The Calabria (Southern Italy) region is characterized by many geological hazardsamong which landslides, due to the geological, geomorphological, and climaticcharacteristics, constitute one of the major cause of significant and widespread damage. Thepresent work aims to exploit a bivariate statistics-based approach for drafting a landslidesusceptibility map in a specific scenario of the region (the Vitravo River catchment) toprovide a useful and easy tool for future land planning. Landslides have been detectedthrough air-photo interpretation and field surveys, by identifying both the landslidedetachment zones (LDZ) and landslide bodies; a geospatial database of predisposing factorshas been constructed using the ESRI ArcView 3.2 GIS. The landslide susceptibility hasbeen assessed by computing the weighting values (Wi) for each class of the predisposingfactors (lithology, proximity to fault and drainage line, land use, slope angle, aspect, plancurvature), thus evaluating the distribution of the landslide detachment zones within eachclass. The extracted predisposing factors maps have then been re-classified on the basis ofthe calculated weighting values (Wi) and by means of overlay processes. Finally, thelandslide susceptibility map has been considered by five classes. It has been determined thata high percentage (61%) of the study area is characterized by a high to very high degree ofsusceptibility; clay and marly lithologies, and slope exceeding 20 in inclination would bemuch prone to landsliding. Furthermore, in order to ascertain the proposed landslide susceptibilityestimate, a validation procedure has been carried out, by splitting the landslidedetachment zones into two groups: a training and a validation set. By means of the trainingset, the susceptibility map has first been produced; then, it has been compared with the validation set. As a result, a great majority of LDZ-validation set (85%) would be located inhighly and very highly susceptible areas. The predictive power of the model is consideredreliable, since more than 50% of the LDZ fall into 20% of the most susceptible areas. Thereliability of the susceptibility map is also suggested by computing the SCAI index, truepositive and false positive rates; nevertheless, the most susceptible areas are overestimated.As a whole, the results indicate that landslide susceptibility assessment based on a bivariatestatistics-based method in a GIS environment may be useful for land planning policy,especially when considering its cost/benefit ratio and the need of using an easy tool.

Application and validation of bivariate GIS-based landslide susceptibility assessment for the Vitravo river catchment (Calabria, south Italy)

ROBUSTELLI, Gaetano;MUTO, Francesco;CRITELLI, Salvatore
2012

Abstract

The Calabria (Southern Italy) region is characterized by many geological hazardsamong which landslides, due to the geological, geomorphological, and climaticcharacteristics, constitute one of the major cause of significant and widespread damage. Thepresent work aims to exploit a bivariate statistics-based approach for drafting a landslidesusceptibility map in a specific scenario of the region (the Vitravo River catchment) toprovide a useful and easy tool for future land planning. Landslides have been detectedthrough air-photo interpretation and field surveys, by identifying both the landslidedetachment zones (LDZ) and landslide bodies; a geospatial database of predisposing factorshas been constructed using the ESRI ArcView 3.2 GIS. The landslide susceptibility hasbeen assessed by computing the weighting values (Wi) for each class of the predisposingfactors (lithology, proximity to fault and drainage line, land use, slope angle, aspect, plancurvature), thus evaluating the distribution of the landslide detachment zones within eachclass. The extracted predisposing factors maps have then been re-classified on the basis ofthe calculated weighting values (Wi) and by means of overlay processes. Finally, thelandslide susceptibility map has been considered by five classes. It has been determined thata high percentage (61%) of the study area is characterized by a high to very high degree ofsusceptibility; clay and marly lithologies, and slope exceeding 20 in inclination would bemuch prone to landsliding. Furthermore, in order to ascertain the proposed landslide susceptibilityestimate, a validation procedure has been carried out, by splitting the landslidedetachment zones into two groups: a training and a validation set. By means of the trainingset, the susceptibility map has first been produced; then, it has been compared with the validation set. As a result, a great majority of LDZ-validation set (85%) would be located inhighly and very highly susceptible areas. The predictive power of the model is consideredreliable, since more than 50% of the LDZ fall into 20% of the most susceptible areas. Thereliability of the susceptibility map is also suggested by computing the SCAI index, truepositive and false positive rates; nevertheless, the most susceptible areas are overestimated.As a whole, the results indicate that landslide susceptibility assessment based on a bivariatestatistics-based method in a GIS environment may be useful for land planning policy,especially when considering its cost/benefit ratio and the need of using an easy tool.
Bivariate statistics; GIS; Landslide susceptibility
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/159389
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