Individual tree identification is a key step for forest surveying and monitoring. To identify individual trees with airborne LiDAR data, a local maximum (LM) filter technique is typically performed. With LM, the highest point in a filtering window is generally considered to represent the tree position. This assumption, however, has great limitations, especially for mixed forests. To address this problem, we developed a new approach, the cluster center of higher points (CCHP), for tree position detection with LiDAR data. CCHP assumes that a tree position is located at the clustering center of higher points within a spatial neighborhood, and the center can be detected by a location-based recursive algorithm. The developed CCHP method was applied to a simulated forest and then verified in two real urban forests. In comparison with the variable window-sized LM filter method and layer stacking method, CCHP successfully identified 97% of trees in the simulated forest, while only 78% and 81% of the trees were recognized by LM and layer stacking methods respectively. The average absolute and relative offsets of CCHP are 0.33 m and 6.59%, respectively, and over 80% of the detected trees have an offset of less than 10% of the tree crown radius. CCHP also correctly detected 93.80% and 88.74% of individual trees in the first and second real forests, respectively, but the detection rates from the variable window-sized LM approach and layer stacking were less than 80%. In addition, the tree positions located by CCHP are considerably more accurate than the other two methods. Therefore, CCHP is proven to be promising for detecting individual tree positions from airborne LiDAR data for both simulated and real forests.

个体树木识别是森林勘测和监测的关键步骤。为了识别带有机载LiDAR数据的树木，通常会执行局部最大值（LM）滤波技术。对于LM，通常认为过滤窗口中的最高点代表树的位置。但是，这种假设有很大的局限性，尤其是对于混交林。为了解决这个问题，我们开发了一种新方法，即更高点的聚类中心（CCHP），用于使用LiDAR数据进行树位置检测。CCHP假定树的位置位于空间邻域内较高点的聚类中心，并且可以通过基于位置的递归算法检测到该中心。将开发的CCHP方法应用于模拟森林，然后在两个真实的城市森林中进行验证。与可变窗口大小的LM滤波方法和层堆叠方法相比，CCHP成功地识别了模拟森林中97％的树木，而LM和层堆叠方法分别识别出78％和81％的树木。CCHP的平均绝对偏移量和相对偏移量分别为0.33 m和6.59％，并且超过80％的被检测树木的偏移量小于树冠半径的10％。CCHP还正确地分别检测了第一和第二个真实森林中的93.80％和88.74％的单个树木，但是可变窗口大小LM方法和层堆叠的检测率不到80％。此外，CCHP定位的树位置比其他两种方法精确得多。所以，