Integrating vegetation indices and geo-environmental factors in GIS-based landslide-susceptibility mapping: using logistic regression

This study aimed to assess the potential of in-situ measured soil and vegetation characteristics in landslide susceptibility analyses. First, data for eight independent variables, i.e., soil moisture content, soil organic content, compaction of soil (soil toughness), plant root strength, crop biomass, tree diameter at knee height, Shannon Wiener Index (SWI) for trees and herbs was assembled from field tests at two historic landslide locations: Aranayaka and Kurukudegama, Sri Lanka. An economical, finer resolution database was obtained as the field tests were not cost-prohibitive. The logistic regression (LR) analysis showed that soil moisture content, compaction of soil, SWI for trees and herbs were statistically significant at P < 0.05. The variance inflation factors (VIFs) were computed to test for multicollinearity. VIF values (< 2) confirmed the absence of multicollinearity between four independent variables in the LR model. Receiver Operating Characteristics (ROC) curve and Confusion Metrix (CM) methods were used to validate the model. In ROC analysis, areas under the curve of Success Rate Curve and Prediction Rate Curve were 84.5% and 96.6%, respectively, demonstrating the model's excellent compatibility and predictability. According to the CM, the model demonstrated a 79.6% accuracy, 63.6% precision, 100% recall, and a F-measure of 77.8%. The model coefficients revealed that the vegetation cover has a more significant contribution to landslide susceptibility than soil characteristics. Finally, the susceptibility map, which was then classified as low, medium, and highly susceptible areas based on the natural breaks (Jenks) method, was generated using geographical information systems (GIS) techniques. All the historic landslide locations fell into the high susceptibility areas. Thus, validation of the model and inspection of the susceptibility map indicated that the in-situ soil and vegetation characteristics used in the model could be employed to demarcate historical landslide patches and identify landslide susceptible locations with high confidence.