Retrospective deformation of the Baige landslide using optical remote sensing images

Detecting slope deformations before major landslides is crucially important for mitigating landslide disasters. In this work, we explored the use of Sentinel-2 images and an image correlation method, Co-Registration of Optically Sensed Images and Correlation (COSI-Corr), to derive slope deformations before the 2018 Baige landslide along the Jinsha River. We found that larger sliding windows outperform smaller ones in extracting the moving slope. Different solar zenith angles in image pairs have an influence on the derived displacements. Although the spatial resolution of Sentinel-2 images is 10 m, uncertainties in estimating slope deformation are found within 2 m. Using the COSI-Corr technique, we monitored sub-pixel slope deformation in Sentinel-2 images from November 2015 to August 2018. We found that the slope moved faster in summer than in winter months, which was probably driven by the monsoon climate in this region. From 2015 to 2018, the slope movement became faster, and an acceleration stage was observed before the slope failed completely. To derive reliable slope displacements, this work suggests testing different sliding window size combinations in the use of image correlation methods, and optical images acquired with similar zenith angles are preferred. This work also demonstrated the capability of using optical remote sensing images to monitor large slope deformations before major landslides. As more optical images with high spatial resolution become available, monitoring minor slope deformations directly would be practicable for large remote mountain regions.