6533b831fe1ef96bd1298463

RESEARCH PRODUCT

Gully erosion susceptibility assessment by means of GIS-based logistic regression: A case of Sicily (Italy)

Silvia Eleonora AngileriChristian ConoscentiChiara CappadoniaValerio AgnesiMichael MärkerMichael MärkerEdoardo Rotigliano

subject

Aerial surveyCalibration (statistics)Settore GEO/04 - Geografia Fisica E GeomorfologiaLogistic regressionErosion susceptibilityRegression analysisStepwise regressionGISLogistic regressionROC curveGully erosionAerial photographyErosionDigital elevation modelGully erosion; GIS; Stochastic Modeling; SiciliaSicilySettore GEO/05 - Geologia ApplicataCartographyGeologyEarth-Surface Processes

description

article i nfo Article history: This research aims at characterizing susceptibility conditions to gully erosion by means of GIS and multivariate statistical analysis. The study area is a 9.5 km 2 river catchment in central-northern Sicily, where agriculture ac- tivities are limited by intense erosion. By means of field surveys and interpretation of aerial images, we prepared a digitalmap of thespatial distribution of 260 gulliesinthestudy area.Inaddition,fromavailable thematicmaps, a 5 m cell size digital elevation model and field checks, we derived 27 environmental attributes that describe the variability of lithology, land use, topography and road position. These attributes were selected for their potential influence on erosion processes, while the dependent variable was given by presence or absence of gullies within two different types of mapping units: 5 m grid cells and slope units (average size = 2.66 ha). The functional re- lationships between gully occurrence and the controlling factors were obtained from forward stepwise logistic regression to calculate the probability to host a gully for each mapping unit. In order to train and test the predictive models, three calibration and three validation subsets, of both grid cells and slope units, were randomly selected. Results of validation, based on ROC (receiving operating characteristic) curves, attest for acceptable to excellent accuracies of the models, showing better predictive skill and more stable performance of the susceptibility model based on grid cells.

10.1016/j.geomorph.2013.08.021http://hdl.handle.net/2158/826130