Monitoring a landslide with a multi-deformation magnitude based on the phase and amplitude information of SAR images: A case study of the Baige landslide in Jinsha River

In recent years, radar remote sensing has been extensively applied to extract high-precision deformation information of landslide surfaces. The techniques used include phase-based interferometry and amplitude-based pixel offset tracking (POT). However, large complex landslides exhibit significantly different deformation magnitudes over the spatio-temporal evolution, complicating the comprehensive monitoring of landslide deformation via single radar remote sensing. Hence, by analyzing the deformation detection capability of radar remote sensing, this study proposed monitoring the whole process of a landslide combined with the phase and amplitude information of synthetic aperture radar (SAR) images. This study investigated the Baige landslide occurring in Jinsha River in 2018 based on Sentinel-1 data from 2014 to 2021 and ALOS-2 data from 2014 to 2018. Combined with time-series interferometric SAR (InSAR) analysis and POT, this study acquired the pre- and post-disaster time-series deformations of the landslide. The results are as follows. Pre-disaster, the trailing edge of the Baige landslide exhibited an average annual rate of 20 mm/a, with deformation of the main landslide area up to about 45 m from December 2014 to July 2018. Post-disaster, the landslide gradually expanded to the trailing edge, with an average annual deformation rate reaching 200 mm/a, threatening the safety of some civilian houses. Therefore, the combined method in this study can achieve the multi-deformation magnitude extraction of large complex landslides from spatio-temporal dimensions.