Modeling Commercial Height in Amazonian Forests: Accuracy of Mixed-Effects Regression Versus Random Forest

Accurate estimation of commercial tree height is essential for volumetric predictions in forest management plans, particularly in Amazonian forests with high species diversity. We assessed two predictive approaches for estimating commercial height, using the sum of actual commercial log lengths as the reference metric. The dataset comprised 1745 harvested trees from Annual Production Unit 8 in the Tapajós National Forest. Three commercial volume groups dominated the structural gradient: 46.1% of the trees Group 1 (<6 m3), 36.7% into Group 2 (6–10 m3), and 17.2% into Group 3 (≥10 m3). The Linear Mixed-Effects Model included diameter at breast height (DBH) as a fixed effect and species as a random effect, whereas the Random Forest model used DBH and species as predictors. The mixed-effects model achieved higher accuracy (r = 0.77; RMSE = 2.95 m), while the Random Forest model performed slightly worse (r = 0.73; RMSE = 3.10 m). Species with greater commercial heights exerted a strong influence on both modelling approaches. Principal Component Analysis revealed structural separation among the three volume groups, driven by DBH, commercial height, number of logs, and log volume. The mixed-effects model provided effective framework for predicting commercial height in heterogeneous tropical forests.