AN EVOLUTIONARY ALGORITHM FOR FAST INTENSITY BASED IMAGE MATCHING BETWEEN OPTICAL AND SAR SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

P. Fischer

2018-04-01

Full Text Available This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

Commercial Satellite Imagery Analysis for Countering Nuclear Proliferation

Science.gov (United States)

Albright, David; Burkhard, Sarah; Lach, Allison

2018-05-01

High-resolution commercial satellite imagery from a growing number of private satellite companies allows nongovernmental analysts to better understand secret or opaque nuclear programs of countries in unstable or tense regions, called proliferant states. They include North Korea, Iran, India, Pakistan, and Israel. By using imagery to make these countries’ aims and capabilities more transparent, nongovernmental groups like the Institute for Science and International Security have affected the policies of governments and the course of public debate. Satellite imagery work has also strengthened the efforts of the International Atomic Energy Agency, thereby helping this key international agency build its case to mount inspections of suspect sites and activities. This work has improved assessments of the nuclear capabilities of proliferant states. Several case studies provide insight into the use of commercial satellite imagery as a key tool to educate policy makers and affect policy.

Thematic mapping from satellite imagery

CERN Document Server

Denègre, J

2013-01-01

Thematic Mapping from Satellite Imagery: A Guidebook discusses methods in producing maps using satellite images. The book is comprised of five chapters; each chapter covers one stage of the process. Chapter 1 tackles the satellite remote sensing imaging and its cartographic significance. Chapter 2 discusses the production processes for extracting information from satellite data. The next chapter covers the methods for combining satellite-derived information with that obtained from conventional sources. Chapter 4 deals with design and semiology for cartographic representation, and Chapter 5 pre

ACCURACY COMPARISON OF VHR SYSTEMATIC-ORTHO SATELLITE IMAGERIES AGAINST VHR ORTHORECTIFIED IMAGERIES USING GCP

Directory of Open Access Journals (Sweden)

E. Widyaningrum

2016-06-01

Full Text Available The Very High Resolution (VHR satellite imageries such us Pleiades, WorldView-2, GeoEye-1 used for precise mapping purpose must be corrected from any distortion to achieve the expected accuracy. Orthorectification is performed to eliminate geometric errors of the VHR satellite imageries. Orthorectification requires main input data such as Digital Elevation Model (DEM and Ground Control Point (GCP. The VHR systematic-ortho imageries were generated using SRTM 30m DEM without using any GCP data. The accuracy value differences of VHR systematic-ortho imageries and VHR orthorectified imageries using GCP currently is not exactly defined. This study aimed to identified the accuracy comparison of VHR systematic-ortho imageries against orthorectified imageries using GCP. Orthorectified imageries using GCP created by using Rigorous model. Accuracy evaluation is calculated by using several independent check points.

A geologic analysis of the Side-Looking Airborne Radar imagery of southern New England

Science.gov (United States)

Banks, Paul T.

1975-01-01

Analysis of the side looking airborn radar imagery of Massachusetts, Connecticut and Rhode Island indicates that radar shows the topography in great detail. Since bedrock geologic features are frequently expressed in the topography the radar lends itself to geologic interpretation. The radar was studied by comparisons with field mapped geologic data first at a scale of approximately 1:125,000 and then at a scale of 1:500,000. The larger scale comparison revealed that faults, minor faults, joint sets, bedding and foliation attitudes, lithology and lithologic contacts all have a topographic expression interpretable on the imagery. Surficial geologic features were far less visible on the imagery over most of the area studied. The smaller scale comparisons revealed a pervasive, near orthogonal fracture set cutting all types and ages of rock and trending roughly N40?E and N30?W. In certain places the strike of bedding and foliation attitudes and some lithologic Contacts were visible in addition to the fractures. Fracturing in southern New England is apparently far more important than has been previously recognized. This new information, together with the visibility of many bedding and foliation attitudes and lithologic contacts, indicates the importance of radar imagery in improving the geologic interpretation of an area.

An approach for flood monitoring by the combined use of Landsat 8 optical imagery and COSMO-SkyMed radar imagery

Science.gov (United States)

Tong, Xiaohua; Luo, Xin; Liu, Shuguang; Xie, Huan; Chao, Wei; Liu, Shuang; Liu, Shijie; Makhinov, A. N.; Makhinova, A. F.; Jiang, Yuying

2018-02-01

Remote sensing techniques offer potential for effective flood detection with the advantages of low-cost, large-scale, and real-time surface observations. The easily accessible data sources of optical remote sensing imagery provide abundant spectral information for accurate surface water body extraction, and synthetic aperture radar (SAR) systems represent a powerful tool for flood monitoring because of their all-weather capability. This paper introduces a new approach for flood monitoring by the combined use of both Landsat 8 optical imagery and COSMO-SkyMed radar imagery. Specifically, the proposed method applies support vector machine and the active contour without edges model for water extent determination in the periods before and during the flood, respectively. A map difference method is used for the flood inundation analysis. The proposed approach is particularly suitable for large-scale flood monitoring, and it was tested on a serious flood that occurred in northeastern China in August 2013, which caused immense loss of human lives and properties. High overall accuracies of 97.46% for the optical imagery and 93.70% for the radar imagery are achieved by the use of the techniques presented in this study. The results show that about 12% of the whole study area was inundated, corresponding to 5466 km2 of land surface.

High resolution satellite imagery : from spies to pipeline management

Energy Technology Data Exchange (ETDEWEB)

Adam, S. [Canadian Geomatic Solutions Ltd., Calgary, AB (Canada); Farrell, M. [TransCanada Transmission, Calgary, AB (Canada)

2000-07-01

The launch of Space Imaging's IKONOS satellite in September 1999 has opened the door for corridor applications. The technology has been successfully implemented by TransCanada PipeLines in mapping over 1500 km of their mainline. IKONOS is the world's first commercial high resolution satellite which collects data at 1-meter black/white and 4-meter multi-spectral. Its use is regulated by the U.S. government. It is the best source of high resolution satellite image data. Other sources include the Indian Space Agency's IRS-1 C/D satellite and the Russian SPIN-2 which provides less reliable coverage. In addition, two more high resolution satellites may be launched this year to provide imagery every day of the year. IKONOS scenes as narrow as 5 km can be purchased. TransCanada conducted a pilot study to determine if high resolution satellite imagery is as effective as ortho-photos for identifying population structures within a buffer of TransCanada's east line right-of-way. The study examined three unique segments where residential, commercial, industrial and public features were compared. It was determined that IKONOS imagery is as good as digital ortho-photos for updating structures from low to very high density areas. The satellite imagery was also logistically easier than ortho-photos to acquire. This will be even more evident when the IKONOS image archives begins to grow. 4 tabs., 3 figs.

Satellite Imagery Analysis for Automated Global Food Security Forecasting

Science.gov (United States)

Moody, D.; Brumby, S. P.; Chartrand, R.; Keisler, R.; Mathis, M.; Beneke, C. M.; Nicholaeff, D.; Skillman, S.; Warren, M. S.; Poehnelt, J.

2017-12-01

The recent computing performance revolution has driven improvements in sensor, communication, and storage technology. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. Cloud computing and storage, combined with recent advances in machine learning, are enabling understanding of the world at a scale and at a level of detail never before feasible. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and that can scale with the high-rate and dimensionality of imagery being collected. We focus on the problem of monitoring food crop productivity across the Middle East and North Africa, and show how an analysis-ready, multi-sensor data platform enables quick prototyping of satellite imagery analysis algorithms, from land use/land cover classification and natural resource mapping, to yearly and monthly vegetative health change trends at the structural field level.

Satellite imagery and the Department of Safeguards

International Nuclear Information System (INIS)

Chitumbo, K.; Bunney, J.; Leve, G.; Robb, S.

2001-01-01

Full text: The presentation examines some of the challenges the Satellite Imagery and Analysis Laboratory (SIAL) is facing in supporting Strengthened Safeguards. It focuses on the analytical process, starting with specifying initial tasking and continuing through to end products that are a direct result of in-house analysis. In addition it also evaluates the advantages and disadvantages of SIAL's mission and introduces external forces that the agency must consider, but cannot itself, predict or control. Although SIAL's contribution to tasks relating to Article 2a(iii) of the Additional Protocol are known and are presently of great benefit to operations areas, this is only one aspect of its work. SIAL's ability to identify and analyze historical satellite imagery data has the advantage of permitting operations to take a more in depth view of a particular area of interest's (AOI) development, and thus may permit operations to confirm or refute specific assertions relating to the AOI's function or abilities. These assertions may originate in-house or may be open source reports the agency feels it is obligated to explore. SIAL's mission is unique in the world of imagery analysis. Its aim is to support all operations areas equally and in doing so it must maintain global focus. The task is tremendous, but the resultant coverage and concentration of unique expertise will allow SIAL to develop and provide operations with datasets that can be exploited in standalone mode or be incorporated into new cutting edge tools to be developed in SGIT. At present SIAL relies on two remote sensors, IKONOS-2 and EROS-AI, for present high- resolution imagery data and is using numerous sources for historical, pre 1999, data. A multiplicity of sources for high-resolution data is very important to SIAL, but is something that it cannot influence. It is hoped that the planned launch of two new sensors by Summer 2002 will be successful and will offer greater flexibility for image collection

Satellite-generated radar images of the earth

International Nuclear Information System (INIS)

Schanda, E.

1980-01-01

The Synthetic Aperture Radar (SAR) on board of SEASAT was the first non-military satellite-borne radar producing high-resolution images of the earth. Several examples of European scenes are discussed to demonstrate the properties of presently available optically processes images. (orig.)

Evaluation of radar imagery for geological and cartographic applications

Science.gov (United States)

Moore, Gerald K.; Sheehan, Cynthia A.

1981-01-01

The House/Senate conference report on H.R. 4930 (96th Congress), the Department of the Interior and Related Agencies Appropriations bill, 1980, stated that the U.S. Geological Survey should "begin the use of side-looking airborne radar imagery for topographic and geological mapping, and geological resource surveys in promising areas, particularly Alaska." In response to this mandate, the Survey acquired radar data and began scientific studies to analyze and interpret these data. About 70 percent of the project funding was used to acquire radar imagery and to evaluate Alaskan applications. Results of these studies indicate that radar images have a unique incremental value for certain geologic and cartographic applications but that the images are best suited for use as supplemental information sources or as primary data sources in areas of persistent cloud cover.The value of radar data is greatest for geologic mapping and resource surveys, particularly for mineral and petroleum exploration, where the objective is to locate any single feature or group of features that may control the occurrences of these resources. Radar images are considered by oil and gas companies to be worth the cost of data acquisition within a limited area of active exploration.Radar images also have incremental value for geologic site studies and hazard mapping. The need in these cases is TO inventory all geologic hazards to human life, property, resources, and the environment. For other geologic applications, radar images have a relatively small incremental value over a combination of Landsat images and aerial photographs.The value of radar images for cartographic applications is minimal, except when they are used as a substitute for aerial photographs and topographic maps in persistently cloud-covered areas. If conventional data sources are not available, radar images provide useful information on terrain relief, landforms, drainage patterns, and land cover. Screen less lithography is a low

Automatic Mosaicking of Satellite Imagery Considering the Clouds

Science.gov (United States)

Kang, Yifei; Pan, Li; Chen, Qi; Zhang, Tong; Zhang, Shasha; Liu, Zhang

2016-06-01

With the rapid development of high resolution remote sensing for earth observation technology, satellite imagery is widely used in the fields of resource investigation, environment protection, and agricultural research. Image mosaicking is an important part of satellite imagery production. However, the existence of clouds leads to lots of disadvantages for automatic image mosaicking, mainly in two aspects: 1) Image blurring may be caused during the process of image dodging, 2) Cloudy areas may be passed through by automatically generated seamlines. To address these problems, an automatic mosaicking method is proposed for cloudy satellite imagery in this paper. Firstly, modified Otsu thresholding and morphological processing are employed to extract cloudy areas and obtain the percentage of cloud cover. Then, cloud detection results are used to optimize the process of dodging and mosaicking. Thus, the mosaic image can be combined with more clear-sky areas instead of cloudy areas. Besides, clear-sky areas will be clear and distortionless. The Chinese GF-1 wide-field-of-view orthoimages are employed as experimental data. The performance of the proposed approach is evaluated in four aspects: the effect of cloud detection, the sharpness of clear-sky areas, the rationality of seamlines and efficiency. The evaluation results demonstrated that the mosaic image obtained by our method has fewer clouds, better internal color consistency and better visual clarity compared with that obtained by traditional method. The time consumed by the proposed method for 17 scenes of GF-1 orthoimages is within 4 hours on a desktop computer. The efficiency can meet the general production requirements for massive satellite imagery.

Radar imagery from the 1994 Lock Linnhe ship wake experiment

Energy Technology Data Exchange (ETDEWEB)

Mullenhoff, C.J.; Lehman, S.K.; Jones, H. [Lawrence Livermore National Lab., CA (United States)] [and others

1994-11-15

The 1994 Loch Linnhe radar ocean imaging trials were held from September 4 through September 17. Two ships were used: the R.V. Colonel Templer, and the RMAS Collie. Thorn EMI, Inc., fielded a dual band, dual polarization radar on a hillside overlooking the loch. A primary purpose of the experiment was to obtain highly visible images of ship generated internal waves. Presented here is imagery for a few of the good ship runs, as well as a study of the environment of the visibility of ship generated internal waves.

Assessment of the Impact of Reservoirs in the Upper Mekong River Using Satellite Radar Altimetry and Remote Sensing Imageries

Directory of Open Access Journals (Sweden)

Kuan-Ting Liu

2016-04-01

Full Text Available Water level (WL and water volume (WV of surface-water bodies are among the most crucial variables used in water-resources assessment and management. They fluctuate as a result of climatic forcing, and they are considered as indicators of climatic impacts on water resources. Quantifying riverine WL and WV, however, usually requires the availability of timely and continuous in situ data, which could be a challenge for rivers in remote regions, including the Mekong River basin. As one of the most developed rivers in the world, with more than 20 dams built or under construction, Mekong River is in need of a monitoring system that could facilitate basin-scale management of water resources facing future climate change. This study used spaceborne sensors to investigate two dams in the upper Mekong River, Xiaowan and Jinghong Dams within China, to examine river flow dynamics after these dams became operational. We integrated multi-mission satellite radar altimetry (RA, Envisat and Jason-2 and Landsat-5/-7/-8 Thematic Mapper (TM/Enhanced Thematic Mapper plus (ETM+/Operational  Land Imager (OLI optical remote sensing (RS imageries to construct composite WL time series with enhanced spatial resolutions and substantially extended WL data records. An empirical relationship between WL variation and water extent was first established for each dam, and then the combined long-term WL time series from Landsat images are reconstructed for the dams. The R2 between altimetry WL and Landsat water area measurements is >0.95. Next, the Tropical Rainfall Measuring Mission (TRMM data were used to diagnose and determine water variation caused by the precipitation anomaly within the basin. Finally, the impact of hydrologic dynamics caused by the impoundment of the dams is assessed. The discrepancy between satellite-derived WL and available in situ gauge data, in term of root-mean-square error (RMSE is at 2–5 m level. The estimated WV variations derived from combined RA

The Potential Uses of Commercial Satellite Imagery in the Middle East

Energy Technology Data Exchange (ETDEWEB)

Vannoni, M.G.

1999-06-08

It became clear during the workshop that the applicability of commercial satellite imagery to the verification of future regional arms control agreements is limited at this time. Non-traditional security topics such as environmental protection, natural resource management, and the development of infrastructure offer the more promising applications for commercial satellite imagery in the short-term. Many problems and opportunities in these topics are regional, or at least multilateral, in nature. A further advantage is that, unlike arms control and nonproliferation applications, cooperative use of imagery in these topics can be done independently of the formal Middle East Peace Process. The value of commercial satellite imagery to regional arms control and nonproliferation, however, will increase during the next three years as new, more capable satellite systems are launched. Aerial imagery, such as that used in the Open Skies Treaty, can also make significant contributions to both traditional and non-traditional security applications but has the disadvantage of requiring access to national airspace and potentially higher cost. There was general consensus that commercial satellite imagery is under-utilized in the Middle East and resources for remote sensing, both human and institutional, are limited. This relative scarcity, however, provides a natural motivation for collaboration in non-traditional security topics. Collaborations between scientists, businesses, universities, and non-governmental organizations can work at the grass-roots level and yield contributions to confidence building as well as scientific and economic results. Joint analysis projects would benefit the region as well as establish precedents for cooperation.

Sea Ice Deformation State From Synthetic Aperture Radar Imagery - Part II: Effects of Spatial Resolution and Noise Level

DEFF Research Database (Denmark)

Dierking, Wolfgang; Dall, Jørgen

2008-01-01

C- and L-band airborne synthetic aperture radar (SAR) imagery acquired at like- and cross-polarization over sea ice under winter conditions is examined with the objective to study the discrimination between level ice and ice deformation features. High-resolution low-noise data were analysed...... in the first paper. In this second paper, the main topics are the effects of spatial resolution and signal-to-noise ratio. Airborne, high-resolution SAR scenes are used to generate a sequence of images with increasingly coarser spatial resolution from 5 m to 25 m, keeping the number of looks constant....... The signal-to-noise ratio is varied between typical noise levels for airborne imagery and satellite data. Areal fraction of deformed ice and average deformation distance are determined for each image product. At L-band, the retrieved values of the areal fraction get larger as the image resolution is degraded...

Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm

Directory of Open Access Journals (Sweden)

Yuxiang He

2018-01-01

Full Text Available This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE that would objectively blend real-time satellite quantitative precipitation estimates (SQPE with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a five-year period between 2003–2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS (National Weather Service operations over two regions: (I Inside radar effective coverage and (II immediately outside radar coverage. The outcomes of the evaluation indicate (a ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and (b blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.

Biomass burning - Combustion emissions, satellite imagery, and biogenic emissions

Science.gov (United States)

Levine, Joel S.; Cofer, Wesley R., III; Winstead, Edward L.; Rhinehart, Robert P.; Cahoon, Donald R., Jr.; Sebacher, Daniel I.; Sebacher, Shirley; Stocks, Brian J.

1991-01-01

After detailing a technique for the estimation of the instantaneous emission of trace gases produced by biomass burning, using satellite imagery, attention is given to the recent discovery that burning results in significant enhancement of biogenic emissions of N2O, NO, and CH4. Biomass burning accordingly has an immediate and long-term impact on the production of atmospheric trace gases. It is presently demonstrated that satellite imagery of fires may be used to estimate combustion emissions, and could be used to estimate long-term postburn biogenic emission of trace gases to the atmosphere.

IAEA Safeguards: Cost/benefit analysis of commercial satellite imagery

International Nuclear Information System (INIS)

Andersson, Christer

1999-03-01

A major milestone in the efforts to strengthen the Safeguards System was reached in May 1997 when the Board of Governors approved a 'Model Protocol Additional to Safeguards Agreements'. The Protocol provides the legal basis necessary to enhance the Agency's ability to detect undeclared nuclear material and activities by using information available from open sources to complement the declarations made by Member States. Commercially available high-resolution satellite data has emerged as one potential complementary open information source to support the traditional and extended Safeguard activities of IAEA. This document constitutes a first report from SSC Satellitbild giving the Agency tentative and initial estimates of the potential cost and time-savings possible with the new proposed technology. The initial cost/benefit simulation will be further finalised in the following 'Implementation Blueprint' study. The general foundation and starting point for the cost/benefit calculation is to simulate a new efficient and relatively small 'imagery unit' within the IAEA, capable of performing advanced image processing as a tool for various safeguards tasks. The image processing capacity is suggested to be task- and interpretation-oriented. The study was performed over a period of 1,5 weeks in late 1998, and is based upon interviews of IAEA staff, reviews of existing IAEA documentation as well as from SSC Satellitbild's long-standing experience of satellite imagery and field missions. The cost/benefit analysis is based on a spreadsheet simulation of five potential applications of commercial satellite imagery: Reference information; Confirmation of Agency acquired and Member State supplied data; Change detection and on-going monitoring; Assessing open source information available to the Agency; Detecting undeclared activities and undeclared sites. The study confirms that the proposed concept of a relatively small 'imagery unit' using high-resolution data will be a sound and

Wind class sampling of satellite SAR imagery for offshore wind resource mapping

DEFF Research Database (Denmark)

Badger, Merete; Badger, Jake; Nielsen, Morten

2010-01-01

developed for mesoscale modeling of wind resources. Its performance in connection with sampling of SAR scenes is tested against two sets of random SAR samples and meteorological observations at three sites in the North Sea during 2005–08. Predictions of the mean wind speed and the Weibull scale parameter......High-resolution wind fields retrieved from satellite synthetic aperture radar (SAR) imagery are combined for mapping of wind resources offshore where site measurements are costly and sparse. A new sampling strategy for the SAR scenes is introduced, based on a method for statistical......-dynamical downscaling of large-scale wind conditions using a set of wind classes that describe representative wind situations. One or more SAR scenes are then selected to represent each wind class and the classes are weighted according to their frequency of occurrence. The wind class methodology was originally...

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

Directory of Open Access Journals (Sweden)

Wei Jin

2016-12-01

Full Text Available Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC, atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

Science.gov (United States)

Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

2016-01-01

Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. PMID:27999261

IAEA Safeguards: Cost/benefit analysis of commercial satellite imagery

Energy Technology Data Exchange (ETDEWEB)

Andersson, Christer [SSC Satellitbild AB, Kiruna (Sweden)

1999-03-01

A major milestone in the efforts to strengthen the Safeguards System was reached in May 1997 when the Board of Governors approved a `Model Protocol Additional to Safeguards Agreements`. The Protocol provides the legal basis necessary to enhance the Agency`s ability to detect undeclared nuclear material and activities by using information available from open sources to complement the declarations made by Member States. Commercially available high-resolution satellite data has emerged as one potential complementary open information source to support the traditional and extended Safeguard activities of IAEA. This document constitutes a first report from SSC Satellitbild giving the Agency tentative and initial estimates of the potential cost and time-savings possible with the new proposed technology. The initial cost/benefit simulation will be further finalised in the following `Implementation Blueprint` study. The general foundation and starting point for the cost/benefit calculation is to simulate a new efficient and relatively small `imagery unit` within the IAEA, capable of performing advanced image processing as a tool for various safeguards tasks. The image processing capacity is suggested to be task- and interpretation-oriented. The study was performed over a period of 1,5 weeks in late 1998, and is based upon interviews of IAEA staff, reviews of existing IAEA documentation as well as from SSC Satellitbild`s long-standing experience of satellite imagery and field missions. The cost/benefit analysis is based on a spreadsheet simulation of five potential applications of commercial satellite imagery: Reference information; Confirmation of Agency acquired and Member State supplied data; Change detection and on-going monitoring; Assessing open source information available to the Agency; Detecting undeclared activities and undeclared sites. The study confirms that the proposed concept of a relatively small `imagery unit` using high-resolution data will be a sound and

Towards an integrated strategy for monitoring wetland inundation with virtual constellations of optical and radar satellites

Science.gov (United States)

DeVries, B.; Huang, W.; Huang, C.; Jones, J. W.; Lang, M. W.; Creed, I. F.; Carroll, M.

2017-12-01

The function of wetlandscapes in hydrological and biogeochemical cycles is largely governed by surface inundation, with small wetlands that experience periodic inundation playing a disproportionately large role in these processes. However, the spatial distribution and temporal dynamics of inundation in these wetland systems are still poorly understood, resulting in large uncertainties in global water, carbon and greenhouse gas budgets. Satellite imagery provides synoptic and repeat views of the Earth's surface and presents opportunities to fill this knowledge gap. Despite the proliferation of Earth Observation satellite missions in the past decade, no single satellite sensor can simultaneously provide the spatial and temporal detail needed to adequately characterize inundation in small, dynamic wetland systems. Surface water data products must therefore integrate observations from multiple satellite sensors in order to address this objective, requiring the development of improved and coordinated algorithms to generate consistent estimates of surface inundation. We present a suite of algorithms designed to detect surface inundation in wetlands using data from a virtual constellation of optical and radar sensors comprising the Landsat and Sentinel missions (DeVries et al., 2017). Both optical and radar algorithms were able to detect inundation in wetlands without the need for external training data, allowing for high-efficiency monitoring of wetland inundation at large spatial and temporal scales. Applying these algorithms across a gradient of wetlands in North America, preliminary findings suggest that while these fully automated algorithms can detect wetland inundation at higher spatial and temporal resolutions than currently available surface water data products, limitations specific to the satellite sensors and their acquisition strategies are responsible for uncertainties in inundation estimates. Further research is needed to investigate strategies for

Volumetric Forest Change Detection Through Vhr Satellite Imagery

Science.gov (United States)

Akca, Devrim; Stylianidis, Efstratios; Smagas, Konstantinos; Hofer, Martin; Poli, Daniela; Gruen, Armin; Sanchez Martin, Victor; Altan, Orhan; Walli, Andreas; Jimeno, Elisa; Garcia, Alejandro

2016-06-01

Quick and economical ways of detecting of planimetric and volumetric changes of forest areas are in high demand. A research platform, called FORSAT (A satellite processing platform for high resolution forest assessment), was developed for the extraction of 3D geometric information from VHR (very-high resolution) imagery from satellite optical sensors and automatic change detection. This 3D forest information solution was developed during a Eurostars project. FORSAT includes two main units. The first one is dedicated to the geometric and radiometric processing of satellite optical imagery and 2D/3D information extraction. This includes: image radiometric pre-processing, image and ground point measurement, improvement of geometric sensor orientation, quasiepipolar image generation for stereo measurements, digital surface model (DSM) extraction by using a precise and robust image matching approach specially designed for VHR satellite imagery, generation of orthoimages, and 3D measurements in single images using mono-plotting and in stereo images as well as triplets. FORSAT supports most of the VHR optically imagery commonly used for civil applications: IKONOS, OrbView - 3, SPOT - 5 HRS, SPOT - 5 HRG, QuickBird, GeoEye-1, WorldView-1/2, Pléiades 1A/1B, SPOT 6/7, and sensors of similar type to be expected in the future. The second unit of FORSAT is dedicated to 3D surface comparison for change detection. It allows users to import digital elevation models (DEMs), align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes between epochs. To this end our 3D surface matching method LS3D is being used. FORSAT is a single source and flexible forest information solution with a very competitive price/quality ratio, allowing expert and non-expert remote sensing users to monitor forests in three and four dimensions from VHR optical imagery for many forest information needs. The capacity and benefits of FORSAT have been tested in

Visualizing Cloud Properties and Satellite Imagery: A Tool for Visualization and Information Integration

Science.gov (United States)

Chee, T.; Nguyen, L.; Smith, W. L., Jr.; Spangenberg, D.; Palikonda, R.; Bedka, K. M.; Minnis, P.; Thieman, M. M.; Nordeen, M.

2017-12-01

Providing public access to research products including cloud macro and microphysical properties and satellite imagery are a key concern for the NASA Langley Research Center Cloud and Radiation Group. This work describes a web based visualization tool and API that allows end users to easily create customized cloud product and satellite imagery, ground site data and satellite ground track information that is generated dynamically. The tool has two uses, one to visualize the dynamically created imagery and the other to provide access to the dynamically generated imagery directly at a later time. Internally, we leverage our practical experience with large, scalable application practices to develop a system that has the largest potential for scalability as well as the ability to be deployed on the cloud to accommodate scalability issues. We build upon NASA Langley Cloud and Radiation Group's experience with making real-time and historical satellite cloud product information, satellite imagery, ground site data and satellite track information accessible and easily searchable. This tool is the culmination of our prior experience with dynamic imagery generation and provides a way to build a "mash-up" of dynamically generated imagery and related kinds of information that are visualized together to add value to disparate but related information. In support of NASA strategic goals, our group aims to make as much scientific knowledge, observations and products available to the citizen science, research and interested communities as well as for automated systems to acquire the same information for data mining or other analytic purposes. This tool and the underlying API's provide a valuable research tool to a wide audience both as a standalone research tool and also as an easily accessed data source that can easily be mined or used with existing tools.

Strengthening IAEA safeguards using high-resolution commercial satellite imagery

International Nuclear Information System (INIS)

Zhang Hui

2001-01-01

Full text: In May 1997, the IAEA Board of Governors adopted the Additional Safeguards Protocol to improve its ability to detect the undeclared production of fissile material. This new strengthened safeguards system has opened the door for the IAEA to use of all types of information, including the potential use of commercial satellite imagery. We have therefore been investigating the feasibility of strengthening IAEA safeguards using commercial satellite imagery. Based on our analysis on a number of one-meter resolution IKONOS satellite images of military nuclear production facilities at nuclear states including Russia, China, India, Pakistan and Israel, we found that the new high-resolution commercial satellite imagery would play a new and valuable role in strengthening IAEA safeguards. Since 1999, images with a resolution of one meter have been available commercially from Space Imaging's IKONOS satellite. One-meter images from other companies are expected to enter the market soon. Although still an order of magnitude less capable than military imaging satellites, the capabilities of these new high-resolution commercial satellites are good enough to detect and identify the major visible characteristics of nuclear production facilities and sites. Unlike the classified spy satellite photos limited to few countries, the commercial satellite imagery is commercially available to anyone who wants to purchase it. Therefore, the new commercial satellite open a new chance that each state, international organizations, and non-governmental groups could use the commercial images to play a more proactive role in monitoring the nuclear activities in related countries and verifying the compliance of non-proliferation agreements. This could help galvanize support for intensified efforts to slow the pace of nuclear proliferation. To produce fissile materials (plutonium and highly enriched uranium) for weapons, a country would operate dedicated plutonium-production reactors and the

Subsidence feature discrimination using deep convolutional neral networks in synthetic aperture radar imagery

CSIR Research Space (South Africa)

Schwegmann, Colin P

2017-07-01

Full Text Available International Geoscience and Remote Sensing Symposium (IGARSS), 23-28 July 2017, Fort Worth, TX, USA SUBSIDENCE FEATURE DISCRIMINATION USING DEEP CONVOLUTIONAL NEURAL NETWORKS IN SYNTHETIC APERTURE RADAR IMAGERY Schwegmann, Colin P Kleynhans, Waldo...

Identification of High-Variation Fields based on Open Satellite Imagery

DEFF Research Database (Denmark)

Jeppesen, Jacob Høxbroe; Jacobsen, Rune Hylsberg; Nyholm Jørgensen, Rasmus

2017-01-01

. The categorization is based on vegetation indices derived from Sentinel-2 satellite imagery. A case study on 7678 winter wheat fields is presented, which employs open data and open source software to analyze the satellite imagery. Furthermore, the method can be automated to deliver categorizations at every update......This paper proposes a simple method for categorizing fields on a regional level, with respect to intra-field variations. It aims to identify fields where the potential benefits of applying precision agricultural practices are highest from an economic and environmental perspective...

A comparison of visual observations of surface oil with Synthetic Aperture Radar imagery of the Sea Empress oil spill

Energy Technology Data Exchange (ETDEWEB)

Jones, B.

2001-06-15

A comparison has been made between the visual observations of surface oil and four satellite-borne Synthetic Aperture Radar (SAR) images taken during the Sea Empress oil spill in February 1996. Whilst the basic oil slick imaging capabilities of SAR are well documented, to be of use at the time of a major oil spill, the imagery must be able to provide information on the thickness of oil. This analysis suggests that, under certain environmental conditions, this is possible. The optimum wind speed for the identification of heavy surface oil is around 5-6 m s{sup -1}. At this wind speed, light and medium sheen is not evident in the imagery and there is a distinction between the backscatter reductions due to heavy sheen and thick brown/black oil. At higher wind speeds, even thick oil slicks readily mix into the water column and their SAR signature weakens. In light winds, pattern recognition is very important to the identification of oil sticks. The images are more sensitive to the presence of sheen within the sheltered waters of Milford Haven than in the open coastal waters, indicating a possible relationship between sheen visibility in satellite-borne SAR and sea state. (author)

Monitoring Areal Snow Cover Using NASA Satellite Imagery

Science.gov (United States)

Harshburger, Brian J.; Blandford, Troy; Moore, Brandon

2011-01-01

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data

"Data Day" and "Data Night" Definitions - Towards Producing Seamless Global Satellite Imagery

Science.gov (United States)

Schmaltz, J. E.

2017-12-01

For centuries, the art and science of cartography has struggled with the challenge of mapping the round earth on to a flat page, or a flat computer monitor. Earth observing satellites with continuous monitoring of our planet have added the additional complexity of the time dimension to this procedure. The most common current practice is to segment this data by 24-hour Coordinated Universal Time (UTC) day and then split the day into sun side "Data Day" and shadow side "Data Night" global imagery that spans from dateline to dateline. Due to the nature of satellite orbits, simply binning the data by UTC date produces significant discontinuities at the dateline for day images and at Greenwich for night images. Instead, imagery could be generated in a fashion that follows the spatial and temporal progression of the satellite which would produce seamless imagery everywhere on the globe for all times. This presentation will explore approaches to produce such imagery but will also address some of the practical and logistical difficulties in implementing such changes. Topics will include composites versus granule/orbit based imagery, day/night versus ascending/descending definitions, and polar versus global projections.

Monitoring of rain water storage in forests with satellite radar

OpenAIRE

de Jong, JJM; Klaassen, W; Kuiper, PJC

2002-01-01

The sensitivity of radar backscatter to the amount of intercepted rain in temperate deciduous forests is analyzed to determine the feasibility of retrieval of this parameter from satellite radar data. A backscatter model is validated with X-band radar measurements of a single tree exposed to rain. A good agreement between simulation and measurements is observed and this demonstrates the ability of radar to measure the amount of intercepted rain. The backscatter model is next applied to simula...

Detection of pear thrips damage using satellite imagery data

Science.gov (United States)

James E. Vogelmann; Barrett N. Rock

1991-01-01

This study evaluates the potential of measuring, mapping and monitoring sugar maple damage caused by pear thrips in southern Vermont and northwestern Massachusetts using satellite imagery data. Landsat Thematic Mapper (TM) data were obtained during a major thrips infestation in June 1988, and were compared with satellite data acquired during June 1984 (before pear...

Satellite Imagery Assisted Road-Based Visual Navigation System

Science.gov (United States)

Volkova, A.; Gibbens, P. W.

2016-06-01

There is a growing demand for unmanned aerial systems as autonomous surveillance, exploration and remote sensing solutions. Among the key concerns for robust operation of these systems is the need to reliably navigate the environment without reliance on global navigation satellite system (GNSS). This is of particular concern in Defence circles, but is also a major safety issue for commercial operations. In these circumstances, the aircraft needs to navigate relying only on information from on-board passive sensors such as digital cameras. An autonomous feature-based visual system presented in this work offers a novel integral approach to the modelling and registration of visual features that responds to the specific needs of the navigation system. It detects visual features from Google Earth* build a feature database. The same algorithm then detects features in an on-board cameras video stream. On one level this serves to localise the vehicle relative to the environment using Simultaneous Localisation and Mapping (SLAM). On a second level it correlates them with the database to localise the vehicle with respect to the inertial frame. The performance of the presented visual navigation system was compared using the satellite imagery from different years. Based on comparison results, an analysis of the effects of seasonal, structural and qualitative changes of the imagery source on the performance of the navigation algorithm is presented. * The algorithm is independent of the source of satellite imagery and another provider can be used

Stormwater Runoff Plumes in Southern California Detected with Satellite SAR and MODIS Imagery - Areas of Increased Contamination Risk

Science.gov (United States)

Trinh, R. C.; Holt, B.; Gierach, M.

2016-12-01

Coastal pollution poses both a major health and environmental hazard, not only for beachgoers and coastal communities, but for marine organisms as well. Stormwater runoff is the largest source of pollution in the coastal waters of the Southern California Bight (SCB). The SCB is the final destination of four major urban watersheds and associated rivers, Ballona Creek, the Los Angeles River, the San Gabriel River, and the Santa Ana River, which act as channels for runoff and pollution during and after episodic rainstorms. Previous studies of SCB water quality have made use of both fine resolution Synthetic Aperture Radar (SAR) imagery and wide-swath medium resolution optical "ocean color" imagery from SeaWiFS and MODIS. In this study, we expand on previous SAR efforts, compiling a more extensive collection of multi-sensor SAR data, spanning from 1992 to 2014, analyzing the surface slick component of stormwater plumes. We demonstrate the use of SAR data in early detection of coastal stormwater plumes, relating plume extent to cumulative river discharge, and shoreline fecal bacteria loads. Intensity maps of the primary extent and direction of plumes were created, identifying coastal areas that may be subject to the greatest risk of environmental contamination. Additionally, we illustrate the differences in the detection of SAR surface plumes with the sediment-related discharge plumes derived from MODIS ocean color imagery. Finally, we provide a concept for satellite monitoring of stormwater plumes, combining both optical and radar sensors, to be used to guide the collection of in situ water quality data and enhance the assessment of related beach closures.

Monitoring civil infrastructure using satellite radar interferometry

NARCIS (Netherlands)

Chang, L.

2015-01-01

Satellite radar interferometry (InSAR) is a precise and efficient technique to monitor deformation on Earth with millimeter precision. Most InSAR applications focus on geophysical phenomena, such as earthquakes, volcanoes, or subsidence. Monitoring civil infrastructure with InSAR is relatively new,

High-resolution satellite imagery is an important yet underutilized resource in conservation biology.

Science.gov (United States)

Boyle, Sarah A; Kennedy, Christina M; Torres, Julio; Colman, Karen; Pérez-Estigarribia, Pastor E; de la Sancha, Noé U

2014-01-01

Technological advances and increasing availability of high-resolution satellite imagery offer the potential for more accurate land cover classifications and pattern analyses, which could greatly improve the detection and quantification of land cover change for conservation. Such remotely-sensed products, however, are often expensive and difficult to acquire, which prohibits or reduces their use. We tested whether imagery of high spatial resolution (≤5 m) differs from lower-resolution imagery (≥30 m) in performance and extent of use for conservation applications. To assess performance, we classified land cover in a heterogeneous region of Interior Atlantic Forest in Paraguay, which has undergone recent and dramatic human-induced habitat loss and fragmentation. We used 4 m multispectral IKONOS and 30 m multispectral Landsat imagery and determined the extent to which resolution influenced the delineation of land cover classes and patch-level metrics. Higher-resolution imagery more accurately delineated cover classes, identified smaller patches, retained patch shape, and detected narrower, linear patches. To assess extent of use, we surveyed three conservation journals (Biological Conservation, Biotropica, Conservation Biology) and found limited application of high-resolution imagery in research, with only 26.8% of land cover studies analyzing satellite imagery, and of these studies only 10.4% used imagery ≤5 m resolution. Our results suggest that high-resolution imagery is warranted yet under-utilized in conservation research, but is needed to adequately monitor and evaluate forest loss and conversion, and to delineate potentially important stepping-stone fragments that may serve as corridors in a human-modified landscape. Greater access to low-cost, multiband, high-resolution satellite imagery would therefore greatly facilitate conservation management and decision-making.

IoSiS: a radar system for imaging of satellites in space

Science.gov (United States)

Jirousek, M.; Anger, S.; Dill, S.; Schreiber, E.; Peichl, M.

2017-05-01

Space debris nowadays is one of the main threats for satellite systems especially in low earth orbit (LEO). More than 700,000 debris objects with potential to destroy or damage a satellite are estimated. The effects of an impact often are not identifiable directly from ground. High-resolution radar images are helpful in analyzing a possible damage. Therefor DLR is currently developing a radar system called IoSiS (Imaging of Satellites in Space), being based on an existing steering antenna structure and our multi-purpose high-performance radar system GigaRad for experimental investigations. GigaRad is a multi-channel system operating at X band and using a bandwidth of up to 4.4 GHz in the IoSiS configuration, providing fully separated transmit (TX) and receive (RX) channels, and separated antennas. For the observation of small satellites or space debris a highpower traveling-wave-tube amplifier (TWTA) is mounted close to the TX antenna feed. For the experimental phase IoSiS uses a 9 m TX and a 1 m RX antenna mounted on a common steerable positioner. High-resolution radar images are obtained by using Inverse Synthetic Aperture Radar (ISAR) techniques. The guided tracking of known objects during overpass allows here wide azimuth observation angles. Thus high azimuth resolution comparable to the range resolution can be achieved. This paper outlines technical main characteristics of the IoSiS radar system including the basic setup of the antenna, the radar instrument with the RF error correction, and the measurement strategy. Also a short description about a simulation tool for the whole instrument and expected images is shown.

Essential climatic variables estimation with satellite imagery

Science.gov (United States)

Kolotii, A.; Kussul, N.; Shelestov, A.; Lavreniuk, M. S.

2016-12-01

According to Sendai Framework for Disaster Risk Reduction 2015 - 2030 Leaf Area Index (LAI) is considered as one of essential climatic variables. This variable represents the amount of leaf material in ecosystems and controls the links between biosphere and atmosphere through various processes and enables monitoring and quantitative assessment of vegetation state. LAI has added value for such important global resources monitoring tasks as drought mapping and crop yield forecasting with use of data from different sources [1-2]. Remote sensing data from space can be used to estimate such biophysical parameter at regional and national scale. High temporal satellite imagery is usually required to capture main parameters of crop growth [3]. Sentinel-2 mission launched in 2015 be ESA is a source of high spatial and temporal resolution satellite imagery for mapping biophysical parameters. Products created with use of automated Sen2-Agri system deployed during Sen2-Agri country level demonstration project for Ukraine will be compared with our independent results of biophysical parameters mapping. References Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., & Kostetsky, O. (2015, July). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1733-1736 Kolotii, A., Kussul, N., Shelestov, A., Skakun, S., Yailymov, B., Basarab, R., ... & Ostapenko, V. (2015). Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 39-44. Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (6), 2500-2508.

Collaborative, Rapid Mapping of Water Extents During Hurricane Harvey Using Optical and Radar Satellite Sensors

Science.gov (United States)

Muench, R.; Jones, M.; Herndon, K. E.; Bell, J. R.; Anderson, E. R.; Markert, K. N.; Molthan, A.; Adams, E. C.; Shultz, L.; Cherrington, E. A.; Flores, A.; Lucey, R.; Munroe, T.; Layne, G.; Pulla, S. T.; Weigel, A. M.; Tondapu, G.

2017-12-01

On August 25, 2017, Hurricane Harvey made landfall between Port Aransas and Port O'Connor, Texas, bringing with it unprecedented amounts of rainfall and flooding. In times of natural disasters of this nature, emergency responders require timely and accurate information about the hazard in order to assess and plan for disaster response. Due to the extreme flooding impacts associated with Hurricane Harvey, delineations of water extent were crucial to inform resource deployment. Through the USGS's Hazards Data Distribution System, government and commercial vendors were able to acquire and distribute various satellite imagery to analysts to create value-added products that can be used by these emergency responders. Rapid-response water extent maps were created through a collaborative multi-organization and multi-sensor approach. One team of researchers created Synthetic Aperture Radar (SAR) water extent maps using modified Copernicus Sentinel data (2017), processed by ESA. This group used backscatter images, pre-processed by the Alaska Satellite Facility's Hybrid Pluggable Processing Pipeline (HyP3), to identify and apply a threshold to identify water in the image. Quality control was conducted by manually examining the image and correcting for potential errors. Another group of researchers and graduate student volunteers derived water masks from high resolution DigitalGlobe and SPOT images. Through a system of standardized image processing, quality control measures, and communication channels the team provided timely and fairly accurate water extent maps to support a larger NASA Disasters Program response. The optical imagery was processed through a combination of various band thresholds by using Normalized Difference Water Index (NDWI), Modified Normalized Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and cloud masking. Several aspects of the pre-processing and image access were run on internal servers to expedite the provision of images to

Collaborative, Rapid Mapping of Water Extents During Hurricane Harvey Using Optical and Radar Satellite Sensors

Science.gov (United States)

Muench, Rebekke; Jones, Madeline; Herndon, Kelsey; Schultz, Lori; Bell, Jordan; Anderson, Eric; Markert, Kel; Molthan, Andrew; Adams, Emily; Cherrington, Emil;

Processing Satellite Imagery To Detect Waste Tire Piles

Science.gov (United States)

Skiles, Joseph; Schmidt, Cynthia; Wuinlan, Becky; Huybrechts, Catherine

2007-01-01

A methodology for processing commercially available satellite spectral imagery has been developed to enable identification and mapping of waste tire piles in California. The California Integrated Waste Management Board initiated the project and provided funding for the method s development. The methodology includes the use of a combination of previously commercially available image-processing and georeferencing software used to develop a model that specifically distinguishes between tire piles and other objects. The methodology reduces the time that must be spent to initially survey a region for tire sites, thereby increasing inspectors and managers time available for remediation of the sites. Remediation is needed because millions of used tires are discarded every year, waste tire piles pose fire hazards, and mosquitoes often breed in water trapped in tires. It should be possible to adapt the methodology to regions outside California by modifying some of the algorithms implemented in the software to account for geographic differences in spectral characteristics associated with terrain and climate. The task of identifying tire piles in satellite imagery is uniquely challenging because of their low reflectance levels: Tires tend to be spectrally confused with shadows and deep water, both of which reflect little light to satellite-borne imaging systems. In this methodology, the challenge is met, in part, by use of software that implements the Tire Identification from Reflectance (TIRe) model. The development of the TIRe model included incorporation of lessons learned in previous research on the detection and mapping of tire piles by use of manual/ visual and/or computational analysis of aerial and satellite imagery. The TIRe model is a computational model for identifying tire piles and discriminating between tire piles and other objects. The input to the TIRe model is the georeferenced but otherwise raw satellite spectral images of a geographic region to be surveyed

Satellite Remote Sensing For Aluminum And Nickel Laterites

Science.gov (United States)

Henderson, Frederick B.; Penfield, Glen T.; Grubbs, Donald K.

1984-08-01

The new LANDSAT-4,-5/Thematic Mapper (TM) land observational satellite remote sensing systems are providing dramatically new and important short wave infrared (SWIR) data, which combined with Landsat's Multi-Spectral Scanner (MSS) visible (VIS), very near infrared (VNIR), and thermal infrared (TI) data greatly improves regional geological mapping on a global scale. The TM will significantly improve clay, iron oxide, aluminum, and nickel laterite mapping capabilities over large areas of the world. It will also improve the ability to discriminate vegetation stress and species distribution associated with lateritic environments. Nickel laterites on Gag Island, Indonesia are defined by MSS imagery. Satellite imagery of the Cape Bougainville and the Darling Range, Australia bauxite deposits show the potential use of MSS data for exploration and mining applications. Examples of satellite syn-thetic aperture radar (SAR) for Jamaica document the use of this method for bauxite exploration. Thematic Mapper data will be combined with the French SPOT satellite's high spatial resolution and stereoscopic digital data, and U.S., Japanese, European, and Canadian Synthetic Aperture Radar (SAR) data to assist with logistics, mine development, and environ-mental concerns associated with aluminum and nickel lateritic deposits worldwide.

Performance of Scattering Matrix Decomposition and Color Spaces for Synthetic Aperture Radar Imagery

Science.gov (United States)

2010-03-01

Color Spaces and Synthetic Aperture Radar (SAR) Multicolor Imaging. 15 2.3.1 Colorimetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.2...III. Decomposition Techniques on SAR Polarimetry and Colorimetry applied to SAR Imagery...space polarimetric SAR systems. Colorimetry is also introduced in this chapter, presenting the fundamentals of the RGB and CMY color spaces, defined for

Integrating satellite imagery with simulation modeling to improve burn severity mapping

Science.gov (United States)

Eva C. Karau; Pamela G. Sikkink; Robert E. Keane; Gregory K. Dillon

2014-01-01

Both satellite imagery and spatial fire effects models are valuable tools for generating burn severity maps that are useful to fire scientists and resource managers. The purpose of this study was to test a new mapping approach that integrates imagery and modeling to create more accurate burn severity maps. We developed and assessed a statistical model that combines the...

Satellite Imagery and In-situ Data Overlay Approach for Fishery Zonation

Directory of Open Access Journals (Sweden)

Fardhi Adria

2010-12-01

Full Text Available Remote sensing technology can be used to better understand the earth’s characteristics. SeaWiFS (sea-viewing wide field-of-view sensor is one of remote sensors used to observe global ocean phenomena. Previous studies showed that the distribution of chlorophyll-a in the ocean indicates the presence of fish. However, only a few studies tried to directly relate the chlorophyll-a distribution obtained through interpretation of satellite imagery to in-situ data of fish distribution. This paper investigates the relation between chlorophyll-a distribution and fish-capturing points in Aceh Province sea waters using overlay image analysis. The results are then used to identify the potential fishing ground in Aceh. The profile of chlorophyll-a concentration is derived from SeaWIFS satellite imagery. Fish-capturing points data is obtained from the fisherman communities of Banda Aceh, starting from June to November 2008. The results showed that the chlorophyll-a profile derived from satellite imagery has a positive relationship to fish-capturing point data. The most potential fish-capturing zone in Aceh sea waters is identified at 5-8º north latitude (N and 96-99º east longitude (E.

Phase 2 Final Report. IAEA Safeguards: Implementation blueprint of commercial satellite imagery

Energy Technology Data Exchange (ETDEWEB)

Andersson, Christer [SSC Satellitbild AB, Solna (Sweden)

2000-01-01

This document - IAEA Safeguards: Implementation Blueprint of Commercial Satellite Imagery - constitutes the second report from SSC Satellitbild giving a structured view and solid guidelines on how to proceed with a conceivable implementation of satellite imagery to support Safeguards activities of the Agency. This Phase 2 report presents a large number of concrete recommendations regarding suggested management issues, work organisation, imagery purchasing and team building. The study has also resulted in several lists of actions and preliminary project plans with GANT schedules concerning training, hardware and software, as well as for the initial pilot studies. In both the Phase 1 and Phase 2 studies it is confirmed that the proposed concept of a relatively small Imagery Unit using high-resolution data will be a sound and feasible undertaking. Such a unit capable of performing advanced image processing as a tool for various safeguard tasks will give the Agency an effective instrument for reference, monitoring, verification, and detection of declared and undeclared activities. The total cost for implementing commercial satellite imagery at the Department for Safeguards, as simulated in these studies, is approximately MUSD 1,5 per year. This cost is founded on an activity scenario with a staff of 4 experts working in an IAEA Imagery Unit with a workload of three dossiers or issues per week. The imagery unit is built around an advanced PC image processing system capable of handling several hundreds of pre-processed images per year. Alternatively a Reduced Scenario with a staff of 3 would need a budget of approximately MUSD 0,9 per year, whereas an Enhanced Imagery Unit including 5 experts and a considerably enlarged capacity would cost MUSD 1,7 per year. The Imagery Unit should be organised so it clearly reflects the objectives and role as set by the Member States and the management of the Agency. We recommend the Imagery Unit to be organised into four main work

Phase 2 Final Report. IAEA Safeguards: Implementation blueprint of commercial satellite imagery

International Nuclear Information System (INIS)

Andersson, Christer

2000-01-01

This document - IAEA Safeguards: Implementation Blueprint of Commercial Satellite Imagery - constitutes the second report from SSC Satellitbild giving a structured view and solid guidelines on how to proceed with a conceivable implementation of satellite imagery to support Safeguards activities of the Agency. This Phase 2 report presents a large number of concrete recommendations regarding suggested management issues, work organisation, imagery purchasing and team building. The study has also resulted in several lists of actions and preliminary project plans with GANT schedules concerning training, hardware and software, as well as for the initial pilot studies. In both the Phase 1 and Phase 2 studies it is confirmed that the proposed concept of a relatively small Imagery Unit using high-resolution data will be a sound and feasible undertaking. Such a unit capable of performing advanced image processing as a tool for various safeguard tasks will give the Agency an effective instrument for reference, monitoring, verification, and detection of declared and undeclared activities. The total cost for implementing commercial satellite imagery at the Department for Safeguards, as simulated in these studies, is approximately MUSD 1,5 per year. This cost is founded on an activity scenario with a staff of 4 experts working in an IAEA Imagery Unit with a workload of three dossiers or issues per week. The imagery unit is built around an advanced PC image processing system capable of handling several hundreds of pre-processed images per year. Alternatively a Reduced Scenario with a staff of 3 would need a budget of approximately MUSD 0,9 per year, whereas an Enhanced Imagery Unit including 5 experts and a considerably enlarged capacity would cost MUSD 1,7 per year. The Imagery Unit should be organised so it clearly reflects the objectives and role as set by the Member States and the management of the Agency. We recommend the Imagery Unit to be organised into four main work

VHR satellite imagery for humanitarian crisis management: a case study

Science.gov (United States)

Bitelli, Gabriele; Eleias, Magdalena; Franci, Francesca; Mandanici, Emanuele

2017-09-01

During the last years, remote sensing data along with GIS have been largely employed for supporting emergency management activities. In this context, the use of satellite images and derived map products has become more common also in the different phases of humanitarian crisis response. In this work very high resolution satellite imagery was processed to assess the evolution of Za'atari Refugee Camp, built in Jordan in 2012 by the UN Refugee Agency to host Syrian refugees. Multispectral satellite scenes of the Za'atari area were processed by means of object-based classifications. The main aim of the present work is the development of a semiautomated procedure for multi-temporal camp monitoring with particular reference to the dwellings detection. Whilst in the emergency mapping domain automation of feature extraction is widely investigated, in the field of humanitarian missions the information is often extracted by means of photointerpretation of the satellite data. This approach requires time for the interpretation; moreover, it is not reliable enough in complex situations, where features of interest are often small, heterogeneous and inconsistent. Therefore, the present paper discusses a methodology to obtain information for assisting humanitarian crisis management, using a semi-automatic classification approach applied to satellite imagery.

Monitoring of the MU radar antenna pattern by Satellite Ohzora (EXOS-C)

Science.gov (United States)

Sato, T.; Inooka, Y.; Fukao, S.; Kato, S.

1986-01-01

As the first attempt among MST (mesosphere stratosphere troposphere) type radars, the MU (middle and upper atmosphere) radar features an active phased array system. Unlike the conventional large VHF radars, in which output power of a large vacuum tube is distributed to individual antenna elements, each of 475 solid state power amplifier feeds each antenna element. This system configuration enables very fast beam steering as well as various flexible operations by dividing the antenna into independent subarrays, because phase shift and signal division/combination are performed at a low signal level using electronic devices under control of a computer network. The antenna beam can be switched within 10 microsec to any direction within the zenith angle of 30 deg. Since a precise phase alignment of each element is crucial to realize the excellent performance of this system, careful calibration of the output phase of each power amplifier and antenna element was carried out. Among various aircraft which may be used for this purpose artificial satellites have an advantage of being able to make a long term monitoring with the same system. An antenna pattern monitoring system for the MU radar was developed using the scientific satellite OHZORA (EXOS-C). A receiver named MUM (MU radar antenna Monitor) on board the satellite measures a CW signal of 100 to 400 watts transmitted from the MU radar. The principle of the measurement and results are discussed.

Satellite radar altimetry for monitoring small rivers and lakes in Indonesia

NARCIS (Netherlands)

Sulistioadi, Y.B.; Tseng, K.H.; Shum, C.K.; Hidayat, Hidayat; Sumaryono, M.; Suhardiman, A.; Setiawan, F.; Sunarso, S.

2015-01-01

Remote sensing and satellite geodetic observations are capable of hydrologic monitoring of freshwater resources. Although satellite radar altimetry has been used in monitoring water level or discharge, its use is often limited to monitoring large rivers (>1 km) with longer interval periods

The development of a land use inventory for regional planning using satellite imagery

Science.gov (United States)

Hessling, A. H.; Mara, T. G.

