Evaluating Machine Learning Algorithms for Modeling Forest Characteristics in Romania Using Remote Sensing Data

Forest attributes such as the standing stock, diameter at the breast height, tree height, and basal area, are essential in forest management. Conventional estimation methods, which are still largely used in many parts of the world, are typically resource intensive. Machine learning algorithms working with remotely sensed data trained by ground measurements may provide a promising, more efficient alternative. This study evaluates the performance of three machine learning algo-rithms, namely Random Forest, Classification and Regression Trees, and Gradient Boosting Tree Algorithm in estimating these forest attributes. Ground truth data was sourced by measurements carried out in relevant forests from Romania and by an independent dataset from Brasov County. The predictive ability of the tested algorithms was examined by considering several spatial resolu-tions. The results showed varying degrees of performance. Random Forest was the best performer, with RMSE and R2 values over 0.8 for all attributes. GBTA excelled in predicting the standing stock, achieving R2 values over 0.9. The validation based on the independent dataset has confirmed higher performance for both RF and GBTA. In contrast, CART excelled in predicting the basal area, but struggled with breast height diameter, standing stock, and tree height. A sensitivity analysis that concerned the spatial resolution revealed high degrees of discrepancy. Random Forest and Gradient Boosting Tree Algorithm were more consistent when estimating the standing stock, but they have shown inconsistency for breast height diameter and tree height, while CART showed important variations. These results provide useful insights into the strengths and weaknesses of these algo-rithms, and provide the information required to select the best option when aiming to use similar solutions for estimation.