Prognoses of diameter and height of trees of eucalyptus using artificial intelligence

Highlights: • We use artificial neural networks to estimate the growth in DBH and height of eucalyptus trees. • Using new techniques in forestry measurement. • The techniques of artificial intelligence showed accuracy in growth estimation in DBH and total height. • The techniques of artificial intelligence are appropriate in estimating the growth of eucalyptus trees. • The techniques used can be adapted to other areas and forest crops. Models of individual trees are composed of sub-models that generally estimate competition, mortality, and growth in height and diameter of each tree. They are usually adopted when we want more detailed information to estimate forest multiproduct. In these models, estimates of growth in diameter at 1.30 m above the ground (DBH) and total height (H) are obtained by regression analysis. Recently, artificial intelligence techniques (AIT) have been used with satisfactory performance in forest measurement. Therefore, the objective of this study was to evaluate the performance of two AIT, artificial neural networks and adaptive neuro-fuzzy inference system, to estimate the growth in DBH and H of eucalyptus trees. We used data of continuous forest inventories of eucalyptus, with annual measurements of DBH, H, and the dominant height of trees of 398 plots, plus two qualitative variables: genetic material and site index. It was observed that the two AIT showed accuracy in growth estimation of DBH and H. Therefore, the two techniques discussed can be used for the prognosis of DBH and H in even-aged eucalyptus stands. The techniques used could also be adapted to other areas and forest species.