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dc.creatorReis, Aliny Aparecida dos-
dc.creatorCarvalho, Mônica Canaan-
dc.creatorMello, José Marcio de-
dc.creatorGomide, Lucas Rezende-
dc.creatorFerraz Filho, Antônio Carlos-
dc.creatorAcerbi Junior, Fausto Weimar-
dc.date.accessioned2019-04-01T17:17:03Z-
dc.date.available2019-04-01T17:17:03Z-
dc.date.issued2018-12-
dc.identifier.citationREIS, A. A. dos et al. Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods. New Zealand Journal of Forestry Science, Rotorua, v. 48, n. 1, p. 1-17, Dec. 2018.pt_BR
dc.identifier.urihttps://link.springer.com/article/10.1186/s40490-017-0108-0pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/33431-
dc.description.abstractBackground: In fast-growing forests such as Eucalyptus plantations, the correct determination of stand productivity is essential to aid decision making processes and ensure the efficiency of the wood supply chain. In the past decade, advances in remote sensing and computational methods have yielded new tools, techniques, and technologies that have led to improvements in forest management and forest productivity assessments. Our aim was to estimate and map the basal area and volume of Eucalyptus stands through the integration of forest inventory, remote sensing, parametric, and nonparametric methods of spatial prediction. Methods: This study was conducted in 20 5-year-old clonal stands (362 ha) of Eucalyptus urophylla S.T.Blake x Eucalyptus camaldulensis Dehnh. The stands are located in the northwest region of Minas Gerais state, Brazil. Basal area and volume data were obtained from forest inventory operations carried out in the field. Spectral data were collected from a Landsat 5 TM satellite image, composed of spectral bands and vegetation indices. Multiple linear regression (MLR), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) methods were used for basal area and volume estimation. Using ordinary kriging, we spatialised the residuals generated by the spatial prediction methods for the correction of trends in the estimates and more detailing of the spatial behaviour of basal area and volume. Results: The ND54 index was the spectral variable that had the best correlation values with basal area (r = − 0.91) and volume (r = − 0.52) and was also the variable that most contributed to basal area and volume estimates by the MLR and RF methods. The RF algorithm presented smaller basal area and volume errors when compared to other machine learning algorithms and MLR. The addition of residual kriging in spatial prediction methods did not necessarily result in relative improvements in the estimations of these methods. Conclusions: Random forest was the best method of spatial prediction and mapping of basal area and volume in the study area. The combination of spatial prediction methods with residual kriging did not result in relative improvement of spatial prediction accuracy of basal area and volume in all methods assessed in this study, and there is not always a spatial dependency structure in the residuals of a spatial prediction method. The approaches used in this study provide a framework for integrating field and multispectral data, highlighting methods that greatly improve spatial prediction of basal area and volume estimation in Eucalyptus stands. This has potential to support fast growth plantation monitoring, offering options for a robust analysis of high-dimensional datapt_BR
dc.languageen_USpt_BR
dc.publisherSpringerpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceNew Zealand Journal of Forestry Sciencept_BR
dc.subjectForest inventorypt_BR
dc.subjectMachine learning algorithmspt_BR
dc.subjectMultiple linear regressionpt_BR
dc.subjectRandom forestpt_BR
dc.subjectSupport vector machinept_BR
dc.subjectArtificial neural networkspt_BR
dc.subjectInventário florestalpt_BR
dc.subjectAlgoritmos de aprendizado de máquinapt_BR
dc.subjectRegressão linear múltiplapt_BR
dc.subjectFloresta aleatóriapt_BR
dc.subjectMáquina de vetor de suportept_BR
dc.subjectRedes neurais artificiaispt_BR
dc.titleSpatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methodspt_BR
dc.typeArtigopt_BR
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