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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.


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.


Bhagia N.,Terrestrial Biosphere and Hydrology Group | Rajak D.R.,Terrestrial Biosphere and Hydrology Group | Patel N.K.,Terrestrial Biosphere and Hydrology Group
Journal of the Indian Society of Remote Sensing | Year: 2011

A study was conducted to improve precision of crop acreage adopting stratified random sampling approach. Remotely sensed data was used to classify mustard crop for the states of Rajasthan, Madhya Pradesh, Uttar Pradesh, Gujarat and Haryana covering 81% of mustard area of India. A grid of size 5 × 5 km was super-imposed on classified image of study area and proportion of mustard crop within the grid was ascertained. Crop proportion was used to determine strata. Stratification was done based on equal interval of proportion, equal sample number and cumulative square root of frequency method. Cumulative square root of frequency method gave highest precision in all the cases. © 2011 Indian Society of Remote Sensing.


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.


Manjunath K.R.,Terrestrial Biosphere and Hydrology Group | Ray S.S.,Terrestrial Biosphere and Hydrology Group | Panigrahy S.,Terrestrial Biosphere and Hydrology Group
Journal of the Indian Society of Remote Sensing | Year: 2011

Hyperspectral remote sensing, because of its large number of narrow bands, has shown possibility of discriminating the crops. Current study was carried out to select the optimum bands for discrimination among pulses, cole crops and ornamental plants using the ground-based Hyperspectral data in Patha village, Lalitpur district, Uttar Pradesh state and Kolkata, West Bengal state. The field observations of reflectance were taken using a 512-channel spectroradiometer with a range of 325-1075 nm. The stepwise discriminant analysis was carried out and separability measures, such as Wilks' lambda and F-Value were used as criteria for identifying the narrow bands. The analysis showed that, the best four bands for pulse crop discrimination lie mostly in NIR and early MIR regions i. e. 750, 800, 940 and 960 nm. Within cole crops discrimination is primarily determined by the green, red and NIR bands of 550, 690, 740, 770 and 980 nm. The separability study showed the bands 420,470,480,570,730,740, 940, 950, 970, 1030 nm are useful for discriminating flowers. © 2011 Indian Society of Remote Sensing.


Tripathy R.,Terrestrial Biosphere and Hydrology Group | Ray S.S.,Terrestrial Biosphere and Hydrology Group | Kaur H.,Punjab Agricultural University | Jalota S.K.,Punjab Agricultural University | And 2 more authors.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2012

The present study investigated the impact of climate change, as projected by the Global Climate Model, HadCM3 for two different storyline (A2, B2), on the total crop production of Punjab state of India and its spatial variability for three future periods (2020, 2050 and 2080). Gridded weather data (1∗1 degree) from India Meteorological Department was used as baseline weather. Daily future weather data were generated from baseline and projected change for each weather parameter (maximum and minimum temperature, rain fall). Both baseline and future weather data were then interpolated to 25∗25 km grid level. The cropping system model, CropSyst was used for simulating the climate change impact on crop productivity. Cropping system map generated from remote sensing data for Punjab was used for finding the major cropping systems in each of the 25 km grid. Using this information cropping system productivity in each grid was estimated for baseline weather as well as for projected weather. Spatial pattern was generated for the difference in grid yield for each scenario. Results showed yield decline in all cropping systems except for few grids during 2020 in B2 scenario. Aggregated district yield indicated that for A2 scenario, in the near future (2020) Roopnagar (in eastern Punjab) will be the most affected district with around 35 % reduction in cropping system yield where as Hoshiarpur (in north-eastern part) will be most affected during 2050 and 2080. For B2 scenario, Hoshiarpur was found to be the most vulnerable region for all the three periods.


Chhabra A.,Terrestrial Biosphere and Hydrology Group | Panigrahy S.,Terrestrial Biosphere and Hydrology Group
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2012

Knowledge of temporal variations of Leaf Area Index (LAI) aids in understanding the climate-vegetation interaction of different vegetative systems. This information is amenable from high temporal remote sensing data. India has around 78.37 million hectare, accounting for 23.84% of the geographic area of the country under forest/tree cover. India has a diverse set of vegetation types ranging from tropical evergreen to dry deciduous. We present a detailed spatio-temporal and inter-seasonal analysis of LAI patterns in different forest types of India using MODIS 8-day composites global LAI/fPAR product for the year 2005 at 1-km spatial resolution. A forest cover mask was generated using SPOT 1-km landuse/landcover classification over the Indian region. The range of estimated LAI varied from 0.1-6.9 among the different forest types. Maximum LAI was observed in tropical evergreen forests in North-Eastern region and Western Ghats. Low LAI was observed in Central Indian region due to predominance of dry deciduous forests. The spatial patterns of seasonal variations detected that for most of the forest types, the peak LAI values were observed during September and October months of the autumn season in contrast to minimum LAI during summer season. The mean LAI and standard deviation for each 8-day LAI composite were also computed and mean monthly LAI profiles were derived for each forest type classified on the basis of their geographical locations. These results are useful indicators for detailed understanding of phenological sequence and may also serve as important inputs for deriving bioclimatic indices for different forest types of India.


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.


Vyas S.,Terrestrial Biosphere and Hydrology Group | Nigam R.,Terrestrial Biosphere and Hydrology Group | Patel N.K.,Terrestrial Biosphere and Hydrology Group | Panigrahy S.,Terrestrial Biosphere and Hydrology Group
Journal of the Indian Society of Remote Sensing | Year: 2013

Monitoring of Agricultural crops using remote sensing data is an emerging tool in recent years. Spatial determination of sowing date is an important input of any crop model. Geostationary satellite has the capability to provide data at high temporal interval to monitor vegetation throughout the entire growth period. A study was conducted to estimate the sowing date of wheat crop in major wheat growing states viz. Punjab, Haryana, Uttar Pradesh (UP), Madhya Pradesh (MP), Rajasthan and Bihar. Data acquired by Charged Couple Detector (CCD) onboard Indian geostationary satellite INSAT 3A have continental (Asia) coverage at 1 km × 1 km spatial resolution in optical spectral bands with high temporal frequency. Daily operational Normalized Difference Vegetation Index (NDVI) product from INSAT 3A CCD available through Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) was used to estimate sowing date of wheat crop in selected six states. Daily NDVI data acquired from September 1, 2010 to December 31, 2010 were used in this study. A composite of 7 days was prepared for further analysis of temporal profile of NDVI. Spatial wheat crop map derived from AWiFS (56 m) were re-sampled at INSAT 3A CCD parent resolution and applied over each 7 day composite. The characteristic temporal profiles of 7 day NDVI composite was used to determine sowing date. NDVI profile showed decreasing trend during maturity of kharif crop, minimum value after harvest and increasing trend after emergence of wheat crop. A mathematical model was made to capture the persistent positive slope of NDVI profile after an inflection point. The change in behavior of NDVI profile was detected on the basis of change in NDVI threshold of 0.3 and sowing date was estimated for wheat crop in six states. Seven days has been deducted after it reached to threshold value with persistent positive slope to get sowing date. The clear distinction between early sowing and late sowing regions was observed in study area. Variation of sowing date was observed ranging from November 1 to December 20. The estimated sowing date was validated with the reported sowing date for the known wheat crop regions. The RMSD of 3.2 (n = 45) has been observed for wheat sowing date. This methodology can also be applied over different crops with the availability of crop maps. © 2013 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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