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The suitability of different vegetation indices to analyses area with landslide propensity using Sentinel-2 Image

dc.contributor.authorGiordano, Lucilia Do Carmo [UNESP]
dc.contributor.authorMarques, Mara Lúcia
dc.contributor.authorReis, Fábio Augusto Gomes Vieira [UNESP]
dc.contributor.authorDos Santos Corrêa, Claudia Vanessa [UNESP]
dc.contributor.authorRiedel, Paulina Setti [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.date.accessioned2025-04-29T18:41:20Z
dc.date.issued2023-01-01
dc.description.abstractVegetation Indices (VIs) provide spatial information on the vegetation state, which has been associated with landslide propensity. To evaluate how VIs information indicate the landslide propensity, the current study analyzed nine different IVs to identify the categories of vegetation states in the hydrographic basin of Pedra Branca before and after landslide event. The different VIs were obtained using Sentinel-2A (2016) and Sentinel-2B (2018) images. All VIs were tested by cross-table analysis regard to the ability to identify the calculated area for landslide scars, and the VIs were also compared to the NDVI reference by error matrix for the analysis of the accuracy in identifying the vegetation state before the landslide occurrence. The areas with landslide scars totalized 86700m² in 2018 image and NDVI matched ~57% of the No Vegetation category. Before the landslide event, almost all VIs indicated a loss of vegetation vigor (with exception of RENDVI and ARVI) in 2016 image. In addition, the indices (exceptionality MSI) also presented high rates of match to the analysis of NDVI in discerning both Intermediate and Vigorous Vegetation states. However, the areas presenting a healthy vegetation state are reduced, which therefore might be indicating the propensity to landslide event before their occurrences.en
dc.description.affiliationUniversidade Estadual Paulista (UNESP) IGCE-UNESPetro Cento de Ciências Naturais Aplicadas, SP
dc.description.affiliationPontifícia Universidade de Campinas Escola de Arquitetura Artes e Design, SP
dc.description.affiliationUniversidade Estadual Paulista (UNESP) IGCE-Departamento de Geologia, SP
dc.description.affiliationUnespUniversidade Estadual Paulista (UNESP) IGCE-UNESPetro Cento de Ciências Naturais Aplicadas, SP
dc.description.affiliationUnespUniversidade Estadual Paulista (UNESP) IGCE-Departamento de Geologia, SP
dc.description.sponsorshipUniversidade Estadual Paulista
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdCNPq: 316574/2021-0
dc.identifierhttp://dx.doi.org/10.1590/s1982-21702023000300008
dc.identifier.citationBoletim de Ciencias Geodesicas, v. 29, n. 3, 2023.
dc.identifier.doi10.1590/s1982-21702023000300008
dc.identifier.issn1982-2170
dc.identifier.issn1413-4853
dc.identifier.scopus2-s2.0-85195866171
dc.identifier.urihttps://hdl.handle.net/11449/299081
dc.language.isoeng
dc.relation.ispartofBoletim de Ciencias Geodesicas
dc.sourceScopus
dc.subjectDigital image processing
dc.subjectLandslide scars
dc.subjectRemote Sensing
dc.subjectVegetation
dc.titleThe suitability of different vegetation indices to analyses area with landslide propensity using Sentinel-2 Imageen
dc.typeArtigopt
dspace.entity.typePublication
unesp.author.orcid0000-0002-1302-6694[1]
unesp.author.orcid0000-0002-1478-565X[2]
unesp.author.orcid0000-0003-3918-6861[3]
unesp.author.orcid0000-0003-2524-8443[4]
unesp.author.orcid0000-0002-5879-9762[5]
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Geociências e Ciências Exatas, Rio Claropt

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