Abstract
This study examines snow covered area (SCA) and associated geohazards in the Bhilangna watershed using the Normalized Difference Snow Index. Two Landsat images from 1990 and 2010 were analyzed. In order to estimate the average elevation of the snowline, a digital elevation model from the Shuttle Radar Topography Mission was used. In 1990, 124 km2 (9 % of the watershed) was snow covered. In 2010, 96 km2 (7 %) was snow covered. Therefore, during the study period (i.e., 1990–2010) SCA decreased by 28 km2 (2 %). Four snow types were identified and mapped: frost, fine, medium and coarse snow. In 1990, 38 km2 (30 % of SCA) was covered by frost snow, 86 km2 (69 %) was covered by fine snow, < 1 km2 (<1 %) was covered by medium granular snow, and an insignificant area was covered by coarse snow. In 2010, frost and fine snow covered 19 km2 (20 %) and 76 km2 (79 %) respectively, medium snow covered 1 km2 (1 %), and coarse snow was not traced. The snowline shifted from 4611 m in 1990 to 4698 m in 2010. These observations show the variability of snow cover in the Bhilangna watershed.
Highlights
Snow cover plays an important role in the climate system by changing the energy and mass transfer between the atmosphere and the surface (Khosla et al, 2011)
Materials and methods The methodology for determining the snow cover pattern and dynamics of Bhilangna watershed is described in the following steps: 1. The base map of study area Bhilangna watershed was prepared in the ArcGIS software by using the survey of India toposheet scaled at 1:50,000 and watershed boundary was checked and corrected by superimposing the DEM data (Digital elevation model) derived from the SRTM digital elevation dataset with 90 m spatial resolution and ± 15 m vertical accuracy
This study demonstrate the usefulness of remote sensing and Geographical Information System (GIS) techniques in analyzing spatial extent, nature and magnitude of snow cover area
Summary
Snow cover plays an important role in the climate system by changing the energy and mass transfer between the atmosphere and the surface (Khosla et al, 2011). Some researchers have introduced the reflectance ratio/index approaches to remove the effects of radiometric errors due to changing effects in the atmosphere and topographical changes across the scene (Slater, 1980) To address this issue, normalized difference snow index (NDSI), along with the threshold tests have been used successfully for snow cover mapping using satellite data (Hall et al, 1995, 2002, Kulkarni and Rathore 2003, 2006; Gupta et al, 2005; Negi et al, 2008).
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