Regional Remote Sensing Center East

Kolkata, India

Regional Remote Sensing Center East

Kolkata, India

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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 | Mohan K.K.,Hyderabad | 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


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


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.


Biswal A.,Regional Remote Sensing Center East | Mukherjee S.,Anthropological Survey of India | Jeyaram A.,Regional Remote Sensing Center East | Krishna Murthy Y.V.N.,Regional Remote Sensing Center East
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2012

The Similipal is a densely forested hill-range in the heart of Mayurbhanj district,Orissa, lying close to the eastern-most end of the Easternghats. Similipal Biosphere Reserve is located in the Mahanadian Biogeographical Region and within the Biotic Province, Chhotanagpur Plateau.There are 4 villages in the core and 61 villages in the buffer area of the biosphere reserve .Agriculture is not well developed and employment opportunities are very poor , most of the people derive their income from collection of NTFP and sale of firewood and timber. A collaborative work is carried out by Regional Remote Sensing Centre(East) and Anthropological survey of India,Kolkata to study the impact of those four villages in the core area of SBR on the conservation of natural resources over the decades.Change in vegetation density as measured by NDVI over the decades is analysed to study the impact of these villages on the core area of Similipal Biosphere Reserve.


Dutta D.,Regional Remote Sensing Center East | Chakraborty A.,Regional Remote Sensing Center East | Jain P.,Gyan Ganga Institute of Technology and Sciences
Proceedings - 2015 International Conference on Computational Intelligence and Communication Networks, CICN 2015 | Year: 2015

Soil moisture is one of the key variables in agricultural water management particularly in scheduling of irrigation in the major irrigation commands. Large number of studies has proved that radar backscatter is sensitive to soil moisture due to its dependence on the complex dielectric permittivity of soil water at microwave frequencies. However, surface roughness and presence of vegetation cover introduces complexities in the precise estimation of soil moisture in large agricultural areas. Numerous approaches have been put forward to predict the backscatter as a function of sensor configuration and surface, as well as vegetation characteristics towards deriving soil moisture information. However, for operational applications in larger areas, a parameterization of these analytical and theoretical models is difficult to achieve. It has always remained a challenge as to how to estimate soil moisture using single configuration radar without field measurement of surface roughness and vegetation cover. In the present study, soil moisture was modeled in two steps. Firstly, SAR angular response was used for surface roughness characterization assuming that the surface roughness and soil moisture does not change in a short period of time in absence of rainfall or irrigation and surface perturbation. The roughness normalized backscattering (RNBS) thus generated consists of contribution from soil moisture and surface vegetation cover. In the second step normalized difference vegetation index (NDVI) generated from IRS LISS III data and RNBS was plotted. Their relationship was studied and iso-moisture lines were generated at different intervals. Specific polynomial model was derived for each level of soil moisture and using those models the SAR image was classified and usable soil moisture map was generated. The approach is valid for C-band radar and gives good result in fallow, and crop cover areas where normalized difference vegetation index (NDVI) value less than 0.4 while using IRS LISS III data. © 2015 IEEE.


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.


Jangir B.,Indian Institute of Technology Kharagpur | Satyanarayana A.N.V.,Indian Institute of Technology Kharagpur | Swati S.,Indian National Remote Sensing Centre | Jayaram C.,Regional Remote Sensing Center East | And 2 more authors.
Natural Hazards | Year: 2016

Odisha coast is a dynamic region wherein both natural and anthropogenic processes affecting the shape and position of the shoreline vis-à-vis the coastal processes are prevalent in this region. Geomorphology such as lagoons, tidal flats, mangrove swamps and shoreline is a rapidly changing phenomenon along this coast mainly due to tides, waves, oceanic currents and storm surges. Keeping in view of the above, the present study mainly focused on decadal changes of shoreline and land cover changes, and delineation of geomorphological units along the Odisha coast using geospatial technologies for the period 1990–2009. Spatio-temporal analysis of remote sensing data indicated notable shoreline changes at cyclone land fall locations along the Odisha coast, wherein erosion (loss) and accretion (gain) rates were observed to be high at Paradeep and Kendrapada. During the study period, the rate of accretion was observed to be higher during 1999–2009 compared to 1990–1999. Coastal geomorphology landforms distributions indicate that the swale complex, older and younger coastal plains, tidal flats and mangrove swamp are the dominant features. Long-term land cover analysis indicated that the vegetation cover loss happened in the high vegetated areas during the period 1990–1999 and regeneration during the period 1999–2009. © 2016 Springer Science+Business Media Dordrecht


