Comparison of vegetation indices and image classification methods for mangrove mapping at semi-detailed scale in southwest of Rio de Janeiro, Brazil
dc.contributor.author | Rodrigues, Flávio Henrique [UNESP] | |
dc.contributor.author | Cerri, Rodrigo Irineu [UNESP] | |
dc.contributor.author | de Andrade Kolya, André [UNESP] | |
dc.contributor.author | Veiga, Vinícius Mendes [UNESP] | |
dc.contributor.author | Gomes Vieira Reis, Fábio Augusto [UNESP] | |
dc.contributor.institution | Universidade Estadual de Campinas (UNICAMP) | |
dc.contributor.institution | Universidade Estadual Paulista (UNESP) | |
dc.date.accessioned | 2023-07-29T13:07:31Z | |
dc.date.available | 2023-07-29T13:07:31Z | |
dc.date.issued | 2023-04-01 | |
dc.description.abstract | This paper presents the mangrove mapping carried out in the Rio de Janeiro City, Brazil, using two remote sensing data processing approaches in order to evaluate their potentialities as a complementary tool for oil spill sensitivity mapping. Ten vegetation indices were computed using the Landsat 8 imagery available in Google Earth Engine, and subsequently their spectral patterns were classified through three supervised and five unsupervised methods. Additionally, one pre-processed Landsat 8 OLI bands composition were classified by these eight classification algorithms. To role as a ground-truth for the comparison of 88 automatically produced maps, a mangrove map was prepared based on the methodological guidelines of Oceanic Atmospheric Administration of United States of America for Environmental Sensitivity Index. The best results were presented by Cobweb unsupervised classification of Mangrove Vegetation Index, properly identifying a great mangrove habitats diversity, such as inland brackish, riverine fringe and seaward forests. | en |
dc.description.affiliation | Department of Geology and Natural Resources Geosciences Institute University of Campinas, PO Box 6152, SP | |
dc.description.affiliation | Department of Geology Institute of Geosciences and Exact Sciences São Paulo State University, SP | |
dc.description.affiliationUnesp | Department of Geology Institute of Geosciences and Exact Sciences São Paulo State University, SP | |
dc.identifier | http://dx.doi.org/10.1016/j.rsase.2023.100965 | |
dc.identifier.citation | Remote Sensing Applications: Society and Environment, v. 30. | |
dc.identifier.doi | 10.1016/j.rsase.2023.100965 | |
dc.identifier.issn | 2352-9385 | |
dc.identifier.scopus | 2-s2.0-85152145149 | |
dc.identifier.uri | http://hdl.handle.net/11449/247144 | |
dc.language.iso | eng | |
dc.relation.ispartof | Remote Sensing Applications: Society and Environment | |
dc.source | Scopus | |
dc.subject | Google earth engine | |
dc.subject | Image classification | |
dc.subject | Mangrove | |
dc.subject | Vegetation index | |
dc.title | Comparison of vegetation indices and image classification methods for mangrove mapping at semi-detailed scale in southwest of Rio de Janeiro, Brazil | en |
dc.type | Artigo | |
unesp.author.orcid | 0000-0002-2016-5334 0000-0002-2016-5334[1] | |
unesp.author.orcid | 0000-0001-8507-2457[4] |