Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/42828
DC FieldValueLanguage
dc.contributor.authorMoresi, Federico Valerioit
dc.contributor.authorMaesano, Mauroit
dc.contributor.authorCollalti, Alessioit
dc.contributor.authorSidle, Roy C.it
dc.contributor.authorMatteucci, Giorgioit
dc.contributor.authorScarascia Mugnozza, Giuseppeit
dc.date.accessioned2021-02-16T16:35:38Z-
dc.date.available2021-02-16T16:35:38Z-
dc.date.issued2020it
dc.identifier.issn2076-3263it
dc.identifier.urihttp://hdl.handle.net/2067/42828-
dc.description.abstract© 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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.it
dc.format.mediumELETTRONICOit
dc.language.isoengit
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.titleMapping landslide prediction through a GIS-based model: A case study in a catchment in southern Italyit
dc.typearticle*
dc.identifier.doi10.3390/geosciences10080309it
dc.identifier.scopus2-s2.0-85090605022it
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85090605022it
local.message.claim2022-06-29T09:59:40.974+0200|||rp00560|||submit_approve|||dc_contributor_author|||None*
dc.relation.journalGEOSCIENCESit
dc.relation.firstpage1it
dc.relation.lastpage22it
dc.relation.volume10it
dc.relation.issue8it
dc.description.internationalit
dc.contributor.countryITAit
dc.contributor.countryAZEit
dc.type.refereeREF_1it
dc.type.miur262it
item.fulltextWith Fulltext-
item.openairetypearticle-
item.cerifentitytypePublications-
item.grantfulltextrestricted-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.journal.journalissn2076-3263-
crisitem.journal.anceE215603-
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