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High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015


High-resolution global maps of annual urban land coverage provide fundamental information of global environmental change and contribute to applications related to climate mitigation and urban planning for sustainable development. Here we map global annual urban dynamics from 1985 to 2015 at a 30 m resolution using numerous surface reflectance data from Landsat satellites. We find that global urban extent has expanded by 9,687 km2 per year. This rate is four times greater than previous reputable estimates from worldwide individual cities, suggesting an unprecedented rate of global urbanization. The rate of urban expansion is notably faster than that of population growth, indicating that the urban land area already exceeds what is needed to sustain population growth. Looking ahead, using these maps in conjunction with integrated assessment models can facilitate greater understanding of the complex environmental impacts of urbanization and help urban planners avoid natural hazards; for example, by limiting new development in flood risk zones.

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Fig. 1: Urban area dynamics during 1985–2015 at the global and continent scales.
Fig. 2: The trend of urban expansion at different scales.
Fig. 3: Urban expansion and green recovery.
Fig. 4: Impacts of urban expansion on land use/cover system.

Data availability

Satellite-derived high-resolution global urban maps from 1985 to 2015, the validation samples used in this study, are freely available at Other ancillary datasets are available on request from X. Liu (

Code availability

The script used for preprocessing the Landsat time series data in GEE (a cloud-based computational platform) is freely available at Analysis scripts are available on request from X. Liu (


  1. IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (SR2) (WPO and UNEP, 2017).

  2. Seto, K. C. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) 927–1000 (Cambridge Univ. Press, 2014).

  3. World Urbanization Prospects: The 2018 Revision (UN, 2019).

  4. Sexton, J. O. et al. Urban growth of the Washington, D.C.–Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover. Remote Sens. Environ. 129, 42–53 (2013).

    Article  Google Scholar 

  5. Seto, K. C. et al. Urban land teleconnections and sustainability. Proc. Natl Acad. Sci. USA 109, 7687–7692 (2012).

    CAS  Article  Google Scholar 

  6. Pesaresi, M. et al. GHS Built-up Grid, Derived from Landsat, Multitemporal (1975, 1990, 2000, 2014) (European Commission, 2015).

  7. Liu, X. et al. 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 (2018).

    Article  Google Scholar 

  8. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    CAS  Article  Google Scholar 

  9. Zhou, L. et al. Evidence for a significant urbanization effect on climate in China. Proc. Natl Acad. Sci. USA 101, 9540–9544 (2004).

    CAS  Article  Google Scholar 

  10. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  11. Kiswanto, Tsuyuki, S., Mardiany & Sumaryono. Completing yearly land cover maps for accurately describing annual changes of tropical landscapes. Glob. Ecol. Conserv. 13, e00384 (2018)..

  12. Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).

    CAS  Article  Google Scholar 

  13. Stokes, E. C. & Seto, K. Characterizing and measuring urban landscapes for sustainability. Environ. Res. Lett. 14, 045002 (2018).

    Article  Google Scholar 

  14. Zhang, W., Villarini, G., Vecchi, G. A. & Smith, J. A. Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston. Nature 563, 384–388 (2018).

    CAS  Article  Google Scholar 

  15. Reducing Disaster Risk by Managing Urban Land Use: Guidance Notes for Planners (Asian Development Bank, 2016).

  16. Gong, P. et al. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sens. Environ. 236, 111510 (2020).

    Article  Google Scholar 

  17. Zhou, Y., Li, X., Asrar, G. R., Smith, S. J. & Imhoff, M. A global record of annual urban dynamics (1992–2013) from nighttime lights. Remote Sens. Environ. 219, 206–220 (2018).

    Article  Google Scholar 

  18. Seto, K. C., Fragkias, M., Güneralp, B. & Reilly, M. K. A meta-analysis of global urban land expansion. PloS ONE 6, e23777 (2011).

    CAS  Article  Google Scholar 

  19. Pesaresi, M. et al. A global human settlement layer from optical HR/VHR RS data: concept and first results. IEEE J Sel. Top. Appl. Earth Obs. Remote Sens. 6, 2102–2131 (2013).

    Article  Google Scholar 

  20. Esch, T. et al. Urban footprint processor—Fully automated processing chain generating settlement masks from global data of the TanDEM-X mission. IEEE Geosci. Remote Sens. Lett. 10, 1617–1621 (2013).

    Article  Google Scholar 

  21. Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).

    Article  Google Scholar 

  22. Taubenböck, H. et al. A new ranking of the world’s largest cities—do administrative units obscure morphological realities? Remote Sens. Environ. 232, 111353 (2019).

    Article  Google Scholar 

  23. Homer, C. G. et al. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogramm. Eng. Remote Sens. 81, 345–354 (2015).

