A comparative study of methods for monitoring and assessing urban green space resources at multiple scales

doi:10.6046/gtzyyg.2020069

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Abstract

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: xiongyuj@mail.sysu.edu.cn;qinlongjun@sz,pku.edu.cn

Issue Date: 18 March 2021

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	Yujiu XIONG
	Shaohua ZHAO
	Chunhua YAN
	Gouyu QIU
	Hua SUN
	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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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020069 OR https://www.gtzyyg.com/EN/Y2021/V33/I1/54

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

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

[1]	United Nations, Department of Economic and Social Affairs, Population Division. World urbanization prospects 2018:Highlights[EB/OL].[2018-11-27]. https://www.un.org/development/desa/pd/content/world-urbanization-prospects-2018-highlights url: https://www.un.org/development/desa/pd/content/world-urbanization-prospects-2018-highlights.
[2]	Bai X, Shi P, Liu Y. Realizing China’s urban dream[J]. Nature, 2014,509:158-160. pmid: 24812683 url: https://www.ncbi.nlm.nih.gov/pubmed/24812683
[3]	Coumou D, Robinson A. Historic and future increase in the global land area affected by monthly heat extremes[J]. Environmental Research Letters, 2013,8:034018.
[4]	Sun X, Sun Q, Zhou X, et al. Heat wave impact on mortality in Pudong New Area,China in 2013[J]. Science of the Total Environment, 2014,493:789-794.
[5]	张玉林. 中国的城市化与生态环境问题——“2018中国人文社会科学环境论坛”研讨述评[J]. 南京工业大学学报(社会科学版), 2019,18(1):1-10.
[5]	Zhang Y L. Urbanization in China and its eco-environmental effects:a review on the discussion of “2018 Chinese humanities and social sciences environment forum”[J]. Journal of Nanjing Tech University (Social Science Edition), 2019,18(1):1-10.
[6]	Haase D, Larondelle N, Andersson E, et al. A quantitative review of urban ecosystem service assessments:Concepts,models,and implementation[J]. AMBIO(43):413-433.
[7]	张云路, 李雄. 基于供给侧的城市绿地系统规划新思考[J]. 中国城市林业, 2017,15(1):1-4.
[7]	Zhang Y L, Li X. New thought on urban green space system planning based on the perspective of supply side[J]. Journal of Chinese Urban Forestry, 2017,15(1):1-4.
[8]	韩依纹, 戴菲. 城市绿色空间的生态系统服务功能研究进展:指标、方法与评估框架[J]. 中国园林, 2018,34(10):55-60.
[8]	Han Y W, Dai F. Review of study on ecosystem services function of urban green spaces:Indicators,methods and assessment framework[J]. Chinese Landscape Architecture, 2018,34(10):55-60.
[9]	李鑫, 马晓冬, 薛小同, 等. 城市绿地空间供需评价与布局优化——以徐州中心城区为例[J]. 地理科学, 2019,39(11):1771-1779.
[9]	Li X, Ma X D, Xue X T, et al. Spatial supply-demand evaluation and layout optimization for urban green space:A case study of Xuzhou central district[J]. Scientia Geographica Sinica, 2019,39(11):1771-1779.
[10]	邱国玉, 张晓楠. 21世纪中国的城市化特点及其生态环境挑战[J]. 地球科学进展, 2019,34(6):640-649.
[10]	Qiu G Y, Zhang X N. China’s urbanization and its ecological environment challenges in the 21st century[J]. Advances in Earth Science, 2019,34(6):640-649.
[11]	赵少华, 刘思含, 刘芹芹, 等. 中国城镇生态环境遥感监测现状及发展趋势[J]. 生态环境学报, 2019,28(6):1261-1271.
[11]	Zhao S H, Liu S H, Liu Q Q, et al. Progress of urban ecological environment monitoring by remote sensing in China[J]. Ecology and Environmental Sciences, 2019,28(6):1261-1271.
