Volume estimation in a Eucalyptus plantation using multi-source remote sensing and digital terrain data: a case study in Minas Gerais State, Brazil

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorReis, Aliny Aparecida dos-
Autor(es): dc.creatorFranklin, Steven E.-
Autor(es): dc.creatorMello, José Marcio de-
Autor(es): dc.creatorAcerbi Junior, Fausto Weimar-
Data de aceite: dc.date.accessioned2026-02-09T11:28:25Z-
Data de disponibilização: dc.date.available2026-02-09T11:28:25Z-
Data de envio: dc.date.issued2019-04-23-
Data de envio: dc.date.issued2019-04-23-
Data de envio: dc.date.issued2019-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/33658-
Fonte completa do material: dc.identifierhttps://www.tandfonline.com/doi/abs/10.1080/01431161.2018.1530808?journalCode=tres20-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1141518-
Descrição: dc.descriptionIn this study, we tested the effectiveness of stand age, multispectral optical imagery obtained from the Landsat 8 Operational Land Imager (OLI), synthetic aperture radar (SAR) data acquired by the Sentinel-1B satellite, and digital terrain attributes extracted from a digital elevation model (DEM), in estimating forest volume in 351 plots in a 1,498 ha Eucalyptus plantation in northern Minas Gerais state, Brazil. A Random Forest (RF) machine learning algorithm was used following the Principal Component Analysis (PCA) of various data combinations, including multispectr al and SAR texture variables and DEM-based geomorphometric derivatives. Using multispectral, SAR or DEM variables alone (i.e. Experiments (ii)–(iv)) did not provide accurate estimates of volume (RMSE (Root Mean Square Error) > 32.00 m3 ha−1) compared to predictions based on age since planting of Eucalyptus stands (Experiment (i)). However, when these datasets were individually combined with stand age (i.e. Experiments (v)–(vii)), the RF models resulted in better volume estimates than those obtained when using the individual multispectral, SAR and DEM datasets (RMSE < 28.00 m3 ha−1). Furthermore, a model that integrated the selected variables of these data with stand age (Experiment (viii)) improved volume estimation significantly (RMSE = 22.33 m3 ha−1). The large and increasing area of Eucalyptus forest plantations in Brazil and elsewhere suggests that this new approach to volume estimation has the potential to support Eucalyptus plantation monitoring and forest management practices.-
Idioma: dc.languageen-
Publicador: dc.publisherTaylor & Francis-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceInternational Journal of Remote Sensing-
Palavras-chave: dc.subjectRemote sensing-
Palavras-chave: dc.subjectOperational land imager-
Palavras-chave: dc.subjectSynthetic aperture radar-
Palavras-chave: dc.subjectDigital elevation model-
Palavras-chave: dc.subjectPrincipal component analysis-
Palavras-chave: dc.subjectSensoriamento remoto-
Palavras-chave: dc.subjectImagens de terra operacional-
Palavras-chave: dc.subjectRadar de abertura sintética-
Palavras-chave: dc.subjectModelo de elevação digital-
Palavras-chave: dc.subjectAnálise do componente principal-
Título: dc.titleVolume estimation in a Eucalyptus plantation using multi-source remote sensing and digital terrain data: a case study in Minas Gerais State, Brazil-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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