Automatic detection and update of landslide inventory before and after impoundments at the Lianghekou reservoir using Sentinel-1 InSAR

Abstract

There is an urgent demand for continuous detection and monitoring of active slopes in wide reservoir areas, as reservoir impoundments may activate unstable slopes. Although satellite time-series InSAR has been widely used in mapping active landslides, the tedious artificial interpretation of InSAR results limits the efficiency and reliability of landslides detection. We propose a set of procedures for deformation monitoring and continuous automatic landslide identification in wide reservoir areas. The local time window estimation can extract the nonlinear deformation in the time series InSAR signal, thus enhancing the deformation field. Spatial adaptive clustering enables the effective extraction of unstable slopes. The abnormal deformation trends of landslides can be updated through the continuous identification of new results and comparison with historical results. The procedure is used to continuously detect unstable slopes in the Lianghekou reservoir area before and after the impoundment. Combining the ascending and descending SAR data of Sentinel-1, 109 historically active landslides were found and 18 new landslides were activated within one year after the first water storage. The distribution and types of unstable slopes were analyzed, followed by two case studies. The first case shows the different effects of the two-stage water storage on slope deformations, indicating rising water levels' critical role in landslide activation. The second case shows the consolidation settlement of the embankment dam and the influence of water storage on slope deformation monitoring. The results demonstrate the effectiveness of the InSAR automatic landslide identification and this study provides a technical reference for similar reservoir area.