【摘要】 树种分类对于森林监测、分析和管理具有战略意义，对于林业可持续发展至关重要。文章提出一种基于机载LiDAR点云进行单木分割，再利用分割后提取出的树冠三维几何特征进行树种分类的方法。首先，利用三角网逐步自适应滤波（TIN滤波）和树点标准化生成树冠高度模型（CHM）；然后，采用局部最大值算法和改进的旋转剖面算法从树冠高度模型中分割出单棵树；再根据树冠的几何特征，使用平行线形状拟合的方式拟合树冠形状，具体使用三角形、矩形和弧形三种基本几何形状来拟合不同树种的树冠形状，对于同一种树冠形状或形状组合，则使用参数化分类；最后，利用中国东北虎豹国家公园的10个样方数据集进行树种分类测试。试验结果表明，树种分类平均精度达到90.9%，最高分类精度达到95.9%，满足林业快速测量的要求。更多还原

【Abstract】 Identifying tree species is of strategic importance for forest monitoring, analysis and management, and is crucial for sustainable forestry development. Therefore, we propose a method for tree species classification based on individual tree segmentation from airborne LiDAR, following by using the threedimensional geometric features of the segmented tree crowns. First, TIN filter and tree points normalization are used to generate the Crown Height Model(CHM). Based on the geometric characteristics of the tree crowns,parallel-line shape fitting is used to fit the crown shapes. Specifically, three basic geometric shapes(triangle,rectangle and arc) are used to fit the crowns of different tree species. For the same crown shape or combinations of shapes, parametric classification is employed. The proposed method is tested using datasets from two different sites in the Tiger and Leopard National Park in Northeast China. The results show that the average accuracy of tree species classification is 90.9%, with the best accuracy reaching 95.9%, meeting the requirements of rapid forestry surveys.更多还原