Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Mapping every adult baobab (Adansonia digitata L.) across the Sahel and relationships to rural livelihoods

Nature Ecology & Evolution volume 8, pages 1632–1640 (2024)Cite this article

Subjects

Abstract

The baobab tree (Adansonia digitata L.) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we use sub-metre-resolution satellite imagery to identify the presence of nearly 2.8 million (underestimation bias 27.1%) baobab trees in the Sahel, a dryland region of 2.4 million km2. This achievement is considered an essential step towards an improved management and monitoring system of valuable woody species. Using Senegal as a case country, we find that 94% of rural buildings have at least one baobab tree in their immediate surroundings and that the abundance of baobabs is associated with a higher likelihood of people consuming a highly nutritious food group: dark green leafy vegetables. The generated database showcases the feasibility of mapping the location of single tree species at a sub-continental scale, providing vital information in times when deforestation and climate change cause the extinction of numerous tree species.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: A wall-to-wall map of baobab trees in the Sahel.
Fig. 2: Mapping evaluation and relationships with rural buildings and food consumption.
Fig. 3: Variables affecting the baobab distribution.
Fig. 4: Baobab mapping with nanosatellites.

Similar content being viewed by others

Data availability

Commercial very-high-resolution satellite images were acquired through NASA under the NextView Imagery End User License Agreement. The copyright remains at Maxar, Inc. and redistribution is not possible. The derived products produced by this study are available via Zenodo at https://doi.org/10.5281/zenodo.10934203 (ref. 63). SkySat images were purchased from Planet Labs and cannot be distributed. Tree species data from the Flotrop database can be downloaded at https://doi.org/10.15468/oxunf1. Global Biodiversity Information Facility data are available under https://www.gbif.org. Google building data are available from https://www.sites.research.google/open-buildings/. The GHS-POP dataset can be accessed via https://ghsl.jrc.ec.europa.eu/ghs_pop2023.php. DHS survey data are available from https://www.dhsprogram.com/methodology/survey/survey-display-457.cfm for the dataset in 2014, https://www.dhsprogram.com/methodology/survey/survey-display-489.cfm for the dataset in 2015, https://www.dhsprogram.com/methodology/survey/survey-display-524.cfm for the dataset in 2016 and https://www.dhsprogram.com/methodology/survey/survey-display-581.cfm for the dataset in 2019. CHIRPS rainfall data are available at https://www.chc.ucsb.edu/data/chirps. Temperature data at 0.1° are the ERA5-Land version of land surface temperature, which is available at the Climate Data Store at https://cds.climate.copernicus.eu/. The gridded soil data at 0.05° spatial resolution are available at https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1247. The human settlement dataset used for the Sahel is the 2.8 arcsec spatial resolution Global Urban Footprint dataset, which is available at https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-9628/16557_read-40454/. Land-use data are available at https://lcviewer.vito.be/download. The river shapefile provided by the Food and Agriculture Organization (FAO) is available at https://data.apps.fao.org/catalog/iso/b891ca64-4cd4-4efd-a7ca-b386e98d52e8.

Code availability

The tree detection framework based on U-Net is publicly available via Zenodo at https://doi.org/10.5281/zenodo.3978185 (ref. 64). The R script for spatial analysing, boosted regression trees and visualization are available from the corresponding author on reasonable request.

References

  1. Herrero, M. et al.Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110, 20888–20893 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Thornton, P. K. & Herrero, M. Adapting to climate change in the mixed crop and livestock farming systems in sub-Saharan Africa. Nat. Clim. Change 5, 830–836 (2015).

    Google Scholar 

  3. Djoudi, H., Vergles, E., Blackie, R. R., Koame, C. K. & Gautier, D. Dry forests, livelihoods and poverty alleviation: understanding current trends. Int. For. Rev. 17, 54–69 (2015).

    Google Scholar 

  4. Lachat, C. et al. Dietary species richness as a measure of food biodiversity and nutritional quality of diets. Proc. Natl Acad. Sci. USA 115, 127–132 (2018).

    CAS  PubMed  Google Scholar 

  5. Zhang, W. M. et al. Ecosystem structural changes controlled by altered rainfall climatology in tropical savannas. Nat. Commun. 10, 671 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Linders, T. E. W. et al. Stakeholder priorities determine the impact of an alien tree invasion on ecosystem multifunctionality. People Nat. 3, 658–672 (2021).

