Science China Earth Sciences

, Volume 60, Issue 6, pp 1098–1109 | Cite as

An EcoCity model for regulating urban land cover structure and thermal environment: Taking Beijing as an example

  • WenHui Kuang
  • TianRong Yang
  • AiLin Liu
  • Chi Zhang
  • DengSheng Lu
  • WenFeng Chi
Research Paper


Urban land-use/cover changes and their effects on the eco-environment have long been an active research topic in the urbanization field. However, the characteristics of urban inner spatial heterogeneity and its quantitative relationship with thermal environment are still poorly understood, resulting in ineffective application in urban ecological planning and management. Through the integration of “spatial structure theory” in urban geography and “surface energy balance” in urban climatology, we proposed a new concept of urban surface structure and thermal environment regulation to reveal the mechanism between urban spatial structure and surface thermal environment. We developed the EcoCity model for regulating urban land cover structure and thermal environment, and established the eco-regulation thresholds of urban surface thermal environments. Based on the comprehensive analysis of experimental observation, remotely sensed and meteorological data, we examined the spatial patterns of urban habitation, industrial, infrastructure service, and ecological spaces. We examined the impacts of internal land-cover components (e.g., urban impervious surfaces, greenness, and water) on surface radiation and heat flux. This research indicated that difference of thermal environments among urban functional areas is closely related to the proportions of the land-cover components. The highly dense impervious surface areas in commercial and residential zones significantly increased land surface temperature through increasing sensible heat flux, while greenness and water decrease land surface temperature through increasing latent heat flux. We also found that different functional zones due to various proportions of green spaces have various heat dissipation roles and ecological thresholds. Urban greening projects in highly dense impervious surfaces areas such as commercial, transportation, and residential zones are especially effective in promoting latent heat dissipation efficiency of vegetation, leading to strongly cooling effect of unit vegetation coverage. This research indicates that the EcoCity model provides the fundamentals to understand the coupled mechanism between urban land use structure and surface flux and the analysis of their spatiotemporal characteristics. This model provides a general computational model system for defining urban heat island mitigation, the greening ratio indexes, and their regulating thresholds for different functional zones.


Urban land-use/cover Urban impervious surface Ecological regulation Thermal environment Remote sensing 


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This work was financially supported by the Major Projects of the National Natural Science Foundation of China (Grant No. 41590842) and General Program of the National Natural Science Foundation of China (Grant No. 41371408).


