The assessment of critical factors in the landslide risk analysis of forest slopes

The reasonableness of landslide risk analysis results for forest slopes poses a significant challenge due to the reliability of environmental data and the complex factors affecting the stability of large-scale forest slopes. This study introduces a novel approach to assessing the impact of critical factors on landslide risk analysis for forest slopes. The factors examined include topsoil thickness, soil strength, hydrological conditions, and vegetation. We utilize the TRIGRS program, a widely used tool in geotechnical engineering, to analyze the safety factor of a large forest slope covering an area of 100 hectares, situated at elevations ranging from 800 to 900 meters in the mountainous region of Kaohsiung, Taiwan. Some areas of the site experienced shallow landslides due to heavy rainfall in 2009. The shallow soil at the forest slope consists mainly of silty sands and clayey materials mixed with decomposed slate. Multiple regression analysis is used to evaluate the sensitivity of these critical factors to the landslide risk analysis results. The critical factors include six independent variables: soil cohesion (c), soil friction angle (f), root cohesion (c_R), coefficient of hydraulic conductivity (Ks), air entry value on soil-water retention curve (a), and topsoil thickness (Z). 

The research findings emphasize the significant role of topsoil thickness and tree root reinforcement in analyzing landslide risks on large-scale forest slopes. Reliable soil strength is crucial for these assessments, while hydrological soil parameters are less important. These findings provide a valuable reference for evaluating landslide risks in extensive forested areas. Notably, the study also highlights the necessity of obtaining trustworthy field data to improve the accuracy of landslide risk assessments. Furthermore, the results underscore the practical implications for future field applications, offering valuable insights for those involved in environmental risk management.