Indian Institute of Remote Sensing

About: Indian Institute of Remote Sensing is a government organization based out in Dehra Dūn, India. It is known for research contribution in the topics: Land cover & Normalized Difference Vegetation Index. The organization has 756 authors who have published 1355 publications receiving 16915 citations. The organization is also known as: Indian Photo-interpretation Institute.

Urbanisation Effect on Hydrological Response: A Case Study of Asan River Watershed, India

Abstract: Human being keeps on modifying the environment especially land use/land cover (LULC), in pursuance of excel, comfort and development. The subsequent impact of urbanization to the environment, especially land cover change, now occurs on scales that significantly affect hydrologic variations. The altering environment makes it necessary to understand and quantify various hydrological components for efficient water resource management. Therefore, in the present study, an attempt was made to study the impact of LULC change on runoff generation potential. Asan River watershed, which lies in Dehradun, capital of newly created Uttarakhand State, India, is selected as study region. A huge industrialization is been taken place within this watershed immediately after declaration of state in year 2000. Initially, LULC change detection analysis was carried out by simple LULC class area difference between two years under consideration i.e. 2000 and 2010. The hydrological simulation using variable infiltration capacity macro-scale hydrological model depicted increase in runoff after urbanization took place. Keywords: Land use land cover change, Urbanization, Impact assessment, hydrological modeling, variable infiltration capacity model, runoff potential

Estimation of forest aboveground biomass using combination of Landsat 8 and Sentinel-1A data with random forest regression algorithm in Himalayan Foothills

Abstract: Forest aboveground biomass (AGB) plays an indispensable role in the terrestrial carbon cycle and its dynamics. It also provides baseline data for developing sustainable management strategies in the region. In the present study, a decision-tree based random forest (RF) algorithm was used to estimate AGB for the different forest types in Doon valley, situated in the Himalayan foothills of India. Fifty-one spectral and textural variables were initially extracted using Landsat 8 Operational Land Imager and Sentinel-1A, which were further reduced to twenty optimum variables using the recursive feature elimination (RFE) method. These optimum variables were finally used to map AGB. Results showed that the spatial distribution of AGB ranged from 46.36 to 596.15 Mg ha−1 with good correlation (R2 = 0.87, RMSEr = 18.7%, RMSE = 62.56 Mg ha−1) between the observed and predicted AGB. This study validated the synergistic use of remote sensing, field data, and RF algorithm to precisely predict the spatial distribution of AGB.

Snow density retrieval using SAR data: algorithm validation and applications in part of North Western Himalaya

Abstract: . The current study has been done using Polarimetric Synthetic Aperture Radar (SAR) data to estimate the dry snow density in Manali sub-basin of Beas River located in state of Himachal Pradesh, India. SAR data from Radarsat-2 (RS2), Environmental Satellite (ENVISAT), Advanced Synthetic Aperture Radar (ASAR) and Advanced Land Observing Satellite (ALOS)-Phased Array type L-band Synthetic Aperture Radar (PALSAR) have been used. The SAR based inversion models were implemented separately for fully polarimetric RS2, PALSAR and dual polarimetric ASAR Alternate polarization System (APS) datasets in Mathematica and MATLAB software and have been used for finding out dry snow dielectric constant and snow density. Masks for forest, built area, layover and shadow were considered in estimating snow parameters. Overall accuracy in terms of R2 value and Root Mean Square Error (RMSE) was calculated as 0.85 and 0.03 g cm−3 for snow density based on the ground truth data. The retrieved snow density is highly useful for snow avalanche and snowmelt runoff modeling related studies of this region.