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http://dx.doi.org/10.7848/ksgpc.2018.36.6.507

Landslide Susceptibility Assessment Using TPI-Slope Combination  

Lee, Han Na (Industry-Academia Collaboration Foundation, Gangneung-Wonju National University)
Kim, Gihong (Dept. of Civil Engineering, Gangneung-Wonju National University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.36, no.6, 2018 , pp. 507-514 More about this Journal
Abstract
TSI (TPI-Slope Index) which is the combination of TPI (Topographic Position Index) and slope was newly proposed for landslide and applied to a landslide susceptibility model. To do this, we first compared the TPIs with various scale factors and found that TPI350 was the best fit for the study area. TPI350 was combined with slope to create TSI. TSI was evaluated using logistic regression. The evaluation showed that TSI can be used as a landslide factor. Then a logistic regression model was developed to assess the landslide susceptibility by adding other topographic factors, geological factors, and forestial factors. For this, landslide-related factors that can be extracted from DEM (Digital Elevation Model), soil map, and forest type map were collected. We checked these factors and excluded those that were highly correlated with other factors or not significant. After these processes, 8 factors of TSI, elevation, slope length, slope aspect, effective soil depth, tree age, tree density, and tree type were selected to be entered into the regression analysis as independent variables. Three models through three variable selection methods of forward selection, backward elimination, and enter method were built and evaluated. Selected variables in the three models were slightly different, but in common, effective soil depth, tree density, and TSI was most significant.
Keywords
Topographic Position Index; TPI-Slope Index; Landslide Susceptibility; Logistic Regression Model;
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