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Remote sensing-based artificial surface cover classification in Asia and spatial pattern analysis

  • Research Paper
  • Special Topic: GlobeLand30 remote sensing mapping innovation and large data analysis
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Abstract

Artificial surfaces, characterized with intensive land-use changes and complex landscape structures, are important indicators of human impacts on terrestrial ecosystems. Without high-resolution land-cover data at continental scale, it is hard to evaluate the impacts of urbanization on regional climate, ecosystem processes and global environment. This study constructed a hierarchical classification system for artificial surfaces, promoted a remote sensing method to retrieve subpixel components of artificial surfaces from 30-m resolution satellite imageries (GlobeLand30) and developed a series of data products of high-precision urban built-up areas including impervious surface and vegetation cover in Asia in 2010. Our assessment, based on multisource data and expert knowledge, showed that the overall accuracy of classification was 90.79%. The mean relative error for the impervious surface components of cities was 0.87. The local error of the extracted information was closely related to the heterogeneity of urban buildings and vegetation in different climate zones. According to our results, the urban built-up area was 18.18×104 km2, accounting for 0.59% of the total land surface areas in Asia; urban impervious surfaces were 11.65×104 km2, accounting for 64.09% of the total urban built-up area in Asia. Vegetation and bare soils accounted for 34.56% of the urban built-up areas. There were three gradients: a concentrated distribution, a scattered distribution and an indeterminate distribution from east to west in terms of spatial pattern of urban impervious surfaces. China, India and Japan ranked as the top three countries with the largest impervious surface areas, which respectively accounted for 32.77%, 16.10% and 11.93% of the urban impervious surface area of Asia. We found the proportions of impervious surface and vegetation cover within urban built-up areas were closely related to the economic development degree of the country and regional climate environment. Built-up areas in developed countries had relatively low impervious surface and high public green vegetation cover, with 50–60% urban impervious surfaces in Japan, South Korea and Singapore. In comparison, the proportion of urban impervious surfaces in developing countries is approaching or exceeding 80% in Asia. In general, the composition and spatial patterns of built-up areas reflected population aggregation and economic development level as well as their impacts on the health of the environment in the sub-watershed.

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Authors and Affiliations

  1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    WenHui Kuang, JiYuan Liu, WenFeng Chi, TianRong Yang & AiLin Liu

  2. National Geomatics Center of China, Beijing, 100830, China

    LiJun Chen

  3. School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China

    WeiNing Xiang

  4. University of North Carolina at Charlotte, NC, 28223, USA

    WeiNing Xiang

  5. School of Environmental & Resource Sciences, Zhejiang A&F University, Hangzhou, 311300, China

    DengSheng Lu

  6. Michigan State University, East Lansing, MI, 48823, USA

    DengSheng Lu

  7. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China

    Tao Pan

  8. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China

    TianRong Yang, Tao Pan & AiLin Liu

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  1. WenHui Kuang
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  2. LiJun Chen
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  8. Tao Pan
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  9. AiLin Liu
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Correspondence to WenHui Kuang.

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Kuang, W., Chen, L., Liu, J. et al. Remote sensing-based artificial surface cover classification in Asia and spatial pattern analysis. Sci. China Earth Sci. 59, 1720–1737 (2016). https://doi.org/10.1007/s11430-016-5295-7

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  • DOI: https://doi.org/10.1007/s11430-016-5295-7

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