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Slow-moving landslide-prone areas (SLAs) are unstable objects on the terrestrial surface that can collapse rapidly when provoked by earthquakes, leading to infrastructure damage. It is critical to identify SLAs prior to earthquake events and assess their hazard-causing probabilities when triggered. An assessment approach of earthquake-triggered geohazards is proposed in this paper by combining interferometric synthetic aperture radar (InSAR) derived SLAs and geological and geomorphological factors. Taking the Ms6.8 Luding earthquake, which occurred in the Sichuan Province of southwestern China on September 5, 2022, as an example, 1320 scenes of Sentinel-1 SAR data in western Sichuan were processed using the small baseline subset (SBAS) InSAR technique before the earthquake. After the earthquake, hazard probability assessment was performed in real-time by filtering the SLAs using a spatial analysis technique with geological and geomorphological factors, e.g., real-time peak ground acceleration (PGA), slope, distance to fault (DTF), and distance to the river (DTR) data. The results show that 11 SLAs were classified into high-risk zones. As verified by the Luding co-seismic landslide dataset from visual interpretation of optical remote sensing images, 142 coseismic landslides were triggered by the earthquake in these high-risk regions. In these areas, an ancient landslide, with high pre-earthquake displacement rates (−50 mm/year) on the scarp was reactivated under the Luding earthquake forces. This method can provide a scientific tool for disaster mitigation and rapid response emergency management. [ABSTRACT FROM AUTHOR]

In high mountain canyon regions, SAR geometric distortion in imaging side may have an inevitable impact on InSAR deformation information, so the effective deformation information acquisition is critical for landslide identification and deformation mechanisms analysis. The landslide deformation around the reservoir of Gushui Hydropower Station located in upstream of the Lancang River has been focused on in the study. Using SAR satellite parameters and topographic information, the visibility analysis of deformation in radar line-of-sight (LOS) direction has been carried out, and a method to obtain LOS effective deformation information based on the visibility analysis has been proposed. The small baseline subsets (SBAS) technique is used to process the L-band and C-band SAR data, and the area affected by the geometric distortion in the InSAR result is masked to obtain the deformation information of the effective deformation region. The landslide identification analysis in the reservoir area has been carried out based on the effective deformation information in LOS direction. Thirteen landslides have been identified, and ten of them are new ones. A new large unstable area (New Zhenggang landslide) has been found near the Zhenggang landslide. The geological survey and displacement time series of the Zhenggang landslide reveals that it is in pull-type landslide mode, that is, due to the local instability of the leading edge of a landslide, the support of the trailing edge may be weakened, which may result in the landslide gradually developing backwards and upwards, and finally becoming a large landslide. The impact of peak rainfall and cumulative rainfall during the rainy season on landslide deformation has been verified in this paper. It indicates that the cumulative precipitation is the dominant factor causing the deformation of the landslide, and it shows that the landslide begins the deformation acceleration period about 12 days after the peak precipitation. The results have shown that the proposed visibility analysis method for extracting the effective deformation information of InSAR results can significantly improve landslide identification and analysis in complex terrain. [ABSTRACT FROM AUTHOR]