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

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Journal of Mountain Science 2022年第2期19卷 477-492页

作者： himasha d.abeysiriwardana Pattiyage I.A.GOMES department of Civil Engineering Faculty of EngineeringSri Lanka Institute of Information TechnologyMalabe 101115Sri Lanka

This study aimed to assess the potential of in-situ measured soil and vegetation characteristics in landslide susceptibility ***,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 *** economical,finer resolution database was obtained as the field tests were not *** logistic regression(LR)analysis showed that soil moisture content,compaction of soil,SWI for trees and herbs were statistically significant at Pd to test for *** values(d the absence of multicollinearity between four independent variables in the LR *** Operating Characteristics(ROC)curve and Confusion Metrix(CM)methods were used to validate the *** 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 *** 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 ***,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)*** the historic landslide locations fell into the high susceptibility ***,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 susceptibl

关键词： Landslide Susceptibility Slope stability GIS Logistic regression Area under the curve Confusion Metrix

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