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A strong but uneven increase in urban tree cover in China over the recent decade

Nature Cities volume 2, pages 460–469 (2025)Cite this article

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Abstract

Trees play a crucial role in urban environments, offering various ecosystem services that contribute to public health and human well-being. China has initiated a range of urban greening policies to increase the number of urban trees, but monitoring urban tree dynamics at a national scale has proven challenging. Here, we used high-resolution nanosatellite images to quantify urban tree cover in all major Chinese cities in 2019 and study changes in tree cover between 2010 and 2019. We show that 11.47% of urban areas were covered by trees in 2019, and 76% of the cities experienced an increase in tree cover compared with 2010. Notably, the increase in tree cover in the mega-cities of Shanghai, Beijing, Shenzhen and Guangzhou (6.64%) was higher than that in other cities analyzed. Tree cover increases also vary between urban land use types, with public service (3.09%) and residential areas (1.79%) having the highest values. The study employed a data-driven approach toward assessing urban tree cover changes, showing clear signs of overall increases that nonetheless do not benefit all cities equally.

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Fig. 1: Mapping urban tree canopies in China using PlanetScope imagery from 2019.
Fig. 2: Urban tree cover at city level in 2019.
Fig. 3: Changes in tree cover in urban areas (2010–2019).
Fig. 4: Urban tree cover related to urban land use types in China.

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Data availability

The high-resolution tree canopy and changes in tree cover are available at https://ee-xzrscph.projects.earthengine.app/view/china-urban-tree-change. PlanetScope imagery and RapidEye imagery in urban areas over China are available via Planet Labs at https://www.planet.com/products/ upon acquiring a license agreement. The GlobeLand30 land cover dataset (2010 and 2020) is available at http://www.globallandcover.com/home_en.html. The ESA WorldCover 2020 land cover map is available at https://worldcover2020.esa.int/. Annual maps for the global artificial impervious areas (GAIA) dataset are available at http://data.ess.tsinghua.edu.cn. The essential urban land use categories map in China (EULUC-China) is available at http://data.ess.tsinghua.edu.cn/. VIIRS-DNB nighttime light is available via the Google Earth Engine at https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMCFG. GDP data are accessible from the National Bureau of Statistics of the People’s Republic of China. Population density data from WorldPop in 2019 are available at https://www.worldpop.org. The administrative boundaries in China are available via the national catalog service for geographic information at https://www.ngcc.cn/.

Code availability

The code for the tree canopy detection framework based on U-Net is available via Zenodo at https://doi.org/10.5281/zenodo.3978185 (ref. 65).

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Acknowledgements

X.Z. was funded by the China Scholarship Council (grant no. 201904910835). M.B. was funded by the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme (grant no. 947757 TOFDRY) and DFF Sapere Aude (grant no. 9064–00049B). Xiaowei Tong was funded by the National Natural Science Foundation of China for Excellent Young Scientists (Overseas) and the National Natural Science Foundation of China (42371129). F.T. acknowledges funding from the National Natural Science Foundation of China (grant no. 42001299) and the Seed Fund Program of the Sino–Foreign Joint Scientific Research Platform of Wuhan University (grant no. WHUZZJJ202205). Y.Y. was funded by the International Partnership Program of the Chinese Academy of Sciences (grant no. 092GJHZ2022029GC) and the CAS Interdisciplinary Innovation Team (grant no. JCTD-2021-16). B.C. acknowledges support from the Research Grants Council of Hong Kong (grant nos. HKU27600222 and HKU17601423), the NSFC/RGC Joint Research Scheme (grant no. N_HKU722/23) and the National Key Research and Development Program of China (grant no. 2022YFB3903703). X.X. was supported by the US National Science Foundation (grant nos. 1911955 and 2200310). R.F. acknowledges support from Villum Fonden through the project Deep Learning and Remote Sensing for Unlocking Global Ecosystem Resource Dynamics (DeReEco, grant no. 34306).

Author information

Authors and Affiliations

  1. Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

    Xiaoxin Zhang, Martin Brandt, Xiaoye Tong, Wenmin Zhang, Florian Reiner, Sizhuo Li & Rasmus Fensholt

  2. Future Urbanity and Sustainable Environment (FUSE) Lab, Division of Landscape Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong, China

    Xiaoxin Zhang & Bin Chen

  3. Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China

    Xiaowei Tong & Yuemin Yue

  4. Guangxi Key Laboratory of Karst Ecological Processes and Services, Huanjiang Observation and Research Station of Karst Ecosystem, Chinese Academy of Sciences, Huanjiang, China

    Xiaowei Tong & Yuemin Yue

  5. Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

    Feng Tian

  6. State Key Laboratory for Ecological Security of Regions and Cities, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Weiqi Zhou

  7. Institute for Climate and Carbon Neutrality, Urban Systems Institute, The University of Hong Kong, Hong Kong, China

    Bin Chen

  8. School of Biological Science, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK, USA

    Xiangming Xiao

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  1. Xiaoxin Zhang
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Contributions

X.Z. and M.B. designed the research. X.Z., M.B., Xiaoye Tong and F.R. helped to collect PlanetScope and RapidEye images, and F.R. and S.L. developed the code for the deep learning pipeline. X.Z. prepared the annotation data and conducted the analysis. X.Z., M.B., W. Zhang and R.F. drafted the first manuscript. Xiaowei Tong, F.T., Y.Y., W. Zhou, B.C. and X.X. reviewed the manuscript.

