Landslide susceptibility assessment using AHP model and multi resolution DEMs along a highway in Manipur, India

摘要

Landslides have been a great threat to the economy as well as human lives in the north-eastern border state, Manipur, India. Highways serve as the main lifelines of the state where hundreds of goods carrying heavy vehicles are transporting every day. However, during the rainy seasons, these highways are often collapsed due to landslides. This study aims to analyze the effect of multi-resolution Digital Elevation Models (DEMs) on landslide susceptibility mapping along a major highway in Manipur, India. Nine-landslide conditioning factors including Land Use Land Cover (LULC), slope, aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), distance to roads, distance to streams, and distance to faults were selected. The topographic factors were extracted from three different DEM sources-CARTOSAT 10 m, ASTER 30 m and SRTM 90 m. The Analytical Hierarchy Process approach is employed to assign appropriate weights to the conditioning factors, and the weighted factors are integrated to produce the landslide susceptibility maps. The assessment of the model accuracies showed that LSM1 (10 m) procured a success accuracy of 83.78 and prediction accuracy of 81.45, while LSM2 (30 m) resolution procured a success accuracy of 72.8 and prediction accuracy of 70.45, and LSM3 (90 m) resolution procured a success accuracy of only 58 and prediction accuracy of 55.87, respectively. In this particular study, the main aim is to analyse the effect of resolution in Landslide Susceptibility mapping along a major highway in Manipur, India, which has been witnessing landslides every year. The study also aims to select the optimal DEM resolution. The selection for optimal DEM resolution is dependent on scale and the topographic setting of the region under consideration. The susceptibility mapping results of this study indicates that CARTOSAT 10 m resolution DEM produced better accuracy as compared to other DEMs for this particular site. Policy makers can use the resulting map in planning purposes and risk management.