Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1124129
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dc.contributor.authorFERREIRA, M. P.
dc.contributor.authorALMEIDA, D. R. A. de
dc.contributor.authorPAPA, D. de A.
dc.contributor.authorMINERVINO, J. B. S.
dc.contributor.authorVERAS, H. F. P.
dc.contributor.authorFORMIGHIERI, A.
dc.contributor.authorSANTOS, C. A. N.
dc.contributor.authorFERREIRA, M. A. D.
dc.contributor.authorFIGUEIREDO, E. O.
dc.contributor.authorFERREIRA, E. J. L.
dc.date.accessioned2020-08-01T11:12:33Z-
dc.date.available2020-08-01T11:12:33Z-
dc.date.created2020-07-31
dc.date.issued2020
dc.identifier.citationForest Ecology and Management, v. 475, n. 118397, p. 1-11, 2020.
dc.identifier.issn0378-1127
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1124129-
dc.descriptionInformation regarding the spatial distribution of palm trees in tropical forests is crucial for commercial exploitation and management. However, spatially continuous knowledge of palms occurrence is scarce and difficult to obtain with conventional approaches such as field inventories. Here, we developed a new method to map Amazonian palm species at the individual tree crown (ITC) level using RGB images acquired by a low-cost unmanned aerial vehicle (UAV). Our approach is based on morphological operations performed in the score maps of palm species derived from a fully convolutional neural network model. We first constructed a labeled dataset by dividing the study area (135 ha within an old-growth Amazon forest) into 28 plots of 250 m×150 m. Then, we manually outlined all palm trees seen in RGB images with 4 cm pixels. We identified three palm species: Attalea butyracea, Euterpe precatoria and Iriartea deltoidea. We randomly selected 22 plots (80%) for training and six plots (20%) for testing. We changed the plots for training and testing to evaluate the variabilityn, in the classification accuracy and assess model generalization. Our method outperformed the average producer?s accuracy of conventional patch-wise semantic segmentation (CSS) in 4.7%. Moreover, our method correctly identified, on average, 34.7 percentage points more ITCs than CSS, which tended to merge trees that are close to each other. The producer's accuracy of A. butyracea, E. precatoria and I. deltoidea was 78.6 ± 5.5%, 8.6 ± 1.4% and 96.6 ± 3.4%, respectively. Fortunately, one of the most exploited and commercialized palm species in the Amazon (E. precatoria, a.k.a, Açaí) was mapped with the highest classification accuracy. Maps of E. precatoria derived from low-cost UAV systems can support management projects and community-based forest monitoring programs in the Amazon.
dc.language.isoeng
dc.rightsopenAccesseng
dc.subjectPalmeira
dc.subjectPalm trees
dc.subjectMapeamento
dc.subjectDrone
dc.subjectAerial surveys
dc.subjectImagem RGB
dc.subjectDeepLabv3+
dc.subjectBosques lluviosos
dc.subjectMadera tropical
dc.subjectTeledetección
dc.subjectVehículos aéreos no tripulados
dc.subjectFotografía aérea
dc.subjectEmbrapa Acre
dc.subjectRio Branco (AC)
dc.subjectAcre
dc.subjectAmazônia Ocidental
dc.subjectWestern Amazon
dc.subjectAmaz
dc.subjectAmazonia Occidental
dc.titleIndividual tree detection and species classification of Amazonian palms using UAV images and deep learning.
dc.typeArtigo de periódico
dc.subject.thesagroFloresta Tropical
dc.subject.thesagroEspécie Nativa
dc.subject.thesagroAçaí
dc.subject.thesagroPopulação de Planta
dc.subject.thesagroBiogeografia
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroAerofotogrametria
dc.subject.nalthesaurusRain forests
dc.subject.nalthesaurusArecaceae
dc.subject.nalthesaurusEuterpe precatoria
dc.subject.nalthesaurusTropical wood
dc.subject.nalthesaurusBiogeography
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusUnmanned aerial vehicles
dc.subject.nalthesaurusAerial photography
riaa.ainfo.id1124129
riaa.ainfo.lastupdate2020-08-31 -03:00:00
dc.identifier.doihttps://doi.org/10.1016/j.foreco.2020.118397
dc.contributor.institutionMatheus Pinheiro Ferreira, Instituto Militar de Engenharia (IME); Danilo Roberti Alves de Almeida, Universidade de São Paulo (USP); DANIEL DE ALMEIDA PAPA, CPAF-AC; Juliano Baldez Silva Minervino, Universidade Federal do Acre (Ufac); Hudson Franklin Pessoa Veras, Universidade Federal do Paraná (UFPR); Arthur Formighieri, Universidade Federal do Acre (Ufac); Caio Alexandre Nascimento Santos, Bolsista Embrapa Acre; Marcio Aurélio Dantas Ferreira, Fundação de Tecnologia do Estado do Acre (Funtac); EVANDRO ORFANO FIGUEIREDO, CPAF-AC; Evandro José Linhares Ferreira, Instituto Nacional de Pesquisas da Amazônia (Inpa).
Aparece nas coleções:Artigo em periódico indexado (CPAF-AC)

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