The suitability of different vegetation indices to analyses area with landslide propensity using Sentinel-2 Image
| dc.contributor.author | Giordano, Lucilia Do Carmo [UNESP] | |
| dc.contributor.author | Marques, Mara Lúcia | |
| dc.contributor.author | Reis, Fábio Augusto Gomes Vieira [UNESP] | |
| dc.contributor.author | Dos Santos Corrêa, Claudia Vanessa [UNESP] | |
| dc.contributor.author | Riedel, Paulina Setti [UNESP] | |
| dc.contributor.institution | Universidade Estadual Paulista (UNESP) | |
| dc.contributor.institution | Universidade Estadual de Campinas (UNICAMP) | |
| dc.date.accessioned | 2025-04-29T18:41:20Z | |
| dc.date.issued | 2023-01-01 | |
| dc.description.abstract | Vegetation 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.affiliation | Universidade Estadual Paulista (UNESP) IGCE-UNESPetro Cento de Ciências Naturais Aplicadas, SP | |
| dc.description.affiliation | Pontifícia Universidade de Campinas Escola de Arquitetura Artes e Design, SP | |
| dc.description.affiliation | Universidade Estadual Paulista (UNESP) IGCE-Departamento de Geologia, SP | |
| dc.description.affiliationUnesp | Universidade Estadual Paulista (UNESP) IGCE-UNESPetro Cento de Ciências Naturais Aplicadas, SP | |
| dc.description.affiliationUnesp | Universidade Estadual Paulista (UNESP) IGCE-Departamento de Geologia, SP | |
| dc.description.sponsorship | Universidade Estadual Paulista | |
| dc.description.sponsorship | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | |
| dc.description.sponsorshipId | CNPq: 316574/2021-0 | |
| dc.identifier | http://dx.doi.org/10.1590/s1982-21702023000300008 | |
| dc.identifier.citation | Boletim de Ciencias Geodesicas, v. 29, n. 3, 2023. | |
| dc.identifier.doi | 10.1590/s1982-21702023000300008 | |
| dc.identifier.issn | 1982-2170 | |
| dc.identifier.issn | 1413-4853 | |
| dc.identifier.scopus | 2-s2.0-85195866171 | |
| dc.identifier.uri | https://hdl.handle.net/11449/299081 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | Boletim de Ciencias Geodesicas | |
| dc.source | Scopus | |
| dc.subject | Digital image processing | |
| dc.subject | Landslide scars | |
| dc.subject | Remote Sensing | |
| dc.subject | Vegetation | |
| dc.title | The suitability of different vegetation indices to analyses area with landslide propensity using Sentinel-2 Image | en |
| dc.type | Artigo | pt |
| dspace.entity.type | Publication | |
| unesp.author.orcid | 0000-0002-1302-6694[1] | |
| unesp.author.orcid | 0000-0002-1478-565X[2] | |
| unesp.author.orcid | 0000-0003-3918-6861[3] | |
| unesp.author.orcid | 0000-0003-2524-8443[4] | |
| unesp.author.orcid | 0000-0002-5879-9762[5] | |
| unesp.campus | Universidade Estadual Paulista (UNESP), Instituto de Geociências e Ciências Exatas, Rio Claro | pt |