    Google Scholar 

  24. Seto, K. C. & Ramankutty, N. Hidden linkages between urbanization and food systems. Science 352, 943–945 (2016).

    CAS  Article  Google Scholar 

  25. McDonald, R. I. et al. Water on an urban planet: urbanization and the reach of urban water infrastructure. Glob. Environ. Change 27, 96–105 (2014).

    Article  Google Scholar 

  26. Fu, Y. C. et al. Characterizing the spatial pattern of annual urban growth by using time series Landsat imagery. Sci. Total Environ. 666, 274–284 (2019).

    CAS  Article  Google Scholar 

  27. d’Amour, C. B. et al. Future urban land expansion and implications for global croplands. Proc. Natl Acad. Sci. USA 114, 8939–8944 (2017).

    Article  Google Scholar 

  28. Wu, Y., Shan, L., Guo, Z. & Peng, Y. Cultivated land protection policies in China facing 2030: dynamic balance system versus basic farmland zoning. Habitat Int. 69, 126–138 (2017).

    Article  Google Scholar 

  29. Stokes, E. C. & Seto, K. C. Principles for minimizing global land impacts of urbanization. Technol. Archit. Des. 3, 5–10 (2019).

    Google Scholar 

  30. Nangini, C. et al. A global dataset of CO2 emissions and ancillary data related to emissions for 343 cities. Sci. Data 6, 180280 (2019).

    CAS  Article  Google Scholar 

  31. Xi, F. et al. Substantial global carbon uptake by cement carbonation. Nat. Geosci. 9, 880–883 (2016).

    CAS  Article  Google Scholar 

  32. Zhang, G. J., Cai, M. & Hu, A. Energy consumption and the unexplained winter warming over northern Asia and North America. Nat. Clim. Change 3, 466–470 (2013).

    Article  Google Scholar 

  33. Li, X. et al. A dataset of 30-meter annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States. Earth Syst. Sci. Data 12, 357–371 (2019).

    Article  Google Scholar 

  34. Doxsey-Whitfield, E. et al. Taking advantage of the improved availability of census data: a first look at the gridded population of the world, version 4. Pap. Appl. Geogr. 1, 226–234 (2015).

    Article  Google Scholar 

  35. Bontemps, S. et al. Consistent global land cover maps for climate modelling communities: current achievements of the ESA’s land cover CCI. In Proc. ESA Living Planet Symposium 9–13 (ESA, 2013).

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This research was funded by the National Key R&D Program of China (grant no. 2017YFA0604404 and grant no. 2019YFA0607203). Z.Z. was supported by the start-up fund provided by Southern University of Science and Technology (grant no. G02296001). We thank the French ANR Convergence Institute CLAND project for support. We thank many students (for example, Z. Lin) for their days and nights validating our GAUD product via high-resolution satellite image interpretation. We also thank K. C. Seto and M. Hansen for their constructive comments on this paper.

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Authors and Affiliations



X. Liu, Xia Li and Z.Z. designed the research; Y.H. and X.X. performed analysis; X. Liu, Xuecao Li and Z.Z. wrote the draft; and all the authors contributed to the interpretation of the results and the writing of the paper.

Corresponding authors

Correspondence to Xia Li or Zhenzhong Zeng.

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The authors declare no competing interests.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The box-plot of the kappa coefficient for fifteen urban ecoregions in 1985 (a) and 2015 (b).

GHSL: global human settlement layer; GAIA: Global Artificial Impervious Areas; GUL: global urban land; GUF: global urban footprint; GAUD: global annual urban dynamics. Note: kappa with negative value suggests the urban product has notable overestimation or underestimation in particular ecoregions.

Extended Data Fig. 2 Overall accuracy of mapped urban land expansion years during periods of 1985–2000 and 2000–2015.

Cities used for validation were randomly selected globally with different sizes, biomes, and climate zones.

Extended Data Fig. 3 Detected urban land expansion years compared with the Landsat-derived time series data for six representative cities.

a, Shanghai (China), b, Chicago (USA), c, Tianjin (China), d, Paris (France), e, Moscow (Russia), and f, Bangkok (Thailand).

Extended Data Fig. 4 Detected green recovery years compared to the Landsat derived time series data for four representative cities.

a, Beijing (China), b, New York (USA), c, Tokyo (Japan), d, Baltimore (USA).

Extended Data Fig. 5 Comparison of growth rates between urban extent and population.

Left: ratio of growth rates between urban area and population. Right: scatter plot of total growth rate versus urban growth rate for each country.

Extended Data Fig. 6 Trend of urban expansion in China, India, and USA regarding the normalized urban area relative to 2015.

Grey lines represent each urban cluster while colored lines represent China, India or the USA.

Supplementary information

Supplementary Information

Supplementary Methods 1–3, Figs. 1–15 and Tables 1 and 2.

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Liu, X., Huang, Y., Xu, X. et al. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nat Sustain 3, 564–570 (2020).

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