[12]	叶远智, 张朝忙, 邓轶, 等. 我国自然资源、自然资源资产监测发展现状及问题分析[J]. 测绘通报, 2019(10):23-29.
[12]	Ye Y Z, Zhang C M, Deng Y, et al. Research on the current situation and problems of natural resources monitoring and natural resources assets monitoring in China[J]. Bulletin of Surveying and Mapping, 2019(10):23-29.
[13]	Grimm N B, Faeth S H, Golubiewski N E, et al. Global change and the ecology of cities[J]. Science, 2008(319):756-760.
[14]	赵娟娟, 欧阳志云, 郑华, 等. 城市植物分层随机抽样调查方案设计的方法探讨[J]. 生态学杂志, 2009,28(7):1430-1436.
[14]	Zhao J J, Ouyang Z Y, Zheng H, et al. Proposed procedure in designing and planning stratified random selection investigation of urban vegetation[J]. Chinese Journal of Ecology, 2009,28(7):1430-1436.
[15]	Yin J, Yang J. Effects of sampling approaches on quantifying urban forest structure[J]. Landscape and Urban Planning, 2020(195):103722.
[16]	王芳, 卓莉, 黎夏, 等. 基于高光谱特征选择和RBFNN的城市植被胁迫程度监测[J]. 地理科学, 2008(1):77-82.
[16]	Wang F, Zhuo L, Li X, et al. Urban vegetation stress level monitoring based on hyperspectral feature selection and RBF neural network[J]. Scientia Geographica Sinica, 2008(1):77-82.
[17]	潘灼坤, 王芳, 夏丽华, 等. 高光谱遥感城市植被胁迫监测研究[J]. 遥感技术与应用, 2012,27(1):68-76.
[17]	Pan Z K, Wang F, Xia L H, et al. Research on urban vegetation stress monitoring by hyperspectral remote sensing[J]. Remote Sensing Technology and Application, 2012,27(1):68-76.
[18]	Scott C T. Sampling methods for estimating change in forest resources[J]. Ecological Applications, 1998,8:228-233.
[19]	史京京, 雷渊才, 赵天忠. 森林资源抽样调查技术方法研究进展[J]. 林业科学研究, 2009,22(1):101-108.
[19]	Shi J J, Lei Y C, Zhao T Z. Progress in sampling technology and methodology in forest inventory[J]. Forest Research, 2009,22(1):101-108.
[20]	He F, Hubbell S. Species-area relationships always overestimate extinction rates from habitat loss[J]. Nature, 2011(473):368-371.
[21]	梁顺林, 白瑞, 陈晓娜, 等. 2019年中国陆表定量遥感发展综述[J]. 遥感学报, 2020,24(6):618-671.
[21]	Liang S L, Bai R, Chen X N, et al. Review of China’s land surface quantitative remote sensing development in 2019[J]. Journal of Remote Sensing, 2020,24(6):618-671.
[22]	Pause M, Schweitzer C, Rosenthal M, et al. In situ/remote sensing integration to assess forest health:A review[J]. Remote Sensing, 2016(8):471.
[23]	Wulder M. Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters[J]. Progress in Physical Geography, 1998,22(4):449-476.
[24]	Mo D, Fuchs H, Fehrmann L, et al. Local parameter estimation of topographic normalization for forest type classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015(12):1998-2002.
[25]	Lausch A, Erasmi S, King D J, et al. Understanding forest health with remote sensing Part I:A review of spectral traits,processes and remote-sensing characteristics[J]. Remote Sensing, 2016(8):1029.
[26]	肖银松. “3S”及抽样技术在森林资源动态监测中的应用[J]. 西南林学院学报, 2004(2):60-64.
[26]	Xiao Y S. Application of “3S” technology and sampling techniques to dynamic monitoring of forest resources[J]. Journal of Southwest Forestry College, 2004(2):60-64.
[27]	李士成, 何凡能, 张学珍. 中国历史时期森林空间格局网格化重建方法研究——以东北地区为例[J]. 地理学报, 2014,69(3):312-322.
[27]	Li S C, He F N, Zhang X Z. An approach of spatially-explicit reconstruction of historical forest in China:A case study in Northeast China[J]. Acta Geographica Sinica, 2014,69(3):312-322.
