一种基于车载激光扫描点云数据的单株行道树信息提取方法

提出了一种基于分层网格点密度的单株树信息提取方法,从车载激光扫描点云数据中提取出组成单株行道树的激光点,并计算树高、冠幅等特征信息.该方法由建立规则网格,基于高程的点云分层,逐层计算网格点密度,逐层提取激光点,提取单株行道树和计算特征等步骤构成.通过实例证明,提取结果较好地保留了组成单株行道树的激光点,并能较准确地计算特征信息.该方法拓展了车载激光扫描系统的应用领域,并可为城市绿化管理提供新的技术方法.

Method for identifying individual street trees from the cloud data of the vehicle-borne laser scanning points

This paper presents a new layered extraction method for identifying laser scanning points that constitute an individual street tree using the grid points density information based on the cloud data of the laser scanning points. The characteristic information, including the height and crown diameter, were derived after an individual tree was identified. The original 3D points cloud data were processed by the following steps: establishing regular grids, layering the points cloud based on elevation value, calculating grid points density for each layer, extracting laser scanning points for each layer, identifying individual tree, and deriving characteristic information. The feasibility of the method was proved through case studies. The results show most of the laser scanning points that constitute an individual tree are extracted correctly. And the derived characteristic information was estimated to be as fairly accurate as the in situ data. The proposed method will expand the application domain of VLS and provide a new approach to the urban green space development and management.