Chakraborty D.,Regional Remote Sensing Center East | Thakur S.,Manipal University India | Jeyaram A.,Regional Remote Sensing Center East | Krishna Murthy Y.V.N.,NRSC | Dadhwal V.K.,NRSC
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2012

RISAT-II or Radar Imaging satellite - II is a microwave-imaging satellite lunched by ISRO to take images of the earth during day and night as well as all weather condition. This satellite enhances the ISRO's capability for disaster management application together with forestry, agricultural, urban and oceanographic applications. The conventional pixel based classification technique cannot classify these type of images since it do not take into account the texture information of the image. This paper presents a method to classify the high-resolution RISAT-II microwave images based on texture analysis. It suppress the speckle noise from the microwave image before analysis the texture of the image since speckle is essentially a form of noise, which degrades the quality of an image; make interpretation (visual or digital) more difficult. A local adaptive median filter is developed that uses local statistics to detect the speckle noise of microwave image and to replace it with a local median value. Local Binary Pattern (LBP) operator is proposed to measure the texture around each pixel of the speckle suppressed microwave image. It considers a series of circles (2D) centered on the pixel with incremental radius values and the intersected pixels on the perimeter of the circles of radius r (where r = 1, 3 and 5) are used for measuring the LBP of the center pixel. The significance of LBP is that it measure the texture around each pixel of the image and computationally simple. ISODATA method is used to cluster the transformed LBP image. The proposed method adequately classifies RISAT-II X band microwave images without human intervention. © 2012 ISPRS.


Das P.K.,Regional Remote Sensing Center East | Sahay B.,Indian National Remote Sensing Centre | Seshasai M.V.R.,Indian National Remote Sensing Centre | Dutta D.,Regional Remote Sensing Center East
Geomatics, Natural Hazards and Risk | Year: 2016

The lack of availability of two shortwave-infrared (SWIR) bands in most of the satellite sensors restricts the utilization of shortwave angle slope index (SASI). The potential of Resourcesat-2 Advanced Wide-Field Sensor (AWiFS) with a single SWIR band was explored to generate surface moisture information comparable to SASI over Haryana, India. The fractional values of several vegetation indices, viz. normalized difference water index (NDWI), SASI and angle-based drought index (ABDI) derived from moderate resolution imaging spectroradiometer (MODIS) were compared. At different phenological stages, ABDI was found to be a better index than NDWI while representing SASI values using regression analysis and Mahalonobis distance measurement. A very high coefficient of regression value (R2 = 0.8) was observed while analyzing the agreement between AWiFS-derived ABDI and MODIS-derived SASI for the year 2012. The regression parameters were deployed on AWiFS-derived ABDI to generate improved moisture images for the year 2013 and were compared with actual SASI images from MODIS data. © 2016 National Remote Sensing Centre - ISRO Published by Informa UK Limited, trading as Taylor & Francis Group


Chacko N.,Regional Remote Sensing Center East | Dutta D.,Regional Remote Sensing Center East | Ali M.M.,Indian National Remote Sensing Centre | Sharma J.R.,Indian National Remote Sensing Centre | Dadhwa V.K.,Indian National Remote Sensing Centre
IEEE Geoscience and Remote Sensing Letters | Year: 2015

Ocean heat content (OHC) is an important parameter in determining the heat flux in the ocean-atmosphere system, which can influence weather systems such as cyclones and monsoons. Hence, regular monitoring of OHC is required, which needs continuous subsurface temperature profiles. Due to the scarcity of in situ temperature profiles in space and time, remotely sensed sea surface temperature (SST) and sea surface height anomalies (SSHAs) are employed in the computation of OHC in the Indian Ocean. OHC derived from in situ temperature profiles from ARGO floats along with collocated SST, SSHA and OHC climatology during the period 2002-2012 are used to estimate OHC700 (heat content up to 700-m depth), using an artificial neural network model. The estimated OHC-700 is validated and is found to be significantly correlated with the observed OHC700. Using this approach, OHC700 is being estimated daily on a near-real-time basis, and the products are available at http://bhuvan.nrsc.gov.in/data/download/index.php. © 2014 IEEE.

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