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

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorNational Land Survey of Finland-
Autor(es): dc.creatorMiyoshi, Gabriela Takahashi [UNESP]-
Autor(es): dc.creatorImai, Nilton Nobuhiro [UNESP]-
Autor(es): dc.creatorTommaselli, Antonio Maria Garcia [UNESP]-
Autor(es): dc.creatorde Moraes, Marcus Vinícius Antunes [UNESP]-
Autor(es): dc.creatorHonkavaara, Eija-
Data de aceite: dc.date.accessioned2022-02-22T00:29:30Z-
Data de disponibilização: dc.date.available2022-02-22T00:29:30Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2019-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/rs12020244-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/200132-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/200132-
Descrição: dc.descriptionThe 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.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionGraduate Program in Cartographic Sciences São Paulo State University (UNESP), Roberto Simonsen 305-
Descrição: dc.descriptionDepartment of Cartography São Paulo State University (UNESP), Roberto Simonsen, 305-
Descrição: dc.descriptionFinnish Geospatial Research Institute National Land Survey of Finland, Geodeetinrinne, 2-
Descrição: dc.descriptionGraduate Program in Cartographic Sciences São Paulo State University (UNESP), Roberto Simonsen 305-
Descrição: dc.descriptionDepartment of Cartography São Paulo State University (UNESP), Roberto Simonsen, 305-
Descrição: dc.descriptionCNPq: 153854/2016-2-
Idioma: dc.languageen-
Relação: dc.relationRemote Sensing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectHyperspectralmultitemporal information;UAV-
Palavras-chave: dc.subjectSemideciduous forest-
Palavras-chave: dc.subjectTree species classification-
Título: dc.titleEvaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forest-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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