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 OPEN ACCESS Remote Sens. 2013, 5 585 measurements. Our method provides an effective tool for extracting various morphological parameters for individual street trees from MLS point cloud data. Urban vegetation is an important component of urban ecosystem and landscape. In particular, street trees play an important role in enhancing environmental quality, maintaining aesthetic beauty of urban landscape, and providing social service for inhabitants. Street trees provide shade for pedestrians, benefit storm-water management [1], reduce air pollutants [2-4] and street noise [5], moderate heat accumulation in urban street canyons [6] [7] [8] , and decrease building energy consumption and CO 2 emissions [9] . However, the growth conditions of street trees are often negatively influenced by some natural and abiotic factors [10] . Some studies show that about half of newly transplanted street trees cannot survive beyond the first two years in some cities [11, 12] . Therefore, acquiring and monitoring the conditions of street trees is important for urban tree planting, maintenance, and management. A comprehensive inventory and classification of street trees need multiple characteristic parameters: tree species, growth conditions, presence or absence of insects or diseases, transplantation requirements, date of planting, etc. [10]. The growth condition and biological function of an individual tree can be inferred from its morphological parameters, such as tree height, diameter at breast height (DBH), crown width, and crown base height (CBH). Traditionally, these parameters of street trees are obtained by field measurements, which are very time-consuming and labor is expensive. Since the size and shape of street trees are changing from year to year, even from season to season, it would be extremely costly to acquire the up-to-date street tree information with the field measurement method. A more efficient method is needed for deriving information about urban street trees. The advent of the laser scanning or LiDAR (Light Detection and Ranging) system represents a technological breakthrough in acquiring three-dimensional (3D) data about surface objects in a rapid, and cost-effective manner. In terms of the carrying platform, laser scanning systems can be classified into four categories: Satellite-based Laser Scanning (SLS), Airborne Laser Scanning (ALS, namely, airborne LiDAR), Mobile Laser Scanning (MLS, in full, Vehicle-borne Laser Scanning, VLS), and Terrestrial Laser Scanning (TLS). The SLS data have sparse sampling points and are not adequate for street tree identification [13] . TLS data have the highest accuracy and sampling density and can be used for structure reconstruction of individual trees [14] [15] [16] . However, the poor mobility of TLS restricts repeated surveys for a large urban area. ALS has the extraordinary capability in gathering highly accurate and dense elevation measurements. Numerous studies demonstrate that ALS data can be employed to investigate urban 3D morphology [17] , to extract building rooftops, footprints, and density information [18] [19] [20] [21] , to estimate urban green volume [22, 23] , and to detect individual trees [24] [25] [26] . Nevertheless, due to the top-down sensing geometry, it is difficult for ALS to capture
REF proposed a voxel-based method to extract individual trees from mobile laser scanning data but their method is not suitable for use with airborne Li-DAR scans.
A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data
Remote Sensing
Remote Sensing
Computer Science Geology