Table of Contents
ISRN Forestry
Volume 2013, Article ID 196832, 12 pages
Research Article

Individual Growth Model for Eucalyptus Stands in Brazil Using Artificial Neural Network

1Department of Forestry, Faculty of Technology, University of Brasília, Campus Darcy Ribeiro, 70904-970 Brasília, DF, Brazil
2Department of Forestry, Federal University of Viçosa, Campus UFV, 36570-000 Viçosa, MG, Brazil
3Department of Forestry, Federal University of the Valleys of Jequitinhonha and Mucuri, Campus Diamantina, 39100-000 Diamantina, MG, Brazil
4Natural Resources Institute, Federal University of Itajubá, Campus Itajubá, 37500-903 Itajubá, MG, Brazil

Received 13 December 2012; Accepted 14 February 2013

Academic Editors: J. Kaitera, P. Newton, T. L. Noland, and P. Robakowski

Copyright © 2013 Renato Vinícius Oliveira Castro et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This work aimed to model the growth and yield of Eucalyptus stands located in northern Brazil, at the individual tree level, by using artificial neural networks (ANNs). Data from permanent plots were used for training the neural networks to predict tree height and diameter as well as mortality probability. Once trained, the networks were evaluated using an independent data set. The first group was composed of 33 plots (11 in each productive capacity class) and was used for artificial neural network training. In five measurements, this group totaled 8,735 cases (measurements of individual trees), as each plot had 53 trees on average throughout this evaluation. The second group was composed of 30 plots (10 in each productive capacity class) and was used for model validation. This group totaled 7,756 cases. Were tested different network architectures Multilayer Perceptron (MLP). Results revealed an underestimation bias for number of surviving trees. However, estimates of diameter, height, and volume per hectare were found to be accurate. This indicates that artificial neural networks are a viable alternative to the traditional growth and yield modeling approach in the forestry sector.