机载雷达点云亚热带针叶林单木分割探究

Exploration of Individual Tree Segmentation Method in Subtropical Coniferous Forest Using Airborne Radar Data

单木是森林的基本构成单元,精准单木检测和高效参数获取对提高林业管理质量和生产效率意义重大。针对亚热带针叶林单木分割存在的分割方法与参数选取问题,探究分水岭算法、距离判别聚类算法和层堆叠算法的关键分割参数选取,比较三种算法对单木集群的分割效果,并结合识别结果与实测数据评价分割精度。试验结果显示:采用距离判别聚类算法和层堆叠算法分割单木集群时,存在着过度分割或者分割不足的情况;而分水岭算法的分割效果最佳,且分割精度最高。

Individual tree is the basic unit of forest.Accurate detection of individual tree and efficient parameter acquisition are of great significance to improving management quality and production efficiency of forestry.In order to solve the problem of selecting the method and parameters of individual tree segmentation in subtropical coniferous forest,the key segmentation parameters of the watershed algorithm,point cloud-based cluster segmentation and layer stacking will be explored.At the same time,the individual tree clusters segmentation results of the three algorithms will be compared,and combine the recognition results with actual measurement data to evaluate the segmentation accuracy.The test results show that when the point cloud-based cluster segmentation and layer stacking segment individual tree clusters,there are over-segmentation or under-segmentation.But the watershed algorithm has the best segmentation results and the highest segmentation accuracy.