Discussion on InSAR Identification Effectivity of Potential Landslides and Factors That Influence the Effectivity

The southwest mountainous area of China is one of the areas with the most landslides in the world. In this paper, we used Ya'an City and Garze Tibetan Autonomous Prefecture in Sichuan Province as the research areas to explore the identification application effects of large-area potential landslides using synthetic aperture radar (SAR) data with different wavelength types (Sentinel-1, ALOS-2), different processing methods (SBAS-InSAR, Stacking-InSAR), and different geological environmental conditions. The results show the following: (1) The effect of identifying landslides with different slope directions is largely affected by the satellite orbit direction; when we identify landslide hazards across a large area, the joint monitoring mode of ascending and descending orbit data is required. (2) The period of monitoring affects the identification effect of potential landslides when landslide identification is carried out in southwestern China; the InSAR monitoring period is recommended to be more than 2 years. (3) In different geological environmental regions, SBAS technology and Stacking technology have their own advantages; Stacking technology identifies more potential landslides, and SBAS technology identifies potential landslides with higher accuracy; (4) the degree of vegetation coverage has a great impact on the landslide identification effect of different SAR data sources. In low-density vegetation coverage areas, the landslide identification result using Sentinel-1 data seems to be better than the result using ALOS-2 data. In high-density vegetation coverage areas, the landslide identification result using ALOS-2 data is better than that using Sentinel-1 data.

Automated summary

This study utilized synthetic aperture radar (SAR) data with different wavelength types and processing methods to explore the identification application effects of large-area potential landslides in the southwest mountainous area of China. The results showed the significant influence of satellite orbit direction, monitoring period, geological environmental conditions, and vegetation coverage on landslide identification.