Applicability Analysis of Potential Landslide Identification by InSAR in Alpine-Canyon Terrain-Case Study on Yalong River

Landslides occur frequently in the western mountainous areas of China, causing huge losses every year. InSAR technology can efficiently and accurately identify potential landslides and is a powerful tool for landslide hazards mitigation. However, the successful application of InSAR technology is limited by several factors, such as geometric distortion and dense vegetation, especially in the area with alpine-canyon terrain. This study investigates the applicability of InSAR observations in identifying potential landslides of the middle section of the Yalong River, which is a typical alpine-canyon terrain area. Using time-series InSAR Sentinel-1 datasets, we detect six potential landslides, which are verified and analyzed by using optical remote sensing images. Then, the applicability analysis is performed considering geometric distortion and band suitability. The results reveal that combining ascending and descending data can increase the detectable area (not in the geometric distortion) from 70% to 92.9%. The comparison of the performance of C-band and L-band data in identifying potential landslides shows that the latter is able to detect potential landslides with high vegetation coverage but it may miss the area with slight displacement. This study demonstrates the use of InSAR for potential landslide identification in alpine-canyon terrain areas and reveals its applicability, which provides a deep understanding of SAR data selection and would play an important role in the InSAR-based landslide geohazard mitigation application.

Automated summary

This study investigates the applicability of InSAR technology in identifying potential landslides in alpine-canyon terrain areas. Using time-series InSAR Sentinel-1 datasets, six potential landslides were detected and analyzed. The results show that combining ascending and descending data increases the detectable area, and L-band data performs better in identifying landslides with high vegetation coverage.