Mapping global impervious surface area and green space within urban environments

Abstract

The mapping of impervious surface area (ISA) and urban green space (UGS) is essential for improving the urban environmental quality toward ecological, livable, and sustainable goals. Currently, accurate ISA and UGS products are lacking in urban areas at the global scale. This study established regression models that estimated the fraction of ISA/UGS in global 30 cities for validation using MODIS NDVI and DMSP/OLS nighttime light imageries. A global dataset of ISA and UGS fraction with a spatial resolution of 250 m×250 m was developed using the regression model, with a mean relative error of 0.19 for its ISA. The results showed the global urban area of 76.29×104 km2, which was primarily distributed in central Europe, eastern Asia, and central and eastern North America. The urban land area in North America, Europe, and Asia was 66.3×104 km2, accounting for 86.91% of the world’s urban area; the urban land area of the top 50 countries accounted for 59.32% of the total urban land area in the world. The global ISA of 45.26×104 km2 was mainly distributed in central and southern North America, eastern Asia, and Europe, as well as coastal regions around the world. The proportion of ISA situated in built-up areas on the continental scale followed the order of Africa (>70%)>South America>Oceania>Asia (>60%)>North America>Europe (>50%), and these areas were mostly in southeastern North America, southwestern Europe, and eastern and western Asia. North America, Europe, and Asia accounted for 89.44% of the world’s total UGS. The cities of developed countries in Europe and North America exposed a dramatic mosaic of ISA and UGS composites in urban construction. Therefore, the proportion of UGS is relatively high in those cities. However, in developing and underdeveloped countries, the proportion of UGS in built-up areas is relatively low, and urban environments need to be improved for livability.

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References

  1. Bierwagen B G, Theobald D M, Pyke C R, Choate A, Groth P, Thomas J V, Morefield P. 2010. National housing and impervious surface scenarios for integrated climate impact assessments. Proc Natl Acad Sci USA, 107: 20887–20892

    Article  Google Scholar 

  2. Cao S S, Hu D Y, Zhao W J, Chen S S, Cheng Q W. 2017. Spatial structure comparison of urban agglomerations between China and USA in a perspective of impervious surface coverage: A case study of Beijing-Tianjin-Hebei and Boswash (in Chinese). Acta Geogr Sin, 72: 1017–1031

    Google Scholar 

  3. Chaudhuri A S, Singh P, Rai S C. 2017. Assessment of impervious surface growth in urban environment through remote sensing estimates. Environ Earth Sci, 76: 541

    Article  Google Scholar 

  4. Chen J, Chen J, Liao A P, Cao X, Chen L J, Chen X H, He C Y, Han G, Peng S, Lu M, Zhang W W, Tong X H, Mills J. 2015. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS-J Photogramm Remote Sens, 103: 7–27

    Article  Google Scholar 

  5. Chen L D, Zhou W Q, Han L J, Sun R H. 2016. Developing key technologies for establishing ecological security patterns at the Beijing-Tianjin-Hebei urban megaregion (in Chinese). Acta Ecol Sin, 36: 7125–7129

    Google Scholar 

  6. Chi W F, Shi W J, Kuang W H. 2015. Spatio-temporal characteristics of intra-urban land cover in the cities of China and USA from 1978 to 2010. J Geogr Sci, 25: 3–18

    Article  Google Scholar 

  7. Creutzig F, Agoston P, Minx J C, Canadell J G, Andrew R M, Quéré C L, Peters G P, Sharifi A, Yamagata Y, Dhakal S. 2016. Urban infrastructure choices structure climate solutions. Nat Clim Change, 6: 1054–1056

    Article  Google Scholar 

  8. Ding Y H. 2018. Impact of climate change and urbanization on extreme rainstorm in China’s megacities (in Chinese). China Flood Drought Manage, 28: 1–2

    Google Scholar 

  9. Elvidge C D, Keith D M, Tuttle B T, Baugh K E. 2010. Spectral identification of lighting type and character. Sensors, 10: 3961–3988

    Article  Google Scholar 

  10. Elvidge C D, Tuttle B T, Sutton P C, Baugh K E, Howard A T, Milesi C, Bhaduri B, Nemani R. 2007. Global distribution and density of constructed impervious surfaces. Sensors, 7: 1962–1979

