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.
Highlights
Large landslides are a global phenomenon causing damage and loss of life [1]
This study provides an approach to landslide semi-automatic identification using the susceptibility-based change detection thresholding (SCDT) method, which integrates susceptibility analysis with the CDT method
The SCDT method improves the accuracy of landslide identification and can reduce labor, time, and monetary investment
Summary
Large landslides are a global phenomenon causing damage and loss of life [1]. As a result, landslide research has become a hot research topic in which landslide identification receives particular attention. Visual interpretation and field surveys have fatal shortcomings. They both require extensive labor or monetary investment. The accuracy of landslide automatic/semi-automatic identification still has plenty of room for improvement; when its accuracy matches that of traditional visual interpretation and field surveys, it can completely replace the latter. Researching landslide automatic/semi-automatic identification methods can compensate for the shortcomings of traditional approaches. Regional landslide automatic/semi-automatic identification research will help governments and relevant institutions develop effective disaster prevention and mitigation policies under reduced investment conditions; for example, maps illustrating the level of damage to affected buildings could prevent construction in historically landslide-prone areas [6]
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