1975-01-01

Water quality planning in Ohio, Kentucky, and Indiana is reviewed in terms of use of land use data and satellite imagery. A land use inventory applicable to water quality planning and developed through computer processing of LANDSAT-1 imagery is described.

Phase calibration of the EISCAT Svalbard Radar interferometer using optical satellite signatures

Directory of Open Access Journals (Sweden)

J. M. Sullivan

2006-09-01

Full Text Available The link between natural ion-line enhancements in radar spectra and auroral activity has been the subject of recent studies but conclusions have been limited by the spatial and temporal resolution previously available. The next challenge is to use shorter sub-second integration times in combination with interferometric programmes to resolve spatial structure within the main radar beam, and so relate enhanced filaments to individual auroral rays. This paper presents initial studies of a technique, using optical and spectral satellite signatures, to calibrate the received phase of a signal with the position of the scattering source along the interferometric baseline of the EISCAT Svalbard Radar. It is shown that a consistent relationship can be found only if the satellite passage through the phase fringes is adjusted from the passage predicted by optical tracking. This required adjustment is interpreted as being due to the vector between the theoretical focusing points of the two antennae, i.e. the true radar baseline, differing from the baseline obtained by survey between the antenna foot points. A method to obtain a measurement of the true interferometric baseline using multiple satellite passes is outlined.

Geospatial Information from Satellite Imagery for Geovisualisation of Smart Cities in India

Science.gov (United States)

Mohan, M.

2016-06-01

In the recent past, there have been large emphasis on extraction of geospatial information from satellite imagery. The Geospatial information are being processed through geospatial technologies which are playing important roles in developing of smart cities, particularly in developing countries of the world like India. The study is based on the latest geospatial satellite imagery available for the multi-date, multi-stage, multi-sensor, and multi-resolution. In addition to this, the latest geospatial technologies have been used for digital image processing of remote sensing satellite imagery and the latest geographic information systems as 3-D GeoVisualisation, geospatial digital mapping and geospatial analysis for developing of smart cities in India. The Geospatial information obtained from RS and GPS systems have complex structure involving space, time and presentation. Such information helps in 3-Dimensional digital modelling for smart cities which involves of spatial and non-spatial information integration for geographic visualisation of smart cites in context to the real world. In other words, the geospatial database provides platform for the information visualisation which is also known as geovisualisation. So, as a result there have been an increasing research interest which are being directed to geospatial analysis, digital mapping, geovisualisation, monitoring and developing of smart cities using geospatial technologies. However, the present research has made an attempt for development of cities in real world scenario particulary to help local, regional and state level planners and policy makers to better understand and address issues attributed to cities using the geospatial information from satellite imagery for geovisualisation of Smart Cities in emerging and developing country, India.

Satellite Imagery Production and Processing Using Apache Hadoop

Science.gov (United States)

Hill, D. V.; Werpy, J.

2011-12-01

The United States Geological Survey's (USGS) Earth Resources Observation and Science (EROS) Center Land Science Research and Development (LSRD) project has devised a method to fulfill its processing needs for Essential Climate Variable (ECV) production from the Landsat archive using Apache Hadoop. Apache Hadoop is the distributed processing technology at the heart of many large-scale, processing solutions implemented at well-known companies such as Yahoo, Amazon, and Facebook. It is a proven framework and can be used to process petabytes of data on thousands of processors concurrently. It is a natural fit for producing satellite imagery and requires only a few simple modifications to serve the needs of science data processing. This presentation provides an invaluable learning opportunity and should be heard by anyone doing large scale image processing today. The session will cover a description of the problem space, evaluation of alternatives, feature set overview, configuration of Hadoop for satellite image processing, real-world performance results, tuning recommendations and finally challenges and ongoing activities. It will also present how the LSRD project built a 102 core processing cluster with no financial hardware investment and achieved ten times the initial daily throughput requirements with a full time staff of only one engineer. Satellite Imagery Production and Processing Using Apache Hadoop is presented by David V. Hill, Principal Software Architect for USGS LSRD.

A practical algorithm for the retrieval of floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar imagery

Directory of Open Access Journals (Sweden)

Byongjun Hwang

2017-07-01

Full Text Available In this study, we present an algorithm for summer sea ice conditions that semi-automatically produces the floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar data. Currently, floe size distribution data from satellite images are very rare in the literature, mainly due to the lack of a reliable algorithm to produce such data. Here, we developed the algorithm by combining various image analysis methods, including Kernel Graph Cuts, distance transformation and watershed transformation, and a rule-based boundary revalidation. The developed algorithm has been validated against the ground truth that was extracted manually with the aid of 1-m resolution visible satellite data. Comprehensive validation analysis has shown both perspectives and limitations. The algorithm tends to fail to detect small floes (mostly less than 100 m in mean caliper diameter compared to ground truth, which is mainly due to limitations in water-ice segmentation. Some variability in the power law exponent of floe size distribution is observed due to the effects of control parameters in the process of de-noising, Kernel Graph Cuts segmentation, thresholds for boundary revalidation and image resolution. Nonetheless, the algorithm, for floes larger than 100 m, has shown a reasonable agreement with ground truth under various selections of these control parameters. Considering that the coverage and spatial resolution of satellite Synthetic Aperture Radar data have increased significantly in recent years, the developed algorithm opens a new possibility to produce large volumes of floe size distribution data, which is essential for improving our understanding and prediction of the Arctic sea ice cover

Biomass burning: Combustion emissions, satellite imagery, and biogenic emissions

International Nuclear Information System (INIS)

Levine, J.S.; Cofer, W.R III; Rhinehart, R.P.; Cahoon, D.R. J.; Winstead, E.L.; Sebacher, S.; Sebacher, D.I.; Stocks, B.J.

1991-01-01

This chapter deals with two different, but related, aspects of biomass burning. The first part of the chapter deals with a technique to estimate the instantaneous emissions of trace gases produced by biomass burning using satellite imagery. The second part of the chapter concerns the recent discovery that burning results in significantly enhanced biogenic emissions of N 2 O, NO, and CH 4 . Hence, biomass burning has both an immediate and long-term impact on the production of trace gases to the atmosphere. The objective of this research is to better assess and quantify the role of this research is to better assess and quantify the role and impact of biomass as a driver for global change. It will be demonstrated that satellite imagery of fires may be used to estimate combustion emissions and may in the future be used to estimate the long-term postburn biogenic emissions of trace gases to the atmosphere

GEOSPATIAL INFORMATION FROM SATELLITE IMAGERY FOR GEOVISUALISATION OF SMART CITIES IN INDIA

Directory of Open Access Journals (Sweden)

M. Mohan

2016-06-01

Full Text Available In the recent past, there have been large emphasis on extraction of geospatial information from satellite imagery. The Geospatial information are being processed through geospatial technologies which are playing important roles in developing of smart cities, particularly in developing countries of the world like India. The study is based on the latest geospatial satellite imagery available for the multi-date, multi-stage, multi-sensor, and multi-resolution. In addition to this, the latest geospatial technologies have been used for digital image processing of remote sensing satellite imagery and the latest geographic information systems as 3-D GeoVisualisation, geospatial digital mapping and geospatial analysis for developing of smart cities in India. The Geospatial information obtained from RS and GPS systems have complex structure involving space, time and presentation. Such information helps in 3-Dimensional digital modelling for smart cities which involves of spatial and non-spatial information integration for geographic visualisation of smart cites in context to the real world. In other words, the geospatial database provides platform for the information visualisation which is also known as geovisualisation. So, as a result there have been an increasing research interest which are being directed to geospatial analysis, digital mapping, geovisualisation, monitoring and developing of smart cities using geospatial technologies. However, the present research has made an attempt for development of cities in real world scenario particulary to help local, regional and state level planners and policy makers to better understand and address issues attributed to cities using the geospatial information from satellite imagery for geovisualisation of Smart Cities in emerging and developing country, India.

Vegetation extraction from high-resolution satellite imagery using the Normalized Difference Vegetation Index (NDVI)

Science.gov (United States)

AlShamsi, Meera R.

2016-10-01

Over the past years, there has been various urban development all over the UAE. Dubai is one of the cities that experienced rapid growth in both development and population. That growth can have a negative effect on the surrounding environment. Hence, there has been a necessity to protect the environment from these fast pace changes. One of the major impacts this growth can have is on vegetation. As technology is evolving day by day, there is a possibility to monitor changes that are happening on different areas in the world using satellite imagery. The data from these imageries can be utilized to identify vegetation in different areas of an image through a process called vegetation detection. Being able to detect and monitor vegetation is very beneficial for municipal planning and management, and environment authorities. Through this, analysts can monitor vegetation growth in various areas and analyze these changes. By utilizing satellite imagery with the necessary data, different types of vegetation can be studied and analyzed, such as parks, farms, and artificial grass in sports fields. In this paper, vegetation features are detected and extracted through SAFIY system (i.e. the Smart Application for Feature extraction and 3D modeling using high resolution satellite ImagerY) by using high-resolution satellite imagery from DubaiSat-2 and DEIMOS-2 satellites, which provide panchromatic images of 1m resolution and spectral bands (red, green, blue and near infrared) of 4m resolution. SAFIY system is a joint collaboration between MBRSC and DEIMOS Space UK. It uses image-processing algorithms to extract different features (roads, water, vegetation, and buildings) to generate vector maps data. The process to extract green areas (vegetation) utilize spectral information (such as, the red and near infrared bands) from the satellite images. These detected vegetation features will be extracted as vector data in SAFIY system and can be updated and edited by end-users, such as

The SUMO Ship Detector Algorithm for Satellite Radar Images

Directory of Open Access Journals (Sweden)

Harm Greidanus

2017-03-01

Full Text Available Search for Unidentified Maritime Objects (SUMO is an algorithm for ship detection in satellite Synthetic Aperture Radar (SAR images. It has been developed over the course of more than 15 years, using a large amount of SAR images from almost all available SAR satellites operating in L-, C- and X-band. As validated by benchmark tests, it performs very well on a wide range of SAR image modes (from Spotlight to ScanSAR and resolutions (from 1–100 m and for all types and sizes of ships, within the physical limits imposed by the radar imaging. This paper describes, in detail, the algorithmic approach in all of the steps of the ship detection: land masking, clutter estimation, detection thresholding, target clustering, ship attribute estimation and false alarm suppression. SUMO is a pixel-based CFAR (Constant False Alarm Rate detector for multi-look radar images. It assumes a K distribution for the sea clutter, corrected however for deviations of the actual sea clutter from this distribution, implementing a fast and robust method for the clutter background estimation. The clustering of detected pixels into targets (ships uses several thresholds to deal with the typically irregular distribution of the radar backscatter over a ship. In a multi-polarization image, the different channels are fused. Azimuth ambiguities, a common source of false alarms in ship detection, are removed. A reliability indicator is computed for each target. In post-processing, using the results of a series of images, additional false alarms from recurrent (fixed targets including range ambiguities are also removed. SUMO can run in semi-automatic mode, where an operator can verify each detected target. It can also run in fully automatic mode, where batches of over 10,000 images have successfully been processed in less than two hours. The number of satellite SAR systems keeps increasing, as does their application to maritime surveillance. The open data policy of the EU�

Limitations and potential of satellite imagery to monitor environmental response to coastal flooding

Science.gov (United States)

Ramsey, Elijah W.; Werle, Dirk; Suzuoki, Yukihiro; Rangoonwala, Amina; Lu, Zhong

2012-01-01

Storm-surge flooding and marsh response throughout the coastal wetlands of Louisiana were mapped using several types of remote sensing data collected before and after Hurricanes Gustav and Ike in 2008. These included synthetic aperture radar (SAR) data obtained from the (1) C-band advance SAR (ASAR) aboard the Environmental Satellite, (2) phased-array type L-band SAR (PALSAR) aboard the Advanced Land Observing Satellite, and (3) optical data obtained from Thematic Mapper (TM) sensor aboard the Land Satellite (Landsat). In estuarine marshes, L-band SAR and C-band ASAR provided accurate flood extent information when depths averaged at least 80 cm, but only L-band SAR provided consistent subcanopy detection when depths averaged 50 cm or less. Low performance of inundation mapping based on C-band ASAR was attributed to an apparent inundation detection limit (>30 cm deep) in tall Spartina alterniflora marshes, a possible canopy collapse of shoreline fresh marsh exposed to repeated storm-surge inundations, wind-roughened water surfaces where water levels reached marsh canopy heights, and relatively high backscatter in the near-range portion of the SAR imagery. A TM-based vegetation index of live biomass indicated that the severity of marsh dieback was linked to differences in dominant species. The severest impacts were not necessarily caused by longer inundation but rather could be caused by repeated exposure of the palustrine marsh to elevated salinity floodwaters. Differential impacts occurred in estuarine marshes. The more brackish marshes on average suffered higher impacts than the more saline marshes, particularly the nearshore coastal marshes occupied by S. alterniflora.

Radar geomorphology of coastal and wetland environments

Science.gov (United States)

Lewis, A. J.; Macdonald, H. C.

1973-01-01

Details regarding the collection of radar imagery over the past ten years are considered together with the geomorphic, geologic, and hydrologic data which have been extracted from radar imagery. Recent investigations were conducted of the Louisiana swamp marsh and the Oregon coast. It was found that radar imagery is a useful tool to the scientist involved in wetland research.

Application of INSAT Satellite Cloud-Imagery Data for Site ...

Indian Academy of Sciences (India)

tribpo

Application of INSAT Satellite Cloud-Imagery Data for Site Evaluation. Work of ... sources like Cyg X-3 and AM-Her binary systems (Bhat et al. 1986; Bhat et al. ... one is dealing with in the very high energy (VHE) and ultra high energy (UHE) .... shows the monthly distribution of 'spectroscopic' hours averaged over the 5-year.

Burn severity mapping using simulation modeling and satellite imagery

Science.gov (United States)

Eva C. Karau; Robert E. Keane

2010-01-01

Although burn severity maps derived from satellite imagery provide a landscape view of fire impacts, fire effects simulation models can provide spatial fire severity estimates and add a biotic context in which to interpret severity. In this project, we evaluated two methods of mapping burn severity in the context of rapid post-fire assessment for four wildfires in...

The use of airborne laser data to calibrate satellite radar altimetry data over ice sheets

DEFF Research Database (Denmark)

Ekholm, Simon; Bamber, J.L.; Krabill, W.B.

2002-01-01

Satellite radar altimetry is the most important data source for ice sheet elevation modeling but it is well established that the accuracy of such data from satellite borne radar altimeters degrade seriously with increasing surface slope and level of roughness. A significant fraction of the slope......-precision airborne laser profiling data from the so-called Arctic Ice Mapping project as a tool to determine that bias and to calibrate the satellite altimetry. This is achieved by a simple statistical analysis of the airborne laser profiles, which defines the mean amplitude of the local surface undulations...

Automated Generation of the Alaska Coastline Using High-Resolution Satellite Imagery

Science.gov (United States)

Roth, G.; Porter, C. C.; Cloutier, M. D.; Clementz, M. E.; Reim, C.; Morin, P. J.

2015-12-01

Previous campaigns to map Alaska's coast at high resolution have relied on airborne, marine, or ground-based surveying and manual digitization. The coarse temporal resolution, inability to scale geographically, and high cost of field data acquisition in these campaigns is inadequate for the scale and speed of recent coastal change in Alaska. Here, we leverage the Polar Geospatial Center (PGC) archive of DigitalGlobe, Inc. satellite imagery to produce a state-wide coastline at 2 meter resolution. We first select multispectral imagery based on time and quality criteria. We then extract the near-infrared (NIR) band from each processed image, and classify each pixel as water or land with a pre-determined NIR threshold value. Processing continues with vectorizing the water-land boundary, removing extraneous data, and attaching metadata. Final coastline raster and vector products maintain the original accuracy of the orthorectified satellite data, which is often within the local tidal range. The repeat frequency of coastline production can range from 1 month to 3 years, depending on factors such as satellite capacity, cloud cover, and floating ice. Shadows from trees or structures complicate the output and merit further data cleaning. The PGC's imagery archive, unique expertise, and computing resources enabled us to map the Alaskan coastline in a few months. The DigitalGlobe archive allows us to update this coastline as new imagery is acquired, and facilitates baseline data for studies of coastal change and improvement of topographic datasets. Our results are not simply a one-time coastline, but rather a system for producing multi-temporal, automated coastlines. Workflows and tools produced with this project can be freely distributed and utilized globally. Researchers and government agencies must now consider how they can incorporate and quality-control this high-frequency, high-resolution data to meet their mapping standards and research objectives.

Multiscale-Driven approach to detecting change in Synthetic Aperture Radar (SAR) imagery

Science.gov (United States)

Gens, R.; Hogenson, K.; Ajadi, O. A.; Meyer, F. J.; Myers, A.; Logan, T. A.; Arnoult, K., Jr.

2017-12-01

Detecting changes between Synthetic Aperture Radar (SAR) images can be a useful but challenging exercise. SAR with its all-weather capabilities can be an important resource in identifying and estimating the expanse of events such as flooding, river ice breakup, earthquake damage, oil spills, and forest growth, as it can overcome shortcomings of optical methods related to cloud cover. However, detecting change in SAR imagery can be impeded by many factors including speckle, complex scattering responses, low temporal sampling, and difficulty delineating boundaries. In this presentation we use a change detection method based on a multiscale-driven approach. By using information at different resolution levels, we attempt to obtain more accurate change detection maps in both heterogeneous and homogeneous regions. Integrated within the processing flow are processes that 1) improve classification performance by combining Expectation-Maximization algorithms with mathematical morphology, 2) achieve high accuracy in preserving boundaries using measurement level fusion techniques, and 3) combine modern non-local filtering and 2D-discrete stationary wavelet transform to provide robustness against noise. This multiscale-driven approach to change detection has recently been incorporated into the Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3) using radiometrically terrain corrected SAR images. Examples primarily from natural hazards are presented to illustrate the capabilities and limitations of the change detection method.

PLASTIC AND GLASS GREENHOUSES DETECTION AND DELINEATION FROM WORLDVIEW-2 SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

D. Koc-San

2016-06-01

Full Text Available Greenhouse detection using remote sensing technologies is an important research area for yield estimation, sustainable development, urban and rural planning and management. An approach was developed in this study for the detection and delineation of greenhouse areas from high resolution satellite imagery. Initially, the candidate greenhouse patches were detected using supervised classification techniques. For this purpose, Maximum Likelihood (ML, Random Forest (RF, and Support Vector Machines (SVM classification techniques were applied and compared. Then, sieve filter and morphological operations were performed for improving the classification results. Finally, the obtained candidate plastic and glass greenhouse areas were delineated using boundary tracing and Douglas Peucker line simplification algorithms. The proposed approach was implemented in the Kumluca district of Antalya, Turkey utilizing pan-sharpened WorldView-2 satellite imageries. Kumluca is the prominent district of Antalya with greenhouse cultivation and includes both plastic and glass greenhouses intensively. When the greenhouse classification results were analysed, it can be stated that the SVM classification provides most accurate results and RF classification follows this. The SVM classification overall accuracy was obtained as 90.28%. When the greenhouse boundary delineation results were considered, the plastic greenhouses were delineated with 92.11% accuracy, while glass greenhouses were delineated with 80.67% accuracy. The obtained results indicate that, generally plastic and glass greenhouses can be detected and delineated successfully from WorldView-2 satellite imagery.

Normalization of satellite imagery

Science.gov (United States)

Kim, Hongsuk H.; Elman, Gregory C.

1990-01-01

Sets of Thematic Mapper (TM) imagery taken over the Washington, DC metropolitan area during the months of November, March and May were converted into a form of ground reflectance imagery. This conversion was accomplished by adjusting the incident sunlight and view angles and by applying a pixel-by-pixel correction for atmospheric effects. Seasonal color changes of the area can be better observed when such normalization is applied to space imagery taken in time series. In normalized imagery, the grey scale depicts variations in surface reflectance and tonal signature of multi-band color imagery can be directly interpreted for quantitative information of the target.

Landsat 7 ETM/1G satellite imagery - Hawaiian Islands cloud-free mosaics

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Cloud-free Landsat satellite imagery mosaics of the islands of the main 8 Hawaiian Islands (Hawaii, Maui, Kahoolawe, Lanai, Molokai, Oahu, Kauai and Niihau). Landsat...

North and northeast Greenland ice discharge from satellite radar interferometry

DEFF Research Database (Denmark)

Rignot, E.J.; Gogineni, S.P.; Krabill, W.B.

1997-01-01

Ice discharge from north and northeast Greenland calculated from satellite radar interferometry data of 14 outlet glaciers is 3.5 times that estimated from iceberg production. The satellite estimates, obtained at the grounding line of the outlet glaciers, differ from those obtained at the glacier...... front, because basal melting is extensive at the underside of the floating glacier sections. The results suggest that the north and northeast parts of the Greenland ice sheet may be thinning and contributing positively to sea-level rise....

Monitoring coastal inundation with Synthetic Aperture Radar satellite data

Science.gov (United States)

Suzuoki, Yukihiro; Rangoonwala, Amina; Ramsey, Elijah W.

2011-01-01

Maps representing the presence and absence of surface inundation in the Louisiana coastal zone were created from available satellite scenes acquired by the Japanese Aerospace Exploration Agency's Advanced Land Observing Satellite and by the European Space Agency's Envisat from late 2006 through summer 2009. Detection of aboveground surface flooding relied on the well-documented and distinct signature of decreased backscatter in Synthetic Aperture Radar (SAR), which is indicative of inundated marsh in the Gulf of Mexico. Even though decreases in backscatter were distinctive, the multiplicity of possible interactions between changing flood depths and canopy height yielded complex SAR-based representations of the marshes.

River monitoring from satellite radar altimetry in the Zambezi River basin

Directory of Open Access Journals (Sweden)

C. I. Michailovsky

2012-07-01

Full Text Available Satellite radar altimetry can be used to monitor surface water levels from space. While current and past altimetry missions were designed to study oceans, retracking the waveforms returned over land allows data to be retrieved for smaller water bodies or narrow rivers. The objective of this study is the assessment of the potential for river monitoring from radar altimetry in terms of water level and discharge in the Zambezi River basin. Retracked Envisat altimetry data were extracted over the Zambezi River basin using a detailed river mask based on Landsat imagery. This allowed for stage measurements to be obtained for rivers down to 80 m wide with an RMSE relative to in situ levels of 0.32 to 0.72 m at different locations. The altimetric levels were then converted to discharge using three different methods adapted to different data-availability scenarios: first with an in situ rating curve available, secondly with one simultaneous field measurement of cross-section and discharge, and finally with only historical discharge data available. For the two locations at which all three methods could be applied, the accuracies of the different methods were found to be comparable, with RMSE values ranging from 4.1 to 6.5% of the mean annual in situ gauged amplitude for the first method and from 6.9 to 13.8% for the second and third methods. The precision obtained with the different methods was analyzed by running Monte Carlo simulations and also showed comparable values for the three approaches with standard deviations found between 5.7 and 7.2% of the mean annual in situ gauged amplitude for the first method and from 8.7 to 13.0% for the second and third methods.

A data mining approach for sharpening satellite thermal imagery over land

Science.gov (United States)

Thermal infrared (TIR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes which are at significant...

CryoSat-2 satellite radar altimetry for river analysis and modelling

DEFF Research Database (Denmark)

Schneider, Raphael

The global coverage of in situ observations of surface water dynamics is insufficient to effectively manage water resources. Moreover, the availability of these data is decreasing, due to the lack of gauging stations and data sharing. Satellite radar altimetry, initially developed to monitor ocean...... water levels, also offers measurements of water levels of rivers and lakes on a global scale. Because of the continuous upstart of new missions, and sensor and processing innovations, the importance of satellite altimetry data for the hydrologic community is increasing. CryoSat-2, launched......) and Synthetic Aperture Radar Interferometric (SARIn) mode. SAR and SARIn have reduced footprint size in the along-track direction owing to delay/Doppler processing, potentially increasing observation accuracy. Second, CryoSat-2 is placed on a unique long-repeat orbit with a cycle of 369 days. This is different...

Spin-image surface matching based target recognition in laser radar range imagery

International Nuclear Information System (INIS)

Li, Wang; Jian-Feng, Sun; Qi, Wang

2010-01-01

We explore the problem of in-plane rotation-invariance existing in the vertical detection of laser radar (Ladar) using the algorithm of spin-image surface matching. The method used to recognize the target in the range imagery of Ladar is time-consuming, owing to its complicated procedure, which violates the requirement of real-time target recognition in practical applications. To simplify the troublesome procedures, we improve the spin-image algorithm by introducing a statistical correlated coefficient into target recognition in range imagery of Ladar. The system performance is demonstrated on sixteen simulated noise range images with targets rotated through an arbitrary angle in plane. A high efficiency and an acceptable recognition rate obtained herein testify the validity of the improved algorithm for practical applications. The proposed algorithm not only solves the problem of in-plane rotation-invariance rationally, but also meets the real-time requirement. This paper ends with a comparison of the proposed method and the previous one. (classical areas of phenomenology)

Simultaneous Radar and Satellite Data Storm-Scale Assimilation Using an Ensemble Kalman Filter Approach for 24 May 2011

Science.gov (United States)

Jones, Thomas A.; Stensrud, David; Wicker, Louis; Minnis, Patrick; Palikonda, Rabindra

2015-01-01

Assimilating high-resolution radar reflectivity and radial velocity into convection-permitting numerical weather prediction models has proven to be an important tool for improving forecast skill of convection. The use of satellite data for the application is much less well understood, only recently receiving significant attention. Since both radar and satellite data provide independent information, combing these two sources of data in a robust manner potentially represents the future of high-resolution data assimilation. This research combines Geostationary Operational Environmental Satellite 13 (GOES-13) cloud water path (CWP) retrievals with Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity to examine the impacts of assimilating each for a severe weather event occurring in Oklahoma on 24 May 2011. Data are assimilated into a 3-km model using an ensemble adjustment Kalman filter approach with 36 members over a 2-h assimilation window between 1800 and 2000 UTC. Forecasts are then generated for 90 min at 5-min intervals starting at 1930 and 2000 UTC. Results show that both satellite and radar data are able to initiate convection, but that assimilating both spins up a storm much faster. Assimilating CWP also performs well at suppressing spurious precipitation and cloud cover in the model as well as capturing the anvil characteristics of developed storms. Radar data are most effective at resolving the 3D characteristics of the core convection. Assimilating both satellite and radar data generally resulted in the best model analysis and most skillful forecast for this event.

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

Directory of Open Access Journals (Sweden)

Sébastien Angelliaume

2017-08-01

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

Wide area change detection with satellite imagery for locating underground nuclear testing

International Nuclear Information System (INIS)

Canty, M.J.; Jasani, B.; Schlittenhardt, J.

2001-01-01

With the advent of high resolution optical imagery from commercial earth observation satellites, the use of remote sensing data for verification of nuclear non-proliferation agreements is becoming increasingly attractive. Non-governmental organizations are routinely publishing high-quality imagery of sensitive nuclear installations round the world, and international verification authorities, such as the International Atomic Energy Agency (IAEA) or the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO), will also want to make use, directly or indirectly, of this additional open source of information. Exact location of the sites of underground nuclear explosions is a task eminently suited to satellite imagery. Here both moderate resolutions for detecting signals in very large testing ranges as well as high resolution images for exact interpretation play important roles. We describe in our paper a particularly sensitive change detection procedure for bitemporal, multispectral satellite imagery which can be used to locate the spall zone of underground nuclear explosions with commercial satellite imagery. The method is based on the multivariate alteration detection (MAD) technique of Nielsen et al. Linear combinations of the spectral channels in two images of the same scene are chosen so as to minimize their positive correlation. This leads to a series of difference images - the so-called MAD components - which are mutually orthogonal (uncorrelated) and ordered according to decreasing variance in their pixel intensities. Since interesting changes in man-made structures may contribute minimally to the overall variance (as the latter may be dominated for instance by seasonal vegetation differences) it is often the case that such changes turn up in a higher order MAD component. This is because they will be uncorrelated with seasonal vegetation changes, stochastic image noise or other major contributions to the overall change signal. This in fact is one of the

Executive summary: Radar imagery interpretation to assess the hydrocarbon potential of four sites in the Phillipines

Energy Technology Data Exchange (ETDEWEB)

1988-11-17

The Republic of the Philippines is intensely interested in the identification, development, and conservation of natural resources. In keeping with this, the Government of the Philippines has recently completed a nationwide sedimentary basin evaluation program to assess hydrocarbon potential and assist in future exploration activities. This program of collection and interpretation of the radar imagery was designed to augment and complement the existing data base. The primary objective of the project was to further the goals of international energy development by aiding the Republic of the Philippines in the assessment of potential petroleum and geothermal prospects within the areas imaged. Secondary goals were to assist the Republic of the Philippines in utilizing state-of-the-art radar remote sensing technology for resource exploration, and to train key Philippines scientists in the use of imaging radar data. 9 refs., 9 figs., 3 tabs.

[Extraction of buildings three-dimensional information from high-resolution satellite imagery based on Barista software].

Science.gov (United States)

Zhang, Pei-feng; Hu, Yuan-man; He, Hong-shi

2010-05-01

The demand for accurate and up-to-date spatial information of urban buildings is becoming more and more important for urban planning, environmental protection, and other vocations. Today's commercial high-resolution satellite imagery offers the potential to extract the three-dimensional information of urban buildings. This paper extracted the three-dimensional information of urban buildings from QuickBird imagery, and validated the precision of the extraction based on Barista software. It was shown that the extraction of three-dimensional information of the buildings from high-resolution satellite imagery based on Barista software had the advantages of low professional level demand, powerful universality, simple operation, and high precision. One pixel level of point positioning and height determination accuracy could be achieved if the digital elevation model (DEM) and sensor orientation model had higher precision and the off-Nadir View Angle was relatively perfect.

Generative Street Addresses from Satellite Imagery

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İlke Demir

2018-03-01

Full Text Available We describe our automatic generative algorithm to create street addresses from satellite images by learning and labeling roads, regions, and address cells. Currently, 75% of the world’s roads lack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude and longitude information into a memorable form for unknown areas. However, settlements are identified by streets, and such addressing schemes are not coherent with the road topology. Instead, we propose a generative address design that maps the globe in accordance with streets. Our algorithm starts with extracting roads from satellite imagery by utilizing deep learning. Then, it uniquely labels the regions, roads, and structures using some graph- and proximity-based algorithms. We also extend our addressing scheme to (i cover inaccessible areas following similar design principles; (ii be inclusive and flexible for changes on the ground; and (iii lead as a pioneer for a unified street-based global geodatabase. We present our results on an example of a developed city and multiple undeveloped cities. We also compare productivity on the basis of current ad hoc and new complete addresses. We conclude by contrasting our generative addresses to current industrial and open solutions.

Polar bears from space: Assessing satellite imagery as a tool to track Arctic wildlife

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Stapleton, Seth P.; LaRue, Michelle A.; Lecomte, Nicolas; Atkinson, Stephen N.; Garshelis, David L.; Porter, Claire; Atwood, Todd C.

2014-01-01

Development of efficient techniques for monitoring wildlife is a priority in the Arctic, where the impacts of climate change are acute and remoteness and logistical constraints hinder access. We evaluated high resolution satellite imagery as a tool to track the distribution and abundance of polar bears. We examined satellite images of a small island in Foxe Basin, Canada, occupied by a high density of bears during the summer ice-free season. Bears were distinguished from other light-colored spots by comparing images collected on different dates. A sample of ground-truthed points demonstrated that we accurately classified bears. Independent observers reviewed images and a population estimate was obtained using mark- recapture models. This estimate (N: 94; 95% Confidence Interval: 92-105) was remarkably similar to an abundance estimate derived from a line transect aerial survey conducted a few days earlier (N: 102; 95% CI: 69-152). Our findings suggest that satellite imagery is a promising tool for monitoring polar bears on land, with implications for use with other Arctic wildlife. Large scale applications may require development of automated detection processes to expedite review and analysis. Future research should assess the utility of multi-spectral imagery and examine sites with different environmental characteristics.

Polar bears from space: assessing satellite imagery as a tool to track Arctic wildlife.

Directory of Open Access Journals (Sweden)

Seth Stapleton

Full Text Available Development of efficient techniques for monitoring wildlife is a priority in the Arctic, where the impacts of climate change are acute and remoteness and logistical constraints hinder access. We evaluated high resolution satellite imagery as a tool to track the distribution and abundance of polar bears. We examined satellite images of a small island in Foxe Basin, Canada, occupied by a high density of bears during the summer ice-free season. Bears were distinguished from other light-colored spots by comparing images collected on different dates. A sample of ground-truthed points demonstrated that we accurately classified bears. Independent observers reviewed images and a population estimate was obtained using mark-recapture models. This estimate (N: 94; 95% Confidence Interval: 92-105 was remarkably similar to an abundance estimate derived from a line transect aerial survey conducted a few days earlier (N: 102; 95% CI: 69-152. Our findings suggest that satellite imagery is a promising tool for monitoring polar bears on land, with implications for use with other Arctic wildlife. Large scale applications may require development of automated detection processes to expedite review and analysis. Future research should assess the utility of multi-spectral imagery and examine sites with different environmental characteristics.

Polar bears from space: assessing satellite imagery as a tool to track Arctic wildlife.

Science.gov (United States)

Stapleton, Seth; LaRue, Michelle; Lecomte, Nicolas; Atkinson, Stephen; Garshelis, David; Porter, Claire; Atwood, Todd

2014-01-01

Development of efficient techniques for monitoring wildlife is a priority in the Arctic, where the impacts of climate change are acute and remoteness and logistical constraints hinder access. We evaluated high resolution satellite imagery as a tool to track the distribution and abundance of polar bears. We examined satellite images of a small island in Foxe Basin, Canada, occupied by a high density of bears during the summer ice-free season. Bears were distinguished from other light-colored spots by comparing images collected on different dates. A sample of ground-truthed points demonstrated that we accurately classified bears. Independent observers reviewed images and a population estimate was obtained using mark-recapture models. This estimate (N: 94; 95% Confidence Interval: 92-105) was remarkably similar to an abundance estimate derived from a line transect aerial survey conducted a few days earlier (N: 102; 95% CI: 69-152). Our findings suggest that satellite imagery is a promising tool for monitoring polar bears on land, with implications for use with other Arctic wildlife. Large scale applications may require development of automated detection processes to expedite review and analysis. Future research should assess the utility of multi-spectral imagery and examine sites with different environmental characteristics.

Nearshore Benthic Habitats of Timor-Leste Derived from WorldView-2 Satellite Imagery

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National Oceanic and Atmospheric Administration, Department of Commerce — Benthic habitat classes were derived for nearshore waters (< 20 m depths) around Timor-Leste from DigitalGlobe WorldView-2 satellite imagery, acquired from Jan 26...

Flood occurrence mapping of the middle Mahakam lowland area using satellite radar

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H. Hidayat

2012-07-01

Full Text Available Floodplain lakes and peatlands in the middle Mahakam lowland area are considered as ecologically important wetland in East Kalimantan, Indonesia. However, due to a lack of data, the hydrological functioning of the region is still poorly understood. Among remote sensing techniques that can increase data availability, radar is well-suitable for the identification, mapping, and measurement of tropical wetlands, for its cloud unimpeded sensing and night and day operation. Here we aim to extract flood extent and flood occurrence information from a series of radar images of the middle Mahakam lowland area. We explore the use of Phased Array L-band Synthetic Aperture Radar (PALSAR imagery for observing flood inundation dynamics by incorporating field water level measurements. Water level measurements were carried out along the river, in lakes and in peatlands, using pressure transducers. For validation of the open water flood occurrence map, bathymetry measurements were carried out in the main lakes. A series of PALSAR images covering the middle and lower Mahakam area in the years 2007 through 2010 were collected. A fully inundated region can be easily recognized on radar images from a dark signature. Open water flood occurrence was mapped using a threshold value taken from radar backscatter of the permanently inundated river and lakes areas. Radar backscatter intensity analysis of the vegetated floodplain area revealed consistently high backscatter values, indicating flood inundation under forest canopy. We used those values as the threshold for flood occurrence mapping in the vegetated area.

MAJOR SOURCE OF NEW RADAR DATA FOR EXPLORATION RESEARCH.

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Kover, Allan N.; Jones, John Edwin; Southworth, C. Scott

1984-01-01

In 1980, the U. S. Geological Survey (USGS) initiated a program to acquire high-quality, side-looking, airborne-radar (SLAR) imagery of selected areas of the United States. The program goals were to demonstrate the usefulness of SLAR imagery for geologic exploration and geoscience applications and to make radar data readily available to the public for additional research and economic applications. Considerable SLAR imagery has been acquired already since 1980 under a mandate from the U. S. Congress. The U. S. Geological Survey is actively engaged in demonstrating the usefulness of radar imagery, and since 1980 has started more than 50 studies addressing geologic, cartographic, and hydrologic applications. All of the radar-imagery products acquired by the USGS during 1980 and 1982 have been archived and are available for public sale.

Coupling Between Doppler Radar Signatures and Tornado Damage Tracks

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Jedlovec, Gary J.; Molthan, Andrew L.; Carey, Lawrence; Carcione, Brian; Smith, Matthew; Schultz, Elise V.; Schultz, Christopher; Lafontaine, Frank

2011-01-01

On April 27, 2011, the southeastern United States was raked with several episodes of severe weather. Numerous tornadoes caused extensive damage, and tragically, the deaths of over 300 people. In Alabama alone, there were 61 confirmed tornados, 4 of them produced EF5 damage, and several were on the ground an hour or more with continuous damage tracks exceeding 80km. The use of Doppler radars covering the region provided reflectivity and velocity signatures that allowed forecasters to monitors the severe storms from beginning to end issuing hundreds of severe weather warnings throughout the day. Meteorologists from the the NWS performed extensive surveys to assess the intensity, duration, and ground track of tornadoes reported during the event. Survey activities included site visits to the affected locations, analysis of radar and satellite data, aerial surveys, and interviews with eyewitnesses. Satellite data from NASA's MODIS and ASTER instruments played a helpful role in determining the location of tornado damage paths and in the assessment. High resolution multispectral and temporal composites helped forecasters corroborate their damage assessments, determine starting and ending points for tornado touchdowns, and helped to provide forecasters with a better big-picture view of the damage region. The imagery also helped to separate damage from the April 27th tornados from severe weather that occurred earlier that month. In a post analysis of the outbreak, tornado damage path signatures observed in the NASA satellite data have been correlated to "debris ball" signatures in the NWS Doppler radars and a special ARMOR dual-polarization radar operated by the University of Alabama Huntsville during the event. The Doppler radar data indicates a circular enhanced reflectivity signal and rotational couplet in the radial velocity likely associated with the tornado that is spatially correlated with the damage tracks in the observed satellite data. An algorithm to detect and

An evaluation of the use of ERTS-1 satellite imagery for grizzly bear habitat analysis

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Varney, J. R.; Craighead, J. J.; Sumner, J.

1973-01-01

Multispectral scanner images taken by the ERTS-1 satellite in August and October, 1972, were examined to determine if they would be useful in identifying and mapping favorable habitat for grizzly bears. It was possible to identify areas having a suitable mixture of alpine meadow and timber, and to eliminate those which did not meet the isolation requirements of grizzlies because of farming or grazing activity. High altitude timbered areas mapped from satellite imagery agreed reasonably well with the distribution of whitebark pine, an important food species. Analysis of satellite imagery appears to be a valuable supplement to present ground observation methods, since it allows the most important areas to be identified for intensive study and many others to be eliminated from consideration. A sampling plan can be developed from such data which will minimize field effort and overall program cost.

Radar Plan Position Indicator Scope

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National Oceanic and Atmospheric Administration, Department of Commerce — Radar Plan Position Indicator Scope is the collection of weather radar imagery for the period prior to the beginning of the Next Generation Radar (NEXRAD) system...

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

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Nahuel R. Peralta

2016-10-01

Full Text Available A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington of Kansas (total of 457 ha. Three basic tests were conducted on the data: (1 spatial dependence on each of the yield and vegetation indices (VIs using Moran’s I test; (2 model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS and spatial econometric (SPL models; and (3 model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG was tested positive and statistically significant for most of the fields (p < 0.05, except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02 was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to

Evaluation of Future Internet Technologies for Processing and Distribution of Satellite Imagery

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Becedas, J.; Perez, R.; Gonzalez, G.; Alvarez, J.; Garcia, F.; Maldonado, F.; Sucari, A.; Garcia, J.

2015-04-01

Satellite imagery data centres are designed to operate a defined number of satellites. For instance, difficulties when new satellites have to be incorporated in the system appear. This occurs because traditional infrastructures are neither flexible nor scalable. With the appearance of Future Internet technologies new solutions can be provided to manage large and variable amounts of data on demand. These technologies optimize resources and facilitate the appearance of new applications and services in the traditional Earth Observation (EO) market. The use of Future Internet technologies for the EO sector were validated with the GEO-Cloud experiment, part of the Fed4FIRE FP7 European project. This work presents the final results of the project, in which a constellation of satellites records the whole Earth surface on a daily basis. The satellite imagery is downloaded into a distributed network of ground stations and ingested in a cloud infrastructure, where the data is processed, stored, archived and distributed to the end users. The processing and transfer times inside the cloud, workload of the processors, automatic cataloguing and accessibility through the Internet are evaluated to validate if Future Internet technologies present advantages over traditional methods. Applicability of these technologies is evaluated to provide high added value services. Finally, the advantages of using federated testbeds to carry out large scale, industry driven experiments are analysed evaluating the feasibility of an experiment developed in the European infrastructure Fed4FIRE and its migration to a commercial cloud: SoftLayer, an IBM Company.

Monsoon Convection during the South China Sea Monsoon Experiment Observed from Shipboard Radar and the TRMM Satellite

Science.gov (United States)

Rickenbach, Tom; Cifelli, Rob; Halverson, Jeff; Kucera, Paul; Atkinson, Lester; Fisher, Brad; Gerlach, John; Harris, Kathy; Kaufman, Cristina; Liu, Ching-Hwang;

Using Satellite Imagery to Identify Tornado Damage Tracks and Recovery from the April 27, 2011 Severe Weather Outbreak

Science.gov (United States)

Cole, Tony A.; Molthan, Andrew L.; Bell, Jordan R.

2014-01-01

Emergency response to natural disasters requires coordination between multiple local, state, and federal agencies. Single, relatively weak tornado events may require comparatively simple response efforts; but larger "outbreak" events with multiple strong, long-track tornadoes can benefit from additional tools to help expedite these efforts. Meteorologists from NOAA's National Weather Service conduct field surveys to map tornado tracks, assess damage, and determine the tornado intensity following each event. Moderate and high resolution satellite imagery can support these surveys by providing a high-level view of the affected areas. Satellite imagery could then be used to target areas for immediate survey or to corroborate the results of the survey after it is completed. In this study, the feasibility of using satellite imagery to identify tornado damage tracks was determined by comparing the characteristics of tracks observed from low-earth orbit to tracks assessed during the official NWS storm survey process. Of the 68 NWS confirmed centerlines, 24 tracks (35.3%) could be distinguished from other surface features using satellite imagery. Within each EF category, 0% of EF-0, 3% of EF-1, 50% of EF-2, 77.7% of EF-3, 87.5% of EF-4 and 100% of EF-5 tornadoes were detected. It was shown that satellite data can be used to identify tornado damage tracks in MODIS and ASTER NDVI imagery, where damage to vegetation creates a sharp drop in values though the minimum EF-category which can be detected is dependent upon the type of sensor used and underlying vegetation. Near-real time data from moderate resolution sensors compare favorably to field surveys after the event and suggest that the data can provide some value in the assessment process.

The concurrent multiplicative-additive approach for gauge-radar/satellite multisensor precipitation estimates

Science.gov (United States)

Garcia-Pintado, J.; Barberá, G. G.; Erena Arrabal, M.; Castillo, V. M.

2010-12-01

Objective analysis schemes (OAS), also called ``succesive correction methods'' or ``observation nudging'', have been proposed for multisensor precipitation estimation combining remote sensing data (meteorological radar or satellite) with data from ground-based raingauge networks. However, opposite to the more complex geostatistical approaches, the OAS techniques for this use are not optimized. On the other hand, geostatistical techniques ideally require, at the least, modelling the covariance from the rain gauge data at every time step evaluated, which commonly cannot be soundly done. Here, we propose a new procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) for operational rainfall estimation using rain gauges and meteorological radar, which does not require explicit modelling of spatial covariances. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on the OAS, whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The approach considers radar estimates as background a priori information (first guess), so that nudging to observations (gauges) may be relaxed smoothly to the first guess, and the relaxation shape is obtained from the sequential

Users, uses, and value of Landsat satellite imagery: results from the 2012 survey of users

Science.gov (United States)

Miller, Holly M.; Richardson, Leslie A.; Koontz, Stephen R.; Loomis, John; Koontz, Lynne

2013-01-01

Landsat satellites have been operating since 1972, providing a continuous global record of the Earth’s land surface. The imagery is currently available at no cost through the U.S. Geological Survey (USGS). Social scientists at the USGS Fort Collins Science Center conducted an extensive survey in early 2012 to explore who uses Landsat imagery, how they use the imagery, and what the value of the imagery is to them. The survey was sent to all users registered with USGS who had accessed Landsat imagery in the year prior to the survey and over 11,000 current Landsat imagery users responded. The results of the survey revealed that respondents from many sectors use Landsat imagery in myriad project locations and scales, as well as application areas. The value of Landsat imagery to these users was demonstrated by the high importance of and dependence on the imagery, the numerous environmental and societal benefits observed from projects using Landsat imagery, the potential negative impacts on users’ work if Landsat imagery was no longer available, and the substantial aggregated annual economic benefit from the imagery. These results represent only the value of Landsat to users registered with USGS; further research would help to determine what the value of the imagery is to a greater segment of the population, such as downstream users of the imagery and imagery-derived products.

Radar and infrared remote sensing of terrain, water resources, arctic sea ice, and agriculture

Science.gov (United States)

Biggs, A. W.

1983-01-01

Radar range measurements, basic waveforms of radar systems, and radar displays are initially described. These are followed by backscatter from several types of terrain and vegetation as a function of frequency and grazing angle. Analytical models for this backscatter include the facet models of radar return, with range-angle, velocity-range, velocity-angle, range, velocity, and angular only discriminations. Several side-looking airborne radar geometries are presented. Radar images of Arctic sea ice, fresh water lake ice, cloud-covered terrain, and related areas are presented to identify applications of radar imagery. Volume scatter models are applied to radar imagery from alpine snowfields. Short pulse ice thickness radar for subsurface probes is discussed in fresh-water ice and sea ice detection. Infrared scanners, including multispectral, are described. Diffusion of cold water into a river, Arctic sea ice, power plant discharges, volcanic heat, and related areas are presented in thermal imagery. Multispectral radar and infrared imagery are discussed, with comparisons of photographic, infrared, and radar imagery of the same terrain or subjects.

Mapping Urban Tree Canopy Cover Using Fused Airborne LIDAR and Satellite Imagery Data

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Parmehr, Ebadat G.; Amati, Marco; Fraser, Clive S.

2016-06-01

Urban green spaces, particularly urban trees, play a key role in enhancing the liveability of cities. The availability of accurate and up-to-date maps of tree canopy cover is important for sustainable development of urban green spaces. LiDAR point clouds are widely used for the mapping of buildings and trees, and several LiDAR point cloud classification techniques have been proposed for automatic mapping. However, the effectiveness of point cloud classification techniques for automated tree extraction from LiDAR data can be impacted to the point of failure by the complexity of tree canopy shapes in urban areas. Multispectral imagery, which provides complementary information to LiDAR data, can improve point cloud classification quality. This paper proposes a reliable method for the extraction of tree canopy cover from fused LiDAR point cloud and multispectral satellite imagery data. The proposed method initially associates each LiDAR point with spectral information from the co-registered satellite imagery data. It calculates the normalised difference vegetation index (NDVI) value for each LiDAR point and corrects tree points which have been misclassified as buildings. Then, region growing of tree points, taking the NDVI value into account, is applied. Finally, the LiDAR points classified as tree points are utilised to generate a canopy cover map. The performance of the proposed tree canopy cover mapping method is experimentally evaluated on a data set of airborne LiDAR and WorldView 2 imagery covering a suburb in Melbourne, Australia.

Analysis on the Utility of Satellite Imagery for Detection of Agricultural Facility

Science.gov (United States)

Kang, J.-M.; Baek, S.-H.; Jung, K.-Y.

2012-07-01

Now that the agricultural facilities are being increase owing to development of technology and diversification of agriculture and the ratio of garden crops that are imported a lot and the crops cultivated in facilities are raised in Korea, the number of vinyl greenhouses is tending upward. So, it is important to grasp the distribution of vinyl greenhouses as much as that of rice fields, dry fields and orchards, but it is difficult to collect the information of wide areas economically and correctly. Remote sensing using satellite imagery is able to obtain data of wide area at the same time, quickly and cost-effectively collect, monitor and analyze information from every object on earth. In this study, in order to analyze the utilization of satellite imagery at detection of agricultural facility, image classification was performed about the agricultural facility, vinyl greenhouse using Formosat-2 satellite imagery. The training set of sea, vegetation, building, bare ground and vinyl greenhouse was set to monitor the agricultural facilities of the object area and the training set for the vinyl greenhouses that are main monitoring object was classified and set again into 3 types according the spectral characteristics. The image classification using 4 kinds of supervise classification methods applied by the same training set were carried out to grasp the image classification method which is effective for monitoring agricultural facilities. And, in order to minimize the misclassification appeared in the classification using the spectral information, the accuracy of classification was intended to be raised by adding texture information. The results of classification were analyzed regarding the accuracy comparing with that of naked-eyed detection. As the results of classification, the method of Mahalanobis distance was shown as more efficient than other methods and the accuracy of classification was higher when adding texture information. Hence the more effective

ANALYSIS ON THE UTILITY OF SATELLITE IMAGERY FOR DETECTION OF AGRICULTURAL FACILITY

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J.-M. Kang

2012-07-01

Full Text Available Now that the agricultural facilities are being increase owing to development of technology and diversification of agriculture and the ratio of garden crops that are imported a lot and the crops cultivated in facilities are raised in Korea, the number of vinyl greenhouses is tending upward. So, it is important to grasp the distribution of vinyl greenhouses as much as that of rice fields, dry fields and orchards, but it is difficult to collect the information of wide areas economically and correctly. Remote sensing using satellite imagery is able to obtain data of wide area at the same time, quickly and cost-effectively collect, monitor and analyze information from every object on earth. In this study, in order to analyze the utilization of satellite imagery at detection of agricultural facility, image classification was performed about the agricultural facility, vinyl greenhouse using Formosat-2 satellite imagery. The training set of sea, vegetation, building, bare ground and vinyl greenhouse was set to monitor the agricultural facilities of the object area and the training set for the vinyl greenhouses that are main monitoring object was classified and set again into 3 types according the spectral characteristics. The image classification using 4 kinds of supervise classification methods applied by the same training set were carried out to grasp the image classification method which is effective for monitoring agricultural facilities. And, in order to minimize the misclassification appeared in the classification using the spectral information, the accuracy of classification was intended to be raised by adding texture information. The results of classification were analyzed regarding the accuracy comparing with that of naked-eyed detection. As the results of classification, the method of Mahalanobis distance was shown as more efficient than other methods and the accuracy of classification was higher when adding texture information. Hence the more

Tracking an Oil Tanker Collision and Spilled Oils in the East China Sea Using Multisensor Day and Night Satellite Imagery

Science.gov (United States)

Sun, Shaojie; Lu, Yingcheng; Liu, Yongxue; Wang, Mengqiu; Hu, Chuanmin

2018-04-01

Satellite remote sensing is well known to play a critical role in monitoring marine accidents such as oil spills, yet the recent SANCHI oil tanker collision event in January 2018 in the East China Sea indicates that traditional techniques using synthetic aperture radar or daytime optical imagery could not provide timely and adequate coverage. In this study, we show the unprecedented value of Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire product and Day/Night Band data in tracking the oil tanker's drifting pathway and locations when all other means are not as effective for the same purpose. Such pathway and locations can also be reproduced with a numerical model, with root-mean-square error of days of the tanker's sinking reveals much larger oil spill area (>350 km2) than previous reports, the impact of the spilled condensate oil on the marine environment requires further research.

Earth mapping - aerial or satellite imagery comparative analysis

Science.gov (United States)

Fotev, Svetlin; Jordanov, Dimitar; Lukarski, Hristo

Nowadays, solving the tasks for revision of existing map products and creation of new maps requires making a choice of the land cover image source. The issue of the effectiveness and cost of the usage of aerial mapping systems versus the efficiency and cost of very-high resolution satellite imagery is topical [1, 2, 3, 4]. The price of any remotely sensed image depends on the product (panchromatic or multispectral), resolution, processing level, scale, urgency of task and on whether the needed image is available in the archive or has to be requested. The purpose of the present work is: to make a comparative analysis between the two approaches for mapping the Earth having in mind two parameters: quality and cost. To suggest an approach for selection of the map information sources - airplane-based or spacecraft-based imaging systems with very-high spatial resolution. Two cases are considered: area that equals approximately one satellite scene and area that equals approximately the territory of Bulgaria.

Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

Science.gov (United States)

Kit, Oleksandr; Lüdeke, Matthias

2013-09-01

This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.

A case of timely satellite image acquisitions in support of coastal emergency environmental response management

Science.gov (United States)

Ramsey, Elijah W.; Werle, Dirk; Lu, Zhong; Rangoonwala, Amina; Suzuoki, Yukihiro

2009-01-01

The synergistic application of optical and radar satellite imagery improves emergency response and advance coastal monitoring from the realm of “opportunistic” to that of “strategic.” As illustrated by the Hurricane Ike example, synthetic aperture radar imaging capabilities are clearly applicable for emergency response operations, but they are also relevant to emergency environmental management. Integrated with optical monitoring, the nearly real-time availability of synthetic aperture radar provides superior consistency in status and trends monitoring and enhanced information concerning causal forces of change that are critical to coastal resource sustainability, including flooding extent, depth, and frequency.

Simulation on change of generic satellite radar cross section via artificially created plasma sprays

International Nuclear Information System (INIS)

Chung, Shen Shou Max; Chuang, Yu-Chou

2016-01-01

Recent advancements in antisatellite missile technologies have proven the effectiveness of such attacks, and the vulnerability of satellites in such exercises inspires a new paradigm in RF Stealth techniques suitable for satellites. In this paper we examine the possibility of using artificially created plasma sprays on the surface of the satellite’s main body to alter its radar cross section (RCS). First, we briefly review past research related to RF Stealth using plasma. Next, we discuss the physics between electromagnetic waves and plasma, and the RCS number game in RF Stealth design. A comparison of RCS in a generic satellite and a more complicated model is made to illustrate the effect of the RCS number game, and its meaning for a simulation model. We also run a comparison between finite-difference-time-domain (FDTD) and multilevel fast multipole method (MLFMM) codes, and find the RCS results are very close. We then compare the RCS of the generic satellite and the plasma-covered satellite. The incident radar wave is a differentiated Gaussian monopulse, with 3 dB bandwidth between 1.2 GHz and 4 GHz, and we simulate three kinds of plasma density, with a characteristic plasma frequency ω P   =  0.1, 1, and 10 GHz. The electron-neutral collision frequency ν en is set at 0.01 GHz. We found the RCS of plasma-covered satellite is not necessarily smaller than the originally satellite. When ω P is 0.1 GHz, the plasma spray behaves like a dielectric, and there is minor reduction in the RCS. When ω P is 1 GHz, the X–Y cut RCS increases. When ω P is 10 GHz, the plasma behaves more like a metal to the radar wave, and stronger RCS dependency to frequency appears. Therefore, to use plasma as an RCS adjustment tool requires careful fine-tuning of plasma density and shape, in order to achieve the so-called plasma stealth effect. (paper)

Development of Deep Learning Based Data Fusion Approach for Accurate Rainfall Estimation Using Ground Radar and Satellite Precipitation Products

Science.gov (United States)

Chen, H.; Chandra, C. V.; Tan, H.; Cifelli, R.; Xie, P.

