purposes of remote sensing data processing and for the retrieval procedures of such parameters of forests like Net Primary Productivity with an ensured accuracy that results from the described here computational procedures.

Remote Sensing for Wind Energy

DEFF Research Database (Denmark)

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

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

Remote Sensing and Imaging Physics

Science.gov (United States)

2012-03-07

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

Accuracy Dimensions in Remote Sensing

Science.gov (United States)

Barsi, Á.; Kugler, Zs.; László, I.; Szabó, Gy.; Abdulmutalib, H. M.

2018-04-01

The technological developments in remote sensing (RS) during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remote sensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications. Based on prior paper of the authors (in cooperation within the Remote Sensing Data Quality working group of ISPRS), which established a taxonomy on the dimensions of data quality in GIS and remote sensing domains, this paper is aiming at focusing on measures of uncertainty in remote sensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users' needs. The present paper gives the theoretic overview of the issue, besides selected, practice

ACCURACY DIMENSIONS IN REMOTE SENSING

Directory of Open Access Journals (Sweden)

Á. Barsi

2018-04-01

Full Text Available The technological developments in remote sensing (RS during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remote sensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications. Based on prior paper of the authors (in cooperation within the Remote Sensing Data Quality working group of ISPRS, which established a taxonomy on the dimensions of data quality in GIS and remote sensing domains, this paper is aiming at focusing on measures of uncertainty in remote sensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users’ needs. The present paper gives the theoretic overview of the issue, besides

An Uneven Illumination Correction Algorithm for Optical Remote Sensing Images Covered with Thin Clouds

Directory of Open Access Journals (Sweden)

Xiaole Shen

2015-09-01

Full Text Available The uneven illumination phenomenon caused by thin clouds will reduce the quality of remote sensing images, and bring adverse effects to the image interpretation. To remove the effect of thin clouds on images, an uneven illumination correction can be applied. In this paper, an effective uneven illumination correction algorithm is proposed to remove the effect of thin clouds and to restore the ground information of the optical remote sensing image. The imaging model of remote sensing images covered by thin clouds is analyzed. Due to the transmission attenuation, reflection, and scattering, the thin cloud cover usually increases region brightness and reduces saturation and contrast of the image. As a result, a wavelet domain enhancement is performed for the image in Hue-Saturation-Value (HSV color space. We use images with thin clouds in Wuhan area captured by QuickBird and ZiYuan-3 (ZY-3 satellites for experiments. Three traditional uneven illumination correction algorithms, i.e., multi-scale Retinex (MSR algorithm, homomorphic filtering (HF-based algorithm, and wavelet transform-based MASK (WT-MASK algorithm are performed for comparison. Five indicators, i.e., mean value, standard deviation, information entropy, average gradient, and hue deviation index (HDI are used to analyze the effect of the algorithms. The experimental results show that the proposed algorithm can effectively eliminate the influences of thin clouds and restore the real color of ground objects under thin clouds.

Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool

Science.gov (United States)

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

2008-01-01

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

Comprehensive, integrated, remote sensing at DOE sites

International Nuclear Information System (INIS)

Lackey, J.G.; Burson, Z.G.

1985-01-01

The Department of Energy has established a program called Comprehensive, Integrated Remote Sensing (CIRS). The overall objective of the program is to provide a state-of-the-art data base of remotely sensed data for all users of such information at large DOE sites. The primary types of remote sensing provided, at present, consist of the following: large format aerial photography, video from aerial platforms, multispectral scanning, and airborne nuclear radiometric surveys. Implementation of the CIRS Program by EG and G Energy Measurements, Inc. began with field operations at the Savannah River Plant in 1982 and is continuing at that DOE site at a level of effort of about $1.5 m per year. Integrated remote sensing studies were subsequently extended to the West Valley Demonstration Project in this summer and fall of 1984. It is expected that the Program will eventually be extended to cover all large DOE sites on a continuing basis

Soil water content and evaporation determined by thermal parameters obtained from ground-based and remote measurements

Science.gov (United States)

Reginato, R. J.; Idso, S. B.; Jackson, R. D.; Vedder, J. F.; Blanchard, M. B.; Goettelman, R.

1976-01-01

Soil water contents from both smooth and rough bare soil were estimated from remotely sensed surface soil and air temperatures. An inverse relationship between two thermal parameters and gravimetric soil water content was found for Avondale loam when its water content was between air-dry and field capacity. These parameters, daily maximum minus minimum surface soil temperature and daily maximum soil minus air temperature, appear to describe the relationship reasonably well. These two parameters also describe relative soil water evaporation (actual/potential). Surface soil temperatures showed good agreement among three measurement techniques: in situ thermocouples, a ground-based infrared radiation thermometer, and the thermal infrared band of an airborne multispectral scanner.

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

Directory of Open Access Journals (Sweden)

Marc Cattet

2010-11-01

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

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

Science.gov (United States)

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

2010-01-01

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

Remote UV Fluorescence Lifetime Spectrometer, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — The goal of this project is to develop, demonstrate, and deliver to NASA an innovative, portable, and power efficient Remote UV Fluorescence Lifetime Spectrometer...

