Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results

Landslide susceptibility is one of the main topics of geomorphological risk studies. Unfortunately, many of these studies applied an exclusively statistical approach with little coherence with the geomorphodynamic models, resulting in susceptibility maps that are difficult to read. Even if many diff...

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Autores principales:	Laura Coco, Debora Macrini, Tommaso Piacentini, Marcello Buccolini
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	landslide susceptibility GIS geo-statistics landslide hazard conditioning factors selection DEM Science Q
Acceso en línea:	https://doaj.org/article/26f9fb62d28d4c8cbd18c85108e8d085
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spelling	oai:doaj.org-article:26f9fb62d28d4c8cbd18c85108e8d0852021-11-11T18:52:50ZLandslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results10.3390/rs132142802072-4292https://doaj.org/article/26f9fb62d28d4c8cbd18c85108e8d0852021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4280https://doaj.org/toc/2072-4292Landslide susceptibility is one of the main topics of geomorphological risk studies. Unfortunately, many of these studies applied an exclusively statistical approach with little coherence with the geomorphodynamic models, resulting in susceptibility maps that are difficult to read. Even if many different models have been developed, those based on statistical techniques applied to slope units (SUs) are among the most promising. SU segmentation divides terrain into homogenous domains and approximates the morphodynamic response of the slope to landslides. This paper presents a landslide susceptibility (LS) analysis at the catchment scale for a key area based on the comparison of two GIS-based bivariate statistical methods using the landslide index (LI) approach. A new simple and reproducible method for delineating SUs is defined with an original GIS-based terrain segmentation based on hydrography. For the first time, the morphometric slope index (MSI) was tested as a predisposing factor for landslides. Beyond the purely statistic values, the susceptibility maps obtained have strong geomorphological significance and highlight the areas with the greatest propensity to landslides. We demonstrate the efficiency of the SU segmentation method and the potential of the proposed statistical methods to perform landslide susceptibility mapping (LSM).Laura CocoDebora MacriniTommaso PiacentiniMarcello BuccoliniMDPI AGarticlelandslide susceptibilityGISgeo-statisticslandslide hazardconditioning factors selectionDEMScienceQENRemote Sensing, Vol 13, Iss 4280, p 4280 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	landslide susceptibility GIS geo-statistics landslide hazard conditioning factors selection DEM Science Q
spellingShingle	landslide susceptibility GIS geo-statistics landslide hazard conditioning factors selection DEM Science Q Laura Coco Debora Macrini Tommaso Piacentini Marcello Buccolini Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results
description	Landslide susceptibility is one of the main topics of geomorphological risk studies. Unfortunately, many of these studies applied an exclusively statistical approach with little coherence with the geomorphodynamic models, resulting in susceptibility maps that are difficult to read. Even if many different models have been developed, those based on statistical techniques applied to slope units (SUs) are among the most promising. SU segmentation divides terrain into homogenous domains and approximates the morphodynamic response of the slope to landslides. This paper presents a landslide susceptibility (LS) analysis at the catchment scale for a key area based on the comparison of two GIS-based bivariate statistical methods using the landslide index (LI) approach. A new simple and reproducible method for delineating SUs is defined with an original GIS-based terrain segmentation based on hydrography. For the first time, the morphometric slope index (MSI) was tested as a predisposing factor for landslides. Beyond the purely statistic values, the susceptibility maps obtained have strong geomorphological significance and highlight the areas with the greatest propensity to landslides. We demonstrate the efficiency of the SU segmentation method and the potential of the proposed statistical methods to perform landslide susceptibility mapping (LSM).
format	article
author	Laura Coco Debora Macrini Tommaso Piacentini Marcello Buccolini
author_facet	Laura Coco Debora Macrini Tommaso Piacentini Marcello Buccolini
author_sort	Laura Coco
title	Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results
title_short	Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results
title_full	Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results
title_fullStr	Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results
title_full_unstemmed	Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results
title_sort	landslide susceptibility mapping by comparing gis-based bivariate methods: a focus on the geomorphological implication of the statistical results
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/26f9fb62d28d4c8cbd18c85108e8d085
work_keys_str_mv	AT lauracoco landslidesusceptibilitymappingbycomparinggisbasedbivariatemethodsafocusonthegeomorphologicalimplicationofthestatisticalresults AT deboramacrini landslidesusceptibilitymappingbycomparinggisbasedbivariatemethodsafocusonthegeomorphologicalimplicationofthestatisticalresults AT tommasopiacentini landslidesusceptibilitymappingbycomparinggisbasedbivariatemethodsafocusonthegeomorphologicalimplicationofthestatisticalresults AT marcellobuccolini landslidesusceptibilitymappingbycomparinggisbasedbivariatemethodsafocusonthegeomorphologicalimplicationofthestatisticalresults
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Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results

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