基于背包式激光雷达测量系统的城市绿地树木三维绿量估算方法

doi:10.13287/j.1001-9332.202210.020

应用生态学报 ›› 2022, Vol. 33 ›› Issue (10): 2777-2784.doi: 10.13287/j.1001-9332.202210.020

基于背包式激光雷达测量系统的城市绿地树木三维绿量估算方法

李肖肖^1,2, 唐丽玉^1,2*, 彭巍^1,2, 陈建新^1,2, 麻霞^1,2

¹福州大学空间数据挖掘与信息共享教育部重点实验室, 福州 350108;
²福州大学地理空间信息技术国家地方联合工程研究中心, 福州 350108

收稿日期:2021-11-15 修回日期:2022-06-28 出版日期:2022-10-15 发布日期:2023-04-15
通讯作者: * E-mail: tangly@fzu.edu.cn
作者简介:李肖肖, 男, 1995年生, 硕士研究生。主要从事激光雷达和影像三维数据获取及分析研究。E-mail: lixiao-xiao0356@foxmail.com
基金资助:
国家自然科学基金项目(41971344)和福建省科技计划项目(2020I0008)资助。

Estimation method of urban green space living vegetation volume based on backpack light detection and ranging

LI Xiao-xiao^1,2, TANG Li-yu^1,2*, PENG Wei^1,2, CHEN Jian-xin^1,2, MA Xia^1,2

¹Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China;
²National Engineering Research Center of Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China

Received:2021-11-15 Revised:2022-06-28 Online:2022-10-15 Published:2023-04-15

摘要/Abstract

摘要： 三维绿量能够客观、准确描述城市绿化水平,可为定量研究城市绿地生态功能的机理提供可靠的数据基础。针对单位附属绿地分布分散、规模较小等特点,本研究提出一种面向该类城市绿地的三维绿量估算方案,该方案包括数据获取、处理、实体分割、分类和单木冠层提取以及三维绿量计算的环节。首先,利用背包式激光雷达测量系统获取三维点云数据,利用变尺度地面点滤波算法剔除地面点云;然后,利用基于密度的聚类算法对非地面点云进行聚类,且基于密度特征的竞争算法对重叠区域进行二次分割,形成独立对象;接着,利用PointNet++模型提取植物点云,根据枝叶点云主方向差异性以及轴向分布密度提取冠层点云;最后,使用凸包法计算单木冠层三维绿量,累计每株木的三维绿量得到区域三维绿量。以某科技园区为例,估算其总三维绿量为21034.95 m³,其中,芒果树株数最多,三维绿量总量最大,为4868.64 m³,占23.2%;单株三维绿量最大的树种为小叶榄仁,平均每株为120.37 m³。本研究方案估算的树木三维绿量与传统方法的相对误差在10.7%～33.7%,平均相对误差为20.9%;与台积法的相对误差在2.7%～16.0%,平均相对误差为8.7%。本研究方案充分利用三维点云数据特性,所用凸多面体逼近树冠的原始形态,更符合树木的实际情况。该三维绿量测量和估算方案可为城市三维绿量快速、精确估算提供新思路。

关键词: 城市绿地, 背包式LiDAR, PointNet++, 点云分割, 三维绿量

Abstract: Living vegetation volume (LVV) can objectively and accurately reflect the urban greenery quality, and provide a reliable data foundation for the quantitative study aiming to reveal the mechanisms underlying urban greenery ecological functions. According to the characteristics of dispersion and small scale of unit affiliated green space, we proposed a LVV estimation scheme for such urban green space, which included data acquisition, processing, entity segmentation, classification, single tree canopy extraction, and LVV calculation. First, point cloud data was obtained with a backpack LiDAR system, and the ground point clouds were eliminated by a multi-scale algorithm. Second, the Density Based Spatial Clustering of Application with Noise (DBSCAN) algorithm was used to cluster the non-ground point clouds, and density feature-based competitive algorithm was used to re-segmented for the overlapping area to generate independent objects. Third, the PointNet++ network model was used to extracted plant point clouds. Then, the canopy point clouds were extracted using the similarity of principal direction between neighboring points and distribution density of branch and leaf points. Finally, the LVV of individual tree canopy was calculated by the convex hull method, and then the LVV of the accessory greenland was summed up. Taking a science and technology park as an example, its total LVV was 21034.95 m³, among which the number of mango trees was the highest, and the total LVV was the largest (4868.64 m³, accounting for 23.2%). The tree species with the largest LVV per plant was Terminalia neotaliala tree, with an average of 120.37 m³ per plant. The relative error between LVV of trees estimated by this scheme compared with traditional method and convex hull method was 10.7%-33.7% and 2.7%-16.0%, with average value of 20.9% and 8.7%, respectively. This scheme could make full use of the characteristics of the three-dimensional point cloud and use a convex polyhedron to simulate the original form of the tree crown, which was more consistent with the actual situation of trees. The measurement and estimation solution of the LVV provided new ideas for rapid and accurate estimation of urban LVV.

Key words: urban green space, backpack LiDAR, PointNet++, point cloud segmentation, living vegetation volume

李肖肖, 唐丽玉, 彭巍, 陈建新, 麻霞. 基于背包式激光雷达测量系统的城市绿地树木三维绿量估算方法[J]. 应用生态学报, 2022, 33(10): 2777-2784.

LI Xiao-xiao, TANG Li-yu, PENG Wei, CHEN Jian-xin, MA Xia. Estimation method of urban green space living vegetation volume based on backpack light detection and ranging[J]. Chinese Journal of Applied Ecology, 2022, 33(10): 2777-2784.

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基于背包式激光雷达测量系统的城市绿地树木三维绿量估算方法

Estimation method of urban green space living vegetation volume based on backpack light detection and ranging

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