Evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forest
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | , , , |
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. |
id |
UNSP_1cb62dd16ba35a879cee82b662ae87a8 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/200132 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
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 |
|
_version_ |
1808129524309688320 |