A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

Remote Sensing

Bin Wu¹  Bailang Yu¹  Wenhui Yue¹  Song Shu¹  Wenqi Tan²  Chunling Hu²  Yan Huang¹  Jianping Wu¹ 

[1] Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, 500 Dongchuan Rd, Shanghai 200241, China; E-Mails:;Shanghai Landscape and City Appearance Administration Information Center, 331 Tongren Rd, Shanghai 200040, China; E-Mails:

关键词: Mobile Laser Scanning (MLS);    Vehicle-borne Laser Scanning (VLS);    point cloud data;    street trees;    voxel;    morphological parameters;    competing growing;    neighborhood search;   

DOI  :  10.3390/rs5020584

来源: mdpi

【 摘 要 】

As an important component of urban vegetation, street trees play an important role in maintenance of environmental quality, aesthetic beauty of urban landscape, and social service for inhabitants. Acquiring accurate and up-to-date inventory information for street trees is required for urban horticultural planning, and municipal urban forest management. This paper presents a new Voxel-based Marked Neighborhood Searching (VMNS) method for efficiently identifying street trees and deriving their morphological parameters from Mobile Laser Scanning (MLS) point cloud data. The VMNS method consists of six technical components: voxelization, calculating values of voxels, searching and marking neighborhoods, extracting potential trees, deriving morphological parameters, and eliminating pole-like objects other than trees. The method is validated and evaluated through two case studies. The evaluation results show that the completeness and correctness of our method for street tree detection are over 98%. The derived morphological parameters, including tree height, crown diameter, diameter at breast height (DBH), and crown base height (CBH), are in a good agreement with the field measurements. Our method provides an effective tool for extracting various morphological parameters for individual street trees from MLS point cloud data.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.