Work

Multi-dimensional canopy structure enables cross-site scalability: a model development workflow for aboveground biomass estimation from terrestrial LiDAR

Year: 2026

Type: article

Abstract: Forest above-ground biomass (AGB) is a crucial measure of forest carbon storage and plays a critical role in global carbon monitoring. While terrestrial laser scanning (TLS) serves as a powerful tool for AGB estimation, existing methods face key limitations. These include reliance on computationally intensive individual-tree segmentation, which struggles in dense forests with overlapping canopies, or on oversimplified structural attributes, such as diameter at breast height (DBH) and tree height, or being site-specific. Here, we present a cross-site scalable and computationally efficient modeling workflow for community-level AGB estimation using multiplatform TLS-derived metrics, without requiring tree-level point cloud segmentation. Besides community-level DBH extracted via a point-cloud-segmentation-free workflow, we selected community-level canopy structure metrics, such as leaf area densities, canopy entropy, etc. to train an AGB estimation model via Random Forest. We used data from five ecologically diverse sites across East Asia, spanning a 20° latitudinal gradient, to train and cross-validate our model. The model achieved high predictive accuracy ( ) and showed consistent performance across all sites. Compared to site-specific models, the cross-site model performed as accurate, with canopy structural features accounting for over 40% of the explained variance and improving performance by 13.89% relative to the site-specific DBH-and-height-only model. Furthermore, our evidence suggested that such improvement was indeed driven by the inclusion of canopy structural information. However, leave-one-site-out validation showed limited transferability to environmentally distinct sites, highlighting the need for broader training datasets. Overall, our workflow provides an efficient alternative for community-level AGB estimation and underscore the importance of multidimensional canopy attributes for improving forest carbon assessment across heterogeneous ecosystems.

Source: Plant Phenomics

DOI: 10.1016/j.plaphe.2026.100280