Indian National Remote Sensing Centre
Hyderabad, India

National Remote Sensing Centre , located at Hyderabad is one of the centres of Indian Space Research Organisation , striving to realise the Indian Space Vision, as a key player in Earth Observation Programme and Disaster Management Support programme. NRSC is responsible for acquisition, processing, supply of aerial and satellite remote sensing data and continuously exploring the practical uses of remote sensing technology for multilevel applications. It provides the necessary trained manpower through capacity building in remote sensing applications.NRSC has wealth of images from Indian and foreign remote sensing satellites in its archives and also has the capability to acquire data pertaining to any part of the globe on demand. NRSC also supports, through ANTRIX, establishment of International Ground Stations and International reseller network to receive, process and market IRS data products globally.NRSC provides end-to-end solutions for utilization of data for natural resource management, geospatial applications and information services. NRSC facilitates several remote sensing & GIS application projects for natural resources and environmental management catering to food security, water security, energy security and sustainable development. NRSC is also providing single window, disaster management support services through the Decision Support Centre. Recently NRSC has started to give its services on Land use Land cover of India under an Information portal called BHOOSAMPADA. The major objective of this portal is- Dissemination and Sharing of Geo-spatial Information Derived from IRS Data on Land use and Land cover of India. Wikipedia.

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Giribabu D.,Indian National Remote Sensing Centre | Srinivasa Rao S.,Indian National Remote Sensing Centre | Krishna Murthy Y.V.N.,Indian Institute of Remote Sensing
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2013

Cartosat-1 is the first Indian Remote Sensing Satellite capable of providing along-track stereo images. Cartosat-1 provides forward stereo images with look angles +26° and -5° with respect to nadir for generating Digital Elevation Models (DEMs), Orthoimages and value added products for various applications. A pitch bias of -21° to the satellite resulted in giving reverse tilt mode stereo pair with look angles of +5° and -26° with respect to nadir. This paper compares DEMs generated using forward, reverse and other possible synthetic stereo pairs for two different types of topographies. Stereo triplet was used to generate DEM for Himalayan mountain topography to overcome the problem of occlusions.For flat to undulating topography it was shown that using Cartosat-1 synthetic stereo pair with look angles of -26° and +26° will produce improved version of DEM. Planimetric and height accuracy (Root Mean Square Error (RMSE)) of less than 2.5. m and 2.95. m respectively were obtained and qualitative analysis shows finer details in comparison with other DEMs. For rugged terrain and steep slopes of Himalayan mountain topography simple stereo pairs may not provide reliable accuracies in DEMs due to occlusions and shadows. Stereo triplet from Cartosat-1 was used to generate DEM for mountainous topography. This DEM shows better reconstruction of elevation model even at occluded region when compared with simple stereo pair based DEM. Planimetric and height accuracy (RMSE) of nearly 3. m were obtained and qualitative analysis shows reduction of outliers at occluded region. © 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

Nayak R.K.,Indian National Remote Sensing Centre | Patel N.R.,Indian Institute of Remote Sensing | Dadhwal V.K.,Indian National Remote Sensing Centre
International Journal of Climatology | Year: 2013

Using satellite observations of Normalized Difference Vegetation Index together with climate data from other sources in a terrestrial biosphere model, inter-annual variability of Net Primary Productivity (NPP) over India during 1981-2006 was studied. It is revealed that the variability is large over mixed shrub and grassland (MGL), moderate over cropland and small over the forest regions. Inter-annual variability of NPP exhibits strong positive coherence with the variability of precipitation, and weak coherence with the variability of temperature and solar radiation. Estimated linear growth rate of annual NPP is 0.005 Pg C Yr-2 which is equivalent to 8.5% over the country during past 25 years. This increase is primarily due to the enhancement of productivity over agricultural lands in the country. NPP has increased over most parts of the country during the early 15-year period (1981-1995) resulting in a 10% growth rate of national NPP budget. On the other hand, the NPP growth rate has been reduced to 2.5% during later 15 years period (1991-2005) owing to large decline of NPP over the Indo-Gangetic plains. Climate had a strong control on NPP growth rate during both the periods. © 2012 Royal Meteorological Society.

