National Remote Sensing Center

Hyderabad, India

National Remote Sensing Center

Hyderabad, India
SEARCH FILTERS
Time filter
Source Type

Agrawal K.M.,Space Applications Center | Mehra R.,Space Applications Center | Ryali U.S.,National Remote Sensing Center
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

NASA-ISRO Synthetic Aperture Radar (NISAR) is a Dual Frequency (L & S band) mission which will be operating in SweepSAR mode. As compared to traditional SAR imaging modes in which Swath and resolution are at trade-off, SweepSAR imaging concept can acquire data over large swath (240 Km) without compromising azimuth resolution (6m approximately). NISAR L-band & S-band sensors will be developed by JPL-NASA and ISRO respectively. NISAR science data will be downloaded at both NASA and ISRO ground stations. SAC-ISRO will develop the SAR processor for both L & S band data to generate products in compliance with science requirements. Moreover, JPL will develop L-band SAR processor and all data products will be available to users. Distributed data processing architecture will be used for handling large volume of data resulting from moderate resolution and larger swath in SweepSAR mode. Data products will be available in multiple processing levels like raw signal products, signal processed single-look and multi-look products, ground range products and Geo-Referenced products in HDF5 & GeoTiff formats. Derived Geo-Referenced Polarimetric and Interferometric data products will also be available for dissemination to the users. A rigorous calibration exercise will be performed by acquiring data over reference targets like Amazon rain-forest & corner reflectors sites for the generation of calibrated data products. Furthermore, various science data products (for science applications) will also be derived from basic data products for operational dissemination. COPYRIGHT © SPIE. Downloading of the abstract is permitted for personal use only.


Wu B.,CAS Institute of Remote Sensing | Yang J.,CAS Institute of Remote Sensing | Zhao Z.,CAS Institute of Remote Sensing | Meng Y.,CAS Institute of Remote Sensing | And 5 more authors.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

Updating the existing land-use maps with remote sensing imagery has become a common method to produce the latest land-use database. An important step is to extract the changed information. In this paper, we propose a novel method to extract the changed parcels in land-use maps by using the holistic feature called 'Spatial Envelope,' which encodes each parcel without segmenting it into homogeneous objects or small regions. The holistic feature is based on the energy spectrum of the windowed Fourier transform (WFT) of each land-use parcel, which is ideal for scene categorization. Unlike the pixel-based change detection using the difference image (DI) leading to speckled results or object-based method which requires a complicated process to segment the land-use parcel into homogeneous land-cover objects, our parcel-based change detection treats each land-use parcel as an entirety by calculating the holistic feature for the former and latter parcels. Then, the distance between the corresponding former and latter parcels is compared against a threshold to select the changed parcels. Experiments have demonstrated that our procedure can extract the changed parcels with the overall accuracy of more than 92%. The performance of our procedure is reliable not only on the high-resolution (HR) images of the same sensor, but also on the images acquired by different sensors with the same or approximate spatial resolution. Comparative experiments have also proved that the holistic feature is better than conventional spectral and textural features in parcel-based change detection. © 2008-2012 IEEE.


Nasanbat E.,National Remote Sensing Center | Lkhamjav O.,Mongolian Geospatial Association
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2016

Grassland fire is a cause of major disturbance to ecosystems and economies throughout the world. This paper investigated to identify risk zone of wildfire distributions on the Eastern Steppe of Mongolia. The study selected variables for wildfire risk assessment using a combination of data collection, including Social Economic, Climate, Geographic Information Systems, Remotely sensed imagery, and statistical yearbook information. Moreover, an evaluation of the result is used field validation data and assessment. The data evaluation resulted divided by main three group factors Environmental, Social Economic factor, Climate factor and Fire information factor into eleven input variables, which were classified into five categories by risk levels important criteria and ranks. All of the explanatory variables were integrated into spatial a model and used to estimate the wildfire risk index. Within the index, five categories were created, based on spatial statistics, to adequately assess respective fire risk: very high risk, high risk, moderate risk, low and very low. Approximately more than half, 68 percent of the study area was predicted accuracy to good within the very high, high risk and moderate risk zones. The percentages of actual fires in each fire risk zone were as follows: very high risk, 42 percent; high risk, 26 percent; moderate risk, 13 percent; low risk, 8 percent; and very low risk, 11 percent. The main overall accuracy to correct prediction from the model was 62 percent. The model and results could be support in spatial decision making support system processes and in preventative wildfire management strategies. Also it could be help to improve ecological and biodiversity conservation management.


