Work

Causative environment-informed active landslide detection with InSAR monitoring: Empowering reservoir slope hazard management

Year: 2026

Type: article

Abstract: The accurate detection of active landslides influenced by reservoir water-level fluctuations is critically important for safeguarding hydropower infrastructure and protecting lives and property along riverbanks. However, most current InSAR-based approaches primarily rely on abrupt changes in phase or velocity gradients, while largely ignoring the causative environmental conditions that control landslide initiation. This study proposes a novel method for causative environment-informed active landslide detection with InSAR monitoring in the reservoir area. First, a four-directional velocity gradient matrix is constructed from time-series deformation data. Spatial morphology analysis is then used to filter out noise, extracting active deformation areas (ADAs) with continuous boundaries. Subsequently, five slope unit-based landslide susceptibility prediction (LSP) models are constructed to learn the causative environmental conditions of reservoir landslides systematically. Finally, an area under the curve (AUC) weighted voting strategy is employed to integrate deformation signals with environmental constraints, enabling the reliable identification of active landslides. Applied to the DHQ and HD reservoir areas of the Lancang River basin, the method identified 89 ADAs, achieving a 96% spatial overlap with manually interpreted deformation areas. Under the constraints of causative environmental factors, the overall F 1 -Score for identifying active landslides in the reservoir area reached 78.15%. Model interpretability analysis revealed that distance to river (DtR) plays a dominant role in both the formation and detection of reservoir landslides. The study further identifies key landslide-causing environment factors (LEFs) and high-susceptibility areas, providing valuable data support and decision-making guidance for reservoir slope disaster management, landslide monitoring, early warning, and risk prevention.

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2025.105079