生物多样性 ›› 2025, Vol. 33 ›› Issue (4): 24237.  DOI: 10.17520/biods.2024237  cstr: 32101.14.biods.2024237

• 三维生态学专题 • 上一篇    下一篇

结合无人机影像和激光点云的城市植物群落树种组成和数量特征提取方法对比

袁敬毅1, 张旭1, 田镇朋1, 王梓柘1, 高永萍1, 姚迪昭1, 关宏灿2, 李文楷3, 刘婧1,4,5, 张宏1,4,5, 马勤6,1,4,5*   

  1. 1. 南京师范大学地理科学学院, 南京 210023; 2. 海南大学热带农林学院, 海口 570228; 3. 中山大学地理科学与规划学院, 广州 510006; 4. 南京师范大学虚拟地理环境教育部重点实验室, 南京 210023; 5. 江苏省地理信息资源开发与利用协同创新中心, 南京 210023; 6. 气候系统预测与变化应对全国重点实验室, 南京 210023
  • 收稿日期:2024-06-14 修回日期:2024-08-27 接受日期:2024-09-18 出版日期:2025-04-20 发布日期:2025-04-29
  • 通讯作者: 马勤

A comparison of methods for extracting tree species composition and quantitative characteristics in urban plant communities via UAV imagery and LiDAR point cloud

Jingyi Yuan1, Xu Zhang1, Zhenpeng Tian1, Zizhe Wang1, Yongping Gao1, Dizhao Yao1, Hongcan Guan2, Wenkai Li3, Jing Liu1,4,5, Hong Zhang1,4,5, Qin Ma6,1,4,5*   

  1. 1 School of Geography, Nanjing Normal University, Nanjing 210023, China 

    2 School of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China 

    3 School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510006, China 

    4 Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China 

    5 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China 

    6 State Key Laboratory of Climate System Prediction and Risk Management, Nanjing 210023, China

  • Received:2024-06-14 Revised:2024-08-27 Accepted:2024-09-18 Online:2025-04-20 Published:2025-04-29
  • Contact: Qin Ma

摘要: 调查城市植被的群落组成及结构特征对评估其生长状况和生态功能至关重要。群落内不同树种的数量特征是定量描述植被群落组成结构的基础。传统的植被调查需要大量人力物力且难以大范围开展, 而单一遥感数据源和方法难以同时提供准确的株数、冠幅和树种信息。为此, 需要探讨多源数据结合的无人机遥感技术在精确获取城市植被群落树种组成和数量特征时的潜力。本研究以庐山风景名胜区的典型城市植被为对象, 分别基于高分辨率可见光影像、激光雷达点云以及两者的结合开展单木分割与树种分类, 进一步对比不同遥感数据源和方法在提取植被群落树种数量特征时的表现。结果表明: (1)采用多源数据结合的方式可以得到最优的单木分割和树种分类精度, 相比于分别使用影像或点云, 单木分割的F值分别提升0.116和0.102, 分类总体精度分别提升12.1%和23.1%; (2)多源数据结合可以更准确地提取群落内树种数量特征的相对关系, 其对各类别树种相对密度和相对盖度的提取误差分别在2.3%和4.8%以内, 而基于单一数据源的方式则对特定树种有明显的高估或低估。研究证明多源数据结合的方法可以通过同时优化单木探测和树种分类过程, 进而提高城市植被群落树种组成及数量特征提取的精度, 为开展城市植被群落结构的无人机遥感监测提供理论支持和方法借鉴。

关键词: 城市植被, 树种组成和数量特征, 无人机遥感, 高分辨率影像, 激光雷达

Abstract

Aims: Investigating the community characteristics of tree species composition and structure is of vital importance in evaluating urban vegetation growth status and its ecological function. The quantitative characteristics of different tree species in a community act as the basis for quantitative descriptions of the composition and structure of vegetation communities. Traditional vegetation surveys require substantial human and material resources and are therefore difficult to carry out on a large scale, other options—such as single remote sensing data source and corresponding method— frequently do not provide accurate information on the number of trees, crown size, and tree species simultaneously. To address these challenges, it is essential to explore the potential of unmanned aerial vehicles (UAV) with remote sensing technology that deploys multi-source datasets to accurately acquire the composition and quantitative characteristics of urban vegetation communities. 

Methods: The study area was located in Lushan, a famous tourist city with high vegetation density and a complex terrain. We employed high-resolution RGB imagery, LiDAR point cloud, and their combination to conduct individual tree detection and species classification. We then compared the performance of different remote sensing data sources and their corresponding methods of extracting quantitative characteristics of tree species in the urban vegetation community in order to assess their efficaciousness. 

Results: The results indicated: (1) The multi-source datasets yielded optimal results for individual tree segmentation and species classification. Compared to using imagery or LiDAR data separately, the F-score for multi-source data on individual tree segmentation increased by 0.116 and 0.102, respectively, and the overall accuracy of tree species classification improved by 12.1% and 23.1%. (2) The combination of multi-source data improved the accuracy when extracting the relative quantitative characteristics of tree species within communities, with errors for the relative density and relative coverage of all tree species categories being within 2.3% and 4.8%, respectively. In contrast, methods based on a single data source suffered from inaccuracies, either overestimating or underestimating the relative quantities of specific tree species. 

Conclusion: The research demonstrated that combining multi-source data improved the accuracy of extracting the composition and quantitative characteristics of urban vegetation communities by simultaneously optimizing the process of individual tree detection and that of species classification. Further, it offers theoretical support and a methodological framework for subsequent UAV remote sensing monitoring and its contribution to the structure of urban vegetation.

Key words: urban vegetation, tree species composition and quantitative characteristics, UAV remote sensing, high-resolution imagery, LiDAR