Light detection and ranging (LiDAR) technology has become one of the most dominant acquisition methods for modeling forest attributes, such as very accurate tree structure information, which is necessary for qualitative forest management and research activities. In this study, we evaluated the efficacy of standalone unmanned aerial vehicle-laser scanning (UAV-LS) and terrestrial laser scanning (TLS) data to accurately estimate forest tree metrics under differing management types. Furthermore, we combined the UAV-LS and TLS data to test whether fusion can improve the mapping of the three-dimensional (3D) structure of individual trees to favor accurate estimates of tree metrics. We initially calculated the percentage of point density per square meter aboveground in each height class at intervals of 1 m for the UAV-LS, TLS, and fusion datasets. This helped illustrate the vertical point density distribution that reflects the structural complexity between broadleaf and conifer trees. We then used tree-level point clouds to assess several tree metrics, such as diameter at breast height (DBH), total tree height (H_T), crown projection area (PA_C), crown width (W_C), crown length (L_C), 3D crown surface (S_C), and 3D crown volume (V_C). Our results indicated that LiDAR fusion can increase the estimation accuracy of DBH and H_T, especially in broadleaves (97.8% accuracy). In addition, fusion significantly reshaped the modeled crown structures in both plots, which led to improved estimates for all crown metrics. The results show empirical evidence that LiDAR fusion can also have a determining role in supporting ecosystem services. For example, detailed 3D mapping of tree crowns can be used to assess the mitigation of rainfall`s kinetic energy by tree crowns concerning soil erosion and sedimentation near habitable zones.

光探测和测距 (LiDAR) 技术已成为森林属性建模的最主要采集方法之一，例如非常准确的树木结构信息，这对于定性森林管理和研究活动是必要的。在这项研究中，我们评估了独立的疗效 无人机激光扫描 (UAV-LS) 和地面激光扫描 (TLS) 数据，以准确估计不同管理类型下的林木指标。此外，我们结合了 UAV-LS 和 TLS 数据来测试融合是否可以改善单个树木的三维 (3D) 结构的映射，从而有利于准确估计树木指标。对于 UAV-LS、TLS 和融合数据集，我们最初以 1 m 的间隔计算了每个高度等级中每平方米地面以上的点密度百分比。这有助于说明反映阔叶树和针叶树之间结构复杂性的垂直点密度分布。然后，我们使用树级点云来评估几个树木指标，例如胸径 (DBH)、总树高 (H _T)、牙冠投影面积 (PA _C )、牙冠宽度 (W _C )、牙冠长度 (L _C )、3D 牙冠表面 (S _C ) 和 3D 牙冠体积 (V _C )。我们的结果表明，LiDAR 融合可以提高 DBH 和 H _{T的估计精度}，尤其是在阔叶树中（97.8% 的准确率）。此外，融合显着重塑了两个图中的建模牙冠结构，从而改进了对所有牙冠指标的估计。结果显示经验证据表明，LiDAR 融合在支持生态系统服务方面也可以发挥决定性作用。例如，树冠的详细 3D 映射可用于评估树冠对可居住区附近土壤侵蚀和沉积的降雨动能的缓解情况。