Reddy C.S.,Indian National Remote Sensing Centre | Jha C.S.,Indian National Remote Sensing Centre | Dadhwal V.K.,Indian National Remote Sensing Centre
Environmental Monitoring and Assessment | Year: 2013

Deforestation and fragmentation are important concerns in managing and conserving tropical forests and have global significance. In the Indian context, in the last one century, the forests have undergone significant changes due to several policies undertaken by government as well as increased population pressure. The present study has brought out spatiotemporal changes in forest cover and variation in forest type in the state of Odisha (Orissa), India, during the last 75 years period. The mapping for the period of 1924-1935, 1975, 1985, 1995 and 2010 indicates that the forest cover accounts for 81,785.6 km2 (52.5 %), 56,661.1 km2 (36.4 %), 51,642.3 km 2 (33.2 %), 49,773 km2 (32 %) and 48,669.4 km2 (31.3 %) of the study area, respectively. The study found the net forest cover decline as 40.5 % of the total forest and mean annual rate of deforestation as 0.69 % year-1 during 1935 to 2010. There is a decline in annual rate of deforestation during 1995 to 2010 which was estimated as 0.15 %. Forest type-wise quantitative loss of forest cover reveals large scale deforestation of dry deciduous forests. The landscape analysis shows that the number of forest patches (per 1,000) are 2.463 in 1935, 10.390 in 1975, 11.899 in 1985, 12.193 in 1995 and 15.102 in 2010, which indicates high anthropogenic pressure on the forests. The mean patch size (km2) of forest decreased from 33.2 in 1935 to 5.5 in 1975 and reached to 3.2 by 2010. The study demonstrated that monitoring of long term forest changes, quantitative loss of forest types and landscape metrics provides critical inputs for management of forest resources. © 2012 Springer Science+Business Media B.V.

Dadhwal V.K.,Indian National Remote Sensing Centre
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2012

Improved national carbon assessments are important for UNFCC communications, policy studies and improving the global assessment. Use of EO for land cover dynamics, forest type, cover and phytomass carbon density, productivity and related soil carbon density and regional extrapolation of point flux measurements. A National Carbon Project (NCP) under the Indian Space Research Organisation - Geosphere Biosphere Programme (ISRO - GBP) aims at improving the understanding and quantification of net carbon balance. The NCP has been implemented with three major components - (A) vegetation carbon pools, (B) Soil carbon pools and (C) Soil and Vegetation - Atmosphere Fluxes. A total of 6500 field plot data from forests and trees outside forests have been collected. 1500 field plots have been inventoried for the soil carbon based on the remotely sensed data stratification. A nationwide network of carbon flux towers in different ecosystems for the measurement and modeling of the net carbon flux using eddy covariance techniques is being established and upscaling using satellite remote sensing data and modelling is under process. The amplitude of the diurnal variation in NEE increased with growth of wheat and reached its peak around the pre-anthesis stage. Besides, under NCP, satellite diurnal CO2 have also analyzed the data obtained from AIRS and SCIAMACHY over India and surrounding oceans and was correlated with surface fluxes. The CASA model simulations over India using NOAA AVHRR NDVI.

Korada D.R.H.V.,Indian National Remote Sensing Centre
Water Resources Management | Year: 2014

Uneven distribution of domestic water in space and time is a major concern in many fast growing cities due to improper planning and lack of scientific approach. This problem is much severe where the maximum domestic water requirements are met from the groundwater resources. Optimising a single groundwater pumping scheme may be an easy task using simple linear programming technique but, if the number of pumping schemes and constraints are more, solutions for identifying such groundwater schemes are more difficult and laborious using conventional methods as the constraints varies in space and time. In this paper, a new technique was developed to identify new groundwater pumping schemes to meet the present and future domestic water requirements in space and time by integrating spatial optimisation technique with the groundwater model. The approach considers the possible optimum rate of groundwater pumping, minimising the cost of water supply scheme and having minimum impact on the downstream side groundwater table using high resolution satellite data (IKONOS), Geographical Information System (GIS) tools and optimisation techniques. Dehradun, which is one of the fast growing cities in India, was considered as a study area to demonstrate the proposed new technique. Domestic water demand for next two decades (up to 2,031) was forecasted and compared with the existing supplies. Nearly 48 additional groundwater pumping schemes were identified to cater the present and future demands. Its impact on the groundwater table was also studied using groundwater modelling technique. © 2014 Springer Science+Business Media Dordrecht.