[28]	姜洋, 李艳. 浙江省森林信息提取及其变化的空间分布[J]. 生态学报, 2014,34(24):7261-7270.
[28]	Jiang Y, Li Y. The extraction of forest information and the spatial distribution of its change in Zhejiang Province[J]. Acta Ecologica Sinica, 2014,34(24):7261-7270.
[29]	Toth C, Jóźków G. Remote sensing platforms and sensors:A survey[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016(115):22-36.
[30]	林辉, 吕勇, 宁晓波. 基于高分辨率卫星图像的立木材积表的编制[J]. 林业科学, 2004,40(4):33-39.
[30]	Lin H, Lyu Y, Ning X B. Compiling the standing volume table of Chinese fir based on the high-resolution satellite image[J]. Scientia Silvae Sinicae, 2004,40(4):33-39.
[31]	张友静, 樊恒通. 城市植被尺度鉴别与分类研究[J]. 地理与地理信息科学, 2007(6):54-57. url: http://d.wanfangdata.com.cn/Periodical_dlxygtyj200706013.aspx
[31]	Zhang Y J, Fan H T. Scale identification for urban vegetation classification using high spatial resolution satellite data[J]. Geography and Geo-Information Science, 2007(6):54-57. url: http://d.wanfangdata.com.cn/Periodical_dlxygtyj200706013.aspx
[32]	陈利, 林辉, 孙华. 基于WorldView-2影像城市绿地信息提取研究[J]. 西北林学院学报, 2014,29(1):155-160.
[32]	Chen L, Lin H, Sun H. WorldView-2 images based urban green space information extraction[J]. Journal of Northwest Forestry university, 2014,29(1):155-160.
[33]	Mora B, Wulder M A, White J C. Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment[J]. Remote Sensing Environment, 2010(114):2474-2484.
[34]	Lim K, Treitz P, Wulder M, et al. LiDAR remote sensing of forest structure[J]. Progress in Physical Geography:Earth and Environment, 2003(27):88-106.
[35]	Sun H, Wang G, Lin H, et al. Retrieval and accuracy assessment of tree and stand parameters for Chinese fir plantation using terrestrial laser scanning[J]. IEEE Geoscience and Remote Sensing Letter, 2015(12):1993-1997.
[36]	Iglhaut J, Cabo C, Puliti S, et al. Structure from motion photogrammetry in forestry:A review[J]. Current Forestry Reports, 2019,5(3):155-168. doi: 10.1007/s40725-019-00094-3 url: https://doi.org/10.1007/s40725-019-00094-3
[37]	Novo A, González-Jorge H, Martínez-Sánchez J, et al. Canopy detection over roads using mobile LiDAR data[J]. International Journal of Remote Sensing, 2020(41):51927-51942.
[38]	郭庆华, 刘瑾, 李玉美, 等. 生物多样性近地面遥感监测:应用现状与前景展望[J]. 生物多样性, 2016,24(11):1249-1266.
[38]	Guo Q H, Liu J, Li Y M, et al. A near-surface remote sensing platform for biodiversity monitoring:Perspectives and prospects[J]. Biodiversity Science, 2016,24(11):1249-1266. doi: 10.17520/biods.2016059 url: <![CDATA[http://www.biodiversity-science.net/EN/10.17520/biods.2016059]]>
[39]	蒋高明. 城市植被:特点、类型与功能[J]. 植物学通报, 1993(3):21-27.
[39]	Jiang G M. Urban vegetation:Its characteristic,type and function[J]. Chinese Bulletin Botany, 1993(3):21-27.
[40]	邱媛, 管东生, 宋巍巍, 等. 惠州城市植被的滞尘效应[J]. 生态学报, 2008(6):2455-2462.
[40]	Qiu Y, Guan D S, Song W W, et al. The dust retention effect of urban vegetation in Huizhou,Guangdong Province[J]. Acat Ecologica Sinica, 2008(6):2455-2462.
[41]	Qiu G Y, Zou Z, Li X, et al. Experimental studies on the effects of green space and evapotranspiration on urban heat island in a subtropical megacity in China[J]. Habitat International, 2017(68):30-42.
[42]	Qiu G Y, Tan S, Wang Y, et al. Characteristics of evapotranspiration of urban lawns in a sub-tropical megacity and its measurement by the “Three Temperature Model + infrared remote sensing” method[J]. Remote Sensing, 2017(9):502.
