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https://doi.org/10.5194/essd-2020-107
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-107
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  25 Jun 2020

25 Jun 2020

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A revised version of this preprint is currently under review for the journal ESSD.

A 30-meter resolution dataset of China's urban impervious surface area and green space fractions, 2000–2018

Wenhui Kuang1, Shu Zhang1,2, Xiaoyong Li2,3, and Dengsheng Lu4 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
  • 3State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
  • 4College of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China

Abstract. Urban impervious surface area (UISA) and urban green space (UGS) are two core components of cities for characterizing urban environments. Although several global or national urban land use/cover products such as Globeland30 and FROM-GLC are available, they cannot effectively delineate the complex intra-urban land cover components. Here we proposed a new approach to map fractional UISA and UGS in China using Google Earth Engine (GEE) based on multiple data sources. The first step is to extract the vector boundaries of urban areas from China's Land Use/cover Dataset (CLUD). The UISA was retrieved using the logistic regression from the Landsat-derived annual maximum Normalized Difference Vegetation Index (NDVI). The UGS was developed through linear calibration between reference UGS from high spatial resolution image and the normalized NDVI. Thus, the China's UISA and UGS fraction datasets (CLUD-Urban) at 30-meter resolution are generated from 2000 to 2018. The overall accuracy of national urban areas is over 92 %. The root mean square errors of UISA and UGS fractions are 0.10 and 0.14, respectively. The datasets indicate that total urban area of China was 7.10 ×104 km2 in 2018, with average fractions of 70.70 % for UISA and 26.54 % for UGS. The UISA and UGS increased with unprecedented annual rates of 1,492.63 km2/yr and 400.43 km2/yr during 2000–2018. CLUD-Urban can enhance our understanding of urbanization impacts on ecological and urban dwellers’ environments, and can be used in such applications as urban planning, urban environmental studies and practices. The datasets can be downloaded from https://doi.org/10.5281/zenodo.3778424 (Kuang et al., 2020).

Wenhui Kuang et al.

Wenhui Kuang et al.

Data sets

A 30-meter resolution dataset of impervious surface area and green space fractions of China's cities, 2000-2018 K. Wenhui, Z. Shu, L. Xiaoyong, and L. Dengsheng https://doi.org/10.5281/zenodo.3778424

Wenhui Kuang et al.

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Latest update: 30 Sep 2020
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Short summary
We propose a hierarchical principle on remotely sensed urban land-use/cover change for mapping intra-urban structure/component dynamics. China’s Land Use/cover Dataset (CLUD) is updated, delineating the imperviousness and green surface conditions in cities from 2000 to 2018. The newly developed datasets can be used to enhance our understanding of urbanization impacts on ecological and regional climatic conditions and urban dwellers’ environments.
We propose a hierarchical principle on remotely sensed urban land-use/cover change for mapping...
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