Bo Z.,Beihang University | Jing Z.,National Remote Sensing Center | Wenwei D.,Beihang University | Longyang Y.,Beihang University
Advanced Materials Research | Year: 2012

A novel method of analyzing the visibility of GNSS satellites in mountainous area is proposed by taking advantage of target area's digital elevation model (DEM) data and GNSS almanac. The key point is calculating the obstacle elevation on the basis of target points' visible range. Simulation results show that both different target points at the same time and same point at different time have different situation of visible satellites on the condition with or without obstacle. On the other hand, this method contributes lots to determine the distribution of pseudolites in mountainous area. © (2012) Trans Tech Publications, Switzerland.


Dung T.T.M.,National Remote Sensing Center | Tuan V.A.,Vietnam Academy of Science and Technology
31st Asian Conference on Remote Sensing 2010, ACRS 2010 | Year: 2010

Ha Noi has been expanded rapidly in the recent 30 years. During the time, the pattern of expansion is change time by time and it linked to the certain socio-economic situation of Viet Nam. However, using the remote sensing data which is available for Ha noi from 80 decades, the expansion pattern can be recognized. The most significant changes in land area, especially in sub-urban area which is the reduction of agricultural land besides the urban area of Ha Noi has also changed itself to the direction of increasing the building density. Estimation the change of Ha Noi in three categories: urban/resident; water; green-land from satellite images acquired from 1986 to 2007, we found the dynamic pattern of changes. The method used for change detection is visual interpretation based on the draft result of object-based classification.


Mathew J.,National Remote Sensing Center | Kundu S.,Kumaun University | Kumar K.V.,National Remote Sensing Center | Pant C.C.,Kumaun University
Geomatics, Natural Hazards and Risk | Year: 2015

This study uses a deterministic approach to evaluate the factor of safety (FS) of the terrain for different hydrological conditions, in part of Indian Lesser Himalaya. The results indicate sudden increase in the percentage unstable area from 7.5% to 13.8% for rainfall intensity variation from 50 to 100 mm/day. For the rainfall intensity of 15 August 2007 which caused many landslides in the study area, 18.5% of the total area was unstable and it increases to 21.7%, 23.5% and 24.7%, respectively, for rainfall intensities corresponding to 10, 25 and 50 year return periods. This increment stagnates at about 260 mm/day, making about 25% of the area unstable. Higher rainfall intensities make progressively gentler slopes unstable, but limited to 25 degrees of slope in this area. The area underlain by granitic gneiss showed 23.1% of area as unstable for 135 mm/day of rainfall intensity, and was followed by those areas underlain by amphibolite (16%), limestone (13.7%) and quartzite (10.4%). Receiver operating characteristic (ROC) curve analysis has given 84.2% accuracy for the model. Conversion of FS to failure probability through Z scores enables identification unstable or marginally unstable areas, for planning selective slope stabilization measures. © 2015 Taylor & Francis


Liu G.,CAS Institute of Remote Sensing | Guo H.,CAS Institute of Remote Sensing | Yue H.,National Remote Sensing Center | Perski Z.,Panstwowy Instytut Geologiczny | And 4 more authors.
Remote Sensing Letters | Year: 2016

Conventional four-pass differential synthetic aperture radar interferometry (DInSAR) assumes that there are no significant changes in the ground during the period between the acquisition times of SAR images for topographic DInSAR pairs. This assumption can rarely be satisfied for glacial areas due to their continuous movement. This letter proposes a modified four-pass DInSAR method without an external digital elevation model (DEM), taking into account glacier movement between the acquisition times of SAR images used to form topographic DInSAR pairs. An explicit expression of theoretical formulas for a modified four-pass DInSAR technique was derived for the first time, revealing that four-pass DInSAR considering ground movement of topographic pairs was equivalent to that of conventional four-pass DInSAR with a spatially varying nominal wavelength. Then the proposed method was tested with four Advanced Land Observing Satellite (ALOS) SAR images covering Dongkemadi glacier located on the Tibetan Plateau, China. An experiment with real data showed that the proposed method could obtain glacial flow patterns efficiently, and that the difference between two-pass DInSAR and the proposed method is a result of DEM bias and glacial thinning. The approach presented in this letter proved to be appropriate for monitoring glacial motion and provides a valuable tool for glacier studies, without the need of an external DEM. © 2015 The Author(s). Published by Taylor & Francis.


