Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forest

Detalhes bibliográficos
Autor(a) principal: Miyoshi, Gabriela Takahashi [UNESP]
Data de Publicação: 2020
Outros Autores: Imai, Nilton Nobuhiro [UNESP], Tommaselli, Antonio Maria Garcia [UNESP], de Moraes, Marcus Vinícius Antunes [UNESP], Honkavaara, Eija
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.3390/rs12020244
http://hdl.handle.net/11449/200132
Resumo: The monitoring of forest resources is crucial for their sustainable management, and tree species identification is one of the fundamental tasks in this process. Unmanned aerial vehicles (UAVs) and miniaturized lightweight sensors can rapidly provide accurate monitoring information. The objective of this study was to investigate the use of multitemporal, UAV-based hyperspectral imagery for tree species identification in the highly diverse Brazilian Atlantic forest. Datasets were captured over three years to identify eight different tree species. The study area comprised initial to medium successional stages of the Brazilian Atlantic forest. Images were acquired with a spatial resolution of 10 cm, and radiometric adjustment processing was performed to reduce the variations caused by different factors, such as the geometry of acquisition. The random forest classification method was applied in a region-based classification approach with leave-one-out cross-validation, followed by computing the area under the receiver operating characteristic (AUCROC) curve. When using each dataset alone, the influence of different weather behaviors on tree species identification was evident. When combining all datasets and minimizing illumination differences over each tree crown, the identification of three tree species was improved. These results show that UAV-based, hyperspectral, multitemporal remote sensing imagery is a promising tool for tree species identification in tropical forests.
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spelling Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forestHyperspectralmultitemporal information;UAVSemideciduous forestTree species classificationThe monitoring of forest resources is crucial for their sustainable management, and tree species identification is one of the fundamental tasks in this process. Unmanned aerial vehicles (UAVs) and miniaturized lightweight sensors can rapidly provide accurate monitoring information. The objective of this study was to investigate the use of multitemporal, UAV-based hyperspectral imagery for tree species identification in the highly diverse Brazilian Atlantic forest. Datasets were captured over three years to identify eight different tree species. The study area comprised initial to medium successional stages of the Brazilian Atlantic forest. Images were acquired with a spatial resolution of 10 cm, and radiometric adjustment processing was performed to reduce the variations caused by different factors, such as the geometry of acquisition. The random forest classification method was applied in a region-based classification approach with leave-one-out cross-validation, followed by computing the area under the receiver operating characteristic (AUCROC) curve. When using each dataset alone, the influence of different weather behaviors on tree species identification was evident. When combining all datasets and minimizing illumination differences over each tree crown, the identification of three tree species was improved. These results show that UAV-based, hyperspectral, multitemporal remote sensing imagery is a promising tool for tree species identification in tropical forests.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Graduate Program in Cartographic Sciences São Paulo State University (UNESP), Roberto Simonsen 305Department of Cartography São Paulo State University (UNESP), Roberto Simonsen, 305Finnish Geospatial Research Institute National Land Survey of Finland, Geodeetinrinne, 2Graduate Program in Cartographic Sciences São Paulo State University (UNESP), Roberto Simonsen 305Department of Cartography São Paulo State University (UNESP), Roberto Simonsen, 305CNPq: 153854/2016-2Universidade Estadual Paulista (Unesp)National Land Survey of FinlandMiyoshi, Gabriela Takahashi [UNESP]Imai, Nilton Nobuhiro [UNESP]Tommaselli, Antonio Maria Garcia [UNESP]de Moraes, Marcus Vinícius Antunes [UNESP]Honkavaara, Eija2020-12-12T01:58:32Z2020-12-12T01:58:32Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/rs12020244Remote Sensing, v. 12, n. 2, 2020.2072-4292http://hdl.handle.net/11449/20013210.3390/rs120202442-s2.0-8508108131429857711025053300000-0003-0516-0567Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRemote Sensinginfo:eu-repo/semantics/openAccess2024-06-18T15:02:06Zoai:repositorio.unesp.br:11449/200132Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:28:40.172428Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forest
title Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forest
spellingShingle Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forest
Miyoshi, Gabriela Takahashi [UNESP]
Hyperspectralmultitemporal information;UAV
Semideciduous forest
Tree species classification
title_short Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forest
title_full Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forest
title_fullStr Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forest
title_full_unstemmed Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forest
title_sort Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forest
author Miyoshi, Gabriela Takahashi [UNESP]
author_facet Miyoshi, Gabriela Takahashi [UNESP]
Imai, Nilton Nobuhiro [UNESP]
Tommaselli, Antonio Maria Garcia [UNESP]
de Moraes, Marcus Vinícius Antunes [UNESP]
Honkavaara, Eija
author_role author
author2 Imai, Nilton Nobuhiro [UNESP]
Tommaselli, Antonio Maria Garcia [UNESP]
de Moraes, Marcus Vinícius Antunes [UNESP]
Honkavaara, Eija
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
National Land Survey of Finland
dc.contributor.author.fl_str_mv Miyoshi, Gabriela Takahashi [UNESP]
Imai, Nilton Nobuhiro [UNESP]
Tommaselli, Antonio Maria Garcia [UNESP]
de Moraes, Marcus Vinícius Antunes [UNESP]
Honkavaara, Eija
dc.subject.por.fl_str_mv Hyperspectralmultitemporal information;UAV
Semideciduous forest
Tree species classification
topic Hyperspectralmultitemporal information;UAV
Semideciduous forest
Tree species classification
description The monitoring of forest resources is crucial for their sustainable management, and tree species identification is one of the fundamental tasks in this process. Unmanned aerial vehicles (UAVs) and miniaturized lightweight sensors can rapidly provide accurate monitoring information. The objective of this study was to investigate the use of multitemporal, UAV-based hyperspectral imagery for tree species identification in the highly diverse Brazilian Atlantic forest. Datasets were captured over three years to identify eight different tree species. The study area comprised initial to medium successional stages of the Brazilian Atlantic forest. Images were acquired with a spatial resolution of 10 cm, and radiometric adjustment processing was performed to reduce the variations caused by different factors, such as the geometry of acquisition. The random forest classification method was applied in a region-based classification approach with leave-one-out cross-validation, followed by computing the area under the receiver operating characteristic (AUCROC) curve. When using each dataset alone, the influence of different weather behaviors on tree species identification was evident. When combining all datasets and minimizing illumination differences over each tree crown, the identification of three tree species was improved. These results show that UAV-based, hyperspectral, multitemporal remote sensing imagery is a promising tool for tree species identification in tropical forests.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-12T01:58:32Z
2020-12-12T01:58:32Z
2020-01-01
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/rs12020244
Remote Sensing, v. 12, n. 2, 2020.
2072-4292
http://hdl.handle.net/11449/200132
10.3390/rs12020244
2-s2.0-85081081314
2985771102505330
0000-0003-0516-0567
url http://dx.doi.org/10.3390/rs12020244
http://hdl.handle.net/11449/200132
identifier_str_mv Remote Sensing, v. 12, n. 2, 2020.
2072-4292
10.3390/rs12020244
2-s2.0-85081081314
2985771102505330
0000-0003-0516-0567
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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