Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands.
TIDOP Research Group, Higher Polytechnic School of Avila, University of Salamanca, Avila, Spain.
PLoS One. 2018 Apr 24;13(4):e0196004. doi: 10.1371/journal.pone.0196004. eCollection 2018.
In an urban context, tree data are used in city planning, in locating hazardous trees and in environmental monitoring. This study focuses on developing an innovative methodology to automatically estimate the most relevant individual structural parameters of urban trees sampled by a Mobile LiDAR System at city level. These parameters include the Diameter at Breast Height (DBH), which was estimated by circle fitting of the points belonging to different height bins using RANSAC. In the case of non-circular trees, DBH is calculated by the maximum distance between extreme points. Tree sizes were extracted through a connectivity analysis. Crown Base Height, defined as the length until the bottom of the live crown, was calculated by voxelization techniques. For estimating Canopy Volume, procedures of mesh generation and α-shape methods were implemented. Also, tree location coordinates were obtained by means of Principal Component Analysis. The workflow has been validated on 29 trees of different species sampling a stretch of road 750 m long in Delft (The Netherlands) and tested on a larger dataset containing 58 individual trees. The validation was done against field measurements. DBH parameter had a correlation R2 value of 0.92 for the height bin of 20 cm which provided the best results. Moreover, the influence of the number of points used for DBH estimation, considering different height bins, was investigated. The assessment of the other inventory parameters yield correlation coefficients higher than 0.91. The quality of the results confirms the feasibility of the proposed methodology, providing scalability to a comprehensive analysis of urban trees.
在城市环境中，树木数据被用于城市规划、定位危险树木和环境监测。本研究专注于开发一种创新的方法，以便自动估算通过移动激光雷达系统在城市级别采样的城市树木的最相关的个体结构参数。这些参数包括胸径（DBH），它通过使用 RANSAC 对属于不同高度箱的点进行圆形拟合来估计。对于非圆形树木，DBH 通过极值点之间的最大距离计算得出。树木大小通过连通性分析提取。冠基高度，定义为从活冠底部到最长距离，通过体素化技术计算得出。为了估算树冠体积，实施了网格生成和α形状方法的程序。此外，通过主成分分析获得了树木位置坐标。该工作流程已在荷兰代尔夫特一条长 750 米的道路上对 29 棵不同物种的树木进行了验证，并在包含 58 棵个体树木的更大数据集上进行了测试。验证是针对实地测量进行的。DBH 参数在 20 厘米高的高度箱中具有 0.92 的相关 R2 值，提供了最佳结果。此外，还研究了考虑不同高度箱时用于 DBH 估算的点数对 DBH 估算的影响。其他库存参数的评估得出的相关系数高于 0.91。结果的质量证实了所提出的方法的可行性，为全面分析城市树木提供了可扩展性。
