Quantifying effectiveness of trees for landslide erosion control

Abstract

We developed a landslide susceptibility model using binary logistic regression for silvopastoral landscapes, which for the first time includes spatial distribution models for individual trees of different vegetation types. Models were trained and tested using a landslide inventory consisting of 43,000 landslide scars mapped across an 843 km2 area. Model performance was very good, with a median AUROC of 0.95 in the final model used for predictions, which equates to an accuracy of 88.7% using a cut-off of 0.5. We investigate the effect of highly skewed continuous tree variables on the maximum likelihood estimator by testing different sampling strategies aimed at reducing positive skewness. With an adequate sample size, we found that highly skewed continuous predictor variables do not result in an inflation of effect size.Using two farms in the study area, we illustrate application of the landslide susceptibility model for quantifying the reduction in shallow landslide erosion due to trees. Landslide erosion was reduced by 16.6% at Site 1 and 42.9% at Site 2 due to all existing vegetation. The effectiveness of individual trees on reducing landslide erosion was shown to be less a function of species than that of targeting highly susceptible areas with adequate plant densities. We found 80% of landslides are triggered in 12.1% and 7.3% of the area of Sites 1 (1700-ha) and 2 (462-ha), respectively, suggesting there is great potential for smarter targeting of erosion mitigation. The high-resolution spatial information provided by the landslide susceptibility maps can be used by decision makers in land management to support the development and targeting of erosion mitigation measures.