Abstract Accurate estimation of individual tree aboveground biomass (AGB) is critical for improving our understanding of forest structure and function, especially in forests with diverse tree species spanning a wide range of sizes. Terrestrial laser scanning (TLS) has proven effective at the tree scale thanks to its ability to provide high-density point cloud data. However, most existing methods rely on high-quality multi-scan TLS data, which requires labor-intensive data collection and complicated preprocessing to achieve high precision, thereby limiting their practical applicability. In contrast, single-scan TLS data offers a simpler and more efficient alternative, though it presents significant challenges due to incomplete coverage, uneven point density, and frequent occlusions. This study presents a novel and effective framework for estimating individual tree AGB from single-scan TLS data by integrating a deep learning network (CoAtNet) with three key innovations: an optimized 3D-to-2D projection strategy, a 3D point cloud-based data augmentation, and an ensemble approach to enhance robustness under data-scarce conditions in local natural forest environments. The framework was evaluated in a mixed hardwood forest and demonstrated strong predictive performance with a coefficient of determination (R2) of 0.73, a median percentage error of 0.99%, and a median absolute percentage error of 25.06%, outperforming two comparison models, Random Forest and Point Transformer. The results indicate the potential of single-scan TLS data to support reliable individual tree biomass when combined with deep learning, even in the presence of data imperfections. The proposed framework provides a practical, scalable approach that contributes to the advancement of remote sensing methodologies for sustainable forest management and facilitates improved monitoring of ecological changes in the context of accelerating climate change.
Minyoung et al. (Wed,) studied this question.