Abstract
Urban trees are crucial for enhancing the social and ecological qualities of urban environments, but their distribution and correlates in informal settlements across African cities remain unclear. Here we map out 53 million individual trees across 54 African cities and investigate their relationship with neighborhood environments along a gradient of urban informality. Our findings reveal the shortage of green infrastructure per capita in the most informal neighborhoods of African cities. Under the assumptions of continued urban expansion, 28 cities are projected to lose tree cover beyond current urban boundaries by 2050. This underscores the urgent need to upgrade informal settlements, although such efforts are often constrained by current socioeconomic conditions. Our study underscores the importance of fine-grained spatial data in informing tree retention strategies during urban upgrading and expansion and emphasizes that trees and green spaces must be recognized as integral components of sustainable urban development in Africa.
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Data availability
GUB data can be downloaded from https://data-starcloud.pcl.ac.cn/. Informal settlements data (that is, BSA) are available at https://www.millionneighborhoods.org/. Population data are derived from WorldPop 2020, and datasets of the building area are derived from OpenStreetMap. Datasets of population and building area at the street-block level are openly available in Million Neighborhoods Map at https://www.millionneighborhoods.org/. Future gridded population data can be downloaded from Figshare at https://doi.org/10.6084/m9.figshare.19608594.v2 (ref. 53). Future land cover dataset data are available via Figshare at https://doi.org/10.6084/m9.figshare.23542860 (ref. 49). Open buildings 2.5D temporal dataset are available at https://sites.research.google/gr/open-buildings/temporal/. Köppen–Geiger climate classification can be downloaded from https://www.gloh2o.org/koppen/. Tree cover maps are available via Zenodo at https://zenodo.org/records/7764460 (ref. 59). The geospatial data of the administrative boundaries were obtained from the Global Administrative Areas (https://gadm.org/). Google satellite imagery used in this study can not be provided due to copyright issues, but is available to download through the Google Static Map API and our shared code. The identified results for individual trees in the 40 sampled cities in this study are available at https://doi.org/10.5061/dryad.jwstqjqn5 (ref. 60). Source data are provided with this paper.
Code availability
The code for the Google imagery download was constructed using the open-source Python packages ‘GeoPandas’, ‘rasterio’ and ‘PIL’. Detecting tree locations is based on the HR-SFANet model (ref. 28), with an updated implementation available. Additionally, a standalone executable for segmentation curve has been made publicly accessible. All related code repositories are publicly accessible at https://doi.org/10.5061/dryad.jwstqjqn5 (ref. 60).
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (grant no. 42371423; to L.J.) and the Fundamental Research Funds for the Central Universities (grant no. 2042023kfyq04; to L.J.). C.M.S. was supported by the SARChI Chairs programme of the Department of Science and Innovation and the National Research Foundation (grant no. 84379). Any opinion, finding, conclusions or recommendation expressed in this material is that of the authors and the National Research Foundation does not accept liability in this regard. We thank K. Armstrong and Y. Qiao for their help.
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X.L. and L.J. designed the research. X.L., W.L., Z.L., B.L., J.Z., R.Y., M.F., H.Z. and N.S.S. contributed to data collection and processing. X.L. and W.L. developed the code and conducted the experiments. X.L. drafted the paper. L.J., C.M.S. and Y.L. reviewed and edited the paper. All authors contributed to the manuscript.
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Nature Cities thanks Olumuyiwa Adegun, Jianhua Guo 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 A flowchart mapping individual urban trees in African cities.
A confidence map generated by the HR-SFANet model (ref. 28), is used to identify tree locations. Segmentation curve module is used to automatically segment the tree crowns based on confidence maps and predicted tree locations.
Extended Data Fig. 2 Schematic of the potential effects of future urban expansion on tree cover.
a, the loss of tree cover caused by the external expansion (First assumption). b, densification of buildings within neighborhoods (Second assumption). c, the increase in population density at the neighborhood scale (Third assumption). d, neighborhood upgrading (Fourth assumption).
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Fifty-four cities chosen for individual tree identification.
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Lian, X., Liu, W., Liu, Z. et al. Tree shortages in informal settlements across African cities. Nat Cities 2, 1049–1059 (2025). https://doi.org/10.1038/s44284-025-00284-0
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DOI: https://doi.org/10.1038/s44284-025-00284-0