  1. Alberti M. 2010. Maintaining ecological integrity and sustaining ecosystem function in urban areas. Curr Opin Environ Sustain, 2: 178–184CrossRefGoogle Scholar
  2. Anderson J W. 1976. Selection of trees for endurance of high temperatures and artificial lights in urban areas. USDA Forest Serv Gen Tech Rep, 22: 67–75Google Scholar
  3. Goldbach A, Kuttler W. 2013. Quantification of turbulent heat fluxes for adaptation strategies within urban planning. Int J Climatol, 33: 143–159CrossRefGoogle Scholar
  4. Grimm N B, Faeth S H, Golubiewski N E, Redman C L, Wu J G, Bai X M, Briggs J M. 2008. Global change and the ecology of cities. Science, 319: 756–760CrossRefGoogle Scholar
  5. Grimmond C S B, Blackett M, Best M J, Barlow J, Baik J J, Belcher S E, Bohnenstengel S I, Calmet I, Chen F, Dandou A, Fortuniak K, Gouvea M L, Hamdi R, Hendry M, Kawai T, Kawamoto Y, Kondo H, Krayenhoff E S, Lee S H, Loridan T, Martilli A, Masson V, Miao S, Oleson K, Pigeon G, Porson A, Ryu Y H, Salamanca F, Shashua-Bar L, Steeneveld G J, Tombrou M, Voogt J, Young D, Zhang N. 2010. The international urban energy balance models comparison project: First results from phase 1. J Appl Meteorol Climatol, 49: 1268–1292CrossRefGoogle Scholar
  6. Heisler G M, Brazel A J. 2010. The urban physical environment: Temperature and urban heat island. In: Aitkenhead-Peterson J, Volder A, eds. Urban Ecosystem Ecology. Madison: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America. 29–56Google Scholar
  7. Hu X F, Weng Q H. 2011. Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method. Geocarto Int, 26: 3–20CrossRefGoogle Scholar
  8. Jones P D, Groisman P Y, Coughlan M, Plummer N, Wang W C, Karl T R. 1990. Assessment of urbanization effects in time series of surface air temperature over land. Nature, 347: 169–172CrossRefGoogle Scholar
  9. Kuang W H. 2011. Simulating dynamic urban expansion at regional scale in Beijing-Tianjin-Tangshan metropolitan area. J Geogr Sci, 21: 317–330CrossRefGoogle Scholar
  10. Kuang W H. 2015. Remote Sensing-Based Analysis of Thermal Environment and Ecological Regulations in Cities. Beijing: Science PressGoogle Scholar
  11. Kuang W H, Chen L J, Liu J Y, Xiang W N, Chi W F, Lu D S, Yang T R, Pan T, Liu A L. 2016. Remote sensing-based artificial surface cover classification in Asia and spatial pattern analysis. Sci China Earth Sci, 59: 1720–1737CrossRefGoogle Scholar
  12. Kuang W H, Chi W F, Lu D S, Dou Y Y. 2014. A comparative analysis of megacity expansions in China and the U.S.: Patterns, rates and driving forces. Landscape Urban Plan, 132: 121–135CrossRefGoogle Scholar
  13. Kuang W H, Dou Y Y, Zhang C, Chi W F, Liu A L, Liu Y, Zhang R H, Liu J Y. 2015. Quantifying the heat flux regulation of metropolitan land use/land cover components by coupling remote sensing modeling with in situ measurement. J Geophys Res-Atmos, 120: 113–130CrossRefGoogle Scholar
  14. Kuang W H, Liu J Y, Zhang Z X, Lu D S, Xiang B. 2013. Spatiotemporal dynamics of impervious surface areas across China during the early 21st century. Chin Sci Bull, 58: 1691–1701CrossRefGoogle Scholar
  15. Kuang W H, Zhang S W, Liu J Y, Shao Q Q. 2010. Methodology for classifying and detecting intra-urban land use change: A case study of Changchun city during the last 100 years (in Chinese). J Remote Sens, 14: 345–355Google Scholar
  16. Li Q, Gu C L. 2015. Study on dynamic Geo-simulation of urban public safety and its emergency response (in Chinese). Sci Sin Terr, 45: 290–304Google Scholar
  17. Liu J Y, Zhang Z X, Xu X L, Kuang W H, Zhou W C, Zhang S W, Li R D, Yan C Z, Yu D S, Wu S X, Jiang N. 2010. Spatial patterns and driving forces of land use change in China during the early 21st century. J Geogr Sci, 20: 483–494CrossRefGoogle Scholar
  18. McGranahan G, Balk D, Anderson B. 2007. The rising tide: Assessing the risks of climate change and human settlements in low elevation coastal zones. Environ Urban, 19: 17–37CrossRefGoogle Scholar
  19. Millennium Ecosystem Assessment. 2003. Ecosystems and Human Wellbeing: A Framework for Assessment. Washington: Island Press. 38Google Scholar
  20. Mooney H A, Duraiappah A, Larigauderie A. 2013. Evolution of natural and social science interactions in global change research programs. Proc Natl Acad Sci USA, 110(Suppl): 3653–3656CrossRefGoogle Scholar
  21. Oke T R. 1984. Methods in urban climatology. Appl Climatol, 14: 19–29Google Scholar
  22. Oke T R. 2006. Towards better scientific communication in urban climate. Theor Appl Climatol, 84: 179–190CrossRefGoogle Scholar
  23. Oke T R, Crowther J M, McNaughton K G, Monteith J L, Gardiner B. 1989. The micrometeorology of the urban forest [and discussion]. Philos Trans R Soc B-Biol Sci, 324: 335–349CrossRefGoogle Scholar
  24. Pickett S T A, Cadenasso M L, Grove J M, Nilon C H, Pouyat R V, Zipperer W C, Costanza R. 2001. Urban ecological systems: Linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas. Annu Rev Ecol Syst, 32: 127–157CrossRefGoogle Scholar
  25. Pielke R A, Adegoke J, BeltraáN-Przekurat A, Hiemstra C A, Lin J, Nair U S, Niyogi D, Nobis T E. 2007. An overview of regional land-use and landcover impacts on rainfall. Tellus B-Chem Phys Meteorol, 59: 587–601CrossRefGoogle Scholar
  26. Ridd M K. 1995. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities. Int J Remote Sens, 16: 2165–2185CrossRefGoogle Scholar
  27. Seto K C, Güneralp B, Hutyra L R. 2012. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc Natl Acad Sci USA, 109: 16083–16088CrossRefGoogle Scholar
  28. Wu J G. 1999. Hierarchy and scaling: Extrapolating information along a scaling ladder. Can J Remote Sens, 25: 367–380CrossRefGoogle Scholar
  29. Wu J G, Jenerette G D, David J L. 2003. Linking land-use change with ecosystem processes: A hierarchical patch dynamic model. In: Guhathakurta S, ed. Integrated Land Use and Environmental Models. Berlin: Springer. 99–119CrossRefGoogle Scholar
  30. Zhang R H, Sun X M, Wang W M, Xu J P, Zhu Z L, Tian J. 2005. An operational two-layer remote sensing model to estimate surface flux in regional scale: Physical background. Sci China Ser D-Earth Sci, 48(Suppl): 225–244Google Scholar
  31. Zhou S Z, Zhang C. 1985. City Climate Introduction (in Chinese). Shanghai: East China Normal University PressGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • WenHui Kuang
    • 1
  • TianRong Yang
    • 1
    • 2
  • AiLin Liu
    • 1
    • 2
  • Chi Zhang
    • 3
  • DengSheng Lu
    • 4
    • 5
  • WenFeng Chi
    • 6
  1. 1.Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqiChina
  4. 4.Zhejiang A&F UniversityHangzhouChina
  5. 5.Michigan State UniversityEast LansingUSA
  6. 6.College of Desert Control Science and EngineeringInner Mongolia Agricultural UniversityHohhotChina

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