Reservoir bank landslides are a frequent phenomenon, and the stability of these landslides is affected by two essential factors: rainfall and reservoir level changes. Studying the response patterns of reservoir bank landslide movements to these variables is crucial in preventing their occurrence and mitigating their effects. To this end, this study employed 103 European Space Agency (ESA) Copernicus Sentinel-1 images and the SBAS-InSAR (small baseline subset interferometric synthetic aperture radar) technique to obtain a time series of the Guobu slope deformation from September 2015 to December 2019. The Guobu slope showed significant toppling damage. The satellite line of sight (LOS) detected a maximum subsidence rate of −447 mm/y (the negative sign indicates movement away from the satellite, i.e., subsidence) in the upper section of the slope. Subsequently, three wavelet tools were used to quantitatively analyze the effect of rainfall and reservoir water level on the deformation of the Guobu slope. The results demonstrate a positive correlation between rainfall and the deformation of the Guobu slope. Moreover, the deformation lags behind the rainfall by approximately 70 days. In contrast, the reservoir water level and the deformation of the Guobu slope exhibit an inverse relationship. The deformation of the leading edge of the slope body lags behind the reservoir level by approximately 19 days, while the middle and upper sections of the slope body, which have the most significant rate of variability, lag by about 80 days. Among these factors, rainfall plays a dominant role in the deformation of the Guobu slope, while reservoir levels play a synergistic role. The findings of this study highlight the importance of monitoring and understanding the impact of changes in rainfall and reservoir water levels on the stability of reservoir bank landslides. This understanding is crucial in preventing the occurrence of such landslides and minimizing their impact. The use of remote sensing techniques, together with wavelet analysis, enables the accurate and timely monitoring of the deformation of the Guobu slope, providing valuable insights for disaster warnings and disaster prevention and reduction efforts.

Building reconstruction using high-resolution satellite-based synthetic SAR tomography (TomoSAR) is of great importance in urban planning and city modeling applications. However, since the imaging mode of SAR is side-by-side, the TomoSAR point cloud of a single orbit cannot achieve a complete observation of buildings. It is difficult for existing methods to extract the same features, as well as to use the overlap rate to achieve the alignment of the homologous TomoSAR point cloud and the cross-source TomoSAR point cloud. Therefore, this paper proposes a robust alignment method for TomoSAR point clouds in urban areas. First, noise points and outlier points are filtered by statistical filtering, and density of projection point (DoPP)-based projection is used to extract TomoSAR building point clouds and obtain the facade points for subsequent calculations based on density clustering. Subsequently, coarse alignment of source and target point clouds was performed using principal component analysis (PCA). Lastly, the rotation and translation coefficients were calculated using the angle of the normal vector of the opposite facade of the building and the distance of the outer end of the facade projection. The experimental results verify the feasibility and robustness of the proposed method. For the homologous TomoSAR point cloud, the experimental results show that the average rotation error of the proposed method was less than 0.1°, and the average translation error was less than 0.25 m. The alignment accuracy of the cross-source TomoSAR point cloud was evaluated for the defined angle and distance, whose values were less than 0.2° and 0.25 m.