2016-12-01

Rainfall estimation based on onboard satellite measurements has been an important topic in satellite meteorology for decades. A number of precipitation products at multiple time and space scales have been developed based upon satellite observations. For example, NOAA Climate Prediction Center has developed a morphing technique (i.e., CMORPH) to produce global precipitation products by combining existing space based rainfall estimates. The CMORPH products are essentially derived based on geostationary satellite IR brightness temperature information and retrievals from passive microwave measurements (Joyce et al. 2004). Although the space-based precipitation products provide an excellent tool for regional and global hydrologic and climate studies as well as improved situational awareness for operational forecasts, its accuracy is limited due to the sampling limitations, particularly for extreme events such as very light and/or heavy rain. On the other hand, ground-based radar is more mature science for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization technique and further enhanced by urban scale radar networks. Therefore, ground radars are often critical for providing local scale rainfall estimation and a "heads-up" for operational forecasters to issue watches and warnings as well as validation of various space measurements and products. The CASA DFW QPE system, which is based on dual-polarization X-band CASA radars and a local S-band WSR-88DP radar, has demonstrated its excellent performance during several years of operation in a variety of precipitation regimes. The real-time CASA DFW QPE products are used extensively for localized hydrometeorological applications such as urban flash flood forecasting. In this paper, a neural network based data fusion mechanism is introduced to improve the satellite-based CMORPH precipitation product by taking into account the ground radar measurements. A deep learning system is

The employment of weather satellite imagery in an effort to identify and locate the forest-tundra ecotone in Canada

Science.gov (United States)

Aldrich, S. A.; Aldrich, F. T.; Rudd, R. D.

1969-01-01

Weather satellite imagery provides the only routinely available orbital imagery depicting the high latitudes. Although resolution is low on this imagery, it is believed that a major natural feature, notably linear in expression, should be mappable on it. The transition zone from forest to tundra, the ecotone, is such a feature. Locational correlation is herein established between a linear signature on the imagery and several ground truth positions of the ecotone in Canada.

Monitoring vegetation change in Abu Dhabi Emirate from 1996 to 2000 and 2004 using Landsat Satellite Imagery

International Nuclear Information System (INIS)

Starbuck, M.J.; Tamayo, J.

2007-01-01

In the fall of 2001, a study was initiated to investigate vegetation changes in the Abu Dhabi Emirates. The vast majority of vegetation present in the region is irrigated and analysis of vegetation change will support groundwater investigations in the region by indicating areas of increased water use. Satellite-based imaging systems provide a good source of data for such an analysis. The recent analysis was completed between February and November 2002 using Landsat 5 Thematic Mapper satellite imagery acquired in 1996 and Landsat 7 Enhanced Thematic Mapper Plus imagery acquired in 2000. These assessments were augmented in 2004with the study of Landsat 7 imagery acquired in early 2004. The total area of vegetation for each of seven study areas was calculated using the Normalized Difference Vegetation Index (NDVI) technique. Multiband image classification was used to differentiate general vegetation types. Change analysis consisted of simple NDVI image differencing and post-classification change matrices. Measurements of total vegetation are for the Abu Dhabi Emirate indicate an increase from 77,200 hectares in 1996 to 162,700 hectares in 2000 (110% increase). Based on comparison with manual interpretation of satellite imagery, the amount of under-reporting of irrigated land is estimated at about 15% of the actual area. From the assessment of 2004 Landset imagery, it was found that the growth of irrigated vegetation in most areas of Emirate had stabilized and had actually slightly decreased in some cases. The decreases are probably due to variability in the measurement technique and not due to actual decreases in area of vegetation. (author)

Monitoring Nuclear Facilities Using Satellite Imagery and Associated Remote Sensing Techniques

International Nuclear Information System (INIS)

Lafitte, Marc; Robin, Jean‑Philippe

2015-01-01

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

Multi-decadal record of ice dynamics on Daugaard Jensen Gletscher, East Greenland, from satellite imagery and terrestrial measurements

DEFF Research Database (Denmark)

Stearns, L.A.; Hamilton, G.S.; Reeh, Niels

2005-01-01

The history of ice velocity and calving front position of Daugaard Jensen Gletscher, a large outlet glacier in East Greenland, is reconstructed from field measurements, aerial photography and satellite imagery for the period 1950-2001. The calving terminus of the glacier has remained in approxima......The history of ice velocity and calving front position of Daugaard Jensen Gletscher, a large outlet glacier in East Greenland, is reconstructed from field measurements, aerial photography and satellite imagery for the period 1950-2001. The calving terminus of the glacier has remained...

User's guide to image processing applications of the NOAA satellite HRPT/AVHRR data. Part 1: Introduction to the satellite system and its applications. Part 2: Processing and analysis of AVHRR imagery

Science.gov (United States)

Huh, Oscar Karl; Leibowitz, Scott G.; Dirosa, Donald; Hill, John M.

1986-01-01

The use of NOAA Advanced Very High Resolution Radar/High Resolution Picture Transmission (AVHRR/HRPT) imagery for earth resource applications is provided for the applications scientist for use within the various Earth science, resource, and agricultural disciplines. A guide to processing NOAA AVHRR data using the hardware and software systems integrated for this NASA project is provided. The processing steps from raw data on computer compatible tapes (1B data format) through usable qualitative and quantitative products for applications are given. The manual is divided into two parts. The first section describes the NOAA satellite system, its sensors, and the theoretical basis for using these data for environmental applications. Part 2 is a hands-on description of how to use a specific image processing system, the International Imaging Systems, Inc. (I2S) Model 75 Array Processor and S575 software, to process these data.

Ionospheric response to daytime auroral electron precipitation: Results and analysis of a coordinated experiment between the AUREOL-3 satellite and the EISCAT radar

International Nuclear Information System (INIS)

Stamnes, K.; Roble, R.G.

1986-01-01

On June 2, 1982 the Soviet-French polar orbiting satellite AUREOL-3 passed over the EISCAT facility in northern Scandinavia. The EISCAT UHF radar measured electron and ion temperatures, electron density and ion composition, while the satellite measured the incident auroral particle spectra (protons and electrons) presumably giving rise to the densities and temperatures inferred from the radar data. The link between the satellite data obtained well above the atmosphere (at about 1300 km), and the radar measurements is an auroral model that simulates the ionospheric response to auroral particle precipitation and solar EUV radiation and makes predictions of ionospheric properties that 1) can be measured by the radar and 2) are the consequence of the satellite-observed particle precipitation. The analysis shows that there is good agreement between model-predicted and radar-inferred electron and ion temperatures and ion composition. However, inference of the ion composition from the radar data is a non-trivial and time-consuming undertaking which requires very good data (i.e. long integration times). Our initial attempts at analyzing the radar data with a fixed ion composition (as commonly practiced) which greatly simplifies the analysis yielded poor agreement between model predictions and radar measurements. Thus, our analysis demonstrates that the proper ion composition is crucial in order to obtain reliable temperature and density results from the measured autocorrelation functions

Automated invariant alignment to improve canonical variates in image fusion of satellite and weather radar data

DEFF Research Database (Denmark)

Vestergaard, Jacob Schack; Nielsen, Allan Aasbjerg

2013-01-01

Canonical correlation analysis (CCA) maximizes correlation between two sets of multivariate data. We applied CCA to multivariate satellite data and univariate radar data in order to produce a subspace descriptive of heavily precipitating clouds. A misalignment, inherent to the nature of the two...... data sets, was observed, corrupting the subspace. A method for aligning the two data sets is proposed, in order to overcome this issue and render a useful subspace projection. The observed corruption of the subspace gives rise to the hypothesis that the optimal correspondence, between a heavily...... precipitating cloud in the radar data and the associated cloud top registered in the satellite data, is found by a scale, rotation and translation invariant transformation together with a temporal displacement. The method starts by determining a conformal transformation of the radar data at the time of maximum...

Surface current measurements in Juan de Fuca Strait using the SeaSonde HF [high frequency] radar

International Nuclear Information System (INIS)

Hodgins, D.O.

1994-09-01

The shore-based SeaSonde high-frequency (HF) radar was deployed for three weeks in summer 1993 to measure surface currents in the Strait of Georgia, British Columbia. Experimental objectives included documenting the complex flow regime generated by large tides and the brackish plume of the Fraser River, and determining the radar performance under low-wind, low-salinity conditions. The radar data showed that surface flows are dominated by the plume jet formed by the Fraser River outflow, giving rise to recurring, energetic eddies with scales of 8-12 km, strong flow meanders, and convergent fronts. These features were continuously modulated by the along-channel tidal flows. Comparisons with a detailed numerical model hindcast gave good correlation between observed and predicted flow fields, especially at tidal and low frequencies. Radar return was found to be correlated with local winds and radar performance was independent of salinity variations in the plume. Synthetic aperture radar (SAR) provides a map of the radar scattering characteristics of the ocean surface on a capillary wave scale. ERS-1 satellite and airborne SAR images for July 28, 1993 were obtained and surface features were examined in the context of the HF radar current fields. Results show that SAR images alone cannot reliably provide the dynamical data required in this region by oil spill models. Under certain conditions, however, the radar imagery offers valuable physical information on phenomena affecting oil slick development. Interpretation of SAR imagery in conjunction with other remote sensing information would offer more quantitative prediction data. 28 refs., 334 figs., 1 tab

Improved wetland classification using eight-band high-resolution satellite imagery and a hybrid approach

Science.gov (United States)

Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of derived wetland maps were limited or often unsatisfactory largely due to the relatively coarse spatial resolution of conventional satellite imagery. This re...

FLIGHT DEVELOPMENT OF A DISTRIBUTED INERTIAL SATELLITE MICRONAVIGATTION SYSTEM FOR SYNTHETIC - APERTURE RADAR

Directory of Open Access Journals (Sweden)

Alexander Vladimirovich Chernodarov

2017-01-01

Full Text Available The current state of the onboard systems is characterized by the integration of aviation and radio-electronic equipment systems for solving problems of navigation and control. These problems include micro-navigation of the anten- na phase center (APC of the radar during the review of the Earth's surface from aboard the aircraft. Increasing of the reso- lution of the radar station (RLS by hardware increasing the antenna size is not always possible due to restrictions on the aircraft onboard equipment weight and dimensions. Therefore the implementation of analytic extension of the radiation pattern by "gluing" the images, obtained by RLS on the aircraft motion trajectory is embodied. The estimations are con- verted into amendments to the signals of RLS with synthetic aperture RSA to compensate instabilities. The purpose of the research is building a theoretical basis and a practical implementation of procedures for evaluating the trajectory APS in- stabilities using a distributed system of inertial-satellite micro-navigation (DSMN taking into account the RSA flight oper- ations actual conditions. The technology of evaluation and compensation of RSA trajectory instabilities via DSMN is con- sidered. The implementation of this technology is based on the mutual support of inertial, satellite and radar systems. Syn- chronization procedures of inertial and satellite measurements in the evaluation of DSMN errors are proposed. The given results of DSMN flight testing justify the possibility and expediency to apply the proposed technology in order to improve the resolution of RSA. The compensation of aircraft trajectory instabilities in RSA signals can be provided by inertial- satellite micro-navigation system, taking into account the actual conditions of the RSA flight operations. The researches show that in order to achieve the required resolution of RSA it seems to be appropriate to define the rational balance be- tween accuracy DSMN characteristics

Dielectric properties of Jovian satellite ice analogs for subsurface radar exploration: A review

Science.gov (United States)

Pettinelli, Elena; Cosciotti, Barbara; Di Paolo, Federico; Lauro, Sebastian Emanuel; Mattei, Elisabetta; Orosei, Roberto; Vannaroni, Giuliano

2015-09-01

The first European mission dedicated to the exploration of Jupiter and its icy moons (JUpiter ICy moons Explorer—JUICE) will be launched in 2022 and will reach its final destination in 2030. The main goals of this mission are to understand the internal structure of the icy crusts of three Galilean satellites (Europa, Ganymede, and Callisto) and, ultimately, to detect Europa's subsurface ocean, which is believed to be the closest to the surface among those hypothesized to exist on these moons. JUICE will be equipped with the 9 MHz subsurface-penetrating radar RIME (Radar for Icy Moon Exploration), which is designed to image the ice down to a depth of 9 km. Moreover, a parallel mission to Europa, which will host onboard REASON (Radar for Europa Assessment and Sounding: Ocean to Near-surface) equipped with 9MHz and 60MHz antennas, has been recently approved by NASA. The success of these experiments strongly relies on the accurate prediction of the radar performance and on the optimal processing and interpretation of radar echoes that, in turn, depend on the dielectric properties of the materials composing the icy satellite crusts. In the present review we report a complete range of potential ice types that may occur on these icy satellites to understand how they may affect the results of the proposed missions. First, we discuss the experimental results on pure and doped water ice in the framework of the Jaccard theory, highlighting the critical aspects in terms of a lack of standard laboratory procedures and inconsistency in data interpretation. We then describe the dielectric behavior of extraterrestrial ice analogs like hydrates and icy mixtures, carbon dioxide ice and ammonia ice. Building on this review, we have selected the most suitable data to compute dielectric attenuation, velocity, vertical resolution, and reflection coefficients for such icy moon environments, with the final goal being to estimate the potential capabilities of the radar missions as a

Sherlock Holmes' or Don Quixote`s certainty? Interpretations of cropmarks on satellite imageries in archaeological investigation

Science.gov (United States)

Wilgocka, Aleksandra; RÄ czkowski, Włodzimierz; Kostyrko, Mikołaj; Ruciński, Dominik

2016-08-01

Years of experience in air-photo interpretations provide us to conclusion that we know what we are looking at, we know why we can see cropmarks, we even can estimate, when are the best opportunities to observe them. But even today cropmarks may be a subject of misinterpretation or wishful thinking. The same problems appear when working with aerial photographs, satellite imageries, ALS, geophysics, etc. In the paper we present several case studies based on data acquired for and within ArchEO - archaeological applications of Earth Observation techniques project to discuss complexity and consequences of archaeological interpretations. While testing usefulness of satellite imagery in Poland on various types of sites, cropmarks were the most frequent indicators of past landscapes as well as archaeological and natural features. Hence, new archaeological sites have been discovered mainly thanks to cropmarks. This situation has given us an opportunity to test not only satellite imageries as a source of data but also confront them with results of other non-invasive methods of data acquisition. When working with variety of data we have met several issues which raised problems of interpretation. Consequently, questions related to the cognitive value of remote sensing data appear and should be discussed. What do the data represent? To what extent the imageries, cropmarks or other visualizations represent the past? How should we deal with ambiguity of data? What can we learn from pitfalls in the interpretation of cropmarks, soilmarks etc. to share more Sherlock's methodology rather than run around Don Quixote's delusions?

Cross-validation Methodology between Ground and GPM Satellite-based Radar Rainfall Product over Dallas-Fort Worth (DFW) Metroplex

Science.gov (United States)

Chen, H.; Chandrasekar, V.; Biswas, S.

2015-12-01

Over the past two decades, a large number of rainfall products have been developed based on satellite, radar, and/or rain gauge observations. However, to produce optimal rainfall estimation for a given region is still challenging due to the space time variability of rainfall at many scales and the spatial and temporal sampling difference of different rainfall instruments. In order to produce high-resolution rainfall products for urban flash flood applications and improve the weather sensing capability in urban environment, the center for Collaborative Adaptive Sensing of the Atmosphere (CASA), in collaboration with National Weather Service (NWS) and North Central Texas Council of Governments (NCTCOG), has developed an urban radar remote sensing network in DFW Metroplex. DFW is the largest inland metropolitan area in the U.S., that experiences a wide range of natural weather hazards such as flash flood and hailstorms. The DFW urban remote sensing network, centered by the deployment of eight dual-polarization X-band radars and a NWS WSR-88DP radar, is expected to provide impacts-based warning and forecasts for benefit of the public safety and economy. High-resolution quantitative precipitation estimation (QPE) is one of the major goals of the development of this urban test bed. In addition to ground radar-based rainfall estimation, satellite-based rainfall products for this area are also of interest for this study. Typical example is the rainfall rate product produced by the Dual-frequency Precipitation Radar (DPR) onboard Global Precipitation Measurement (GPM) Core Observatory satellite. Therefore, cross-comparison between ground and space-based rainfall estimation is critical to building an optimal regional rainfall system, which can take advantages of the sampling differences of different sensors. This paper presents the real-time high-resolution QPE system developed for DFW urban radar network, which is based upon the combination of S-band WSR-88DP and X

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

Directory of Open Access Journals (Sweden)

Dimitris G. Stavrakoudis

2014-07-01

Full Text Available This study investigates the effectiveness of combining multispectral very high resolution (VHR and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM. The classification result from the hyperspectral image is then resampled to the multispectral’s spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements.

Application of satellite imagery to monitoring human rights abuse of vulnerable communities, with minimal risk to relief staff

International Nuclear Information System (INIS)

Lavers, C; Bishop, C; Hawkins, O; Grealey, E; Cox, C; Thomas, D; Trimel, S

2009-01-01

Space imagery offers remote surveillance of ethnic people groups at risk of human rights abuse. We highlight work in alleged violations in Burma and Sudan, using satellite imagery for verification with Amnesty International. We consider how imaging may effectively support small to medium-sized Non Governmental Organisations and charities, e.g. HART, working in dangerous zones on the ground. Satellite based sensing applications are now at a sufficiently mature stage for moderate Governmental funding levels to help prevent human rights abuse, rather than the greater cost of rebuilding communities and healing sectarian divisions after abuse has taken place.

Application of satellite imagery to monitoring human rights abuse of vulnerable communities, with minimal risk to relief staff

Energy Technology Data Exchange (ETDEWEB)

Lavers, C; Bishop, C; Hawkins, O; Grealey, E; Cox, C; Thomas, D; Trimel, S, E-mail: brnc-radarcomms1@nrta.mod.u [Sensors Team, Plymouth University at Britannia Royal Naval College, Dartmouth (United Kingdom); DMC International Imaging, Tycho House, Surrey Research Park, Guildford (United Kingdom); Qinetiq, Cody Technology Park, Cody Building, Ively Road, Farnborough (United Kingdom); Humanitarian Aid Relief Trust (HART), 3 Arnellan House, Kingsbury, London (United Kingdom); Amnesty International USA, 5 Penn Plaza, New York (United States)

2009-07-01

Space imagery offers remote surveillance of ethnic people groups at risk of human rights abuse. We highlight work in alleged violations in Burma and Sudan, using satellite imagery for verification with Amnesty International. We consider how imaging may effectively support small to medium-sized Non Governmental Organisations and charities, e.g. HART, working in dangerous zones on the ground. Satellite based sensing applications are now at a sufficiently mature stage for moderate Governmental funding levels to help prevent human rights abuse, rather than the greater cost of rebuilding communities and healing sectarian divisions after abuse has taken place.

Applying NASA Imaging Radar Datasets to Investigate the Geomorphology of the Amazon's Planalto

Science.gov (United States)

McDonald, K. C.; Campbell, K.; Islam, R.; Alexander, P. M.; Cracraft, J.

2016-12-01

The Amazon basin is a biodiversity rich biome and plays a significant role into shaping Earth's climate, ocean and atmospheric gases. Understanding the history of the formation of this basin is essential to our understanding of the region's biodiversity and its response to climate change. During March 2013, the NASA/JPL L-band polarimetric airborne imaging radar, UAVSAR, conducted airborne studies over regions of South America including portions of the western Amazon basin. We utilize UAVSAR imagery acquired during that time over the Planalto, in the Madre de Dios region of southeastern Peru in an assessment of the underlying geomorphology, its relationship to the current distribution of vegetation, and its relationship to geologic processes through deep time. We employ UAVSAR data collections to assess the utility of these high quality imaging radar data for use in identifying geomorphologic features and vegetation communities within the context of improving the understanding of evolutionary processes, and their utility in aiding interpretation of datasets from Earth-orbiting satellites to support a basin-wide characterization across the Amazon. We derive maps of landcover and river branching structure from UAVSAR imagery. We compare these maps to those derived using imaging radar datasets from the Japanese Space Agency's ALOS PALSAR and Digital Elevation Models (DEMs) from NASA's Shuttle Radar Topography Mission (SRTM). Results provide an understanding of the underlying geomorphology of the Amazon planalto as well as its relationship to geologic processes and will support interpretation of the evolutionary history of the Amazon Basin. Portions of this work have been carried out within the framework of the ALOS Kyoto & Carbon Initiative. PALSAR data were provided by JAXA/EORC and the Alaska Satellite Facility.This work is carried out with support from the NASA Biodiversity Program and the NSF DIMENSIONS of Biodiversity Program.

Detecting Weather Radar Clutter by Information Fusion With Satellite Images and Numerical Weather Prediction Model Output

DEFF Research Database (Denmark)

Bøvith, Thomas; Nielsen, Allan Aasbjerg; Hansen, Lars Kai

2006-01-01

A method for detecting clutter in weather radar images by information fusion is presented. Radar data, satellite images, and output from a numerical weather prediction model are combined and the radar echoes are classified using supervised classification. The presented method uses indirect...... information on precipitation in the atmosphere from Meteosat-8 multispectral images and near-surface temperature estimates from the DMI-HIRLAM-S05 numerical weather prediction model. Alternatively, an operational nowcasting product called 'Precipitating Clouds' based on Meteosat-8 input is used. A scale...

The use of radar for bathymetry assessment

NARCIS (Netherlands)

Aardoom, J.H.; Greidanus, H.S.F.

1998-01-01

The bottom topography in shallow seas can be observed by air- and spaceborne imaging radar. Bathymetric information derived from radar data is limited in accuracy, but radar has a good spatial coverage. The accuracy can be increased by assimilating the radar imagery into existing or insitu gathered

Simultaneous measurements from the Millstone Hill radar and the Active satellite during the SAID/SAR arc event of the March 1990 CEDAR storm

Directory of Open Access Journals (Sweden)

M. Förster

Full Text Available During a nearby passage of the Active satellite above the Millstone Hill radar on 21 March 1990 at local sunset, the satellite and the radar performed simultaneous measurements of upper ionospheric parameters in nearly the same spatial volume. For this purpose the radar carried out a special azimuth-elevation scan to track the satellite. Direct comparisons of radar data and in situ satellite measurements have been carried out quite rarely. In this case, the coincidence of co-ordinated measurements and active ionospheric-magnetospheric processes during an extended storm recovery phase presents a unique occasion resulting in a very valuable data set. The measurements show generally good agreement both during quiet prestorm and storm conditions and the combination of radar and satellite observations gives a more comprehensive picture of the physical processes involved. We find a close relationship between the rapid westward ion drift peak at subauroral latitudes (SAID event and the occurrence of a stable auroral red (SAR arc observed after sunset by an all-sky imager and reported in an earlier study of this event. The SAID electric field is caused by the penetration of energetic ions with energies between about 1 keV and 100 keV into the outer plasmasphere to a latitude equatorward of the extent of the plasmasheet electrons. Charge separation results in the observed polarisation field and the SAID. Unusually high molecular ion densities measured by the satellite at altitudes of 700-870 km at subauroral and auroral latitudes point on strong upward-directed ion acceleration processes and an intense neutral gas upwelling. These structures are collocated with a narrow trough in electron density and an electron temperature peak as observed simultaneously by the radar and the satellite probes.

Key words. Ionosphere (ionosphere-magnetosphere interactions; plasma temperature and density; Magnetospheric physics (plasmasphere.

Perception-oriented fusion of multi-sensor imagery: visible, IR, and SAR

Science.gov (United States)

Sidorchuk, D.; Volkov, V.; Gladilin, S.

2018-04-01

This paper addresses the problem of image fusion of optical (visible and thermal domain) data and radar data for the purpose of visualization. These types of images typically contain a lot of complimentary information, and their joint visualization can be useful and more convenient for human user than a set of individual images. To solve the image fusion problem we propose a novel algorithm that utilizes some peculiarities of human color perception and based on the grey-scale structural visualization. Benefits of presented algorithm are exemplified by satellite imagery.

Ship detection in satellite imagery using rank-order greyscale hit-or-miss transforms

Energy Technology Data Exchange (ETDEWEB)

Harvey, Neal R [Los Alamos National Laboratory; Porter, Reid B [Los Alamos National Laboratory; Theiler, James [Los Alamos National Laboratory

2010-01-01

Ship detection from satellite imagery is something that has great utility in various communities. Knowing where ships are and their types provides useful intelligence information. However, detecting and recognizing ships is a difficult problem. Existing techniques suffer from too many false-alarms. We describe approaches we have taken in trying to build ship detection algorithms that have reduced false alarms. Our approach uses a version of the grayscale morphological Hit-or-Miss transform. While this is well known and used in its standard form, we use a version in which we use a rank-order selection for the dilation and erosion parts of the transform, instead of the standard maximum and minimum operators. This provides some slack in the fitting that the algorithm employs and provides a method for tuning the algorithm's performance for particular detection problems. We describe our algorithms, show the effect of the rank-order parameter on the algorithm's performance and illustrate the use of this approach for real ship detection problems with panchromatic satellite imagery.

Operational Satellite-based Surface Oil Analyses (Invited)

Science.gov (United States)

Streett, D.; Warren, C.

2010-12-01

During the Deepwater Horizon spill, NOAA imagery analysts in the Satellite Analysis Branch (SAB) issued more than 300 near-real-time satellite-based oil spill analyses. These analyses were used by the oil spill response community for planning, issuing surface oil trajectories and tasking assets (e.g., oil containment booms, skimmers, overflights). SAB analysts used both Synthetic Aperture Radar (SAR) and high resolution visible/near IR multispectral satellite imagery as well as a variety of ancillary datasets. Satellite imagery used included ENVISAT ASAR (ESA), TerraSAR-X (DLR), Cosmo-Skymed (ASI), ALOS (JAXA), Radarsat (MDA), ENVISAT MERIS (ESA), SPOT (SPOT Image Corp.), Aster (NASA), MODIS (NASA), and AVHRR (NOAA). Ancillary datasets included ocean current information, wind information, location of natural oil seeps and a variety of in situ oil observations. The analyses were available as jpegs, pdfs, shapefiles and through Google, KML files and also available on a variety of websites including Geoplatform and ERMA. From the very first analysis issued just 5 hours after the rig sank through the final analysis issued in August, the complete archive is still publicly available on the NOAA/NESDIS website http://www.ssd.noaa.gov/PS/MPS/deepwater.html SAB personnel also served as the Deepwater Horizon International Disaster Charter Project Manager (at the official request of the USGS). The Project Manager’s primary responsibility was to acquire and oversee the processing and dissemination of satellite data generously donated by numerous private companies and nations in support of the oil spill response including some of the imagery described above. SAB has begun to address a number of goals that will improve our routine oil spill response as well as help assure that we are ready for the next spill of national significance. We hope to (1) secure a steady, abundant and timely stream of suitable satellite imagery even in the absence of large-scale emergencies such as

Estimated Depth Maps of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery (Draft)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Estimated shallow-water, depth maps were produced using rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations in the...

Monsoon Convective During the South China Sea Monsoon Experiment: Observations from Ground-Based Radar and the TRMM Satellite

Science.gov (United States)

Cifelli, Rob; Rickenbach, Tom; Halverson, Jeff; Keenan, Tom; Kucera, Paul; Atkinson, Lester; Fisher, Brad; Gerlach, John; Harris, Kathy; Kaufman, Cristina

1999-01-01

A main goal of the recent South China Sea Monsoon Experiment (SCSMEX) was to study convective processes associated with the onset of the Southeast Asian summer monsoon. The NASA TOGA C-band scanning radar was deployed on the Chinese research vessel Shi Yan #3 for two 20 day cruises, collecting dual-Doppler measurements in conjunction with the BMRC C-Pol dual-polarimetric radar on Dongsha Island. Soundings and surface meteorological data were also collected with an NCAR Integrated Sounding System (ISS). This experiment was the first major tropical field campaign following the launch of the Tropical Rainfall Measuring Mission (TRMM) satellite. These observations of tropical oceanic convection provided an opportunity to make comparisons between surface radar measurements and the Precipitation Radar (PR) aboard the TRMM satellite in an oceanic environment. Nearly continuous radar operations were conducted during two Intensive Observing Periods (IOPS) straddling the onset of the monsoon (5-25 May 1998 and 5-25 June 1998). Mesoscale lines of convection with widespread regions of both trailing and forward stratiform precipitation were observed following the onset of the active monsoon in the northern South China Sea region. The vertical structure of the convection during periods of strong westerly flow and relatively moist environmental conditions in the lower to mid-troposphere contrasted sharply with convection observed during periods of low level easterlies, weak shear, and relatively dry conditions in the mid to upper troposphere. Several examples of mesoscale convection will be shown from the ground (ship)-based and spaceborne radar data during times of TRMM satellite overpasses. Examples of pre-monsoon convection, characterized by isolated cumulonimbus and shallow, precipitating congestus clouds, will also be discussed.

On land-use modeling: A treatise of satellite imagery data and misclassification error

Science.gov (United States)

Sandler, Austin M.

Recent availability of satellite-based land-use data sets, including data sets with contiguous spatial coverage over large areas, relatively long temporal coverage, and fine-scale land cover classifications, is providing new opportunities for land-use research. However, care must be used when working with these datasets due to misclassification error, which causes inconsistent parameter estimates in the discrete choice models typically used to model land-use. I therefore adapt the empirical correction methods developed for other contexts (e.g., epidemiology) so that they can be applied to land-use modeling. I then use a Monte Carlo simulation, and an empirical application using actual satellite imagery data from the Northern Great Plains, to compare the results of a traditional model ignoring misclassification to those from models accounting for misclassification. Results from both the simulation and application indicate that ignoring misclassification will lead to biased results. Even seemingly insignificant levels of misclassification error (e.g., 1%) result in biased parameter estimates, which alter marginal effects enough to affect policy inference. At the levels of misclassification typical in current satellite imagery datasets (e.g., as high as 35%), ignoring misclassification can lead to systematically erroneous land-use probabilities and substantially biased marginal effects. The correction methods I propose, however, generate consistent parameter estimates and therefore consistent estimates of marginal effects and predicted land-use probabilities.

Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery

Directory of Open Access Journals (Sweden)

M. C. Anderson

2011-01-01

Full Text Available Thermal infrared (TIR remote sensing of land-surface temperature (LST provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (e.g., air temperature, advection are affecting plant functioning. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil + canopy land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5 to 10-km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions on the order of 10 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe, Africa

Inter-comparison of Rainfall Estimation from Radar and Satellite During 2016 June 23 Yancheng Tornado Event over Eastern China

Science.gov (United States)

Huang, C.; Chen, S.; Liang, Z.; Hu, B.

2017-12-01

ABSTRACT: On the afternoon of June 23, 2016, Yancheng city in eastern China was hit by a severe thunderstorm that produced a devastating tornado. This tornado was ranked as an EF4 on the Enhanced Fujita scale by China Meteorological Administration, and killed at least 99 people and injured 846 others (152 seriously). This study evaluates rainfall estimates from ground radar network and four satellite algorithms with a relatively dense rain gauge network over eastern China including Jiangsu province and its adjacent regions for the Yancheng June 23 Tornado extreme convective storm in different spatiotemporal scales (from 0.04° to 0.1° and hourly to event total accumulation). The radar network is composed of about 6 S-band Doppler weather radars. Satellite precipitation products include Integrated Multi-satellitE Retrievals for GPM (IMERG), Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), and Global Satellite Mapping of Precipitation (GSMap). Relative Bias (RB), Root-Mean-Squared Error (RMSE), Correlation Coefficient (CC), Probability Of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) are used to quantify the performance of these precipitation products.

Observing and Modelling the HighWater Level from Satellite Radar Altimetry During Tropical Cyclones

DEFF Research Database (Denmark)

Deng, Xiaoli; Gharineiat, Zahra; Andersen, Ole Baltazar

2016-01-01

This paper investigates the capability of observing tropical cyclones using satellite radar altimetry. Two representative cyclones Yasi (February 2011) and Larry (March 2006) in the northeast Australian coastal area are selected based also on available tide gauge sea level measurements. It is sho...

Compressing interpreted satellite imagery for geographic information systems applications over extensive regions

Science.gov (United States)

Miller, Stephan W.

1981-01-01

Image processing systems (IPS) and techniques effectively transform satellite imagery into data for input into a spatial database. Geographic information systems (GIS), consisting of graphic input and spatial database management subsystems, are capable of processing digital map and map overlay data to build and manipulate a spatial database. These systems can be successfully integrated to create a successful spatial data handling capability provided certain obstacle are understood and overcome.

Applying High Resolution Imagery to Understand the Role of Dynamics in the Diminishing Arctic Sea Ice Cover

Science.gov (United States)

2015-09-30

describe contemporary ice pack thickness, MODIS , AVHRR, RadarSat-2 (satellite imagery) that describe ice pack deformation features on large scales, as well...high-resolution visible-band images of the Arctic ice pack that are available at the GFL, USGS. The statistics related to the available images are...University of Maryland team as a Faculty Research Assistant, working under the guidance of Co-PI Farrell. Ms. Faber is responsible for analysis of MODIS

Surface Characteristics of Green Island Wakes from Satellite Imagery

Science.gov (United States)

Cheng, Kai-Ho; Hsu, Po-Chun; Ho, Chung-Ru

2017-04-01

Characteristics of an island wake induced by the Kuroshio Current flows pass by Green Island, a small island 40 km off southeast of Taiwan is investigated by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. The MODIS sea surface temperature (SST) and chlorophyll-a (chl-a) imagery is produced at 250-meter resolution from 2014 to 2015 using the SeaDAS software package which is developed by the National Aeronautics and Space Administration. The wake occurrence is 59% observed from SST images during the data span. The average cooling area is 190 km2, but the area is significantly changed with wind directions. The wake area is increased during southerly winds and is reduced during northerly winds. Besides, the average cooling SST was about 2.1 oC between the front and rear island. Comparing the temperature difference between the wake and its left side, the difference is 1.96 oC. In addition, the wakes have 1 3 times higher than normal in chlorophyll concentration. The results indicate the island mass effect makes the surface water of Green island wake colder and chl-a higher.

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

Directory of Open Access Journals (Sweden)

Oscar Rojas

2013-04-01

Full Text Available Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the availability of long time series as needed by the majority of the yield prediction methods used today.

Upper atmospheric gravity wave details revealed in nightglow satellite imagery

Science.gov (United States)

Miller, Steven D.; Straka, William C.; Yue, Jia; Smith, Steven M.; Alexander, M. Joan; Hoffmann, Lars; Setvák, Martin; Partain, Philip T.

2015-01-01

Gravity waves (disturbances to the density structure of the atmosphere whose restoring forces are gravity and buoyancy) comprise the principal form of energy exchange between the lower and upper atmosphere. Wave breaking drives the mean upper atmospheric circulation, determining boundary conditions to stratospheric processes, which in turn influence tropospheric weather and climate patterns on various spatial and temporal scales. Despite their recognized importance, very little is known about upper-level gravity wave characteristics. The knowledge gap is mainly due to lack of global, high-resolution observations from currently available satellite observing systems. Consequently, representations of wave-related processes in global models are crude, highly parameterized, and poorly constrained, limiting the description of various processes influenced by them. Here we highlight, through a series of examples, the unanticipated ability of the Day/Night Band (DNB) on the NOAA/NASA Suomi National Polar-orbiting Partnership environmental satellite to resolve gravity structures near the mesopause via nightglow emissions at unprecedented subkilometric detail. On moonless nights, the Day/Night Band observations provide all-weather viewing of waves as they modulate the nightglow layer located near the mesopause (∼90 km above mean sea level). These waves are launched by a variety of physical mechanisms, ranging from orography to convection, intensifying fronts, and even seismic and volcanic events. Cross-referencing the Day/Night Band imagery with conventional thermal infrared imagery also available helps to discern nightglow structures and in some cases to attribute their sources. The capability stands to advance our basic understanding of a critical yet poorly constrained driver of the atmospheric circulation. PMID:26630004

OVERVIEW OF MODERN RESEARCH OF LANDSLIDES ACCORDING TO AERIAL AND SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

K. M. Lyapishev

2015-01-01

Full Text Available This article is an overview of researches of landslides using remote sensing methods such as aerial photography, satellite images, radar interferometry, and their combination with the use of GIS technology. Modern methods of investigation of landslides are very diverse. The authors propose different approaches to the identification, classification and monitoring of landslides. Data analysis techniques can help in creating more sophisticated approach to the analysis of landslides.

The use of radar for bathymetry assessment

OpenAIRE

Aardoom, J.H.; Greidanus, H.S.F.

1998-01-01

The bottom topography in shallow seas can be observed by air- and spaceborne imaging radar. Bathymetric information derived from radar data is limited in accuracy, but radar has a good spatial coverage. The accuracy can be increased by assimilating the radar imagery into existing or insitu gathered bathymetric data. The paper reviews the concepts of bathymetry assessment by radar, the radar imaging mechanism, and the possibilities and limitations of the use of radar data in rapid assessment.

Fusion of Pixel-based and Object-based Features for Road Centerline Extraction from High-resolution Satellite Imagery

Directory of Open Access Journals (Sweden)

CAO Yungang

2016-10-01

Full Text Available A novel approach for road centerline extraction from high spatial resolution satellite imagery is proposed by fusing both pixel-based and object-based features. Firstly, texture and shape features are extracted at the pixel level, and spectral features are extracted at the object level based on multi-scale image segmentation maps. Then, extracted multiple features are utilized in the fusion framework of Dempster-Shafer evidence theory to roughly identify the road network regions. Finally, an automatic noise removing algorithm combined with the tensor voting strategy is presented to accurately extract the road centerline. Experimental results using high-resolution satellite imageries with different scenes and spatial resolutions showed that the proposed approach compared favorably with the traditional methods, particularly in the aspect of eliminating the salt noise and conglutination phenomenon.

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

Science.gov (United States)

Hester, David Barry

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

Assessing the population coverage of a health demographic surveillance system using satellite imagery and crowd-sourcing

NARCIS (Netherlands)

Pasquale, Di Aurelio; Mc Cann, Robert; Maire, Nicolas

2017-01-01

Remotely sensed data can serve as an independent source of information about the location of residential structures in areas under demographic and health surveillance. We report on results obtained combining satellite imagery, imported from Bing, with location data routinely collected using the

Linear feature extraction from radar imagery: SBIR (Small Business Innovative Research), phase 2, option 2

Science.gov (United States)

Milgram, David L.; Kahn, Philip; Conner, Gary D.; Lawton, Daryl T.

1988-12-01

The goal of this effort is to develop and demonstrate prototype processing capabilities for a knowledge-based system to automatically extract and analyze features from Synthetic Aperture Radar (SAR) imagery. This effort constitutes Phase 2 funding through the Defense Small Business Innovative Research (SBIR) Program. Previous work examined the feasibility of and technology issues involved in the development of an automated linear feature extraction system. This final report documents this examination and the technologies involved in automating this image understanding task. In particular, it reports on a major software delivery containing an image processing algorithmic base, a perceptual structures manipulation package, a preliminary hypothesis management framework and an enhanced user interface.

Using satellite imagery to assess the influence of urban development on the impacts of extreme rainfall

DEFF Research Database (Denmark)

Kaspersen, Per Skougaard; Drews, Martin; Madsen, Henrik

We investigate the applicability of medium resolution Landsat satellite imagery for mapping temporal changes in urban land cover for direct use in urban flood models. The overarching aim is to provide accurate and cost- and resource-efficient quantification of temporal changes in risk towards...

Detection and Prediction of Hail Storms in Satellite Imagery using Deep Learning

Science.gov (United States)

Pullman, M.; Gurung, I.; Ramachandran, R.; Maskey, M.

2017-12-01

Natural hazards, such as damaging hail storms, dramatically disrupt both industry and agriculture, having significant socio-economic impacts in the United States. In 2016, hail was responsible for 3.5 billion and 23 million dollars in damage to property and crops, respectively, making it the second costliest 2016 weather phenomenon in the United States. The destructive nature and high cost of hail storms has driven research into the development of more accurate hail-prediction algorithms in an effort to mitigate societal impacts. Recently, weather forecasting efforts have turned to deep learning neural networks because neural networks can more effectively model complex, nonlinear, dynamical phenomenon that exist in large datasets through multiple stages of transformation and representation. In an effort to improve hail-prediction techniques, we propose a deep learning technique that leverages satellite imagery to detect and predict the occurrence of hail storms. The technique is applied to satellite imagery from 2006 to 2016 for the contiguous United States and incorporates hail reports obtained from the National Center for Environmental Information Storm Events Database for training and validation purposes. In this presentation, we describe a novel approach to predicting hail via a neural network model that creates a large labeled dataset of hail storms, the accuracy and results of the model, and its applications for improving hail forecasting.

NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE): Changing patterns in the use of NRT satellite imagery

Science.gov (United States)

Davies, D.; Michael, K.; Schmaltz, J. E.; Harrison, S.; Ding, F.; Durbin, P. B.; Boller, R. A.; Cechini, M. F.; Rinsland, P. L.; Ye, G.; Mauoka, E.

2015-12-01

NASA's Land, Atmosphere Near real-time Capability for EOS (Earth Observing System) (LANCE) provides data and imagery approximately 3 hours from satellite observation, to monitor natural events globally and to meet the needs of the near real-time (NRT) applications community. This article describes LANCE, and how the use of NRT data and imagery has evolved. Since 2010 there has been a four-fold increase in both the volume of data and the number of files downloaded. Over the last year there has been a marked shift in the way in which users are accessing NRT imagery; users are gravitating towards Worldview and the Global Imagery Browse Services (GIBS) and away from MODIS Rapid Response, in part due to the increased exposure through social media. In turn this is leading to a broader range of users viewing NASA NRT imagery. This article also describes new, and planned, product enhancements to LANCE. Over the last year, LANCE has expanded to support NRT products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), and the Multi-angle Imaging SpectroRadiometer (MISR). LANCE elements are also planning to ingest and process NRT data from the Visible Infrared Imager Radiometer Suite (VIIRS), and the advanced Ozone Mapping and Profiler Suite (OMPS) instruments onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite in the near future.

Cloud cover typing from environmental satellite imagery. Discriminating cloud structure with Fast Fourier Transforms (FFT)

Science.gov (United States)

Logan, T. L.; Huning, J. R.; Glackin, D. L.

1983-01-01

The use of two dimensional Fast Fourier Transforms (FFTs) subjected to pattern recognition technology for the identification and classification of low altitude stratus cloud structure from Geostationary Operational Environmental Satellite (GOES) imagery was examined. The development of a scene independent pattern recognition methodology, unconstrained by conventional cloud morphological classifications was emphasized. A technique for extracting cloud shape, direction, and size attributes from GOES visual imagery was developed. These attributes were combined with two statistical attributes (cloud mean brightness, cloud standard deviation), and interrogated using unsupervised clustering amd maximum likelihood classification techniques. Results indicate that: (1) the key cloud discrimination attributes are mean brightness, direction, shape, and minimum size; (2) cloud structure can be differentiated at given pixel scales; (3) cloud type may be identifiable at coarser scales; (4) there are positive indications of scene independence which would permit development of a cloud signature bank; (5) edge enhancement of GOES imagery does not appreciably improve cloud classification over the use of raw data; and (6) the GOES imagery must be apodized before generation of FFTs.

Rigorous Line-Based Transformation Model Using the Generalized Point Strategy for the Rectification of High Resolution Satellite Imagery

Directory of Open Access Journals (Sweden)

Kun Hu

2016-09-01

Full Text Available High precision geometric rectification of High Resolution Satellite Imagery (HRSI is the basis of digital mapping and Three-Dimensional (3D modeling. Taking advantage of line features as basic geometric control conditions instead of control points, the Line-Based Transformation Model (LBTM provides a practical and efficient way of image rectification. It is competent to build the mathematical relationship between image space and the corresponding object space accurately, while it reduces the workloads of ground control and feature recognition dramatically. Based on generalization and the analysis of existing LBTMs, a novel rigorous LBTM is proposed in this paper, which can further eliminate the geometric deformation caused by sensor inclination and terrain variation. This improved nonlinear LBTM is constructed based on a generalized point strategy and resolved by least squares overall adjustment. Geo-positioning accuracy experiments with IKONOS, GeoEye-1 and ZiYuan-3 satellite imagery are performed to compare rigorous LBTM with other relevant line-based and point-based transformation models. Both theoretic analysis and experimental results demonstrate that the rigorous LBTM is more accurate and reliable without adding extra ground control. The geo-positioning accuracy of satellite imagery rectified by rigorous LBTM can reach about one pixel with eight control lines and can be further improved by optimizing the horizontal and vertical distribution of control lines.

Super-Resolution for "Jilin-1" Satellite Video Imagery via a Convolutional Network.

Science.gov (United States)

Xiao, Aoran; Wang, Zhongyuan; Wang, Lei; Ren, Yexian

2018-04-13

Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method's practicality. Experimental results on "Jilin-1" satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods.

Marine parameters from synergy of optical and radar satellite data

Science.gov (United States)

Lehner, S.; Hoja, D.; Schulz-Stellenfleth, J.

In 2001 the European Space Agency ESA will launch the earth observation satellite ENVISAT. It will carry several instruments that provide new opportunities to measure oceanographic variables. Together, they represent the main measurement techniques of satellite oceanography, and complement each other in an ideal manner. These instruments are to be used in synergy to: Improve the analysis of measured wind and ocean wave fields, and thereby improve weather forecasting at weather centers; Determine the extent and variables of sea ice and develop a five-day sea ice prediction model, to support maritime shipping and offshore activities; Monitor and map sediment and suspended matter transport in coastal regions, especially in areas with large river estuaries, which greatly affects shipping lanes, harbors, and dredging activities; Monitor hydrobiological and bio-geochemical variables related to water quality in coastal regions and large inland waters, which affects ecology, coastal development, aquaculture, drinking water supplies, and tourism. To prepare the oceanographic community to make best use of the ENVISAT sensors in the pre-launch phase, existing algorithms to derive marine parameters are used and validated using data from the ERS SAR, the ERS RA, SeaWiFS and IRS MOS sensors now in operation. Derived products are used to address problems that can best be tackled using the synergy of radar and optical data, such as the effect of surface slicks on radar wind measurements, of sea state on ocean color, of wind and waves on the resuspension of suspended matter, and of wind and waves on sea ice variables.

Satellite imagery-based monitoring of archaeological site damage in the Syrian civil war.

Science.gov (United States)

Casana, Jesse; Laugier, Elise Jakoby

2017-01-01

Since the start of the Syrian civil war in 2011, the rich archaeological heritage of Syria and northern Iraq has faced severe threats, including looting, combat-related damage, and intentional demolition of monuments. However, the inaccessibility of the conflict zone to archaeologists or cultural heritage specialists has made it difficult to produce accurate damage assessments, impeding efforts to develop mitigation strategies and policies. This paper presents results of a project, undertaken in collaboration with the American Schools of Oriental Research (ASOR) and the US Department of State, to monitor damage to archaeological sites in Syria, northern Iraq, and southern Turkey using recent, high-resolution satellite imagery. Leveraging a large database of archaeological and heritage sites throughout the region, as well as access to continually updated satellite imagery from DigitalGlobe, this project has developed a flexible and efficient methodology to log observations of damage in a manner that facilitates spatial and temporal queries. With nearly 5000 sites carefully evaluated, analysis reveals unexpected patterns in the timing, severity, and location of damage, helping us to better understand the evolving cultural heritage crisis in Syria and Iraq. Results also offer a model for future remote sensing-based archaeological and heritage monitoring efforts in the Middle East and beyond.

Satellite imagery-based monitoring of archaeological site damage in the Syrian civil war.

Directory of Open Access Journals (Sweden)

Jesse Casana

Full Text Available Since the start of the Syrian civil war in 2011, the rich archaeological heritage of Syria and northern Iraq has faced severe threats, including looting, combat-related damage, and intentional demolition of monuments. However, the inaccessibility of the conflict zone to archaeologists or cultural heritage specialists has made it difficult to produce accurate damage assessments, impeding efforts to develop mitigation strategies and policies. This paper presents results of a project, undertaken in collaboration with the American Schools of Oriental Research (ASOR and the US Department of State, to monitor damage to archaeological sites in Syria, northern Iraq, and southern Turkey using recent, high-resolution satellite imagery. Leveraging a large database of archaeological and heritage sites throughout the region, as well as access to continually updated satellite imagery from DigitalGlobe, this project has developed a flexible and efficient methodology to log observations of damage in a manner that facilitates spatial and temporal queries. With nearly 5000 sites carefully evaluated, analysis reveals unexpected patterns in the timing, severity, and location of damage, helping us to better understand the evolving cultural heritage crisis in Syria and Iraq. Results also offer a model for future remote sensing-based archaeological and heritage monitoring efforts in the Middle East and beyond.

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

International Nuclear Information System (INIS)

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

2010-01-01

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

Radar image enhancement and simulation as an aid to interpretation and training

Science.gov (United States)

Frost, V. S.; Stiles, J. A.; Holtzman, J. C.; Dellwig, L. F.; Held, D. N.

1980-01-01

Greatly increased activity in the field of radar image applications in the coming years demands that techniques of radar image analysis, enhancement, and simulation be developed now. Since the statistical nature of radar imagery differs from that of photographic imagery, one finds that the required digital image processing algorithms (e.g., for improved viewing and feature extraction) differ from those currently existing. This paper addresses these problems and discusses work at the Remote Sensing Laboratory in image simulation and processing, especially for systems comparable to the formerly operational SEASAT synthetic aperture radar.

The Role of Satellite Imagery to Improve Pastureland Estimates in South America

Science.gov (United States)

Graesser, J.

2015-12-01

Agriculture has changed substantially across the globe over the past half century. While much work has been done to improve spatial-temporal estimates of agricultural changes, we still know more about the extent of row-crop agriculture than livestock-grazed land. The gap between cropland and pastureland estimates exists largely because it is challenging to characterize natural versus grazed grasslands from a remote sensing perspective. However, the impasse of pastureland estimates is set to break, with an increasing number of spaceborne sensors and freely available satellite data. The Landsat satellite archive in particular provides researchers with immense amounts of data to improve pastureland information. Here we focus on South America, where pastureland expansion has been scrutinized for the past few decades. We explore the challenges of estimating pastureland using temporal Landsat imagery and focus on key agricultural countries, regions, and ecosystems. We focus on the suggested shift of pastureland from the Argentine Pampas to northern Argentina, and the mixing of small-scale and large-scale ranching in eastern Paraguay and how it could impact the Chaco forest to the west. Further, the Beni Savannahs of northern Bolivia and the Colombian Llanos—both grassland and savannah regions historically used for livestock grazing—have been hinted at as future areas for cropland expansion. There are certainly environmental concerns with pastureland expansion into forests; but what are the environmental implications when well-managed pasture systems are converted to intensive soybean or palm oil plantation? Tropical, grazed grasslands are important habitats for biodiversity, and pasturelands can mitigate soil erosion when well managed. Thus, we must improve estimates of grazed land before we can make informed policy and conservation decisions. This talk presents insights into pastureland estimates in South America and discusses the feasibility to improve current

Linear feature extraction from radar imagery: SBIR (Small Business Innovative Research) phase 2, option 1

Science.gov (United States)

Conner, Gary D.; Milgram, David L.; Lawton, Daryl T.; McConnell, Christopher C.

1988-04-01

The goal of this effort is to develop and demonstrate prototype processing capabilities for a knowledge-based system to automatically extract and analyze linear features from synthetic aperture radar (SAR) imagery. This effort constitutes Phase 2 funding through the Defense Small Business Innovative Research (SBIR) Program. Previous work examined the feasibility of the technology issues involved in the development of an automatedlinear feature extraction system. This Option 1 Final Report documents this examination and the technologies involved in automating this image understanding task. In particular, it reports on a major software delivery containing an image processing algorithmic base, a perceptual structures manipulation package, a preliminary hypothesis management framework and an enhanced user interface.

A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land

Directory of Open Access Journals (Sweden)

Feng Gao

2012-10-01

Full Text Available Thermal infrared (TIR imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes that are at significantly finer spatial scales. Consequently, thermal sharpening techniques have been developed to sharpen TIR imagery to shortwave band pixel resolutions, which are often fine enough for field-scale applications. A classic thermal sharpening technique, TsHARP, uses a relationship between land surface temperature (LST and Normalized Difference Vegetation Index (NDVI developed empirically at the TIR pixel resolution and applied at the NDVI pixel resolution. However, recent studies show that unique relationships between temperature and NDVI may only exist for a limited class of landscapes, with mostly green vegetation and homogeneous air and soil conditions. To extend application of thermal sharpening to more complex conditions, a new data mining sharpener (DMS technique is developed. The DMS approach builds regression trees between TIR band brightness temperatures and shortwave spectral reflectances based on intrinsic sample characteristics. A comparison of sharpening techniques applied over a rainfed agricultural area in central Iowa, an irrigated agricultural region in the Texas High Plains, and a heterogeneous naturally vegetated landscape in Alaska indicates that the DMS outperformed TsHARP in all cases. The artificial box-like patterns in LST generated by the TsHARP approach are greatly reduced using the DMS scheme, especially for areas containing irrigated crops, water bodies, thin clouds or terrain. While the DMS technique can provide fine resolution TIR imagery, there are limits to the sharpening ratios that can be reasonably implemented. Consequently, sharpening techniques cannot replace actual thermal band imagery at fine resolutions or missions that

Wetland Classification for Black Duck Habitat Management Using Combined Polarimetric RADARSAT 2 and SPOT Imagery

Science.gov (United States)

Zhang, W.; Hu, B.; Brown, G.

2018-04-01

The black duck population has decreased significantly due to loss of its breeding habitat. Wetlands are an important feature that relates to habitat management and requires monitoring. Synthetic Aperture Radar (SAR) systems are helpful to map the wetland as the microwave signals are sensitive to water content and can be used to map surface water extent, saturated soils, and flooded vegetation. In this study, RadarSat 2 Polarimetric data is employed to map surface water and track changes in extent over the years through image thresholding and reviewed different approaches of Polarimetric decompositions for detecting flooded vegetation. Also, object-based analysis associated with beaver activity is conducted with combined multispectral SPOT satellite imagery. Results show SAR data has proven ability to improve mapping open water areas and locate flooded vegetation areas.

Testing methods for using high-resolution satellite imagery to monitor polar bear abundance and distribution

Science.gov (United States)

LaRue, Michelle A.; Stapleton, Seth P.; Porter, Claire; Atkinson, Stephen N.; Atwood, Todd C.; Dyck, Markus; Lecomte, Nicolas

2015-01-01

High-resolution satellite imagery is a promising tool for providing coarse information about polar species abundance and distribution, but current applications are limited. With polar bears (Ursus maritimus), the technique has only proven effective on landscapes with little topographic relief that are devoid of snow and ice, and time-consuming manual review of imagery is required to identify bears. Here, we evaluated mechanisms to further develop methods for satellite imagery by examining data from Rowley Island, Canada. We attempted to automate and expedite detection via a supervised spectral classification and image differencing to expedite image review. We also assessed what proportion of a region should be sampled to obtain reliable estimates of density and abundance. Although the spectral signature of polar bears differed from nontarget objects, these differences were insufficient to yield useful results via a supervised classification process. Conversely, automated image differencing—or subtracting one image from another—correctly identified nearly 90% of polar bear locations. This technique, however, also yielded false positives, suggesting that manual review will still be required to confirm polar bear locations. On Rowley Island, bear distribution approximated a Poisson distribution across a range of plot sizes, and resampling suggests that sampling >50% of the site facilitates reliable estimation of density (CV large-scale applications remain limited because of the challenges in automation and the limited environments in which the method can be effectively applied. Improvements in resolution may expand opportunities for its future uses.

