Regional Landslide Identification Based on Susceptibility Analysis and Change Detection

Abstract

Landslide identification is an increasingly important research topic in remote sensing and the study of natural hazards. It is essential for hazard prevention, mitigation, and vulnerability assessments. Despite great efforts over the past few years, its accuracy and efficiency can be further improved. Thus, this study combines the two most popular approaches: susceptibility analysis and change detection thresholding, to derive a landslide identification method employing novel identification criteria. Through a quantitative evaluation of the proposed method and masked change detection thresholding method, the proposed method exhibits improved accuracy to some extent. Our susceptibility-based change detection thresholding method has the following benefits: (1) it is a semi-automatic landslide identification method that effectively integrates a pixel-based approach with an object-oriented image analysis approach to achieve more precise landslide identification; (2) integration of the change detection result with the susceptibility analysis result represents a novel approach in the landslide identification research field.