The objective of this study was to propose a methodology by using Artificial Neural Networks (ANN) to reduce the number of trees to be scaled during the process of generating volumetric equations. The data used in this study were originated from measurements of 2,700 trees of clonal eucalyptus plantations located in southern Bahia State, Brazil. The training of ANN was performed in order to obtain networks for estimating the volume with and without bark. As input variables, we used the diameter at breast height (dbh) - 1.30 m, height, and diameter at 0.0, 0.5, 1.0, 1.5, 2.0 and 4.0 m above the ground and the volumes obtained until 2 and 4 m. The accuracy of the method was carried out using the test L & O. We also evaluated the dispersion of percentage errors, frequency histogram of the percentage error and the root mean square error (RMSE). The methodology proposed in this study proved to be efficient for estimating the volume of trees, and is indicated to obtain the total volume with and without bark of eucalyptus, enabling the reduction of costs for the construction of volumetric equations.
Scaling; Forest inventory; Forest measurement