Testing methods for using high-resolution satellite imagery to monitor polar bear abundance and distribution

Science.gov (United States)

LaRue, Michelle A.; Stapleton, Seth P.; Porter, Claire; Atkinson, Stephen N.; Atwood, Todd C.; Dyck, Markus; Lecomte, Nicolas

2015-01-01

High-resolution satellite imagery is a promising tool for providing coarse information about polar species abundance and distribution, but current applications are limited. With polar bears (Ursus maritimus), the technique has only proven effective on landscapes with little topographic relief that are devoid of snow and ice, and time-consuming manual review of imagery is required to identify bears. Here, we evaluated mechanisms to further develop methods for satellite imagery by examining data from Rowley Island, Canada. We attempted to automate and expedite detection via a supervised spectral classification and image differencing to expedite image review. We also assessed what proportion of a region should be sampled to obtain reliable estimates of density and abundance. Although the spectral signature of polar bears differed from nontarget objects, these differences were insufficient to yield useful results via a supervised classification process. Conversely, automated image differencing—or subtracting one image from another—correctly identified nearly 90% of polar bear locations. This technique, however, also yielded false positives, suggesting that manual review will still be required to confirm polar bear locations. On Rowley Island, bear distribution approximated a Poisson distribution across a range of plot sizes, and resampling suggests that sampling >50% of the site facilitates reliable estimation of density (CV in certain areas, but large-scale applications remain limited because of the challenges in automation and the limited environments in which the method can be effectively applied. Improvements in resolution may expand opportunities for its future uses.

Assessing Field-Specific Risk of Soybean Sudden Death Syndrome Using Satellite Imagery in Iowa.

Science.gov (United States)

Yang, S; Li, X; Chen, C; Kyveryga, P; Yang, X B

2016-08-01

Moderate resolution imaging spectroradiometer (MODIS) satellite imagery from 2004 to 2013 were used to assess the field-specific risks of soybean sudden death syndrome (SDS) caused by Fusarium virguliforme in Iowa. Fields with a high frequency of significant decrease (>10%) of the normalized difference vegetation index (NDVI) observed in late July to middle August on historical imagery were hypothetically considered as high SDS risk. These high-risk fields had higher slopes and shorter distances to flowlines, e.g., creeks and drainages, particularly in the Des Moines lobe. Field data in 2014 showed a significantly higher SDS level in the high-risk fields than fields selected without considering NDVI information. On average, low-risk fields had 10 times lower F. virguliforme soil density, determined by quantitative polymerase chain reaction, compared with other surveyed fields. Ordinal logistic regression identified positive correlations between SDS and slope, June NDVI, and May maximum temperature, but high June maximum temperature hindered SDS. A modeled SDS risk map showed a clear trend of potential disease occurrences across Iowa. Landsat imagery was analyzed similarly, to discuss the ability to utilize higher spatial resolution data. The results demonstrated the great potential of both MODIS and Landsat imagery for SDS field-specific risk assessment.

Significant wave height retrieval from synthetic radar images

NARCIS (Netherlands)

Wijaya, Andreas Parama; van Groesen, Embrecht W.C.

2014-01-01

In many offshore activities radar imagery is used to observe and predict ocean waves. An important issue in analyzing the radar images is to resolve the significant wave height. Different from 3DFFT methods that use an estimate related to the square root of the signal-to-noise ratio of radar images,

NOAA high resolution sea surface winds data from Synthetic Aperture Radar (SAR) on the Sentinel-1 satellites

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of high resolution sea surface winds data produced from Synthetic Aperture Radar (SAR) on board Sentinel-1A and Sentinel-1B satellites. This...

Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis

Directory of Open Access Journals (Sweden)

Jane Southworth

2010-12-01

Full Text Available Savanna ecosystems are an important component of dryland regions and yet are exceedingly difficult to study using satellite imagery. Savannas are composed are varying amounts of trees, shrubs and grasses and typically traditional classification schemes or vegetation indices cannot differentiate across class type. This research utilizes object based classification (OBC for a region in Namibia, using IKONOS imagery, to help differentiate tree canopies and therefore woodland savanna, from shrub or grasslands. The methodology involved the identification and isolation of tree canopies within the imagery and the creation of tree polygon layers had an overall accuracy of 84%. In addition, the results were scaled up to a corresponding Landsat image of the same region, and the OBC results compared to corresponding pixel values of NDVI. The results were not compelling, indicating once more the problems of these traditional image analysis techniques for savanna ecosystems. Overall, the use of the OBC holds great promise for this ecosystem and could be utilized more frequently in studies of vegetation structure.

Advanced Land Observing Satellite (ALOS) Phased Array Type L-Band Synthetic Aperture Radar (PALSAR) mosaic for the Kahiltna terrane, Alaska, 2007-2010

Science.gov (United States)

Cole, Christopher J.; Johnson, Michaela R.; Graham, Garth E.

2015-01-01

The U.S. Geological Survey (USGS) has initiated a multi-disciplinary study investigating the applicability of remote sensing technologies for geologic mapping and identification of prospective areas for base and precious metal deposits in remote parts of Alaska. The Kahiltna terrane in southwestern Alaska was selected for investigation because of its known mineral deposits and potential for additional mineral resources. An assortment of technologies is being investigated to aid in remote analysis of terrain, and includes imaging spectroscopy (hyperspectral remote sensing), high spatial resolution electro-optical imagery, and Synthetic Aperture Radar (SAR). However, there are significant challenges to applying imaging spectroscopy and electro-optical imagery technologies in this area because of the low solar angle for parts of the year, seasonal periods of darkness and snow cover, and the frequently cloudy weather that characterizes Alaska. Synthetic Aperture Radar (SAR) was selected because this technology does not rely on solar illumination and has all-weather capability.

Comparison of cloud top heights derived from FY-2 meteorological satellites with heights derived from ground-based millimeter wavelength cloud radar

Science.gov (United States)

Wang, Zhe; Wang, Zhenhui; Cao, Xiaozhong; Tao, Fa

2018-01-01

Clouds are currently observed by both ground-based and satellite remote sensing techniques. Each technique has its own strengths and weaknesses depending on the observation method, instrument performance and the methods used for retrieval. It is important to study synergistic cloud measurements to improve the reliability of the observations and to verify the different techniques. The FY-2 geostationary orbiting meteorological satellites continuously observe the sky over China. Their cloud top temperature product can be processed to retrieve the cloud top height (CTH). The ground-based millimeter wavelength cloud radar can acquire information about the vertical structure of clouds-such as the cloud base height (CBH), CTH and the cloud thickness-and can continuously monitor changes in the vertical profiles of clouds. The CTHs were retrieved using both cloud top temperature data from the FY-2 satellites and the cloud radar reflectivity data for the same time period (June 2015 to May 2016) and the resulting datasets were compared in order to evaluate the accuracy of CTH retrievals using FY-2 satellites. The results show that the concordance rate of cloud detection between the two datasets was 78.1%. Higher consistencies were obtained for thicker clouds with larger echo intensity and for more continuous clouds. The average difference in the CTH between the two techniques was 1.46 km. The difference in CTH between low- and mid-level clouds was less than that for high-level clouds. An attenuation threshold of the cloud radar for rainfall was 0.2 mm/min; a rainfall intensity below this threshold had no effect on the CTH. The satellite CTH can be used to compensate for the attenuation error in the cloud radar data.

Development of Radar-Satellite Blended QPF (Quantitative Precipitation Forecast) Technique for heavy rainfall

Science.gov (United States)

Jang, Sangmin; Yoon, Sunkwon; Rhee, Jinyoung; Park, Kyungwon

2016-04-01

Due to the recent extreme weather and climate change, a frequency and size of localized heavy rainfall increases and it may bring various hazards including sediment-related disasters, flooding and inundation. To prevent and mitigate damage from such disasters, very short range forecasting and nowcasting of precipitation amounts are very important. Weather radar data very useful in monitoring and forecasting because weather radar has high resolution in spatial and temporal. Generally, extrapolation based on the motion vector is the best method of precipitation forecasting using radar rainfall data for a time frame within a few hours from the present. However, there is a need for improvement due to the radar rainfall being less accurate than rain-gauge on surface. To improve the radar rainfall and to take advantage of the COMS (Communication, Ocean and Meteorological Satellite) data, a technique to blend the different data types for very short range forecasting purposes was developed in the present study. The motion vector of precipitation systems are estimated using 1.5km CAPPI (Constant Altitude Plan Position Indicator) reflectivity by pattern matching method, which indicates the systems' direction and speed of movement and blended radar-COMS rain field is used for initial data. Since the original horizontal resolution of COMS is 4 km while that of radar is about 1 km, spatial downscaling technique is used to downscale the COMS data from 4 to 1 km pixels in order to match with the radar data. The accuracies of rainfall forecasting data were verified utilizing AWS (Automatic Weather System) observed data for an extreme rainfall occurred in the southern part of Korean Peninsula on 25 August 2014. The results of this study will be used as input data for an urban stream real-time flood early warning system and a prediction model of landslide. Acknowledgement This research was supported by a grant (13SCIPS04) from Smart Civil Infrastructure Research Program funded by

Feature Detection Systems Enhance Satellite Imagery

Science.gov (United States)

2009-01-01

In 1963, during the ninth orbit of the Faith 7 capsule, astronaut Gordon Cooper skipped his nap and took some photos of the Earth below using a Hasselblad camera. The sole flier on the Mercury-Atlas 9 mission, Cooper took 24 photos - never-before-seen images including the Tibetan plateau, the crinkled heights of the Himalayas, and the jagged coast of Burma. From his lofty perch over 100 miles above the Earth, Cooper noted villages, roads, rivers, and even, on occasion, individual houses. In 1965, encouraged by the effectiveness of NASA s orbital photography experiments during the Mercury and subsequent Gemini manned space flight missions, U.S. Geological Survey (USGS) director William Pecora put forward a plan for a remote sensing satellite program that would collect information about the planet never before attainable. By 1972, NASA had built and launched Landsat 1, the first in a series of Landsat sensors that have combined to provide the longest continuous collection of space-based Earth imagery. The archived Landsat data - 37 years worth and counting - has provided a vast library of information allowing not only the extensive mapping of Earth s surface but also the study of its environmental changes, from receding glaciers and tropical deforestation to urban growth and crop harvests. Developed and launched by NASA with data collection operated at various times by the Agency, the National Oceanic and Atmospheric Administration (NOAA), Earth Observation Satellite Company (EOSAT, a private sector partnership that became Space Imaging Corporation in 1996), and USGS, Landsat sensors have recorded flooding from Hurricane Katrina, the building boom in Dubai, and the extinction of the Aral Sea, offering scientists invaluable insights into the natural and manmade changes that shape the world. Of the seven Landsat sensors launched since 1972, Landsat 5 and Landsat 7 are still operational. Though both are in use well beyond their intended lifespans, the mid

AgSat Imagery Collection Footprints

Data.gov (United States)

Farm Service Agency, Department of Agriculture — The AgSat Imagery Collection Footprints map shows the imagery footprints which have been collected under the USDA satellite blanket purchase agreement. Click on a...

Environmental monitoring of El Hierro Island submarine volcano, by combining low and high resolution satellite imagery

Science.gov (United States)

Eugenio, F.; Martin, J.; Marcello, J.; Fraile-Nuez, E.

2014-06-01

El Hierro Island, located at the Canary Islands Archipelago in the Atlantic coast of North Africa, has been rocked by thousands of tremors and earthquakes since July 2011. Finally, an underwater volcanic eruption started 300 m below sea level on October 10, 2011. Since then, regular multidisciplinary monitoring has been carried out in order to quantify the environmental impacts caused by the submarine eruption. Thanks to this natural tracer release, multisensorial satellite imagery obtained from MODIS and MERIS sensors have been processed to monitor the volcano activity and to provide information on the concentration of biological, chemical and physical marine parameters. Specifically, low resolution satellite estimations of optimal diffuse attenuation coefficient (Kd) and chlorophyll-a (Chl-a) concentration under these abnormal conditions have been assessed. These remote sensing data have played a fundamental role during field campaigns guiding the oceanographic vessel to the appropriate sampling areas. In addition, to analyze El Hierro submarine volcano area, WorldView-2 high resolution satellite spectral bands were atmospherically and deglinted processed prior to obtain a high-resolution optimal diffuse attenuation coefficient model. This novel algorithm was developed using a matchup data set with MERIS and MODIS data, in situ transmittances measurements and a seawater radiative transfer model. Multisensor and multitemporal imagery processed from satellite remote sensing sensors have demonstrated to be a powerful tool for monitoring the submarine volcanic activities, such as discolored seawater, floating material and volcanic plume, having shown the capabilities to improve the understanding of submarine volcanic processes.

Mapping Pyroclastic Flow Inundation Using Radar and Optical Satellite Images and Lahar Modeling

Directory of Open Access Journals (Sweden)

Chang-Wook Lee

2018-01-01

Full Text Available Sinabung volcano, located above the Sumatra subduction of the Indo-Australian plate under the Eurasian plate, became active in 2010 after about 400 years of quiescence. We use ALOS/PALSAR interferometric synthetic aperture radar (InSAR images to measure surface deformation from February 2007 to January 2011. We model the observed preeruption inflation and coeruption deflation using Mogi and prolate spheroid sources to infer volume changes of the magma chamber. We interpret that the inflation was due to magma accumulation in a shallow reservoir beneath Mount Sinabung and attribute the deflation due to magma withdrawal from the shallow reservoir during the eruption as well as thermoelastic compaction of erupted material. The pyroclastic flow extent during the eruption is then derived from the LAHARZ model based on the coeruption volume from InSAR modeling and compared to that derived from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+ image. The pyroclastic flow inundation extents between the two different methods agree at about 86%, suggesting the capability of mapping pyroclastic flow inundation by combing radar and optical imagery as well as flow modeling.

Investigating the backscatter contrast anomaly in synthetic aperture radar (SAR) imagery of the dunes along the Israel-Egypt border

Science.gov (United States)

Rozenstein, Offer; Siegal, Zehava; Blumberg, Dan G.; Adamowski, Jan

2016-04-01

The dune field intersected by the Israel-Egypt borderline has attracted many remote sensing studies over the years because it exhibits unique optical phenomena in several domains, from the visual to the thermal infrared. These phenomena are the result of land-use policies implemented by the two countries, which have differing effects on the two ecosystems. This study explores the surface properties that affect radar backscatter, namely the surface roughness and dielectric properties, in order to determine the cause for the variation across the border. The backscatter contrast was demonstrated for SIR-C, the first synthetic aperture radar (SAR) sensor to capture this phenomenon, as well as ASAR imagery that coincides with complementary ground observations. These field observations along the border, together with an aerial image from the same year as the SIR-C acquisition were used to analyze differences in vegetation patterns that can affect the surface roughness. The dielectric permittivity of two kinds of topsoil (sand, biocrust) was measured in the field and in the laboratory. The results suggest that the vegetation structure and spatial distribution differ between the two sides of the border in a manner that is consistent with the radar observations. The dielectric permittivity of sand and biocrust was found to be similar, although they are not constant across the radar spectral region (50 MHz-20 GHz). These findings support the hypothesis that changes to the vegetation, as a consequence of the different land-use practices in Israel and Egypt, are the cause for the radar backscatter contrast across the border.

MAJOR SOURCE OF SIDE-LOOKING AIRBORNE RADAR IMAGERY FOR RESEARCH AND EXPLORATION: THE U. S. GEOLOGICAL SURVEY.

Science.gov (United States)

Kover, Allan N.; Jones, John Edwin; ,

1985-01-01

The US Geological Survey (USGS) instituted a program in 1980 to acquire side-looking airbore radar (SLAR) data and make these data readily available to the public in a mosaic format comparable to the USGS 1:250,000-scale topographic map series. The SLAR data are also available as strip images at an acquisition scale of 1:250,000 or 1:400,000 (depending on the acquisition system), as a variety of print products and indexes, and in a limited amount in digital form on computer compatible tapes. Three different commercial X-band (3-cm) systems were used to acquire the imagery for producing the mosaics.

Detailed Maps Depicting the Shallow-Water Benthic Habitats of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Detailed, shallow-water coral reef ecosystem maps were generated by rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations...

An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery

Directory of Open Access Journals (Sweden)

Xiao Ling

2016-08-01

Full Text Available This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC, which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3, Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results.

Nonlinear bias compensation of ZiYuan-3 satellite imagery with cubic splines

Science.gov (United States)

Cao, Jinshan; Fu, Jianhong; Yuan, Xiuxiao; Gong, Jianya

2017-11-01

Like many high-resolution satellites such as the ALOS, MOMS-2P, QuickBird, and ZiYuan1-02C satellites, the ZiYuan-3 satellite suffers from different levels of attitude oscillations. As a result of such oscillations, the rational polynomial coefficients (RPCs) obtained using a terrain-independent scenario often have nonlinear biases. In the sensor orientation of ZiYuan-3 imagery based on a rational function model (RFM), these nonlinear biases cannot be effectively compensated by an affine transformation. The sensor orientation accuracy is thereby worse than expected. In order to eliminate the influence of attitude oscillations on the RFM-based sensor orientation, a feasible nonlinear bias compensation approach for ZiYuan-3 imagery with cubic splines is proposed. In this approach, no actual ground control points (GCPs) are required to determine the cubic splines. First, the RPCs are calculated using a three-dimensional virtual control grid generated based on a physical sensor model. Second, one cubic spline is used to model the residual errors of the virtual control points in the row direction and another cubic spline is used to model the residual errors in the column direction. Then, the estimated cubic splines are used to compensate the nonlinear biases in the RPCs. Finally, the affine transformation parameters are used to compensate the residual biases in the RPCs. Three ZiYuan-3 images were tested. The experimental results showed that before the nonlinear bias compensation, the residual errors of the independent check points were nonlinearly biased. Even if the number of GCPs used to determine the affine transformation parameters was increased from 4 to 16, these nonlinear biases could not be effectively compensated. After the nonlinear bias compensation with the estimated cubic splines, the influence of the attitude oscillations could be eliminated. The RFM-based sensor orientation accuracies of the three ZiYuan-3 images reached 0.981 pixels, 0.890 pixels, and 1

Evaluation of radar imagery of the North Louisiana Salt Dome Area

International Nuclear Information System (INIS)

Dellwig, L.F.

1977-01-01

Radar (SLAR) is basically a reconnaissance tool. It has proved to be most effective in the identification of regional structures, trends or fracture patterns which might be expressed in such a way as to affect (1) the roughness of the target (soil, rock or vegetation), (2) its dielectric properties (chemical composition, moisture content), or (3) its topographic expression. Topographic expression of domes is only poorly to moderately identifiable. Significant associated fractures cannot be detected. Vegetative anomalies are associated with many known domes, and are suggestive of the existence of numerous others. The validity of such anomalies as indicators of domes has yet to be, but should be, determined by field investigations. In the light of the poor to near lack of topographic expression of the domes or associated faults it is obvious that identification of recent movement is not to be expected. Such conclusions apply only to this environment and do not in any way mean to degrade the quality of the imagery. They do, however, point out the importance of understanding energy-target interaction and target and terrain characteristics in the mission planning phase of any further investigations

Ships as salient objects in synthetic aperture radar imaginary

CSIR Research Space (South Africa)

Schwegmann, Colin P

2016-07-01

Full Text Available The widespread access to Synthetic Aperture Radar data has created a need for more precise ship extraction, specifically in low-to-medium resolution imagery. While Synthetic Aperture Radar pixel resolution is improving for a large swaths...

Time series for water levels in virtual gauge stations in the Amazon basin using satellite radar altimetry

Directory of Open Access Journals (Sweden)

Juan Gabriel León Hernández

2009-01-01

Full Text Available Using satellite altimeter radar technology for monitoring changes in water levels at continental scale is a relatively recent ad- vance. Several studies have demonstrated the interest being shown in applying this technology to monitoring the hydrographic patterns of large-scale basins worldwide. The current study presents the inference of time series representing changes in water le- vel for bodies of water by defining virtual gauge stations deduced for two very different rivers in terms of their biophysical and to- pographic characteristics; the two rivers were the Rio Negro in the Brazilian Amazon Basin and the Caqueta River on the Colombian side. The differences between the two rivers revealed the limits of satellite radar altimeter when applied to continental waters (±20cm and ±40 cm precision for Río Negro and Río Caquetá, respectively. However, applying this technology seems very promising, since new missions have been scheduled to be put into orbit by the end of 2008.

Detection of macroalgae blooms by complex SAR imagery

International Nuclear Information System (INIS)

Shen, Hui; Perrie, William; Liu, Qingrong; He, Yijun

2014-01-01

Highlights: • Complex SAR imagery enables better recognition of macroalgae patches. • Combination of different information in SAR matrix forms new index factors. • Proposed index factors contribute to unsupervised recognition of macroalgae. -- Abstract: Increased frequency and enhanced damage to the marine environment and to human society caused by green macroalgae blooms demand improved high-resolution early detection methods. Conventional satellite remote sensing methods via spectra radiometers do not work in cloud-covered areas, and therefore cannot meet these demands for operational applications. We present a methodology for green macroalgae bloom detection based on RADARSAT-2 synthetic aperture radar (SAR) images. Green macroalgae patches exhibit different polarimetric characteristics compared to the open ocean surface, in both the amplitude and phase domains of SAR-measured complex radar backscatter returns. In this study, new index factors are defined which have opposite signs in green macroalgae-covered areas, compared to the open water surface. These index factors enable unsupervised detection from SAR images, providing a high-resolution new tool for detection of green macroalgae blooms, which can potentially contribute to a better understanding of the mechanisms related to outbreaks of green macroalgae blooms in coastal areas throughout the world ocean

Change Detection with Polarimetric SAR Imagery for Nuclear Verification

International Nuclear Information System (INIS)

Canty, M.

2015-01-01

This paper investigates the application of multivariate statistical change detection with high-resolution polarimetric SAR imagery acquired from commercial satellite platforms for observation and verification of nuclear activities. A prototype software tool comprising a processing chain starting from single look complex (SLC) multitemporal data through to change detection maps is presented. Multivariate change detection algorithms applied to polarimetric SAR data are not common. This is because, up until recently, not many researchers or practitioners have had access to polarimetric data. However with the advent of several spaceborne polarimetric SAR instruments such as the Japanese ALOS, the Canadian Radarsat-2, the German TerraSAR-X, the Italian COSMO-SkyMed missions and the European Sentinal SAR platform, the situation has greatly improved. There is now a rich source of weather-independent satellite radar data which can be exploited for Nuclear Safeguards purposes. The method will also work for univariate data, that is, it is also applicable to scalar or single polarimetric SAR data. The change detection procedure investigated here exploits the complex Wishart distribution of dual and quad polarimetric imagery in look-averaged covariance matrix format in order to define a per-pixel change/no-change hypothesis test. It includes approximations for the probability distribution of the test statistic, and so permits quantitative significance levels to be quoted for change pixels. The method has been demonstrated previously with polarimetric images from the airborne EMISAR sensor, but is applied here for the first time to satellite platforms. In addition, an improved multivariate method is used to estimate the so-called equivalent number of looks (ENL), which is a critical parameter of the hypothesis test. (author)

Volcanic and Tectonic Activity in the Red Sea Region (2004-2013): Insights from Satellite Radar Interferometry and Optical Imagery

KAUST Repository

Xu, Wenbin

2015-04-01

Studying recent volcanic and tectonic events in the Red Sea region is important for improving our knowledge of the Red Sea plate boundary and for regional geohazard assessments. However, limited information has been available about the past activity due to insufficient in-situ data and remoteness of some of the activity. In this dissertation, I have used satellite remote sensing to derive new information about several recent volcanic and tectonic events in the Red Sea region. I first report on three volcanic eruptions in the southern Red Sea, the 2007-8 Jebel at Tair eruption and the 2011-12 & 2013 Zubair eruptions, which resulted in formation of two new islands. Series of high- resolution optical images were used to map the extent of lava flows and to observe and analyze the growth and destructive processes of the new islands. I used Interferometric Synthetic Aperture Radar (InSAR) data to study the evolution of lava flows, to estimate their volumes, as well as to generate ground displacements maps, which were used to model the dikes that fed the eruptions. I then report on my work of the 2009 Harrat Lunayyir dike intrusion and the 2004 Tabuk earthquake sequence in western Saudi Arabia. I used InSAR observations and stress calculations to study the intruding dike at Harrat Lunayyir, while I combined InSAR data and Bayesian estimation to study the Tabuk earthquake activity. The key findings of the thesis are: 1) The recent volcanic eruptions in the southern Red Sea indicate that the area is magmatically more active than previously acknowledged and that a rifting episode has been taken place in the southern Red Sea; 2) Stress interactions between an ascending dike intrusion and normal faulting on graben-bounding faults above the dike can inhibit vertical propagation of magma towards the surface; 3) InSAR observations can improve locations of shallow earthquakes and fault model uncertainties are useful to associate earthquake activity with mapped faults; 4). The

Using Online Citizen Science to Assess Giant Kelp Abundances Across the Globe with Satellite Imagery

Science.gov (United States)

Byrnes, J.; Cavanaugh, K. C.; Haupt, A. J.; Trouille, L.; Rosenthal, I.; Bell, T. W.; Rassweiler, A.; Pérez-Matus, A.; Assis, J.

2017-12-01

Global scale long-term data sets that document the patterns and variability of human impacts on marine ecosystems are rare. This lack is particularly glaring for underwater species - even moreso for ecologically important ones. Here we demonstrate how online Citizen Science combined with Landsat satellite imagery can help build a picture of change in the dynamics of giant kelp, an important coastal foundation species around the globe, from the 1984 to the present. Giant kelp canopy is visible from Landsat images, but these images defy easy machine classification. To get useful data, images must be processed by hand. While academic researchers have applied this method successfully at sub-regional scales, unlocking the value of the full global dataset has not been possible until given the massive effort required. Here we present Floating Forests (http://floatingforests.org), an international collaboration between kelp forest researchers and the citizen science organization Zooniverse. Floating Forests provides an interface that allows citizen scientists to identify canopy cover of giant kelp on Landsat images, enabling us to scale up the dataset to the globe. We discuss lessons learned from the initial version of the project launched in 2014, a prototype of an image processing pipeline to bring Landsat imagery to citizen science platforms, methods of assessing accuracy of citizen scientists, and preliminary data from our relaunch of the project. Through this project we have developed generalizable tools to facilitate citizen science-based analysis of Landsat and other satellite and aerial imagery. We hope that this create a powerful dataset to unlock our understanding of how global change has altered these critically important species in the sea.

An estimation model of population in China using time series DMSP night-time satellite imagery from 2002-2010

Science.gov (United States)

Zhang, Xiaoyong; Zhang, Zhijie; Chang, Yuguang; Chen, Zhengchao

2015-12-01

Accurate data on the spatial distribution and potential growth estimation of human population are playing pivotal role in addressing and mitigating heavy lose caused by earthquake. Traditional demographic data is limited in its spatial resolution and is extremely hard to update. With the accessibility of massive DMSP/OLS night time imagery, it is possible to model population distribution at the county level across China. In order to compare and improve the continuity and consistency of time-series DMSP night-time satellite imagery obtained by different satellites in same year or different years by the same satellite from 2002-2010, normalized method was deployed for the inter-correction among imageries. And we referred to the reference F162007 Jixi city, whose social-economic has been relatively stable. Through binomial model, with average R2 0.90, then derived the correction factor of each year. The normalization obviously improved consistency comparing to previous data, which enhanced the correspondent accuracy of model. Then conducted the model of population density between average night-time light intensity in eight-economic districts. According to the two parameters variation law of consecutive years, established the prediction model of next following years with R2of slope and constant typically 0.85 to 0.95 in different regions. To validate the model, taking the year of 2005 as example, retrieved quantitatively population distribution in per square kilometer based on the model, then compared the results to the statistical data based on census, the difference of the result is acceptable. In summary, the estimation model facilitates the quick estimation and prediction in relieving the damage to people, which is significant in decision-making.

Satellite based radar interferometry to estimate large-scale soil water depletion from clay shrinkage: possibilities and limitations

NARCIS (Netherlands)

Brake, te B.; Hanssen, R.F.; Ploeg, van der M.J.; Rooij, de G.H.

2013-01-01

Satellite-based radar interferometry is a technique capable of measuring small surface elevation changes at large scales and with a high resolution. In vadose zone hydrology, it has been recognized for a long time that surface elevation changes due to swell and shrinkage of clayey soils can serve as

Using high resolution satellite multi-temporal interferometry for landslide hazard detection in tropical environments: the case of Haiti

Science.gov (United States)

Wasowski, Janusz; Nutricato, Raffaele; Nitti, Davide Oscar; Bovenga, Fabio; Chiaradia, Maria Teresa; Piard, Boby Emmanuel; Mondesir, Philemon

2015-04-01

Synthetic aperture radar (SAR) multi-temporal interferometry (MTI) is one of the most promising satellite-based remote sensing techniques for fostering new opportunities in landslide hazard detection and assessment. MTI is attractive because it can provide very precise quantitative information on slow slope displacements of the ground surface over huge areas with limited vegetation cover. Although MTI is a mature technique, we are only beginning to realize the benefits of the high-resolution imagery that is currently acquired by the new generation radar satellites (e.g., COSMO-SkyMed, TerraSAR-X). In this work we demonstrate the potential of high resolution X-band MTI for wide-area detection of slope instability hazards even in tropical environments that are typically very harsh (eg. coherence loss) for differential interferometry applications. This is done by presenting an example from the island of Haiti, a tropical region characterized by dense and rapidly growing vegetation, as well as by significant climatic variability (two rainy seasons) with intense precipitation events. Despite the unfavorable setting, MTI processing of nearly 100 COSMO-SkyMed (CSK) mages (2011-2013) resulted in the identification of numerous radar targets even in some rural (inhabited) areas thanks to the high resolution (3 m) of CSK radar imagery, the adoption of a patch wise processing SPINUA approach and the presence of many man-made structures dispersed in heavily vegetated terrain. In particular, the density of the targets resulted suitable for the detection of some deep-seated and shallower landslides, as well as localized, very slow slope deformations. The interpretation and widespread exploitation of high resolution MTI data was facilitated by Google EarthTM tools with the associated high resolution optical imagery. Furthermore, our reconnaissance in situ checks confirmed that MTI results provided useful information on landslides and marginally stable slopes that can represent a

Detailed Maps Depicting the Shallow-Water Benthic Habitats of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery (Draft)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Detailed, shallow-water coral reef ecosystem maps were generated by rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations...

Mapping plasma structures in the high-latitude ionosphere using beacon satellite, incoherent scatter radar and ground-based magnetometer observations

Directory of Open Access Journals (Sweden)

T. Neubert

2002-06-01

Full Text Available In the autumn of the year 2000, four radio receivers capable of tracking various beacon satellites were set up along the southwestern coast of Greenland. They are used to reconstruct images of the ionospheric plasma density distribution via the tomographic method. In order to test and validate tomographic imaging under the highly variable conditions often prevailing in the high-latitude ionosphere, a time interval was selected when the Sondrestrom incoherent scatter radar conducted measurements of the ionospheric plasma density while the radio receivers tracked a number of beacon satellites. A comparison between two-dimensional images of the plasma density distribution obtained from the radar and the satellite receivers revealed generally good agreement between radar measurements and tomographic images. Observed discrepancies can be attributed to F region plasma patches moving through the field of view with a speed of several hundred meters per second, thereby smearing out the tomographic image. A notable mismatch occurred around local magnetic midnight when a magnetospheric substorm breakup occurred in the vicinity of southwest Greenland (identified from ground-based magnetometer observations. The breakup was associated with a sudden intensification of the westward auroral electrojet which was centered at about 69 and extended up to some 73 corrected geomagnetic latitude. Ground-based magnetometer data may thus have the potential of indicating when the tomographic method is at risk and may fail. We finally outline the application of tomographic imaging, when combined with magnetic field data, to estimate ionospheric Joule heating rates.

Mapping invasive Phragmites australis in the coastal Great Lakes with ALOS PALSAR satellite imagery for decision support

Science.gov (United States)

Bourgeau-Chavez, Laura L.; Kowalski, Kurt P.; Carlson Mazur, Martha L.; Scarbrough, Kirk A.; Powell, Richard B.; Brooks, Colin N.; Huberty, Brian; Jenkins, Liza K.; Banda, Elizabeth C.; Galbraith, David M.; Laubach, Zachary M.; Riordan, Kevin

2013-01-01

The invasive variety of Phragmites australis (common reed) forms dense stands that can cause negative impacts on coastal Great Lakes wetlands including habitat degradation and reduced biological diversity. Early treatment is key to controlling Phragmites, therefore a map of the current distribution is needed. ALOS PALSAR imagery was used to produce the first basin-wide distribution map showing the extent of large, dense invasive Phragmites-dominated habitats in wetlands and other coastal ecosystems along the U.S. shore of the Great Lakes. PALSAR is a satellite imaging radar sensor that is sensitive to differences in plant biomass and inundation patterns, allowing for the detection and delineation of these tall (up to 5 m), high density, high biomass invasive Phragmites stands. Classification was based on multi-season ALOS PALSAR L-band (23 cm wavelength) HH and HV polarization data. Seasonal (spring, summer, and fall) datasets were used to improve discrimination of Phragmites by taking advantage of phenological changes in vegetation and inundation patterns over the seasons. Extensive field collections of training and randomly selected validation data were conducted in 2010–2011 to aid in mapping and for accuracy assessments. Overall basin-wide map accuracy was 87%, with 86% producer's accuracy and 43% user's accuracy for invasive Phragmites. The invasive Phragmites maps are being used to identify major environmental drivers of this invader's distribution, to assess areas vulnerable to new invasion, and to provide information to regional stakeholders through a decision support tool.

Algorithmic Foundation of Spectral Rarefaction for Measuring Satellite Imagery Heterogeneity at Multiple Spatial Scales

Science.gov (United States)

Rocchini, Duccio

2009-01-01

Measuring heterogeneity in satellite imagery is an important task to deal with. Most measures of spectral diversity have been based on Shannon Information theory. However, this approach does not inherently address different scales, ranging from local (hereafter referred to alpha diversity) to global scales (gamma diversity). The aim of this paper is to propose a method for measuring spectral heterogeneity at multiple scales based on rarefaction curves. An algorithmic solution of rarefaction applied to image pixel values (Digital Numbers, DNs) is provided and discussed. PMID:22389600

A Decadal Historical Satellite Data and Rainfall Trend Analysis (2001–2016 for Flood Hazard Mapping in Sri Lanka

Directory of Open Access Journals (Sweden)

Niranga Alahacoon

2018-03-01

Full Text Available Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE gridded rainfall data with flood maps derived from Synthetic Aperture Radar (SAR and multispectral satellite to arrive at holistic spatio-temporal patterns of floods in Sri Lanka. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR data were used to map flood extents for emergency relief operations while eight-day Moderate Resolution Imaging Spectroradiometer (MODIS surface reflectance data for the time period from 2001 to 2016 were used to map long term flood-affected areas. The inundation maps produced for rapid response were published within three hours upon the availability of satellite imagery in web platforms, with the aim of supporting a wide range of stakeholders in emergency response and flood relief operations. The aggregated time series of flood extents mapped using MODIS data were used to develop a flood occurrence map (2001–2016 for Sri Lanka. Flood hotpots identified using both optical and synthetic aperture average of 325 km2 for the years 2006–2015 and exceptional flooding in 2016 with inundation extent of approximately 1400 km2. The time series rainfall data explains increasing trend in the extreme rainfall indices with similar observation derived from satellite imagery. The results demonstrate the feasibility of using multi-sensor flood mapping approaches, which will aid Disaster Management Center (DMC and other multi-lateral agencies involved in managing rapid response operations and preparing mitigation measures.

Validity and feasibility of a satellite imagery-based method for rapid estimation of displaced populations.

Science.gov (United States)

Checchi, Francesco; Stewart, Barclay T; Palmer, Jennifer J; Grundy, Chris

2013-01-23

Estimating the size of forcibly displaced populations is key to documenting their plight and allocating sufficient resources to their assistance, but is often not done, particularly during the acute phase of displacement, due to methodological challenges and inaccessibility. In this study, we explored the potential use of very high resolution satellite imagery to remotely estimate forcibly displaced populations. Our method consisted of multiplying (i) manual counts of assumed residential structures on a satellite image and (ii) estimates of the mean number of people per structure (structure occupancy) obtained from publicly available reports. We computed population estimates for 11 sites in Bangladesh, Chad, Democratic Republic of Congo, Ethiopia, Haiti, Kenya and Mozambique (six refugee camps, three internally displaced persons' camps and two urban neighbourhoods with a mixture of residents and displaced) ranging in population from 1,969 to 90,547, and compared these to "gold standard" reference population figures from census or other robust methods. Structure counts by independent analysts were reasonably consistent. Between one and 11 occupancy reports were available per site and most of these reported people per household rather than per structure. The imagery-based method had a precision relative to reference population figures of layout. For each site, estimates were produced in 2-5 working person-days. In settings with clearly distinguishable individual structures, the remote, imagery-based method had reasonable accuracy for the purposes of rapid estimation, was simple and quick to implement, and would likely perform better in more current application. However, it may have insurmountable limitations in settings featuring connected buildings or shelters, a complex pattern of roofs and multi-level buildings. Based on these results, we discuss possible ways forward for the method's development.

Terrain feature recognition for synthetic aperture radar (SAR) imagery employing spatial attributes of targets

Science.gov (United States)

Iisaka, Joji; Sakurai-Amano, Takako

1994-08-01

This paper describes an integrated approach to terrain feature detection and several methods to estimate spatial information from SAR (synthetic aperture radar) imagery. Spatial information of image features as well as spatial association are key elements in terrain feature detection. After applying a small feature preserving despeckling operation, spatial information such as edginess, texture (smoothness), region-likeliness and line-likeness of objects, target sizes, and target shapes were estimated. Then a trapezoid shape fuzzy membership function was assigned to each spatial feature attribute. Fuzzy classification logic was employed to detect terrain features. Terrain features such as urban areas, mountain ridges, lakes and other water bodies as well as vegetated areas were successfully identified from a sub-image of a JERS-1 SAR image. In the course of shape analysis, a quantitative method was developed to classify spatial patterns by expanding a spatial pattern through the use of a series of pattern primitives.

Enhanced processing of SPOT multispectral satellite imagery for environmental monitoring and modelling

Energy Technology Data Exchange (ETDEWEB)

Clark, B.

2010-07-01

The Taita Hills in southeastern Kenya form the northernmost part of Africa's Eastern Arc Mountains, which have been identified by Conservation International as one of the top ten biodiversity hotspots on Earth. As with many areas of the developing world, over recent decades the Taita Hills have experienced significant population growth leading to associated major changes in land use and land cover (LULC), as well as escalating land degradation, particularly soil erosion. Multi-temporal medium resolution multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors, provides a valuable source of information for environmental monitoring and modelling at a landscape level at local and regional scales. However, utilization of multi-temporal SPOT data in quantitative remote sensing studies requires the removal of atmospheric effects and the derivation of surface reflectance factor (rho{sub s}). Furthermore, for areas of rugged terrain, such as the Taita Hills, topographic correction is necessary to derive comparable (rho{sub s}) throughout a SPOT scene. Reliable monitoring of LULC change over time and modelling of land degradation and human population distribution and abundance are of crucial importance to sustainable development, natural resource management, biodiversity conservation, and understanding and mitigating climate change and its impacts. The main purpose of this thesis was to develop and validate enhanced processing of SPOT satellite imagery for use in environmental monitoring and modelling at a landscape level, in regions of the developing world with limited ancillary data availability. The Taita Hills formed the application study site, whilst the Helsinki metropolitan region was used as a control site for validation and assessment of the applied atmospheric correction techniques, where multiangular (rho{sub s}) field measurements were taken and where horizontal visibility meteorological data concurrent with image

The Radar locates spills of Petroleum Sea inside

International Nuclear Information System (INIS)

Acantland Sylvie; De Biegert

1996-01-01

The satellite information is helping to the petroleum geologists to determine the potential of new petroleum reserves all over the world. Particularly, radar technology recently available is providing an increased dependability, an improved effectiveness of costs and a quicker access to the information that can be vital to detect and to supervise the petroleum spills that naturally happen. Several projects have been carrying out to evaluate the best use in the technology of the satellite information, specifically radar information for satellite, in sea inside exploration. The authors comment about of the kindness and benefits in the radar use

Super-Resolution for “Jilin-1” Satellite Video Imagery via a Convolutional Network

Directory of Open Access Journals (Sweden)

Aoran Xiao

2018-04-01

Full Text Available Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method’s practicality. Experimental results on “Jilin-1” satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods.

Assessment of spatial distribution of soil heavy metals using ANN-GA, MSLR and satellite imagery.

Science.gov (United States)

Naderi, Arman; Delavar, Mohammad Amir; Kaboudin, Babak; Askari, Mohammad Sadegh

2017-05-01

This study aims to assess and compare heavy metal distribution models developed using stepwise multiple linear regression (MSLR) and neural network-genetic algorithm model (ANN-GA) based on satellite imagery. The source identification of heavy metals was also explored using local Moran index. Soil samples (n = 300) were collected based on a grid and pH, organic matter, clay, iron oxide contents cadmium (Cd), lead (Pb) and zinc (Zn) concentrations were determined for each sample. Visible/near-infrared reflectance (VNIR) within the electromagnetic ranges of satellite imagery was applied to estimate heavy metal concentrations in the soil using MSLR and ANN-GA models. The models were evaluated and ANN-GA model demonstrated higher accuracy, and the autocorrelation results showed higher significant clusters of heavy metals around the industrial zone. The higher concentration of Cd, Pb and Zn was noted under industrial lands and irrigation farming in comparison to barren and dryland farming. Accumulation of industrial wastes in roads and streams was identified as main sources of pollution, and the concentration of soil heavy metals was reduced by increasing the distance from these sources. In comparison to MLSR, ANN-GA provided a more accurate indirect assessment of heavy metal concentrations in highly polluted soils. The clustering analysis provided reliable information about the spatial distribution of soil heavy metals and their sources.

Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields

NARCIS (Netherlands)

Hamzeh, Saied; Naseri, Abd Ali; Alavipanah, Seyed Kazem; Bartholomeus, Harm; Herold, Martin

2016-01-01

This study evaluates the feasibility of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields located in the southwest of Iran. For this purpose a Hyperion image acquired on September 2, 2010 and a Landsat7 ETM+ image

Space Radar Image of Central Sumatra, Indonesia

Science.gov (United States)

1994-01-01

This is a radar image of the central part of the island of Sumatra in Indonesia that shows how the tropical rainforest typical of this country is being impacted by human activity. Native forest appears in green in this image, while prominent pink areas represent places where the native forest has been cleared. The large rectangular areas have been cleared for palm oil plantations. The bright pink zones are areas that have been cleared since 1989, while the dark pink zones are areas that were cleared before 1989. These radar data were processed as part of an effort to assist oil and gas companies working in the area to assess the environmental impact of both their drilling operations and the activities of the local population. Radar images are useful in these areas because heavy cloud cover and the persistent smoke and haze associated with deforestation have prevented usable visible-light imagery from being acquired since 1989. The dark shapes in the upper right (northeast) corner of the image are a chain of lakes in flat coastal marshes. This image was acquired in October 1994 by the Spaceborne Imaging Radar C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) onboard the space shuttle Endeavour. Environmental changes can be easily documented by comparing this image with visible-light data that were acquired in previous years by the Landsat satellite. The image is centered at 0.9 degrees north latitude and 101.3 degrees east longitude. The area shown is 50 kilometers by 100 kilometers (31 miles by 62 miles). The colors in the image are assigned to different frequencies and polarizations of the radar as follows: red is L-band horizontally transmitted, horizontally received; green is L-band horizontally transmitted, vertically received; blue is L-band vertically transmitted, vertically received. SIR-C/X-SAR, a joint mission of the German, Italian and United States space agencies, is part of NASA's Mission to Planet Earth program.

A signature correlation study of ground target VHF/UHF ISAR imagery

Science.gov (United States)

Gatesman, Andrew J.; Beaudoin, Christopher J.; Giles, Robert H.; Kersey, William T.; Waldman, Jerry; Carter, Steve; Nixon, William E.

2003-09-01

VV and HH-polarized radar signatures of several ground targets were acquired in the VHF/UHF band (171-342 MHz) by using 1/35th scale models and an indoor radar range operating from 6 to 12 GHz. Data were processed into medianized radar cross sections as well as focused, ISAR imagery. Measurement validation was confirmed by comparing the radar cross section of a test object with a method of moments radar cross section prediction code. The signatures of several vehicles from three vehicle classes (tanks, trunks, and TELs) were measured and a signature cross-correlation study was performed. The VHF/UHF band is currently being exploited for its foliage penetration ability, however, the coarse image resolution which results from the relatively long radar wavelengths suggests a more challenging target recognition problem. One of the study's goals was to determine the amount of unique signature content in VHF/UHF ISAR imagery of military ground vehicles. Open-field signatures are compared with each other as well as with simplified shapes of similar size. Signatures were also acquired on one vehicle in a variety of configurations to determine the impact of monitor target variations on the signature content at these frequencies.

The users, uses, and value of Landsat and other moderate-resolution satellite imagery in the United States-Executive report

Science.gov (United States)

Miller, Holly M.; Sexton, Natalie R.; Koontz, Lynne; Loomis, John; Koontz, Stephen R.; Hermans, Caroline

2011-01-01

Moderate-resolution imagery (MRI), such as that provided by the Landsat satellites, provides unique spatial information for use by many people both within and outside of the United States (U.S.). However, exactly who these users are, how they use the imagery, and the value and benefits derived from the information are, to a large extent, unknown. To explore these issues, social scientists at the USGS Fort Collins Science Center conducted a study of U.S.-based MRI users from 2008 through 2010 in two parts: 1) a user identification and 2) a user survey. The objectives for this study were to: 1) identify and classify U.S.-based users of this imagery; 2) better understand how and why MRI, and specifically Landsat, is being used; and 3) qualitatively and quantitatively measure the value and societal benefits of MRI (focusing on Landsat specifically). The results of the survey revealed that respondents from multiple sectors use Landsat imagery in many different ways, as demonstrated by the breadth of project locations and scales, as well as application areas. The value of Landsat imagery to these users was demonstrated by the high importance placed on the imagery, the numerous benefits received from projects using Landsat imagery, the negative impacts if Landsat imagery was no longer available, and the substantial willingness to pay for replacement imagery in the event of a data gap. The survey collected information from users who are both part of and apart from the known user community. The diversity of the sample delivered results that provide a baseline of knowledge about the users, uses, and value of Landsat imagery. While the results supply a wealth of information on their own, they can also be built upon through further research to generate a more complete picture of the population of Landsat users as a whole.

Relative abundance of 'Bacillus' spp., surfactant-associated bacterium present in a natural sea slick observed by satellite SAR imagery over the Gulf of Mexico

Directory of Open Access Journals (Sweden)

Kathryn Lynn Howe

2018-01-01

Full Text Available The damping of short gravity-capillary waves (Bragg waves due to surfactant accumulation under low wind speed conditions results in the formation of natural sea slicks. These slicks are detectable visually and in synthetic aperture radar satellite imagery. Surfactants are produced by natural life processes of many marine organisms, including bacteria, phytoplankton, seaweed, and zooplankton. In this work, samples were collected in the Gulf of Mexico during a research cruise on the R/V 'F.G. Walton Smith' to evaluate the relative abundance of 'Bacillus' spp., surfactant-associated bacteria, in the sea surface microlayer compared to the subsurface water at 0.2 m depth. A method to reduce potential contamination of microlayer samples during their collection on polycarbonate filters was implemented and advanced, including increasing the number of successive samples per location and changing sample storage procedures. By using DNA analysis (real-time polymerase chain reaction to target 'Bacillus' spp., we found that in the slick areas, these surfactant-associated bacteria tended to reside mostly in subsurface waters, lending support to the concept that the surfactants they may produce move to the surface where they accumulate under calm conditions and enrich the sea surface microlayer.

Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana: A Case Study in Bundaberg, Australia

Directory of Open Access Journals (Sweden)

Andrew Robson

2017-11-01

Full Text Available Accurate pre-harvest estimation of avocado (Persea americana cv. Haas yield offers a range of benefits to industry and growers. Currently there is no commercial yield monitor available for avocado tree crops and the manual count method used for yield forecasting can be highly inaccurate. Remote sensing using satellite imagery offers a potential means to achieve accurate pre-harvest yield forecasting. This study evaluated the accuracies of high resolution WorldView (WV 2 and 3 satellite imagery and targeted field sampling for the pre-harvest prediction of total fruit weight (kg·tree−1 and average fruit size (g and for mapping the spatial distribution of these yield parameters across the orchard block. WV 2 satellite imagery was acquired over two avocado orchards during 2014, and WV3 imagery was acquired in 2016 and 2017 over these same two orchards plus an additional three orchards. Sample trees representing high, medium and low vigour zones were selected from normalised difference vegetation index (NDVI derived from the WV images and sampled for total fruit weight (kg·tree−1 and average fruit size (g per tree. For each sample tree, spectral reflectance data was extracted from the eight band multispectral WV imagery and 18 vegetation indices (VIs derived. Principal component analysis (PCA and non-linear regression analysis was applied to each of the derived VIs to determine the index with the strongest relationship to the measured total fruit weight and average fruit size. For all trees measured over the three year period (2014, 2016, and 2017 a consistent positive relationship was identified between the VI using near infrared band one and the red edge band (RENDVI1 to both total fruit weight (kg·tree−1 (R2 = 0.45, 0.28, and 0.29 respectively and average fruit size (g (R2 = 0.56, 0.37, and 0.29 respectively across all orchard blocks. Separate analysis of each orchard block produced higher R2 values as well as identifying different

Geostationary Satellite (GOES) Images

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Visible and Infrared satellite imagery taken from radiometer instruments on SMS (ATS) and GOES satellites in geostationary orbit. These satellites produced...

The accuracy of satellite radar altimeter data over the Greenland ice sheet determined from airborne laser data

DEFF Research Database (Denmark)

Bamber, J.L.; Ekholm, Simon; Krabill, W.

1998-01-01

with airborne laser altimeter data an absolute accuracy typically in the range 2-10 cm +/- 10 cm. Comparison of differences between the radar and laser derived elevations, showed a correlation with surface slope. The difference between the two data sets ranged from 84 cm +/- 79 cm for slopes below 0.1 degrees......The 336 days of the geodetic phase of ERS-1 provides dense coverage, by satellite radar altimetry, of the whole of the Greenland ice sheet. These data have been used to produce a digital elevation model of the ice sheet. The errors present in the altimeter data were investigated via a comparison......, to 10.3 m +/- 8.4 m for a slope of 0.7 degrees ( the half power beam-width of the ERS-1 radar altimeter). An explanation for the behaviour of the difference as a function of surface slope is given in terms of the pattern of surface roughness on the ice sheet....

Stellwagen Bank National Marine Sanctuary - Synthetic Aperture Radar (SAR) Imagery

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This geodatabase contains Synthetic Aperture Radar images (SAR), which consist of a fine resolution (12.5-50m), two-dimensional radar backscatter map of the...

Optical and Physical Methods for Mapping Flooding with Satellite Imagery

Science.gov (United States)

Fayne, Jessica Fayne; Bolten, John; Lakshmi, Venkat; Ahamed, Aakash

2016-01-01

Flood and surface water mapping is becoming increasingly necessary, as extreme flooding events worldwide can damage crop yields and contribute to billions of dollars economic damages as well as social effects including fatalities and destroyed communities (Xaio et al. 2004; Kwak et al. 2015; Mueller et al. 2016).Utilizing earth observing satellite data to map standing water from space is indispensable to flood mapping for disaster response, mitigation, prevention, and warning (McFeeters 1996; Brakenridge and Anderson 2006). Since the early 1970s(Landsat, USGS 2013), researchers have been able to remotely sense surface processes such as extreme flood events to help offset some of these problems. Researchers have demonstrated countless methods and modifications of those methods to help increase knowledge of areas at risk and areas that are flooded using remote sensing data from optical and radar systems, as well as free publically available and costly commercial datasets.

TESTFIELD TRENTO: GEOMETRIC EVALUATION OF VERY HIGH RESOLUTION SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

G. Agugiaro

2012-07-01

Full Text Available Today the use of spaceborne Very High Spatial Resolution (VHSR optical sensors for automatic 3D information extraction is increasing in the scientific and civil communities. The 3D Optical Metrology (3DOM Unit of the Bruno Kessler Foundation (FBK in Trento (Italy has collected stereo VHSR satellite imagery, as well as aerial and terrestrial data over Trento, with the aim to create a complete data collection with state-of-the-art datasets for investigations on image analysis, automatic digital surface model (DSM generation, 2D/3D feature extraction, city modelling and data fusion. The testfield region covers the city of Trento, characterised by very dense urban (historical centre, residential and industrial areas, and the surrounding hills and steep mountains (approximate height range 200-2100 m with cultivations, forests and bare soil. This paper reports the analysis conducted in FBK on the VHSR spaceborne imagery of Trento testfield for 3D information extraction. The data include two stereo-pairs acquired by WorldView-2 in August 2010 and by GeoEye-1 in September 2011 in panchromatic and multispectral mode, together with their original Rational Polynomial Coefficients (RPC, and the position and description of well distributed ground points. For reference and validation, a DSM from airborne LiDAR acquisition is used. The paper gives details on the project and the dataset characteristics. The results achieved by 3DOM on DSM extraction from WorldView-2 and GeoEye-1 stereo-pairs are shown and commented.

Fusion of Satellite Multispectral Images Based on Ground-Penetrating Radar (GPR Data for the Investigation of Buried Concealed Archaeological Remains

Directory of Open Access Journals (Sweden)

Athos Agapiou

2017-06-01

Full Text Available The paper investigates the superficial layers of an archaeological landscape based on the integration of various remote sensing techniques. It is well known in the literature that shallow depths may be rich in archeological remains, which generate different signal responses depending on the applied technique. In this study three main technologies are examined, namely ground-penetrating radar (GPR, ground spectroscopy, and multispectral satellite imagery. The study aims to propose a methodology to enhance optical remote sensing satellite images, intended for archaeological research, based on the integration of ground based and satellite datasets. For this task, a regression model between the ground spectroradiometer and GPR is established which is then projected to a high resolution sub-meter optical image. The overall methodology consists of nine steps. Beyond the acquirement of the in-situ measurements and their calibration (Steps 1–3, various regression models are examined for more than 70 different vegetation indices (Steps 4–5. The specific data analysis indicated that the red-edge position (REP hyperspectral index was the most appropriate for developing a local fusion model between ground spectroscopy data and GPR datasets (Step 6, providing comparable results with the in situ GPR measurements (Step 7. Other vegetation indices, such as the normalized difference vegetation index (NDVI, have also been examined, providing significant correlation between the two datasets (R = 0.50. The model is then projected to a high-resolution image over the area of interest (Step 8. The proposed methodology was evaluated with a series of field data collected from the Vésztő-Mágor Tell in the eastern part of Hungary. The results were compared with in situ magnetic gradiometry measurements, indicating common interpretation results. The results were also compatible with the preliminary archaeological investigations of the area (Step 9. The overall

A Subpixel Classification of Multispectral Satellite Imagery for Interpetation of Tundra-Taiga Ecotone Vegetation (Case Study on Tuliok River Valley, Khibiny, Russia)

Science.gov (United States)

Mikheeva, A. I.; Tutubalina, O. V.; Zimin, M. V.; Golubeva, E. I.

