The Machine-Learning-Driven Transformation of Forest Biometrics: Progress and Pathways Ahead Review

Forest biometrics has emerged as one of the fastest-growing scientific disciplines within environmental sciences. Machine learning (ML), an increasingly essential approach that uses effective algorithms, has proven to be an accurate and cost-efficient solution to forest-related problems. Recently, ML methods have evolved, from traditional machine learning (TML) algorithms to more sophisticated approaches, such as deep learning (DL) and ensemble (ENS) methods. To uncover these developments, a structured review and analysis of 150 peer-reviewed studies was conducted, following a standardized workflow. The analysis reveals clear shifts in methodological adoption. During the most recent five-year period (2021–2025), DL and shallow neural network (SNN) methods dominated the literature, accounting for 37.5% of published studies, followed by ENS and TML methods, contributing 29.2% and 27.1%, respectively, presenting a marked increase in the utilization of artificial neural networks (ANNs) and related algorithms across the domains of forest biometrics. Nevertheless, overall trends indicate that the benefits of TML methods still need further exploration for ground-based received data. Advances in remote sensing and satellite data have brought large-scale remotely sensed data into environmental research, further boosting ML utilization. However, each field could be strengthened by implementing standardized evaluation metrics and broader geographic representation. In this way, robust and widely transferable modeling frameworks for forest ecosystems can be developed. At the same time, further research on algorithms and their applicability to natural resources proves a key component for comprehensive and sustainable forest management.