AI 中文总结
该研究提出一种利用多平台激光扫描数据的框架来估计单棵树木胸径和树干体积生长。通过深度学习分割勾勒树木轮廓，结合多平台数据得出树干曲线和高度估计，利用缩放模型重建属性及估计生长，提供了无需多次冠层下扫描的可靠生长估计框架。
AI 中文摘要
利用激光扫描数据进行精确的树木级森林监测，需要可靠的树木轮廓划分、跨多期点云的一致树木对应关系，以及对树木属性及其变化的准确估计。在北方森林中重建树木生长具有挑战性，因为历史树干级数据稀缺，旧传感器的误差会传播到变化估计中，且生长速率存在测量不确定性。本研究调查了一个框架，该框架使用2014年至2025年期间在北方森林测试站点通过11台扫描仪在机载（ALS）、移动（MLS）和地面激光扫描（TLS）平台上获取的136个点云，来估计单棵树木的胸径（DBH）和树干体积生长。通过基于深度学习的分割从MLS点云勾勒树木轮廓，并将其转移到其余点云，实现了可靠的多期树木对应。从MLS/TLS数据得出树干曲线，利用ALS数据进行高度估计，从而实现DBH和体积估计及时间序列分析。使用基于高度增长的缩放模型来重建不同时间的树干属性并估计生长。结果表明，与从点云独立估计属性的差异相比，建模生长与人工生长估计的一致性更高。根据地块难度，建模-人工5年和10年生长的RMSE对于DBH分别为55%-111%和26%-67%，对于体积分别为31%-87%和21%-67%。缩放模型在时间上具有鲁棒性，误差在5-6年后保持稳定或趋于稳定，12年后DBH的最大RMSE为8%-12%，体积为12%-23%。将MLS/TLS得出的树干测量值与多期ALS得出的高度相结合，提供了一个无需多次冠层下扫描即可进行单棵树木生长估计的强大框架。
英文摘要
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.
