As a core content of forest management, the height to crown base (HCB) model can provide a theoretical basis for the study of forest growth and yield. In this study, 8364 trees of Larix olgensis within 118 sample plots from 11 sites were measured to establish a two-level nonlinear mixed effect (NLME) HCB model. All predictors were derived from an unmanned aerial vehicle light detection and ranging (UAV-LiDAR) laser scanning system, which is reliable for extensive forest measurement. The effects of the different individual trees, stand factors, and their combinations on the HCB were analyzed, and the leave-one-site-out cross-validation was utilized for model validation. The results showed that the NLME model significantly improved the prediction accuracy compared to the base model, with a mean absolute error and relative mean absolute error of 0.89% and 9.71%, respectively. In addition, both site-level and plot-level sampling strategies were simulated for NLME model calibration. According to different prediction scale and accuracy requirements, selecting 15 trees randomly per site or selecting the three largest trees and three medium-size trees per plot was considered the most favorable option, especially when both investigations cost and the model’s accuracy are primarily considered. The newly established HCB model will provide valuable tools to effectively utilize the UAV-LiDAR data for facilitating decision making in larch plantations management.

中文翻译：

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利用UAV-LiDAR数据和非线性混合效应模型预测东北落叶松冠基高度

高冠基数模型是森林管理的核心内容，可以为森林生长和单产的研究提供理论依据。在这项研究中，落叶松有8364棵树在来自11个地点的118个样地中，进行了测量，以建立两级非线性混合效应（NLME）HCB模型。所有的预测指标均来自无人驾驶飞机的光检测和测距（UAV-LiDAR）激光扫描系统，该系统可进行广泛的森林测量。分析了不同个体树，林分因素及其组合对六氯苯的影响，并利用留一留出的交叉验证进行模型验证。结果表明，与基本模型相比，NLME模型显着提高了预测准确性，其平均绝对误差和相对平均绝对误差分别为0.89％和9.71％。此外，还针对NLME模型校准模拟了站点级别和样地级别的采样策略。根据不同的预测规模和准确性要求，每个站点随机选择15棵树或每个地块选择三棵最大的树和三棵中等大小的树被认为是最有利的选择，尤其是在同时考虑调查成本和模型准确性的情况下。新建立的HCB模型将提供有效利用UAV-LiDAR数据的有价值的工具，以促进落叶松人工林管理中的决策。