结合无人机高分辨率可见光影像和激光雷达点云的城市植物群落树种组成和数量特征提取方法对比

doi:10.17520/biods.2024237

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

结合无人机高分辨率可见光影像和激光雷达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.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 接受日期:2024-09-10 出版日期:2025-04-20 发布日期:2025-04-29
通讯作者: 马勤
基金资助:
国家重点研发计划(2023YFB3907401);国家自然科学基金(42201366);江苏省特聘教授项目和南京师范大学启动基金(184080H202B349)

A comparison of methods for extracting tree species composition and quantitative characteristics in urban plant communities using UAV high-resolution RGB imagery and LiDAR point cloud

Yuan Jingyi¹, Zhang Xu¹, Tian Zhenpeng¹, Wang Zizhe¹, Gao Yongping¹, Yao Dizhao¹, Guan Hongcan², Li Wenkai³, Liu Jing¹^,⁴^,⁵, Zhang Hong¹^,⁴^,⁵, Ma Qin⁶^,¹^,⁴^,⁵^,^*()

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, Ministry of Education, Nanjing Normal University, 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 Accepted:2024-09-10 Online:2025-04-20 Published:2025-04-29
Contact: Ma Qin
Supported by:
the National Key Research and Development Program of China(2023YFB3907401);National Natural Science Foundation of China(42201366);Jiangsu Specially-Appointed Professor Program, and Research Fund Provided by Nanjing Normal University(184080H202B349)

摘要/Abstract

摘要： 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物群落组成结构的基础。但传统的调查方法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物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, 对比不同遥感数据源和方法在提取植物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。本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, 进而提高城市植物群落树种组成及数量特征提取的精度, 可为开展城市植物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, 数量特征, 无人机遥感, 高分辨率可见光影像, 激光雷达

Abstract

Aims:Tree species composition and structure are vital for 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 plant 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 plant 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 plant community in order to assess their efficaciousness.

Results: (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 plant 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, quantitative characteristics, UAV remote sensing, high-resolution RGB imagery, LiDAR

袁敬毅, 张旭, 田镇朋, 王梓柘, 高永萍, 姚迪昭, 关宏灿, 李文楷, 刘婧, 张宏, 马勤 (2025) 结合无人机高分辨率可见光影像和激光雷达点云的城市植物群落树种组成和数量特征提取方法对比. 生物多样性, 33, 24237. DOI: 10.17520/biods.2024237.

Yuan Jingyi, Zhang Xu, Tian Zhenpeng, Wang Zizhe, Gao Yongping, Yao Dizhao, Guan Hongcan, Li Wenkai, Liu Jing, Zhang Hong, Ma Qin (2025) A comparison of methods for extracting tree species composition and quantitative characteristics in urban plant communities using UAV high-resolution RGB imagery and LiDAR point cloud. Biodiversity Science, 33, 24237. DOI: 10.17520/biods.2024237.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2024237

https://www.biodiversity-science.net/CN/Y2025/V33/I4/24237

图/表 10

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分类方案 Classification schemes	类别数 Number of categories	具体类别 Specific categories
A	2	黄山松 Pinus hwangshanensis
A	2	其他树种 Other tree species
B	2	二球悬铃木 Platanus × acerifolia
B	2	其他树种 Other tree species
C	2	针叶类 Coniferous trees
C	2	阔叶类 Broadleaf trees
D	3	优势针叶类 Dominant coniferous trees
		优势阔叶类 Dominant broadleaf trees
		其他类 Other tree species
E	5	优势针叶类 Dominant coniferous trees
		优势阔叶类 Dominant broadleaf trees
		伴生针叶类 Companion coniferous trees
		伴生阔叶类 Companion broadleaf trees
		其他类 Other tree species
F	11	黄山松 Pinus hwangshanensis
		日本柳杉 Cryptomeria japonica
		二球悬铃木 Platanus × acerifolia
		日本扁柏 Chamaecyparis obtusa
		金钱松 Pseudolarix amabilis
		鹅掌楸 Liriodendron chinense
		灯台树 Cornus controversa
		鸡爪槭 Acer palmatum
		山樱花 Prunus serrulata
		偶见针叶类 Occasional coniferous trees
		偶见阔叶类 Occasional broadleaf trees

分类方案 Classification schemes	类别数 Number of categories	具体类别 Specific categories
A	2	黄山松 Pinus hwangshanensis
A	2	其他树种 Other tree species
B	2	二球悬铃木 Platanus × acerifolia
B	2	其他树种 Other tree species
C	2	针叶类 Coniferous trees
C	2	阔叶类 Broadleaf trees
D	3	优势针叶类 Dominant coniferous trees
		优势阔叶类 Dominant broadleaf trees
		其他类 Other tree species
E	5	优势针叶类 Dominant coniferous trees
		优势阔叶类 Dominant broadleaf trees
		伴生针叶类 Companion coniferous trees
		伴生阔叶类 Companion broadleaf trees
		其他类 Other tree species
F	11	黄山松 Pinus hwangshanensis
		日本柳杉 Cryptomeria japonica
		二球悬铃木 Platanus × acerifolia
		日本扁柏 Chamaecyparis obtusa
		金钱松 Pseudolarix amabilis
		鹅掌楸 Liriodendron chinense
		灯台树 Cornus controversa
		鸡爪槭 Acer palmatum
		山樱花 Prunus serrulata
		偶见针叶类 Occasional coniferous trees
		偶见阔叶类 Occasional broadleaf trees