A Remote Sensing Based Forage Biomass Yield Inversion Model of Alpine-cold Meadow during Grass-withering Period in Sanjiangyuan Area

International Nuclear Information System (INIS)

Song, Weize; Jia, Haifeng; Liang, Shidong; Wang, Zheng; Liu, Shujie; Hao, Lizhuang; Chai, Shatuo

2014-01-01

Estimating forage biomass yield remotely from space is still challenging nowadays. Field experiments were conducted and ground measurements correlated to remote sensing data to estimate the forage biomass yield of Alpine-cold meadow grassland during the grass and grass-withering period in Sanjiangyuan area in Yushu county. Both Shapiro-Wilk and Kolmogorov-Smirnov two-tailed tests showed that the field training samples are normally distributed, the Spearman coefficient indicated that the parametric correlation analysis had significant differences. The optimal regression models were developed based on the Landsat Thematic Mapper Normalized Difference Vegetation Index (TM-NDVI) and the forage biomass field data during the grass and the grass-withering periods, respectively. Then an integration model was used to predict forage biomass yield of alpine-cold meadow in the grass-withering period. The model showed good prediction accuracy and reliability. It was found that this approach can not only estimate forage yield in large scale efficiently but also overcome the seasonal limitation of remote sensing inversion. This technique can provides valuable guidance to animal husbandry to resource more efficiently in winter

Space-Based Remote Sensing of the Earth: A Report to the Congress

Science.gov (United States)

1987-01-01

The commercialization of the LANDSAT Satellites, remote sensing research and development as applied to the Earth and its atmosphere as studied by NASA and NOAA is presented. Major gaps in the knowledge of the Earth and its atmosphere are identified and a series of space based measurement objectives are derived. The near-term space observations programs of the United States and other countries are detailed. The start is presented of the planning process to develop an integrated national program for research and development in Earth remote sensing for the remainder of this century and the many existing and proposed satellite and sensor systems that the program may include are described.

Remote sensing in meteorology, oceanography and hydrology

Energy Technology Data Exchange (ETDEWEB)

Cracknell, A P [ed.

1981-01-01

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

JEarth | Analytical Remote Sensing Imagery Application for Researchers and Practitioners

Science.gov (United States)

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

2009-12-01

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

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

Zhaoqin Li

2014-11-01

Full Text Available 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.

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

Science.gov (United States)

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

The Use of Remote Sensing to Resolve the Aerosol Radiative Forcing

Science.gov (United States)

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

1999-01-01

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

Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models

Science.gov (United States)

H. Viana; J. Aranha; D. Lopes; Warren B. Cohen

2012-01-01

Spatially crown biomass of Pinus pinaster stands and shrubland above-ground biomass (AGB) estimation was carried-out in a region located in Centre-North Portugal, by means of different approaches including forest inventory data, remotely sensed imagery and spatial prediction models. Two cover types (pine stands and shrubland) were inventoried and...

Far-ultraviolet imaging spectrograph and scanning grating spectrometers for the Remote Atmospheric and Ionospheric Detection System

International Nuclear Information System (INIS)

McCoy, R.P.; Meier, R.R.; Wolfram, K.D.; Picone, J.M.; Thonnard, S.E.; Fritz, G.G.; Morrill, J.S.; Christensen, A.B.; Kayser, D.C.; Pranke, J.B.; Straus, P.R.

1994-01-01

The Remote Atmospheric and Ionospheric Detection System (RAIDS) experiment is an optical remote sensing platform consisting of eight sensors, (spectrographs, spectrometers, and photometers) covering the wavelength range 550 to 8744 angstrom. RAIDS employs a mechanical scan platform to view the Earth's limb and measure line-of-sight column emission from tangent altitudes from 50 to 750 km. These measurements provide vertical profiles of atmospheric dayglow and nightglow from the mesosphere to the upper regions of the F-region ionosphere. RAIDS will be flown on the National Oceanographic and Atmospheric Administration (NOAA) J weather satellite through the auspices of the US Air Force Space Test Program. The RAIDS wavelength and altitude coverage allows remote sensing of the major and many minor constituents in the thermosphere and ionosphere. These measurements will be used as part of a proof of concept for remote sensing of ionospheric and neutral density profiles. The RAIDS database will be used to study composition, thermal structure, and couplings between the mesosphere, thermosphere, thermal structure, and couplings between the mesosphere, thermosphere, and ionosphere. RAIDS is a joint venture of the Naval Research Laboratory (NRL) and the Aerospace Corporation. The authors describe the subset of RAIDS instruments developed at NRL covering the far to near UV regions (1,300 to 4,000 angstrom)

Remote sensing programs and courses in engineering and water resources

Science.gov (United States)

Kiefer, R. W.

1981-01-01

The content of typical basic and advanced remote sensing and image interpretation courses are described and typical remote sensing graduate programs of study in civil engineering and in interdisciplinary environmental remote sensing and water resources management programs are outlined. Ideally, graduate programs with an emphasis on remote sensing and image interpretation should be built around a core of five courses: (1) a basic course in fundamentals of remote sensing upon which the more specialized advanced remote sensing courses can build; (2) a course dealing with visual image interpretation; (3) a course dealing with quantitative (computer-based) image interpretation; (4) a basic photogrammetry course; and (5) a basic surveying course. These five courses comprise up to one-half of the course work required for the M.S. degree. The nature of other course work and thesis requirements vary greatly, depending on the department in which the degree is being awarded.

Remote sensing applications for the dam industry

Energy Technology Data Exchange (ETDEWEB)

Pryse-Phillips, A.; Woolgar, R. [Hatch Ltd., St. John' s, NL (Canada); Puestow, T.; Warren, S. [Memorial Univ. of Newfoundland, St. John' s, NL (Canada). C-Core; Rogers, K. [Nalcor Energy, St. John' s, NL (Canada); Khan, A. [Government of Newfoundland and Labrador, St. Johns, NL (Canada)

2009-07-01

There has been an increase in the earth observation missions providing satellite imagery for operational monitoring applications. This technique has been found to be especially useful for the surveillance of large, remote areas, which is challenging to achieve in a cost-effective manner by conventional field-based or aerial means. This paper discussed the utility of satellite-based monitoring for different applications relevant to hydrology and water resources management. Emphasis was placed on the monitoring of river ice covers in near, real-time and water resources management. The paper first outlined river ice monitoring using remote sensing on the Lower Churchill River. The benefits of remote sensing over traditional survey methods for the dam industry was then outlined. Satellite image acquisition and interpretation for the Churchill River was then presented. Several images were offered. Watershed physiographic characterization using remote sensing was also described. It was concluded that satellite imagery proved to be a useful tool to develop physiographic characteristics when conducting rainfall-runoff modelling. 3 refs., 1 tab., 11 figs.