    Google Scholar 

  7. Vinceti, B. et al. Food tree species selection for nutrition-sensitive forest landscape restoration in Burkina Faso. Plants People Planet 4, 667–684 (2022).

    Google Scholar 

  8. Linders, T. E. W. et al. Direct and indirect effects of invasive species: biodiversity loss is a major mechanism by which an invasive tree affects ecosystem functioning. J. Ecol. 107, 2660–2672 (2019).

    Google Scholar 

  9. Graziosi, I., Tembo, M., Kuate, J. & Muchugi, A. Pests and diseases of trees in Africa: a growing continental emergency. Plants People Planet 2, 14–28 (2020).

    Google Scholar 

  10. Schumann, K., Wittig, R., Thiombiano, A., Becker, U. & Hahn, K. Impact of land-use type and bark- and leaf-harvesting on population structure and fruit production of the baobab tree (Adansonia digitata L.) in a semi-arid savanna, West Africa. For. Ecol. Manag. 260, 2035–2044 (2010).

    Google Scholar 

  11. Jama, B. A., Mohamed, A. M., Mulatya, J. & Njui, A. N. Comparing the ‘Big Five’: a framework for the sustainable management of indigenous fruit trees in the drylands of East and Central Africa. Ecol. Indic. 8, 170–179 (2008).

    Google Scholar 

  12. Gebauer, J. et al. Africa’s wooden elephant: the baobab tree (Adansonia digitata L.) in Sudan and Kenya: a review. Genet. Resour. Crop Evol. 63, 377–399 (2016).

    Google Scholar 

  13. Gebauer, J., El-Siddig, K. & Ebert, G. Baobab (Adansonia digitata L.): a review on a multipurpose tree with promising future in the Sudan. Gartenbauwissenschaft 67, 155–160 (2002).

    CAS  Google Scholar 

  14. Chadare, F. J., Linnemann, A. R., Hounhouigan, J. D., Nout, M. J. R. & Van Boekel, M. Baobab food products: a review on their composition and nutritional value. Crit. Rev. Food Sci. Nutr. 49, 254–274 (2009).

    CAS  PubMed  Google Scholar 

  15. Khoja, K. K., Aslam, M. F., Sharp, P. A. & Latunde-Dada, G. O. In vitro bioaccessibility and bioavailability of iron from fenugreek, baobab and moringa. Food Chem. 335, 127671 (2021).

    CAS  PubMed  Google Scholar 

  16. Buchmann, C., Prehsler, S., Hartl, A. & Vogl, C. R. The importance of baobab (Adansonia digitata L.) in rural West African subsistence suggestion of a cautionary approach to international market export of baobab fruits. Ecol. Food Nutr. 49, 145–172 (2010).

    PubMed  Google Scholar 

  17. Kamatou, G. P. P., Vermaak, I. & Viljoen, A. M. An updated review of Adansonia digitata: a commercially important African tree. S. Afr. J. Bot. 77, 908–919 (2011).

    Google Scholar 

  18. Meinhold, K., Dumenu, W. K. & Darr, D. Connecting rural non-timber forest product collectors to global markets: the case of baobab (Adansonia digitata L.). For. Policy Econ. 134, 102628 (2022).

    Google Scholar 

  19. Assogbadjo, A. E., Chadare, F. J., Manda, L. & Sinsin, B. A 20-year journey through an orphan African baobab (Adansonia digitata L.) towards improved food and nutrition security in Africa. Front. Sustain. Food Syst. https://doi.org/10.3389/fsufs.2021.675382 (2021).

  20. Gurevitch, J. Managing forests for competing goals. Science 376, 792–793 (2022).

    CAS  PubMed  Google Scholar 

  21. Hua, F. et al. The biodiversity and ecosystem service contributions and trade-offs of forest restoration approaches. Science 376, 839–844 (2022).

    CAS  PubMed  Google Scholar 

  22. Ranaivoson, T., Brinkmann, K., Rakouth, B. & Buerkert, A. Distribution, biomass and local importance of tamarind trees in south-western Madagascar. Glob. Ecol. Conserv. 4, 14–25 (2015).

