The purpose of this study was to prepare a landslide susceptibility map using a GIS-based spatial multi criteria (SMC) approach in a landslide-prone area, the Amendolara territory in Calabria (Southern Italy). In the first step, landslide locations were identified from aero-photo interpretation and field surveys. In the second step, six data layers were chosen as landslide predisposing factors for susceptibility mapping. These factors are slope, aspect, lithology, land use, distance from roads and distance from rivers. Next, landslide-susceptible areas were analyzed using the SMC approach and mapped using landslide conditioning factors. The agreement between the susceptibility map generated using the SMC approach and the landslide locations was tested by analyzing their receveir operating characteristic (ROC) curve. Landslide scarps locations were used in the validation. area under curve (AUC) value of the SMC method was calculated as 0.96. According to this result the produced map exhibited good performance.

Landslide susceptibility mapping using a spatial multi-criteria methodology in the town of Amendolara (southern Italy).

MUTO, Francesco;
2016

Abstract

The purpose of this study was to prepare a landslide susceptibility map using a GIS-based spatial multi criteria (SMC) approach in a landslide-prone area, the Amendolara territory in Calabria (Southern Italy). In the first step, landslide locations were identified from aero-photo interpretation and field surveys. In the second step, six data layers were chosen as landslide predisposing factors for susceptibility mapping. These factors are slope, aspect, lithology, land use, distance from roads and distance from rivers. Next, landslide-susceptible areas were analyzed using the SMC approach and mapped using landslide conditioning factors. The agreement between the susceptibility map generated using the SMC approach and the landslide locations was tested by analyzing their receveir operating characteristic (ROC) curve. Landslide scarps locations were used in the validation. area under curve (AUC) value of the SMC method was calculated as 0.96. According to this result the produced map exhibited good performance.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/132987
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