Understanding Pan-Arctic Tundra Vegetation Change Through Long-term Remotely Sensed Data

Science.gov (United States)

Bhatt, U.; Walker, D. A.; Bieniek, P.; Raynolds, M. K.; Epstein, H. E.; Comiso, J. C.; Pinzon, J. E.; Tucker, C. J.

2012-12-01

The goal of this paper is to present an analysis of the seasonality of tundra vegetation variability and change using long-term remotely sensed data as well as ground based measurements and reanalyses. An increase of Pan-Arctic tundra vegetation greenness has been documented using the remotely sensed Normalized Difference Vegetation Index (NDVI). Coherent variability between NDVI, springtime coastal sea ice (passive microwave) and land surface temperatures (AVHRR) has also been established. Satellite based snow and cloud cover data sets are being incorporated into this analysis. The Arctic tundra is divided into domains based on Treshnikov divisions that are modified based on floristic provinces. There is notable heterogeneity in Pan-Arctic vegetation and climate trends, which necessitates a regional analysis. This study uses remotely sensed weekly 25-km sea ice concentration, weekly surface temperature, and bi-weekly NDVI from 1982 to 2010. The GIMMS NDVI3g data has been corrected for biases during the spring and fall, with special focus on the Arctic. Trends of Maximum NDVI (MaxNDVI), Time Integrated NDVI (TI-NDVI), Summer Warmth Index (SWI, sum of degree months above freezing during May-August), and open water area are calculated for the Pan Arctic. Remotely sensed snow data trends suggest varying patterns throughout the Arctic and may in part explain the heterogeneous MaxNDVI trends. Standard climate data (station, reanalysis, and model data) and ground observations are used in the analysis to provide additional support for hypothesized mechanisms. Overall, we find that trends over the 30-year record are changing as evidenced by the following examples from recent years. The sea ice decline has increased in Eurasia and slowed in North America. The weekly AVHRR landsurface temperatures reveal that there has been summer cooling over Eurasia and that the warming over North America has slowed. The MaxNDVI rates of change have diverged between N. America and Eurasia

Autonomous target recognition using remotely sensed surface vibration measurements

Science.gov (United States)

Geurts, James; Ruck, Dennis W.; Rogers, Steven K.; Oxley, Mark E.; Barr, Dallas N.

1993-09-01

The remotely measured surface vibration signatures of tactical military ground vehicles are investigated for use in target classification and identification friend or foe (IFF) systems. The use of remote surface vibration sensing by a laser radar reduces the effects of partial occlusion, concealment, and camouflage experienced by automatic target recognition systems using traditional imagery in a tactical battlefield environment. Linear Predictive Coding (LPC) efficiently represents the vibration signatures and nearest neighbor classifiers exploit the LPC feature set using a variety of distortion metrics. Nearest neighbor classifiers achieve an 88 percent classification rate in an eight class problem, representing a classification performance increase of thirty percent from previous efforts. A novel confidence figure of merit is implemented to attain a 100 percent classification rate with less than 60 percent rejection. The high classification rates are achieved on a target set which would pose significant problems to traditional image-based recognition systems. The targets are presented to the sensor in a variety of aspects and engine speeds at a range of 1 kilometer. The classification rates achieved demonstrate the benefits of using remote vibration measurement in a ground IFF system. The signature modeling and classification system can also be used to identify rotary and fixed-wing targets.

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

Science.gov (United States)

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

2018-04-01

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

Remote Sensing and the Earth.

Science.gov (United States)

Brosius, Craig A.; And Others

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

Remote sensing technology: symposium proceedings

International Nuclear Information System (INIS)

1985-01-01

Papers were presented in four subject areas: applications of remote sensing; data analysis, digital and analog; acquisition systems; and general. Abstracts of individual items from the conference were prepared separately for the data base

Radar Remote Sensing

Science.gov (United States)

Rosen, Paul A.

2012-01-01

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

Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling

Directory of Open Access Journals (Sweden)

Yvonne Walz

2015-11-01

Full Text Available Schistosomiasis is a widespread water-based disease that puts close to 800 million people at risk of infection with more than 250 million infected, mainly in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and the frequency, duration and extent of human bodies exposed to infested water sources during human water contact. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. Since schistosomiasis risk profiling based on remote sensing data inherits a conceptual drawback if school-based disease prevalence data are directly related to the remote sensing measurements extracted at the location of the school, because the disease transmission usually does not exactly occur at the school, we took the local environment around the schools into account by explicitly linking ecologically relevant environmental information of potential disease transmission sites to survey measurements of disease prevalence. Our models were validated at two sites with different landscapes in Côte d’Ivoire using high- and moderateresolution remote sensing data based on random forest and partial least squares regression. We found that the ecologically relevant modelling approach explained up to 70% of the variation in Schistosoma infection prevalence and performed better compared to a purely pixelbased modelling approach. Furthermore, our study showed that model performance increased as a function of enlarging the school catchment area, confirming the hypothesis that suitable environments for schistosomiasis transmission rarely occur at the location of survey measurements.

Remote sensing strategies for global resource exploration and environmental management

Science.gov (United States)

Henderson, Frederick B.

