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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (1) : 54-62     DOI: 10.6046/gtzyyg.2020069
A comparative study of methods for monitoring and assessing urban green space resources at multiple scales
XIONG Yujiu1,2(), ZHAO Shaohua3, YAN Chunhua4, QIU Gouyu4, SUN Hua5,6,7, WANG Yanlin8, QIN Longjun4()
1. School of Civil Engineering, Sun Yat-Sen University, Guangzhou 510275, China
2. Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Guangzhou 510275, China
3. Satellite Environment Center, Ministry of Ecology and Environment/State Environmental Protection Key Laboratory of Satellite Remote Sensing, Beijing 100094, China
4. School of Environment and Energy, Peking University Shenzhen Graduate School, Peking University, Shenzhen 518055, China
5. Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
6. Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China
7. Key Laboratory of State Forestry & Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China
8. Guangzhou Municipal Planning and Natural Resources Bureau, Guangzhou 510000, China
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Urban green spaces are important ecological resources in cities; therefore, quantitative assessment of these green space resources as well as establishment of monitoring system at multiple scales is urgently required for assisting natural resources management and eco-city construction. The objectives of this study are to summarize major methods used to assess and monitor two typical urban green space resources, i.e., vegetation and water bodies, in terms of quantity, quality, and ecosystem service value, and to discuss advantage and disadvantage of these methods. Some results have been obtained: ① Although traditional sampling methods can obtain quantitative information for urban vegetation, fragmentation and patch of urban vegetation has limited scaling such information to larger scales; ② Satellite remote sensing (RS), which can provide information such as spatial distribution, area, vegetation classification, and water quality, is an effective method to assess and monitor urban green spaces; nonetheless, detailed information, such as biomass and water volume, requires high spatial resolution (e.g., < 5 m) RS data as well as corresponding methods to process the data; ③ Unmanned aerial vehicle (UAV) can provide land surface information at high spatial resolution (e.g., < 5 cm); however, UAV has limitations, such as limited data coverage and challenged data processing; ④ Lots of studies focus on the relationship between urban green spaces and urban heat islands, but the mechanism, i.e., how much energy is consumed by evapotranspiration and its impact on cooling effect, is less focused, which is likely due to a relatively low spatial resolution of available thermal infrared RS data. In summary, there are still lots of challenges in assessing and monitoring nature resources, including urban green spaces.

Keywords natural resources      urban green space      assessment and monitoring      remote sensing      unmanned aerial vehicle (UAV)     
ZTFLH:  TP79  
Corresponding Authors: QIN Longjun     E-mail:;qinlongjun@sz,
Issue Date: 18 March 2021
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Shaohua ZHAO
Chunhua YAN
Gouyu QIU
Yanlin WANG
Longjun QIN
Cite this article:   
Yujiu XIONG,Shaohua ZHAO,Chunhua YAN, et al. A comparative study of methods for monitoring and assessing urban green space resources at multiple scales[J]. Remote Sensing for Land & Resources, 2021, 33(1): 54-62.
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Fig.1  Different methods used to assess and monitor urban green space resources
Fig.2  Examples showing assessment and monitoring of urban trees based on three-dimensional laser scanning
传感器 卫星平台 幅宽/
TM Landsat(美国) 185 120 16 d
ETM+ 60
OLI 100
ASTER Terra(美国) 60 90 9~16 d
IRMSS CBERS-01/02(中国) 120 156 26 d
IRS HJ-1B(中国) 720 300 4 d
VIIRS NPP(美国) 3 000 400 4 h
MODIS Terra/Aqua(美国) 2 330 1 000 1 d
AVHRR NOAA(美国) 2 800 1 100 0.5 d
SEVIRI MSG(欧盟) 全球 3 000 15 min
SVISSR FY-2(中国) 全球 5 000 1 h
Tab.1  Typical thermal infrared remote sensing data
Fig.3  Distribution of average land surface temperature based on ASTER product (AG100v003) for Haizhu District, Guangzhou City during 2000—2008
Fig.4  A mobile traverse observation method to study the relationship between air temperature and different types of urban green spaces
Fig.5  An unmanned aerial vehicle and thermal infrared based method to study the mechanical impact of ET on LST
Fig.6  Examples showing chlorophyll-a concentration (mg/L) monitoring at Dajingshan Reservoir, Zhuhai City, Guangdong, based on Landsat TM/ETM+ series datasets
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