Predicting teak tree (Tectona grandis Linn F.) height using generic models and artificial neural networks

Abstract

The continuous monitoring of dendrometric variables provides estimates that assist in conducting fast-growing stands. In this study, we aimed to investigate the performance of generic models and artificial neural networks to estimate total height of Tectona grandis in a forest stand in the Eastern Amazon. Continuous forest inventory was performed in this population, where total height and diameter at breast height were measured. The variables such as age and the square root of the average diameter (dg) of the plots were used to compose the methods adopted to estimate the height of the trees. The accuracy of these methods was assessed using the residual standard error of the estimate, the coefficient of correlation, and the graphical analysis of residues. The aggregated difference and ANOVA were calculated to compare the methods. The independent variables mentioned were able to describe the height behavior of individuals. We concluded that the methods have good residual dispersion, normal distribution of errors and little tendency to overestimate height. It was found that the generic models and the ANNs do not differ significantly from each other and are efficient to estimate the height of individuals. We also concluded that the ANNs, especially those that included dg, presented superior statistical indicators