Since 1972, satellite remote sensing, when integrated with other exploration techniques, has demonstrated operational exploration and engineering cost savings and reduced exploration risks through improved geological mapping. Land and ocean remote sensing satellite systems under development for the 1990's by the United States, France, Japan, Canada, ESA, Russia, China, and others, will significantly increase our ability to explore for, develop, and manage energy and mineral resources worldwide. A major difference between these systems is the "Open Skies" and "Non-Discriminatory Access to Data" policies as have been practiced by the U.S. and France and the restrictive nationalistic data policies as have been practiced by Russia and India. Global exploration will use satellite remote sensing to better map regional structural and basin-like features that control the distribution of energy and mineral resources. Improved sensors will better map lithologic and stratigraphic units and identify alteration effects in rocks, soils, and vegetation cover indicative of undiscovered subsurface resources. These same sensors will also map and monitor resource development. The use of satellite remote sensing data will grow substantially through increasing integration with other geophysical, geochemical, and geologic data using improved geographic information systems (GIS). International exploration will focus on underdeveloped countries rather than on mature exploration areas such as the United States, Europe, and Japan. Energy and mineral companies and government agencies in these countries and others will utilize available remote sensing data to acquire economic intelligence on global resources. If the "Non-Discriminatory Access to Data" principle is observed by satellite producing countries, exploration will remain competitive "on the ground". In this manner, remote sensing technology will continue to be developed to better explore for and manage the world's needed resources

A DNA-based semantic fusion model for remote sensing data.

Directory of Open Access Journals (Sweden)

Heng Sun

Full Text Available Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology.

A DNA-based semantic fusion model for remote sensing data.

Science.gov (United States)

Sun, Heng; Weng, Jian; Yu, Guangchuang; Massawe, Richard H

2013-01-01

Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology.

A hierarchic approach for examining panarctic vegeta0on with a focus on the linkages between remote-sensing and plot-based studies: A prototype example from Toolik Lake, Alaska

DEFF Research Database (Denmark)

Walker, D.A.; Bhatt, U.S.; Breen, A.L.

A circumpolar view of Arctic vegetation developed with the advent of satellite-derived remote-sensing products. Interpretations of what the revealed patterns mean are dependent on a foundation of in-situ plot-based observations. Despite the importance of ground-based observations, only a few areas...... of species composition, canopy structure, biomass, leaf-area index, and NDVI, along with high-resolution satellite-based remote-sensing products at the same time....... classification, plot markings, and standardized approaches to describe the local environment, including photo points showing the vegetation and soils up close and in landscape view. (3) Standardized approaches for collecting in-situ time-series of spectral data. Standardized methods for collecting and analyzing...

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

Directory of Open Access Journals (Sweden)

LIN Xiangguo

2017-06-01

Full Text Available Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. In this article, an object-based morphological building index (OBMBI is constructed based on both image segmentation and graph-based top-hat reconstruction, and OBMBI is used for building extraction from high resolution remote sensing images. First, bidirectional mapping relationship between pixels, objects and graph-nodes are constructed. Second, the OBMBI image is built based on both graph-based top-hat reconstruction and the above mapping relationship. Third, a binary thresholding is performed on the OBMBI image, and the binary image is converted into vector format to derive the building polygons. Finally, the post-processing is made to optimize the extracted building polygons. Two images, including an aerial image and a panchromatic satellite image, are used to test both the proposed method and classic PanTex method. The experimental results suggest that our proposed method has a higher accuracy in building extraction than the classic PanTex method. On average, the correctness, the completeness and the quality of our method are respectively 9.49%, 11.26% and 14.11% better than those of the PanTex.

A component-based system for agricultural drought monitoring by remote sensing.

Directory of Open Access Journals (Sweden)

Heng Dong

Full Text Available 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.

Remote Sensing Training for Middle School through the Center of Excellence in Remote Sensing Education

Science.gov (United States)

Hayden, L. B.; Johnson, D.; Baltrop, J.

2012-12-01

Remote sensing has steadily become an integral part of multiple disciplines, research, and education. Remote sensing can be defined as the process of acquiring information about an object or area of interest without physical contact. As remote sensing becomes a necessity in solving real world problems and scientific questions an important question to consider is why remote sensing training is significant to education and is it relevant to training students in this discipline. What has been discovered is the interest in Science, Technology, Engineering and Mathematics (STEM) fields, specifically remote sensing, has declined in our youth. The Center of Excellence in Remote Sensing Education and Research (CERSER) continuously strives to provide education and research opportunities on ice sheet, coastal, ocean, and marine science. One of those continued outreach efforts are Center for Remote Sensing of Ice Sheets (CReSIS) Middle School Program. Sponsored by the National Science Foundation CReSIS Middle School Program offers hands on experience for middle school students. CERSER and NSF offer students the opportunity to study and learn about remote sensing and its vital role in today's society as it relate to climate change and real world problems. The CReSIS Middle School Program is an annual two-week effort that offers middle school students experience with remote sensing and its applications. Specifically, participants received training with Global Positioning Systems (GPS) where the students learned the tools, mechanisms, and applications of a Garmin 60 GPS. As a part of the program the students were required to complete a fieldwork assignment where several longitude and latitude points were given throughout campus. The students had to then enter the longitude and latitude points into the Garmin 60 GPS, navigate their way to each location while also accurately reading the GPS to make sure travel was in the right direction. Upon completion of GPS training the

Remote sensing for water quality

International Nuclear Information System (INIS)

Giardino, Claudia

2006-01-01

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

Assessment of the performance of a compact concentric spectrometer system for Atmospheric Differential Optical Absorption Spectroscopy

Science.gov (United States)

Whyte, C.; Leigh, R. J.; Lobb, D.; Williams, T.; Remedios, J. J.; Cutter, M.; Monks, P. S.