[43]	Zou Z D, Yang Y J, Qiu G Y. Quantifying the evapotranspiration rate and its cooling effects of urban hedges based on Three-Temperature model and infrared remote sensing[J]. Remote Sensing, 2019(11):202.
[44]	Bowler D E, Buyung-Ali L, Knight T M, et al. Urban greening to cool towns and cities:A systematic review of the empirical evidence[J]. Landscape and Urban Planning, 2010,97(3):147-155.
[45]	Stewart I, Oke T. Local climate zones for urban temperature studies[J]. Bulletin of the American Meteorological Society, 2012,93(12):1879-1900.
[46]	Yang J, Pyrgou A, Chong A, et al. Green and cool roofs’ urban heat island mitigation potential in tropical climate[J]. Solar Energy, 2018(173):597-609.
[47]	Oke T. City size and the urban heat island[J]. Atmospheric Environment, 1973,7(8):769-779.
[48]	Meehl G A, Tebaldi C. More intense,more frequent,and longer lasting heat waves in the 21st century[J]. Science, 2004(305):994-997.
[49]	Guerreiro S, Dawson R, Kilsby C, et al. Future heat-waves,droughts and floods in 571 European cities[J]. Environmental Research Letters, 2018,13(3):034009.
[50]	Oleson K, Anderson G, Jones B, et al. Avoided climate impacts of urban and rural heat and cold waves over the U.S. using large climate model ensembles for RCP8.5 and RCP4.5[J]. Climatic Change, 2018(146):377-392.
[51]	秦大河. 气候变化科学与人类可持续发展[J]. 地理科学进展, 2014,33(7):874-883.
[51]	Qin D H. Climate change science and sustainable development[J]. Progress in Geography, 2014,33(7):874-883.
[52]	Grimmond C, Oke T. An evapotranspiration-interception model for urban areas[J]. Water Resources Research, 1991,27(7):1739-1755.
[53]	Qiu G Y, Yu X H, Wen H Y, et al. An advanced approach for measuring the transpiration rate of individual urban trees by the 3D three-temperature model and thermal infrared remote sensing[J]. Journal of Hydrology, 2020(587):125034.
[54]	Wang K, Dickinson R. A review on global terrestrial evapotranspiration:Observation,modeling,climatology,and climatic variability[J]. Reviews of Geophysics, 2012(50):569.
[55]	Zhang K, Kimball J, Running S. A review of remote sensing based actual evapotranspiration estimation[J]. Wiley Interdisciplinary Reviews-Water, 2016(3):834-853.
[56]	杨大文, 徐宗学, 李哲, 等. 水文学研究进展与展望[J]. 地理科学进展, 2018,37(1):36-45.
[56]	Yang D W, Xu Z X, Li Z, et al. Progress and prospect of hydrological sciences[J]. Progress in Geography, 2018,37(1):36-45.
[57]	陈发虎, 傅伯杰, 夏军, 等. 近70年来中国自然地理与生存环境基础研究的重要进展与展望[J]. 中国科学:地球科学, 2019,49(11):1659-1696.
[57]	Chen F H, Fu B J, Xia J, et al. Major advances in studies of the physical geography and living environment of China during the past 70 years and future prospects[J]. Science China Earth Sciences, 2019,49(11):1659-1696.
[58]	Pataki D McCarthy H Litvak E, et al. Transpiration of urban forests in the Los Angeles metropolitan area[J]. Ecological Applications, 2011(21):661-677.
[59]	王晓娟, 孔繁花, 尹海伟, 等. 高温天气植被蒸腾与遮荫降温效应的变化特征[J]. 生态学报, 2018,38(12):4234-4244.
[59]	Wang X J, Kong F H, Yin H W, et al. Characteristics of vegetation shading and transpiration cooling effects during hot summer[J]. Acat Ecologica Sinica, 2018,38(12):4234-4244.
[60]	Segovia-Cardozo D, Rodríguez-Sinobas L, Zubelzu S. Living green walls:Estimation of water requirements and assessment of irrigation management[J]. Urban Forestry & Urban Greening, 2019(46):126458.
