树木是构成森林的基础，精确获取森林中每一棵树的位置和结构信息，是推进林业资源管理的现代化、准确计算森林蓄积量和碳储量的关键。其中，基于机载激光雷达点云数据，通过单木分割提取每一棵树的信息正在成为代替人工森林清查的主要手段和发展智慧林业的重要研究方向。

本文基于模拟和实测的机载激光雷达点云数据，定量对比和分析树种、森林密度对不同单木分割算法精度的影响，并研究单木分割算法的适用性。论文首先通过构建同密度不同树种、不同树密度等三维森林场景，利用LESS模型模拟生成了机载激光雷达点云数据，选取分水岭算法和点云区域增长算法两种经典的单木分割方法，对模拟的点云数据进行了单木分割。通过对单木分割结果的精度和性能评估，定量分析了不同树林密度不同树种等条件下对单木分割精度的影响。然后，使用塞罕坝林场真实数据作为验证。研究结果表明，针对0.1-0.7trees/m²密度的森林场景，两种方法分割精度都随树密度增加而降低，且分水岭算法对冠层高度模型分辨率参数敏感。在简单的森林场景中，两种分割方法表现都较为稳定和准确；在树木高差较大的复杂环境下，分水岭算法分割会丢失低矮树木，点云区域增长算法易造成欠分割，两种算法都需要进一步优化和改进。单木分割方法的适用性和性能受到森林结构、树木形态和密度等因素的影响，需要根据具体情况选择合适的方法以提高分割效果。

Trees are the fundamental components of forests, and accurately obtaining the location and structural information of each tree is crucial for advancing the modernization of forestry resource management and the precise calculation of forest volume and carbon storage. This study delves into the precision of individual tree segmentation in forestry, a critical factor for resource management and ecological assessments. Utilizing both simulated and actual airborne LiDAR data, it evaluates how tree species and forest density affect segmentation accuracy. The research employs the LESS model to create 3D forest scenarios with varying densities and species, followed by the application of watershed and point cloud region growing algorithms. Real-world validation is conducted at Saihanba Forest Farm. Findings reveal that increased tree density correlates with reduced segmentation accuracy, particularly impacting the watershed algorithm due to its sensitivity to canopy height model resolution. While both algorithms demonstrate reliable performance in uniform forests, complex terrains with height variances pose challenges, leading to potential omissions or under-segmentation. The study underscores the need for algorithmic refinement and advocates for method selection tailored to specific forest conditions to optimize segmentation outcomes.