## 4 Discussion  
Earthquakes in Karst regions quickly increase the susceptibility to landslides and spark a large number of geological hazards. In this study, the refined LSM aimed to update the susceptibility classes in the time series and decrease the number of potential false negatives using the RF model. In this study, it was possible to compare the results of various assessments of landslide susceptibility in Jiuzhaigou and provide an effective correction method for a refined LSM. A refined LSM could reduce the number of errors using only the RF model and update the susceptibility classes in the time series. This method proved to be very effective in delineating areas of different susceptibility. The MS 7.0 earthquake in this region in 2017 caused a significant increase in landslide susceptibility in the epicenter region, but the time series of regional deformation showed a gradual decrease in LSM in most of the region after the earthquake, which was associated with stress release and manual intervention. Over the past four years, a total of 270,000 trees have been replanted in the Jiuzhaigou scenic area, with a restoration and protection area of 1.72 km\({}^{2}\), and the scenic area's ecosystem had been effectively restored [71]. As shown in Figure 15, we compare the NDVI after ecological restoration with the NDVI during the earthquake period. The change in NDVI indicates that there is increased vegetation and that ecological restoration has had an effect. On the other hand, in the Zhangzha and Jiuzhaigou National Park, human activities have increased landslide susceptibility. This rapid update method provides the ability to dynamically update the LSM to provide a  
Figure 14: Earthquake-induced landslide events in Jiuzhaigou were documented through photographs; for site location and LSM, see Figure 13. On the left is an enlarged LSM (Figure 13) for each site: (**a**) YG145 landslide in Zhangzha, (**b**) YG159 landslide in the Heye moutain. On the right, (**c**) and (**d**) are photo records of the selected EIL events (2018). reference for planners. In addition, dynamic landslide susceptibility demonstrates that appropriate ecological protection is an effective means of reducing landslide susceptibility. There are many factors that influence landslides, and different combinations of factors can have a profound impact on the results. In this study, the 12 influencing factors and landslide inventory were selected by experts from the Sichuan Ecological and Environmental Restoration Institute based on years of ground investigation and research, taking into account topography, river, climate, vegetation, human activity, earthquakes, and faults. In the model calculation, grid search and Bayesian methods were used to find suitable model parameters by considering the ROC, F1 score, and other indicators. Meanwhile, in the Karst Region, difficulties including temporal decorrelation and atmospheric phase screen hindered the use of PSI techniques over multi-vegetation regions. To overcome this limitation, this research developed a distributed scatterer technique by forming an optimal interferogram network and employing spatial adaptive filtering to reduce noise and improve the density of distributed scatterers in sparsely vegetated landscapes. Despite the fact that PS/DS points from the C-band dataset had a lower density in vegetated areas than those from the L-band dataset, Sentinel data have fast revisit cycles, a simple data collecting process, and a huge image range. Using Sentinel data significantly decreased temporal decorrelation, making displacement detection possible over landslides covered in rangeland, grassland, bushes, and bare soils. Evaluation of the models for the initial LSM and the rapidly updated LSM showed that the modified LSM could improve the susceptibility model according to the regional deformation rate. The susceptibility of areas of high deformation was adjusted upwards and vice versa. Distance from rivers was found to be a significant factor in this study.