2017-12-01

The tundra-taiga ecotone plays significant role in northern ecosystems. Due to global climatic changes, the vegetation of the ecotone is the key object of many remote-sensing studies. The interpretation of vegetation and nonvegetation objects of the tundra-taiga ecotone on satellite imageries of a moderate resolution is complicated by the difficulty of extracting these objects from the spectral and spatial mixtures within a pixel. This article describes a method for the subpixel classification of Terra ASTER satellite image for vegetation mapping of the tundra-taiga ecotone in the Tuliok River, Khibiny Mountains, Russia. It was demonstrated that this method allows to determine the position of the boundaries of ecotone objects and their abundance on the basis of quantitative criteria, which provides a more accurate characteristic of ecotone vegetation when compared to the per-pixel approach of automatic imagery interpretation.

Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method

Science.gov (United States)

Brovelli, Maria Antonia; Crespi, Mattia; Fratarcangeli, Francesca; Giannone, Francesca; Realini, Eugenio

Interest in high-resolution satellite imagery (HRSI) is spreading in several application fields, at both scientific and commercial levels. Fundamental and critical goals for the geometric use of this kind of imagery are their orientation and orthorectification, processes able to georeference the imagery and correct the geometric deformations they undergo during acquisition. In order to exploit the actual potentialities of orthorectified imagery in Geomatics applications, the definition of a methodology to assess the spatial accuracy achievable from oriented imagery is a crucial topic. In this paper we want to propose a new method for accuracy assessment based on the Leave-One-Out Cross-Validation (LOOCV), a model validation method already applied in different fields such as machine learning, bioinformatics and generally in any other field requiring an evaluation of the performance of a learning algorithm (e.g. in geostatistics), but never applied to HRSI orientation accuracy assessment. The proposed method exhibits interesting features which are able to overcome the most remarkable drawbacks involved by the commonly used method (Hold-Out Validation — HOV), based on the partitioning of the known ground points in two sets: the first is used in the orientation-orthorectification model (GCPs — Ground Control Points) and the second is used to validate the model itself (CPs — Check Points). In fact the HOV is generally not reliable and it is not applicable when a low number of ground points is available. To test the proposed method we implemented a new routine that performs the LOOCV in the software SISAR, developed by the Geodesy and Geomatics Team at the Sapienza University of Rome to perform the rigorous orientation of HRSI; this routine was tested on some EROS-A and QuickBird images. Moreover, these images were also oriented using the world recognized commercial software OrthoEngine v. 10 (included in the Geomatica suite by PCI), manually performing the LOOCV

The potential of satellite spectro-imagery for monitoring CO2 emissions from large cities

Science.gov (United States)

Broquet, Grégoire; Bréon, François-Marie; Renault, Emmanuel; Buchwitz, Michael; Reuter, Maximilian; Bovensmann, Heinrich; Chevallier, Frédéric; Wu, Lin; Ciais, Philippe

2018-02-01

This study assesses the potential of 2 to 10 km resolution imagery of CO2 concentrations retrieved from the shortwave infrared measurements of a space-borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA). This assessment is based on observing system simulation experiments (OSSEs) with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 h before a given satellite overpass. These 6 h correspond to the period during which emissions produce CO2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge with 50 % uncertainty on the hourly or sectorial emissions, and with ˜ 25 % uncertainty on the 6 h mean emissions, from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically integrated column of CO2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif) and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbiting missions, corresponding to the specifications of CarbonSat and Sentinel-5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system is perfectly informed about the

The potential of satellite spectro-imagery for monitoring CO2 emissions from large cities

Directory of Open Access Journals (Sweden)

G. Broquet

2018-02-01

Full Text Available This study assesses the potential of 2 to 10 km resolution imagery of CO2 concentrations retrieved from the shortwave infrared measurements of a space-borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA. This assessment is based on observing system simulation experiments (OSSEs with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 h before a given satellite overpass. These 6 h correspond to the period during which emissions produce CO2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge with 50 % uncertainty on the hourly or sectorial emissions, and with ∼ 25 % uncertainty on the 6 h mean emissions, from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically integrated column of CO2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbiting missions, corresponding to the specifications of CarbonSat and Sentinel-5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system

Seasonally-managed wetland footprint delineation using Landsat ETM+ satellite imagery

Energy Technology Data Exchange (ETDEWEB)

Quinn, Nigel W. T. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Epshtein, Olga [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Arizona State Univ., Tempe, AZ (United States). School of Sustainable Engineering and the Built Environment

2014-01-09

One major challenge in water resource management is the estimation of evapotranspiration losses from seasonally managed wetlands. Quantifying these losses is complicated by the dynamic nature of the wetlands' areal footprint during the periods of flood-up and drawdown. In this paper, we present a data-lean solution to this problem using an example application in the San Joaquin Basin, California. Through analysis of high-resolution Landsat Enhanced Thematic Mapper Plus (ETM+) satellite imagery, we develop a metric to better capture the extent of total flooded wetland area. The procedure is validated using year-long, continuously-logged field datasets for two wetlands within the study area. The proposed classification which uses a Landsat ETM + Band 5 (mid-IR wavelength) to Band 2 (visible green wavelength) ratio improves estimates by 30–50% relative to previous wetland delineation studies. Finally, requiring modest ancillary data, the study results provide a practical and efficient option for wetland management in data-sparse regions or un-gauged watersheds.

Multisensor satellite observations of meso- and submesoscale surface circulation in the Liguro-Provençal Basin

Science.gov (United States)

Karimova, Svetlana; Alvera-Azcarate, Aida

2017-04-01

Despite great efforts being paid to studying circulation of the Western Mediterranean Basin and the factors triggering bioproductivity of its marine ecosystem, the evidence provided by satellite imagery has not been fully analysed yet. In the present paper, we concentrate our attention on mesoscale and submesoscale circulation features of the Liguro-Provençal Basin captured by satellite radiometer, spectroradiometer, and radar images. Using such a dataset makes it possible to observe the circulation features from a wide spatial range, from the basin scale through mesoscale to the scales of a few kilometers. Mesoscale features in this study are being mostly observed with thermal infrared imagery retrieved by AVHRR and AATSR sensors. Special attention in the work was paid to an analysis of the data coming from a geostationary satellite, namely ones provided by SEVIRI. Due to their uniquely high temporal resolution, such imagery allows observing circulation features in their evolution. During the winter blooming events, surface circulation at meso- to submesoscales in the region of interest was additionally highlighted by images obtained in the visible range. Full spatial resolution images provided by Envisat MERIS, Sentinel-2 MSI, and Landsat TM/ETM+/OLI made the greatest contribution to this part. The smallest scales (namely submesoscale) are being observed with synthetic aperture radar (SAR) imagery provided by Envisat ASAR and Sentinel-1 SAR. During an analysis of SAR images, it was noted that there was strikingly great amount of biogenic surfactants on the water surface in the region of interest. Apparently, low biological productivity typical for the Western Mediterranean ecosystem is not a limiting factor for the formation of surfactant films seen in SAR imagery. This finding though requires further consideration in some other researches, and hereafter we just benefited from the presence of surfactants, because they behave as good tracers of surface currents

Advances in the Processing of VHR Optical Imagery in Support of Safeguards Verification

International Nuclear Information System (INIS)

Niemeyer, I.; Listner, C.; Canty, M.

2015-01-01

Under the Additional Protocol of the Non-Proliferation Treaty (NPT) complementing the safeguards agreements between States and the International Atomic Energy Agency, commercial satellite imagery, preferably acquired by very high-resolution (VHR) satellite sensors, is an important source of safeguards-relevant information. Satellite imagery can assist in the evaluation of site declarations, design information verification, the detection of undeclared nuclear facilities, and the preparation of inspections or other visits. With the IAEA's Geospatial Exploitation System (GES), satellite imagery and other geospatial information such as site plans of nuclear facilities are available for a broad range of inspectors, analysts and country officers. The demand for spatial information and new tools to analyze this data is growing, together with the rising number of nuclear facilities under safeguards worldwide. Automated computer-driven processing of satellite imagery could therefore add a big value in the safeguards verification process. These could be, for example, satellite imagery pre-processing algorithms specially developed for new sensors, tools for pixel or object-based image analysis, or geoprocessing tools that generate additional safeguards-relevant information. In the last decade procedures for automated (pre-) processing of satellite imagery have considerably evolved. This paper aims at testing some pixel-based and object-based procedures for automated change detection and classification in support of safeguards verification. Taking different nuclear sites as examples, these methods will be evaluated and compared with regard to their suitability to (semi-) automatically extract safeguards-relevant information. (author)

Extraction of advanced geospatial intelligence (AGI) from commercial synthetic aperture radar imagery

Science.gov (United States)

Kanberoglu, Berkay; Frakes, David

2017-04-01

The extraction of objects from advanced geospatial intelligence (AGI) products based on synthetic aperture radar (SAR) imagery is complicated by a number of factors. For example, accurate detection of temporal changes represented in two-color multiview (2CMV) AGI products can be challenging because of speckle noise susceptibility and false positives that result from small orientation differences between objects imaged at different times. These cases of apparent motion can result in 2CMV detection, but they obviously differ greatly in terms of significance. In investigating the state-of-the-art in SAR image processing, we have found that differentiating between these two general cases is a problem that has not been well addressed. We propose a framework of methods to address these problems. For the detection of the temporal changes while reducing the number of false positives, we propose using adaptive object intensity and area thresholding in conjunction with relaxed brightness optical flow algorithms that track the motion of objects across time in small regions of interest. The proposed framework for distinguishing between actual motion and misregistration can lead to more accurate and meaningful change detection and improve object extraction from a SAR AGI product. Results demonstrate the ability of our techniques to reduce false positives up to 60%.

Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar

Directory of Open Access Journals (Sweden)

Ramesh K. Ningthoujam

2016-07-01

Full Text Available Assessments of forest cover, forest carbon stocks and carbon emissions from deforestation and degradation are increasingly important components of sustainable resource management, for combating biodiversity loss and in climate mitigation policies. Satellite remote sensing provides the only means for mapping global forest cover regularly. However, forest classification with optical data is limited by its insensitivity to three-dimensional canopy structure and cloud cover obscuring many forest regions. Synthetic Aperture Radar (SAR sensors are increasingly being used to mitigate these problems, mainly in the L-, C- and X-band domains of the electromagnetic spectrum. S-band has not been systematically studied for this purpose. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest characterisation. The Michigan Microwave Canopy Scattering (MIMICS-I radiative transfer model is utilised to understand the scattering mechanisms in forest canopies at S-band. The MIMICS-I model reveals strong S-band backscatter sensitivity to the forest canopy in comparison to soil characteristics across all polarisations and incidence angles. Airborne S-band SAR imagery over the temperate mixed forest of Savernake Forest in southern England is analysed for its information content. Based on the modelling results, S-band HH- and VV-polarisation radar backscatter and the Radar Forest Degradation Index (RFDI are used in a forest/non-forest Maximum Likelihood classification at a spatial resolution of 6 m (70% overall accuracy, κ = 0.41 and 20 m (63% overall accuracy, κ = 0.27. The conclusion is that S-band SAR such as from NovaSAR-S is likely to be suitable for monitoring forest cover and its changes.

Radar and electronic navigation

CERN Document Server

Sonnenberg, G J

2013-01-01

Radar and Electronic Navigation, Sixth Edition discusses radar in marine navigation, underwater navigational aids, direction finding, the Decca navigator system, and the Omega system. The book also describes the Loran system for position fixing, the navy navigation satellite system, and the global positioning system (GPS). It reviews the principles, operation, presentations, specifications, and uses of radar. It also describes GPS, a real time position-fixing system in three dimensions (longitude, latitude, altitude), plus velocity information with Universal Time Coordinated (UTC). It is accur

Monitoring of oil pollution in the Arabian Gulf based on medium resolution satellite imagery

Science.gov (United States)

Zhao, J.; Ghedira, H.

2013-12-01

A large number of inland and offshore oil fields are located in the Arabian Gulf where about 25% of the world's oil is produced by the countries surrounding the Arabian Gulf region. Almost all of this oil production is shipped by sea worldwide through the Strait of Hormuz making the region vulnerable to environmental and ecological threats that might arise from accidental or intentional oil spills. Remote sensing technologies have the unique capability to detect and monitor oil pollutions over large temporal and spatial scales. Synoptic satellite imaging can date back to 1972 when Landsat-1 was launched. Landsat satellite missions provide long time series of imagery with a spatial resolution of 30 m. MODIS sensors onboard NASA's Terra and Aqua satellites provide a wide and frequent coverage at medium spatial resolution, i.e. 250 m and 500, twice a day. In this study, the capability of medium resolution MODIS and Landsat data in detecting and monitoring oil pollutions in the Arabian Gulf was tested. Oil spills and slicks show negative or positive contrasts in satellite derived RGB images compared with surrounding clean waters depending on the solar/viewing geometry, oil thickness and evolution, etc. Oil-contaminated areas show different spectral characteristics compared with surrounding waters. Rayleigh-corrected reflectance at the seven medium resolution bands of MODIS is lower in oil affected areas. This is caused by high light absorption of oil slicks. 30-m Landsat image indicated the occurrence of oil spill on May 26 2000 in the Arabian Gulf. The oil spill showed positive contrast and lower temperature than surrounding areas. Floating algae index (FAI) images are also used to detect oil pollution. Oil-contaminated areas were found to have lower FAI values. To track the movement of oil slicks found on October 21 2007, ocean circulations from a HYCOM model were examined and demonstrated that the oil slicks were advected toward the coastal areas of United Arab

High-precision positioning of radar scatterers

NARCIS (Netherlands)

Dheenathayalan, P.; Small, D.; Schubert, A.; Hanssen, R.F.

2016-01-01

Remote sensing radar satellites cover wide areas and provide spatially dense measurements, with millions of scatterers. Knowledge of the precise position of each radar scatterer is essential to identify the corresponding object and interpret the estimated deformation. The absolute position accuracy

Cloud detection method for Chinese moderate high resolution satellite imagery (Conference Presentation)

Science.gov (United States)

Zhong, Bo; Chen, Wuhan; Wu, Shanlong; Liu, Qinhuo

2016-10-01

Cloud detection of satellite imagery is very important for quantitative remote sensing research and remote sensing applications. However, many satellite sensors don't have enough bands for a quick, accurate, and simple detection of clouds. Particularly, the newly launched moderate to high spatial resolution satellite sensors of China, such as the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) and the wide field of view (WFV) sensor on-board the Gao Fen 1 (GF-1), only have four available bands including blue, green, red, and near infrared bands, which are far from the requirements of most could detection methods. In order to solve this problem, an improved and automated cloud detection method for Chinese satellite sensors called OCM (Object oriented Cloud and cloud-shadow Matching method) is presented in this paper. It firstly modified the Automatic Cloud Cover Assessment (ACCA) method, which was developed for Landsat-7 data, to get an initial cloud map. The modified ACCA method is mainly based on threshold and different threshold setting produces different cloud map. Subsequently, a strict threshold is used to produce a cloud map with high confidence and large amount of cloud omission and a loose threshold is used to produce a cloud map with low confidence and large amount of commission. Secondly, a corresponding cloud-shadow map is also produced using the threshold of near-infrared band. Thirdly, the cloud maps and cloud-shadow map are transferred to cloud objects and cloud-shadow objects. Cloud and cloud-shadow are usually in pairs; consequently, the final cloud and cloud-shadow maps are made based on the relationship between cloud and cloud-shadow objects. OCM method was tested using almost 200 HJ-1/CCD images across China and the overall accuracy of cloud detection is close to 90%.

New uses of shadow enhancement. [interpretation of geologic structures from photographic or scanner imagery of Colorado

Science.gov (United States)

Lee, K. (Principal Investigator); Sawatzky, D. L.

1974-01-01

The author has identified the following significant results. Shadow enhancement of topographic linears in photographic or scanner images is a valuable tool for interpretation of geologic structures. Whether linears will be enhanced or subdued depends on sun angle and azimuth. The relationship of the sun's attitude to topographic slopes determines which trends are available for interpretation in existing imagery, and it can be used to select the time of day, surface properties, and film and filter characteristics in planning aircraft flights or satellite orbital passes. The technique of selective shadow enhancement can be applied to all photographic or imaging experiments, but its best for snow-covered scenes, side-looking radar images, and painted relief models.

Polar-Orbiting Satellite (POES) Images

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Visible and Infrared satellite imagery taken from camera systems or radiometer instruments on satellites in orbit around the poles. Satellite campaigns include...

Calculating Viewing Angles Pixel by Pixel in Optical Remote Sensing Satellite Imagery Using the Rational Function Model

OpenAIRE

Kai Xu; Guo Zhang; Qingjun Zhang; Deren Li

2018-01-01

In studies involving the extraction of surface physical parameters using optical remote sensing satellite imagery, sun-sensor geometry must be known, especially for sensor viewing angles. However, while pixel-by-pixel acquisitions of sensor viewing angles are of critical importance to many studies, currently available algorithms for calculating sensor-viewing angles focus only on the center-point pixel or are complicated and are not well known. Thus, this study aims to provide a simple and ge...

Quantifying South East Asia's forest degradation using latest generation optical and radar satellite remote sensing

Science.gov (United States)

Broich, M.; Tulbure, M. G.; Wijaya, A.; Weisse, M.; Stolle, F.

2017-12-01

Deforestation and forest degradation form the 2nd largest source of anthropogenic CO2 emissions. While deforestation is being globally mapped with satellite image time series, degradation remains insufficiently quantified. Previous studies quantified degradation for small scale, local sites. A method suitable for accurate mapping across large areas has not yet been developed due to the variability of the low magnitude and short-lived degradation signal and the absence of data with suitable resolution properties. Here we use a combination of newly available streams of free optical and radar image time series acquired by NASA and ESA, and HPC-based data science algorithms to innovatively quantify degradation consistently across Southeast Asia (SEA). We used Sentinel1 c-band radar data and NASA's new Harmonized Landsat8 (L8) Sentinel2 (S2) product (HLS) for cloud free optical images. Our results show that dense time series of cloud penetrating Sentinel 1 c-band radar can provide degradation alarm flags, while the HLS product of cloud-free optical images can unambiguously confirm degradation alarms. The detectability of degradation differed across SEA. In the seasonal forest of continental SEA the reliability of our radar-based alarm flags increased as the variability in landscape moisture decreases in the dry season. We reliably confirmed alarms with optical image time series during the late dry season, where degradation in open canopy forests becomes detectable once the undergrowth vegetation has died down. Conversely, in insular SEA landscape moisture is low, the radar time series generated degradation alarms flags with moderate to high reliability throughout the year, further confirmed with the HLS product. Based on the HLS product we can now confirm degradation within time series provides better results than either one on its own. Our results provide significant information with application for carbon trading policy and land management.

Satellite remote sensing at the Sea Empress spill - a help or potential hindrance

International Nuclear Information System (INIS)

Lunel, T.

1996-01-01

The application of satellite images in an oil spill response operation, was discussed. The oil movement and satellite imagery of the Sea Empress spill was described in detail. There were large discrepancies in the predictions by Radarsat satellite imagery and the actual oil movement, and in this instance, the satellite imagery proved to be more of a distraction than a useful tool. It was suggested that the greatest potential for satellite imagery is in detecting smaller releases of oil, such as from illegal tank washings, ballast waters from ships, or operational malfunctions at oil rigs. 4 refs., 10 figs

Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations

Science.gov (United States)

We analyzed 10 established and 4 new satellite reflectance algorithms for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense water truth collected within one hour of image acquisition to develop si...

Synthetic aperture radar imaging simulator for pulse envelope evaluation

Science.gov (United States)

Balster, Eric J.; Scarpino, Frank A.; Kordik, Andrew M.; Hill, Kerry L.

2017-10-01

A simulator for spotlight synthetic aperture radar (SAR) image formation is presented. The simulator produces radar returns from a virtual radar positioned at an arbitrary distance and altitude. The radar returns are produced from a source image, where the return is a weighted summation of linear frequency-modulated (LFM) pulse signals delayed by the distance of each pixel in the image to the radar. The imagery is resampled into polar format to ensure consistent range profiles to the position of the radar. The SAR simulator provides a capability enabling the objective analysis of formed SAR imagery, comparing it to an original source image. This capability allows for analysis of various SAR signal processing techniques previously determined by impulse response function (IPF) analysis. The results suggest that IPF analysis provides results that may not be directly related to formed SAR image quality. Instead, the SAR simulator uses image quality metrics, such as peak signal-to-noise ratio (PSNR) and structured similarity index (SSIM), for formed SAR image quality analysis. To showcase the capability of the SAR simulator, it is used to investigate the performance of various envelopes applied to LFM pulses. A power-raised cosine window with a power p=0.35 and roll-off factor of β=0.15 is shown to maximize the quality of the formed SAR images by improving PSNR by 0.84 dB and SSIM by 0.06 from images formed utilizing a rectangular pulse, on average.

Detection of Coccolithophore Blooms in Ocean Color Satellite Imagery: a Generalized Approach for Use with Multiple Sensors

Science.gov (United States)

Moore, Timothy; Dowell, Mark; Franz, Bryan A.

2012-01-01

A generalized coccolithophore bloom classifier has been developed for use with ocean color imagery. The bloom classifier was developed using extracted satellite reflectance data from SeaWiFS images screened by the default bloom detection mask. In the current application, we extend the optical water type (OWT) classification scheme by adding a new coccolithophore bloom class formed from these extracted reflectances. Based on an in situ coccolithophore data set from the North Atlantic, the detection levels with the new scheme were between 1,500 and 1,800 coccolithophore cellsmL and 43,000 and 78,000 lithsmL. The detected bloom area using the OWT method was an average of 1.75 times greater than the default bloom detector based on a collection of SeaWiFS 1 km imagery. The versatility of the scheme is shown with SeaWiFS, MODIS Aqua, CZCS and MERIS imagery at the 1 km scale. The OWT scheme was applied to the daily global SeaWiFS imagery mission data set (years 19972010). Based on our results, average annual coccolithophore bloom area was more than two times greater in the southern hemisphere compared to the northern hemi- sphere with values of 2.00 106 km2 and 0.75 106 km2, respectively. The new algorithm detects larger bloom areas in the Southern Ocean compared to the default algorithm, and our revised global annual average of 2.75106 km2 is dominated by contributions from the Southern Ocean.

Location of irrigated land classified from satellite imagery - High Plains Area, nominal date 1992

Science.gov (United States)

Qi, Sharon L.; Konduris, Alexandria; Litke, David W.; Dupree, Jean

2002-01-01

Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land overlying the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a water-quality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated-land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres).

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

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Hasituya

2017-03-01

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

The Use of Radar Imagery in Climatological Research. Resource Paper No. 21.

Science.gov (United States)

Williams, Aaron, Jr.

Intended to supplement undergraduate college geography courses, this resource paper investigates the need and use of radar in weather phenomena research. Radar can be used to study weather phenomena over a wide area, thus improving the results of statistical analyses previously limited by inadequate data. Radar techniques are also useful for…

Geometric Positioning Accuracy Improvement of ZY-3 Satellite Imagery Based on Statistical Learning Theory

Directory of Open Access Journals (Sweden)

Niangang Jiao

2018-05-01

Full Text Available With the increasing demand for high-resolution remote sensing images for mapping and monitoring the Earth’s environment, geometric positioning accuracy improvement plays a significant role in the image preprocessing step. Based on the statistical learning theory, we propose a new method to improve the geometric positioning accuracy without ground control points (GCPs. Multi-temporal images from the ZY-3 satellite are tested and the bias-compensated rational function model (RFM is applied as the block adjustment model in our experiment. An easy and stable weight strategy and the fast iterative shrinkage-thresholding (FIST algorithm which is widely used in the field of compressive sensing are improved and utilized to define the normal equation matrix and solve it. Then, the residual errors after traditional block adjustment are acquired and tested with the newly proposed inherent error compensation model based on statistical learning theory. The final results indicate that the geometric positioning accuracy of ZY-3 satellite imagery can be improved greatly with our proposed method.

Assessing the population coverage of a health demographic surveillance system using satellite imagery and crowd-sourcing.

Directory of Open Access Journals (Sweden)

Aurelio Di Pasquale

Full Text Available Remotely sensed data can serve as an independent source of information about the location of residential structures in areas under demographic and health surveillance. We report on results obtained combining satellite imagery, imported from Bing, with location data routinely collected using the built-in GPS sensors of tablet computers, to assess completeness of population coverage in a Health and Demographic Surveillance System in Malawi. The Majete Malaria Project Health and Demographic Surveillance System, in Malawi, started in 2014 to support a project with the aim of studying the reduction of malaria using an integrated control approach by rolling out insecticide treated nets and improved case management supplemented with house improvement and larval source management. In order to support the monitoring of the trial a Health and Demographic Surveillance System was established in the area that surrounds the Majete Wildlife Reserve (1600 km2, using the OpenHDS data system. We compared house locations obtained using GPS recordings on mobile devices during the demographic surveillance census round with those acquired from satellite imagery. Volunteers were recruited through the crowdcrafting.org platform to identify building structures on the images, which enabled the compilation of a database with coordinates of potential residences. For every building identified on these satellite images by the volunteers (11,046 buildings identified of which 3424 (ca. 30% were part of the censused area, we calculated the distance to the nearest house enumerated on the ground by fieldworkers during the census round of the HDSS. A random sample of buildings (85 structures identified on satellite images without a nearby location enrolled in the census were visited by a fieldworker to determine how many were missed during the baseline census survey, if any were missed. The findings from this ground-truthing effort suggest that a high population coverage was

Monitoring Termite-Mediated Ecosystem Processes Using Moderate and High Resolution Satellite Imagery

Science.gov (United States)

Lind, B. M.; Hanan, N. P.

2016-12-01

Termites are considered dominant decomposers and prominent ecosystem engineers in the global tropics and they build some of the largest and architecturally most complex non-human-made structures in the world. Termite mounds significantly alter soil texture, structure, and nutrients, and have major implications for local hydrological dynamics, vegetation characteristics, and biological diversity. An understanding of how these processes change across large scales has been limited by our ability to detect termite mounds at high spatial resolutions. Our research develops methods to detect large termite mounds in savannas across extensive geographic areas using moderate and high resolution satellite imagery. We also investigate the effect of termite mounds on vegetation productivity using Landsat-8 maximum composite NDVI data as a proxy for production. Large termite mounds in arid and semi-arid Senegal generate highly reflective `mound scars' with diameters ranging from 10 m at minimum to greater than 30 m. As Sentinel-2 has several bands with 10 m resolution and Landsat-8 has improved calibration, higher radiometric resolution, 15 m spatial resolution (pansharpened), and improved contrast between vegetated and bare surfaces compared to previous Landsat missions, we found that the largest and most influential mounds in the landscape can be detected. Because mounds as small as 4 m in diameter are easily detected in high resolution imagery we used these data to validate detection results and quantify omission errors for smaller mounds.

Meteorite Falls Observed in U.S. Weather Radar Data in 2015 and 2016 (To Date)

Science.gov (United States)

Fries, Marc; Fries, Jeffrey; Hankey, Mike; Matson, Robert

2016-01-01

To date, over twenty meteorite falls have been located in the weather radar imagery of the National Oceanic and Atmospheric Administration (NOAA)'s NEXRAD radar network. We present here the most prominent events recorded since the last Meteoritical Society meeting, covering most of 2015 and early 2016. Meteorite Falls: The following events produced evidence of falling meteorites in radar imagery and resulted in meteorites recovered at the fall site. Creston, CA (24 Oct 2015 0531 UTC): This event generated 218 eyewitness reports submitted to the American Meteor Society (AMS) and is recorded as event #2635 for 2015 on the AMS website. Witnesses reported a bright fireball with fragmentation terminating near the city of Creston, CA, north of Los Angeles. Sonic booms and electrophonic noise were reported in the vicinity of the event. Weather radar imagery records signatures consistent with falling meteorites in data from the KMUX, KVTX, KHNX and KVBX. The Meteoritical Society records the Creston fall as an L6 meteorite with a total recovered mass of 688g. Osceola, FL (24 Jan 2016 1527 UTC): This daytime fireball generated 134 eyewitness reports on AMS report number 266 for 2016, with one credible sonic boom report. The fireball traveled roughly NE to SW with a terminus location north of Lake City, FL in sparsely populated, forested countryside. Radar imagery shows distinct and prominent evidence of a significant meteorite fall with radar signatures seen in data from the KJAX and KVAX radars. Searchers at the fall site found that recoveries were restricted to road sites by the difficult terrain, and yet several meteorites were recovered. Evidence indicates that this was a relatively large meteorite fall where most of the meteorites are unrecoverable due to terrain. Osceola is an L6 meteorite with 991 g total mass recovered to date. Mount Blanco, TX (18 Feb 2016 0343 UTC): This event produced only 39 eyewitness reports and is recorded as AMS event #635 for 2016. No

Classification of irrigated land using satellite imagery, the High Plains aquifer, nominal date 1992

Science.gov (United States)

Qi, Sharon L.; Konduris, Alexandria; Litke, David W.; Dupree, Jean

2002-01-01

Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land across the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a waterquality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres). This information was also compared to a similar data set based on 1980 imagery. The 1980 data classified 13.7 million acres as irrigated. Although the change in the amount of irrigated land between the two times was not substantial, the location of the irrigated land did shift from areas where there were large ground-water-level declines to other areas where ground-water levels were static or rising.

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

Directory of Open Access Journals (Sweden)

Qicong Peng

2010-03-01

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

Current Operational Use of and Future Needs for Microwave Imagery at NOAA

Science.gov (United States)

Goldberg, M.; McWilliams, G.; Chang, P.

2017-12-01

There are many applications of microwave imagery served by NOAA's operational products and services. They include the use of microwave imagery and derived products for monitoring precipitation, tropical cyclones, sea surface temperature under all weather conditions, wind speed, snow and ice cover, and even soil moisture. All of NOAA's line offices including the National Weather Service, National Ocean Service, National Marine Fisheries Service, and Office of Oceanic and Atmospheric Research rely on microwave imagery. Currently microwave imagery products used by NOAA come from a constellation of satellites that includes Air Force's Special Sensor Microwave Imager Sounder (SSMIS), the Japanese Advanced Microwave Scanning Radiometer (AMSR), the Navy's WindSat, and NASA's Global Precipitation Monitoring (GPM) Microwave Imager (GMI). Follow-on missions for SSMIS are very uncertain, JAXA approval for a follow-on to AMSR2 is still pending, and GMI is a research satellite (lacking high-latitude coverage) with no commitment for operational continuity. Operational continuity refers to a series of satellites, so when one satellite reaches its design life a new satellite is launched. EUMETSAT has made a commitment to fly a microwave imager in the mid-morning orbit. China and Russia have demonstrated on-orbit microwave imagers. Of utmost importance to NOAA, however, is the quality, access, and latency of the data This presentation will focus on NOAA's current requirements for microwave imagery data which, for the most part, are being fulfilled by AMSR2, SSMIS, and WindSat. It will include examples of products and applications of microwave imagery at NOAA. We will also discuss future needs, especially for improved temporal resolution which hopefully can be met by an international constellation of microwave imagers. Finally, we will discuss what we are doing to address the potential gap in imagery.

Spatial and temporal changes in household structure locations using high-resolution satellite imagery for population assessment: an analysis in southern Zambia, 2006-2011

Directory of Open Access Journals (Sweden)

Timothy Shields

2016-05-01

Full Text Available Satellite imagery is increasingly available at high spatial resolution and can be used for various purposes in public health research and programme implementation. Comparing a census generated from two satellite images of the same region in rural southern Zambia obtained four and a half years apart identified patterns of household locations and change over time. The length of time that a satellite image-based census is accurate determines its utility. Households were enumerated manually from satellite images obtained in 2006 and 2011 of the same area. Spatial statistics were used to describe clustering, cluster detection, and spatial variation in the location of households. A total of 3821 household locations were enumerated in 2006 and 4256 in 2011, a net change of 435 houses (11.4% increase. Comparison of the images indicated that 971 (25.4% structures were added and 536 (14.0% removed. Further analysis suggested similar household clustering in the two images and no substantial difference in concentration of households across the study area. Cluster detection analysis identified a small area where significantly more household structures were removed than expected; however, the amount of change was of limited practical significance. These findings suggest that random sampling of households for study participation would not induce geographic bias if based on a 4.5-year-old image in this region. Application of spatial statistical methods provides insights into the population distribution changes between two time periods and can be helpful in assessing the accuracy of satellite imagery.

Bistatic SAR: Imagery & Image Products.

Energy Technology Data Exchange (ETDEWEB)

Yocky, David A.; Wahl, Daniel E.; Jakowatz, Charles V,

2014-10-01

While typical SAR imaging employs a co-located (monostatic) RADAR transmitter and receiver, bistatic SAR imaging separates the transmitter and receiver locations. The transmitter and receiver geometry determines if the scattered signal is back scatter, forward scatter, or side scatter. The monostatic SAR image is backscatter. Therefore, depending on the transmitter/receiver collection geometry, the captured imagery may be quite different that that sensed at the monostatic SAR. This document presents imagery and image products formed from captured signals during the validation stage of the bistatic SAR research. Image quality and image characteristics are discussed first. Then image products such as two-color multi-view (2CMV) and coherent change detection (CCD) are presented.

Algorithm and Application of Gcp-Independent Block Adjustment for Super Large-Scale Domestic High Resolution Optical Satellite Imagery

Science.gov (United States)

Sun, Y. S.; Zhang, L.; Xu, B.; Zhang, Y.

2018-04-01

The accurate positioning of optical satellite image without control is the precondition for remote sensing application and small/medium scale mapping in large abroad areas or with large-scale images. In this paper, aiming at the geometric features of optical satellite image, based on a widely used optimization method of constraint problem which is called Alternating Direction Method of Multipliers (ADMM) and RFM least-squares block adjustment, we propose a GCP independent block adjustment method for the large-scale domestic high resolution optical satellite image - GISIBA (GCP-Independent Satellite Imagery Block Adjustment), which is easy to parallelize and highly efficient. In this method, the virtual "average" control points are built to solve the rank defect problem and qualitative and quantitative analysis in block adjustment without control. The test results prove that the horizontal and vertical accuracy of multi-covered and multi-temporal satellite images are better than 10 m and 6 m. Meanwhile the mosaic problem of the adjacent areas in large area DOM production can be solved if the public geographic information data is introduced as horizontal and vertical constraints in the block adjustment process. Finally, through the experiments by using GF-1 and ZY-3 satellite images over several typical test areas, the reliability, accuracy and performance of our developed procedure will be presented and studied in this paper.

Employing high resolution satellite imagery to document a rapid glacier surge in the Karakoram - risks and opportunities for hazard assessment

Science.gov (United States)

Steiner, J. F.; Kraaijenbrink, P. D. A.; Jiduc, S. G.; Immerzeel, W. W.

2017-12-01

Glacier surges occur regularly in the Karakoram but their driving mechanisms, recurrence and its relation to climatic change remain unclear. Since many glacier tongues in the region reach to very low elevations, local populations are often exposed to glacial hazards. While the scientific interpretation of hazard is one challenge, adequately communicating results to possibly affected stakeholders poses a different set of hurdles. Using DEMs as well as Landsat imagery in combination with high-resolution Planet imagery we quantify surface elevation changes and flow velocities to document a glacier surge of the Khurdopin glacier, located in a remote valley in Pakistan, in the first half of 2017. Results reveal that an accumulation of ice mass leads to a rapid surge in peaking with velocities above 5000 m a-1 or 0.5 m h-1 during a few days. Velocities increase steadily during a four-year build-up phase prior to the actual surge, while the remaining 15 years of the recurring cycle the glacier is quiescent. It is hypothesized that the surge is mainly initiated as a result of increased pressure melting caused by ice accumulation. However, surface observations show increased crevassing and disappearance of supra glacial ponds, which could have led to increased lubrication of the glacier bed. As a consequence of the surging tongue blocking the main valley a lake has formed and grown continuously in size over two months at a rate of up to 3000 m2 per day. Using satellite imagery with a frequent overpass rate we are able to (a) characterize the nature of glacier surges in the region with greater detail and (b) monitor the surge as well as the formation of the lake as it develops. Having developed a connection to local stakeholders we were able to provide rapid hazard assessments to affected communities, which can be employed to define possible actions. We show the potential of satellite imagery - freely available Landsat in combination with commercial Planet imagery -, which

Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

Science.gov (United States)

Prat, O. P.; Nelson, B. R.

2014-10-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets (bias-adjusted TMPA 3B42, near-real time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (± 6%). However, differences at the RFC are more important in particular for near-real time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near real time counterpart 3B42RT. However, large biases remained for 3B42 over the Western US for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in day-1) over the Northwest. Furthermore, the conditional analysis and the contingency analysis conducted illustrated the challenge of retrieving extreme precipitation from remote sensing estimates.

Defense Meteorological Satellite Program (DMSP)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Defense Meteorological Satellite Program (DMSP) satellites collect visible and infrared cloud imagery as well as monitoring the atmospheric, oceanographic,...

Blending satellite data and RADAR tool for rapid flood damage assessment in Agriculture: A case study in Sri Lanka

Science.gov (United States)

Amarnath, Giriraj; Inada, Yoshiaki; Inoue, Ryosuke; Alahacoon, Niranga; Smakhtin, Vladimir

2014-05-01

During the catastrophic flooding it is critically important to estimate losses as it is essential for facilitating good decision making at the district, province and national levels of government and to appraise aid agencies for necessary assistance. Flood loss estimates can also be used to evaluate the cost effectiveness of alternative approaches to strengthening flood control measures. In the case of Sri Lanka there were limited knowledge and application system exist for carrying out rapid damage assessment for Agriculture in Sri Lanka. FAO has developed the tool "Rapid Agricultural Disaster Assessment Routine" (RADAR) based on theoretical approach that uses simple tools for assessing the impact on agriculture of a disastrous event. There are two knowledge bases that contain information needed for calculation of the value loss or damage. The procedure of rapid impact assessment implies the use of knowledge-bases, database and GIS. In this study, the user friendly application of RADAR system has been developed. Three components were considered including agriculture, livestock and farmers asset to estimate the losses. The application will allow estimating flood damage at various scales and this being tested at district level and specific example for the 2011 floods in Sri Lanka. In order to understand flood inundation cycle, time-series optical MODIS satellite data (2000-2011) and microwave ALOS PALSAR (2006-2011) were used to derive annual flood extent, flood duration and recurrent areas to identify flood risk and impact of seasonal flooding on agriculture. This study demonstrates how RADAR & satellite-based flood products can be effectively used for rapid damage assessment and managing the floods.

Environmental waste site characterization utilizing aerial photographs and satellite imagery: Three sites in New Mexico, USA

International Nuclear Information System (INIS)

Van Eeckhout, E.; Pope, P.; Becker, N.; Wells, B.; Lewis, A.; David, N.

1996-01-01

The proper handling and characterization of past hazardous waste sites is becoming more and more important as world population extends into areas previously deemed undesirable. Historical photographs, past records, current aerial satellite imagery can play an important role in characterizing these sites. These data provide clear insight into defining problem areas which can be surface samples for further detail. Three such areas are discussed in this paper: (1) nuclear wastes buried in trenches at Los Alamos National Laboratory, (2) surface dumping at one site at Los Alamos National Laboratory, and (3) the historical development of a municipal landfill near Las Cruces, New Mexico

Joint synthetic aperture radar plus ground moving target indicator from single-channel radar using compressive sensing

Science.gov (United States)

Thompson, Douglas; Hallquist, Aaron; Anderson, Hyrum

2017-10-17

The various embodiments presented herein relate to utilizing an operational single-channel radar to collect and process synthetic aperture radar (SAR) and ground moving target indicator (GMTI) imagery from a same set of radar returns. In an embodiment, data is collected by randomly staggering a slow-time pulse repetition interval (PRI) over a SAR aperture such that a number of transmitted pulses in the SAR aperture is preserved with respect to standard SAR, but many of the pulses are spaced very closely enabling movers (e.g., targets) to be resolved, wherein a relative velocity of the movers places them outside of the SAR ground patch. The various embodiments of image reconstruction can be based on compressed sensing inversion from undersampled data, which can be solved efficiently using such techniques as Bregman iteration. The various embodiments enable high-quality SAR reconstruction, and high-quality GMTI reconstruction from the same set of radar returns.

Wind energy applications of synthetic aperture radar

DEFF Research Database (Denmark)

Badger, Merete

Synthetic aperture radars (SAR), mounted on satellites or aircraft, have proven useful for ocean wind mapping. Wind speeds at the height 10 m may be retrieved from measurements of radar backscatter using empirical model functions. The resulting windfields are valuable in offshore wind energy plan...

Mapping rice ecosystem dynamics and greenhouse gas emissions using multiscale imagery and biogeochemical models

Science.gov (United States)

Salas, W.; Torbick, N.

2017-12-01

Rice greenhouse gas (GHG) emissions in production hot spots have been mapped using multiscale satellite imagery and a processed-based biogeochemical model. The multiscale Synthetic Aperture Radar (SAR) and optical imagery were co-processed and fed into a machine leanring framework to map paddy attributes that are tuned using field observations and surveys. Geospatial maps of rice extent, crop calendar, hydroperiod, and cropping intensity were then used to parameterize the DeNitrification-DeComposition (DNDC) model to estimate emissions. Results, in the Red River Detla for example, show total methane emissions at 345.4 million kgCH4-C equivalent to 11.5 million tonnes CO2e (carbon dioxide equivalent). We further assessed the role of Alternative Wetting and Drying and the impact on GHG and yield across production hot spots with uncertainty estimates. The approach described in this research provides a framework for using SAR to derive maps of rice and landscape characteristics to drive process models like DNDC. These types of tools and approaches will support the next generation of Monitoring, Reporting, and Verification (MRV) to combat climate change and support ecosystem service markets.

Assessment on spatiotemporal relationship between rainfall and cloud top temperature from new generation weather satellite imagery

Science.gov (United States)

Wei, Chiang; Yeh, Hui-Chung; Chen, Yen-Chang

2017-04-01

This study addressed the relationship between rainfall and cloud top temperature (CCT) from new generation satellite Himawari-8 imagery at different spatiotemporal scale. This satellite provides higher band, more bits for data format, spatial and temporal resolution compared with previous GMS series. The multi-infrared channels with 10-minute and 1-2 km resolution make it possible for rainfall estimating/forecasting in small/medium watershed. The preliminary result investigated at Chenyulan watershed (443.6 square kilometer) of Central Taiwan in 2016 Typhoon Megi shows the regression coefficient fitted by negative exponential equation of largest rainfall vs. CCT (B8 band) at pixel scale increases as time scales enlarges and reach 0.462 for 120-minute accumulative rainfall; the value (CTT of B15 band) decreases from 0.635 for 10-minute to 0.423 for 120-minute accumulative rainfall at basin-wide scale. More rainfall events for different regime are yet to evaluate to get solid results.

Integrating Radar Image Data with Google Maps

Science.gov (United States)

Chapman, Bruce D.; Gibas, Sarah

2010-01-01

A public Web site has been developed as a method for displaying the multitude of radar imagery collected by NASA s Airborne Synthetic Aperture Radar (AIRSAR) instrument during its 16-year mission. Utilizing NASA s internal AIRSAR site, the new Web site features more sophisticated visualization tools that enable the general public to have access to these images. The site was originally maintained at NASA on six computers: one that held the Oracle database, two that took care of the software for the interactive map, and three that were for the Web site itself. Several tasks were involved in moving this complicated setup to just one computer. First, the AIRSAR database was migrated from Oracle to MySQL. Then the back-end of the AIRSAR Web site was updated in order to access the MySQL database. To do this, a few of the scripts needed to be modified; specifically three Perl scripts that query that database. The database connections were then updated from Oracle to MySQL, numerous syntax errors were corrected, and a query was implemented that replaced one of the stored Oracle procedures. Lastly, the interactive map was designed, implemented, and tested so that users could easily browse and access the radar imagery through the Google Maps interface.

Sport Transition of JPSS VIIRS Imagery for Night-time Applications

Science.gov (United States)

Fuell, Kevin; LeRoy, Anita; Smith, Matt; Miller, Steve; Kann, Diedre; Bernhardt, David; Reydell, Nezette; Cox, Robert

2014-01-01

The NASA/Short-term Prediction, Research, and Transition (SPoRT) Program and NOAA/Cooperative Institute for Research in the Atmosphere (CIRA) work within the NOAA/Joint Polar Satellite System (JPSS) Proving Ground to demonstrate the unique capabilities of the VIIRS instrument. Very similar to MODIS, the VIIRS instrument provides many high-resolution visible and infrared channels in a broad spectrum. In addition, VIIRS is equipped with a low-light sensor that is able to detect light emissions from the land and atmosphere as well as reflected sunlight by the lunar surface. This band is referred to as the Day-Night Band due to the sunlight being used at night to see cloud and topographic features just as one would typically see in day-time visible imagery. NWS forecast offices that collaborate with SPoRT and CIRA have utilized MODIS imagery in operations, but have longed for more frequent passes of polar-orbiting data. The VIIRS instrument enhances SPoRT collaborations with WFOs by providing another day and night-time pass, and at times two additional passes due to its large swath width. This means that multi-spectral, RGB imagery composites are more readily available to prepare users for their use in GOES-R era and high-resolution imagery for use in high-latitudes is more frequently able to supplement standard GOES imagery within the SPoRT Hybrid GEO-LEO product. The transition of VIIRS also introduces the new Day-Night Band capability to forecast operations. An Intensive Evaluation Period (IEP) was conducted in Summer 2013 with a group of "Front Range" NWS offices related to VIIRS night-time imagery. VIIRS single-channel imagery is able to better analyze the specific location of fire hotspots and other land features, as well as provide a more true measurement of various cloud and aerosol properties than geostationary measurements, especially at night. Viewed within the SPoRT Hybrid imagery, the VIIRS data allows forecasters to better interpret the more frequent, but

Technical Description of Radar and Optical Sensors Contributing to Joint UK-Australian Satellite Tracking, Data-fusion and Cueing Experiment

Science.gov (United States)

Eastment, J.; Ladd, D.; Donnelly, P.; Ash, A.; Harwood, N.; Ritchie, I.; Smith, C.; Bennett, J.; Rutten, M.; Gordon, N.

2014-09-01

DSTL, DSTO, EOS and STFC have recently participated in a campaign of co-ordinated observations with both radar and optical sensors in order to demonstrate and to refine methodologies for orbit determination, data fusion and cross-sensor cueing. The experimental programme is described in detail in the companion paper by Harwood et al. At the STFC Chilbolton Observatory in Southern England, an S-band radar on a 25 m diameter fully-steerable dish antenna was used to measure object range and radar cross-section. At the EOS Space Systems facility on Mount Stromlo, near Canberra, Australia, an optical system comprising a 2 m alt / az observatory, with Coude path laser tracking at 400W power, was used to acquire, lock and laser track the cued objects, providing accurate orbit determinations for each. DSTO, located at Edinburgh, Australia, operated an optical system consisting of a small commercial telescope and mount, measuring the direction to the objects. Observation times were limited to the evening solar terminator period. Data from these systems was processed independently, using DSTL-developed and DSTO / EOS-developed algorithms, to perform orbit determination and to cross-cue: (i) the radar, based on the optical measurements; (ii) the optical system, based on the radar measurements; and (iii) the radar, using its own prior observations (self-cueing). In some cases, TLEs were used to initialise the orbit determination process; in other cases, the cues were derived entirely from sensor data. In all 3 scenarios, positive results were obtained for a variety of satellites in low earth orbits, demonstrating the feasibility of the different cue generation techniques. The purpose of this paper is to describe the technical characteristics of the radar and optical systems used, the modes of operation employed to acquire the observations, and details of the parameters measured and the data formats.

Aercibo S-band radar program

International Nuclear Information System (INIS)

Campbell, D.B.

1988-01-01

The high powered 12.6 cm wavelength radar on the 1000-ft Arecibo reflector is utilized for a number of solar system studies. Chief among these are: (1) surface reflectivity mapping of Venus, Mercury and the Moon. Resolutions achievable on Venus are less than 1.5 km over some areas, for Mercury about 30 km and for the Moon 200 m at present, (2) high time resolution ranging measurements to the surfaces of the terrestrial planets. These measurements are used to obtain profiles and scattering parameters in the equatorial region. They can also be used to test relativistic and gravitational theories by monitoring the rate of advance of the perihelion of the orbit of Mercury and placing limits on the stability of the gravitational constant, (3) measurements of the orbital parameters, figure, spin vector and surface properties of asteroids and comets, and (4) observations of the Galilean Satellites of Jupiter and the satellites of Mars, Phobos and Deimos. The Galilean Satellites of Jupiter were re-observed with the 12.6 cm radar for the first time since 1981. Much more accurate measurements of the scattering properties of the three icy satellites were obtained that generally confirmed previous observations. Unambiguous measurements of the cross section and circular polarizations ratio of Io were also obtained for the first time. The radar scattering properties of four mainbelt asteroids and one near-earth asteroid were studied

A Platform for Scalable Satellite and Geospatial Data Analysis

Science.gov (United States)

Beneke, C. M.; Skillman, S.; Warren, M. S.; Kelton, T.; Brumby, S. P.; Chartrand, R.; Mathis, M.

2017-12-01

At Descartes Labs, we use the commercial cloud to run global-scale machine learning applications over satellite imagery. We have processed over 5 Petabytes of public and commercial satellite imagery, including the full Landsat and Sentinel archives. By combining open-source tools with a FUSE-based filesystem for cloud storage, we have enabled a scalable compute platform that has demonstrated reading over 200 GB/s of satellite imagery into cloud compute nodes. In one application, we generated global 15m Landsat-8, 20m Sentinel-1, and 10m Sentinel-2 composites from 15 trillion pixels, using over 10,000 CPUs. We recently created a public open-source Python client library that can be used to query and access preprocessed public satellite imagery from within our platform, and made this platform available to researchers for non-commercial projects. In this session, we will describe how you can use the Descartes Labs Platform for rapid prototyping and scaling of geospatial analyses and demonstrate examples in land cover classification.

POST-DISASTER DAMAGE ASSESSMENT THROUGH COHERENT CHANGE DETECTION ON SAR IMAGERY

Directory of Open Access Journals (Sweden)

L. Guida

2018-04-01

Full Text Available Damage assessment is a fundamental step to support emergency response and recovery activities in a post-earthquake scenario. In recent years, UAVs and satellite optical imagery was applied to assess major structural damages before technicians could reach the areas affected by the earthquake. However, bad weather conditions may harm the quality of these optical assessments, thus limiting the practical applicability of these techniques. In this paper, the application of Synthetic Aperture Radar (SAR imagery is investigated and a novel approach to SAR-based damage assessment is presented. Coherent Change Detection (CCD algorithms on multiple interferometrically pre-processed SAR images of the area affected by the seismic event are exploited to automatically detect potential damages to buildings and other physical structures. As a case study, the 2016 Central Italy earthquake involving the cities of Amatrice and Accumoli was selected. The main contribution of the research outlined above is the integration of a complex process, requiring the coordination of a variety of methods and tools, into a unitary framework, which allows end-to-end application of the approach from SAR data pre-processing to result visualization in a Geographic Information System (GIS. A prototype of this pipeline was implemented, and the outcomes of this methodology were validated through an extended comparison with traditional damage assessment maps, created through photo-interpretation of high resolution aerial imagery. The results indicate that the proposed methodology is able to perform damage detection with a good level of accuracy, as most of the detected points of change are concentrated around highly damaged buildings.

Post-Disaster Damage Assessment Through Coherent Change Detection on SAR Imagery

Science.gov (United States)

Guida, L.; Boccardo, P.; Donevski, I.; Lo Schiavo, L.; Molinari, M. E.; Monti-Guarnieri, A.; Oxoli, D.; Brovelli, M. A.

2018-04-01

Damage assessment is a fundamental step to support emergency response and recovery activities in a post-earthquake scenario. In recent years, UAVs and satellite optical imagery was applied to assess major structural damages before technicians could reach the areas affected by the earthquake. However, bad weather conditions may harm the quality of these optical assessments, thus limiting the practical applicability of these techniques. In this paper, the application of Synthetic Aperture Radar (SAR) imagery is investigated and a novel approach to SAR-based damage assessment is presented. Coherent Change Detection (CCD) algorithms on multiple interferometrically pre-processed SAR images of the area affected by the seismic event are exploited to automatically detect potential damages to buildings and other physical structures. As a case study, the 2016 Central Italy earthquake involving the cities of Amatrice and Accumoli was selected. The main contribution of the research outlined above is the integration of a complex process, requiring the coordination of a variety of methods and tools, into a unitary framework, which allows end-to-end application of the approach from SAR data pre-processing to result visualization in a Geographic Information System (GIS). A prototype of this pipeline was implemented, and the outcomes of this methodology were validated through an extended comparison with traditional damage assessment maps, created through photo-interpretation of high resolution aerial imagery. The results indicate that the proposed methodology is able to perform damage detection with a good level of accuracy, as most of the detected points of change are concentrated around highly damaged buildings.

Visual attention based detection of signs of anthropogenic activities in satellite imagery

Energy Technology Data Exchange (ETDEWEB)

Skurikhin, Alexei N [Los Alamos National Laboratory

2010-10-13

With increasing deployment of satellite imaging systems, only a small fraction of collected data can be subject to expert scrutiny. We present and evaluate a two-tier approach to broad area search for signs of anthropogenic activities in high-resolution commercial satellite imagery. The method filters image information using semantically oriented interest points by combining Harris corner detection and spatial pyramid matching. The idea is that anthropogenic structures, such as rooftop outlines, fence corners, road junctions, are locally arranged in specific angular relations to each other. They are often oriented at approximately right angles to each other (which is known as rectilinearity relation). Detecting the rectilinearity provides an opportunity to highlight regions most likely to contain anthropogenic activity. This is followed by supervised classification of regions surrounding the detected corner points as man-made vs. natural scenes. We consider, in particular, a search for anthropogenic activities in uncluttered areas. In this paper, we proposed and evaluated a two-tier approach to broad area search for signs of anthropogenic activities. Results from experiments on high-resolution ({approx}0.6m) commercial satellite image data showed the potential applicability of this approach and its ability of achieving both high precision and recall rates. The main advantage of combining corner-based cueing with general object recognition is that the incorporation of domain specific knowledge even in its more general form, such as presence of comers, provides a useful cue to narrow the focus of search for signs of anthropogenic activities. Combination of comer based cueing with spatial pyramid matching addressed the issue of comer categorization. An important practical issue for further research is optimizing the balance between false positive and false negative rates. While the results presented in the paper are encouraging, the problem of an automated broad area

Estimation Of The Spatial Distribution Of Crop Coefficient (Kc) From Landsat Satellite Imagery

International Nuclear Information System (INIS)

Abou EI-Magd, I.H.

2009-01-01

Single crop coefficient factor (K c ) is an essential component for crop water allocation for efficient irrigation scheduling and irrigation water management. Kc is basically defined as the ratio of actual evapotranspiration and grass/alfalfa reference evapotranspiration and always measured by lysimeter in localized area in the field, which then generalized on the whole irrigated land. The lack of precise information about the crop coefficient particularly in our country together with both small sized fields and heterogeneity of agricultural crops calls for developing a new methodology for computing a real time crop coefficient from remotely sensed data. This paper discusses the methodology developed for obtaining a real time single crop coefficient from Landsat Satellite ETM + 7 imageries. The methodology was applied and optimized on one irrigation field with two different dates and crop cover in the northern Delta of Egypt

A multi-source precipitation approach to fill gaps over a radar precipitation field

Science.gov (United States)

Tesfagiorgis, K. B.; Mahani, S. E.; Khanbilvardi, R.

