A Two-Dimensional InSAR-Based Framework for Landslide Identification and Movement Pattern Classification

Frequent extreme climate events have intensified landslide hazards in mountainous regions, necessitating efficient identification and classification to understand movement mechanisms and mitigate risks. This study develops a novel, non-contact InSAR framework that seamlessly integrates three key steps—Identification, Inversion, and Classification—to address this challenge. By applying this framework to ascending and descending Sentinel-1 data in the complex terrain of the Jishi Mountain region, we first introduce geometric distortion masking and a C-Index deformation consistency check, which enables the reliable identification of 530 active landslides, with 154 detected in both orbits. Second, we employ a local parallel flow model to invert the landslide movement geometry without relying on DEM-derived prior assumptions, successfully retrieving the two-dimensional (sliding and normal direction) deformation fields for all 154 consistent landslides. Finally, by synthesizing these 2D deformation patterns with geomorphological features, we achieve a systematic classification of movement types, categorizing them into retrogressive translational (31), progressive translational (66), rotational (19), composite (24), and earthflows (14). This integrated methodology provides a validated, transferable solution for deciphering landslide mechanisms and assessing risks in remote, complex mountainous areas.