    Google Scholar 

  23. Duvall, C. S. Human settlement and baobab distribution in south-western Mali. J. Biogeogr. 34, 1947–1961 (2007).

    Google Scholar 

  24. Pelletier, J., Chidumayo, E., Trainor, A., Siampale, A. & Mbindo, K. Distribution of tree species with high economic and livelihood value for Zambia. For. Ecol. Manag. 441, 280–292 (2019).

    Google Scholar 

  25. Navarro, J. A. et al. Integration of UAV, sentinel-1, and sentinel-2 data for mangrove plantation aboveground biomass monitoring in Senegal. Remote Sens. 11, 77 (2019).

    Google Scholar 

  26. Piiroinen, R. et al. Invasive tree species detection in the Eastern Arc Mountains biodiversity hotspot using one class classification. Remote Sens. Environ. 218, 119–131 (2018).

    Google Scholar 

  27. Richardson, D. M. et al. Accommodating scenarios of climate change and management in modelling the distribution of the invasive tree Schinus molle in South Africa. Ecography 33, 1049–1061 (2010).

    Google Scholar 

  28. Sanchez, A. C., Osborne, P. E. & Haq, N. Identifying the global potential for baobab tree cultivation using ecological niche modelling. Agrofor. Syst. 80, 191–201 (2010).

    Google Scholar 

  29. Kolarik, N. E. et al. A multi-plot assessment of vegetation structure using a micro-unmanned aerial system (UAS) in a semi-arid savanna environment. ISPRS J. Photogramm. Remote Sens. 164, 84–96 (2020).

    Google Scholar 

  30. Brandt, M. et al. An unexpectedly large count of trees in the West African Sahara and Sahel. Nature 587, 78–82 (2020).

    PubMed  Google Scholar 

  31. den Braber, B. et al. Even low levels of tree cover improve dietary quality in West Africa. PNAS nexus 3, 67–75 (2024).

    Google Scholar 

  32. Liu, S. Y. et al. The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe. Sci. Adv. 9, 14 (2023).

    Google Scholar 

  33. Mugabowindekwe, M. et al. Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda. Nat. Clim. Change 13, 91–97 (2023).

    Google Scholar 

  34. Tucker, C. et al. Sub-continental-scale carbon stocks of individual trees in African drylands. Nature 615, 80–86 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Gamfeldt, L. et al. Higher levels of multiple ecosystem services are found in forests with more tree species. Nat. Commun. 4, 1340 (2013).

    PubMed  Google Scholar 

  36. Reiner, F. et al. More than one quarter of Africa’s tree cover is found outside areas previously classified as forest. Nat. Commun. 14, 2258–2258 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. La filière baobab en region de Kayes. Pour un développement économique garant de la souveraineté alimentaire (GRDR, 2012).

  38. Diémé, J. S., Diouf, M., Armas, C., Rusch, G. M. & Pugnaire, F. I. Functional groups of Sahelian trees in a semiarid agroforestry system of Senegal. J. Plant Ecol. 11, 375–384 (2018).

    Google Scholar 

  39. Rashford, J. Baobab: The Hadza of Tanzania and the Baobab as Humanity’s Tree of Life (Springer Nature, 2023).

  40. Kiptot, E. & Franzel, S. Gender and agroforestry in Africa: a review of women’s participation. Agrofor. Syst. 84, 35–58 (2012).

    Google Scholar 

  41. Patrut, A. et al. The demise of the largest and oldest African baobabs. Nat. Plants 4, 423–426 (2018).

    PubMed  Google Scholar 

  42. Venter, S. M. & Witkowski, E. T. F. Fruits of our labour: contribution of commercial baobab (Adansonia digitata L.) fruit harvesting to the livelihoods of marginalized people in northern Venda, South Africa. Agrofor. Syst. 87, 159–172 (2013).

    Google Scholar 

  43. Boonman, C. C. F. et al. More than 17,000 tree species are at risk from rapid global change. Nat. Commun. 15, 166–166 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Stevart, T. et al. A third of the tropical African flora is potentially threatened with extinction. Sci. Adv. 5, eaax9444 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Talluto, M. V., Boulangeat, I., Vissault, S., Thuiller, W. & Gravel, D. Extinction debt and colonization credit delay range shifts of eastern North American trees. Nat. Ecol. Evol. 1, 0182 (2017).