Vu T.,National Remote Sensing Center | Nguyen D.,Hanoi University of Mining and Geology
33rd Asian Conference on Remote Sensing 2012, ACRS 2012 | Year: 2012

Application of remote sensing and GIS technologies in the field of natural hazard was developed in the last century and is continued until now. Remote sensing imagery supply information when natural hazard occurs (before, during and after) that will be valuable to monitor, manage, assess and estimate the economic losses. In the tropical region, during flooding time, cloud is covered almost so that the optical image would be not sharp. Radar imagery is selected to extract the water boundary and estimate the area flooded instead of optical image. The research on natural hazard is focused in different looking angles. Some research is concentrated in extracting the water boundary only; the other research is going further by applying the GIS analysis to estimate the economic losses due to that natural hazard. Some function in set of GIS tools was also used for computing area of land use types under water during flooding time. Those approaches are quickly and accurately. The United Nations Economic Commission for Latin America and the Caribbean (ECLAC) has extensive expertise in post-disaster impact assessment in 2004. Followed by ELAC methodology, evaluation of economic losses consists of three types as direct damage, indirect damage and secondary that information will help to consider where will be geographical regions or what will be social or economic sectors must be given priority in the rehabilitation and reconstruction process.


Guha A.,National Remote Sensing Center | Chakraborty D.,Geological Survey of India | Ekka A.B.,Geological Survey of India | Pramanik K.,Geological Survey of India | And 4 more authors.
Journal of the Geological Society of India | Year: 2012

Recent developments in sensor technology have given an onset for studying the earth surface features based on the detailed spectroscopic observation of different rocks and minerals. The spectroscopic profiles of the rocks are always quite different than their constituent minerals however, the spectral profile of a rock can be broadly reconstituted from the spectral profile of each constituent minerals. Interpretation of rock spectra using the spectra of constituent minerals based on relative spectral matching can bring out interesting information on the rock. Present study is an effort toward this and it highlights how visible-near infrared-shortwave-infrared (VNIR-SWIR) rock spectroscopy acts as an useful tool for understanding the rock-mineralogy in indirect and rapid way. It has also been observed that spectral signatures of rocks; studied in present case, are related to spectral signatures of constituent minerals although absorption features of constituent mineral in the rock are also modified by the other minerals juxtaposed in the rock fabric. However, each rock of the study area has their significant absorption features, but many of the absorption signatures are closely spaced, as altered rock has significant absorption at 2305 nm whereas amphibolite has its important absorption signature in 2385 nm and metabasalt has its significant absorption at 2342 nm. Therefore spectral measurement of high spectral resolution with appreciable signal to noise ratio (SNR) only can detect rocks from each other based on the absorption signatures mentioned above (each of which is 10 to 20 nm apart from the other) and therefore spectroscopy of rock is an innovative technique to map rocks and minerals based on the spectral signatures.


Karnieli A.,Ben - Gurion University of the Negev | Bayarjargal Y.,Ben - Gurion University of the Negev | Bayasgalan M.,National Remote Sensing Center | Mandakh B.,Mongolian Academy of science | And 5 more authors.
International Journal of Remote Sensing | Year: 2013

Space and ground observations were applied to explore the ability of remote sensing techniques to assess the effect of grazing on vegetation degradation. The steppe biome of Mongolia was used as the study area, in which several pairs of sites were investigated - each pair comprised an ungrazed (fenced-off) area and a heavily grazed area. For each pair, the enhanced vegetation index (EVI), computed from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data, along with field-observed biophysical variables (e.g. plant density, species composite, above-ground biomass (AGB), and percentage cover) and plant spectral reflectance data were collected. As expected, plant density, AGB, and percentage cover values were significantly higher in the ungrazed areas than in the adjacent grazed ones. However, unexpectedly, the grazed areas had significantly higher EVI values than the ungrazed areas. It was found that unpalatable species had invaded into the grazed areas, substituting the native grasses. These invasive species, mostly characterized by denser leaf structure, induced higher spectral responses in the near infrared (NIR) region of the electromagnetic spectrum. EVI is the preferred vegetation index to use for detecting this phenomenon, since it is more sensitive to variations in leaf cellular structural as expressed in the NIR (rather than the red) portion of the spectrum. The current study contradicts the general assumption that the higher the vegetation index value, the better the grazing conditions. © 2013 Copyright Taylor and Francis Group, LLC.

Loading National Remote Sensing Center collaborators
Loading National Remote Sensing Center collaborators