树种 Tree species	生活型 Life form	多度 Abundance	相对密度 Relative density (%)	树冠面积 Canopy area (m²)	相对盖度 Relative coverage (%)
黄山松 Pinus hwangshanensis	ECT	152	24.3	3,105.5	12.1
日本柳杉 Cryptomeria japonica	ECT	149	23.8	4,063.4	15.9
二球悬铃木 Platanus × acerifolia	DBT	92	14.7	11,545.1	45.1
日本扁柏 Chamaecyparis obtusa	ECT	79	12.6	1,847.1	7.2
金钱松 Pseudolarix amabilis	DCT	17	2.7	348.5	1.4
鹅掌楸 Liriodendron chinense	DBT	13	2.1	793.9	3.1
灯台树 Cornus controversa	DBT	14	2.2	652.2	2.5
鸡爪槭 Acer palmatum	DBT	11	1.8	216.1	0.8
山樱花 Prunus serrulata	DBT	11	1.8	185.6	0.7
其他针叶树 Other coniferous trees		17	2.7	523.8	2.0
其他阔叶树 Other broadleaf trees		70	11.2	2,321.7	9.1
总计 Total		625	100.0	25,602.9	100.0

树种 Tree species	生活型 Life form	多度 Abundance	相对密度 Relative density (%)	树冠面积 Canopy area (m²)	相对盖度 Relative coverage (%)
黄山松 Pinus hwangshanensis	ECT	152	24.3	3,105.5	12.1
日本柳杉 Cryptomeria japonica	ECT	149	23.8	4,063.4	15.9
二球悬铃木 Platanus × acerifolia	DBT	92	14.7	11,545.1	45.1
日本扁柏 Chamaecyparis obtusa	ECT	79	12.6	1,847.1	7.2
金钱松 Pseudolarix amabilis	DCT	17	2.7	348.5	1.4
鹅掌楸 Liriodendron chinense	DBT	13	2.1	793.9	3.1
灯台树 Cornus controversa	DBT	14	2.2	652.2	2.5
鸡爪槭 Acer palmatum	DBT	11	1.8	216.1	0.8
山樱花 Prunus serrulata	DBT	11	1.8	185.6	0.7
其他针叶树 Other coniferous trees		17	2.7	523.8	2.0
其他阔叶树 Other broadleaf trees		70	11.2	2,321.7	9.1
总计 Total		625	100.0	25,602.9	100.0

数据 Database	方法 Methods	正确分割 True positive	过分割 False positive	欠分割 False negative	召回率 Recall	精确率 Precision	F值 F-score
高分辨率可见光影像 High-resolution RGB imagery	多尺度分割 Multi-scale segmentation	349	300	178	0.662	0.538	0.594
激光雷达点云 LiDAR point cloud	点云分割 Point cloud segmentation	343	259	184	0.651	0.570	0.608
高分辨率可见光影像 + 激光雷达点云 High-resolution RGB imagery + LiDAR point cloud	单木分割优化 Optimized individual tree segmentation	337	85	190	0.639	0.799	0.710

结合无人机高分辨率可见光影像和激光雷达点云的城市植物群落树种组成和数量特征提取方法对比

A comparison of methods for extracting tree species composition and quantitative characteristics in urban plant communities using UAV high-resolution RGB imagery and LiDAR point cloud

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数据 Database	方法 Methods	类别 Categories	生产者精度 Producer’s accuracy (%)	用户精度 User’s accuracy (%)	总体精度 Overall accuracy (%)	Kappa系数 Kappa coefficient
高分辨率可见光影像 High-resolution RGB imagery	面向对象分类 Object-oriented classification	优势针叶类 Dominant coniferous trees	61.7	78.4	74.6	0.608
		优势阔叶类 Dominant broadleaf trees	85.0	91.1
		其他类 Other tree species	73.9	45.9
激光雷达点云 LiDAR point cloud	面向单木点云分类 Point-cloud-oriented classification	优势针叶类 Dominant coniferous trees	75.0	55.9	63.6	0.443
		优势阔叶类 Dominant broadleaf trees	64.4	74.4
		其他类 Other tree species	46.9	65.2
高分辨率可见光影像 + 激光雷达点云 High-resolution RGB imagery + LiDAR point cloud	面向种子点分类 Seed-point-oriented classification	优势针叶类 Dominant coniferous trees	83.1	81.7	86.7	0.783
		优势阔叶类 Dominant broadleaf trees	87.3	99.2
		其他类 Other tree species	88.9	68.6

研究区域 Study region	树种/类别数量 No. of species/ categories	数据源 Databases	分类精度 Classification accuracy	参考文献 Reference
森林 Forest	7	可见光影像 RGB imagery	79.0	滕文秀等, 2019
城市 Urban	9	可见光影像 RGB imagery	92.4	陈逊龙等, 2024
城市 Urban	2	激光雷达点云 LiDAR point cloud	83.8	Cetin & Yastikli, 2022
森林 Forest	2	激光雷达点云 LiDAR point cloud	86.7	刘茂华等, 2022
城市 Urban	10	可见光影像和激光雷达点云 RGB imagery and LiDAR point cloud	74.1	Wu et al, 2024
城市 Urban	15	激光雷达点云和高光谱影像 LiDAR point cloud and hyperspectral imagery	70.0	Liu et al, 2017
森林 Forest	18	可见光影像、激光雷达点云和高光谱影像 RGB imagery, LiDAR point cloud, and hyperspectral imagery	91.8	Qin et al, 2022

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