    Google Scholar 

  46. Shackleton, S., Shanley, P. & Ndoye, O. Invisible but viable: recognising local markets for nontimber forest products. Int. For. Rev. 9, 697–712 (2007).

    Google Scholar 

  47. Barrett, M. A., Brown, J. L. & Yoder, A. D. Protection for trade of precious rosewood. Nature 499, 29–29 (2013).

    CAS  PubMed  Google Scholar 

  48. Humphreys, A. M., Govaerts, R., Ficinski, S. Z., Lughadha, E. N. & Vorontsova, M. S. Global dataset shows geography and life form predict modern plant extinction and rediscovery. Nat. Ecol. Evol. 3, 1043–1047 (2019).

    PubMed  Google Scholar 

  49. Leakey, R. R. B. et al. The future of food: domestication and commercialization of indigenous food crops in Africa over the third decade (2012–2021). Sustainability 14, 2355 (2022).

    CAS  Google Scholar 

  50. Omotayo, A. O. & Aremu, A. O. Underutilized African indigenous fruit trees and food-nutrition security: opportunities, challenges, and prospects. Food Energy Secur. 9, e220 (2020).

  51. Adansonia digitata L. Occurrences. GBIF https://doi.org/10.15468/dl.4dcbgt (2021).

  52. Taugourdeau, S. et al. FLOTROP, a massive contribution to plant diversity data for open ecosystems in northern tropical Africa. Sci. Data 6, 118 (2019).

  53. Sirko, W. et al. Continental-scale building detection from high resolution satellite imagery. Preprint at https://doi.org/10.48550/arXiv.2107.12283 (2021).

  54. Schiavina, M., Freire, S. & MacManus, K. J. GHS-POP R2022A—GHS population grid multitemporal (1975–2030). European Commission Joint Research Centre https://doi.org/10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE (2022).

  55. Buchhorn, M. S. B. et al. Copernicus Global Land Service: land cover 100m: collection 3: epoch 2015: Globe (Version V3.0.1). Zenodo https://doi.org/10.5281/zenodo.3939038 (2020).

  56. Agence Nationale de la Statistique et de la Démographie (ANSD) & ICF. Senegal: Continuous Demographic and Health Survey (EDS-Continuous) 2014 https://www.dhsprogram.com/methodology/survey/survey-display-457.cfm (2015).

  57. Agence Nationale de la Statistique et de la Démographie (ANSD) & ICF. Senegal: Continuous Demographic and Health Survey (EDS-Continuous) 2015 https://www.dhsprogram.com/methodology/survey/survey-display-489.cfm (2016).

  58. Agence Nationale de la Statistique et de la Démographie (ANSD) & ICF. Senegal: Continuous Demographic and Health Survey (EDS-Continuous) 2016 https://www.dhsprogram.com/methodology/survey/survey-display-524.cfm (2017).

  59. Agence Nationale de la Statistique et de la Démographie (ANSD) & ICF. Senegal: Continuous Demographic and Health Survey (EDS-Continuous) 2019 https://www.dhsprogram.com/methodology/survey/survey-display-581.cfm (2020).

  60. Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 1–21 (2015).

  61. Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).

  62. Wieder, W. R., Boehnert, J., Bonan, G. B. & Langseth, M. Regridded harmonized world soil database v1.2. ORNL Distrubuted Active Archive Center https://doi.org/10.3334/ORNLDAAC/1247 (2014).

  63. Huang, K. et al. The Adult Adansonia Digitata L. (baobab Tree) distribution map derived from very high-resolution satelite imagery across the Sahel at 1km resolution (2010s). Zenodo https://doi.org/10.5281/zenodo.10934203 (2024).

  64. Ankit. ankitkariryaa/An-unexpectedly-large-count-of-trees-in-the-western-Sahara-and-Sahel: paper version (v1.0.0). Zenodo https://doi.org/10.5281/zenodo.3978185 (2020).

Download references

Acknowledgements

We thank Maxar, Inc. for providing commercial satellite data through the NextView Imagery End User License Agreement. This study was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 947757 TOFDRY) and a DFF Sapere Aude grant (number 9064–00049B) to M.B., the European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement 853222 FORESTDIET) to L.V.R. and the Villum Fonden through the project Deep Learning and Remote Sensing for Unlocking Global Ecosystem Resource Dynamics (DeReEco, 34306) to R.F. and A.K.

