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Discussion papers
https://doi.org/10.5194/essd-2019-65
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-65
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 25 Jul 2019

Submitted as: data description paper | 25 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

A 30-meter resolution national urban land-cover dataset of China, 2000–2015

Wenhui Kuang1, Shu Zhang1,2, Xiaoyong Li1,2, and Dengsheng Lu3 Wenhui Kuang et al.
  • 1Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 10049, China
  • 3College of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China

Abstract. Accurate urban land-cover datasets are essential for mapping urban environments. However, a series of national urban land-cover data covering more than 15 years that characterizes urban environments is relatively rare. Here we propose a hierarchical principle on remotely sensed urban land-use/cover classification for mapping intra-urban structure/component dynamics. China's Land Use/cover Dataset (CLUD) is updated, delineating the imperviousness, green surface, waterbody and bare land conditions in cities. A new data subset called CLUD-Urban is created from 2000 to 2015 at five-year intervals with a medium spatial resolution (30 m). The first step is a prerequisite to extract the vector boundaries covered with urban areas from CLUD. A new method is then proposed using logistic regression between urban impervious surface area (ISA) and the annual maximum Normalized Difference Vegetation Index (NDVI) value retrieved from Landsat images based on a big-data platform with Google Earth Engine. National ISA and urban green space (UGS) fraction datasets for China are generated at 30-meter resolution with five-year intervals from 2000 to 2015. The overall classification accuracy of national urban areas is 92 %. The root mean square error values of ISA and UGS fractions are 0.10 and 0.14, respectively. The datasets indicate that the total urban area of China was 6.28 × 104 km2 in 2015, with average fractions of 70.70 % and 26.54 % for ISA and UGS, respectively. The ISA and UGS increased between 2000 and 2015 with unprecedented annual rates of 1,311.13 km2/yr and 405.30 km2/yr, respectively. CLUD-Urban can be used to enhance our understanding of urbanization impacts on ecological and regional climatic conditions and urban dwellers' environments. CLUD-Urban can be applied in future researches on urban environmental research and practices in the future. The datasets can be downloaded from https://doi.org/10.5281/zenodo.2644932.

Wenhui Kuang et al.
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Wenhui Kuang et al.
Data sets

A national urban land use/cover change dataset (CLUD-Urban) in China, 2000-2015 W. Kuang, S. Zhang, X. Li, and D. Lu https://doi.org/10.5281/zenodo.2644932

Wenhui Kuang et al.
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Short summary
Urban land use/cover dynamics datasets play a vital role in urban planning and management. However, a series of national urban land-cover data covering more than 15 years is relatively rare. Here we developed a new data subset called CLUD-Urban from 2000 to 2015 at five-year intervals with a 30 m resolution. The total urban area of China was 62800 km2 in 2015, with average fractions of 70.70 % and 26.54 % for ISA and UGS, respectively. CLUD-Urban will be useful in urban environment.
Urban land use/cover dynamics datasets play a vital role in urban planning and management....
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