CROWN MORPHOMETRIC INDEXES OF EUCALYPT ESTIMATED BY LOGISTIC REGRESSION AND SUPPORT VECTOR MACHINES

Resumo

The proper choice of the modeling method for morphometric tree crown estimates is important to optimize measurement and support silvicultural decision-making. This study aims to evaluate the efficiency of interdimensional morphometric relationships modeling of eucalypt crown under different spacings using logistic regression and Support Vector Machines (SVM). The experiment was set up with four spacings (T1: 3.0 × 0.5 m; T2: 3.0 × 1.0 m; T3: 3.0 × 1.5 m and T4: 3.0 × 2.0 m). A continuous forest inventory was carried out at the ages of 24, 37, 48, 59 and 72 months. Two modeling methods, one using nonlinear regression (logistic model) and the other using SVM, were tested. The range, salience and vital space indexes decreased with increasing tree stem dimensions, tending to stabilization. The logistic model was satisfactorily adapted to the problems, more specifically in prediction of the first two indexes. SVM modeling using radial base Kernel function can be used with good precision for crown morphometric indexes estimation of eucalypt, simultaneously, for different planting spacings.