2009-12-01

A breadboard demonstrator of a novel UV/VIS grating spectrometer has been developed based upon a concentric arrangement of a spherical meniscus lens, concave spherical mirror and curved diffraction grating suitable for a range of atmospheric remote sensing applications from the ground or space. The spectrometer is compact and provides high optical efficiency and performance benefits over traditional instruments. The concentric design is capable of handling high relative apertures, owing to spherical aberration and comma being near zero at all surfaces. The design also provides correction for transverse chromatic aberration and distortion, in addition to correcting for the distortion called "smile", the curvature of the slit image formed at each wavelength. These properties render this design capable of superior spectral and spatial performance with size and weight budgets significantly lower than standard configurations. This form of spectrometer design offers the potential for exceptionally compact instrument for differential optical absorption spectroscopy (DOAS) applications from LEO, GEO, HAP or ground-based platforms. The breadboard demonstrator has been shown to offer high throughput and a stable Gaussian line shape with a spectral range from 300 to 450 nm at 0.5 nm resolution, suitable for a number of typical DOAS applications.

In Situ/Remote Sensing Integration to Assess Forest Health—A Review

Directory of Open Access Journals (Sweden)

Marion Pause

2016-06-01

Full Text Available For mapping, quantifying and monitoring regional and global forest health, satellite remote sensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large quantity, operational applications are still limited by deficits of in situ verification. In situ sampling data as input is required in order to add value to physical imaging remote sensing observations and possibilities to interlink the forest health assessment with biotic and abiotic factors. Numerous methods on how to link remote sensing and in situ data have been presented in the scientific literature using e.g. empirical and physical-based models. In situ data differs in type, quality and quantity between case studies. The irregular subsets of in situ data availability limit the exploitation of available satellite remote sensing data. To achieve a broad implementation of satellite remote sensing data in forest monitoring and management, a standardization of in situ data, workflows and products is essential and necessary for user acceptance. The key focus of the review is a discussion of concept and is designed to bridge gaps of understanding between forestry and remote sensing science community. Methodological approaches for in situ/remote-sensing implementation are organized and evaluated with respect to qualifying for forest monitoring. Research gaps and recommendations for standardization of remote-sensing based products are discussed. Concluding the importance of outstanding organizational work to provide a legally accepted framework for new information products in forestry are highlighted.

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

Science.gov (United States)

Mendiguren, Gorka; Koch, Julian; Stisen, Simon

2017-11-01

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

Geospatial Image Stream Processing: Models, techniques, and applications in remote sensing change detection

Science.gov (United States)

Rueda-Velasquez, Carlos Alberto

GISP framework with representative remote sensing applications including land cover detection, wildfire detection, and near real-time validation of surface temperature measurements integrating ground- and satellite-based data.

Proceedings of the twelfth international symposium on remote sensing of environment

Energy Technology Data Exchange (ETDEWEB)

1978-01-01

This is the third of three volumes of the proceedings of the Twelfth International Symposium on Remote Sensing of Environment, held 20 to 26 April 1978 in Manila, Philippines. This symposium is part of a continuing program investigating current activities in the field of remote sensing. The meeting is intended to promote increased international cooperation in research, development and application of this technology, and to stimulate an exchange of information on all aspects of this multidisciplinary field through the presentation of reports on work planned, in progress or completed. Presentations include those concerned with the utilization of this technology in various national and international programs as well as in numerous applications for monitoring and managing the earth's resources and man's global environment. Ground-based, airborne, and spaceborne sensor systems and both manual and machine-assisted data analysis and interpretation are included. All papers included in their entirety were abstracted and indexed for EDB/ERA.

A study of the Oklahoma City urban heat island using ground measurements and remote sensing

Energy Technology Data Exchange (ETDEWEB)

Brown, M. J. (Michael J.); Ivey, A. (Austin); McPherson, T. N. (Timothy N.); Boswell, D. (David); Pardyjak, E. R. (Eric R.)

2004-01-01

Measurements of temperature and position were collected during the night from an instrumented van on routes through Oklahoma City and the rural outskirts. The measurements were taken as part of the Joint URBAN 2003 Tracer Field Experiment conducted in Oklahoma City from June 29, 2003 to July 30, 2003 (Allwine et al., 2004). The instrumented van was driven over four primary routes that included legs from the downtown core to four different 'rural' areas. Each route went through residential areas and most often went by a line of permanently fixed temperature probes (Allwine et al., 2004) for cross-checking purposes. Each route took from 20 to 40 minutes to complete. Based on seven nights of data, initial analyses indicate that there was a temperature difference of 0.5-6.5 C between the urban core and nearby 'rural' areas. Analyses also suggest that there were significant fine scale temperature differences over distances of tens of meters within the city and in the nearby rural areas. The temperature measurements that were collected are intended to supplement the meteorological measurements taken during the Joint URBAN 2003 Field Experiment, to assess the importance of the urban heat island phenomenon in Oklahoma City, and to test new urban canopy parameterizations that have been developed for regional scale meteorological codes (e.g., Chin et al., 2000; Holt and Shi, 2004). In addition to the ground measurements, skin temperature measurements were also analyzed from remotely sensed images taken from the Earth Observing System's Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). A surface kinetic temperature thermal infrared image captured by the ASTER of the Oklahoma City area on July 21, 2001 was analyzed within ESRI's ArcGIS 8.3 to correlate variations in temperature with land use type. Analysis of this imagery suggests distinct variations in temperature across different land use categories. Through the use of

A remote sensing driven distributed hydrological model of the Senegal River basin

DEFF Research Database (Denmark)

Stisen, Simon; Jensen, Karsten Høgh; Sandholt, Inge

2008-01-01

outputs of AET from both model setups was carried out. This revealed substantial differences in the spatial patterns of AET for the examined subcatchment, in spite of similar values of predicted discharge and average AET. The potential for driving large scale hydrological models using remote sensing data......Distributed hydrological models require extensive data amounts for driving the models and for parameterization of the land surface and subsurface. This study investigates the potential of applying remote sensing (RS) based input data in a hydrological model for the 350,000 km2 Senegal River basin...... in West Africa. By utilizing remote sensing data to estimate precipitation, potential evapotranspiration (PET) and leaf area index (LAI) the model was driven entirely by remote sensing based data and independent of traditional meteorological data. The remote sensing retrievals were based on data from...

