Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data

TLDR

The study evaluates landslide hazard in Penang, Malaysia by integrating GIS and remote sensing data. Landslide locations were mapped from aerial imagery and field surveys, a spatial database of topographic, geological, land‑use, and vegetation variables was constructed, and logistic regression was applied to model hazard, with results validated against observed landslides and compared to a probabilistic model. The logistic regression model outperformed the probabilistic model in predicting landslide occurrence.

Abstract

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographical Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Thematic Mapper (TM) satellite images; and the vegetation index value from Système Probatoire de l'Observation de la Terre (SPOT) satellite images. Landslide hazardous areas were analysed and mapped using the landslide‐occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better in prediction than probabilistic model.