# Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation
###### Abstract  
Aiming at the limited degree of revealing vegetation growth pattern by simple topographic factors, it is essential to find an index that can explain the vegetation cover mechanism to a greater extent. By introducing a certainty factor into vegetation cover evaluation, LSW, LS, topographic wetness index, and aspect were also chosen to carry out control group experiments. The grid digital elevation model of 10 disaster-stricken counties (cities) in Sichuan Province was used as the basis to obtain LSW, while vegetation susceptibility levels were categorized using the natural breaks classification. The results of the multi-factor analysis demonstrated that the certainty factor corresponding to LSW climbed linearly from negative to positive values as the susceptibility level rose, indicating that it was highly correlated with vegetation cover and had an obvious advantage in revealing the vegetation growth potential. Statistically, the vegetation occurrence rate increased regularly with the improvement of the susceptibility level, in which the high and very high susceptibility zones corresponded to 83.66% and 89.95% of vegetation coverage respectively, suggesting a good consistency between the delineated high susceptibility areas and the actual vegetation cover. The findings proved that LSW has a substantial advantage in evaluating vegetation cover, with an evaluation model accuracy of 72.9%. It has been established that LSW will serve as a promising new model for assessing vegetation restoration and planning. vegetation growth potential; digital terrain analysis; LSW factor; certainty factor 202313180613390/app13211180612231318061223131806122313180612231318061223131806122313180612231318061223131806122313180612231318061223231318061223231318061223231323180612232313231806122323  
high surface biomass conditions [4], and the RVI is significantly less sensitive when applied to areas with low vegetation cover. The positional relationship between the sensor and the vegetation, such as the vegetation coverage obtained by observing from the tree tops (where the observed object is located at the substellar point), the light-facing side (forward scattering), and the back-lighting side (back scattering), etc., will make a difference [5; 6], which results in unavoidable errors in the vegetation cover assessment. Moreover, the obtained vegetation indices can be biased due to atmospheric scattering and absorption, soil background, and angular variations [6; 7]. It is important to note that vegetation indices have limitations when used for assessing vegetation growth potential, even though they are widely used for vegetation cover evaluation. [PERSON] et al. [8] found that an increase in forest canopy NDVI did not correlate with tree growth trends when used to assess long-term trends in greenness and land surface phenology. [PERSON] et al. [9] pointed out that the correspondence between vegetation index and vegetation biomass will be ambiguous for high-altitude forested areas and low-elevation areas with greater species richness (covering trees, shrubs, grasslands, etc.). Existing vegetation indices determine whether vegetation is covered or not only by the magnitude of their values, ignoring the influence of the site environment on the growth potential of vegetation, and failing to reveal the mechanism of vegetation within the slope. Therefore, it is urgent to seek a new vegetation appraisal method. Topography is the carrying container of ecosystems, and topographic factors are gradually gaining attention in resource and environmental planning and management as a sort of intuitive representation [10; 11]. In recent years, many scholars have started to explore the interconnection between topography and vegetation cover, and have conducted studies with different emphases based on their respective fields of application.