[61]	van de Wouw P, Brouwers H. Precipitation collection and evapo(transpi)ration of living wall systems:A comparative study between a panel system and a planter box system[J]. Building and Environment, 2017(126):221-237.
[62]	Costello L, Matheny N, Clark J, et al. A guide to estimating irrigation water needs of landscape plantings in California[R]. University of California Cooperative Extension,California Department of Water Resources, 2000.
[63]	Azeñas V, Janner I, Medrano H, et al. Performance evaluation of five Mediterranean species to optimize ecosystem services of green roofs under water-limited conditions[J]. Journal of Environmental Management, 2018(212):236-247.
[64]	Rana G, De Lorenzi F, Mazza G, et al. Tree transpiration in a multi-species Mediterranean garden[J]. Agricultural and Forest Meteorology, 2020(280):107767.
[65]	Li Z, Tang B, Wu H, et al. Satellite-derived land surface temperature:Current status and perspectives[J]. Remote Sensing of Environment, 2013(131):14-37. doi: 10.1016/j.rse.2012.12.008 url: http://dx.doi.org/10.1016/j.rse.2012.12.008
[66]	Chen X, Su Y, Li D, et al. Study on the cooling effects of urban parks on surrounding environments using Landsat TM data:A case study in Guangzhou,southern China[J]. International Journal of Remote Sensing, 2012(33):5889-5914. doi: 10.1080/01431161.2012.676743 url: http://dx.doi.org/10.1080/01431161.2012.676743
[67]	Feyisa G, Dons K, Meilby H. Efficiency of parks in mitigating urban heat island effect:An example from Addis Ababa[J]. Landscape and Urban Planning, 2014(123):87-95. doi: 10.1016/j.landurbplan.2013.12.008 url: http://dx.doi.org/10.1016/j.landurbplan.2013.12.008
[68]	Cong Z, Shen Q, Zhou L, et al. Evapotranspiration estimation considering anthropogenic heat based on remote sensing in urban area[J]. Science China Earth Sciences, 2017(60):659-671.
[69]	Jiang Y, Weng Q. Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces[J]. GIScience & Remote Sensing, 2017,54(1):95-117.
[70]	Liu W, Hong Y, Khan S, et al. Actual evapotranspiration estimation for different land use and land cover in urban regions using Landsat 5 data[J]. Journal of Applied Remote Sensing, 2010(4):041873.
[71]	张敏霞, 梅丹英, 高伟俊, 等. 无人机遥感技术在城市绿地监测中的应用进展[J]. 中国城市林业, 2019,17(5):5-11.
[71]	Zhang M X, Mei D Y, Gao W J, et al. Review on the applications of UAV remote sensing technology to urban green space monitoring[J]. Journal of Chinese Urban Forestry, 2019,17(5):5-11.
[72]	Mouw C, Greb S, Aurin D, et al. Aquatic color radiometry remote sensing of coastal and inland waters:Challenges and recommendations for future satellite missions[J]. Remote Sensing of Environment, 2015(160):15-30.
[73]	Xiong Y, Ran Y, Zhao S, et al. Remotely assessing and monitoring coastal and inland water quality in China:Progress,challenges and outlook[J]. Critical Reviews in Environmental Science and Technology, 2019,50(12), 1266-1302.
[74]	中华人民共和国水利部, 中华人民共和国国家统计局. 第一次全国水利普查公报[M]. 北京: 中国水利水电出版社, 2013: 3.
[74]	Ministry of Water Resources of the People’s Republic of China, National Bureau of Statistics of the People’s Republic of China. Bulletin of first National Census for Water[M]. Beijing: China Water and Power Press, 2013: 3.

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