Estimation of inside-bark diameter and heartwood diameter for Tectona grandis Linn. trees using artificial neural networks

Abstract

The objective of this study was to obtain neural networks that would precisely estimate inside-bark diameter (dib) and heartwood diameter (dh) and compare to the results obtained by the Taper models. The databank was formed so as to eliminate inconsistent and biased data, and stratified: minimum dib of 4, 6 and 8 cm and minimum dh of 10, 15 and 20 cm. The adjusted Taper model used was the Kozak model. For the fitting of artificial neural networks (ANN), tests were performed to identify the independent variables and the database scope level, i.e., the following input variables were tested: diameter at breast height (dbh), total height (H), height at diameter dib or dh (h) and outside-bark diameter at h (dob), bark thickness at 1.3 m and project, and the scope at database level or project level. The estimates obtained by the neural networks and Kozak model were evaluated by residual graphs in function of the respective diameter observed and graph of the observed versus estimated values. ANN were found to be more efficient in estimating inside-bark and heartwood diameters for Tectona grandis trees than the Kozak model. The variables that must be used to fit the networks are dbh, H, h and dob. Stratification by project results in precision gain, with precision being higher for wider commercial diameters. Thus, linear-type artificial neural networks can be efficient in describing the taper of Tectona grandis trees.