Institution
Indian Institute of Remote Sensing
Government•Dehra Dūn, India•
About: Indian Institute of Remote Sensing is a government organization based out in Dehra Dūn, India. It is known for research contribution in the topics: Land cover & Normalized Difference Vegetation Index. The organization has 756 authors who have published 1355 publications receiving 16915 citations. The organization is also known as: Indian Photo-interpretation Institute.
Topics: Land cover, Normalized Difference Vegetation Index, Population, Land use, Synthetic aperture radar
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors reported the results of a study carried out in the Lesser Himalayan foothills in India (Dun valley) on potential distribution modeling for Malabar nut using Maxent model.
409 citations
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TL;DR: A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data.
Abstract: A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3‐band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time‐series for 1997–1999, (b) Systeme pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50 km monthly time series for 1961–2000, (d) Global 30 Arc‐Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite‐1 Synthetic Aperture Radar (JERS‐1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992–1993. A single mega‐file data‐cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re‐sampling different data types into a common 1 km resolutio...
365 citations
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TL;DR: The Joint Research Centre of the European Commission (JRC), in partnership with 30 institutions, has produced a global land cover map for the year 2000, the GLC 2000 map, and the validation of the product has now been completed.
Abstract: The Joint Research Centre of the European Commission (JRC), in partnership with 30 institutions, has produced a global land cover map for the year 2000, the GLC 2000 map. The validation of the GLC2000 product has now been completed. The accuracy assessment relied on two methods: a confidence-building method (quality control based on a comparison with ancillary data) and a quantitative accuracy assessment based on a stratified random sampling of reference data. The sample site stratification used an underlying grid of Landsat data and was based on the proportion of priority land cover classes and on the landscape complexity. A total of 1265 sample sites have been interpreted. The first results indicate an overall accuracy of 68.6%. The GLC2000 validation exercise has provided important experiences. The design-based inference conforms to the CEOS Cal-Val recommendations and has proven to be successful. Both the GLC2000 legend development and reference data interpretations used the FAO Land Cover Classification System (LCCS). Problems in the validation process were identified for areas with heterogeneous land cover. This issue appears in both in the GLC2000 (neighborhood pixel variations) and in the reference data (cartographic and thematic mixed units). Another interesting outcome of the GLC2000 validation is the accuracy reporting. Error statistics are provided from both the producer and user perspective and incorporates measures of thematic similarity between land cover classes derived from LCCS
295 citations
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TL;DR: The Urban Neighborhood Green Index (UNGI) as discussed by the authors aims to assess the greenness and can help in identifying the critical areas, which can be used to identify action areas for improving the quality of green.
246 citations
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TL;DR: In this paper, the authors suggest approaches for using satellite remote sensing data for regional biomass mapping in Madhav National Park (MP) using stratified random sampling in the homogeneous vegetation strata.
Abstract: Vegetation type and its biomass are considered important components affecting biosphere-atmosphere interactions. The measurements of biomass per unit area and productivity have been set as one of the goals for International Geosphere-Biosphere Programme (IGBP). Ground assessment of biomass, however, has been found insufficient to present spatial extent of the biomass. The present study suggests approaches for using satellite remote sensing data for regional biomass mapping in Madhav National Park (MP). The stratified random sampling in the homogeneous vegetation strata mapped using satellite remote sensing has been effectively utilized to extrapolate the sample point biomass observations in the first approach.
245 citations
Authors
Showing all 777 results
Name | H-index | Papers | Citations |
---|---|---|---|
Rakesh Kumar | 91 | 1959 | 39017 |
Sanjay K. Srivastava | 73 | 366 | 15587 |
Masako Osumi | 44 | 200 | 6683 |
Vinay Kumar Dadhwal | 40 | 322 | 6217 |
Pramod Kumar | 39 | 170 | 4248 |
Anil K. Mishra | 38 | 300 | 4907 |
Partha Sarathi Roy | 37 | 174 | 5119 |
Pawan Kumar Joshi | 36 | 170 | 4268 |
Kiran Singh | 34 | 156 | 3525 |
Priyanka Singh | 34 | 129 | 3839 |
Chandrashekhar Biradar | 33 | 100 | 3529 |
Amit K. Tiwari | 33 | 146 | 4422 |
Debashis Mitra | 32 | 117 | 2926 |
Suresh Kumar | 29 | 407 | 3580 |
Nidhi Chauhan | 27 | 107 | 2319 |