2012-12-01

Satellite Precipitation Estimates (SPEs) may be the only available source of information for operational hydrologic and flash flood prediction due to spatial limitations of radar and gauge products. The present work develops an approach to seamlessly blend satellite, radar, climatological and gauge precipitation products to fill gaps over ground-based radar precipitation fields. To mix different precipitation products, the bias of any of the products relative to each other should be removed. For bias correction, the study used an ensemble-based method which aims to estimate spatially varying multiplicative biases in SPEs using a radar rainfall product. Bias factors were calculated for a randomly selected sample of rainy pixels in the study area. Spatial fields of estimated bias were generated taking into account spatial variation and random errors in the sampled values. A weighted Successive Correction Method (SCM) is proposed to make the merging between error corrected satellite and radar rainfall estimates. In addition to SCM, we use a Bayesian spatial method for merging the gap free radar with rain gauges, climatological rainfall sources and SPEs. We demonstrate the method using SPE Hydro-Estimator (HE), radar- based Stage-II, a climatological product PRISM and rain gauge dataset for several rain events from 2006 to 2008 over three different geographical locations of the United States. Results show that: the SCM method in combination with the Bayesian spatial model produced a precipitation product in good agreement with independent measurements. The study implies that using the available radar pixels surrounding the gap area, rain gauge, PRISM and satellite products, a radar like product is achievable over radar gap areas that benefits the scientific community.

Global surveys of reservoirs and lakes from satellites and regional application to the Syrdarya river basin

Science.gov (United States)

Jean-François, Crétaux; Sylvain, Biancamaria; Adalbert, Arsen; Muriel, Bergé-Nguyen; Mélanie, Becker

2015-01-01

Large reservoirs along rivers regulate downstream flows to generate hydropower but may also store water for irrigation and urban sectors. Reservoir management therefore becomes critical, particularly for transboundary basins, where coordination between riparian countries is needed. Reservoir management is even more important in semiarid regions where downstream water users may be totally reliant on upstream reservoir releases. If the water resources are shared between upstream and downstream countries, potentially opposite interests arise as is the case in the Syrdarya river in Central Asia. In this case study, remote sensing data (radar altimetry and optical imagery) are used to highlight the potential of satellite data to monitor water resources: water height, areal extent and storage variations. New results from 20 years of monitoring using satellites over the Syrdarya basin are presented. The accuracy of satellite data is 0.6 km3 using a combination of MODIS data and satellite altimetry, and only 0.2 km3 with Landsat images representing 2-4% of average annual reservoir volume variations in the reservoirs in the Syrdarya basin. With future missions such as Sentinel-3A (S3A), Sentinel-3B (S3B) and surface water and ocean topography (SWOT), significant improvement is expected. The SWOT mission’s main payload (a radar interferometer in Ka band) will furthermore provide 2D maps of water height, reservoirs, lakes, rivers and floodplains, with a temporal resolution of 21 days. At the global scale, the SWOT mission will cover reservoirs with areal extents greater than 250 × 250 m with 20 cm accuracy.

Global surveys of reservoirs and lakes from satellites and regional application to the Syrdarya river basin

International Nuclear Information System (INIS)

Jean-François, Crétaux; Adalbert, Arsen; Muriel, Bergé-Nguyen; Sylvain, Biancamaria; Mélanie, Becker

2015-01-01

Large reservoirs along rivers regulate downstream flows to generate hydropower but may also store water for irrigation and urban sectors. Reservoir management therefore becomes critical, particularly for transboundary basins, where coordination between riparian countries is needed. Reservoir management is even more important in semiarid regions where downstream water users may be totally reliant on upstream reservoir releases. If the water resources are shared between upstream and downstream countries, potentially opposite interests arise as is the case in the Syrdarya river in Central Asia. In this case study, remote sensing data (radar altimetry and optical imagery) are used to highlight the potential of satellite data to monitor water resources: water height, areal extent and storage variations. New results from 20 years of monitoring using satellites over the Syrdarya basin are presented. The accuracy of satellite data is 0.6 km 3 using a combination of MODIS data and satellite altimetry, and only 0.2 km 3 with Landsat images representing 2–4% of average annual reservoir volume variations in the reservoirs in the Syrdarya basin. With future missions such as Sentinel-3A (S3A), Sentinel-3B (S3B) and surface water and ocean topography (SWOT), significant improvement is expected. The SWOT mission’s main payload (a radar interferometer in Ka band) will furthermore provide 2D maps of water height, reservoirs, lakes, rivers and floodplains, with a temporal resolution of 21 days. At the global scale, the SWOT mission will cover reservoirs with areal extents greater than 250  ×  250 m with 20 cm accuracy. (letter)

BUILT-UP AREA AND LAND COVER EXTRACTION USING HIGH RESOLUTION PLEIADES SATELLITE IMAGERY FOR MIDRAND, IN GAUTENG PROVINCE, SOUTH AFRICA

Directory of Open Access Journals (Sweden)

E. Fundisi

2017-09-01

Full Text Available Urban areas, particularly in developing countries face immense challenges such as climate change, poverty, lack of resources poor land use management systems, and week environmental management practices. Mitigating against these challenges is often hampered by lack of data on urban expansion, urban footprint and land cover. To support the recently adopted new urban agenda 2030 there is need for the provision of information to support decision making in the urban areas. Earth observation has been identified as a tool to foster sustainable urban planning and smarter cities as recognized by the new urban agenda, because it is a solution to unavailability of data. Accordingly, this study uses high resolution EO data Pleiades satellite imagery to map and document land cover for the rapidly expanding area of Midrand in Johannesburg, South Africa. An unsupervised land cover classification of the Pleiades satellite imagery was carried out using ENVI software, whereas NDVI was derived using ArcGIS software. The land cover had an accuracy of 85% that is highly adequate to document the land cover in Midrand. The results are useful because it provides a highly accurate land cover and NDVI datasets at localised spatial scale that can be used to support land use management strategies within Midrand and the City of Johannesburg South Africa.

Built-Up Area and Land Cover Extraction Using High Resolution Pleiades Satellite Imagery for Midrand, in Gauteng Province, South Africa

Science.gov (United States)

Fundisi, E.; Musakwa, W.

2017-09-01

Urban areas, particularly in developing countries face immense challenges such as climate change, poverty, lack of resources poor land use management systems, and week environmental management practices. Mitigating against these challenges is often hampered by lack of data on urban expansion, urban footprint and land cover. To support the recently adopted new urban agenda 2030 there is need for the provision of information to support decision making in the urban areas. Earth observation has been identified as a tool to foster sustainable urban planning and smarter cities as recognized by the new urban agenda, because it is a solution to unavailability of data. Accordingly, this study uses high resolution EO data Pleiades satellite imagery to map and document land cover for the rapidly expanding area of Midrand in Johannesburg, South Africa. An unsupervised land cover classification of the Pleiades satellite imagery was carried out using ENVI software, whereas NDVI was derived using ArcGIS software. The land cover had an accuracy of 85% that is highly adequate to document the land cover in Midrand. The results are useful because it provides a highly accurate land cover and NDVI datasets at localised spatial scale that can be used to support land use management strategies within Midrand and the City of Johannesburg South Africa.

gProcess and ESIP Platforms for Satellite Imagery Processing over the Grid

Science.gov (United States)

Bacu, Victor; Gorgan, Dorian; Rodila, Denisa; Pop, Florin; Neagu, Gabriel; Petcu, Dana

2010-05-01

The Environment oriented Satellite Data Processing Platform (ESIP) is developed through the SEE-GRID-SCI (SEE-GRID eInfrastructure for regional eScience) co-funded by the European Commission through FP7 [1]. The gProcess Platform [2] is a set of tools and services supporting the development and the execution over the Grid of the workflow based processing, and particularly the satelite imagery processing. The ESIP [3], [4] is build on top of the gProcess platform by adding a set of satellite image processing software modules and meteorological algorithms. The satellite images can reveal and supply important information on earth surface parameters, climate data, pollution level, weather conditions that can be used in different research areas. Generally, the processing algorithms of the satellite images can be decomposed in a set of modules that forms a graph representation of the processing workflow. Two types of workflows can be defined in the gProcess platform: abstract workflow (PDG - Process Description Graph), in which the user defines conceptually the algorithm, and instantiated workflow (iPDG - instantiated PDG), which is the mapping of the PDG pattern on particular satellite image and meteorological data [5]. The gProcess platform allows the definition of complex workflows by combining data resources, operators, services and sub-graphs. The gProcess platform is developed for the gLite middleware that is available in EGEE and SEE-GRID infrastructures [6]. gProcess exposes the specific functionality through web services [7]. The Editor Web Service retrieves information on available resources that are used to develop complex workflows (available operators, sub-graphs, services, supported resources, etc.). The Manager Web Service deals with resources management (uploading new resources such as workflows, operators, services, data, etc.) and in addition retrieves information on workflows. The Executor Web Service manages the execution of the instantiated workflows

Bringing satellite winds to hub-height

DEFF Research Database (Denmark)

Badger, Merete; Pena Diaz, Alfredo; Bredesen, Rolv Erlend

2012-01-01

Satellite observations of the ocean surface can provide detailed information about the spatial wind variability over large areas. This is very valuable for the mapping of wind resources offshore where other measurements are costly and sparse. Satellite sensors operating at microwave frequencies...... measure the amount of radar backscatter from the sea surface, which is a function of the instant wind speed, wind direction, and satellite viewing geometry. A major limitation related to wind retrievals from satellite observations is that existing empirical model functions relate the radar backscatter...... to wind speed at the height 10 m only. The extrapolation of satellite wind fields to higher heights, which are more relevant for wind energy, remains a challenge which cannot be addressed by means of satellite data alone. As part of the EU-NORSEWInD project (2008-12), a hybrid method has been developed...

Incidence angle normalization of radar backscatter data

Science.gov (United States)

NASA’s Soil Moisture Passive Active (SMAP) satellite (~2014) will include a radar system that will provide L-band multi-polarization backscatter at a constant incidence angle of 40º. During the pre-launch phase of the project there is a need for observations that will support the radar-based soil mo...

Detection of Weather Radar Clutter

DEFF Research Database (Denmark)

Bøvith, Thomas

2008-01-01

classification and use a range of different techniques and input data. The first method uses external information from multispectral satellite images to detect clutter. The information in the visual, near-infrared, and infrared parts of the spectrum can be used to distinguish between cloud and cloud-free areas......Weather radars provide valuable information on precipitation in the atmosphere but due to the way radars work, not only precipitation is observed by the weather radar. Weather radar clutter, echoes from non-precipitating targets, occur frequently in the data, resulting in lowered data quality....... Especially in the application of weather radar data in quantitative precipitation estimation and forecasting a high data quality is important. Clutter detection is one of the key components in achieving this goal. This thesis presents three methods for detection of clutter. The methods use supervised...

JPSS Preparations at the Satellite Proving Ground for Marine, Precipitation, and Satellite Analysis

Science.gov (United States)

Folmer, M. J.; Berndt, E.; Clark, J.; Orrison, A.; Kibler, J.; Sienkiewicz, J. M.; Nelson, J. A., Jr.; Goldberg, M.

2016-12-01

The National Oceanic and Atmospheric Administration (NOAA) Satellite Proving Ground (PG) for Marine, Precipitation, and Satellite Analysis (MPS) has been demonstrating and evaluating Suomi National Polar-orbiting Partnership (S-NPP) products along with other polar-orbiting satellite platforms in preparation for the Joint Polar Satellite System - 1 (JPSS-1) launch in March 2017. The first S-NPP imagery was made available to the MPS PG during the evolution of Hurricane Sandy in October 2012 and has since been popular in operations. Since this event the MPS PG Satellite Liaison has been working with forecasters on ways to integrate single-channel and multispectral imagery from the Visible Infrared Imaging Radiometer Suite (VIIRS), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Advanced Very High Resolution Radiometer (AVHRR)into operations to complement numerical weather prediction and geostationary satellite savvy National Weather Service (NWS) National Centers. Additional unique products have been introduced to operations to address specific forecast challenges, including the Cooperative Institute for Research in the Atmosphere (CIRA) Layered Precipitable Water, the National Environmental Satellite, Data, and Information Service (NESDIS) Snowfall Rate product, NOAA Unique Combined Atmospheric Processing System (NUCAPS) Soundings, ozone products from the Atmospheric Infrared Sounder (AIRS), Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS), and Infrared Atmospheric Sounding Interferometer (IASI). In addition, new satellite domains have been created to provide forecasters at the NWS Ocean Prediction Center and Weather Prediction Center with better quality imagery at high latitudes. This has led to research projects that are addressing forecast challenges such as tropical to extratropical transition and explosive cyclogenesis. This presentation will provide examples of how the MPS PG has been introducing and integrating

Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

Directory of Open Access Journals (Sweden)

Shasha Jiang

2016-05-01

Full Text Available Automated remote sensing methods have not gained widespread usage for damage assessment after hurricane events, especially for low-rise buildings, such as individual houses and small businesses. Hurricane wind, storm surge with waves, and inland flooding have unique damage signatures, further complicating the development of robust automated assessment methodologies. As a step toward realizing automated damage assessment for multi-hazard hurricane events, this paper presents a mono-temporal image classification methodology that quickly and accurately differentiates urban debris from non-debris areas using post-event images. Three classification approaches are presented: spectral, textural, and combined spectral–textural. The methodology is demonstrated for Gulfport, Mississippi, using IKONOS panchromatic satellite and NOAA aerial colour imagery collected after 2005 Hurricane Katrina. The results show that multivariate texture information significantly improves debris class detection performance by decreasing the confusion between debris and other land cover types, and the extracted debris zone accurately captures debris distribution. Additionally, the extracted debris boundary is approximately equivalent regardless of imagery type, demonstrating the flexibility and robustness of the debris mapping methodology. While the test case presents results for hurricane hazards, the proposed methodology is generally developed and expected to be effective in delineating debris zones for other natural hazards, including tsunamis, tornadoes, and earthquakes.

Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring

Directory of Open Access Journals (Sweden)

Pauline Dusseux

2014-06-01

Full Text Available The aim of this study was to assess the ability of optical images, SAR (Synthetic Aperture Radar images and the combination of both types of data to discriminate between grasslands and crops in agricultural areas where cloud cover is very high most of the time, which restricts the use of visible and near-infrared satellite data. We compared the performances of variables extracted from four optical and five SAR satellite images with high/very high spatial resolutions acquired during the growing season. A vegetation index, namely the NDVI (Normalized Difference Vegetation Index, and two biophysical variables, the LAI (Leaf Area Index and the fCOVER (fraction of Vegetation Cover were computed using optical time series and polarization (HH, VV, HV, VH. The polarization ratio and polarimetric decomposition (Freeman–Durden and Cloude–Pottier were calculated using SAR time series. Then, variables derived from optical, SAR and both types of remotely-sensed data were successively classified using the Support Vector Machine (SVM technique. The results show that the classification accuracy of SAR variables is higher than those using optical data (0.98 compared to 0.81. They also highlight that the combination of optical and SAR time series data is of prime interest to discriminate grasslands from crops, allowing an improved classification accuracy.

Using phase for radar scatterer classification

Science.gov (United States)

Moore, Linda J.; Rigling, Brian D.; Penno, Robert P.; Zelnio, Edmund G.

2017-04-01

Traditional synthetic aperture radar (SAR) systems tend to discard phase information of formed complex radar imagery prior to automatic target recognition (ATR). This practice has historically been driven by available hardware storage, processing capabilities, and data link capacity. Recent advances in high performance computing (HPC) have enabled extremely dense storage and processing solutions. Therefore, previous motives for discarding radar phase information in ATR applications have been mitigated. First, we characterize the value of phase in one-dimensional (1-D) radar range profiles with respect to the ability to correctly estimate target features, which are currently employed in ATR algorithms for target discrimination. These features correspond to physical characteristics of targets through radio frequency (RF) scattering phenomenology. Physics-based electromagnetic scattering models developed from the geometrical theory of diffraction are utilized for the information analysis presented here. Information is quantified by the error of target parameter estimates from noisy radar signals when phase is either retained or discarded. Operating conditions (OCs) of signal-tonoise ratio (SNR) and bandwidth are considered. Second, we investigate the value of phase in 1-D radar returns with respect to the ability to correctly classify canonical targets. Classification performance is evaluated via logistic regression for three targets (sphere, plate, tophat). Phase information is demonstrated to improve radar target classification rates, particularly at low SNRs and low bandwidths.

Multi-Temporal Satellite Imagery for Urban Expansion Assessment at Sharjah City /UAE

International Nuclear Information System (INIS)

Al-Ruzouq, R; Shanableh, A

2014-01-01

Change detection is the process of identifying differences in land cover over time. As human and natural forces continue to alter the landscape, it is important to develop monitoring methods to assess and quantify these changes. Recent advances in satellite imagery, in terms of improved spatial and temporal resolutions, are allowing for efficient identification of change patterns and the prediction of areas of growth. Sharjah is the third largest and most populous city in the United Arab Emirates (UAE). It is located along the northern coast of the Persian Gulf on the Arabian Peninsula. After the discovery of oil and its export in the last four decades at UAE, it has experienced very rapid growth in industry, economy and population. The main purpose of this study is to detect urban development in Sharjah city by detecting and registering linear features in multi-temporal Landsat images. This paper used linear features for image registration that were chosen since they can be reliably extracted from imagery with significantly different geometric and radiometric properties. Derived edges from the registered images are used as the basis for change detection. Image registration and pixel-pixel subtraction has been implement using multi- temporal Landsat images for Sharjah City. Straight-line segments have been used for accurate co-registration as well as main element for a reliable change detection procedure. Results illustrate that highest range of growth that represented by linear features (building and roads) have been accrued during 1976 – 1987 and stand for 36.24% of the total urban features inside Sharjah city. Moreover, result shows that since 1976 to 2010, the cumulative urban expansion inside Sharjah city is 71.9%

The study of fresh-water lake ice using multiplexed imaging radar

Science.gov (United States)

Leonard, Bryan M.; Larson, R.W.

1975-01-01

The study of ice in the upper Great Lakes, both from the operational and the scientific points of view, is receiving continued attention. Quantitative and qualitative field work is being conducted to provide the needed background for accurate interpretation of remotely sensed data. The data under discussion in this paper were obtained by a side-looking multiplexed airborne radar (SLAR) supplemented with ground-truth data.Because of its ability to penetrate adverse weather, radar is an especially important instrument for monitoring ice in the upper Great Lakes. It has previously been shown that imaging radars can provide maps of ice cover in these areas. However, questions concerning both the nature of the surfaces reflecting radar energy and the interpretation of the radar imagery continually arise.Our analysis of ice in Whitefish Bay (Lake Superior) indicates that the combination of the ice/water interlace and the ice/air interface is the major contributor to the radar backscatter as seen on the imagery At these frequencies the ice has a very low relative dielectric permittivity (types studied include newly formed black ice, pancake ice, and frozen and consolidated pack and brash ice.Although ice thickness cannot be measured directly from the received signals, it is suspected that by combining the information pertaining to radar backscatter with data on the meteorological and sea-state history of the area, together with some basic ground truth, better estimates of the ice thickness may be provided. In addition, certain ice features (e.g. ridges, ice-foot formation, areas of brash ice) may be identified with reasonable confidence. There is a continued need for additional ground work to verify the validity of imaging radars for these types of interpretations.

Using Satellite Imagery to Monitor the Major Lakes; Case Study Lake Hamun

Science.gov (United States)

Norouzi, H.; Islam, R.; Bah, A.; AghaKouchak, A.

2015-12-01

Proper lakes function can ease the impact of floods and drought especially in arid and semi-arid regions. They are important environmentally and can directly affect human lives. Better understanding of the effect of climate change and human-driven changes on lakes would provide invaluable information for policy-makers and local people. As part of a comprehensive study, we aim to monitor the land-cover/ land-use changes in the world's major lakes using satellite observations. As a case study, Hamun Lake which is a pluvial Lake, also known as shallow Lake, located on the south-east of Iran and adjacent to Afghanistan, and Pakistan borders is investigated. The Lake is the main source of resources (agriculture, fishing and hunting) for the people around it and politically important in the region since it is shared among three different countries. The purpose of the research is to find the Lake's area from 1972 to 2015 and to see if any drought or water resources management has affected the lake. Analyzing satellites imagery from Landsat shows that the area of the Lake changes seasonally and intra-annually. Significant seasonal effects are found in 1975,1977, 1987, 1993, 1996, 1998, 2000, 2009 and 2011, as well as, substantial amount of shallow water is found throughout the years. The precipitation records as well as drought historical records are studied for the lake's basin. Meteorological studies suggest that the drought, decrease of rainfalls in the province and the improper management of the Lake have caused environmental, economic and geographical consequences. The results reveal that lake has experienced at least two prolong dryings since 1972 which drought cannot solely be blamed as main forcing factor.Proper lakes function can ease the impact of floods and drought especially in arid and semi-arid regions. They are important environmentally and can directly affect human lives. Better understanding of the effect of climate change and human-driven changes on lakes

Challenges for Greenland-wide mass balance from Cryosat-2 radar-altimetry

DEFF Research Database (Denmark)

Simonsen, Sebastian Bjerregaard; Forsberg, René; Sørensen, Louise Sandberg

As the Greenland ice sheet warms, a change in the structure of the upper snow/firn occurs. This change further induces changes in the reflective properties of the firn seen from satellite radar altimetry. If not identified as changes in the reflective properties of the firn, these may be interpre......As the Greenland ice sheet warms, a change in the structure of the upper snow/firn occurs. This change further induces changes in the reflective properties of the firn seen from satellite radar altimetry. If not identified as changes in the reflective properties of the firn, these may...... be interpreted as actual surface elevation changes seen from the satellite radar altimetry (Nilsson et al., 2015).Here, we investigate how to correct the elevation change observed from the ESA Cryosat-2 radar altimetry mission to derive elevation change of the air/snow interface of the Greenland ice sheet....... The elevation change of this “real” physical surface is crucial, if the goal is to derive Greenland mass balance as done for LiDAR missions.The investigations look into waveform parameters to correct for the observed bias between Radar and LiDAR observations when using Croysat-2 level-2 data. Based...

Using high-resolution satellite radar to measure lava flow morphology, rheology, effusion rate and subsidence at El Reventador Volcano, Ecuador.

Science.gov (United States)

Biggs, J.; Arnold, D. W. D.; Mothes, P. A.; Anderson, K. R.; Albino, F.; Wadge, G.; Vallejo Vargas, S.; Ebmeier, S. K.

2017-12-01

There are relatively few studies of active lava flows of an andesitic rather than basaltic composition. The flow field at El Reventador volcano, Ecuador is a good example, but observations are hampered by persistent cloud cover. We use high resolution satellite radar from Radarsat-2 and TanDEM-X to map the dimensions of 43 lava flows extruded between 9 Feb 2012 and 24 Aug 2016. Flow height is measured using the width of radar shadow cast by steep sided features, or the difference in radar phase between two sensors separated in space. The cumulative volume of erupted material was 44.8M m3 dense rock equivalent with an average rate of 0.31 ± 0.02 m3s-1, similar to the long term average. The flows were mostly emplaced over durations shorter than the satellite repeat interval of 24 days and ranged in length from 0.3 to 1.7 km. We use the dimensions of the levees to estimate the flow yield strengths and compare measurements of diversions around barriers with observations from laboratory experiments. The rate of effusion, flow length and flow volume all decrease with time, and simple physics-based models can be equally well fit by a closed reservoir depressurising during the eruption with no magma recharge, or an open reservoir with a time-constant magma recharge rate of up to 0.35 ± 0.01 m3s-1. We propose that the conduit acts as magma capacitor and individual flows are volume-limited. Emplaced flows are subsiding at rates proportional to lava thickness that decay with time following a square-root relationship. Radar observations, such as those presented here, could be used to map and measure properties of evolving lava flow fields at other remote or difficult to monitor volcanoes. Physics-based models can be run into the future, but a sudden increase in flow length in 2017 seen by Sentinel illustrates that changes in magma supply can cause rapid changes in behavior, which remain challenging to forecast.

Laser radar IV; Proceedings of the Meeting, Orlando, FL, Mar. 29, 30, 1989

Science.gov (United States)

Becherer, Richard J.

1989-09-01

Various papers on laser radars are presented. Individual topics considered include: frequency chirp of a low-pressure hybrid TE CO2 laser, design of a high-power isotopic CO2 laser amplifier, monolithic beam steering for large aperture laser radar, laser radar receiver using a Digicon detector, all-solid-state CO2 laser driver, noise in an acoustooptic-modulated laser source, laser signature prediction using the Value computer program, laser radar acquisition and tracking, concept of a moving target indicator search ladar, system design philosophy for laser radar wavelength determination, imaging three-frequency CO2 laser radar, backscatter-modulation semiconductor laser radar, three-dimensional imaging using a single laser pulse, design and manufacture of a high-resolution laser radar scanner, calculations of vibrational signatures for coherent ladar, coherent subaperture ultraviolet imagery, and range-Doppler resolution degradation associated with amplitude distortion.

Mapping urban impervious surface using object-based image analysis with WorldView-3 satellite imagery

Science.gov (United States)

Iabchoon, Sanwit; Wongsai, Sangdao; Chankon, Kanoksuk

2017-10-01

Land use and land cover (LULC) data are important to monitor and assess environmental change. LULC classification using satellite images is a method widely used on a global and local scale. Especially, urban areas that have various LULC types are important components of the urban landscape and ecosystem. This study aims to classify urban LULC using WorldView-3 (WV-3) very high-spatial resolution satellite imagery and the object-based image analysis method. A decision rules set was applied to classify the WV-3 images in Kathu subdistrict, Phuket province, Thailand. The main steps were as follows: (1) the image was ortho-rectified with ground control points and using the digital elevation model, (2) multiscale image segmentation was applied to divide the image pixel level into image object level, (3) development of the decision ruleset for LULC classification using spectral bands, spectral indices, spatial and contextual information, and (4) accuracy assessment was computed using testing data, which sampled by statistical random sampling. The results show that seven LULC classes (water, vegetation, open space, road, residential, building, and bare soil) were successfully classified with overall classification accuracy of 94.14% and a kappa coefficient of 92.91%.

Photogrammetric Processing Using ZY-3 Satellite Imagery

Science.gov (United States)

Kornus, W.; Magariños, A.; Pla, M.; Soler, E.; Perez, F.

2015-03-01

This paper evaluates the stereoscopic capacities of the Chinese sensor ZiYuan-3 (ZY-3) for the generation of photogrammetric products. The satellite was launched on January 9, 2012 and carries three high-resolution panchromatic cameras viewing in forward (22º), nadir (0º) and backward direction (-22º) and an infrared multi-spectral scanner (IRMSS), which is slightly looking forward (6º). The ground sampling distance (GSD) is 2.1m for the nadir image, 3.5m for the two oblique stereo images and 5.8m for the multispectral image. The evaluated ZY-3 imagery consists of a full set of threefold-stereo and a multi-spectral image covering an area of ca. 50km x 50km north-west of Barcelona, Spain. The complete photogrammetric processing chain was executed including image orientation, the generation of a digital surface model (DSM), radiometric image correction, pansharpening, orthoimage generation and digital stereo plotting. All 4 images are oriented by estimating affine transformation parameters between observed and nominal RPC (rational polynomial coefficients) image positions of 17 ground control points (GCP) and a subsequent calculation of refined RPC. From 10 independent check points RMS errors of 2.2m, 2.0m and 2.7m in X, Y and H are obtained. Subsequently, a DSM of 5m grid spacing is generated fully automatically. A comparison with the Lidar data results in an overall DSM accuracy of approximately 3m. In moderate and flat terrain higher accuracies in the order of 2.5m and better are achieved. In a next step orthoimages from the high resolution nadir image and the multispectral image are generated using the refined RPC geometry and the DSM. After radiometric corrections a fused high resolution colour orthoimage with 2.1m pixel size is created using an adaptive HSL method. The pansharpen process is performed after the individual geocorrection due to the different viewing angles between the two images. In a detailed analysis of the colour orthoimage artifacts are

Using Satellite Imagery to Quantify Water Quality Impacts and Recovery from Hurricane Harvey

Science.gov (United States)

Sobel, R. S.; Kiaghadi, A.; Rifai, H. S.

2017-12-01

Record rainfall during Hurricane Harvey in the Houston-Galveston region generated record flows containing suspended sediment that was likely contaminated. Conventional water quality monitoring requires resource intensive field campaigns, and produces sparse datasets. In this study, satellite data were used to quantify suspended sediment (TSS) concentrations and mass within the region's estuary system and to estimate sediment deposition and transport. A conservative two band, red-green empirical regression was developed from the Sentinel 2 satellite to calculate TSS concentrations and masses. The regression was calibrated with an R2 = 0.73 (n=28) and validated with an R2 = 0.75 (n=12) using 2016 & 2017 imagery. TSS concentrations four days, 14 days, and 44 days post-storm were compared with a reference condition three days before storm arrival. Results indicated that TSS concentrations were an average of 100% higher four days post-storm, and 150% higher after 14 days, however, the average concentration on day 144 was only seven percent higher than the reference condition, suggesting the estuary system is approaching recovery to pre-storm conditions. Sediment masses were determined from the regressed concentrations and water volumes estimated from a bottom elevation grid combined with water surface elevations observed coincidently with the satellite image. While water volumes were only 13% higher on both day four and day 14 post-storm; sediment masses were 195% and 227% higher than the reference condition, respectively. By day 44, estuary sediment mass returned to just 2.9% above the reference load. From a mechanistic standpoint, the elevated TSS concentrations on day four indicated an advection-based regime due to stormwater runoff draining through the estuarine system. Sometime, however, between days 14 and 44, transport conditions switched from advection-dominated to deposition-driven as indicated by the near normal TSS concentrations on day 44.

Use of multispectral satellite imagery and hyperspectral endmember libraries for urban land cover mapping at the metropolitan scale

Science.gov (United States)

Priem, Frederik; Okujeni, Akpona; van der Linden, Sebastian; Canters, Frank

2016-10-01

The value of characteristic reflectance features for mapping urban materials has been demonstrated in many experiments with airborne imaging spectrometry. Analysis of larger areas requires satellite-based multispectral imagery, which typically lacks the spatial and spectral detail of airborne data. Consequently the need arises to develop mapping methods that exploit the complementary strengths of both data sources. In this paper a workflow for sub-pixel quantification of Vegetation-Impervious-Soil urban land cover is presented, using medium resolution multispectral satellite imagery, hyperspectral endmember libraries and Support Vector Regression. A Landsat 8 Operational Land Imager surface reflectance image covering the greater metropolitan area of Brussels is selected for mapping. Two spectral libraries developed for the cities of Brussels and Berlin based on airborne hyperspectral APEX and HyMap data are used. First the combined endmember library is resampled to match the spectral response of the Landsat sensor. The library is then optimized to avoid spectral redundancy and confusion. Subsequently the spectra of the endmember library are synthetically mixed to produce training data for unmixing. Mapping is carried out using Support Vector Regression models trained with spectra selected through stratified sampling of the mixed library. Validation on building block level (mean size = 46.8 Landsat pixels) yields an overall good fit between reference data and estimation with Mean Absolute Errors of 0.06, 0.06 and 0.08 for vegetation, impervious and soil respectively. Findings of this work may contribute to the use of universal spectral libraries for regional scale land cover fraction mapping using regression approaches.

The US-China Face-Off in the Asia-Pacific Region: What Answer Can the Offense-Defense Theory Provide

Science.gov (United States)

2015-06-01

invasions of various nomadic groups and barbarians of the Eurasian Steppe. Other scholars, however, doubt the assertion that the intention behind...Republic of China 2014, pp. 31-32. 37 satellites for digital imagery in the visual and near infrared spectrum; synthetic aperture radar satellites

The Matsu Wheel: A Cloud-Based Framework for Efficient Analysis and Reanalysis of Earth Satellite Imagery

Science.gov (United States)

Patterson, Maria T.; Anderson, Nicholas; Bennett, Collin; Bruggemann, Jacob; Grossman, Robert L.; Handy, Matthew; Ly, Vuong; Mandl, Daniel J.; Pederson, Shane; Pivarski, James;

Estimation of daily global solar irradiation by coupling ground measurements of bright sunshine hours to satellite imagery

International Nuclear Information System (INIS)

Ener Rusen, Selmin; Hammer, Annette; Akinoglu, Bulent G.

2013-01-01

In this work, the current version of the satellite-based HELIOSAT method and ground-based linear Ångström–Prescott type relations are used in combination. The first approach is based on the use of a correlation between daily bright sunshine hours (s) and cloud index (n). In the second approach a new correlation is proposed between daily solar irradiation and daily data of s and n which is based on a physical parameterization. The performances of the proposed two combined models are tested against conventional methods. We test the use of obtained correlation coefficients for nearby locations. Our results show that the use of sunshine duration together with the cloud index is quite satisfactory in the estimation of daily horizontal global solar irradiation. We propose to use the new approaches to estimate daily global irradiation when the bright sunshine hours data is available for the location of interest, provided that some regression coefficients are determined using the data of a nearby station. In addition, if surface data for a close location does not exist then it is recommended to use satellite models like HELIOSAT or the new approaches instead the Ångström type models. - Highlights: • Satellite imagery together with surface measurements in solar radiation estimation. • The new coupled and conventional models (satellite and ground-based) are analyzed. • New models result in highly accurate estimation of daily global solar irradiation

ANALYSIS OF RADAR AND OPTICAL SPACE BORNE DATA FOR LARGE SCALE TOPOGRAPHICAL MAPPING

Directory of Open Access Journals (Sweden)

W. Tampubolon

2015-03-01

Full Text Available Normally, in order to provide high resolution 3 Dimension (3D geospatial data, large scale topographical mapping needs input from conventional airborne campaigns which are in Indonesia bureaucratically complicated especially during legal administration procedures i.e. security clearance from military/defense ministry. This often causes additional time delays besides technical constraints such as weather and limited aircraft availability for airborne campaigns. Of course the geospatial data quality is an important issue for many applications. The increasing demand of geospatial data nowadays consequently requires high resolution datasets as well as a sufficient level of accuracy. Therefore an integration of different technologies is required in many cases to gain the expected result especially in the context of disaster preparedness and emergency response. Another important issue in this context is the fast delivery of relevant data which is expressed by the term “Rapid Mapping”. In this paper we present first results of an on-going research to integrate different data sources like space borne radar and optical platforms. Initially the orthorectification of Very High Resolution Satellite (VHRS imagery i.e. SPOT-6 has been done as a continuous process to the DEM generation using TerraSAR-X/TanDEM-X data. The role of Ground Control Points (GCPs from GNSS surveys is mandatory in order to fulfil geometrical accuracy. In addition, this research aims on providing suitable processing algorithm of space borne data for large scale topographical mapping as described in section 3.2. Recently, radar space borne data has been used for the medium scale topographical mapping e.g. for 1:50.000 map scale in Indonesian territories. The goal of this on-going research is to increase the accuracy of remote sensing data by different activities, e.g. the integration of different data sources (optical and radar or the usage of the GCPs in both, the optical and the

About uncertainties in sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise

Science.gov (United States)

Kern, S.; Khvorostovsky, K.; Skourup, H.; Rinne, E.; Parsakhoo, Z. S.; Djepa, V.; Wadhams, P.; Sandven, S.

2014-03-01

One goal of the European Space Agency Climate Change Initiative sea ice Essential Climate Variable project is to provide a quality controlled 20 year long data set of Arctic Ocean winter-time sea ice thickness distribution. An important step to achieve this goal is to assess the accuracy of sea ice thickness retrieval based on satellite radar altimetry. For this purpose a data base is created comprising sea ice freeboard derived from satellite radar altimetry between 1993 and 2012 and collocated observations of snow and sea ice freeboard from Operation Ice Bridge (OIB) and CryoSat Validation Experiment (CryoVEx) air-borne campaigns, of sea ice draft from moored and submarine Upward Looking Sonar (ULS), and of snow depth from OIB campaigns, Advanced Microwave Scanning Radiometer aboard EOS (AMSR-E) and the Warren Climatology (Warren et al., 1999). An inter-comparison of the snow depth data sets stresses the limited usefulness of Warren climatology snow depth for freeboard-to-thickness conversion under current Arctic Ocean conditions reported in other studies. This is confirmed by a comparison of snow freeboard measured during OIB and CryoVEx and snow freeboard computed from radar altimetry. For first-year ice the agreement between OIB and AMSR-E snow depth within 0.02 m suggests AMSR-E snow depth as an appropriate alternative. Different freeboard-to-thickness and freeboard-to-draft conversion approaches are realized. The mean observed ULS sea ice draft agrees with the mean sea ice draft computed from radar altimetry within the uncertainty bounds of the data sets involved. However, none of the realized approaches is able to reproduce the seasonal cycle in sea ice draft observed by moored ULS satisfactorily. A sensitivity analysis of the freeboard-to-thickness conversion suggests: in order to obtain sea ice thickness as accurate as 0.5 m from radar altimetry, besides a freeboard estimate with centimetre accuracy, an ice-type dependent sea ice density is as mandatory

Meteorite Fall Detection and Analysis via Weather Radar: Worldwide Potential for Citizen Science

Science.gov (United States)

Fries, M.; Bresky, C.; Laird, C.; Reddy, V.; Hankey, M.

2017-12-01

Meteorite falls can be detected using weather radars, facilitating rapid recovery of meteorites to minimize terrestrial alteration. Imagery from the US NEXRAD radar network reveals over two dozen meteorite falls where meteorites have been recovered, and about another dozen that remain unrecovered. Discovery of new meteorite falls is well suited to "citizen science" and similar outreach activities, as well as automation of computational components into internet-based search tools. Also, there are many more weather radars employed worldwide than those in the US NEXRAD system. Utilization of weather radars worldwide for meteorite recovery can not only expand citizen science opportunities but can also lead to significant improvement in the number of freshly-fallen meteorites available for research. We will discuss the methodologies behind locating and analyzing meteorite falls using weather radar, and how to make them available for citizen science efforts. An important example is the Aquarius Project, a Chicago-area consortium recently formed with the goal of recovering meteorites from Lake Michigan. This project has extensive student involvement geared toward development of actual hardware for recovering meteorites from the lake floor. Those meteorites were identified in weather radar imagery as they fell into the lake from a large meteor on 06 Feb 2017. Another example of public interaction is the meteor detection systems operated by the American Meteor Society (AMS). The AMS website has been developed to allow public reporting of meteors, effectively enabling citizen science to locate and describe significant meteor events worldwide.

Detection and Classification of Objects in Synthetic Aperture Radar Imagery

National Research Council Canada - National Science Library

Cooke, Tristrom

2006-01-01

.... The reports concern the detection of faint trails, and the theory and evaluation of a number of existing and novel methods for the detection and classification of ground and maritime targets with SAR imagery...

Wide-Angle Multistatic Synthetic Aperture Radar: Focused Image Formation and Aliasing Artifact Mitigation

National Research Council Canada - National Science Library

Luminati, Jonathan E

2005-01-01

...) imagery from a Radar Cross Section (RCS) chamber validates this approach. The second implementation problem stems from the large Doppler spread in the wide-angle scene, leading to severe aliasing problems...

Satellite radar interferometry for monitoring and early-stage warning of structural instability in archaeological sites

International Nuclear Information System (INIS)

Tapete, D; Fanti, R; Casagli, N; Cecchi, R; Petrangeli, P

2012-01-01

Satellite interferometric synthetic aperture radar (InSAR) monitoring campaigns were performed on the archaeological heritage of the Roman Forum, Palatino and Oppio Hills in the centre of Rome, Italy, to test the capabilities of persistent scatterer interferometry techniques for the preventive diagnosis of deformation threatening the structural stability of archaeological monuments and buried structures. ERS-1/2 and RADARSAT-1/2 SAR images were processed with the permanent scatterers InSAR (PSInSAR) and SqueeSAR approaches, and the identified measurement points (MP) were radar-interpreted to map the conservation criticalities in relation to the local geohazard factors and active deterioration processes. The multi-temporal reconstruction of past/recent instability events based on the MP deformation time series provided evidences of stabilization for the Domus Tiberiana as a consequence of recent restoration works, as well as of persistent deformation for the Temple of Magna Mater on the Palatino Hill and the structures of the Baths of Trajan on the Oppio Hill. Detailed time series analysis was also exploited to back monitor and understand the nature of the 2010 collapse that occurred close to Nero's Golden House, and to establish an early-stage warning procedure useful to preventively detect potential instability. (paper)

The Role of Cloud and Precipitation Radars in Convoys and Constellations

Science.gov (United States)

Tanelli, Simone; Durden, Stephen L.; Im, Eastwood; Sadowy, Gregory A.

2013-01-01

We provide an overview of which benefits a radar, and only a radar, can provide to any constellation of satellites monitoring Earth's atmosphere; which aspects instead are most useful to complement a radar instrument to provide accurate and complete description of the state of the troposphere; and finally which goals can be given a lower priority assuming that other types of sensors will be flying in formation with a radar.

Interpretation of earthquake-induced landslides triggered by the 12 May 2008, M7.9 Wenchuan earthquake in the Beichuan area, Sichuan Province, China using satellite imagery and Google Earth

Science.gov (United States)

Sato, H.P.; Harp, E.L.

2009-01-01

The 12 May 2008 M7.9 Wenchuan earthquake in the People's Republic of China represented a unique opportunity for the international community to use commonly available GIS (Geographic Information System) tools, like Google Earth (GE), to rapidly evaluate and assess landslide hazards triggered by the destructive earthquake and its aftershocks. In order to map earthquake-triggered landslides, we provide details on the applicability and limitations of publicly available 3-day-post- and pre-earthquake imagery provided by GE from the FORMOSAT-2 (formerly ROCSAT-2; Republic of China Satellite 2). We interpreted landslides on the 8-m-resolution FORMOSAT-2 image by GE; as a result, 257 large landslides were mapped with the highest concentration along the Beichuan fault. An estimated density of 0.3 landslides/km2 represents a minimum bound on density given the resolution of available imagery; higher resolution data would have identified more landslides. This is a preliminary study, and further study is needed to understand the landslide characteristics in detail. Although it is best to obtain landslide locations and measurements from satellite imagery having high resolution, it was found that GE is an effective and rapid reconnaissance tool. ?? 2009 Springer-Verlag.

Research at the Stanford Center for Radar Astronomy

Science.gov (United States)

1972-01-01

The research is reported in the applications of radar and radio techniques to the study of the solar system, and to space programs. Experiments reported include: bistatic-radar on Apollo missions, development of an unmanned geophysical observatory in the Antartic, Bragg scattering probes of sea states, characteristics of dense solar wind disturbances, and satellite communications for Alaska.

Accuracy of three-dimensional glacier surface volocities derived from radar interfeometry and ice-soundin radar measurements

DEFF Research Database (Denmark)

Mohr, Johan Jacob; Reeh, Niels; Madsen, Søren Nørvang

2003-01-01

We present a method for analyzing the errors involved in measuring three-dimensional glacier velocities with interferometric radar. We address the surface-parallel flow assumption and an augmented approach with a flux-divergence (FD) term. The errors in an interferometric ERS-1/-2 satellite radar...... dataset with ascending- and descending-orbit data covering Storstrommen glacier, northeast Greenland, are assessed. The FD error assessment is carried out on airborne 60 MHz ice-sounding radar data from the same area. A simple model of an interferometric radar system is developed and analyzed. The error...... sources considered include phase noise, atmospheric distortions, baseline calibration errors, a dry snow layer, and the stationary-flow assumption used in differential interferometry. The additional error sources in the analysis of FD errors are noise, bias and unknown variations of the ice thickness...

Quantifying the Value of Satellite Imagery in Agriculture and other Sectors

Science.gov (United States)

Brown, M. E.; Abbott, P. C.; Escobar, V. M.

2013-12-01

This study focused on quantifying the commercial value of satellite remote sensing for agriculture. Commercial value from satellite imagery arises when improved information leads to better economic decisions. We identified five areas of application of remote sensing to agriculture where there is this potential: crop management (precision agriculture), insurance, real estate assessment, crop forecasting, and environmental monitoring. These applications can be divided between public information (crop forecasting) and those that may generate private commercial value (crop management), with both public and private information dimensions in some categories. Public information applications of remote sensing have been more successful in the past, and are likely to generate more economic value in the future. It was found that several issues have limited realization of the potential to generate private value from remote sensing in agriculture. The scale of use is small to the high cost of acquiring and interpreting large images has limited the cost effectiveness to individual farmers. Insurance, environmental monitoring, and crop management services by cooperatives or consultants may be cases overcoming this limitation. The greatest opportunities for potential commercial value from agriculture are probably in the crop forecasting area, especially where agricultural statistics services are not as well developed, since public market information benefits a broad range of economic actors, not limited to countries where forecasts are made. We estimate here the value from components of USDA's World Agricultural Supply and Demand Estimates (WASDE) forecasts for corn, indicating potential value increasing in the range of 60 to 240 million if improved satellite based information enhances those forecasts. The research was conducted by agricultural economists at Purdue University, and will be the basis for further evaluation of the use of satellite data within the NASA Carbon

Coastal flood inundation monitoring with Satellite C-band and L-band Synthetic Aperture Radar data

Science.gov (United States)

Ramsey, Elijah W.; Rangoonwala, Amina; Bannister, Terri

2013-01-01

Satellite Synthetic Aperture Radar (SAR) was evaluated as a method to operationally monitor the occurrence and distribution of storm- and tidal-related flooding of spatially extensive coastal marshes within the north-central Gulf of Mexico. Maps representing the occurrence of marsh surface inundation were created from available Advanced Land Observation Satellite (ALOS) Phased Array type L-Band SAR (PALSAR) (L-band) (21 scenes with HH polarizations in Wide Beam [100 m]) data and Environmental Satellite (ENVISAT) Advanced SAR (ASAR) (C-band) data (24 scenes with VV and HH polarizations in Wide Swath [150 m]) during 2006-2009 covering 500 km of the Louisiana coastal zone. Mapping was primarily based on a decrease in backscatter between reference and target scenes, and as an extension of previous studies, the flood inundation mapping performance was assessed by the degree of correspondence between inundation mapping and inland water levels. Both PALSAR- and ASAR-based mapping at times were based on suboptimal reference scenes; however, ASAR performance seemed more sensitive to reference-scene quality and other types of scene variability. Related to water depth, PALSAR and ASAR mapping accuracies tended to be lower when water depths were shallow and increased as water levels decreased below or increased above the ground surface, but this pattern was more pronounced with ASAR. Overall, PALSAR-based inundation accuracies averaged 84% (n = 160), while ASAR-based mapping accuracies averaged 62% (n = 245).

Providing Access and Visualization to Global Cloud Properties from GEO Satellites

Science.gov (United States)

Chee, T.; Nguyen, L.; Minnis, P.; Spangenberg, D.; Palikonda, R.; Ayers, J. K.

2015-12-01

Providing public access to cloud macro and microphysical properties is a key concern for the NASA Langley Research Center Cloud and Radiation Group. This work describes a tool and method that allows end users to easily browse and access cloud information that is otherwise difficult to acquire and manipulate. The core of the tool is an application-programming interface that is made available to the public. One goal of the tool is to provide a demonstration to end users so that they can use the dynamically generated imagery as an input into their own work flows for both image generation and cloud product requisition. This project builds upon NASA Langley Cloud and Radiation Group's experience with making real-time and historical satellite cloud product imagery accessible and easily searchable. As we see the increasing use of virtual supply chains that provide additional value at each link there is value in making satellite derived cloud product information available through a simple access method as well as allowing users to browse and view that imagery as they need rather than in a manner most convenient for the data provider. Using the Open Geospatial Consortium's Web Processing Service as our access method, we describe a system that uses a hybrid local and cloud based parallel processing system that can return both satellite imagery and cloud product imagery as well as the binary data used to generate them in multiple formats. The images and cloud products are sourced from multiple satellites and also "merged" datasets created by temporally and spatially matching satellite sensors. Finally, the tool and API allow users to access information that spans the time ranges that our group has information available. In the case of satellite imagery, the temporal range can span the entire lifetime of the sensor.

Monitoring of Conservation Tillage and Tillage Intensity by Ground and Satellite Imagery

Directory of Open Access Journals (Sweden)

M.A Rostami

2014-09-01

Full Text Available Local information about tillage intensity and ground residue coverage is useful for policies in agricultural extension, tillage implement design and upgrading management methods. The current methods for assessing crop residue coverage and tillage intensity such as residue weighing methods, line-transect and photo comparison methods are tedious and time-consuming. The present study was devoted to investigate accurate methods for monitoring residue management and tillage practices. The satellite imagery technique was used as a rapid and spatially explicit method for delineating crop residue coverage and as an estimator of conservation tillage adoption and intensity. The potential of multispectral high-spatial resolution WorldView-2 local data was evaluated using the total of eleven satellite spectral indices and Linear Spectral Unmixing Analysis (LSUA. The total of ninety locations was selected for this study and for each location the residue coverage was measured by the image processing method and recorded as ground control. The output of indices and LSUA method were individually correlated to the control and the relevant R2 was calculated. Results indicated that crop residue cover was related to IPVI, RVI1, RVI2 and GNDVI spectral indices and satisfactory correlations were established (0.74 - 0.81. The crop residue coverage estimated from the LSUA approach was found to be correlated with the ground residue data (0.75. Two effective indices named as Infrared Percentage Vegetation Index (IPVI and Ratio Vegetation Index (RVI with maximum R2 were considered for classification of tillage intensity. Results indicated that the classification accuracy with IPVI and RVI indices in different conditions varied from 78-100 percent and therefore in good agreement with ground measurement, observations and field records.

A CA-CFAR and localized wavelet ship detector for sentinel-1 imagery

CSIR Research Space (South Africa)

Schwegmann, CP

2015-07-01

Full Text Available affordable. Using the basis of a cell-averaging constant false alarm rate prescreening method as input, this paper presents a novel method for detecting ships within Synthetic Aperture Radar imagery using a Gabor wavelet correlator. The method proposed allows...

Recognition and characterization of networks of water bodies in the Arctic ice-wedge polygonal tundra using high-resolution satellite imagery

Science.gov (United States)

Skurikhin, A. N.; Gangodagamage, C.; Rowland, J. C.; Wilson, C. J.

2013-12-01

Arctic lowland landscapes underlain by permafrost are often characterized by polygon-like patterns such as ice-wedge polygons outlined by networks of ice wedges and complemented with polygon rims, troughs, shallow ponds and thermokarst lakes. Polygonal patterns and corresponding features are relatively easy to recognize in high spatial resolution satellite imagery by a human, but their automated recognition is challenging due to the variability in their spectral appearance, the irregularity of individual trough spacing and orientation within the patterns, and a lack of unique spectral response attributable to troughs with widths commonly between 1 m and 2 m. Accurate identification of fine scale elements of ice-wedge polygonal tundra is important as their imprecise recognition may bias estimates of water, heat and carbon fluxes in large-scale climate models. Our focus is on the problem of identification of Arctic polygonal tundra fine-scale landscape elements (as small as 1 m - 2 m width). The challenge of the considered problem is that while large water bodies (e.g. lakes and rivers) can be recognized based on spectral response, reliable recognition of troughs is more difficult. Troughs do not have unique spectral signature, their appearance is noisy (edges are not strong), their width is small, and they often form connected networks with ponds and lakes, and thus they have overlapping spectral response with other water bodies and surrounding non-water bodies. We present a semi-automated approach to identify and classify Arctic polygonal tundra landscape components across the range of spatial scales, such as troughs, ponds, river- and lake-like objects, using high spatial resolution satellite imagery. The novelty of the approach lies in: (1) the combined use of segmentation and shape-based classification to identify a broad range of water bodies, including troughs, and (2) the use of high-resolution WorldView-2 satellite imagery (with resolution of 0.6 m) for this

Highly Enhanced Risk Management Emergency Satellite

DEFF Research Database (Denmark)

Dalmeir, Michael; Gataullin, Yunir; Indrajit, Agung

HERMES (Highly Enhanced Risk Management Emergency Satellite) is potential European satellite mission for global flood management, being implemented by Technical University Munich and European Space Agency. With its main instrument - a reliable and precise Synthetic Aperture Radar (SAR) antenna...

Inexpensive land-use maps extracted from satellite data

Science.gov (United States)

Barney, T. W.; Barr, D. J.; Elifrits, C. D.; Johannsen, C. J.

1979-01-01

Satellite images are interpretable with minimal skill and equipment by employing method which uses false color composite print of image of area transmitted from Landsat satellite. Method is effective for those who have little experience with satellite imagery, little time, and little money available.

Site Monitoring with Synthetic Aperture Radar Satellite Imagery

DEFF Research Database (Denmark)

Canty, Morton J.; Nielsen, Allan Aasbjerg

2017-01-01

Based on a statistical test for the equality of polarimetric matrices following the complex Wishart distribution and a factorization of the test statistic, change analysis in a time series of multi-look polarimetric SAR data in variance-covariance or polarimetric matrix representation is carried...

Effects of an assimilation of radar and satellite data on a very-short range forecast of heavy convective rainfalls

Czech Academy of Sciences Publication Activity Database

Sokol, Zbyněk

2009-01-01

Roč. 93, 1-3 (2009), s. 188-206 ISSN 0169-8095. [European Conference on Severe Storms /4./. Miramare -Trieste, 10.09.2007-14.09.2007] R&D Projects: GA ČR GA205/07/0905; GA MŠk OC 112; GA MŠk 1P05ME748 Institutional research plan: CEZ:AV0Z30420517 Keywords : Precipitation forecast * NWP model * Assimilation of radar and satellite data * Local convective precipitation Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.811, year: 2009 http://www.sciencedirect.com/science/journal/01698095

The development of deep learning in synthetic aperture radar imagery

CSIR Research Space (South Africa)

Schwegmann, Colin P

2017-05-01

Full Text Available sensing techniques but comes at the price of additional complexities. To adequately cope with these, researchers have begun to employ advanced machine learning techniques known as deep learning to Synthetic Aperture Radar data. Deep learning represents...

An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery

Directory of Open Access Journals (Sweden)

Xiangguang Leng

2016-08-01

Full Text Available With the rapid development of spaceborne synthetic aperture radar (SAR and the increasing need of ship detection, research on adaptive ship detection in spaceborne SAR imagery is of great importance. Focusing on practical problems of ship detection, this paper presents a highly adaptive ship detection scheme for spaceborne SAR imagery. It is able to process a wide range of sensors, imaging modes and resolutions. Two main stages are identified in this paper, namely: ship candidate detection and ship discrimination. Firstly, this paper proposes an adaptive land masking method using ship size and pixel size. Secondly, taking into account the imaging mode, incidence angle, and polarization channel of SAR imagery, it implements adaptive ship candidate detection in spaceborne SAR imagery by applying different strategies to different resolution SAR images. Finally, aiming at different types of typical false alarms, this paper proposes a comprehensive ship discrimination method in spaceborne SAR imagery based on confidence level and complexity analysis. Experimental results based on RADARSAT-1, RADARSAT-2, TerraSAR-X, RS-1, and RS-3 images demonstrate that the adaptive scheme proposed in this paper is able to detect ship targets in a fast, efficient and robust way.

