Eucalyptus growth recognition using machine learning methods and spectral variables

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de Mato Grosso do Sul (UFMS)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorEstadual de Mato Grosso (UNEMAT)-
Autor(es): dc.contributorUniversidade Tecnológica Federal do Paraná (UTFPR)-
Autor(es): dc.creatorde Oliveira, Bruno Rodrigues-
Autor(es): dc.creatorda Silva, Arlindo Ananias Pereira [UNESP]-
Autor(es): dc.creatorTeodoro, Larissa Pereira Ribeiro-
Autor(es): dc.creatorde Azevedo, Gileno Brito-
Autor(es): dc.creatorAzevedo, Glauce Taís de Oliveira Sousa-
Autor(es): dc.creatorBaio, Fábio Henrique Rojo-
Autor(es): dc.creatorSobrinho, Renato Lustosa-
Autor(es): dc.creatorda Silva Junior, Carlos Antonio-
Autor(es): dc.creatorTeodoro, Paulo Eduardo [UNESP]-
Data de aceite: dc.date.accessioned2022-08-04T22:10:22Z-
Data de disponibilização: dc.date.available2022-08-04T22:10:22Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2021-10-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.foreco.2021.119496-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/221953-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/221953-
Descrição: dc.descriptionGrowth and production models can help to simulate the growth of tree dimensions to predict forest productivity at different levels. In this context, the following questions arise: (i) is it possible to recognize the growth pattern of eucalyptus species based on spectral features using machine learning (ML) for data modeling? (ii) what spectral features provides better accuracy? and (iii) what ML algorithms are most accurate for performing this modeling? To answer these questions, the present study evaluated the use of ML techniques using breast height and total plant height to classify the growth of five species of eucalyptus and Corymbria citriodora in an unsupervised learning, and the obtained classes for induce ML algorithms to recognize the species with relation to their growth using vegetation indices (VIs) and spectral bands (SBs). It were evaluated five eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis e E. urograndis) and C. citriodora in experimental design of randomized blocks with four replicates, with 20 plants inside each experimental plot. The diameter at breast height and total plant height at stand level were obtained by measuring five trees in each experimental unit in seven measurements. During this same period, a flight was carried out using a remotely piloted aircraft for the acquisition of spectral variables (SBs and VIs). For recognition of eucalyptus species in relation to their growth two machine learning approaches were employed: supervised and unsupervised. The average accuracy obtained from 10-fold cross-validation, employing Random Forest algorithm and 24 features, was 0.76. This result shows that the proposed approach is appropriate to recognize different eucalyptus species based on their growth.-
Descrição: dc.descriptionUniversidade Federal de Mato Grosso do Sul (UFMS), Rodovia MS 306, Km. 305-
Descrição: dc.descriptionUniversidade Estadual Paulista (UNESP), Av. Brasil Sul, 56 – Centro-
Descrição: dc.descriptionDepartment of Geography Universidade Estadual de Mato Grosso (UNEMAT), Av. dos Ingas, 3001, Jardim Imperial-
Descrição: dc.descriptionUniversidade Tecnológica Federal do Paraná (UTFPR), Via do Conhecimento – Km 01-
Descrição: dc.descriptionUniversidade Estadual Paulista (UNESP), Av. Brasil Sul, 56 – Centro-
Idioma: dc.languageen-
Relação: dc.relationForest Ecology and Management-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectClassification-
Palavras-chave: dc.subjectRandom forest-
Palavras-chave: dc.subjectVegetation index-
Título: dc.titleEucalyptus growth recognition using machine learning methods and spectral variables-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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