Exploitation of commercial remote sensing images: reality ignored?

Science.gov (United States)

Allen, Paul C.

1999-12-01

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

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

Directory of Open Access Journals (Sweden)

Gonthier P

2012-05-01

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

An improved optimum-path forest clustering algorithm for remote sensing image segmentation

Science.gov (United States)

Chen, Siya; Sun, Tieli; Yang, Fengqin; Sun, Hongguang; Guan, Yu

2018-03-01

Remote sensing image segmentation is a key technology for processing remote sensing images. The image segmentation results can be used for feature extraction, target identification and object description. Thus, image segmentation directly affects the subsequent processing results. This paper proposes a novel Optimum-Path Forest (OPF) clustering algorithm that can be used for remote sensing segmentation. The method utilizes the principle that the cluster centres are characterized based on their densities and the distances between the centres and samples with higher densities. A new OPF clustering algorithm probability density function is defined based on this principle and applied to remote sensing image segmentation. Experiments are conducted using five remote sensing land cover images. The experimental results illustrate that the proposed method can outperform the original OPF approach.

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

Science.gov (United States)

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

2003-04-01

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

Remote sensing of growing conditions of rice plants by landsat MSS data and color IR aerial photograph

International Nuclear Information System (INIS)

Miyama, K.; Sato, H.

1985-01-01

Remote sensing is the technique of deriving information about an object or a phenomenon on the ground without actually coming in contact with it. The quantity measured in remote sensing systems is the electromagnetic energy which is reflected or radiated from the object of interest. Therefore, the remote sensing data rare usually collected by the remote-senser on board the airplane or the satellite. This technique is very useful for the measurement or investigation of earth surface conditions, for example, in the field of agriculture, geology, environment, etc., and the practical application of this technique is expected

Remote sensing of growing conditions of rice plants by landsat MSS data and color IR aerial photograph

Energy Technology Data Exchange (ETDEWEB)

Miyama, K.; Sato, H. [Hokkaido National Agricultural Experiment Station, Sapporo (Japan)

1985-12-15

Remote sensing is the technique of deriving information about an object or a phenomenon on the ground without actually coming in contact with it. The quantity measured in remote sensing systems is the electromagnetic energy which is reflected or radiated from the object of interest. Therefore, the remote sensing data rare usually collected by the remote-senser on board the airplane or the satellite. This technique is very useful for the measurement or investigation of earth surface conditions, for example, in the field of agriculture, geology, environment, etc., and the practical application of this technique is expected.

Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation

Science.gov (United States)

Jana, Raghavendra B.; Mohanty, Binayak P.

2011-03-01

scale of the training data for the BNNs by sequentially aggregating finer resolution training data to coarser resolutions, and the applicability of the technique to upscaling problems. The BNN outputs are corrected for bias using a non-linear CDF-matching technique. Final results show good promise of the suitability of this Bayesian Neural Network approach for soil hydraulic parameter estimation across spatial scales using ground-, air-, or space-based remotely sensed geophysical parameters. Inclusion of remotely sensed data such as elevation and LAI in addition to in situ soil physical properties improved the estimation capabilities of the BNN-based PTF in certain conditions.

An Adaptive Web-Based Learning Environment for the Application of Remote Sensing in Schools

Science.gov (United States)

Wolf, N.; Fuchsgruber, V.; Riembauer, G.; Siegmund, A.

2016-06-01

Satellite images have great educational potential for teaching on environmental issues and can promote the motivation of young people to enter careers in natural science and technology. Due to the importance and ubiquity of remote sensing in science, industry and the public, the use of satellite imagery has been included into many school curricular in Germany. However, its implementation into school practice is still hesitant, mainly due to lack of teachers' know-how and education materials that align with the curricula. In the project "Space4Geography" a web-based learning platform is developed with the aim to facilitate the application of satellite imagery in secondary school teaching and to foster effective student learning experiences in geography and other related subjects in an interdisciplinary way. The platform features ten learning modules demonstrating the exemplary application of original high spatial resolution remote sensing data (RapidEye and TerraSAR-X) to examine current environmental issues such as droughts, deforestation and urban sprawl. In this way, students will be introduced into the versatile applications of spaceborne earth observation and geospatial technologies. The integrated web-based remote sensing software "BLIF" equips the students with a toolset to explore, process and analyze the satellite images, thereby fostering the competence of students to work on geographical and environmental questions without requiring prior knowledge of remote sensing. This contribution presents the educational concept of the learning environment and its realization by the example of the learning module "Deforestation of the rainforest in Brasil".

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

Science.gov (United States)

Rokhmana, Catur Aries; Andaru, Ruli

2017-07-01

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

Texture-based classification for characterizing regions on remote sensing images

Science.gov (United States)

Borne, Frédéric; Viennois, Gaëlle

2017-07-01

Remote sensing classification methods mostly use only the physical properties of pixels or complex texture indexes but do not lead to recommendation for practical applications. Our objective was to design a texture-based method, called the Paysages A PRIori method (PAPRI), which works both at pixel and neighborhood level and which can handle different spatial scales of analysis. The aim was to stay close to the logic of a human expert and to deal with co-occurrences in a more efficient way than other methods. The PAPRI method is pixelwise and based on a comparison of statistical and spatial reference properties provided by the expert with local properties computed in varying size windows centered on the pixel. A specific distance is computed for different windows around the pixel and a local minimum leads to choosing the class in which the pixel is to be placed. The PAPRI method brings a significant improvement in classification quality for different kinds of images, including aerial, lidar, high-resolution satellite images as well as texture images from the Brodatz and Vistex databases. This work shows the importance of texture analysis in understanding remote sensing images and for future developments.

