Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images

Detalhes bibliográficos
Autor(a) principal: Gonçalves, Vinicius Paiva
Data de Publicação: 2022
Outros Autores: Ribeiro, Eduardo Augusto Werneck, Imai, Nilton Nobuhiro [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.3390/rs14122805
http://hdl.handle.net/11449/241186
Resumo: Invasive alien species reduce biodiversity. In southern Brazil, the genus Pinus is considered invasive, and its dispersal by humans has resulted in this species reaching ecosystems that are more sensitive and less suitable for cultivation, as is the case for the restingas on the island of Santa Catarina. Invasion control requires persistent efforts to identify and treat each new invasion case as a priority. In this study, areas invaded by Pinus sp. in restingas were mapped using images taken by a remotely piloted aircraft system (RPAS, or drone) to identify the invasion areas in great detail, enabling management to be planned for the most recently invaded areas, where management is simpler, more effective, and less costly. Geographic object-based image analysis (GEOBIA) was applied on images taken from a conventional RGB camera embedded in an RPAS, which resulted in a global accuracy of 89.56%, a mean kappa index of 0.86, and an F-score of 0.90 for Pinus sp. Processing was conducted with open-source software to reduce operational costs.
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spelling Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color ImagesdroneGEOBIAmachine learningPinusRPASInvasive alien species reduce biodiversity. In southern Brazil, the genus Pinus is considered invasive, and its dispersal by humans has resulted in this species reaching ecosystems that are more sensitive and less suitable for cultivation, as is the case for the restingas on the island of Santa Catarina. Invasion control requires persistent efforts to identify and treat each new invasion case as a priority. In this study, areas invaded by Pinus sp. in restingas were mapped using images taken by a remotely piloted aircraft system (RPAS, or drone) to identify the invasion areas in great detail, enabling management to be planned for the most recently invaded areas, where management is simpler, more effective, and less costly. Geographic object-based image analysis (GEOBIA) was applied on images taken from a conventional RGB camera embedded in an RPAS, which resulted in a global accuracy of 89.56%, a mean kappa index of 0.86, and an F-score of 0.90 for Pinus sp. Processing was conducted with open-source software to reduce operational costs.Department of Health and Services Federal Institute of Santa Catarina—IFSC, Av. Mauro Ramos, 950, SCSão Francisco do Sul Campus Federal Catarinense Institute—IFC, Duque de Caxias Highway, 6750, Iperoba, SCDepartment of Cartography São Paulo State University—UNESP, Roberto Simonsen St., 305, SPDepartment of Cartography São Paulo State University—UNESP, Roberto Simonsen St., 305, SPFederal Institute of Santa Catarina—IFSCFederal Catarinense Institute—IFCUniversidade Estadual Paulista (UNESP)Gonçalves, Vinicius PaivaRibeiro, Eduardo Augusto WerneckImai, Nilton Nobuhiro [UNESP]2023-03-01T20:50:49Z2023-03-01T20:50:49Z2022-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/rs14122805Remote Sensing, v. 14, n. 12, 2022.2072-4292http://hdl.handle.net/11449/24118610.3390/rs141228052-s2.0-85132300583Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRemote Sensinginfo:eu-repo/semantics/openAccess2024-06-18T15:01:03Zoai:repositorio.unesp.br:11449/241186Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:07:20.636033Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images
title Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images
spellingShingle Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images
Gonçalves, Vinicius Paiva
drone
GEOBIA
machine learning
Pinus
RPAS
title_short Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images
title_full Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images
title_fullStr Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images
title_full_unstemmed Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images
title_sort Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images
author Gonçalves, Vinicius Paiva
author_facet Gonçalves, Vinicius Paiva
Ribeiro, Eduardo Augusto Werneck
Imai, Nilton Nobuhiro [UNESP]
author_role author
author2 Ribeiro, Eduardo Augusto Werneck
Imai, Nilton Nobuhiro [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Federal Institute of Santa Catarina—IFSC
Federal Catarinense Institute—IFC
Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Gonçalves, Vinicius Paiva
Ribeiro, Eduardo Augusto Werneck
Imai, Nilton Nobuhiro [UNESP]
dc.subject.por.fl_str_mv drone
GEOBIA
machine learning
Pinus
RPAS
topic drone
GEOBIA
machine learning
Pinus
RPAS
description Invasive alien species reduce biodiversity. In southern Brazil, the genus Pinus is considered invasive, and its dispersal by humans has resulted in this species reaching ecosystems that are more sensitive and less suitable for cultivation, as is the case for the restingas on the island of Santa Catarina. Invasion control requires persistent efforts to identify and treat each new invasion case as a priority. In this study, areas invaded by Pinus sp. in restingas were mapped using images taken by a remotely piloted aircraft system (RPAS, or drone) to identify the invasion areas in great detail, enabling management to be planned for the most recently invaded areas, where management is simpler, more effective, and less costly. Geographic object-based image analysis (GEOBIA) was applied on images taken from a conventional RGB camera embedded in an RPAS, which resulted in a global accuracy of 89.56%, a mean kappa index of 0.86, and an F-score of 0.90 for Pinus sp. Processing was conducted with open-source software to reduce operational costs.
publishDate 2022
dc.date.none.fl_str_mv 2022-06-01
2023-03-01T20:50:49Z
2023-03-01T20:50:49Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.3390/rs14122805
Remote Sensing, v. 14, n. 12, 2022.
2072-4292
http://hdl.handle.net/11449/241186
10.3390/rs14122805
2-s2.0-85132300583
url http://dx.doi.org/10.3390/rs14122805
http://hdl.handle.net/11449/241186
identifier_str_mv Remote Sensing, v. 14, n. 12, 2022.
2072-4292
10.3390/rs14122805
2-s2.0-85132300583
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Remote Sensing
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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