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Gupta P.K.,Space Applications Center | Panigrahy S.,Terrestrial Biosphere and Hydrology Group | Parihar J.S.,Space Applications Center
Journal of the Indian Society of Remote Sensing | Year: 2011

The effects of climate change on hydrological regimes have become a priority area for water and catchment management strategies. The terrestrial hydrology driven by monsoon rainfall plays a crucial role in shaping the agriculture, surface and ground water scenario in India. Thus, it is imperative to assess the impact of the changing climatic scenario projected under various climate change scenario towards the hydrological aspects for India. Runoff is one of the key parameters used as an indicator of hydrological process. A study was taken up to analyse the climate change impact on the runoff of river basins of India. The global circulation model output of Hadley centre (HADCM3) projected climate change data was used. Scenario for 2080 (A2 scenario indicating more industrial growth) was selected. The runoff was modeled using the curve number method in spatial domain using satellite derived current landuse/cover map. The derived runoff was compared with the runoff using normal climatic data (1951-1980). The results showed that there is a decline in the future climatic runoff in most of the river basins of India compared to normal climatic runoff. However, significant reduction was observed for the river basins in the eastern region viz: lower part of Ganga, Bahamani-Baitrani, Subarnrekha and upper parts of the Mahanadi. The mean projected runoff reduction during monsoon season (June-September) were 18 Billion Cubic Meter (BCM), 3.2 BCM, 3.5 BCM and 5.9 BCM for Brahmaputra-Barak Subarnrekha, Subarnarekha and Brahmini-Baitrani basin, respectively in comparison to normal climatic runoff. Overall reduction in seasonal runoff was high for Subarnrekha basin (54.1%). Rainfall to runoff conversion was high for Brahmaputra-Barak basin (72%), whereas coefficient of variation for runoff was more for Mahanadi basin (1.88) considering the monsoon season. Study indicates that eastern India agriculture may be affected due to shortage of surface water availability. © 2011 Indian Society of Remote Sensing.

Kumar T.,Terrestrial Biosphere and Hydrology Group | Patnaik C.,Advanced Techniques Development Group
International Journal of Applied Earth Observation and Geoinformation | Year: 2013

C-band dual polarization (HH, HV) Synthetic Aperture Radar (SAR) data from Radarsat-2 were used to discriminate and characterize mangrove forests of the Sundarbans. Multi-temporal data acquired during winter and rainy seasons were analysed for the segregation of mangrove forest area. A decision rule based classification involvingcombination of three-date HH (range -11 to -2 dB) with single-date crosspolarization ratio (2-8) was applied on the datasets for discriminating mangrove forests from other land cover classes. Application of textural measures(entropy and angular second moment) in the aforesaid decision rule based classification produced three broad homogeneous mangrove classes. The area covered by the most homogeneous class increased from January to March and decreased from July to September, and correlated well to the change in the phenological status of the mangroves. Extent of homogeneous areas was more in the eastern region of the Sundarbans than that of the central and westernside. Thus, the study revealed that textural measures combined with multi-temporal HH backscatter and single-date cross-polarization ratio in a decision rule classification could be satisfactorily used for characterization of the mangrove forests. © 2012 Elsevier B.V.

Gupta P.K.,Space Applications Center | Punalekar S.,Space Applications Center | Panigrahy S.,Terrestrial Biosphere and Hydrology Group | Sonakia A.,APCCF projects | Parihar J.S.,Space Applications Center
Journal of Hydrologic Engineering | Year: 2012

Abstract: A hybrid technique was used for the runoff production and its routing in an agro-forested watershed located within the Kanha National Park in Central India with the use of remote sensing and geographic information system (GIS) data. In this technique, a modified Soil Conservation Service curve number (SCS-CN) method and a two-dimensional overland flow model were combined. Modified SCS-CN method estimated daily net rainfall fractions were used as an input to the overland flow model along with other remote-sensing-derived inputs such as the digital elevation model (DEM), rainfall, and roughness factor for routing of the produced runoff. The model works on a cell basis and routs produced runoff from one cell to next following the maximum downslope directions. The flow model uses the diffusive wave approximations of the St. Venant equations for routing surface water. The model was tested by calibrating the Strickler coefficient K, which is inversely proportional to resistance to flow, and comparing the observed and simulated daily change in the water levels for two gauging sites. The calibrated average values of K for different subcatchments were 15.7, 21.7, 23.4, and 28.4 for Kurkuti, Sijhora, between the gauging sites, and downstream catchments, respectively. The model was tested for some statistical parameters like the Nash Sutcliffe coefficientand RMSE using residuals between observed and simulated data, and found to be within the acceptable limits. The results show that the hybrid technique works well to extend the application of curve number to address the routing phase of runoff. © 2012 American Society of Civil Engineers.

Ray S.S.,Terrestrial Biosphere and Hydrology Group | Jain N.,Terrestrial Biosphere and Hydrology Group | Arora R.K.,Central Potato Research Station | Chavan S.,Terrestrial Biosphere and Hydrology Group | Panigrahy S.,Terrestrial Biosphere and Hydrology Group
Journal of the Indian Society of Remote Sensing | Year: 2011

The study was carried out to investigate the utility of hyperspectral reflectance data for potato late blight disease detection. The hyperspectral data was collected for potato crop at different level of disease infestation using hand-held spectroradiometer over the spectral range of 325-1075 nm. The data was averaged into 10-nm wide wavebands, resulting in 75 narrowbands. The reflectance curve was partitioned into five regions, viz. 400-500 nm, 520-590 nm, 620-680 nm, 770-860 nm and 920-1050 nm. The notable differences in healthy and diseased potato plants were noticed in 770-860 nm and 920-1050 nm range. Vegetation indices, namely NDVI, SR, SAVI and red edge were calculated using reflectance values. The differences between the vegetation indices for plants at different levels of disease infestation were found highly significant. The optimal hyperspectral wavebands to discriminate the healthy plants from disease infested plants were 540, 610, 620, 700, 710, 730, 780 and 1040 nm whereas upto 25% infestation could be discriminated using reflectance at 710, 720 and 750 nm. © 2011 Indian Society of Remote Sensing.

Tripathy R.,Terrestrial Biosphere and Hydrology Group | Chaudhari K.N.,Terrestrial Biosphere and Hydrology Group | Mukherjee J.,Punjab Agricultural University | Ray S.S.,Terrestrial Biosphere and Hydrology Group | And 3 more authors.
Remote Sensing Letters | Year: 2013

An attempt has been made to assimilate remotely sensed input data in mechanistic crop simulation model World Food Studies (WOFOST) for in-season wheat yield forecasting in Punjab state of India. Spatial weather data at '5 km × 5 km' grid were generated through interpolation of daily available weather data. Grid-wise sowing date was estimated from time-series normalized difference vegetation index (NDVI) data product from vegetation sensor of SPOT satellite (SPOT-VGT). The leaf area index (LAI) derived from remotely sensed data was used in the simulation model WOFOST for predicting spatial yield. The simulated wheat grain yield for each grid was aggregated to district level using the actual wheat fraction for each grid derived from remote sensing-based wheat crop map. A comparison was made between the estimated yield and that reported by Department of Agriculture. The procedure was repeated for three crop seasons to check the reliability. The results indicated that this technique could be used for spatial yield prediction at regional level with a root mean square error (RMSE) of <0.4 tonnes ha-1 at state level. © 2012 Taylor & Francis.

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