2026-05-17 中国科学院（CAS）
中国科学院植物研究所のSU Yanjun教授らは、樹木の地上部バイオマスを非破壊で高精度に推定するLiDAR（地上レーザースキャニング）手法を開発した。森林の炭素循環や炭素固定能力の評価には樹木バイオマスの正確な把握が不可欠だが、従来法は伐採を伴う破壊計測や経験式（アロメトリー）に依存しており、労力や精度に課題があった。研究チームは、木部と葉の分離、ボクセルベースの葉量推定、3次元再構築を統合した新しいレーザースキャン解析フローを構築し、幹・枝・葉ごとのバイオマス配分を詳細に推定できるようにした。検証の結果、この手法は従来のアロメトリーモデルよりも総地上部バイオマス推定で高精度を示し、細い枝レベルでも安定した性能を維持した。さらに、希少・絶滅危惧樹種であるCathaya argyrophyllaにも適用可能であることが確認された。本成果は、森林生態系の長期観測、炭素循環研究、希少植物の保全や個体群回復研究を支援する有力な非破壊計測技術として期待される。
＜関連情報＞
- https://english.cas.cn/newsroom/research-news/202605/t20260518_1159538.shtml
- https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210x.70299
地上レーザースキャンに基づくワークフローによる、個々の樹木の地上部バイオマスの構成要素別推定 A terrestrial laser scanning-based workflow for component-wise estimation of individual tree above-ground biomass
Qi Dong, Tianyu Hu, Xiaoyong Wu, Jiatong Wang, Xuanhao Jiang, Jiabo Yan, Xiaoqiang Liu, Yuhao Feng, Zhichao Wei, Lingli Liu, Xiao-Quan Wang, Yanjun Su
Methods in Ecology and Evolution Published: 10 April 2026
DOI:https://doi.org/10.1111/2041-210x.70299
Abstract
- Accurate estimation of individual tree above-ground biomass (AGB) and its component-wise allocation is crucial for advancing ecological research and forest management. However, current biomass estimation methods, such as destructive sampling and allometric equation–based approaches, face limitations in both operational efficiency and cost-effectiveness, and only destructive sampling can provide component-wise biomass measurements, which is impractical for large-scale studies or repeated measurements.
- In this study, we present a terrestrial laser scanning (TLS)-based workflow integrating wood–leaf separation, voxel-based foliage estimation and detailed 3D reconstruction of tree architecture to achieve accurate estimation of individual tree AGB and its component-wise allocation. A total of 68 trees were scanned to obtain high-resolution TLS data and subsequently destructively harvested to acquire field reference measurements for validation.
- The results demonstrate that the workflow achieved high accuracy in predicting AGB at the individual tree level (coefficient of determination/R2 = 0.88, root mean squared error/RMSE = 16.83 kg, mean absolute error/MAE = 12.18 kg), significantly outperforming estimates derived from locally calibrated allometric equations (R2 = 0.61, RMSE = 29.86 kg, MAE = 24.52 kg). Furthermore, this study provides evidence of the strong capability of TLS in estimating branch-level biomass, with high accuracy achieved across branch orders (R2 ranging from 0.66 to 0.91, RMSE from 3.55 to 380 g and MAE from 2.97 to 290 g).
- By providing precise, non-destructive estimates of biomass distribution across branches and leaves, this workflow demonstrates strong potential for improving the accuracy of tree biomass quantification, supporting investigations of resource allocation strategies, and enhancing forest carbon monitoring.
