Single-tree detection is one of the main research topics in quantifying the structural properties of forests. Drone LiDAR systems and terrestrial laser scanning systems produce high-density point clouds that offer great promise for forest inventories in limited areas. However, most studies have focused on the upper canopy layer and neglected the lower forest structure. This paper describes an innovative tree detection method using drone LiDAR data from a new perspective of the under-canopy structure. This method relies on trunk point clouds, with under-canopy sections split into heights ranging from 1 to 7 ​m, which were processed and compared, to determine a suitable height threshold to detect trees. The method was tested in a dense cedar plantation forest in the Aichi Prefecture, Japan, which has a stem density of 1140 stems·ha⁻¹ and an average tree age of 42 years. Dense point cloud data were generated from the drone LiDAR system and terrestrial laser scanning with an average point density of 5000 and 6500 points·m⁻², respectively. Tree detection was achieved by drawing point-cloud section projections of tree trunks at different heights and calculating the center coordinates. The results show that this trunk-section-based method significantly reduces the difficulty of tree detection in dense plantation forests with high accuracy ($F1-\mathrm{S}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}$ ​= ​0.9395). This method can be extended to different forest scenarios or conditions by changing section parameters.

单树检测是量化森林结构特性的主要研究课题之一。无人机 LiDAR 系统和地面激光扫描系统产生高密度点云，为有限区域的森林清查提供了巨大希望。然而，大多数研究都集中在上层冠层而忽略了下层森林结构。本文从树冠下结构的新角度描述了一种使用无人机 LiDAR 数据的创新树木检测方法。该方法依赖于树干点云，将树冠下部分分成 1 到 7 米的高度，对其进行处理和比较，以确定合适的高度阈值来检测树木。该方法在日本爱知县茂密的雪松人工林中进行了测试，该人工林的茎密度为 1140 株公顷⁻¹，平均树龄为 42 年。密集点云数据由无人机 LiDAR 系统和地面激光扫描生成，平均点密度分别为 5000 点和 6500 点 m ^-2。树木检测是通过绘制树干在不同高度的点云截面投影并计算中心坐标来实现的。结果表明，这种基于树干截面的方法显着降低了茂密人工林中树木检测的难度，精度高（$F\mathrm{1个}-\mathrm{小号}\mathrm{对比}\mathrm{哦}\mathrm{r}\mathrm{电子}$ = 0.9395）。通过更改截面参数，可以将此方法扩展到不同的森林场景或条件。