The southwestern mountainous region of China (SMRC), characterized by complex geological environments, experiences frequent landslide disasters that pose significant threats to local residents. This study focuses on the Qijiang District of Chongqing, where we conduct a systematic evaluation of wavelength and observation geometry effects on InSAR-based landslide monitoring. Utilizing multi-sensor SAR imagery (Sentinel-1 C-band, ALOS-2 L-band, and LUTAN-1 L-band) acquired between 2018 and 2025, we integrate time-series InSAR analysis with geological records, high-resolution topographic data, and field investigation findings to assess representative landslide-susceptible zones in the Qijiang District. The results indicate the following: (1) L-band SAR data demonstrates superior monitoring precision compared to C-band SAR data in the SMRC; (2) the combined use of LUTAN-1 ascending/descending orbits significantly improved spatial accuracy and detection completeness in complex landscapes; (3) multi-source data fusion effectively mitigated limitations of single SAR systems, enhancing identification of small- to medium-scale landslides. This study provides critical technical support for multi-source landslide monitoring and early warning systems in Southwest China while demonstrating the applicability of China’s SAR satellites for geohazard applications.

To address the challenges of high cost, complex deployment, and difficulties in real-time early warning for small landslides near residential areas, a portable and low-cost high-frequency monitoring device based on Micro-Electro-Mechanical Systems (MEMSs) was developed, and an advanced warning algorithm based on anomaly intensity factors was constructed. The device is easy to install and can collect and transmit data to the cloud in real time. Equipped with edge computing capabilities, it can independently analyze data in the absence of network connectivity and transmit real-time early warning information to terminals within a range of 5 km. To verify the performance of the system, a large-scale outdoor landslide simulation test site was constructed, where slope failure was induced through artificial rainfall to obtain the complete process data from deformation to failure. The experimental results demonstrate that the proposed early warning algorithm effectively identified different stability levels, providing warnings up to 13 s prior to catastrophic failure, confirming the high sensitivity and reliability of the device. This study offers a cost-effective and efficient approach to landslide monitoring and early warning, with notable prospects for broader implementation in practice.

This study addresses the key scientific problem of urban safety in complex, hazard-inducing geological environments by focusing on representative towns in the Three Gorges Reservoir area. Through the integrated use of field investigations, numerical simulations, and multivariate statistical analysis, we developed a comprehensive model for assessing geological safety risk in reservoir-area towns. A four-tier deep safety evaluation system was constructed for two types of hazard-inducing geological environments, and a classification scheme for shallow susceptibility was proposed. On this basis, a five-tier integrated urban geological safety risk evaluation model was established that combines deep safety level, engineering sensitivity, shallow susceptibility, and prevention difficulty. The model exhibited strong performance (pseudo R² ≥ 0.914, p < 0.001) and indicates that risk is predominantly moderate (Grade III), with 85.7% of the 21 representative areas in Wushan, Fengjie, and other “2 + 4” towns falling into this category. Overall, the results provide an operational tool to support disaster risk reduction, risk-informed land-use governance, and resilient infrastructure planning, thereby contributing to sustainable urban development in reservoir-side mountainous regions.