Sharma N.,Indian National Remote Sensing Centre | Ali M.M.,Indian National Remote Sensing Centre
IEEE Geoscience and Remote Sensing Letters | Year: 2013

Tropospheric temperature measurements at high temporal, spatial, and vertical resolutions are required for many meteorological studies. Radiosonde and Global Positioning System radio occultation (GPSRO) observations have very high vertical resolutions but poor in spatial and temporal coverage. Although the sounders on geostationary satellites can provide high temporal and spatial resolutions, their vertical resolution is poor. In this letter, we proposed a method to increase the vertical resolution of tropospheric temperature profiles obtained from geostationary satellite observations based on an artificial neural network (ANN) approach so that high-resolution temperature profiles are available in all four dimensions. We simulated the pressure levels of the forthcoming Indian National Satellite System (INSAT) 3-D temperature measurements from 950 to 100 hPa using 1-D variational temperature profiles of the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC). We used these low-resolution simulated profiles as the predictors and the high-resolution GPSRO COSMIC profiles as predictants. The data during 2007 and 2008 were used to develop the model, and the data during 2009 were used for validation. The correlation coefficient of greater than 0.94 is observed throughout the pressure levels for all the three data sets. The root-mean-square differences of training, selection, and validation sets are 0.43, 0.46, and 0.51, respectively. A scatter index of less than 0.002 for all the three data sets indicates the accuracy of the estimations. © 2004-2012 IEEE.

Martha T.R.,Indian National Remote Sensing Centre | Vinod Kumar K.,Indian National Remote Sensing Centre
Landslides | Year: 2013

The Okhimath area in the Uttarakhand state of India witnessed a large-scale occurrence of landslides on 14 September 2012 due to intense rainfall. As per news reports, this event resulted in the death of 51 people and a significant loss of property. In this study, the damage assessment results of the Okhimath landslides derived from the analysis of very high resolution (VHR) images received from Cartosat-2, Resourcesat-2, Kompsat-2 and GeoEye-1 satellites are presented. These datasets were acquired through a coordinated effort of the Indian Space Research Organisation and International Charter Space and Major Disasters. A total of 126 buildings, 34.5 ha of agricultural land and 7.78 km of road were identified as damaged through the VHR satellite data analysis. Using a semi-automatic landslide detection technique, 473 landslides covering a 2.25-km2 area were also identified. Villages such as Mangali, Chunni, Brahman Kholi, Semla, Paldwadi, Saari and Giriyagaon are found to be most affected due to this event. The damage is mainly attributed to rock slides which originated in the escarpment zone which later converted to debris flows by scouring the material along the run-out zone. © 2013 Springer-Verlag Berlin Heidelberg.

Raj K.B.G.,Indian National Remote Sensing Centre
Geomatics, Natural Hazards and Risk | Year: 2010

The climate change of the 20th century has had a pronounced effect on glacier environments of the Himalayas. The formation of moraine dammed glacial lakes and outburst floods from such lakes are a major concern in countries such as Bhutan, Tibet (China), India, Nepal and Pakistan. The hazardous lakes, however, are situated in remote areas and are very difficult to monitor through ground surveys due to the rugged terrain and extreme climatic conditions. This paper depicts the growth of a glacial lake in Reru Valley, Zanskar Himalaya based on the observations made from temporal satellite data. The change detection studies show the glacier retreating at an average rate of 12 m per year, causing lateral growth of the lake. Peak discharge from the lake is estimated using empirical formulas and varies from 1.7 m 3 s -1 to 196 m 3 s -1. © 2010 Taylor & Francis.

Jawak S.D.,Indian National Remote Sensing Centre | Luis A.J.,Indian National Remote Sensing Centre
Photogrammetric Engineering and Remote Sensing | Year: 2014

We devised a semiautomatic approach for extracting lake features based on a customized set of normalized difference water index (NDWI) information which was obtained by incorporating high resolution, 8-band WorldView-2 data. An extensive accuracy assessment was carried out for three semiautomatic feature extraction approaches for extracting 36 lake features on Larsemann Hills, Antarctica. The method was tested on five existing PAN-sharpening algorithms, which suggest that the customized NDWI approach renders intermediate performance (root mean square error varies from ~227 to ~235 m2) and highest stability when compared with existing feature extraction techniques. In general, the customized NDWI rendered a least misclassification (≈11 percent), followed by target detection (≈16 percent) and spectral processing (≈17 percent) methods for extraction of 36 lakes. We also found that customized NDWI caused consistently least misclassification (≈21 percent) than the target detection (≈23 percent) and spectral processing (≈30 percent) methods for extraction of partially snow or ice-covered 11 lakes. Our results indicate that the use of the customized NDWI approach and appropriate PAN-sharpening algorithm can greatly improve the semiautomatic extraction of lake features in cryospheric environment. © 2014 American Society for Photogrammetry and Remote Sensing.

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