This paper presents GIS-aided procedures for the evaluation of gullying susceptibility on a statistical basis. Field surveys and air-photo interpretation allowed us to identify gullies, geology and land use, while some predisposing factors were derived from a 7.5m cell-size DEM. In order to investigate the role of each instability factor in controlling the spatial distribution of gullies, we implemented four models that differ in the types of method (bivariate vs. multivariate), training set sampling (polygon or point-based) and variables (continuous or categorical). The models were applied to the Turbolo river catchment, a tributary of the Crati River, Northern Calabria, Italy. The susceptibility values were classified into five categories. Model performance was evaluated using prediction rate curves and by comparing the proportion of each susceptibility class with gully distribution in the validation set. Although all models showed relatively good performances, the bivariate method overestimated the extent of the high susceptibility classes. The multivariate logistic regression model using continuous variables was found to be the best model in predicting gullying susceptibility of the study area. The area classified as stable increased with DEM cell size, meaning that a reasonably fine DEM should be used.

Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy

LUCA' F;ROBUSTELLI, Gaetano
2011

Abstract

This paper presents GIS-aided procedures for the evaluation of gullying susceptibility on a statistical basis. Field surveys and air-photo interpretation allowed us to identify gullies, geology and land use, while some predisposing factors were derived from a 7.5m cell-size DEM. In order to investigate the role of each instability factor in controlling the spatial distribution of gullies, we implemented four models that differ in the types of method (bivariate vs. multivariate), training set sampling (polygon or point-based) and variables (continuous or categorical). The models were applied to the Turbolo river catchment, a tributary of the Crati River, Northern Calabria, Italy. The susceptibility values were classified into five categories. Model performance was evaluated using prediction rate curves and by comparing the proportion of each susceptibility class with gully distribution in the validation set. Although all models showed relatively good performances, the bivariate method overestimated the extent of the high susceptibility classes. The multivariate logistic regression model using continuous variables was found to be the best model in predicting gullying susceptibility of the study area. The area classified as stable increased with DEM cell size, meaning that a reasonably fine DEM should be used.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/159388
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