Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1150165
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dc.contributor.authorVERAS, H. F. P.
dc.contributor.authorFERREIRA, M. P.
dc.contributor.authorCUNHA NETO, E. M. da
dc.contributor.authorFIGUEIREDO, E. O.
dc.contributor.authorDALLA CORTE, A. P.
dc.contributor.authorSANQUETTA, C. R.
dc.date.accessioned2022-12-21T14:02:03Z-
dc.date.available2022-12-21T14:02:03Z-
dc.date.created2022-12-21
dc.date.issued2022
dc.identifier.citationEcological Informatics, v. 71, 101815, 2022.
dc.identifier.issn1574-9541
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1150165-
dc.descriptionRemote sensing images obtained by unoccupied aircraft systems (UAS) across different seasons enabled capturing of species-specific phenological patterns of tropical trees. The application of UAS multi-season images to classify tropical tree species is still poorly understood. In this study, we used RGB images from different seasons obtained by a low-cost UAS and convolutional neural networks (CNNs) to map tree species in an Amazonian forest. Individual tree crowns (ITC) were outlined in the UAS images and identified to the species level using forest inventory data. The CNN model was trained with images obtained in February, May, August, and November. The classification accuracy in the rainy season (November and February) was higher than in the dry season (May and August). Fusing images from multiple seasons improved the average accuracy of tree species classification by up to 21.1 percentage points, reaching 90.5%. The CNN model can learn species-specific phenological characteristics that impact the classification accuracy, such as leaf fall in the dry season, which highlights its potential to discriminate species in various conditions. We produced high-quality individual tree crown maps of the species using a post-processing procedure. The combination of multi-season UAS images and CNNs has the potential to map tree species in the Amazon, providing valuable insights for forest management and conservation initiatives.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectAmazonia Occidentaleng
dc.subjectFusão de imagenseng
dc.subjectImagem RGBeng
dc.subjectModelo CNNeng
dc.subjectMapeamento de espécieseng
dc.subjectImagem multitemporadaeng
dc.subjectTeledeteccióneng
dc.subjectBosques tropicaleseng
dc.subjectIdentificación de especieseng
dc.subjectBosques experimentaleseng
dc.subjectEmbrapa Acreeng
dc.subjectRio Branco (AC)eng
dc.subjectAcreeng
dc.subjectAmazônia Ocidentaleng
dc.subjectWestern Amazoneng
dc.titleFusing multi-season UAS images with convolutional neural networks to map tree species in Amazonian forests.
dc.typeArtigo de periódico
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroFenologiaeng
dc.subject.thesagroCampo Experimentaleng
dc.subject.thesagroEspécie Nativaeng
dc.subject.thesagroFloresta Tropicaleng
dc.subject.thesagroIdentificaçãoeng
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusExperimental forestseng
dc.subject.nalthesaurusTropical forestseng
dc.subject.nalthesaurusSpecies identificationeng
dc.subject.nalthesaurusPhenologyeng
riaa.ainfo.id1150165
riaa.ainfo.lastupdate2022-12-21
dc.identifier.doihttps://doi.org/10.1016/j.ecoinf.2022.101815
dc.contributor.institutionHUDSON FRANKLIN PESSOA VERAS, UNIVERSIDADE FEDERAL DO PARANÁ; MATHEUS PINHEIRO FERREIRA, INSTITUTO MILITAR DE ENGENHARIA; ERNANDES MACEDO DA CUNHA NETO, UNIVERSIDADE FEDERAL DO PARANÁ; EVANDRO ORFANO FIGUEIREDO, CPAF-AC; ANA PAULA DALLA CORTE, UNIVERSIDADE FEDERAL DO PARANÁ; CARLOS ROBERTO SANQUETTA, UNIVERSIDADE FEDERAL DO PARANÁ.
Aparece nas coleções:Artigo em periódico indexado (CPAF-AC)

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