Classification of Eucalyptus plantation Site Index (SI) and Mean Annual Increment (MAI) prediction using DEM-based geomorphometric and climatic variables in Brazil

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorReis, Aliny Aparecida dos-
Autor(es): dc.creatorFranklin, Steven E.-
Autor(es): dc.creatorAcerbi Junior, Fausto Weimar-
Autor(es): dc.creatorFerraz Filho, Antonio Carlos-
Autor(es): dc.creatorMello, José Marcio de-
Data de aceite: dc.date.accessioned2026-02-09T12:02:36Z-
Data de disponibilização: dc.date.available2026-02-09T12:02:36Z-
Data de envio: dc.date.issued2020-08-21-
Data de envio: dc.date.issued2020-08-21-
Data de envio: dc.date.issued2019-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/42585-
Fonte completa do material: dc.identifierhttps://www.tandfonline.com/doi/abs/10.1080/10106049.2020.1778103?journalCode=tgei20-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1153074-
Descrição: dc.descriptionDigital elevation model (DEM) data were used with climate data to estimate productivity in 19 Eucalyptus plantations in Minas Gerais state, Brazil. Typically, plantation and individual stand growth and productivity estimates, such as Site Index (SI) and Mean Annual Increment (MAI), are based on field measures of height, tree diameter and age. Using a Random Forest modelling approach, SI and MAI were related to: (i) DEM-based geomorphometric variables and (ii) WorldClim historical macro-climatic measures. Three operational SI classes (high, medium and low productivity) in 180 stands were mapped with an overall accuracy of 91.6%. Medium and high productivity sites were the most accurately classified. Low productivity sites had 76.5% producer’s accuracy and 92.9% user’s accuracy, and were the most extensive in the study area. Such sites are considered of high importance from a plantation management perspective since additional forestry operations are likely required to address low productivity and growth.-
Idioma: dc.languageen-
Publicador: dc.publisherTaylor & Francis-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceGeocarto International-
Palavras-chave: dc.subjectDigital elevation model-
Palavras-chave: dc.subjectGeomorphometrics-
Palavras-chave: dc.subjectSite index-
Palavras-chave: dc.subjectRandom forest-
Palavras-chave: dc.subjectEucalyptus plantation-
Palavras-chave: dc.subjectModelo digital de elevação-
Palavras-chave: dc.subjectGeomorfometria-
Palavras-chave: dc.subjectFloresta aleatória-
Palavras-chave: dc.subjectPlantação de eucalipto-
Título: dc.titleClassification of Eucalyptus plantation Site Index (SI) and Mean Annual Increment (MAI) prediction using DEM-based geomorphometric and climatic variables in Brazil-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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