Decoding vegetation's role in landslide susceptibility mapping: An integrated review of techniques and future directions

Rainfall-induced landslides, exacerbated by climate change, require urgent attention to identify vulnerable regions and propose effective risk mitigation measures. Extensive research underscores the significant impact of vegetation on soil properties and slope stability, emphasizing the necessity to incorporate vegetation effects into regional landslide susceptibility mapping. This review thoroughly examines research integrating vegetation into landslide susceptibility mapping, encompassing qualitative, semi-quantitative, and quantitative forecasting methods. It highlights the importance of incorporating vegetation aspects into these methods for comprehensive and accurate landslide susceptibility assessment. This review explores the diverse roles of vegetation in slope stability, covering both aggregated impacts and individual influences, including mechanical and hydrological effects on soil properties, as well as the implications of evapotranspiration and rainwater interception on slope stability. While aggregated roles are integrated into non-deterministic methods as input layers, individual roles are considered in deterministic methods. In the application of deterministic methods, it is noteworthy that a considerable number of studies primarily concentrate on the mechanical impact, particularly the reinforcement provided by root cohesion. The review also explores limitations and highlights future research prospects. In the context of mapping landslide susceptibility amid changing climatic conditions, data-driven techniques encounter challenges, while deterministic methods present their advantages. Stressing the significance of hydrological impacts, the paper recommends incorporating vegetation influences on unsaturated soil properties, including the soil water characteristic curve and soil permeability, along with pre-wetting suction due to evapotranspiration and potential rainwater interception.