A Remote Sensing Image Fusion Method based on adaptive dictionary learning

Science.gov (United States)

He, Tongdi; Che, Zongxi

2018-01-01

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

Lithological mapping of Kanjamalai hill using hyperspectral remote sensing tools in Salem district, Tamil Nadu, India

Science.gov (United States)

Arulbalaji, Palanisamy; Balasubramanian, Gurugnanam

2017-07-01

This study uses advanced spaceborne thermal emission and reflection radiometer (ASTER) hyperspectral remote sensing techniques to discriminate rock types composing Kanjamalai hill located in the Salem district of Tamil Nadu, India. Kanjamalai hill is of particular interest because it contains economically viable iron ore deposits. ASTER hyperspectral data were subjected to principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) to improve identification of lithologies remotely and to compare these digital data results with published geologic maps. Hyperspectral remote sensing analysis indicates that PCA (R∶G∶B=2∶1∶3), MNF (R∶G∶B=3∶2∶1), and ICA (R∶G∶B=1∶3∶2) provide the best band combination for effective discrimination of lithological rock types composing Kanjamalai hill. The remote sensing-derived lithological map compares favorably with a published geological map from Geological Survey of India and has been verified with ground truth field investigations. Therefore, ASTER data-based lithological mapping provides fast, cost-effective, and accurate geologic data useful for lithological discrimination and identification of ore deposits.

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

Directory of Open Access Journals (Sweden)

X. Tan

2017-09-01

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

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

Science.gov (United States)

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

2017-09-01

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

Introductory remote sensing principles and concepts principles and concepts

CERN Document Server

Gibson, Paul

2013-01-01

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

Mississippi Sound Remote Sensing Study

Science.gov (United States)

Atwell, B. H.

1973-01-01

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

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase one, volume 3 : use of scanning LiDAR in structural evaluation of bridges.

Science.gov (United States)

2009-12-01

This volume introduces several applications of remote bridge inspection technologies studied in : this Integrated Remote Sensing and Visualization (IRSV) study using ground-based LiDAR : systems. In particular, the application of terrestrial LiDAR fo...

Satellite and ground-based remote sensing of aerosols during intense haze event of October 2013 over lahore, Pakistan

Science.gov (United States)

Tariq, Salman; Zia, ul-Haq; Ali, Muhammad

2016-02-01

Due to increase in population and economic development, the mega-cities are facing increased haze events which are causing important effects on the regional environment and climate. In order to understand these effects, we require an in-depth knowledge of optical and physical properties of aerosols in intense haze conditions. In this paper an effort has been made to analyze the microphysical and optical properties of aerosols during intense haze event over mega-city of Lahore by using remote sensing data obtained from satellites (Terra/Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)) and ground based instrument (AErosol RObotic NETwork (AERONET)) during 6-14 October 2013. The instantaneous highest value of Aerosol Optical Depth (AOD) is observed to be 3.70 on 9 October 2013 followed by 3.12 on 8 October 2013. The primary cause of such high values is large scale crop residue burning and urban-industrial emissions in the study region. AERONET observations show daily mean AOD of 2.36 which is eight times higher than the observed values on normal day. The observed fine mode volume concentration is more than 1.5 times greater than the coarse mode volume concentration on the high aerosol burden day. We also find high values (~0.95) of Single Scattering Albedo (SSA) on 9 October 2013. Scatter-plot between AOD (500 nm) and Angstrom exponent (440-870 nm) reveals that biomass burning/urban-industrial aerosols are the dominant aerosol type on the heavy aerosol loading day over Lahore. MODIS fire activity image suggests that the areas in the southeast of Lahore across the border with India are dominated by biomass burning activities. A Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model backward trajectory showed that the winds at 1000 m above the ground are responsible for transport from southeast region of biomass burning to Lahore. CALIPSO derived sub-types of

Concept of an advanced hyperspectral remote sensing system for pipeline monitoring

Science.gov (United States)

Keskin, Göksu; Teutsch, Caroline D.; Lenz, Andreas; Middelmann, Wolfgang

2015-10-01

Areas occupied by oil pipelines and storage facilities are prone to severe contamination due to leaks caused by natural forces, poor maintenance or third parties. These threats have to be detected as quickly as possible in order to prevent serious environmental damage. Periodical and emergency monitoring activities need to be carried out for successful disaster management and pollution minimization. Airborne remote sensing stands out as an appropriate choice to operate either in an emergency or periodically. Hydrocarbon Index (HI) and Hydrocarbon Detection Index (HDI) utilize the unique absorption features of hydrocarbon based materials at SWIR spectral region. These band ratio based methods require no a priori knowledge of the reference spectrum and can be calculated in real time. This work introduces a flexible airborne pipeline monitoring system based on the online quasi-operational hyperspectral remote sensing system developed at Fraunhofer IOSB, utilizing HI and HDI for oil leak detection on the data acquired by an SWIR imaging sensor. Robustness of HI and HDI compared to state of the art detection algorithms is evaluated in an experimental setup using a synthetic dataset, which was prepared in a systematic way to simulate linear mixtures of selected background and oil spectra consisting of gradually decreasing percentages of oil content. Real airborne measurements in Ettlingen, Germany are used to gather background data while the crude oil spectrum was measured with a field spectrometer. The results indicate that the system can be utilized for online and offline monitoring activities.

