Extreme rainfall has caused severe road damage and landslide disasters in mountainous areas. Rainfall forecasting derived from remote sensing data has been widely adopted for disaster prevention and early warning as a trend in recent years. By integrating high-resolution radar rain data, for example, the QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system provides a great opportunity to establish the extreme climate-based landslide susceptibility model, which would be helpful in the prevention of hillslope disasters under climate change. QPESUMS was adopted to obtain spatio-temporal rainfall patterns, and further, multi-temporal landslide inventories (2003–2018) would integrate with other explanatory factors and therefore, we can establish the logistic regression method for prediction of landslide susceptibility sites in the Laonong River watershed, which was devastated by Typhoon Morakot in 2009. Simulations of landslide susceptibility under the critical rainfall (300, 600, and 900 mm) were designed to verify the model’s sensitivity. Due to the orographic effect, rainfall was concentrated at the low mountainous and middle elevation areas in the southern Laonong River watershed. Landslide change analysis indicates that the landslide ratio increased from 1.5% to 7.0% after Typhoon Morakot in 2009. Subsequently, the landslide ratio fluctuated between 3.5% and 4.5% after 2012, which indicates that the recovery of landslide areas is still in progress. The validation results showed that the calibrated model of 2005 is preferred in the general period, with an accuracy of 78%. For extreme rainfall typhoons, the calibrated model of 2009 would perform better (72%). This study presented that the integration of multi-temporal landslide inventories in a logistic regression model is capable of predicting rainfall-triggered landslide risk under climate change.

Large-scale landslides often result in severe soil displacement and the exposure of bedrock, particularly combined with heavy rainfall. This condition significantly increases the risk of sediment-related disasters. Consequently, vegetation restoration and succession following landslide events are critical strategies for mitigating such hazards and enhancing disaster resilience. In this study, we integrated multi-temporal remote sensing imagery, land use classification, and Markov chain change simulations to evaluate the dynamic restoration of vegetation in a large-scale landslide area. Field surveys were conducted to validate the observed patterns of vegetation recovery. The results showed high accuracy in land use classifications derived from eight temporal images, with overall accuracy surpassing 80% and Kappa coefficients exceeding 0.7. The primary areas of vegetation recovery were identified as forests, followed by grasslands. Spatial change simulations indicated that full vegetation stability is expected to be reached after 2075. We emphasized the efficacy of combining remote sensing and modeling techniques for long-term monitoring of vegetation dynamics and offer critical insights for formulating sustainable strategies for disaster management.