    Article  Google Scholar 

  11. Fang C L, Yang J Y, Kuang W H. 2017. Basic schemes and suggestions of multi-planning integration in progress of Xiongan New Area planning (in Chinese). Bull Chin Acad Sci, 32: 1192–1198

    Google Scholar 

  12. Georgescu M, Morefield P E, Bierwagen B G, Weaver C P. 2014. Urban adaptation can roll back warming of emerging megapolitan regions. Proc Natl Acad Sci USA, 111: 2909–2914

    Article  Google Scholar 

  13. Georgescu M, Moustaoui M, Mahalov A, Dudhia J. 2013. Summer-time climate impacts of projected megapolitan expansion in Arizona. Nat Clim Change, 3: 37–41

    Article  Google Scholar 

  14. Grimm N B, Faeth S H, Golubiewski N E, Redman C L, Wu J, Bai X, Briggs J M. 2008. Global change and the ecology of cities. Science, 319: 756–760

    Article  Google Scholar 

  15. Homer C H, Fry J A, Barnes C A. 2012. The national land cover database. USGS Fact Sheet, 3020: 1–4

    Google Scholar 

  16. Jones B, O’Neill B C, McDaniel L, McGinnis S, Mearns L O, Tebaldi C. 2015. Future population exposure to US heat extremes. Nat Clim Change, 5: 652–655

    Article  Google Scholar 

  17. 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. 2016a. Remote sensing-based artificial surface cover classification in Asia and spatial pattern analysis. Sci China Earth Sci, 59: 1720–1737

    Article  Google Scholar 

  18. 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: 121–135

    Article  Google Scholar 

  19. Kuang W H, Liu J Y, Dong J W, Chi W F, Zhang C. 2016b. The rapid and massive urban and industrial land expansions in China between 1990 and 2010: A CLUD-based analysis of their trajectories, patterns, and drivers. Landscape Urban Plan, 145: 21–33

    Article  Google Scholar 

  20. 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: 1691–1701

    Article  Google Scholar 

  21. Lee C, Kim K, Lee H. 2018. GIS based optimal impervious surface map generation using various spatial data for urban nonpoint source management. J Environ Manage, 206: 587–601

    Article  Google Scholar 

  22. Li L W, Lu D S, Kuang W H. 2016. Examining urban impervious surface distribution and its dynamic change in Hangzhou metropolis. Remote Sens, 8: 265

    Article  Google Scholar 

  23. Liu X P, Hu G H, Chen Y M, Li X, Xu X C, Li S Y, Pei F S, Wang S J. 2018. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sens Environ, 209: 227–239

    Article  Google Scholar 

  24. Liu Z F, He C Y, Zhou Y Y, Wu J G. 2014. How much of the world’s land has been urbanized, really? A hierarchical framework for avoiding confusion. Landscape Ecol, 29: 763–771

    Article  Google Scholar 

  25. Lu D S, Hetrick S, Moran E. 2011. Impervious surface mapping with Quickbird imagery. Int J Remote Sens, 32: 2519–2533

    Article  Google Scholar 

  26. Lu D S, Tian H Q, Zhou G M. 2008. Regional mapping of human settlements in southeastern China with multisensor remotely sensed data. Remote Sens Environ, 112: 3668–3679

    Article  Google Scholar 

  27. Lu D S, Weng Q H. 2006. Use of impervious surface in urban land-use classification. Remote Sens Environ, 102: 146–160

    Article  Google Scholar 

  28. Pan J H, Li X X, Liu C Y. 2009. Urban impervious surface abundance estimation in Lanzhou City based on remote sensing (in Chinese). J Northwest Nor Univ-Nat Sci, 45: 95–100

    Google Scholar 

  29. 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: 2165–2185

    Article  Google Scholar 

  30. Sanchez Rodriguez R, Ürge-Vorsatz D, Barau A S. 2018. Sustainable development goals and climate change adaptation in cities. Nat Clim Change, 8: 181–183

    Article  Google Scholar 

  31. Schneider A, Friedl M A, Potere D. 2009. A new map of global urban extent from MODIS satellite data. Environ Res Lett, 4: 044003–44011

    Article  Google Scholar 

  32. Schneider A, Friedl M A, Potere D. 2010. Mapping global urban areas using MODIS 500-m data: New methods and datasets based on ‘urban ecoregions’. Remote Sens Environ, 114: 1733–1746

