融合多平台激光扫描数据，重建温带森林单株树木生长过程。
A Framework for Individual Tree Growth Reconstruction Using Multi-Platform Laser Scanning
- 用深度学习分割移动激光点云，跨平台匹配树木个体
- 结合高程变化与茎干曲线建模，估算胸径和材积年际增长
- 模型对5年以上生长预测稳定，适合长期森林监测
利用2014–2025年间在北方森林样地采集的136个点云数据（涵盖11台机载、移动及地面激光扫描仪），本研究提出一种个体树木生长重建框架。通过移动激光点云的深度学习分割实现树冠识别，并迁移至其他平台点云以建立时间一致性对应关系。基于移动/地面激光数据构建茎干曲线，机载激光数据用于高度估计，进而实现胸径（DBH）与材积的时序估算。采用基于高度生长的缩放模型重建全周期茎干属性并计算生长量。结果显示，模型预测值与人工测量值的偏差优于直接差分法：5年与10年生长预测的DBH RMSE为55–111%和26–67%，材积为31–87%和21–67%，受样地难度影响；模型在5–6年后误差趋于稳定，12年时最大RMSE为DBH 8–12%、材积12–23%。该方法无需多次林下扫描，可高效实现个体树生长动态监测。
原文摘要 · Abstract (English)
Accurate tree-level forest monitoring using laser scanning data requires reliable tree delineation, consistent tree correspondence across multitemporal point clouds, and accurate estimation of tree attributes and their change. Reconstructing tree growth in boreal forests is challenging due to the scarcity of historical stem-level data, propagation of errors from older sensors into change estimation, and growth rates with a magnitude of measurement uncertainty. This study investigates a framework for estimating individual tree diameter at breast height (DBH) and stem volume growth using 136 point clouds acquired between 2014--2025 with 11 scanners on airborne (ALS), mobile (MLS), and terrestrial laser scanning (TLS) platforms across boreal forest test sites. Trees were delineated from an MLS point cloud using deep learning-based segmentation which was transferred to the remaining point clouds, resulting in reliable multitemporal tree correspondence. Stem curves were derived from MLS/TLS data, with ALS data used for height estimation, enabling DBH and volume estimation and time series. A height growth-based scaling model was used to reconstruct stem attributes across time and estimate growth. Results showed that modeled growth achieved higher agreement with manual growth estimates than differencing independently estimated attributes from point clouds. The modeled-manual 5- and 10-year growth RMSEs were 55--111\% and 26--67\% for DBH, and 31--87\% and 21--67\% for volume, respectively, depending on plot difficulty. The scaling model was temporally robust, with errors remaining stable or stabilizing after 5--6 years, reaching maximum RMSEs of 8--12\% for DBH and 12--23\% for volume after 12 years. Combining MLS/TLS-derived stem measurements with multitemporal ALS-derived heights provided a robust framework for individual tree growth estimation without requiring multiple under-canopy scans.
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