Recursive diameter prediction and volume calculation of eucalyptus trees using Multilayer Perceptron Networks

A major challenge in forest management is the ability to quickly and accurately predict bole volume of standing trees. This study presents a new model that uses Multilayer Perceptron (MLP) artificial neural networks for predicting tree diameters values. The model requires three diameter measures at the base of the tree, and recursively predicts other diameter measures. The predicted diameters allow for calculating tree volume using the Smalian method. The performance of the proposed model was satisfactory when compared with data obtained from tree scaling and volume equations.

► With the known diameter of three neighboring samples points on a trunk as input an MLP can predict a diameter ahead. ► Predictions are close to actual measurements. ► Recursively an MLP can predict all diameters of a tree to its tip. ► Calculated volumes from the predicted diameters are as good as volumetric equations.