Abstract
To prepare a landslide susceptibility map of Shiv-khola watershed, one of the landslide prone parts of Darjeeling Himalaya, remote sensing and GIS tools were used to integrate 10 landslide triggering parameters: lithology, slope angle, slope aspect, slope curvature, drainage density, upslope contributing area (UCA), lineament, settlement density, road contributing area (RCA), and land use and land cover (LULC). The Analytical Hierarchy Process (AHP) was applied to derive factor weights using MATLAB with reasonable consistency ratio (CR). The frequency ratio (FR) model was used to derive class frequency ratio or class weights that indicate the relative importance of individual classes for each factor. The weighted linear combination (WLC) method was used to determine the landslide susceptibility index value (LSIV) on a GIS platform, by incorporating both factor weights and class weights. The Shiv-khola watershed is classified into five landslide susceptibility zones. The overall classification accuracy is 99.22 and Kappa Statistics is 0.894.
Highlights
The identification of the causative factors is the basis of many methods of landslide susceptibility assessment
Ten landslide triggering factors including lithology, slope angle, drainage, slope aspect, slope curvature, lineament, upslope contributing area (UCA), land use and land cover (LULC), road contributing area (RCA), and settlement density were taken into account to prepare the landslide susceptibility map of the Shiv-khola watershed and their hierarchical arrangement was made on priority basis
A pairwise comparison matrix for the study area was constructed on the basis of the preference of a factor as compared with the other factor and arithmetic mean method was applied to arrange landslide triggering factors hierarchically and to determine the prioritized factor rating value/eigenvector (PFRV) with reasonable consistency ratio (CR), based on Saaty (1977, 1980) and Saaty and Vargas (2000), using MATLAB (Table 2)
Summary
The identification of the causative factors is the basis of many methods of landslide susceptibility assessment. Integration between factor weight (FW) and class frequency ratio (FR) was performed with the help of a linear combination model This is done to derive pixelwise landslide susceptibility index values (LSIV) and prepare a landslide susceptibility map. In the Shiv-khola watershed, Lower Paglajhora, Tindharia, Shiviter, Gayabari, and Mahanadi are the major and prominent landslide locations where settlements, communication lines, and tea garden areas are being affected severely by the frequent occurrence of landslides. Thematic data layers of all the landslide inducing factors were integrated to prepare a landslide susceptibility map using a linear combination model in GIS. The integration between PFRV and PCRV was made in a linear combination model on a GIS platform to estimate the landslide susceptibility index value (LSIV) for each pixel and a suitable classification technique was incorporated to prepare the landslide susceptibility map of the Shiv-khola watershed. The following section presents the methods and results of the landslide analyses in this study
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