Seeing is believing I: The use of thermal sensing from satellite imagery to predict crop yield

International Nuclear Information System (INIS)

Potgieter A B; Rodriguez D; Power B; Mclean J; Davis P

2014-01-01

Volatility in crop production has been part of the Australian environment since cropping began with the arrival of the first European settlers. Climate variability is the main factor affecting crop production at national, state and local scales. At field level spatial patterns on yield production are also determined by spatially changing soil properties in interaction with seasonal climate conditions and weather patterns at critical stages in the crop development. Here we used a combination of field level weather records, canopy characteristics, and satellite information to determine the spatial performance of a large field of wheat. The main objective of this research is to determine the ability of remote sensing technologies to capture yield losses due to water stress at the canopy level. The yield, canopy characteristics (i.e. canopy temperature and ground cover) and seasonal conditions of a field of wheat (∼1400ha) (-29.402° South and 149.508°, New South Wales, Australia) were continuously monitored during the winter of 2011. Weather and crop variables were continuously monitored by installing three automatic weather stations in a transect covering different positions and soils in the landscape. Weather variables included rainfall, minimum and maximum temperatures and relative humidity, and crop characteristics included ground cover and canopy temperature. Satellite imagery Landsat TM 5 and 7 was collected at five different stages in the crop cycle. Weather variables and crop characteristics were used to calculate a crop stress index (CSI) at point and field scale (39 fields). Field data was used to validate a spatial satellite image derived index. Spatial yield data was downloaded from the harvester at the different locations in the field. We used the thermal band (land surface temperature, LST) and enhanced vegetation index (EVI) bands from the MODIS (250 m for visible bands and 1km for thermal band) and a derived EVI from Landsat TM 7 (25 m for visible

Improved spatial mapping of rainfall events with spaceborne SAR imagery

Science.gov (United States)

Ulaby, F. T.; Brisco, B.; Dobson, C.

1983-01-01

The Seasat satellite acquired the first spaceborne synthetic-aperture radar (SAR) images of the earth's surface, in 1978, at a frequency of 1.275 GHz (L-band) in a like-polarization mode at incidence angles of 23 + or - 3 deg. Although this may not be the optimum system configuration for radar remote sensing of soil moisture, interpretation of two Seasat images of Iowa demonstrates the sensitivity of microwave backscatter to soil moisture content. In both scenes, increased image brightness, which represents more radar backscatter, can be related to previous rainfall activity in the two areas. Comparison of these images with ground-based rainfall observations illustrates the increased spatial coverage of the rainfall event that can be obtained from the satellite SAR data. These data can then be color-enhanced by a digital computer to produce aesthetically pleasing output products for the user community.

Satellite-based Tropical Cyclone Monitoring Capabilities

Science.gov (United States)

Hawkins, J.; Richardson, K.; Surratt, M.; Yang, S.; Lee, T. F.; Sampson, C. R.; Solbrig, J.; Kuciauskas, A. P.; Miller, S. D.; Kent, J.

2012-12-01

Satellite remote sensing capabilities to monitor tropical cyclone (TC) location, structure, and intensity have evolved by utilizing a combination of operational and research and development (R&D) sensors. The microwave imagers from the operational Defense Meteorological Satellite Program [Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS)] form the "base" for structure observations due to their ability to view through upper-level clouds, modest size swaths and ability to capture most storm structure features. The NASA TRMM microwave imager and precipitation radar continue their 15+ yearlong missions in serving the TC warning and research communities. The cessation of NASA's QuikSCAT satellite after more than a decade of service is sorely missed, but India's OceanSat-2 scatterometer is now providing crucial ocean surface wind vectors in addition to the Navy's WindSat ocean surface wind vector retrievals. Another Advanced Scatterometer (ASCAT) onboard EUMETSAT's MetOp-2 satellite is slated for launch soon. Passive microwave imagery has received a much needed boost with the launch of the French/Indian Megha Tropiques imager in September 2011, basically greatly supplementing the very successful NASA TRMM pathfinder with a larger swath and more frequent temporal sampling. While initial data issues have delayed data utilization, current news indicates this data will be available in 2013. Future NASA Global Precipitation Mission (GPM) sensors starting in 2014 will provide enhanced capabilities. Also, the inclusion of the new microwave sounder data from the NPP ATMS (Oct 2011) will assist in mapping TC convective structures. The National Polar orbiting Partnership (NPP) program's VIIRS sensor includes a day night band (DNB) with the capability to view TC cloud structure at night when sufficient lunar illumination exits. Examples highlighting this new capability will be discussed in concert with additional data fusion efforts.

Forest Biomass Mapping From Lidar and Radar Synergies

Science.gov (United States)

Sun, Guoqing; Ranson, K. Jon; Guo, Z.; Zhang, Z.; Montesano, P.; Kimes, D.

2011-01-01

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed. In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R(exp 2) of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI Mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R2 of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar- SAR synergy 63 was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the

Volcanic and Tectonic Activity in the Red Sea Region (2004-2013): Insights from Satellite Radar Interferometry and Optical Imagery

KAUST Repository

Xu, Wenbin

2015-01-01

due to insufficient in-situ data and remoteness of some of the activity. In this dissertation, I have used satellite remote sensing to derive new information about several recent volcanic and tectonic events in the Red Sea region. I first report

Land cover classification accuracy from electro-optical, X, C, and L-band Synthetic Aperture Radar data fusion

Science.gov (United States)

Hammann, Mark Gregory

The fusion of electro-optical (EO) multi-spectral satellite imagery with Synthetic Aperture Radar (SAR) data was explored with the working hypothesis that the addition of multi-band SAR will increase the land-cover (LC) classification accuracy compared to EO alone. Three satellite sources for SAR imagery were used: X-band from TerraSAR-X, C-band from RADARSAT-2, and L-band from PALSAR. Images from the RapidEye satellites were the source of the EO imagery. Imagery from the GeoEye-1 and WorldView-2 satellites aided the selection of ground truth. Three study areas were chosen: Wad Medani, Sudan; Campinas, Brazil; and Fresno- Kings Counties, USA. EO imagery were radiometrically calibrated, atmospherically compensated, orthorectifed, co-registered, and clipped to a common area of interest (AOI). SAR imagery were radiometrically calibrated, and geometrically corrected for terrain and incidence angle by converting to ground range and Sigma Naught (?0). The original SAR HH data were included in the fused image stack after despeckling with a 3x3 Enhanced Lee filter. The variance and Gray-Level-Co-occurrence Matrix (GLCM) texture measures of contrast, entropy, and correlation were derived from the non-despeckled SAR HH bands. Data fusion was done with layer stacking and all data were resampled to a common spatial resolution. The Support Vector Machine (SVM) decision rule was used for the supervised classifications. Similar LC classes were identified and tested for each study area. For Wad Medani, nine classes were tested: low and medium intensity urban, sparse forest, water, barren ground, and four agriculture classes (fallow, bare agricultural ground, green crops, and orchards). For Campinas, Brazil, five generic classes were tested: urban, agriculture, forest, water, and barren ground. For the Fresno-Kings Counties location 11 classes were studied: three generic classes (urban, water, barren land), and eight specific crops. In all cases the addition of SAR to EO resulted

New and Emerging Satellite Imaging Capabilities in Support of Safeguards

International Nuclear Information System (INIS)

Johnson, M.; Paquette, J.P.; Spyropoulos, N.; Rainville, L.; Schichor, P.; Hong, M.

2015-01-01

This abstract is focused on new and emerging commercial satellite imagery (CSI) capabilities. For more than a decade, experienced imagery analysts have been exploiting and analyzing CSI in support of the Department of Safeguards. As the remote sensing industry continues to evolve, additional CSI imagery types are becoming available that could enhance our ability to evaluate and verify States' declarations and to investigate the possible presence of undeclared activities. A newly available and promising CSI capability that may have a Safeguards application is Full Motion Video (FMV) imagery collection from satellites. For quite some time, FMV imagery has been collected from airborne platforms, but now FMV sensors are being deployed into space. Like its airborne counterpart, satellite FMV imagery could provide analysts with a great deal of information, including insight into the operational status of facilities and patterns of activity. From a Safeguards perspective, FMV imagery could help the Agency in the evaluation and verification of States' declared facilities and activities. There are advantages of FMV imaging capabilities that cannot be duplicated with other CSI capabilities, including the ability to loiter over areas of interest and the potential to revisit sites multiple times per day. Additional sensor capabilities applicable to the Safeguards mission include, but are not limited to, the following sensors: · Thermal Infrared imaging sensors will be launched in late 2014 to monitor operational status, e.g., heat from a transformer. · High resolution ShortWave Infrared sensors able to characterize materials that could support verification of Additional Protocol declarations under Article 2.a(v). · Unmanned Aerial Vehicles with individual sensors or specific sensor combinations. The Safeguards Symposium provides a forum to showcase and demonstrate safeguards applications for these emerging satellite imaging capabilities. (author)

3D high resolution tracking of ice flow using mutli-temporal stereo satellite imagery, Franz Josef Glacier, New Zealand

Science.gov (United States)

Leprince, S.; Lin, J.; Ayoub, F.; Herman, F.; Avouac, J.

2013-12-01

We present the latest capabilities added to the Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) software, which aim at analyzing time-series of stereoscopic imagery to document 3D variations of the ground surface. We review the processing chain and present the new and improved modules for satellite pushbroom imagery, in particular the N-image bundle block adjustment to jointly optimize the viewing geometry of multiple acquisitions, the improved multi-scale image matching based on Semi-Global Matching (SGM) to extract high resolution topography, and the triangulation of multi-temporal disparity maps to derive 3D ground motion. In particular, processes are optimized to run on a cluster computing environment. This new suite of algorithms is applied to the study of Worldview stereo imagery above the Franz Josef, Fox, and Tasman Glaciers, New Zealand, acquired on 01/30/2013, 02/09/2013, and 02/28/2013. We derive high resolution (1m post-spacing) maps of ice flow in three dimensions, where ice velocities of up to 4 m/day are recorded. Images were collected in early summer during a dry and sunny period, which followed two weeks of unsettled weather with several heavy rainfall events across the Southern Alps. The 3D tracking of ice flow highlights the surface response of the glaciers to changes in effective pressure at the ice-bedrock interface due to heavy rainfall, at an unprecedented spatial resolution.

Towards monitoring surface and subsurface lakes on the Greenland Ice Sheet using Sentinel-1 SAR and Landsat-8 OLI imagery

Science.gov (United States)

Miles, Katie E.; Willis, Ian C.; Benedek, Corinne L.; Williamson, Andrew G.; Tedesco, Marco

2017-07-01

Supraglacial lakes are an important component of the Greenland Ice Sheet’s mass balance and hydrology, with their drainage affecting ice dynamics. This study uses imagery from the recently launched Sentinel-1A Synthetic Aperture Radar (SAR) satellite to investigate supraglacial lakes in West Greenland. A semi-automated algorithm is developed to detect surface lakes from Sentinel-1 images during the 2015 summer. A combined Landsat-8 and Sentinel-1 dataset, which has a comparable temporal resolution to MODIS (3 days versus daily) but a higher spatial resolution (25-40 m versus 250-500 m), is then used together with a fully-automated lake drainage detection algorithm. Rapid (days) and slow (> 4 days) drainages are investigated for both small (summer. Drainage events of small lakes occur at lower elevations (mean 159 m), and slightly earlier (mean 4.5 days) in the melt season than those of large lakes. The analysis is extended manually into the early winter to calculate the dates and elevations of lake freeze-through more precisely than is possible with optical imagery (mean 30 August; 1270 m mean elevation). Finally, the Sentinel-1 imagery is used to detect subsurface lakes and, for the first time, their dates of appearance and freeze-through (mean 9 August and 7 October, respectively). These subsurface lakes occur at higher elevations than the surface lakes detected in this study (mean 1593 m and 1185 m, respectively). Sentinel-1 imagery therefore provides great potential for tracking melting, water movement and freezing within both the firn zone and ablation area of the Greenland Ice Sheet.

Global, Persistent, Real-time Multi-sensor Automated Satellite Image Analysis and Crop Forecasting in Commercial Cloud

Science.gov (United States)

Brumby, S. P.; Warren, M. S.; Keisler, R.; Chartrand, R.; Skillman, S.; Franco, E.; Kontgis, C.; Moody, D.; Kelton, T.; Mathis, M.

2016-12-01

Cloud computing, combined with recent advances in machine learning for computer vision, is enabling understanding of the world at a scale and at a level of space and time granularity never before feasible. Multi-decadal Earth remote sensing datasets at the petabyte scale (8×10^15 bits) are now available in commercial cloud, and new satellite constellations will generate daily global coverage at a few meters per pixel. Public and commercial satellite observations now provide a wide range of sensor modalities, from traditional visible/infrared to dual-polarity synthetic aperture radar (SAR). This provides the opportunity to build a continuously updated map of the world supporting the academic community and decision-makers in government, finanace and industry. We report on work demonstrating country-scale agricultural forecasting, and global-scale land cover/land, use mapping using a range of public and commercial satellite imagery. We describe processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work combining this imagery with time-series SAR collected by ESA Sentinel 1. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. We apply remote sensing science and machine learning algorithms to detect and classify agricultural crops and then estimate crop yields and detect threats to food security (e.g., flooding, drought). The software platform and analysis methodology also support monitoring water resources, forests and other general

Preliminary hard and soft bottom seafloor substrate map derived from an supervised classification of bathymetry derived from multispectral World View-2 satellite imagery of Ni'ihau Island, Territory of Main Hawaiian Islands, USA

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary hard and soft seafloor substrate map derived from a supervised classification from multispectral World View-2 satellite imagery of Ni'ihau Island,...

COMBINATION OF GENETIC ALGORITHM AND DEMPSTER-SHAFER THEORY OF EVIDENCE FOR LAND COVER CLASSIFICATION USING INTEGRATION OF SAR AND OPTICAL SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

H. T. Chu

2012-07-01

Full Text Available The integration of different kinds of remotely sensed data, in particular Synthetic Aperture Radar (SAR and optical satellite imagery, is considered a promising approach for land cover classification because of the complimentary properties of each data source. However, the challenges are: how to fully exploit the capabilities of these multiple data sources, which combined datasets should be used and which data processing and classification techniques are most appropriate in order to achieve the best results. In this paper an approach, in which synergistic use of a feature selection (FS methods with Genetic Algorithm (GA and multiple classifiers combination based on Dempster-Shafer Theory of Evidence, is proposed and evaluated for classifying land cover features in New South Wales, Australia. Multi-date SAR data, including ALOS/PALSAR, ENVISAT/ASAR and optical (Landsat 5 TM+ images, were used for this study. Textural information were also derived and integrated with the original images. Various combined datasets were generated for classification. Three classifiers, namely Artificial Neural Network (ANN, Support Vector Machines (SVMs and Self-Organizing Map (SOM were employed. Firstly, feature selection using GA was applied for each classifier and dataset to determine the optimal input features and parameters. Then the results of three classifiers on particular datasets were combined using the Dempster-Shafer theory of Evidence. Results of this study demonstrate the advantages of the proposed method for land cover mapping using complex datasets. It is revealed that the use of GA in conjunction with the Dempster-Shafer Theory of Evidence can significantly improve the classification accuracy. Furthermore, integration of SAR and optical data often outperform single-type datasets.

Vessel and oil spill early detection using COSMO satellite imagery

Science.gov (United States)

Revollo, Natalia V.; Delrieux, Claudio A.

2017-10-01

Oil spillage is one of the most common sources of environmental damage in places where coastal wild life is found in natural reservoirs. This is especially the case in the Patagonian coast, with a littoral more than 5000 km long and a surface above a million and half square km. In addition, furtive fishery activities in Argentine waters are depleting the food supplies of several species, altering the ecological equilibrium. For this reason, early oil spills and vessel detection is an imperative surveillance task for environmental and governmental authorities. However, given the huge geographical extension, human assisted monitoring is unfeasible, and therefore real time remote sensing technologies are the only operative and economically feasible solution. In this work we describe the theoretical foundations and implementation details of a system specifically designed to take advantage of the SAR imagery delivered by two satellite constellations (the SAOCOM mission, developed by the Argentine Space Agency, and the COSMO mission, developed by the Italian Space Agency), to provide real-time detection of vessels and oil spills. The core of the system is based on pattern recognition over a statistical characterization of the texture patterns arising in the positive and negative conditions (i.e., vessel, oil, or plain sea surfaces). Training patterns were collected from a large number of previously reported contacts tagged by experts in the National Commission on Space Activities (CONAE). The resulting system performs well above the sensitivity and specificity of other avalilable systems.

Using limnological and optical knowledge to detect discharges from nuclear facilities - Potential application of satellite imagery for international safeguards

International Nuclear Information System (INIS)

Borstad, G.; Truong, Q.S. Bob; Keeffe, R.; Baines, P.; Staenz, K.; Neville, R.

2001-01-01

Previous work carried out under the Canadian Safeguards Support Program, has shown that thermal imagery from the American Landsat satellites could be used to detect the cooling water discharges, and could therefore be used to verify the operational status of nuclear facilities. In some images, thermal plumes could be easily detected in single band imagery with no mathematical manipulation and little image enhancement because there was a very strong thermal contrast between the effluent and the receiving water. However, for certain situations such as discharges into well mixed conditions (cold water and violent tides) the thermal plume may be more subtle. We show here that the visible bands of Landsat and IKONOS images often contain additional information, and that the thermal signature of a discharge from a nuclear facility is not the only signal available to describe its operation. This paper introduces some important hydrological phenomena that govern the biological and physical organization of water bodies, and discusses some basic concepts of marine and aquatic optics that are relevant to the safeguards problem. Using image analysis techniques that have been used widely in ocean optics work and in applications in the mapping and monitoring of water quality, we have re-analyzed data that were obtained under a joint project between various Canadian government departments. We present a preliminary examination of imagery from both satellite multispectral and aircraft hyperspectral sensors, and discuss methods to extract information that could be useful in the detection and verification of declared or undeclared nuclear activities. In one example of an IKONOS image of the Canadian Bruce Nuclear Generating Facility, simple enhancement techniques failed to find any plume other than a small jet visible in the surface wave field. With knowledge of limnology, oceanography and aquatic optics, we have been able to separate and remove the surface reflection, and detect a

Developing and Evaluating RGB Composite MODIS Imagery for Applications in National Weather Service Forecast Offices

Science.gov (United States)

Oswald, Hayden; Molthan, Andrew L.

2011-01-01

Satellite remote sensing has gained widespread use in the field of operational meteorology. Although raw satellite imagery is useful, several techniques exist which can convey multiple types of data in a more efficient way. One of these techniques is multispectral compositing. The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed two multispectral satellite imagery products which utilize data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra and Aqua satellites, based upon products currently generated and used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The nighttime microphysics product allows users to identify clouds occurring at different altitudes, but emphasizes fog and low cloud detection. This product improves upon current spectral difference and single channel infrared techniques. Each of the current products has its own set of advantages for nocturnal fog detection, but each also has limiting drawbacks which can hamper the analysis process. The multispectral product combines each current product with a third channel difference. Since the final image is enhanced with color, it simplifies the fog identification process. Analysis has shown that the nighttime microphysics imagery product represents a substantial improvement to conventional fog detection techniques, as well as provides a preview of future satellite capabilities to forecasters.

A Commercial Architecture for Satellite Imagery

National Research Council Canada - National Science Library

Didier, Christopher J

2006-01-01

.... This study focuses on the concept of the U.S. government purchasing proven and successful commercial satellites with minimal non-recurring engineering costs to help augment current national systems...

Identifying and Allocating Geodetic Systems to historical oil gas wells by using high-resolution satellite imagery

Science.gov (United States)

Alvarez, Gabriel O.

2018-05-01

Hydrocarbon exploration in Argentina started long before the IGM created a single, high-precision geodetic reference network for the whole country. Several geodetic surveys were conducted in every producing basin, which have ever since then supported well placement. Currently, every basin has a huge amount of information referenced to the so-called "local" geodetic systems, such as Chos Malal - Quiñi Huao in the Neuquén Basin, and Pampa del Castillo in the San Jorge Basin, which differ to a greater or lesser extent from the national Campo Inchauspe datum established by the IGM in 1969 as the official geodetic network. However, technology development over the last few years and the expansion of satellite positioning systems such as GPS resulted in a new world geodetic order. Argentina rapidly joined this new geodetic order through the implementation of a new national geodetic system by the IGM: POSGAR network, which replaced the old national Campo Inchauspe system. However, this only helped to worsen the data georeferencing issue for oil companies, as a third reference system was added to each basin. Now every basin has a local system, the national system until 1997 (Campo Inchauspe), and finally the newly created POSGAR network national satellite system, which is geocentric unlike the former two planimetric datums. The purpose of this paper is to identify and allocate geodetic systems of coordinates to historical wells, whose geodetic system is missing or has been erroneously allocated, by using currently available technological resources such as geographic information systems and high-resolution satellite imagery.

Change of the high-latitude ionosphere during heating by a powerful short radio wave of the EISCAT/Heating complex according to signals of the GLONASS satellite and the incoherent scattering radar

Directory of Open Access Journals (Sweden)

Tereshchenko E. D.

2018-03-01

Full Text Available Results of observations of variations of temperature, electron concentration and total electron content of the high-latitude region of the ionosphere during its modification by powerful short radio waves of the heating complex EISCAT/Heating (Tromsø, Norway according to signals of the GLONASS satellites and the incoherent scattering UHF EISCAT radar (Tromsø, Norway have been provided. The geometry of passes of the GLONASS and GPS satellites for operating conditions of the heating complex in Tromsø has been considered. It has been shown that during the experiments on the EISCAT/Heating complex for the study of the modified structure of the high-latitude ionosphere it is more convenient to use the GLONASS satellites. Parameters of orbits of these satellites allow researching changes of total electron content in the direction along the geomagnetic field line at the place of observation. It has been shown that during heating of the ionosphere by powerful short radio waves its structure is becoming an irregular one. Operation of the heating complex in the mode "switched on – switched off" has caused appearance of wavy variations of total electron content with the periods close to the heating period. The main features of behavior of the total electron content in the case of the continuous heating of the ionosphere in the direction of the magnetic zenith according to the GLONASS satellite are: reduction of total electron content in the central zone of the antenna diagram, i. e. in the direction of the magnetic zenith, and presence of the increased values of total electron content at the edges of the heating zone. According to the incoherent scattering radar the heating of the ionosphere by the powerful short radio wave has created the region of the increased electron temperature and electron concentration along the direction of the magnetic zenith. The behavior of total electron content according to the GLONASS satellite and the radar of

Utilizing Weather RADAR for Rapid Location of Meteorite Falls and Space Debris Re-Entry

Science.gov (United States)

Fries, Marc D.

2016-01-01

This activity utilizes existing NOAA weather RADAR imagery to locate meteorite falls and space debris falls. The near-real-time availability and spatial accuracy of these data allow rapid recovery of material from both meteorite falls and space debris re-entry events. To date, at least 22 meteorite fall recoveries have benefitted from RADAR detection and fall modeling, and multiple debris re-entry events over the United States have been observed in unprecedented detail.

Space communication and radar with lasers

NARCIS (Netherlands)

Witteman, W.J.

2005-01-01

Sensitive heterodyne detection with lasers applied .to radar and satellite communication is seriously hampered by the large electronic bandwidth due to random Doppler shift and frequency instability. These drawbacks can be circumvented by dual signal heterodyne detection. The system consists of

Monitoring of Damage in Sunflower and Maize Parcels Using Radar and Optical Time Series Data

Directory of Open Access Journals (Sweden)

György Surek

2015-01-01

Full Text Available The objective of this paper is to monitor the temporal behaviour of geometrical structural change of cropland affected by four different types of damage: weed infection, Western Corn Rootworm (WCR, storm damage, and drought by time series of different type of optical and quad-pol RADARSAT2 data. Based on our results it is established that ragweed infection in sunflower can be well identified by evaluation of radar (mid-June and optical (mid-August satellite images. Effect of drought in sunflower is well recognizable by spectral indices derived from optical as well as “I”-component of Shannon entropy (SEI from radar satellite images acquired during the first decade of July. Evaluation of radar and optical satellite images acquired between the last decade of July and mid-August proven to be the most efficient for detecting damages in maize fields caused by either by WCR or storm. Components of Shannon entropy are proven to have significant role in identification. Our project demonstrates the potential in integrated usage of polarimetric radar and optical satellite images for monitoring several types of agricultural damage.

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

Science.gov (United States)

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

2017-10-01

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

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

Science.gov (United States)

Aten, Michelle L.

2003-04-01

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

Essential Technology and Application of Jitter Detection and Compensation for High Resolution Satellites

Directory of Open Access Journals (Sweden)

TONG Xiaohua

2017-10-01

Full Text Available Satellite jitter is a common and complex phenomenon for the on-orbit high resolution satellites, which may affect the mapping accuracy and quality of imagery. A framework of jitter detection and compensation integrating data processing of multiple sensors is proposed in this paper. Jitter detection is performed based on multispectral imagery, three-line-array imagery, dense ground control and attitude measurement data, and jitter compensation is conducted both on image and on attitude with the sensor model. The platform jitter of ZY-3 satellite is processed and analyzed using the proposed technology, and the results demonstrate the feasibility and reliability of jitter detection and compensation. The variation law analysis of jitter indicates that the frequencies of jitter of ZY-3 satellite hold in the range between 0.6 and 0.7 Hz, while the amplitudes of jitter of ZY-3 satellite drop from 1 pixel in the early stage to below 0.4 pixels and tend to remain stable in the following stage.

Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery

Science.gov (United States)

Bai, Ting; Sun, Kaimin; Deng, Shiquan; Chen, Yan

2018-03-01

High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.

Coastal erosion and accretion in Pak Phanang, Thailand by GIS analysis of maps and satellite imagery

Directory of Open Access Journals (Sweden)

Sayedur Rahman Chowdhury

2013-12-01

Full Text Available Coastal erosion and accretion in Pak Phanang of southern Thailand between 1973 and 2003 was measured using multi-temporal topographic maps and Landsat satellite imageries. Within a GIS environment landward and seaward movements of shoreline was estimated by a transect-based analysis, and amounts of land accretion and erosion were estimated by a parcel-based geoprocessing. The whole longitudinal extent of the 58 kilometer coast was classified based on the erosion and accretion trends during this period using agglomerative hierarchical clustering approach. Erosion and accretion were found variable over time and space, and periodic reversal of status was also noticed in many places. Estimates of erosion were evaluated against field-survey based data, and found reasonably accurate where the rates were relatively great. Smoothing of shoreline datasets was found desirable as its impacts on the estimates remained within tolerable limits.

A general framework of TOPSIS method for integration of airborne geophysics, satellite imagery, geochemical and geological data

Science.gov (United States)

Abedi, Maysam; Norouzi, Gholam-Hossain

2016-04-01

This work presents the promising application of three variants of TOPSIS method (namely the conventional, adjusted and modified versions) as a straightforward knowledge-driven technique in multi criteria decision making processes for data fusion of a broad exploratory geo-dataset in mineral potential/prospectivity mapping. The method is implemented to airborne geophysical data (e.g. potassium radiometry, aeromagnetic and frequency domain electromagnetic data), surface geological layers (fault and host rock zones), extracted alteration layers from remote sensing satellite imagery data, and five evidential attributes from stream sediment geochemical data. The central Iranian volcanic-sedimentary belt in Kerman province at the SE of Iran that is embedded in the Urumieh-Dokhtar Magmatic Assemblage arc (UDMA) is chosen to integrate broad evidential layers in the region of prospect. The studied area has high potential of ore mineral occurrences especially porphyry copper/molybdenum and the generated mineral potential maps aim to outline new prospect zones for further investigation in future. Two evidential layers of the downward continued aeromagnetic data and its analytic signal filter are prepared to be incorporated in fusion process as geophysical plausible footprints of the porphyry type mineralization. The low values of the apparent resistivity layer calculated from the airborne frequency domain electromagnetic data are also used as an electrical criterion in this investigation. Four remote sensing evidential layers of argillic, phyllic, propylitic and hydroxyl alterations were extracted from ASTER images in order to map the altered areas associated with porphyry type deposits, whilst the ETM+ satellite imagery data were used as well to map iron oxide layer. Since potassium alteration is generally the mainstay of porphyry ore mineralization, the airborne potassium radiometry data was used. The geochemical layers of Cu/B/Pb/Zn elements and the first component of PCA

Image Fusion Applied to Satellite Imagery for the Improved Mapping and Monitoring of Coral Reefs: a Proposal

Science.gov (United States)

Gholoum, M.; Bruce, D.; Hazeam, S. Al

2012-07-01

A coral reef ecosystem, one of the most complex marine environmental systems on the planet, is defined as biologically diverse and immense. It plays an important role in maintaining a vast biological diversity for future generations and functions as an essential spawning, nursery, breeding and feeding ground for many kinds of marine species. In addition, coral reef ecosystems provide valuable benefits such as fisheries, ecological goods and services and recreational activities to many communities. However, this valuable resource is highly threatened by a number of environmental changes and anthropogenic impacts that can lead to reduced coral growth and production, mass coral mortality and loss of coral diversity. With the growth of these threats on coral reef ecosystems, there is a strong management need for mapping and monitoring of coral reef ecosystems. Remote sensing technology can be a valuable tool for mapping and monitoring of these ecosystems. However, the diversity and complexity of coral reef ecosystems, the resolution capabilities of satellite sensors and the low reflectivity of shallow water increases the difficulties to identify and classify its features. This paper reviews the methods used in mapping and monitoring coral reef ecosystems. In addition, this paper proposes improved methods for mapping and monitoring coral reef ecosystems based on image fusion techniques. This image fusion techniques will be applied to satellite images exhibiting high spatial and low to medium spectral resolution with images exhibiting low spatial and high spectral resolution. Furthermore, a new method will be developed to fuse hyperspectral imagery with multispectral imagery. The fused image will have a large number of spectral bands and it will have all pairs of corresponding spatial objects. This will potentially help to accurately classify the image data. Accuracy assessment use ground truth will be performed for the selected methods to determine the quality of the

IMAGE FUSION APPLIED TO SATELLITE IMAGERY FOR THE IMPROVED MAPPING AND MONITORING OF CORAL REEFS: A PROPOSAL

Directory of Open Access Journals (Sweden)

M. Gholoum

2012-07-01

Full Text Available A coral reef ecosystem, one of the most complex marine environmental systems on the planet, is defined as biologically diverse and immense. It plays an important role in maintaining a vast biological diversity for future generations and functions as an essential spawning, nursery, breeding and feeding ground for many kinds of marine species. In addition, coral reef ecosystems provide valuable benefits such as fisheries, ecological goods and services and recreational activities to many communities. However, this valuable resource is highly threatened by a number of environmental changes and anthropogenic impacts that can lead to reduced coral growth and production, mass coral mortality and loss of coral diversity. With the growth of these threats on coral reef ecosystems, there is a strong management need for mapping and monitoring of coral reef ecosystems. Remote sensing technology can be a valuable tool for mapping and monitoring of these ecosystems. However, the diversity and complexity of coral reef ecosystems, the resolution capabilities of satellite sensors and the low reflectivity of shallow water increases the difficulties to identify and classify its features. This paper reviews the methods used in mapping and monitoring coral reef ecosystems. In addition, this paper proposes improved methods for mapping and monitoring coral reef ecosystems based on image fusion techniques. This image fusion techniques will be applied to satellite images exhibiting high spatial and low to medium spectral resolution with images exhibiting low spatial and high spectral resolution. Furthermore, a new method will be developed to fuse hyperspectral imagery with multispectral imagery. The fused image will have a large number of spectral bands and it will have all pairs of corresponding spatial objects. This will potentially help to accurately classify the image data. Accuracy assessment use ground truth will be performed for the selected methods to determine

Satellite Remote Sensing in Offshore Wind Energy

DEFF Research Database (Denmark)

Hasager, Charlotte Bay; Badger, Merete; Astrup, Poul

2013-01-01

Satellite remote sensing of ocean surface winds are presented with focus on wind energy applications. The history on operational and research-based satellite ocean wind mapping is briefly described for passive microwave, scatterometer and synthetic aperture radar (SAR). Currently 6 GW installed...

Ground and Space Radar Volume Matching and Comparison Software

Science.gov (United States)

Morris, Kenneth; Schwaller, Mathew

2010-01-01

This software enables easy comparison of ground- and space-based radar observations. The software was initially designed to compare ground radar reflectivity from operational, ground based Sand C-band meteorological radars with comparable measurements from the Tropical Rainfall Measuring Mission (TRMM) satellite s Precipitation Radar (PR) instrument. The software is also applicable to other ground-based and space-based radars. The ground and space radar volume matching and comparison software was developed in response to requirements defined by the Ground Validation System (GVS) of Goddard s Global Precipitation Mission (GPM) project. This software innovation is specifically concerned with simplifying the comparison of ground- and spacebased radar measurements for the purpose of GPM algorithm and data product validation. This software is unique in that it provides an operational environment to routinely create comparison products, and uses a direct geometric approach to derive common volumes of space- and ground-based radar data. In this approach, spatially coincident volumes are defined by the intersection of individual space-based Precipitation Radar rays with the each of the conical elevation sweeps of the ground radar. Thus, the resampled volume elements of the space and ground radar reflectivity can be directly compared to one another.

Airborne Radar Observations of Severe Hailstorms: Implications for Future Spaceborne Radar

Science.gov (United States)

Heymsfield, Gerald M.; Tian, Lin; Li, Lihua; McLinden, Matthew; Cervantes, Jaime I.

2013-01-01

A new dual-frequency (Ku and Ka band) nadir-pointing Doppler radar on the high-altitude NASA ER-2 aircraft, called the High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP), has collected data over severe thunderstorms in Oklahoma and Kansas during the Midlatitude Continental Convective Clouds Experiment (MC3E). The overarching motivation for this study is to understand the behavior of the dualwavelength airborne radar measurements in a global variety of thunderstorms and how these may relate to future spaceborne-radar measurements. HIWRAP is operated at frequencies that are similar to those of the precipitation radar on the Tropical Rainfall Measuring Mission (Ku band) and the upcoming Global Precipitation Measurement mission satellite's dual-frequency (Ku and Ka bands) precipitation radar. The aircraft measurements of strong hailstorms have been combined with ground-based polarimetric measurements to obtain a better understanding of the response of the Ku- and Ka-band radar to the vertical distribution of the hydrometeors, including hail. Data from two flight lines on 24 May 2011 are presented. Doppler velocities were approx. 39m/s2at 10.7-km altitude from the first flight line early on 24 May, and the lower value of approx. 25m/s on a second flight line later in the day. Vertical motions estimated using a fall speed estimate for large graupel and hail suggested that the first storm had an updraft that possibly exceeded 60m/s for the more intense part of the storm. This large updraft speed along with reports of 5-cm hail at the surface, reflectivities reaching 70 dBZ at S band in the storm cores, and hail signals from polarimetric data provide a highly challenging situation for spaceborne-radar measurements in intense convective systems. The Ku- and Ka-band reflectivities rarely exceed approx. 47 and approx. 37 dBZ, respectively, in these storms.

Use of Openly Available Satellite Images for Remote Sensing Education

Science.gov (United States)

Wang, C.-K.

2011-09-01

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

Feature extraction and classification of clouds in high resolution panchromatic satellite imagery

Science.gov (United States)

Sharghi, Elan

The development of sophisticated remote sensing sensors is rapidly increasing, and the vast amount of satellite imagery collected is too much to be analyzed manually by a human image analyst. It has become necessary for a tool to be developed to automate the job of an image analyst. This tool would need to intelligently detect and classify objects of interest through computer vision algorithms. Existing software called the Rapid Image Exploitation Resource (RAPIER®) was designed by engineers at Space and Naval Warfare Systems Center Pacific (SSC PAC) to perform exactly this function. This software automatically searches for anomalies in the ocean and reports the detections as a possible ship object. However, if the image contains a high percentage of cloud coverage, a high number of false positives are triggered by the clouds. The focus of this thesis is to explore various feature extraction and classification methods to accurately distinguish clouds from ship objects. An examination of a texture analysis method, line detection using the Hough transform, and edge detection using wavelets are explored as possible feature extraction methods. The features are then supplied to a K-Nearest Neighbors (KNN) or Support Vector Machine (SVM) classifier. Parameter options for these classifiers are explored and the optimal parameters are determined.

Methods and limitations in radar target imagery

Science.gov (United States)

Bertrand, P.

An analytical examination of the reflectivity of radar targets is presented for the two-dimensional case of flat targets. A complex backscattering coefficient is defined for the amplitude and phase of the received field in comparison with the emitted field. The coefficient is dependent on the frequency of the emitted signal and the orientation of the target with respect to the transmitter. The target reflection is modeled in terms of the density of illumined, colored points independent from one another. The target therefore is represented as an infinite family of densities indexed by the observational angle. Attention is given to the reflectivity parameters and their distribution function, and to the conjunct distribution function for the color, position, and the directivity of bright points. It is shown that a fundamental ambiguity exists between the localization of the illumined points and the determination of their directivity and color.

Detection and Characterization of Hedgerows Using TerraSAR-X Imagery

Directory of Open Access Journals (Sweden)

Julie Betbeder

2014-04-01

Full Text Available Whilst most hedgerow functions depend upon hedgerow structure and hedgerow network patterns, in many ecological studies information on the fragmentation of hedgerows network and canopy structure is often retrieved in the field in small areas using accurate ground surveys and estimated over landscapes in a semi-quantitative manner. This paper explores the use of radar SAR imagery to (i detect hedgerow networks; and (ii describe the hedgerow canopy heterogeneity using TerraSAR-X imagery. The extraction of hedgerow networks was achieved using an object-oriented method using two polarimetric parameters: the Single Bounce and the Shannon Entropy derived from one TerraSAR-X image. The hedgerow canopy heterogeneity estimated from field measurements was compared with two backscattering coefficients and three polarimetric parameters derived from the same image. The results show that the hedgerow network and its fragmentation can be identified with a very good accuracy (Kappa index: 0.92. This study also reveals the high correlation between one polarimetric parameter, the Shannon entropy, and the canopy fragmentation measured in the field. Therefore, VHSR radar images can both precisely detect the presence of wooded hedgerow networks and characterize their structure, which cannot be achieved with optical images.

U.S. Government Open Internet Access to Sub-meter Satellite Data

Science.gov (United States)

Neigh, Christopher S. R> Masek, Jeffery G.; Nickeson, Jaime E.

2012-01-01

The National Geospatial-Intelligence Agency (NGA) has contracted United States commercial remote sensing companies GeoEye and Digital Globe to provide very high resolution commercial quality satellite imagery to federal/state government agencies and those projects/people who support government interests. Under NextView contract terms, those engaged in official government programs/projects can gain online access to NGA's vast global archive. Additionally, data from vendor's archives of IKONOS-2 (IK-2), OrbView-3 (OB-3), GeoEye-1 (GE-1), QuickBird-1 (QB-1), WorldView-1 (WV-1), and WorldView-2 (WV-2), sensors can also be requested under these agreements. We report here the current extent of this archive, how to gain access, and the applications of these data by Earth science investigators to improve discoverability and community use of these data. Satellite commercial quality imagery (CQI) at very high resolution (source to U.S. federal, state, and local governments for many different purposes. The rapid growth of free global CQI data has been slow to disseminate to NASA Earth Science community and programs such as the Land-Cover Land-Use Change (LCLUC) program which sees potential benefit from unprecedented access. This article evolved from a workshop held on February 23rd, 2012 between representatives from NGA, NASA, and NASA LCLUC Scientists discussion on how to extend this resource to a broader license approved community. Many investigators are unaware of NGA's archive availability or find it difficult to access CQI data from NGA. Results of studies, both quality and breadth, could be improved with CQI data by combining them with other moderate to coarse resolution passive optical Earth observation remote sensing satellites, or with RADAR or LiDAR instruments to better understand Earth system dynamics at the scale of human activities. We provide the evolution of this effort, a guide for qualified user access, and describe current to potential use of these data in

Patchiness in semi-arid dwarf shrublands: evidence from satellite ...

African Journals Online (AJOL)

... Plants; Remote sensing; Rhigozum obovatum Burch; Satellite-derived vegetation indices; Woody species; patchiness; semi-arid; dwarf shrubland; shrublands; co2; assimilation; karoo; south africa; ndvi; satellite imagery; geochemical mound; rhigozum obovatum; eriocephalus ericoides; pentzia incana; vegetation; botany

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

Science.gov (United States)

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

2014-12-01

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

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

Science.gov (United States)

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

2016-06-01

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

Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment

DEFF Research Database (Denmark)

Milzow, Christian; Krogh, Pernille Engelbredt; Bauer-Gottwein, Peter

2011-01-01

The availability of data is a major challenge for hydrological modelling in large parts of the world. Remote sensing data can be exploited to improve models of ungauged or poorly gauged catchments. In this study we combine three datasets for calibration of a rainfall-runoff model of the poorly...... gauged Okavango catchment in Southern Africa: (i) surface soil moisture (SSM) estimates derived from radar measurements onboard the Envisat satellite; (ii) radar altimetry measurements by Envisat providing river stages in the tributaries of the Okavango catchment, down to a minimum river width of about...... one hundred meters; and (iii) temporal changes of the Earth's gravity field recorded by the Gravity Recovery and Climate Experiment (GRACE) caused by total water storage changes in the catchment. The SSM data are shown to be helpful in identifying periods with over-respectively underestimation...

Radar Images of the Earth and the World Wide Web

Science.gov (United States)

Chapman, B.; Freeman, A.

1995-01-01

A perspective of NASA's Jet Propulsion Laboratory as a center of planetary exploration, and its involvement in studying the earth from space is given. Remote sensing, radar maps, land topography, snow cover properties, vegetation type, biomass content, moisture levels, and ocean data are items discussed related to earth orbiting satellite imaging radar. World Wide Web viewing of this content is discussed.

Phenology-based Spartina alterniflora mapping in coastal wetland of the Yangtze Estuary using time series of GaoFen satellite no. 1 wide field of view imagery

Science.gov (United States)

Ai, Jinquan; Gao, Wei; Gao, Zhiqiang; Shi, Runhe; Zhang, Chao

2017-04-01

Spartina alterniflora is an aggressive invasive plant species that replaces native species, changes the structure and function of the ecosystem across coastal wetlands in China, and is thus a major conservation concern. Mapping the spread of its invasion is a necessary first step for the implementation of effective ecological management strategies. The performance of a phenology-based approach for S. alterniflora mapping is explored in the coastal wetland of the Yangtze Estuary using a time series of GaoFen satellite no. 1 wide field of view camera (GF-1 WFV) imagery. First, a time series of the normalized difference vegetation index (NDVI) was constructed to evaluate the phenology of S. alterniflora. Two phenological stages (the senescence stage from November to mid-December and the green-up stage from late April to May) were determined as important for S. alterniflora detection in the study area based on NDVI temporal profiles, spectral reflectance curves of S. alterniflora and its coexistent species, and field surveys. Three phenology feature sets representing three major phenology-based detection strategies were then compared to map S. alterniflora: (1) the single-date imagery acquired within the optimal phenological window, (2) the multitemporal imagery, including four images from the two important phenological windows, and (3) the monthly NDVI time series imagery. Support vector machines and maximum likelihood classifiers were applied on each phenology feature set at different training sample sizes. For all phenology feature sets, the overall results were produced consistently with high mapping accuracies under sufficient training samples sizes, although significantly improved classification accuracies (10%) were obtained when the monthly NDVI time series imagery was employed. The optimal single-date imagery had the lowest accuracies of all detection strategies. The multitemporal analysis demonstrated little reduction in the overall accuracy compared with the

On the use of wavelet for extracting feature patterns from Multitemporal google earth satellite data sets

Science.gov (United States)

Lasaponara, R.

2012-04-01

, Masini N (2006b) Identification of archaeological buried remains based on Normalized Difference Vegetation Index (NDVI) from Quickbird satellite data. IEEE Geosci Remote S 3(3): 325-328. Lasaponara R, Masini N (2007a) Detection of archaeological crop marks by using satellite QuickBird multispectral imagery. J Archaeol Sci 34: 214-21. Lasaponara R, Masini N (2007b) Improving satellite Quickbird - based identification of landscape archaeological features trough tasselled cup transformation and PCA. 21st CIPA Symposium, Atene, 1-6 giugno 2007. Lasaponara R, Masini N (2010) Facing the archaeological looting in Peru by local spatial autocorrelation statistics of Very high resolution satellite imagery. In: Taniar D et al (Eds), Proceedings of ICSSA, The 2010 International Conference on Computational Science and its Application (Fukuoka-Japan, March 23 - 26, 2010), Springer, Berlin, 261-269. Lasaponara R, Masini N (2011) Satellite Remote Sensing in Archaeology : past, present and future. J Archaeol Sc 38: 1995-2002. Lasaponara R, Masini N, Rizzo E, Orefici G (2011) New discoveries in the Piramide Naranjada in Cahuachi (Peru) using satellite, Ground Probing Radar and magnetic investigations. J Archaeol Sci 38: 2031-2039. Lasaponara R, Masini N, Scardozzi G (2008) Satellite based archaeological research in ancient territory of Hierapolis. 1st International EARSeL Workshop. Advances in Remote Sensing for Archaeology and Cultural Heritage Management", CNR, Rome, September 30-October 4, Aracne, Rome, pp.11-16. Lillesand T M, Kiefer R W (2000) Remote Sensing and Image interpretation. John Wiley and Sons, New York. Masini N, Lasaponara R (2006) Satellite-based recognition of landscape archaeological features related to ancient human transformation. J Geophys Eng 3: 230-235, doi:10.1088/1742-2132/3/3/004. Masini N, Lasaponara R (2007) Investigating the spectral capability of QuickBird data to detect archaeological remains buried under vegetated and not vegetated areas. J Cult Heri 8 (1

Wave activity (planetary, tidal) throughout the middle atmosphere (20-100km) over the CUJO network: Satellite (TOMS) and Medium Frequency (MF) radar observations

OpenAIRE

A. H. Manson; C. E. Meek; T. Chshyolkova; S. K. Avery; D. Thorsen; J. W. MacDougall; W. Hocking; Y. Murayama; K. Igarashi

2005-01-01

Planetary and tidal wave activity in the tropopause-lower stratosphere and mesosphere-lower thermosphere (MLT) is studied using combinations of ground-based (GB) and satellite instruments (2000-2002). The relatively new MFR (medium frequency radar) at Platteville (40° N, 105° W) has provided the opportunity to create an operational network of middle-latitude MFRs, stretching from 81° W-142° E, which provides winds and tides 70-100km. CUJO (Canada U.S. Japan Opp...

Ambiguity Function and Resolution Characteristic Analysis of DVB-S Signal for Passive Radar

Directory of Open Access Journals (Sweden)

Jin Wei

2012-12-01

Full Text Available This paper gives the performance research on the ambiguity function and resolution of passive radar based on DVB-S (Digital Video Broadcasting-Satellite signal. The radar system structure and signal model of DVB-S signal are firstly studied, then the ambiguity function of DVB-S signal is analyzed. At last, it has been obtained how the bistatic radar position impacts the resolution. Theoretical analyses and computer simulation show that DVB-S signal is applicable as an illuminator for passive radar.

Modelling avian biodiversity using raw, unclassified satellite imagery.

Science.gov (United States)

St-Louis, Véronique; Pidgeon, Anna M; Kuemmerle, Tobias; Sonnenschein, Ruth; Radeloff, Volker C; Clayton, Murray K; Locke, Brian A; Bash, Dallas; Hostert, Patrick

2014-01-01

Applications of remote sensing for biodiversity conservation typically rely on image classifications that do not capture variability within coarse land cover classes. Here, we compare two measures derived from unclassified remotely sensed data, a measure of habitat heterogeneity and a measure of habitat composition, for explaining bird species richness and the spatial distribution of 10 species in a semi-arid landscape of New Mexico. We surveyed bird abundance from 1996 to 1998 at 42 plots located in the McGregor Range of Fort Bliss Army Reserve. Normalized Difference Vegetation Index values of two May 1997 Landsat scenes were the basis for among-pixel habitat heterogeneity (image texture), and we used the raw imagery to decompose each pixel into different habitat components (spectral mixture analysis). We used model averaging to relate measures of avian biodiversity to measures of image texture and spectral mixture analysis fractions. Measures of habitat heterogeneity, particularly angular second moment and standard deviation, provide higher explanatory power for bird species richness and the abundance of most species than measures of habitat composition. Using image texture, alone or in combination with other classified imagery-based approaches, for monitoring statuses and trends in biological diversity can greatly improve conservation efforts and habitat management.

Dual-Polarized L-Band SAR Imagery for Temporal Monitoring of Marine Oil Slick Concentration

Directory of Open Access Journals (Sweden)

Sébastien Angelliaume

2018-06-01

Full Text Available SAR sensors are usually used in the offshore domain to detect marine oil slicks which allows the authorities to guide cleanup operations or prosecute polluters. As radar imagery can be used any time of day or year and in almost any weather conditions, the use and programming of such remote sensing data is usually favored over optical imagery. Nevertheless, images collected in the optical domain provide access to key information not accessible today by SAR instruments, such as the thickness or the amount of pollutant. To address this knowledge gap, a methodology based on the joint use of a scattering model (U-WCA and remote sensing data collected by a low frequency (e.g., L-band imaging radar over controlled release of mineral oil spill is reported in this paper. The proposed method allows estimation of the concentration of pollutant within an oil-in-water mixture as well as the temporal variation of this quantity due to weathering processes.

Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations

Directory of Open Access Journals (Sweden)

Ana F. Militino

2018-03-01

Full Text Available Multitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated to properly exploit the remote sensing information. In this study, we show how ground-truth data from rain gauge stations can improve the quality of satellite imagery. To this end, a simulation study is conducted wherein different sizes of outlier outbreaks are spread and randomly introduced in the normalized difference vegetation index (NDVI and the day and night land surface temperature (LST of composite images from Navarre (Spain between 2011 and 2015. To remove outliers, a new method called thin-plate splines with covariates (TpsWc is proposed. This method consists of smoothing the median anomalies with a thin-plate spline model, whereby transformed ground-truth data are the external covariates of the model. The performance of the proposed method is measured with the square root of the mean square error (RMSE, calculated as the root of the pixel-by-pixel mean square differences between the original data and the predicted data with the TpsWc model and with a state-space model with and without covariates. The study shows that the use of ground-truth data reduces the RMSE in both the TpsWc model and the state-space model used for comparison purposes. The new method successfully removes the abnormal data while preserving the phenology of the raw data. The RMSE reduction percentage varies according to the derived variables (NDVI or LST, but reductions of up to 20% are achieved with the new proposal.

Proposed satellite position determination systems and techniques for Geostationary Synthetic Aperture Radar

OpenAIRE

Martin Fuster, Roger; Fernández Usón, Marc; Casado Blanco, David; Broquetas Ibars, Antoni

2016-01-01

This paper proposes two different calibration techniques for Geostationary Synthetic Aperture Radar (GEOSAR) missions requiring a high precision positioning, based on Active Radar Calibrators and Ground Based Interferometry. The research is enclosed in the preparation studies of a future GEOSAR mission providing continuous monitoring at continental scale. Peer Reviewed

Synergism of optical and radar data for forest structure and biomass / Sinergismo entre dados ópticos e de radar da estrutura da floresta e biomassa

Directory of Open Access Journals (Sweden)

Sassan S. Saatchi

2010-10-01

Full Text Available AbstractThe structure of forests, the three-dimensional arrangement of individual trees, has a profound effect on how ecosystems function and carbon cycle, water and nutrients. Repeated optical satellite observations of vegetation patterns in two-dimensions have made significant contributions to our understanding of the state and dynamics of the global biosphere. Recent advances in Remote Sensing technology allow us to view the biosphere in three-dimensions and provide us with refined measurements of horizontal as well as vertical structure of forests. This paper provides an overview of the recent advances in fusion of optical and radar imagery in assessing terrestrial ecosystem structure and aboveground biomass. In particular, the paper will focus on radar and LIDAR sensors from recent and planned spaceborne missions and provide theoretical and practical applications of the measurements. Finally, the relevance of these measurements for reducing the uncertainties of terrestrial carbon cycle and the response of ecosystems to future climate will be discussed in details. ResumoA estrutura de florestas, o arranjo tridimensional de árvores individuais, tem um efeito profundo sobre o funcionamento dos ecossistemas e do ciclo do carbono, água e nutrientes. Repetidas observações de satélite óptico de padrões de vegetação em duas dimensões trouxeram contribuições significativas para a nossa compreensão do estado e da dinâmica da biosfera global. Recentes avanços na tecnologia de Sensoriamento Remoto nos permitem ver a biosfera em três dimensões e nos fornecer medições apuradas da estrutura horizontal, bem como a vertical das florestas. Esse artigo fornece uma visão geral dos recentes avanços na fusão de imagens ópticas e de radar para avaliar a estrutura do ecossistema terrestre e biomassa. Em particular, o trabalho concentra-se em sensores radar e LIDAR de recentes missões espaciais planejadas e fornece aplicações teóricas e

Stormwater runoff plumes in the Southern California Bight: A comparison study with SAR and MODIS imagery.

Science.gov (United States)

Holt, Benjamin; Trinh, Rebecca; Gierach, Michelle M

2017-05-15

Stormwater runoff is the largest source of pollution in the Southern California Bight (SCB), resulting from untreated runoff and pollutants from urban watersheds entering the coastal waters after rainstorms. We make use of both satellite SAR and MODIS-Aqua ocean color imagery to examine two different components of runoff plumes, the surface slick and the sediment discharge. We expand on earlier satellite SAR studies by examining an extensive collection of multi-platform SAR imagery, spanning from 1992 to 2014, that provides a more comprehensive view of the plume surface slick characteristics, illustrated with distribution maps of the extent and flow direction of the plumes. The SAR-detected surface plumes are compared with coincident rain and runoff measurements, and with available measured shoreline fecal bacteria loads. We illustrate differences in the detection of SAR surface plumes with the sediment-related discharge plumes derived from MODIS imagery. A conceptual satellite stormwater runoff monitoring approach is presented. Copyright © 2017 Elsevier Ltd. All rights reserved.

Enhanced tactical radar correlator (ETRAC): true interoperability of the 1990s

Science.gov (United States)

Guillen, Frank J.

1994-10-01

The enhanced tactical radar correlator (ETRAC) system is under development at Westinghouse Electric Corporation for the Army Space Program Office (ASPO). ETRAC is a real-time synthetic aperture radar (SAR) processing system that provides tactical IMINT to the corps commander. It features an open architecture comprised of ruggedized commercial-off-the-shelf (COTS), UNIX based workstations and processors. The architecture features the DoD common SAR processor (CSP), a multisensor computing platform to accommodate a variety of current and future imaging needs. ETRAC's principal functions include: (1) Mission planning and control -- ETRAC provides mission planning and control for the U-2R and ASARS-2 sensor, including capability for auto replanning, retasking, and immediate spot. (2) Image formation -- the image formation processor (IFP) provides the CPU intensive processing capability to produce real-time imagery for all ASARS imaging modes of operation. (3) Image exploitation -- two exploitation workstations are provided for first-phase image exploitation, manipulation, and annotation. Products include INTEL reports, annotated NITF SID imagery, high resolution hard copy prints and targeting data. ETRAC is transportable via two C-130 aircraft, with autonomous drive on/off capability for high mobility. Other autonomous capabilities include rapid setup/tear down, extended stand-alone support, internal environmental control units (ECUs) and power generation. ETRAC's mission is to provide the Army field commander with accurate, reliable, and timely imagery intelligence derived from collections made by the ASARS-2 sensor, located on-board the U-2R aircraft. To accomplish this mission, ETRAC receives video phase history (VPH) directly from the U-2R aircraft and converts it in real time into soft copy imagery for immediate exploitation and dissemination to the tactical users.

