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浙江大学学报（农业与生命科学版）
浙江大学学报（农业与生命科学版）  2024, Vol. 50 Issue (2): 190-199    DOI: 10.3785/j.issn.1008-9209.2023.10.071
综述     
城市植被遥感分类研究进展与展望
孟庆岩1,2,3,4(),杜弘宇1,3,4,王莉萍2,张琳琳1,2,3(),吴嘉豪1,5,康佳琦1,6
1.中国科学院空天信息创新研究院，北京 100094
2.杭州国际城市学研究中心浙江省城市治理研究中心，浙江 杭州 310000
3.海南空天信息研究院，海南省地球观测重点实验室，海南 三亚 572029
4.中国科学院大学，北京 100049
5.澳门大学智慧城市物联网国家重点实验室，澳门 999078
6.桂林电子科技大学生命与环境科学学院，广西 桂林 541004
Research progress and prospects of remote sensing classification of urban vegetation
Qingyan MENG1,2,3,4(),Hongyu DU1,3,4,Liping WANG2,Linlin ZHANG1,2,3(),Jiahao WU1,5,Jiaqi KANG1,6
1.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.Center for Urban Governance Studies of Zhejiang Province, Hangzhou International Urbanology Research Center, Hangzhou 310000, Zhejiang, China
3.Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, Hainan, China
4.University of Chinese Academy of Sciences, Beijing 100049, China
5.State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao 999078, China
6.School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China
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摘要：

城市植被是城市环境的重要组成部分，城市植被遥感分类是对城市绿度空间监测分析的重要方式。本文通过梳理国内外城市植被遥感分类研究进展，从遥感数据源和分类方法入手，分析该领域目前面临的问题及发展趋势，以期为城市绿度空间研究提供参考。首先，概述了光学数据、激光雷达数据及地面传感数据等数据源在城市植被遥感分类领域的应用，对不同数据源的优势与不足进行了深入分析；其次，基于阈值分割、机器学习和深度学习3种分类方法的研究，总结了应用于城市植被遥感分类领域各方法的特点；最后，提出了城市植被遥感分类研究中现存问题和未来发展方向。
关键词： 城市植被;  城市遥感;  图像分类    
Abstract:

Urban vegetation is an important part of the urban environment, and remote sensing classification of urban vegetation is an important way to monitor and analyze urban green space. By sorting the research progress of remote sensing classification of urban vegetation at home and abroad, we started from two aspects of remote sensing data sources and classification methods, and analyzed the current problems and development trends in this field, in order to provide references for urban green space research. First, the applications of optical data, light detection and ranging (LiDAR) data and ground sensing data in the remote sensing classification of urban vegetation were summarized, and the advantages and disadvantages of different data sources were analyzed in depth. Second, the characteristics of classification methods applied in the remote sensing classification of urban vegetation were summarized through the study of three classification methods, including threshold segmentation, machine learning, and deep learning. Finally, the existing problems and future development directions in the remote sensing classification of urban vegetation were proposed.
Key words: urban vegetation    urban remote sensing    image classification
收稿日期: 2023-10-07 出版日期: 2024-04-30
CLC:  P237  
基金资助: 海南省自然科学基金项目(423CXTD390);国家自然科学基金面上项目(42171357);风云三号03批气象卫星工程地面应用系统生态监测评估应用项目（第一期）(ZQC-R22227);中国科学院青年创新促进会项目(2023139)
通讯作者: 张琳琳     E-mail: mengqy@radi.ac.cn;zhangll@aircas.ac.cn
作者简介: 孟庆岩（https://orcid.org/0000-0002-5440-4081），E-mail：mengqy@radi.ac.cn
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引用本文:

孟庆岩,杜弘宇,王莉萍,张琳琳,吴嘉豪,康佳琦. 城市植被遥感分类研究进展与展望[J]. 浙江大学学报（农业与生命科学版）, 2024, 50(2): 190-199.

Qingyan MENG,Hongyu DU,Liping WANG,Linlin ZHANG,Jiahao WU,Jiaqi KANG. Research progress and prospects of remote sensing classification of urban vegetation. Journal of Zhejiang University (Agriculture and Life Sciences), 2024, 50(2): 190-199.

链接本文:

https://www.zjujournals.com/agr/CN/10.3785/j.issn.1008-9209.2023.10.071    或    https://www.zjujournals.com/agr/CN/Y2024/V50/I2/190

图1  城市植被分类研究年发文量（2001—2023年）
图2  文献第一作者所属国家（A）及文献研究方向（B）

数据源

Data source

优势

Advantage

不足

Disadvantage

主要应用

Main application

光学数据

Optical data

中空间分辨率

（＞10 m）

遥感数据监测时序长空间分辨率低，难以满足要求植被提取

高空间分辨率

（1～10 m）

空间分辨率满足城市植被

提取分类的要求；

空间分辨率越高，纹理特

征的作用越明显

随着空间分辨率的提高，数据

处理的难度也增大

植被提取；植被分类；树冠提取

亚米级分辨率

（＜1 m）

空间分辨率满足精细化分

类的要求

获取成本较高，大多依赖机载

平台，所受限制较多

植被分类；树冠分割

激光雷达数据

LiDAR data

提供树冠形态信息；

减少城市阴影的干扰

获取成本较高；

受城市其他地物影响较大

植被分类；树冠分割

地面传感数据

Ground sensing

data

观测树木垂直结构；

从行人视角感知城市绿地

数据空间覆盖范围有限，局限在

道路周围的绿地；

光谱波段有限，缺乏红外等波段

居民感知；植被分类
表1  城市植被遥感提取分类数据源对比
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