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

Nature Sustainability volume 3, pages 564–570 (2020)Cite this article

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Abstract

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.

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Data availability

Satellite-derived high-resolution global urban maps from 1985 to 2015, the validation samples used in this study, are freely available at https://doi.org/10.6084/m9.figshare.11513178.v1. Other ancillary datasets are available on request from X. Liu (liuxp3@mail.sysu.edu.cn).

Code availability

The script used for preprocessing the Landsat time series data in GEE (a cloud-based computational platform) is freely available at https://doi.org/10.6084/m9.figshare.11513178.v1. Analysis scripts are available on request from X. Liu (liuxp3@mail.sysu.edu.cn).

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Acknowledgements

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

  1. Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-Sen University, Guangzhou, China

    Xiaoping Liu, Yinghuai Huang, Xiaocong Xu, Yimin Chen & Shaojian Wang

  2. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China

    Xiaoping Liu

  3. Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, USA

    Xuecao Li

  4. Key Lab of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, China

    Xia Li & Guohua Hu

  5. Laboratoire des Sciences du Climat et de l’Environnement, UMR 1572 CEA-CNRS-UVSQ, Gif-sur-Yvette, France

    Philippe Ciais

  6. Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA

    Peirong Lin, Kai Gong & Zhenzhong Zeng

  7. Mae Jo University, Mae Jo, Chiang Mai, Thailand

    Alan D. Ziegler

  8. Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA

    Anping Chen

  9. Ministry of Education Key Laboratory of Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China

    Peng Gong

  10. National Geomatics Center of China, Beijing, China

    Jun Chen

  11. Department of Geography, University of Tennessee, Knoxville, TN, USA

    Qiusheng Wu

  12. Yale School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA

    Kangning Huang

  13. Graduate School of Geography, Clark University, Worcester, MA, USA

    Lyndon Estes

  14. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Zhenzhong Zeng

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  1. Xiaoping Liu
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Contributions

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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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). https://doi.org/10.1038/s41893-020-0521-x

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