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Bhaskar T.V.S.U.,Indian National Center for Ocean Information Services | Jayaram C.,Regional Remote Sensing Center East | Bansal S.,Indian National Remote Sensing Centre | Swain D.,Indian Institute of Technology Bhubaneswar
Journal of the Indian Society of Remote Sensing | Year: 2016

The Oceansat-2 scatterometer (OSCAT) of the Indian Space Research Organization (ISRO), provides surface wind speed and direction with a spatial resolution of 50 km × 50 km. With a revisit time of 2 days it had provided ocean surface wind vectors over the global oceans. In the present work, an attempt has been made to generate two day composite of OSCAT wind vectors using Data-Interpolating Variational Analysis (DIVA) and compare them with daily composite winds to check how better is the two day composites in comparison to daily composites. The daily and two days composite wind vectors of zonal (U) and meridional (V) components have been validated with wind measurements from in situ buoys and Advanced Scatterometer (ASCAT) for the year 2012 over the tropical Indian Ocean region. The statistical comparison with the in situ measurements and ASCAT has shown that the two-day OSCAT wind composites are slightly better than the daily composite winds. The improvement in the statistics can be attributed to the use of ascending and descending passes pertaining to two days which results in fewer gaps between passes, thereby reducing the interpolation errors. © 2016 Indian Society of Remote Sensing

Girishkumar M.S.,Indian National Center for Ocean Information Services | Suprit K.,Indian National Center for Ocean Information Services | Chiranjivi J.,Regional Remote Sensing Center East | Udaya Bhaskar T.V.S.,Indian National Center for Ocean Information Services | And 3 more authors.
Ocean Dynamics | Year: 2014

Upper oceanographic and surface meteorological time-series observations from a moored buoy located at 9.98 N, 88 E in the south-western Bay of Bengal (BoB) were used to quantify variability in upper ocean, forced by a tropical cyclone (TC) Jal during November 2010. Before the passage of TC Jal, salinity and temperature profiles showed a typical BoB post-monsoon structure with relatively warm (30 C) and low-saline (32.8 psu) waters in the upper 30- to 40-m layer, and relatively cooler and higher salinity (35 psu) waters below. After the passage of cyclone, an abrupt increase of 1 psu (decrease of 1 C) in salinity (temperature) in the near-surface layers (up to 40-m depth) was observed from buoy measurements, which persisted up to 10-12 days during the relaxation stage of cyclone. Mixed layer heat budget analysis showed that vertical processes are the dominant contributors towards the observed cooling. The net surface heat flux and horizontal advection together contributed approximately 33 % of observed cooling, during TC Jal forced stage. Analysis showed the existence of strong inertial oscillation in the thermocline region and currents with periodicity of ∼2.8 days. During the relaxation stage of the cyclone, upward movement of thermocline in near-inertial frequencies played significant role in mixed layer temperature and salinity variability, by much freer turbulent exchange between the mixed layer and thermocline. © 2014 Springer-Verlag Berlin Heidelberg.

Chowdary V.M.,Regional Remote Sensing Center East | Desai V.R.,Indian Institute of Technology Kharagpur | Gupta M.,Indian Institute of Technology Kharagpur | Jeyaram A.,Regional Remote Sensing Center East | Murthy Y.V.N.K.,Indian National Remote Sensing Centre
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2012

Distributed hydrological modeling has the capability of simulating distributed watershed basin processes, by dividing a heterogeneous and complex land surface divided into computational elements such as Hydrologic Response Units (HRU), grid cell or sub watersheds. The present study was taken up to simulate spatial hydrological processes from a case study area of Kansavati watershed in Purulia district of West Bengal, India having diverse geographical features using distributed hydrological modelling approach. In the present study, overland flow in terms of direct runoff from storm rainfall was computed using USDA Soil Conservation Services (SCS) curve number technique and subsequently it served as input to channel routing model. For channel flow routing, Muskingum-Cunge flood routing technique was used, specifically to route surface runoff from the different sub watershed outlet points to the outlet point of the watershed. Model parameters were derived for each grid cell either from remote sensing data or conventional maps under GIS environment. For distributed approach, validation show reasonable fit between the simulated and measured data and CMR value in all the cases is negative and ranges from -0.1 to -0.3. Further, this study investigates the effect of cell size on runoff simulation for different grid cell sizes of 23, 46, 92, 184, 368, 736, 1472 m resolution. The difference between simulated and observed runoff values increases with the increase of grid size beyond 184 m more prominently. Further, this model can be used to evaluate futuristic water availability scenarios for an agricultural watershed in eastern India.

Paul A.,Regional Remote Sensing Center East | Bhattacharya S.,Institute of Engineering and Management | Dutta D.,Regional Remote Sensing Center East | Sharma J.R.,Indian National Remote Sensing Centre | Dadhwal V.K.,Indian National Remote Sensing Centre
GIScience and Remote Sensing | Year: 2015

Dimensionality reduction of hyperspectral images is essential for reduction of computational complexity and faster analysis. A novel method for band reduction has been proposed here, which has been adapted from the genetic algorithm (GA) along with spatial clustering. Spatial clustering generates overall signature variation present in a particular scene and in turn removes huge redundancy present in the raster data set. GA is applied on the clustered signatures to extract the reduced set of bands that is computed to be the “fittest” i.e., those bands that provide the most discriminating information in a hyperspectral image. This has been computed by taking the sum of Kullback–Leibler divergences (KLD) between consecutive selected bands. A higher KLD value amongst adjacent selected band implies higher divergence in value. The selected band-set image has been classified and the accuracy indices are evaluated respectively. The proposed method shows high performance on the basis of classification accuracy and efficient execution while comparing with two other state-of-the-art methods. © 2015 Taylor & Francis

Paul A.,Regional Remote Sensing Center East | Chowdary V.M.,Regional Remote Sensing Center East | Srivastava Y.K.,Regional Remote Sensing Center East | Dutta D.,Regional Remote Sensing Center East | Sharma J.R.,Regional Centres
Geocarto International | Year: 2016

Automatic change detection of land cover features using high-resolution satellite images, is a challenging problem in the field of intelligent remote sensing data interpretation, and is becoming more and more effective for its applications viz. urban planning and monitoring, disaster assessment etc. In the present study, a change in detection approach based on the image morphology that analyses change in the local image grids is proposed. In this approach, edges from both the images are extracted and grid wise comparison is made by probabilistic thresholding and power spectral density analysis for identifying change area. One of the advantages of the proposed methodology is that the temporal images used in the change analysis need not be radiometrically corrected as analysis is based on edge extractions. The grid-based analysis further reduces the error, which might have been introduced by image mis-registration. The proposed methodology is validated by finding the temporal changes in the linear land cover features in parts of Kolkata city, India using three different image data-sets from LISS IV, Cartosat-1 and Google earth having varied spatial resolutions of 5.8 m, 2.5 m and about 1 m, respectively. The overall accuracy in identifying changes is found to be 64.82, 73.86 and 80.93% for LISS IV, Cartosat-1 and Google earth data-set, respectively. © 2016 Informa UK Limited, trading as Taylor & Francis Group

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