Landslide Susceptibility Analysis of Shiv-Khola Watershed, Darjiling: A Remote Sensing & GIS Based Analytical Hierar
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RESEARCH ARTICLE
Landslide Susceptibility Analysis of Shiv-Khola Watershed, Darjiling: A Remote Sensing & GIS Based Analytical Hierarchy Process (AHP) Sujit Mondal & Ramkrishna Maiti
Received: 12 April 2011 / Accepted: 31 August 2011 / Published online: 18 October 2011 # Indian Society of Remote Sensing 2011
Abstract In the present study, Remote Sensing Technique and GIS tools were used to prepare landslide susceptibility map of Shiv-khola watershed, one of the landslide prone part of Darjiling Himalaya, based on 9 landslide inducing parameters like lithology, slope gradient, slope aspect, slope curvature, drainage density, upslope contributing area, land use and land cover, road contributing area and settlement density applying Analytical Hierarchy Approach (AHA). In this approach, quantification of the factors was executed on priority basis by pair-wise comparison of the factors. Couple comparing matrix of the factors were being made with reasonable consistency for understanding relative dominance of the factors as well as for assigning weighted mean/ prioritized factor rating value for each landslide triggering factors through arithmetic mean method using MATLAB Software. The factor maps/thematic data layers were generated with the help of SOI Topo-
S. Mondal (*) Department of Geography, Raja N.L.Khan Women’s College, Paschim Medinipur, West Bengal 721102, India e-mail: [email protected] R. Maiti Department of Geography and Environment Management, Vidyasagar University, Paschim Medinipur, West Bengal 721102, India e-mail: [email protected]
sheet, LIIS-III Satellite Image (IRS P6/Sensor-LISSIII, Path-107, Row-052, date-18/03/2010) by using Erdas Imagine 8.5, PCI Geomatica, Arc View and ARC GIS Software. Landslide frequency (%) for each class of all the thematic data layers was calculated to assign the class weight value/rank value. Then, weighted linear combination (WLC) model was implied to determine the landslide susceptibility coefficient value (LSCV or ‘M’) integrating factors weight and assigned class weight on GIS platform. Greater the value of M, higher is the propensity of landslide susceptibility over the space. Then Shivkhola watershed was classified into seven landslide susceptibility zones and the result was verified by ground truth assessment of existing landslide location where the classification accuracy was 92.86 and overall Kappa statistics was 0.8919. Keywords Remote sensing & GIS . Analytical Hierarchy Process (AHA) . Landslide Susceptibility . Frequency Ratio (FR)
Introduction Several attempts to reduce landslide risk were made through studying the history of management of landslide terrain by constructing protective structures or monitoring and warning systems, or through the ever-increasing sophisticated methods for mapping and delineating areas prone to landslides (Dai and
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Lee 2002). Landslides were the result of two interacting sets of forces; ‘the precondition factors’, naturally induced which govern the stability conditions of slopes, and ‘the preparatory and
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