Assessing sampling efficiency and artificial neural networks for tree height prediction and site index classification in forest stands

Forest management relies on accurate growth and harvest stock estimations, typically obtained through forest inventories. This study evaluated sampling strategies for training Artificial Neural Networks (ANNs) to predict tree heights, aiming to minimize sampling effort and associated costs, and assessed the efficiency of using these predictions for site index classification. Data were collected from Pinus taeda L. spacing experiment in southern Brazil, encompassing measurements from 3 to 15 years of age. Four scenarios were evaluated: (a) adjusting height-diameter equations with varying sample sizes, (b) applying equations from one age to the next, (c) training ANNs with different sample sizes, and (d) using ANNs trained in one year for subsequent years. Results showed that ANNs, particularly with a sample of three trees per plot (Scenario c), provided superior height predictions compared to traditional height-diameter relationships, achieving higher correlation and lower error. While applying ANNs trained in one year to the next (Scenario d) showed reduced accuracy, Scenario (c) maintained high precision. The use of ANN-predicted heights for site index classification was also deemed viable. The study demonstrates that ANNs can significantly reduce the sampling effort required for forest inventories while maintaining accuracy in height estimation and site productivity assessment. The findings support the continued use of the established practice of measuring three trees per plot for ANN training, optimizing both cost and precision in forest inventory processes.