## 2 Materials and Methods
### Multi-Temporal InSAR Method  
In order to adjust the post-earthquake LSM using the slope deformation, multi-temporal InSAR was used to calculate surface deformation [51; 52]. For efficient and swift processing of SAR data, the SAR images are processed in Figure 4. The topography of the study area was highly variable, and the surface cover consisted mainly of hills, vegetation, a small amount of agricultural land, and human-made structures. Therefore, there were few PS points in the region and dense vegetation with significant shading, which caused difficulties for traditional PS-InSAR techniques. The scatterer points in this study were divided into two parts. One was the PS points determined by thresholds of the mean to sigma ratio (MSR) (1.4) and spectral coherence (0.32) [53; 54]; the number of such points was small and they were concentrated in urban areas. The other was DS points, including  
Figure 3: Influencing factors in Jiuzhaigou. (**a**) Elevation; (**b**) slope; (**c**) plan curvature; (**d**) profile curvature; land use (**e**) aspect lithology; (**f**) TWI; (**g**) distance to a river; (**h**) distance to a fault; (**i**) land use and land cover; (**j**) lithology; (**k**) rainfall and (**l**) NDVI. mountains, highways, woods, and rocks etc. To obtain more detailed regional landslide deformation, this study implemented distributed scatterer technology. This technology could significantly increase the number of observed targets in the region so as to obtain more accurate ground deformation information. To discover distributed scatterers, SAR data were analyzed for statistical hypothesis testing (KD test), as previously reported by [PERSON] et al. [55; 56]. Prior to the deformation analysis, the Delaunay triangle network was built to ensure the spatial connectivity of all permanent scatterer and distributed scatterer points [57]. On the completion of constructing the network, the process of selecting the best point set was carried out using distance and connectivity threshold. We initially chose a high-quality point set to establish a spatial backbone network by utilizing the period diagram approach to compute the linear deformation and elevation residual error for all linked edges. The region growth approach was then utilized to identify the edge generation branch network that fit the requirements based on the backbone network [58]. Then, using the corresponding spatial baseline (250 m) and temporal baseline (60 d) thresholds, the interferogram was calculated by means of the differential conjugate multiplication of the interferometric pairs. The differential interferogram was processed using adaptive filtering and the filtered interferogram was unwrapped using minimum cost flow (MCF) [59; 60]. After removing the elevation phase and atmospheric phase from the phase model, multiple regression was used to estimate the deformation phase [54]. The interferometric pairs of multiple baselines were then reduced to time series using singular value decomposition (SVD) [61]. It is worth noting that the above method obtained the deformation velocity of the radar line of sight (LOS). Deformation of slope (\(v_{slope}\)) was utilized in the study instead of \(v_{Los}\) as it better reflected the movement of landslides. The transfer approach was implemented according to the procedure of [PERSON] et al. and [PERSON] et al. [62; 63]. Based on the satellite motion, the ground factor, and triangulation, the trans  
Figure 4: SAR processing flow, indicating the processing flow from obtaining PS/DS points to obtaining deformation. formation equations can be calculated using Formula (1): the DEM calculated the aspect and slope angle, and the satellite parameter file obtained the azimuth and incidence angle.