Identification of Street Trees’ Main Nonphotosynthetic Components from Mobile Laser Scanning Data

Laser scanning technique is an important area of the optical and laser technology, which makes the access of 3D individual tree information becomes available. In order to deal with the biomass and structure estimation of the urban forest, many algorithms have been developed for 3D point clouds to extract individual tree information, including tree counts, tree locations, branching structure and tree heights. However, due to the fact that the urban forest environment is complex, i.e. tree stems are non-vertical, tree crowns are overlapped and tree branches are in different structures, the existing methods are far from being desired in terms of the identification accuracy and robustness. The goal of this paper is to present a novel tree mapping algorithm that provides both tree stems and main branches, i.e. main nonphotosynthetic components, for inadequately identifying branches information. This work is based on an iterative clustering method to group point clouds and uses a growing strategy to merge tree branches and trunks with the help of the Euclidean distance and elevation difference information. The experiment dataset contains different types of roadside trees collected by the mobile laser scanning technique. Results show that the correctness and completeness of the proposed method are 95.2 and 88.5%, respectively, in the clustering of trees’ main nonphotosynthetic components, which presents a promising approach for street trees identification.