    Article  Google Scholar 

  33. Tigges J, Lakes T, Hostert P. 2013. Urban vegetation classification: Benefits of multitemporal RapidEye satellite data. Remote Sens Environ, 136: 66–75

    Article  Google Scholar 

  34. Ürge-Vorsatz D, Rosenzweig C, Dawson R J, Rodriguez R S, Bai X M, Barau A S, Seto K C, Dhakal S. 2018. Locking in positive climate responses in cities. Nat Clim Change, 8: 174–177

    Article  Google Scholar 

  35. Wang L, Li C C, Ying Q, Cheng X, Wang X Y, Li X Y, Hu Y Y, Liang L, Yu L, Huang H B, Gong P. 2012. China’s urban expansion from 1990 to 2010 determined with satellite remote sensing (in Chinese). Chin Sci Bull, 57: 1388–1399

    Article  Google Scholar 

  36. Wickham J D, Stehman S V, Gass L, Dewitz J, Fry J A, Wade T G. 2013. Accuracy assessment of NLCD 2006 land cover and impervious surface. Remote Sens Environ, 130: 294–304

    Article  Google Scholar 

  37. William S, Cynthia R, Shobhakar D, Debra R, Aliyu S B, Seth S, Ürge-Vorsatz D. 2018. City transformations in a 1.5°C warmer world. Nat Clim Change, 8: 177–181

    Article  Google Scholar 

  38. Wu C S, Murray A T. 2003. Estimating impervious surface distribution by spectral mixture analysis. Remote Sens Environ, 84: 493–505

    Article  Google Scholar 

  39. Wu L Y. 2018. Planning and constructing healthy cities is the key to improve the livability of cities (in Chinese). Chin Sci Bull, 63: 985–985

    Article  Google Scholar 

  40. Yang L M, Huang C G, Homer C G, Wylie B K, Coan M J. 2003. An approach for mapping large-area impervious surfaces: Synergistic use of Landsat-7 ETM+ and high spatial resolution imagery. Canadian J Remote Sens, 29: 230–240

    Article  Google Scholar 

  41. Yu S S, Sun Z X, Guo H D, Zhao X W, Sun L, Wu M F. 2017. Monitoring and analyzing the spatial dynamics and patterns of megacities along the Maritime Silk Road (in Chinese). J Remote Sens, 21: 169–181

    Google Scholar 

  42. Yue W Z, Xu J H, Xu L H. 2006. An analysis on eco-environmental effect of urban land use based on remote sensing images: A case study of urban thermal environment and NDVI (in Chinese). Acta Ecol Sin, 26: 1450–1460

    Google Scholar 

  43. Zhang J, Zhou Y K, Li R Q, Zhou Z J, Zhang L Q, Shi Q D, Pan X L. 2010. Accuracy assessments and uncertainty analysis of spatially explicit modeling for land use/cover change and urbanization: A case in Beijing metropolitan area. Sci China Earth Sci, 53: 173–180

    Article  Google Scholar 

  44. Zhang L, Weng Q H. 2016. Annual dynamics of impervious surface in the Pearl River Delta, China, from 1988 to 2013, using time series Landsat imagery. ISPRS-J Photogramm Remote Sens, 113: 86–96

    Article  Google Scholar 

  45. Zhuo L, Shi Q L, Tao H Y, Zheng J, Li Q P. 2018. An improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data. ISPRS-J Photogramm Remote Sens, 142: 64–77

    Article  Google Scholar 

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Acknowledgements

We appreciate the constructive comments and suggestions from three anonymous reviewers. We also thank Prof. Chen Jun for sharing the GlobeLand30 data and Tao Pan, Tianrong Yang, and Xiaoyong Li for their help with data processing. This work was supported by the Major Projects of the National Natural Science Foundation of China (Grant No. 41590842), the Strategic Priority Research Program of the Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE) (Grant No. XDA20040400) and the National High Technology Research and Development Program of China (Grant No. 2013AA122802).

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Correspondence to Wenhui Kuang.

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Kuang, W. Mapping global impervious surface area and green space within urban environments. Sci. China Earth Sci. 62, 1591–1606 (2019). https://doi.org/10.1007/s11430-018-9342-3

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Keywords

  • Impervious surface area
  • Green space
  • Habitat environment
  • Remote sensing classification
  • Global scale