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浙江大学学报（农业与生命科学版）
Journal of Zhejiang University (Agriculture and Life Sciences)  2024, Vol. 50 Issue (2): 190-199    DOI: 10.3785/j.issn.1008-9209.2023.10.071
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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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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     
Received: 07 October 2023      Published: 30 April 2024
CLC:  P237  
Corresponding Authors: Linlin ZHANG     E-mail: mengqy@radi.ac.cn;zhangll@aircas.ac.cn
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Qingyan MENG
Hongyu DU
Liping WANG
Linlin ZHANG
Jiahao WU
Jiaqi KANG
Cite this article:

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.

URL:

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


城市植被遥感分类研究进展与展望

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 
Fig. 1 Numbers of annual published papers of urban vegetation classification research (2001—2023)
Fig. 2 Countries of papers’ first author (A) and research directions of papers (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
Table 1 Comparisons of data sources for urban vegetation remote sensing extraction and classification
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