Abstract
Landslide disasters have characteristics of frequent occurrence, widespread impact, and high destructiveness, posing serious threats to human lives, property, and the ecological environment. Timely and accurate early identification of landslides remains an urgent issue within the disaster prevention field. This study focuses on Jianzha County, Qinghai Province, integrating PS-InSAR、SBAS-InSAR and optical remote sensing techniques to delineate potential landslide-prone areas. Utilizing Google Earth imagery and existing landslide datasets, potential landslide points were identified through a deep learning model. The results indicate that: (1) In Jianzha County, the variation trend of the average surface velocity monitored by PS-InSAR and SBAS-InSAR technology is consistent, and the deformation monitoring results are reliable. (2) Utilizing the deep learning model, 56 potential landslide points were identified, comprising 39 high-risk points and 17 medium-risk points. By integrating the spatial distribution data of historical geological disaster points, it was found that 10 out of 13 previously occurred landslide disaster points were located at the identified high-risk landslide points, achieving a detection accuracy of 76.92%. (3) The spatial distribution of landslide points exhibits clustering, with slopes ranging from 10–40°, elevations between 15–30 m, and slope orientations predominantly towards the northeast. (4) Landslide formation is correlated with seasonal precipitation concentrations and temperature fluctuations. This method can provide a crucial basis for large-scale surface deformation monitoring and early identification of landslide risks.