Landslide Susceptibility Assessment by Using Publicly‐Available Remote Sensing and Geospatial Data to Assist Risk Management and Geological Safety: A Case Study of the Wugongshan Area, South China

ABSTRACT The Wugongshan area has undergone complex geological evolution since Neoproterozoic, creating a wealth of natural relics that support tourism development, but also triggering frequent landslides that threaten local sustainability. However, the primary triggering factors remain unclear. Based on 692 historical landslides in this area, eight factors potentially related to landslides were selected for susceptibility assessment, including elevation, slope, aspect, lithology, distance to geological fault, distance to water system, distance to transportation system, and Normalized Difference Vegetation Index. They are then applied with the information quantity model and the information value‐logistic regression coupled model, respectively, to assess susceptibility and generate risk zoning maps on the ArcGIS platform. The accuracy values of the two models are 0.787 and 0.786, respectively, suggesting a reliable assessment result. The validation results also suggest that the information value‐logistic regression coupled model is better at dealing with larger datasets in complex environments. Elevation, geological fault, lithology, and distance to transportation system are mainly responsible for the landslides in Wugongshan. The framework developed by using public data is relevant for improving the quality of geohazard prevention and geological safety in Wugongshan and other similar areas worldwide.