Assimilation of remote sensing data into a process-based ecosystem model for monitoring changes of soil water content in croplands

Science.gov (United States)

Ju, Weimin; Gao, Ping; Wang, Jun; Li, Xianfeng; Chen, Shu

2008-10-01

Soil water content (SWC) is an important factor affecting photosynthesis, growth, and final yields of crops. The information on SWC is of importance for mitigating the reduction of crop yields caused by drought through proper agricultural water management. A variety of methodologies have been developed to estimate SWC at local and regional scales, including field sampling, remote sensing monitoring and model simulations. The reliability of regional SWC simulation depends largely on the accuracy of spatial input datasets, including vegetation parameters, soil and meteorological data. Remote sensing has been proved to be an effective technique for controlling uncertainties in vegetation parameters. In this study, the vegetation parameters (leaf area index and land cover type) derived from the Moderate Resolution Imaging Spectrometer (MODIS) were assimilated into a process-based ecosystem model BEPS for simulating the variations of SWC in croplands of Jiangsu province, China. Validation shows that the BEPS model is able to capture 81% and 83% of across-site variations of SWC at 10 and 20 cm depths during the period from September to December, 2006 when a serous autumn drought occurred. The simulated SWC responded the events of rainfall well at regional scale, demonstrating the usefulness of our methodology for SWC and practical agricultural water management at large scales.

Improved drought monitoring in the Greater Horn of Africa by combining meteorological and remote sensing based indicators

DEFF Research Database (Denmark)

Horion, Stéphanie Marie Anne F; Kurnik, Blaz; Barbosa, Paulo

2010-01-01

, and therefore to better trigger timely and appropriate actions on the field. In this study, meteorological and remote sensing based drought indicators were compared over the Greater Horn of Africa in order to better understand: (i) how they depict historical drought events ; (ii) if they could be combined...... distribution. Two remote sensing based indicators were tested: the Normalized Difference Water Index (NDWI) derived from SPOT-VEGETATION and the Global Vegetation Index (VGI) derived form MERIS. The first index is sensitive to change in leaf water content of vegetation canopies while the second is a proxy...... of the amount and vigour of vegetation. For both indexes, anomalies were estimated using available satellite archives. Cross-correlations between remote sensing based anomalies and SPI were analysed for five land covers (forest, shrubland, grassland, sparse grassland, cropland and bare soil) over different...

Remote sensing and implications for variable-rate application using agricultural aircraft

Science.gov (United States)

Thomson, Steven J.; Smith, Lowrey A.; Ray, Jeffrey D.; Zimba, Paul V.

2004-01-01

Aircraft routinely used for agricultural spray application are finding utility for remote sensing. Data obtained from remote sensing can be used for prescription application of pesticides, fertilizers, cotton growth regulators, and water (the latter with the assistance of hyperspectral indices and thermal imaging). Digital video was used to detect weeds in early cotton, and preliminary data were obtained to see if nitrogen status could be detected in early soybeans. Weeds were differentiable from early cotton at very low altitudes (65-m), with the aid of supervised classification algorithms in the ENVI image analysis software. The camera was flown at very low altitude for acceptable pixel resolution. Nitrogen status was not detectable by statistical analysis of digital numbers (DNs) obtained from images, but soybean cultivar differences were statistically discernable (F=26, p=0.01). Spectroradiometer data are being analyzed to identify narrow spectral bands that might aid in selecting camera filters for determination of plant nitrogen status. Multiple camera configurations are proposed to allow vegetative indices to be developed more readily. Both remotely sensed field images and ground data are to be used for decision-making in a proposed variable-rate application system for agricultural aircraft. For this system, prescriptions generated from digital imagery and data will be coupled with GPS-based swath guidance and programmable flow control.

Photogrammetry - Remote Sensing and Geoinformation

Science.gov (United States)

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

2012-07-01

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

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

Science.gov (United States)

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

2018-01-01

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

Image Fusion Technologies In Commercial Remote Sensing Packages

OpenAIRE

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

2013-01-01

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

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

Science.gov (United States)

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.

Remote Sensing Digital Image Analysis An Introduction

CERN Document Server

Richards, John A

2013-01-01

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

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

Science.gov (United States)

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

2017-12-01

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

Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data

Directory of Open Access Journals (Sweden)

Wei Tu

2018-01-01

Full Text Available Portraying urban functional zones provides useful insights into understanding complex urban systems and establishing rational urban planning. Although several studies have confirmed the efficacy of remote sensing imagery in urban studies, coupling remote sensing and new human sensing data like mobile phone positioning data to identify urban functional zones has still not been investigated. In this study, a new framework integrating remote sensing imagery and mobile phone positioning data was developed to analyze urban functional zones with landscape and human activity metrics. Landscapes metrics were calculated based on land cover from remote sensing images. Human activities were extracted from massive mobile phone positioning data. By integrating them, urban functional zones (urban center, sub-center, suburbs, urban buffer, transit region and ecological area were identified by a hierarchical clustering. Finally, gradient analysis in three typical transects was conducted to investigate the pattern of landscapes and human activities. Taking Shenzhen, China, as an example, the conducted experiment shows that the pattern of landscapes and human activities in the urban functional zones in Shenzhen does not totally conform to the classical urban theories. It demonstrates that the fusion of remote sensing imagery and human sensing data can characterize the complex urban spatial structure in Shenzhen well. Urban functional zones have the potential to act as bridges between the urban structure, human activity and urban planning policy, providing scientific support for rational urban planning and sustainable urban development policymaking.

International Commercial Remote Sensing Practices and Policies: A Comparative Analysis

Science.gov (United States)

Stryker, Timothy

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