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Correspondence to Xiaoxin Zhang or Martin Brandt.

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Nature Cities thanks Kangning Huang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison of PlanetScope tree canopy mapping with other products.

a, Google Earth satellite images (Google, 2023 Maxar Technologies). b, PlanetScope Image 2019 (RGB: NIR/G/B). Credit: Planet Labs PBC. c, PlanetScope tree canopy mapping 2019. d, Tree canopy from the ESA 2020 Land cover map32. e, Tree canopy based on the Esri land cover map 202059. f, RapidEye image 2019 (RGB: NIR/G/B). Credit: Planet Labs PBC. g, RapidEye tree canopy cover in 2019.

Extended Data Fig. 2 Examples showing changes in tree canopy cover from 2010 to 2019.

a, Change in tree cover in 1 ha grids (2010–2019). b, The prediction of tree canopy cover is based on RapidEye imagery in 2010. c, The prediction of tree canopy cover is based on PlanetScope imagery in 2019. d, Google Earth historical imagery in 2010 (Google, 2024 Maxar Technologies). e, Google Earth historical imagery in 2019 (Google, 2024 Maxar Technologies).

Extended Data Fig. 3 Comparison of PlanetScope and RapidEye in 2019.

a, Google Earth satellite images (Google, 2024 Maxar Technologies). b, PlanetScope Image 2019 (RGB: NIR/G/B). c, PlanetScope tree canopy cover in 2019. d, RapidEye image 2019 (RGB: NIR/G/B). e, RapidEye tree canopy cover in 2019.

Extended Data Fig. 4 Examples of urban greening in various urban land use types.

a, Google Earth satellite images (Google, 2024 Maxar Technologies). b, RapidEye Image 2010 (RGB: NIR/G/B). c, RapidEye image 2019 (RGB: NIR/G/B). d, Areas of increasing and decreasing urban tree canopy cover between 2010 and 2019, with unchanged canopy areas excluded. Credit: a, Google Earth; b,c: Planet Labs PBC.

Extended Data Fig. 5 Cities studied in 2010 (n = 144) and 2019 (n = 242).

a, Spatial distribution of cities studied in 2010 and 2019. b, Mean temperature and annual precipitation of cities analyzed. c, Number of cities in the different geographical zones. d, Urban areas of the analyzed cities. e, Cities grouped by their population size (see Methods).

Extended Data Fig. 6 Comparison between manually labeled areas from the test dataset and the corresponding predictions for 185 patches (the size of each patch is 1 ha).

a, Location of patches for evaluation. Map data retrieved from Google, 2023 Maxar Technologies. b, Examples of patches with labeled tree canopy cover for 2010 (b1) and 2019 (b2) and prediction (b3). c, Comparison between predictions and manual labeling for PlanetScope 2019 tree canopy cover. d, Comparison between predictions and manual labeling for RapidEye 2010 tree canopy cover. e, Comparison of tree canopy cover changes from 2010 to 2019 between model predictions and manual labeling. f, Statistical evaluation metrics for the PlanetScope 2019 tree canopy cover mapping (※: mean value; –: median value). g, Statistical evaluation metrics for the RapidEye 2010 tree canopy cover mapping (※: mean value; –: median value). Basemap in a is from Google Maps Google Earth Satellite Imagery from 2023 (Imagery 2023, Maxar Technologies). In the box plots the lower and upper box limits are the 25th and 75th percentiles, the central line is the median, and the upper (lower) whiskers extend to 1.5 (−1.5) times the interquartile range.

Extended Data Fig. 7 Comparison of tree cover predictions from PlanetScope and other tree cover products in urban areas.

a, Density plot for the PlanetScope-based 2019 tree cover and MOD44B 2019 tree cover. b, Box plot for the PlanetScope-based 2019 tree cover and ESA WorldCover 202032 tree cover (×: mean value; –: median value). c, Histogram of PlanetScope 2019 tree cover and ESA WorldCover 202032 tree cover in urban areas. In the box plots the lower and upper box limits are the 25th and 75th percentiles, the central line is the median, and the upper (lower) whiskers extend to 1.5 (−1.5) times the interquartile range.

Extended Data Table 1 Mean tree cover by land cover class. Land cover classes are derived from the ESA WorldCover 2020 map32
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Supplementary Figs. 1–11, Tables 1–3 and References.

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Zhang, X., Brandt, M., Tong, X. et al. A strong but uneven increase in urban tree cover in China over the recent decade. Nat Cities 2, 460–469 (2025). https://doi.org/10.1038/s44284-025-00227-9

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