| Journal: | Boletim de ciencias geodesicas |
| Database: | PERIÓDICA |
| System number: | 000458636 |
| ISSN: | 1413-4853 |
| Authors: | Giordano, Lucilia do Carmo1 Marques, Mara Lucia2 Reis, Fabio Augusto Gomes Vieira1 Correa, Claudia Vanessa dos Santos1 Riedel, Paulina Setti1 |
| Institutions: | 1Universidade Estadual Paulista "Julio de Mesquita Filho", Instituto de Geociencias e Ciencias Exatas, Rio Claro, Sao Paulo. Brasil 2Pontificia Universidade Catolica de Campinas, Escola de Arquitetura, Artes e Design, Campinas, Sao Paulo. Brasil |
| Year: | 2023 |
| Volumen: | 29 |
| Number: | 3 |
| Country: | Brasil |
| Language: | Inglés |
| Document type: | Artículo |
| Approach: | Analítico, descriptivo |
| English 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 |
| Disciplines: | Geociencias |
| Keyword: | Cartografía, Vegetación, Deslizamientos, Procesamiento de imágenes digitales, Percepción remota, Brasil |
| Keyword: | Cartography, Brazil, Vegetation, Landslides, Digital image processing, Remote sensing |
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