Mapping Landslide Prediction through a GIS-Based Model: A Case Study in a Catchment in Southern Italy

Federico Valerio Moresi; Mauro Maesano; Alessio Collalti; Roy C Sidle; Giorgio Matteucci; Giuseppe Scarascia Mugnozza

doi:10.3390/geosciences10080309

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Mapping Landslide Prediction through a GIS-Based Model: A Case Study in a Catchment in Southern Italy

Journal article

Open access

Peer reviewed

Mapping Landslide Prediction through a GIS-Based Model: A Case Study in a Catchment in Southern Italy

Federico Valerio Moresi, Mauro Maesano, Alessio Collalti, Roy C Sidle, Giorgio Matteucci and Giuseppe Scarascia Mugnozza

Geosciences , Vol.10(8), pp.1-22

2020

DOI: https://doi.org/10.3390/geosciences10080309

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Abstract

shallow landslide

root cohesion

infinite slope analysis

integrated modelling

GIS

forest management

Shallow landslides are an increasing concern in Italy and worldwide because of the frequent association with vegetation management. As vegetation cover plays a fundamental role in slope stability, we developed a GIS-based model to evaluate the influence of plant roots on slope safety, and also included a landslide susceptibility map. The GIS-based model, 4SLIDE, is a physically based predictor for shallow landslides that combines geological, topographical, and hydrogeological data. The 4SLIDE combines the infinite slope model, TOPMODEL (for the estimation of the saturated water level), and a vegetation root strength model, which facilitates prediction of locations that are more susceptible for shallow landslides as a function of forest cover. The aim is to define the spatial distribution of Factor of Safety (FS) in steep-forested areas. The GIS-based model 4SLIDE was tested in a forest mountain watershed located in the Sila Greca (Cosenza, Calabria, South Italy) where almost 93% of the area is covered by forest. The sensitive ROC analysis (Receiver Operating Characteristic) indicates that the model has good predictive capability in identifying the areas sensitive to shallow landslides. The localization of areas at risk of landslides plays an important role in land management activities because landslides are among the most costly and dangerous hazards.

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Title: Mapping Landslide Prediction through a GIS-Based Model: A Case Study in a Catchment in Southern Italy
Authors: Federico Valerio Moresi (Author) - Università degli Studi della Tuscia
Mauro Maesano (Author) - Università degli Studi della Tuscia
Alessio Collalti (Author) - Università degli Studi della Tuscia
Roy C Sidle (Author) - University of the Sunshine Coast
Giorgio Matteucci (Author) - Institute of BioEconomy
Giuseppe Scarascia Mugnozza (Author) - Università degli Studi della Tuscia
Publication details: Geosciences , Vol.10(8), pp.1-22
Publisher: MDPI AG
Date published: 2020
DOI: 10.3390/geosciences10080309
Copyright note: Copyright (c) 2020 The authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Organisation Unit: University of the Sunshine Coast, Queensland; Sustainability Research Cluster
Language: English
Record Identifier: 99482308102621
Output Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Geosciences, Multidisciplinary

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