Please use this identifier to cite or link to this item: http://localhost:80/xmlui/handle/123456789/581
Title: Modeling of potential gully erosion hazard using geo-spatial technology at Garbheta block, West Bengal in India
Authors: Shit, Pravat Kumar
Paira, Rumpa
Bhunia, GouriSankar
Maiti, Ramkrishna
Keywords: Gully erosion
Bivariate statistics
GIS based method
Gully proneness mapping
Issue Date: May-2015
Publisher: Modeling Earth Systems and Environment (Springer)
Abstract: The gully erosion is the most serious environmental problem in West Bengal in India. Present study focused on delineation the gully affected areas and characterization of geo-environmental factor in the gully affected region to prevent future problems. Ground investigation and geo-spatial data along with bivariate statistical approach were employed to identity the most crucial factors among lithology, dynamic and slope inclination, landuse, aspect, plan curvature, stream power index, topographical wetness index and length-slope factor and also understand the most dominant class of each factor associated the gully erosion in the area under study. All the information were integrated into geographical information system platform and categorized in zones of very high, high, moderate, and low gully erosion susceptibility. Weight index overlay method is used to validate the gully proneness map. Results showed land use factor (barren land and waste land), slope ([20), topographical wetness index values ([1.2), length-slope index ([4.00), fragments of pebbles, boulder and gravels, older alluvium and lateritic soil play important roles in gully processes. Model validation indicated that the resulting map of areas prone to gully erosion has a prediction accuracy of 88.25 %. The methodology adopted for gully erosion proneness mapping can be exercised in other gully vulnerability areas that could be an excellent approach to defend the natural resources and progress in the land use conservation.
URI: http://111.93.204.14:8080/xmlui/handle/123456789/581
ISSN: 2363-6203
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