Landslide Susceptibility Mapping Using Remotely Sensed Data through Conditional Probability Analysis Using Seed Cell and Point Sampling Techniques

Abstract

Rapid urbanization, intense infra-structure development and increased tourism related activities have resulted in the change of landscape of the Kodaikkanal town and its surrounding, a popular hill town in Tamilnadu, South India. As an after effect, the numbers of landslides and rock-falls have increased steadily in the past decade. Landslide susceptibility analysis is carried out for this area using conditional probability analysis. The geo-spatial database for mapping landslide susceptibility consists of the factors - Relief, Slope, Aspect, Curvature, Weathering, Land use, Topographic Wetness Index and Proximity to road. Two sampling strategies – point and seed-cell are compared for landslide susceptibility mapping. The Landslide Susceptibility map developed using conditional probability method is verified using R index for both sampling strategies. The study shows that both the sampling strategies perform with good accuracy, seed cell technique excels slightly over point sampling. 86.11% of the landslides fall in the high and critical susceptible zones. The results show that conditional probability technique provides a simple tool for susceptibility analysis. The method can be used at regional scale and is a valuable input for planning purpose.