ALTERNATIVES TO ESTIMATE THE VOLUME OF INDIVIDUAL TREES IN FOREST FORMATIONS IN THE STATE OF MINAS GERAIS

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Jadson Coelho de Abreu

Abstract

The objective of this study was to compare different alternatives to estimate the volume of individual trees in four forest formations in the state of Minas Gerais. The data were obtained in a forest inventory procedure performed by the Minas Gerais Technological Center Foundation. The trunk volumes were obtained by the Smalian expression for the minimum diameter with bark of 4 cm. Using the volume data with bark, diameters (DBH) and total heights, the Schumacher and Hall model was adjusted for each forest formation, considering the structures of a linear model of fixed effect and a mixed linear model. Next, 100 Multilayer Perceptron artificial neural networks (ANN) were trained in a supervised manner. In addition, we evaluated eight support-vector machine regression (SVMR). The criteria to evaluate the performance of all the alternatives studied were: the correlation between the observed and estimated volumes, the square root of the mean square error and the frequency distribution by percentage relative error class. After the analyzes, all the alternatives were verified to estimate the volume of the individual trees in the different forest formations. Although the alternatives presented close statistics in the validation process, the graphical analysis of the error distribution showed greater precision of the estimates of the mixed linear models for the four formations. Given the results, it was concluded that there is no absolute superiority of one alternative over the others, and that all of them should be evaluated to find the one that best describes or explains the dataset

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How to Cite
DE ABREU, Jadson Coelho. ALTERNATIVES TO ESTIMATE THE VOLUME OF INDIVIDUAL TREES IN FOREST FORMATIONS IN THE STATE OF MINAS GERAIS. CERNE, [S.l.], p. 393-402, nov. 2020. ISSN 2317-6342. Available at: <http://cerne.ufla.br/site/index.php/CERNE/article/view/2413>. Date accessed: 01 dec. 2020.
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