Using satellite imagery for qualitative evaluation of plume transport in modeling the effects of the Kuwait oil fire smoke plumes

International Nuclear Information System (INIS)

Bass, A.; Janota, P.

1992-01-01

To forecast the behavior of the Kuwait oil fire smoke plumes and their possible acute or chronic health effects over the Arabian Gulf region, TASC created a comprehensive health and environmental impacts modeling system. A specially-adapted Lagrangian puff transport model was used to create (a) short-term (multiday) forecasts of plume transport and ground-level concentrations of soot and SO 2 ; and (b) long-term (seasonal and longer) estimates of average surface concentrations and depositions. EPA-approved algorithms were used to transform exposures to SO 2 and soot (as PAH/BaP) into morbidity, mortality and crop damage risks. Absent any ground truth, satellite imagery from the NOAA Polar Orbiter and the ESA Geostationary Meteosat offered the only opportunity for timely qualitative evaluation of the long-range plume transport and diffusion predictions. This paper shows the use of actual satellite images (including animated loops of hourly Meteosat images) to evaluate plume forecasts in near-real-time, and to sanity-check the meso- and long-range plume transport projections for the long-term estimates. Example modeled concentrations, depositions and health effects are shown

National Satellite Land Remote Sensing Data Archive

Science.gov (United States)

Faundeen, John L.; Longhenry, Ryan

2018-06-13

The National Satellite Land Remote Sensing Data Archive is managed on behalf of the Secretary of the Interior by the U.S. Geological Survey’s Earth Resources Observation and Science Center. The Land Remote Sensing Policy Act of 1992 (51 U.S.C. §601) directed the U.S. Department of the Interior to establish a permanent global archive consisting of imagery over land areas obtained from satellites orbiting the Earth. The law also directed the U.S. Department of the Interior, delegated to the U.S. Geological Survey, to ensure proper storage and preservation of imagery, and timely access for all parties. Since 2008, these images have been available at no cost to the user.

Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment

Directory of Open Access Journals (Sweden)

C. Milzow

2011-06-01

Full Text Available The availability of data is a major challenge for hydrological modelling in large parts of the world. Remote sensing data can be exploited to improve models of ungauged or poorly gauged catchments. In this study we combine three datasets for calibration of a rainfall-runoff model of the poorly gauged Okavango catchment in Southern Africa: (i surface soil moisture (SSM estimates derived from radar measurements onboard the Envisat satellite; (ii radar altimetry measurements by Envisat providing river stages in the tributaries of the Okavango catchment, down to a minimum river width of about one hundred meters; and (iii temporal changes of the Earth's gravity field recorded by the Gravity Recovery and Climate Experiment (GRACE caused by total water storage changes in the catchment. The SSM data are shown to be helpful in identifying periods with over-respectively underestimation of the precipitation input. The accuracy of the radar altimetry data is validated on gauged subbasins of the catchment and altimetry data of an ungauged subbasin is used for model calibration. The radar altimetry data are important to condition model parameters related to channel morphology such as Manning's roughness. GRACE data are used to validate the model and to condition model parameters related to various storage compartments in the hydrological model (e.g. soil, groundwater, bank storage etc.. As precipitation input the FEWS-Net RFE, TRMM 3B42 and ECMWF ERA-Interim datasets are considered and compared.

Coastline changes in North Bengkalis Island, Indonesia: satellite imagery analysis and observation

Directory of Open Access Journals (Sweden)

M Mubarak

2018-01-01

Full Text Available Coastal area activity on human exploitation greatly affected aquatic ecosystems. Land changes disturbed the level of soil stability, soil will be easily eroded by the flow of water, the surface tide ran off to the sea. North waters of the island of Bengkalis is a place boiling down to several rivers, including the river Jangkang and river Liung. The rivers have affected the concentration of total suspended solid (TSS in the strait waters of North Bengkalis Island. This research demonstrated water sampling by using sampling point determined by purposive sampling method mixing the layer of water depth ratio. The results based on satellite imagery data showed that TSS was quite high in the West season period until the transition period I (West to East with a large concentration value of 200 mg / L. For the lowest TSS concentration occurred in the East season i.e., between 0 - 200 mg/L. TSS concentrations that dominated in the East season ranged from 51 to 75 mg/L This value was higher than the TSS concentration of field data analysis, i.e., between 23 - 39 mg/L. Changes of coastal coastline of North Bengkalis during the last 20 years continue to change the size of the land area, with a land area of 131 ha lost.

Mapping the Distribution of Cloud Forests Using MODIS Imagery

Science.gov (United States)

Douglas, M. W.; Mejia, J.; Murillo, J.; Orozco, R.

2007-05-01

Tropical cloud forests - those forests that are frequently immersed in clouds or otherwise very humid, are extremely difficult to map from the ground, and are not easily distinguished in satellite imagery from other forest types, but they have a very different flora and fauna than lowland rainforest. Cloud forests, although found in many parts of the tropics, have a very restricted vertical extent and thus are also restricted horizontally. As a result, they are subject to both human disturbance (coffee growing for example) and the effects of possible climate change. Motivated by a desire to seek meteorological explanations for the distribution of cloud forests, we have begun to map cloudiness using MODIS Terra and Aqua visible imagery. This imagery, at ~1030 LT and 1330 LT, is an approximation for mid-day cloudiness. In tropical regions the amount of mid-day cloudiness strongly controls the shortwave radiation and thus the potential for evaporation (and aridity). We have mapped cloudiness using a simple algorithm that distinguishes between the cloud-free background brightness and the generally more reflective clouds to separate clouds from the underlying background. A major advantage of MODIS imagery over many other sources of satellite imagery is its high spatial resolution (~250m). This, coupled with precisely navigated images, means that detailed maps of cloudiness can be produced. The cloudiness maps can then be related to the underlying topography to further refine the location of the cloud forests. An advantage of this technique is that we are mapping the potential cloud forest, based on cloudiness, rather than the actual cloud forest, which are commonly based on forest estimates from satellite and digital elevation data. We do not derive precipitation, only estimates of daytime cloudiness. Although only a few years of MODIS imagery has been used in our studies, we will show that this is sufficient to describe the climatology of cloudiness with acceptable

Accuracy Analysis Comparison of Supervised Classification Methods for Anomaly Detection on Levees Using SAR Imagery

Directory of Open Access Journals (Sweden)

Ramakalavathi Marapareddy

2017-10-01

Full Text Available This paper analyzes the use of a synthetic aperture radar (SAR imagery to support levee condition assessment by detecting potential slide areas in an efficient and cost-effective manner. Levees are prone to a failure in the form of internal erosion within the earthen structure and landslides (also called slough or slump slides. If not repaired, slough slides may lead to levee failures. In this paper, we compare the accuracy of the supervised classification methods minimum distance (MD using Euclidean and Mahalanobis distance, support vector machine (SVM, and maximum likelihood (ML, using SAR technology to detect slough slides on earthen levees. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s uninhabited aerial vehicle synthetic aperture radar (UAVSAR. The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers.

Characterization of precipitation features over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

Science.gov (United States)

Prat, O. P.; Nelson, B. R.

2013-12-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, surface observations, and models to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets of TMPA 3B42, CMORPH, and PERSIANN. The satellite based QPEs are compared over the concurrent period with the NCEP Stage IV product, which is a near real time product providing precipitation data at the hourly temporal scale gridded at a nominal 4-km spatial resolution. In addition, remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model), which provides gridded precipitation estimates that are used as a baseline for multi-sensor QPE products comparison. The comparisons are performed at the annual, seasonal, monthly, and daily scales with focus on selected river basins (Southeastern US, Pacific Northwest, Great Plains). While, unconditional annual rain rates present a satisfying agreement between all products, results suggest that satellite QPE datasets exhibit important biases in particular at higher rain rates (≥4 mm/day). Conversely, on seasonal scales differences between remotely sensed data and ground surface observations can be greater than 50% and up to 90% for low daily accumulation (≤1 mm/day) such as in the Western US (summer) and Central US (winter). The conditional analysis performed using different daily rainfall accumulation thresholds (from low rainfall intensity to intense precipitation) shows that while intense events measured at the ground are infrequent (around 2% for daily accumulation above 2 inches/day), remotely sensed products displayed differences from 20-50% and up to 90-100%. A discussion on the impact of differing spatial and temporal resolutions with respect to the datasets ability to capture extreme

Synthetic aperture radar ship discrimination, generation and latent variable extraction using information maximizing generative adversarial networks

CSIR Research Space (South Africa)

Schwegmann, Colin P

2017-07-01

Full Text Available such as Synthetic Aperture Radar imagery. To aid in the creation of improved machine learning-based ship detection and discrimination methods this paper applies a type of neural network known as an Information Maximizing Generative Adversarial Network. Generative...

Supraglacial Lakes in the Percolation Zone of the Western Greenland Ice Sheet: Formation and Development using Operation IceBridge Snow Radar and ATM (2009-2014)

Science.gov (United States)

Chen, C.; Howat, I. M.; de la Peña, S.

2015-12-01

Surface meltwater lakes on the Greenland Ice Sheet have appeared at higher elevations, extending well into the percolation zone, under recent warming, with the largest expansion occurring in the western Greenland Ice Sheet. The conditions that allow lakes to form atop firn are poorly constrained, but the formation of new lakes imply changes in the permeability of the firn at high elevations, promoting meltwater runoff. We explore the formation and evolution of new surface lakes in this region above 1500 meters, using a combination of satellite imagery and repeat Snow (2-6.5 GHz) radar echograms and LIDAR measurements from NASA's Operation IceBridge of 2009-2014. We identify conditions for surface lake formation at their farthest inland extent and suggest behaviors of persistence and lake drainage are due to differences in regional ice dynamics.

Spaceborne Radar for Mapping Forest and Land Use Changes

DEFF Research Database (Denmark)

Joshi, Neha Pankaj

Degradation (REDD+). The implementation and effectiveness of such mechanisms relies partially on continuous observations of forests using satellite technology and partially on ground-based measurements of forest aboveground volume/biomass (AGV/AGB), carbon density and changes therein. Together, these means...... of forest monitoring enable the development of policies and measures to alter current trends in global forest and biodiversity loss. This thesis investigates the use of long wavelength (~23 cm, L-band) spaceborne radar, which has all-weather and canopy-penetration capabilities, acquired by the Advanced Land...... Observing Satellite (ALOS) for forest monitoring. Using a combination of local expert knowledge, plot inventories, and data from lidar and optical sensors, it aims to understand (1) whether forest disturbance dynamics may be detected with radar, and (2) what physical and macroecological properties influence...

Correcting bias in the rational polynomial coefficients of satellite imagery using thin-plate smoothing splines

Science.gov (United States)

Shen, Xiang; Liu, Bin; Li, Qing-Quan

2017-03-01

The Rational Function Model (RFM) has proven to be a viable alternative to the rigorous sensor models used for geo-processing of high-resolution satellite imagery. Because of various errors in the satellite ephemeris and instrument calibration, the Rational Polynomial Coefficients (RPCs) supplied by image vendors are often not sufficiently accurate, and there is therefore a clear need to correct the systematic biases in order to meet the requirements of high-precision topographic mapping. In this paper, we propose a new RPC bias-correction method using the thin-plate spline modeling technique. Benefiting from its excellent performance and high flexibility in data fitting, the thin-plate spline model has the potential to remove complex distortions in vendor-provided RPCs, such as the errors caused by short-period orbital perturbations. The performance of the new method was evaluated by using Ziyuan-3 satellite images and was compared against the recently developed least-squares collocation approach, as well as the classical affine-transformation and quadratic-polynomial based methods. The results show that the accuracies of the thin-plate spline and the least-squares collocation approaches were better than the other two methods, which indicates that strong non-rigid deformations exist in the test data because they cannot be adequately modeled by simple polynomial-based methods. The performance of the thin-plate spline method was close to that of the least-squares collocation approach when only a few Ground Control Points (GCPs) were used, and it improved more rapidly with an increase in the number of redundant observations. In the test scenario using 21 GCPs (some of them located at the four corners of the scene), the correction residuals of the thin-plate spline method were about 36%, 37%, and 19% smaller than those of the affine transformation method, the quadratic polynomial method, and the least-squares collocation algorithm, respectively, which demonstrates

Automated Extraction of Crop Area Statistics from Medium-Resolution Imagery, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — This project will focus on the strategic, routine incorporation of medium-resolution satellite imagery into operational agricultural assessments for the global crop...

VHF/UHF imagery and RCS measurements of ground targets in forested terrain

Science.gov (United States)

Gatesman, Andrew J.; Beaudoin, Christopher J.; Giles, Robert H.; Waldman, Jerry; Nixon, William E.

2002-08-01

The monostatic VV and HH-polarized radar signatures of several targets and trees have been measured at foliage penetration frequencies (VHF/UHF) by using 1/35th scale models and an indoor radar range operating at X-band. An array of high-fidelity scale model ground vehicles and test objects as well as scaled ground terrain and trees have been fabricated for the study. Radar measurement accuracy has been confirmed by comparing the signature of a test object with a method of moments radar cross section prediction code. In addition to acquiring signatures of targets located on a smooth, dielectric ground plane, data have also been acquired with targets located in simulated wooded terrain that included scaled tree trunks and tree branches. In order to assure the correct backscattering behavior, all dielectric properties of live tree wood and moist soil were scaled properly to match the complex dielectric constant of the full-scale materials. The impact of the surrounding tree clutter on the VHF/UHF radar signatures of ground vehicles was accessed. Data were processed into high-resolution, polar-formatted ISAR imagery and signature comparisons are made between targets in open-field and forested scenarios.

Imaging of concrete specimens using inverse synthetic aperture radar

International Nuclear Information System (INIS)

Rhim, Hong C.; Buyukozturk, Oral

2000-01-01

Radar Measurement results of laboratory size concrete specimens are presented in this paper. The purpose of this research work is to study various aspects of the radar method in an effort to develop an improved radar system for nondestructive testing of concrete structures. The radar system used for the study is an Inverse Synthetic Aperture Radar (ISAR), which is capable of transmitting microwaves at three different frequency ranges of 2-3.4, 3.4-5.8, and 8-12 GHz. Radar measurement setup is such that the radar is locates 14.4 m away from a concrete target to satisfy a far-field criterion. The concrete target is rotated for 20 degrees during the measurements for the generation of two-dimensional (cross-range) imagery. Concrete targets used for the measurements have the dimensions of 305 mm (width)x305 mm (height)x92 mm (thickness) with different inside configurations. Comparisons are made for dry and wet specimens, specimens with and without inclusions. Each specimen is made to model various situations that a concrete structure can have in reality. Results show that center frequency, frequency bandwidth, and polarization of the incident wave have different effects on identifying the thickness or inclusions inside concrete specimens. Results also suggest that a certain combination of measurement parameters is suitable for a specific application area. Thus, measurement parameters can be optimized for a specific problem. The findings are presented and discussed in details in the paper. Signal processing schemes implemented for imaging of the specimens are also discussed

Ocean Wave Parameters Retrieval from Sentinel-1 SAR Imagery

Directory of Open Access Journals (Sweden)

Weizeng Shao

2016-08-01

Full Text Available In this paper, a semi-empirical algorithm for significant wave height (Hs and mean wave period (Tmw retrieval from C-band VV-polarization Sentinel-1 synthetic aperture radar (SAR imagery is presented. We develop a semi-empirical function for Hs retrieval, which describes the relation between Hs and cutoff wavelength, radar incidence angle, and wave propagation direction relative to radar look direction. Additionally, Tmw can be also calculated through Hs and cutoff wavelength by using another empirical function. We collected 106 C-band stripmap mode Sentinel-1 SAR images in VV-polarization and wave measurements from in situ buoys. There are a total of 150 matchup points. We used 93 matchups to tune the coefficients of the semi-empirical algorithm and the rest 57 matchups for validation. The comparison shows a 0.69 m root mean square error (RMSE of Hs with a 18.6% of scatter index (SI and 1.98 s RMSE of Tmw with a 24.8% of SI. Results indicate that the algorithm is suitable for wave parameters retrieval from Sentinel-1 SAR data.

Landsat imagery: a unique resource

Science.gov (United States)

Miller, H.; Sexton, N.; Koontz, L.

2011-01-01

Landsat satellites provide high-quality, multi-spectral imagery of the surface of the Earth. These moderate-resolution, remotely sensed images are not just pictures, but contain many layers of data collected at different points along the visible and invisible light spectrum. These data can be manipulated to reveal what the Earth’s surface looks like, including what types of vegetation are present or how a natural disaster has impacted an area (Fig. 1).

Transition, Training, and Assessment of Multispectral Composite Imagery in Support of the NWS Aviation Forecast Mission

Science.gov (United States)

Fuell, Kevin; Jedlovec, Gary; Leroy, Anita; Schultz, Lori

2015-01-01

The NASA/Short-term Prediction, Research, and Transition (SPoRT) Program works closely with NOAA/NWS weather forecasters to transition unique satellite data and capabilities into operations in order to assist with nowcasting and short-term forecasting issues. Several multispectral composite imagery (i.e. RGB) products were introduced to users in the early 2000s to support hydrometeorology and aviation challenges as well as incident support. These activities lead to SPoRT collaboration with the GOES-R Proving Ground efforts where instruments such as MODIS (Aqua, Terra) and S-NPP/VIIRS imagers began to be used as near-realtime proxies to future capabilities of the Advanced Baseline Imager (ABI). One of the composite imagery products introduced to users was the Night-time Microphysics RGB, originally developed by EUMETSAT. SPoRT worked to transition this imagery to NWS users, provide region-specific training, and assess the impact of the imagery to aviation forecast needs. This presentation discusses the method used to interact with users to address specific aviation forecast challenges, including training activities undertaken to prepare for a product assessment. Users who assessed the multispectral imagery ranged from southern U.S. inland and coastal NWS weather forecast offices (WFOs), to those in the Rocky Mountain Front Range region and West Coast, as well as highlatitude forecasters of Alaska. These user-based assessments were documented and shared with the satellite community to support product developers and the broad users of new generation satellite data.

Etude par imagerie radar des pollutions pétrolières

Directory of Open Access Journals (Sweden)

G. Rees

2004-06-01

Full Text Available En Russie, chaque année, les déversements de pétrole représentent un cinquième de la production totale. Les plus importants se produisent dans le domaine périglaciaire où le milieu naturel est d’une extrême fragilité. Les pipelines sont soumis à de rudes conditions notamment à la corrosion et aux processus cryogéniques. Le risque de rupture augmente en conséquence. La surveillance des déversements d’hydrocarbures, contrainte par l’immensité et la fréquente inaccessibilité du réseau de pipelines, peut être réalisée par le recours à la télédétection. L’objectif de ce travail est de fournir, à travers l’exemple de la catastrophe d’Usinsk (Rép. de Komi survenue en 1994, des outils d’analyse des images radar. Outre leur capacité à s’affranchir du couvert nuageux, les capteurs radar apportent des informations complémentaires à celles fournies par des capteurs optiques. L’exploitation thématique de ces images est rendue difficile par un certain nombre de facteurs perturbateurs tels que les caractéristiques propres du capteur, la direction de visée, la topographie et le phénomène de chatoiement (speckle. Le travail présenté montre le rôle prépondérant joué par ces corrections qui permettent de disposer de données quantitatives comparables d’une date à une autre. La méthode mise au point ici pour le suivi temporel de l’épanchement de pétrole d’Usinsk à partir de trois images radar, est discutée notamment dans ses limites et son aspect opérationnel possible.

Toward Monitoring Surface and Subsurface Lakes on the Greenland Ice Sheet Using Sentinel-1 SAR and Landsat-8 OLI Imagery

Directory of Open Access Journals (Sweden)

Katie E. Miles

2017-07-01

Full Text Available Supraglacial lakes are an important component of the Greenland Ice Sheet's mass balance and hydrology, with their drainage affecting ice dynamics. This study uses imagery from the recently launched Sentinel-1A Synthetic Aperture Radar (SAR satellite to investigate supraglacial lakes in West Greenland. A semi-automated algorithm is developed to detect surface lakes from Sentinel-1 images during the 2015 summer. A combined Landsat-8 and Sentinel-1 dataset, which has a comparable temporal resolution to MODIS (3 days vs. daily but a higher spatial resolution (25–40 vs. 250–500 m, is then used together with a fully automated lake drainage detection algorithm. Rapid (<4 days and slow (>4 days drainages are investigated for both small (<0.125 km2, the minimum size detectable by MODIS and large (≥0.125 km2 lakes through the summer. Drainage events of small lakes occur at lower elevations (mean 159 m, and slightly earlier (mean 4.5 days in the melt season than those of large lakes. The analysis is extended manually into the early winter to calculate the dates and elevations of lake freeze-through more precisely than is possible with optical imagery (mean 30 August; 1,270 m mean elevation. Finally, the Sentinel-1 imagery is used to detect subsurface lakes and, for the first time, their dates of appearance and freeze-through (mean 9 August and 7 October, respectively. These subsurface lakes occur at higher elevations than the surface lakes detected in this study (mean 1,593 and 1,185 m, respectively. Sentinel-1 imagery therefore provides great potential for tracking melting, water movement and freezing within both the firn zone and ablation area of the Greenland Ice Sheet.

Forest-cover-type separation using RADARSAT-1 synthetic aperture radar imagery

Science.gov (United States)

Mark D. Nelson; Kathleen T. Ward; Marvin E. Bauer

2009-01-01

RADARSAT-1 synthetic aperture radar data, speckle reduction, and texture measures provided for separation among forest types within the Twin Cities metropolitan area, MN, USA. The highest transformed divergence values for 16-bit data resulted from speckle filtering while the highest values for 8-bit data resulted from the orthorectified image, before and after...

Assimilation of radar altimetry to a routing model of the Brahmaputra River

DEFF Research Database (Denmark)

Michailovsky, Claire Irene B.; Milzow, Christian; Bauer-Gottwein, Peter

2013-01-01

.78 to 0.84. The method uses very little in situ data and is easily implemented as an add-on to hydrological models, and it therefore has the potential for large-scale application to improve hydrological predictions in many river basins. ©2013. American Geophysical Union. All Rights Reserved.......While satellite-based remote sensing has provided hydrologists with valuable new data sets, integration of such data sets in operational modeling systems is usually not straightforward due to spatial or temporal resolution issues or because remote sensing does not directly measure the hydrological...... quantities of interest. This is the case for satellite-based radar altimetry. River-level variations can be tracked using radar altimetry at a temporal resolution between 10 and 35 days, depending on the satellite, but hydrologists are typically interested in river flows rather than levels and require...

Spatial estimation of air PM2.5 emissions using activity data, local emission factors and land cover derived from satellite imagery

Science.gov (United States)

Gibe, Hezron P.; Cayetano, Mylene G.

2017-09-01

Exposure to particulate matter (PM) is a serious environmental problem in many urban areas on Earth. In the Philippines, most existing studies and emission inventories have mainly focused on point and mobile sources, while research involving human exposures to particulate pollutants is rare. This paper presents a method for estimating the amount of fine particulate (PM2.5) emissions in a test study site in the city of Cabanatuan, Nueva Ecija, in the Philippines, by utilizing local emission factors, regionally procured data, and land cover/land use (activity data) interpreted from satellite imagery. Geographic information system (GIS) software was used to map the estimated emissions in the study area. The present results suggest that vehicular emissions from motorcycles and tricycles, as well as fuels used by households (charcoal) and burning of agricultural waste, largely contribute to PM2.5 emissions in Cabanatuan. Overall, the method used in this study can be applied in other small urbanizing cities, as long as on-site specific activity, emission factor, and satellite-imaged land cover data are available.

Laser radar cross-section estimation from high-resolution image data.

Science.gov (United States)

Osche, G R; Seeber, K N; Lok, Y F; Young, D S

1992-05-10

A methodology for the estimation of ladar cross sections from high-resolution image data of geometrically complex targets is presented. Coherent CO(2) laser radar was used to generate high-resolution amplitude imagery of a UC-8 Buffalo test aircraft at a range of 1.3 km at nine different aspect angles. The average target ladar cross section was synthesized from these data and calculated to be sigma(T) = 15.4 dBsm, which is similar to the expected microwave radar cross sections. The aspect angle dependence of the cross section shows pronounced peaks at nose on and broadside, which are also in agreement with radar results. Strong variations in both the mean amplitude and the statistical distributions of amplitude with the aspect angle have also been observed. The relative mix of diffuse and specular returns causes significant deviations from a simple Lambertian or Swerling II target, especially at broadside where large normal surfaces are present.

DSM GENERATION FROM HIGH RESOLUTION COSMO-SKYMED IMAGERY WITH RADARGRAMMETRIC MODEL

OpenAIRE

P. Capaldo; M. Crespi; F. Fratarcangeli; A. Nascetti; F. Pieralice

2012-01-01

The availability of new high resolution radar spaceborne sensors offers new interesting potentialities for the geomatics application: spatial and temporal change detection, features extraction, generation of Digital Surface (DSMs). As regards the DSMs generation from new high resolution data (as SpotLight imagery), the development and the accuracy assessment of method based on radargrammetric approach are topics of great interest and relevance. The aim of this investigation is the DSM generat...

Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002-2012)

Science.gov (United States)

Prat, O. P.; Nelson, B. R.

2015-04-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over the contiguous United States (CONUS) for the period 2002-2012. This comparison effort includes satellite multi-sensor data sets (bias-adjusted TMPA 3B42, near-real-time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation data sets are compared with surface observations from the Global Historical Climatology Network-Daily (GHCN-D) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (±6%). However, differences at the RFC are more important in particular for near-real-time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near-real-time counterpart 3B42RT. However, large biases remained for 3B42 over the western USA for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in. day-1) over the Pacific Northwest. Furthermore, the conditional analysis and a contingency analysis conducted illustrated the challenge in retrieving extreme precipitation from remote sensing estimates.

Cassini RADAR Observations of Phoebe, Iapetus, Enceladus, and Rhea

Science.gov (United States)

Ostro, S. J.; West, R. D.; Janssen, M. A.; Zebker, H. A.; Wye, L. C.; Lunine, J. I.; Lopes, R. M.; Kelleher, K.; Hamilton, G. A.; Gim, Y.; Anderson, Y. Z.; Boehmer, R. A.; Lorenz, R. D.

2005-12-01

Operating in its scatterometry mode, the Cassini radar has obtained 2.2-cm-wavelength echo power spectra from Phoebe on the inbound and outbound legs of its flyby (subradar points at W. Long, Lat. = 245,-22 deg and 328,+27 deg), from Iapetus' leading side (66,+39 deg) and trailing side (296,+44 deg) on the inbound and outbound legs of orbit BC, from Enceladus during orbits 3 (0,0 deg) and 4 (70,-13 deg), and from Rhea during orbit 11 (64,-77 deg). Our echo spectra, obtained in the same linear (SL) polarization as transmitted, are broad, nearly featureless, and much stronger than expected if the echoes were due just to single backreflections. Rather, volume scattering from the subsurface probably is primarily responsible for the echoes. This conclusion is supported by the strong anticorrelation between our targets' radar albedos (radar cross section divided by target projected area) and disc brightness temperatures estimated from passive radiometric measurements obtained during each radar flyby. Taking advantage of the available information about the radar properties of the icy satellites of Saturn and Jupiter, especially the linear- and circular-polarization characteristics of groundbased echoes from the icy Galilean satellites (Ostro et al. 1992, J. Geophys. Res. 97, 18227-18244), we estimate our targets' 2.2-cm total-power (TP) albedos and compare them to Arecibo and Goldstone values for icy satellites at 3.5, 13, and 70 cm. Our four targets' albedos span an order of magnitude and decrease in the same order as their optical albedos: Enceladus/Rhea/Iapetus/Phoebe. This sequence most likely corresponds to increasing contamination of near-surface water ice, whose extremely low electrical loss at radio wavelengths permits the multiple scattering responsible for high radar albedos. Plausible candidates for contaminants causing variations in radar albedo include ammonia, silicates, and polar organics. Modeling of icy Galilean satellite echoes indicates that penetration

Comparison of Hyperspectral and Multispectral Satellites for Discriminating Land Cover in Northern California

Science.gov (United States)

Clark, M. L.; Kilham, N. E.

2015-12-01

Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Most land-cover maps at regional to global scales are produced with remote sensing techniques applied to multispectral satellite imagery with 30-500 m pixel sizes (e.g., Landsat, MODIS). Hyperspectral, or imaging spectrometer, imagery measuring the visible to shortwave infrared regions (VSWIR) of the spectrum have shown impressive capacity to map plant species and coarser land-cover associations, yet techniques have not been widely tested at regional and greater spatial scales. The Hyperspectral Infrared Imager (HyspIRI) mission is a VSWIR hyperspectral and thermal satellite being considered for development by NASA. The goal of this study was to assess multi-temporal, HyspIRI-like satellite imagery for improved land cover mapping relative to multispectral satellites. We mapped FAO Land Cover Classification System (LCCS) classes over 22,500 km2 in the San Francisco Bay Area, California using 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery simulated from data acquired by NASA's AVIRIS airborne sensor. Random Forests (RF) and Multiple-Endmember Spectral Mixture Analysis (MESMA) classifiers were applied to the simulated images and accuracies were compared to those from real Landsat 8 images. The RF classifier was superior to MESMA, and multi-temporal data yielded higher accuracy than summer-only data. With RF, hyperspectral data had overall accuracy of 72.2% and 85.1% with full 20-class and reduced 12-class schemes, respectively. Multispectral imagery had lower accuracy. For example, simulated and real Landsat data had 7.5% and 4.6% lower accuracy than HyspIRI data with 12 classes, respectively. In summary, our results indicate increased mapping accuracy using HyspIRI multi-temporal imagery, particularly in discriminating different natural vegetation types, such as

On results using automated wideband instrumentation for radar measurements and characterization

Science.gov (United States)

Govoni, Mark A.; Dogaru, Traian; Le, Calvin; Sobczak, Kevin

2017-05-01

Experiences are shared from a recent radar measurement and characterization effort. A regimented data collection procedure ensures repeatability and provides an expedited alternative to typical narrowband capabilities. Commercially-available instrumentation is repurposed to support wideband data collections spanning a contiguous range of frequencies from 700 MHz to 40 GHz. Utilizing a 4-port network analyzer, both monostatic and quasi-monostatic measurements are achievable. Polarization is varied by way of a custom-designed antenna mount that allows for the mechanical reorientation of the antennas. Computational electromagnetic modeling is briefly introduced and serves in validating the legitimacy of the collection capability. Data products presented will include high-range resolution profiles and inverse synthetic aperture radar (ISAR) imagery.

A new approach for river flood extent delineation in rural and urban areas combining RADARSAT-2 imagery and flood recurrence interval data

Science.gov (United States)

Tanguy, Marion; Bernier, Monique; Chokmani, Karem

2015-04-01

When a flood hits an inhabited area, managers and services responsible for public safety need precise, reliable and up to date maps of the areas affected by the flood, in order to quickly roll out and to coordinate the adequate intervention and assistance plans required to limit the human and material damages caused by the disaster. Synthetic aperture radar (SAR) sensors are now considered as one of the most adapted tool for flood detection and mapping in a context of crisis management. Indeed, due to their capacity to acquire data night and day, in almost all meteorological conditions, SAR sensors allow the acquisition of synoptic but detailed views of the areas affected by the flood, even during the active phases of the event. Moreover, new generation sensors such as RADARSAT-2, TerraSAR-X, COSMO-SkyMed, are providing very high resolution images of the disaster (down to 1m ground resolution). Further, critical improvements have been made on the temporal repetitivity of acquisitions and on data availability, through the development of satellite constellations (i.e the four COSMO-Skymed or the Sentinel-1A and 1B satellites) and thanks to the implementation of the International Charter "Space and Major Disasters", which guarantees high priority images acquisition and delivery with 4 to 12 hours. If detection of open water flooded areas is relatively straightforward with SAR imagery, flood detection in built-up areas is often associated with important issues. Indeed, because of the side looking geometry of the SAR sensors, structures such as tall vegetation and structures parallel to the satellite direction of travel may produce shadow and layover effects, leading to important over and under-detections of flooded pixels. Besides, the numerous permanent water-surfaces like radar response areas present in built-up environments, such as parking lots, roads etc., may be mixed up with flooded areas, resulting in substantial inaccuracies in the final flood map. In spite of

Toward a Framework for Systematic Error Modeling of NASA Spaceborne Radar with NOAA/NSSL Ground Radar-Based National Mosaic QPE

Science.gov (United States)

Kirstettier, Pierre-Emmanual; Honh, Y.; Gourley, J. J.; Chen, S.; Flamig, Z.; Zhang, J.; Howard, K.; Schwaller, M.; Petersen, W.; Amitai, E.

2011-01-01

Characterization of the error associated to satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving space-born passive and active microwave measurement") for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. We focus here on the error structure of NASA's Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar-based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a three-month data sample in the southern part of US. The primary contribution of this study is the presentation of the detailed steps required to derive trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relics on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors arc revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfall rate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall rate estimates from other sensors onboard low-earth orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission.

Marsh dieback, loss, and recovery mapped with satellite optical, airborne polarimetric radar, and field data

Science.gov (United States)

Ramsey, Elijah W.; Rangoonwala, Amina; Chi, Zhaohui; Jones, Cathleen E.; Bannister, Terri

2014-01-01

Landsat Thematic Mapper and Satellite Pour l'Observation de la Terre (SPOT) satellite based optical sensors, NASA Uninhabited Aerial Vehicle synthetic aperture radar (UAVSAR) polarimetric SAR (PolSAR), and field data captured the occurrence and the recovery of an undetected dieback that occurred between the summers of 2010, 2011, and 2012 in the Spartina alterniflora marshes of coastal Louisiana. Field measurements recorded the dramatic biomass decrease from 2010 to 2011 and a biomass recovery in 2012 dominated by a decrease of live biomass, and the loss of marsh as part of the dieback event. Based on an established relationship, the near-infrared/red vegetation index (VI) and site-specific measurements delineated a contiguous expanse of marsh dieback encompassing 6649.9 ha of 18,292.3 ha of S. alterniflora marshes within the study region. PolSAR data were transformed to variables used in biophysical mapping, and of this variable suite, the cross-polarization HV (horizontal send and vertical receive) backscatter was the best single indicator of marsh dieback and recovery. HV backscatter exhibited substantial and significant changes over the dieback and recovery period, tracked measured biomass changes, and significantly correlated with the live/dead biomass ratio. Within the context of regional trends, both HV and VI indicators started higher in pre-dieback marshes and exhibited substantially and statistically higher variability from year to year than that exhibited in the non-dieback marshes. That distinct difference allowed the capturing of the S. alterniflora marsh dieback and recovery; however, these changes were incorporated in a regional trend exhibiting similar but more subtle biomass composition changes.

Mapping of elements at risk for landslides in the tropics using airborne laser scanning

NARCIS (Netherlands)

Razak, Khamarrul Azahari; van Westen, C.J.; Straatsma, Menno; ... [et al.],

2011-01-01

Mapping elements at risk for landslides in the tropics pose as a challenging task. Aerial-photograph, satellite imagery, and synthetic perture radar images are less effective to accurately provide physical presence of objects in a relatively short time. In this paper, we utilized an airborne laser

Real-time new satellite product demonstration from microwave sensors and GOES-16 at NRL TC web

Science.gov (United States)

Cossuth, J.; Richardson, K.; Surratt, M. L.; Bankert, R.

2017-12-01

The Naval Research Laboratory (NRL) Tropical Cyclone (TC) satellite webpage (https://www.nrlmry.navy.mil/TC.html) provides demonstration analyses of storm imagery to benefit operational TC forecast centers around the world. With the availability of new spectral information provided by GOES-16 satellite data and recent research into improved visualization methods of microwave data, experimental imagery was operationally tested to visualize the structural changes of TCs during the 2017 hurricane season. This presentation provides an introduction into these innovative satellite analysis methods, NRL's next generation satellite analysis system (the Geolocated Information Processing System, GeoIPSTM), and demonstration the added value of additional spectral frequencies when monitoring storms in near-realtime.

Detailed maps of tropical forest types are within reach: forest tree communities for Trinidad and Tobago mapped with multiseason Landsat and multiseason fine-resolution imagery

Science.gov (United States)

Eileen H. Helmer; Thomas S. Ruzycki; Jay Benner; Shannon M. Voggesser; Barbara P. Scobie; Courtenay Park; David W. Fanning; Seepersad. Ramnarine

2012-01-01

Tropical forest managers need detailed maps of forest types for REDD+, but spectral similarity among forest types; cloud and scan-line gaps; and scarce vegetation ground plots make producing such maps with satellite imagery difficult. How can managers map tropical forest tree communities with satellite imagery given these challenges? Here we describe a case study of...

Bistatic Forward Scattering Radar Detection and Imaging

Directory of Open Access Journals (Sweden)

Hu Cheng

2016-06-01

Full Text Available Forward Scattering Radar (FSR is a special type of bistatic radar that can implement image detection, imaging, and identification using the forward scattering signals provided by the moving targets that cross the baseline between the transmitter and receiver. Because the forward scattering effect has a vital significance in increasing the targets’ Radar Cross Section (RCS, FSR is quite advantageous for use in counter stealth detection. This paper first introduces the front line technology used in forward scattering RCS, FSR detection, and Shadow Inverse Synthetic Aperture Radar (SISAR imaging and key problems such as the statistical characteristics of forward scattering clutter, accurate parameter estimation, and multitarget discrimination are then analyzed. Subsequently, the current research progress in FSR detection and SISAR imaging are described in detail, including the theories and experiments. In addition, with reference to the BeiDou navigation satellite, the results of forward scattering experiments in civil aircraft detection are shown. Finally, this paper considers future developments in FSR target detection and imaging and presents a new, promising technique for stealth target detection.

Radar-to-Radar Interference Suppression for Distributed Radar Sensor Networks

Directory of Open Access Journals (Sweden)

Wen-Qin Wang

2014-01-01

Full Text Available Radar sensor networks, including bi- and multi-static radars, provide several operational advantages, like reduced vulnerability, good system flexibility and an increased radar cross-section. However, radar-to-radar interference suppression is a major problem in distributed radar sensor networks. In this paper, we present a cross-matched filtering-based radar-to-radar interference suppression algorithm. This algorithm first uses an iterative filtering algorithm to suppress the radar-to-radar interferences and, then, separately matched filtering for each radar. Besides the detailed algorithm derivation, extensive numerical simulation examples are performed with the down-chirp and up-chirp waveforms, partially overlapped or inverse chirp rate linearly frequency modulation (LFM waveforms and orthogonal frequency division multiplexing (ODFM chirp diverse waveforms. The effectiveness of the algorithm is verified by the simulation results.

A Tool and Application Programming Interface for Browsing Historical Geostationary Satellite Data

Science.gov (United States)

Chee, T.; Nguyen, L.; Minnis, P.; Spangenberg, D.; Ayers, J.

2013-12-01

Providing access to information is a key concern for NASA Langley Research Center. We describe a tool and method that allows end users to easily browse and access information that is otherwise difficult to acquire and manipulate. The tool described has as its core the application-programming interface that is made available to the public. One goal of the tool is to provide a demonstration to end users so that they can use the enhanced imagery as an input into their own work flows. This project builds upon NASA Langley Cloud and Radiation Group's experience with making real-time and historical satellite imagery accessible and easily searchable. As we see the increasing use of virtual supply chains that provide additional value at each link there is value in making satellite imagery available through a simple access method as well as allowing users to browse and view that imagery as they need rather than in a manner most convenient for the data provider.

ARM Cloud Radar Simulator Package for Global Climate Models Value-Added Product

Energy Technology Data Exchange (ETDEWEB)

Zhang, Yuying [North Carolina State Univ., Raleigh, NC (United States); Xie, Shaocheng [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-05-01

It has been challenging to directly compare U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility ground-based cloud radar measurements with climate model output because of limitations or features of the observing processes and the spatial gap between model and the single-point measurements. To facilitate the use of ARM radar data in numerical models, an ARM cloud radar simulator was developed to converts model data into pseudo-ARM cloud radar observations that mimic the instrument view of a narrow atmospheric column (as compared to a large global climate model [GCM] grid-cell), thus allowing meaningful comparison between model output and ARM cloud observations. The ARM cloud radar simulator value-added product (VAP) was developed based on the CloudSat simulator contained in the community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) (Bodas-Salcedo et al., 2011), which has been widely used in climate model evaluation with satellite data (Klein et al., 2013, Zhang et al., 2010). The essential part of the CloudSat simulator is the QuickBeam radar simulator that is used to produce CloudSat-like radar reflectivity, but is capable of simulating reflectivity for other radars (Marchand et al., 2009; Haynes et al., 2007). Adapting QuickBeam to the ARM cloud radar simulator within COSP required two primary changes: one was to set the frequency to 35 GHz for the ARM Ka-band cloud radar, as opposed to 94 GHz used for the CloudSat W-band radar, and the second was to invert the view from the ground to space so as to attenuate the beam correctly. In addition, the ARM cloud radar simulator uses a finer vertical resolution (100 m compared to 500 m for CloudSat) to resolve the more detailed structure of clouds captured by the ARM radars. The ARM simulator has been developed following the COSP workflow (Figure 1) and using the capabilities available in COSP

A General Approach to Enhance Short Wave Satellite Imagery by Removing Background Atmospheric Effects

Directory of Open Access Journals (Sweden)

Ronald Scheirer

2018-04-01

Full Text Available Atmospheric interaction distorts the surface signal received by a space-borne instrument. Images derived from visible channels appear often too bright and with reduced contrast. This hampers the use of RGB imagery otherwise useful in ocean color applications and in forecasting or operational disaster monitoring, for example forest fires. In order to correct for the dominant source of atmospheric noise, a simple, fast and flexible algorithm has been developed. The algorithm is implemented in Python and freely available in PySpectral which is part of the PyTroll family of open source packages, allowing easy access to powerful real-time image-processing tools. Pre-calculated look-up tables of top of atmosphere reflectance are derived by off-line calculations with RTM DISORT as part of the LibRadtran package. The approach is independent of platform and sensor bands, and allows it to be applied to any band in the visible spectral range. Due to the use of standard atmospheric profiles and standard aerosol loads, it is possible just to reduce the background disturbance. Thus signals from excess aerosols become more discernible. Examples of uncorrected and corrected satellite images demonstrate that this flexible real-time algorithm is a useful tool for atmospheric correction.

Assessment of Mining Extent and Expansion in Myanmar Based on Freely-Available Satellite Imagery

Directory of Open Access Journals (Sweden)

Katherine J. LaJeunesse Connette

2016-11-01

Full Text Available Using freely-available data and open-source software, we developed a remote sensing methodology to identify mining areas and assess recent mining expansion in Myanmar. Our country-wide analysis used Landsat 8 satellite data from a select number of mining areas to create a raster layer of potential mining areas. We used this layer to guide a systematic scan of freely-available fine-resolution imagery, such as Google Earth, in order to digitize likely mining areas. During this process, each mining area was assigned a ranking indicating our certainty in correct identification of the mining land use. Finally, we identified areas of recent mining expansion based on the change in albedo, or brightness, between Landsat images from 2002 and 2015. We identified 90,041 ha of potential mining areas in Myanmar, of which 58% (52,312 ha was assigned high certainty, 29% (26,251 ha medium certainty, and 13% (11,478 ha low certainty. Of the high-certainty mining areas, 62% of bare ground was disturbed (had a large increase in albedo since 2002. This four-month project provides the first publicly-available database of mining areas in Myanmar, and it demonstrates an approach for large-scale assessment of mining extent and expansion based on freely-available data.

Resolution Enhancement of Multilook Imagery

Energy Technology Data Exchange (ETDEWEB)

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

2004-07-01

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

Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

Science.gov (United States)

Husak, G. J.; Marshall, M. T.; Michaelsen, J.; Pedreros, D.; Funk, C.; Galu, G.

2008-07-01

Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

a Fuzzy Automatic CAR Detection Method Based on High Resolution Satellite Imagery and Geodesic Morphology

Science.gov (United States)

Zarrinpanjeh, N.; Dadrassjavan, F.

2017-09-01

Automatic car detection and recognition from aerial and satellite images is mostly practiced for the purpose of easy and fast traffic monitoring in cities and rural areas where direct approaches are proved to be costly and inefficient. Towards the goal of automatic car detection and in parallel with many other published solutions, in this paper, morphological operators and specifically Geodesic dilation are studied and applied on GeoEye-1 images to extract car items in accordance with available vector maps. The results of Geodesic dilation are then segmented and labeled to generate primitive car items to be introduced to a fuzzy decision making system, to be verified. The verification is performed inspecting major and minor axes of each region and the orientations of the cars with respect to the road direction. The proposed method is implemented and tested using GeoEye-1 pansharpen imagery. Generating the results it is observed that the proposed method is successful according to overall accuracy of 83%. It is also concluded that the results are sensitive to the quality of available vector map and to overcome the shortcomings of this method, it is recommended to consider spectral information in the process of hypothesis verification.

Analysis of Satellite and Airborne Imagery for Detection of Water Hyacinth and Other Invasive Floating Macrophytes and Tracking of Aquatic Weed Control Efficacy

Science.gov (United States)

Potter, Christopher

2016-01-01

Waterways of the Sacramento San Joaquin Delta have recently become infested with invasive aquatic weeds such as floating water hyacinth (Eichhoria crassipes) and water primrose (Ludwigia peploides). These invasive plants cause many negative impacts, including, but not limited to: the blocking of waterways for commercial shipping and boating; clogging of irrigation screens, pumps and canals; and degradation of biological habitat through shading. Zhang et al. (1997, Ecological Applications, 7(3), 1039-1053) used NASA Landsat satellite imagery together with field calibration measurements to map physical and biological processes within marshlands of the San Francisco Bay. Live green biomass (LGB) and related variables were correlated with a simple vegetation index ratio of red and near infra-red bands from Landsat images. More recently, the percent (water area) cover of water hyacinth plotted against estimated LGB of emergent aquatic vegetation in the Delta from September 2014 Landsat imagery showed an 80 percent overall accuracy. For the past two years, we have partnered with the U. S. Department of Agriculture (USDA) and the Department of Plant Sciences, University of California at Davis to conduct new validation surveys of water hyacinth and water primrose coverage and LGB in Delta waterways. A plan is underway to transfer decision support tools developed at NASA's Ames Research Center based on Landsat satellite images to improve Delta-wide integrated management of floating aquatic weeds, while reducing chemical control costs. The main end-user for this application project will be the Division of Boating and Waterways (DBW) of the California Department of Parks and Recreation, who has the responsibility for chemical control of water hyacinth in the Delta.

The 2017 Hurricane Season: A Revolution in Geostationary Weather Satellite Imaging and Data Processing

Science.gov (United States)

Weiner, A. M.; Gundy, J.; Brown-Bertold, B.; Yates, H.; Dobler, J. T.

2017-12-01

Since their introduction, geostationary weather satellites have enabled us to track hurricane life-cycle movement from development to dissipation. During the 2017 hurricane season, the new GOES-16 geostationary satellite demonstrated just how far we have progressed technologically in geostationary satellite imaging, with hurricane imagery showing never-before-seen detail of the hurricane eye and eyewall structure and life cycle. In addition, new ground system technology, leveraging high-performance computing, delivered imagery and data to forecasters with unprecedented speed—and with updates as often as every 30 seconds. As additional satellites and new products become operational, forecasters will be able to track hurricanes with even greater accuracy and assist in aftermath evaluations. This presentation will present glimpses into the past, a look at the present, and a prediction for the future utilization of geostationary satellites with respect to all facets of hurricane support.

Satellite Image Classification of Building Damages Using Airborne and Satellite Image Samples in a Deep Learning Approach

Science.gov (United States)

Duarte, D.; Nex, F.; Kerle, N.; Vosselman, G.

2018-05-01

The localization and detailed assessment of damaged buildings after a disastrous event is of utmost importance to guide response operations, recovery tasks or for insurance purposes. Several remote sensing platforms and sensors are currently used for the manual detection of building damages. However, there is an overall interest in the use of automated methods to perform this task, regardless of the used platform. Owing to its synoptic coverage and predictable availability, satellite imagery is currently used as input for the identification of building damages by the International Charter, as well as the Copernicus Emergency Management Service for the production of damage grading and reference maps. Recently proposed methods to perform image classification of building damages rely on convolutional neural networks (CNN). These are usually trained with only satellite image samples in a binary classification problem, however the number of samples derived from these images is often limited, affecting the quality of the classification results. The use of up/down-sampling image samples during the training of a CNN, has demonstrated to improve several image recognition tasks in remote sensing. However, it is currently unclear if this multi resolution information can also be captured from images with different spatial resolutions like satellite and airborne imagery (from both manned and unmanned platforms). In this paper, a CNN framework using residual connections and dilated convolutions is used considering both manned and unmanned aerial image samples to perform the satellite image classification of building damages. Three network configurations, trained with multi-resolution image samples are compared against two benchmark networks where only satellite image samples are used. Combining feature maps generated from airborne and satellite image samples, and refining these using only the satellite image samples, improved nearly 4 % the overall satellite image

Application of spectrometer cropscan MSR 16R and Landsat imagery for identification the spectral characteristics of land cover

Science.gov (United States)

Tampubolon, Togi; Abdullah, Khiruddin bin; San, Lim Hwee

2013-09-01

The spectral characteristics of land cover are basic references in classifying satellite image for geophysics analysis. It can be obtained from the measurements using spectrometer and satellite image processing. The aims of this study to investigate the spectral characteristics of land cover based on the results of measurement using Spectrometer Cropscan MSR 16R and Landsat satellite imagery. The area of study in this research is in Medan, (Deli Serdang, North Sumatera) Indonesia. The scope of this study is the basic survey from the measurements of spectral land cover which is covered several type of land such as a cultivated and managed terrestrial areas, natural and semi-natural, cultivated aquatic or regularly flooded areas, natural and semi-natural aquatic, artificial surfaces and associated areas, bare areas, artificial waterbodies and natural waterbodies. The measurement and verification were conducted using a spectrometer provided their spectral characteristics and Landsat imagery, respectively. The results of the spectral characteristics of land cover shows that each type of land cover have a unique characteristic. The correlation of spectral land cover based on spectrometer Cropscan MSR 16R and Landsat satellite image are above 90 %. However, the land cover of artificial waterbodiese have a correlation under 40 %. That is because the measurement of spectrometer Cropscan MSR 16R and acquisition of Landsat satellite imagery has a time different.

Fully Convolutional Network Based Shadow Extraction from GF-2 Imagery

Science.gov (United States)

Li, Z.; Cai, G.; Ren, H.

2018-04-01

There are many shadows on the high spatial resolution satellite images, especially in the urban areas. Although shadows on imagery severely affect the information extraction of land cover or land use, they provide auxiliary information for building extraction which is hard to achieve a satisfactory accuracy through image classification itself. This paper focused on the method of building shadow extraction by designing a fully convolutional network and training samples collected from GF-2 satellite imagery in the urban region of Changchun city. By means of spatial filtering and calculation of adjacent relationship along the sunlight direction, the small patches from vegetation or bridges have been eliminated from the preliminary extracted shadows. Finally, the building shadows were separated. The extracted building shadow information from the proposed method in this paper was compared with the results from the traditional object-oriented supervised classification algorihtms. It showed that the deep learning network approach can improve the accuracy to a large extent.

TerraSAR-X basierte Pre- und Post- Desaster Analyse zur Abschätzung vulkanbedingter Landbedeckungsveränderungen Fallbeispiel: Merapi 2010

OpenAIRE

Kalia, Andre Cahyadi

2011-01-01

This work demonstrates how Synthetic Aperture Radar (SAR) image analysis can successfully be used for supporting disaster and crisis-management concerning volcanic eruptions. In October/November 2010 the strato-volcano Mt. Merapi erupted leading to about 300 deaths and more than 380 000 refugees. Satellite imagery can be a very useful source to derive rapid crisis information for post-disaster relief efforts. Modern, space borne radar sensors like TerraSAR-X can deliver very high resolution r...

GOLD MINERAL PROSPECTING USING PHASED ARRAY TYPE L-BAND SYNTHETIC APERTURE RADAR (PALSAR SATELLITE REMOTE SENSING DATA, CENTRAL GOLD BELT, MALAYSIA

Directory of Open Access Journals (Sweden)

A. Beiranvand Pour

2016-06-01

Full Text Available The Bentong-Raub Suture Zone (BRSZ of Peninsular Malaysia is one of the significant structural zones in Sundaland, Southeast Asia. It forms the boundary between the Gondwana-derived Sibumasu terrane in the west and Sukhothai arc in the east. The BRSZ is also genetically related to the sediment-hosted/orogenic gold deposits associated with the major lineaments and form-lines in the central gold belt Central Gold Belt of Peninsular Malaysia. In tropical environments, heavy tropical rainforest and intense weathering makes it impossible to map geological structures over long distances. Advances in remote sensing technology allow the application of Synthetic Aperture Radar (SAR data in geological structural analysis for tropical environments. In this investigation, the Phased Array type L-band Synthetic Aperture Radar (PALSAR satellite remote sensing data were used to analyse major geological structures in Peninsular Malaysia and provide detailed characterization of lineaments and form-lines in the BRSZ, as well as its implication for sediment-hosted/orogenic gold exploration in tropical environments. The major geological structure directions of the BRSZ are N-S, NNE-SSW, NE-SW and NW-SE, which derived from directional filtering analysis to PALSAR data. The pervasive array of N-S faults in the study area and surrounding terrain is mainly linked to the N-S trending of the Suture Zone. N-S striking lineaments are often cut by younger NE-SW and NW-SE-trending lineaments. Gold mineralized trends lineaments are associated with the intersection of N-S, NE-SW, NNW-SSE and ESE-WNW faults and curvilinear features in shearing and alteration zones. Lineament analysis on PALSAR satellite remote sensing data is a useful tool for detecting the boundary between the Gondwana-derived terranes and major geological features associated with suture zone especially for large inaccessible regions in tropical environments.

Spatiotemporal estimation of air temperature patterns at the street level using high resolution satellite imagery.

Science.gov (United States)

Pelta, Ran; Chudnovsky, Alexandra A

2017-02-01

Although meteorological monitoring stations provide accurate measurements of Air Temperature (AT), their spatial coverage within a given region is limited and thus is often insufficient for exposure and epidemiological studies. In many applications, satellite imagery measures energy flux, which is spatially continuous, and calculates Brightness Temperature (BT) that used as an input parameter. Although both quantities (AT-BT) are physically related, the correlation between them is not straightforward, and varies daily due to parameters such as meteorological conditions, surface moisture, land use, satellite-surface geometry and others. In this paper we first investigate the relationship between AT and BT as measured by 39 meteorological stations in Israel during 1984-2015. Thereafter, we apply mixed regression models with daily random slopes to calibrate Landsat BT data with monitored AT measurements for the period 1984-2015. Results show that AT can be predicted with high accuracy by using BT with high spatial resolution. The model shows relatively high accuracy estimation of AT (R 2 =0.92, RMSE=1.58°C, slope=0.90). Incorporating meteorological parameters into the model generates better accuracy (R 2 =0.935) than the AT-BT model (R 2 =0.92). Furthermore, based on the relatively high model accuracy, we investigated the spatial patterns of AT within the study domain. In the latter we focused on July-August, as these two months are characterized by relativity stable synoptic conditions in the study area. In addition, a temporal change in AT during the last 30years was estimated and verified using available meteorological stations and two additional remote sensing platforms. Finally, the impact of different land coverage on AT were estimated, as an example of future application of the presented approach. Copyright © 2016 Elsevier B.V. All rights reserved.