Author information

Authors and Affiliations

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

    Ke Huang, Martin Brandt, Laura Vang Rasmussen, Florian Reiner, Sizhuo Li, Ankit Kariryaa, Maurice Mugabowindekwe, Bowy den Braber & Rasmus Fensholt

  2. Department of Food and Resource Economics, University of Copenhagen, Copenhagen, Denmark

    Ke Huang

  3. Science Systems and Applications, Inc., NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Martin Brandt, Pierre Hiernaux, Ankit Kariryaa, Jennifer Small & Scott Sino

  4. Pastoralisme Conseil, Caylus, France

    Pierre Hiernaux

  5. Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Compton J. Tucker, Jennifer Small & Scott Sino

  6. Département Sciences de la terre et de l’univers, espace, Université Paris-Saclay, Paris, France

    Sizhuo Li

  7. Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

    Ankit Kariryaa

Authors
  1. Ke Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Brandt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pierre Hiernaux
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Compton J. Tucker
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Laura Vang Rasmussen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Florian Reiner
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sizhuo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ankit Kariryaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Maurice Mugabowindekwe
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Bowy den Braber
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jennifer Small
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Scott Sino
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Rasmus Fensholt
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.H., M.B. and R.F. conceptualized and designed the study. C.J.T., J.S. and S.S. prepared the satellite data. K.H. and P.H. identified the trees used for training the model. The codes were done by K.H., A.K., S.L., F.R. and M.M. K.H. and M.B. conducted the analyses and performed the visualization. B.d.B. and L.V.R. prepared the DHS data. M.B. and K.H. prepared the paper with support from the other authors.

Corresponding authors

Correspondence to Ke Huang or Martin Brandt.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Julius Anchang, Aliyu Barau, Niall Hanan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Examples of baobab trees as seen in Skysat satellite imagery.

All images were captured during the dry season, with data spanning from 2019 to 2023, showcasing baobab trees in various landscapes across the Sahel. Image © 2023 Planet Labs PBC.

Extended Data Fig. 2 Hotspot region of baobab trees from our map and local report in Kayes, Mali.

Local report ref. 37 showing a high density map of baobab trees in Kayes, Mali. This area was also detected by our study as a hotspot region of baobab trees. The basemap was created using ArcGIS® software by Esri.

Extended Data Fig. 3 Baobab densities at country-scale in and outside croplands.

a, Baobab density plot for 8 countries. b, Baobab density for 5 countries with higher densities. The color of each circle represents the proportion of cropland in the country. In Senegal, Niger, Nigeria, and Sudan, more than 1/5 of the land area is cropland. The size of the circle indicates the percentage of baobabs found in cropland. In Senegal, Burkina Faso, Niger, and Nigeria, more than 2% of the baobab trees are found in croplands. Senegal, Mali, Burkina Faso, Niger and Nigeria have relatively higher baobab densities in croplands with > 1 baobab tree per km2.

Extended Data Fig. 4 DHS data for Senegal used in this study.

a, Statistics of the DHS dataset from different years. In total, 183 clusters (4,708 households) were investigated from 2014, 2015, 2016, 2019. For Senegal, 128 clusters (3020 households) are used for our study, the remaining clusters are outside of the studied rainfall zone. Each slice of the inner circle of the donut pie chart represents the number of clusters for each investigated year, which is regarded as the cluster-year record, while the outer circle shows the number of households across those cluster-year records. b, The violin plot shows the distribution of households for each cluster during the investigation period, each cluster includes 2–20 households. Box, interquartile range (IQR); whiskers, 1.5 × IQR; horizontal line within the each violin, median; points, observations for each cluster. c, Spatial distribution of the DHS clusters. The grey points are clusters from the DHS datasets that were available but outside of the 150–750 mm rainfall zone or located in urban areas and thus not used, while the red points are clusters that were used in our study. d, Histogram of household counts per cluster-year. In total, there are 320 cluster-year records that were included, and 3020 household records.

Extended Data Fig. 5 Presence of baobabs and food consumption at two zones based on rainfall.

DHS data on rural household clusters in Senegal were split into two classes with an almost equal number of household clusters, representing clusters with high and low consumption levels of dark green leafy vegetables. Data are presented for two (overlapping) rainfall range zones: a.750 mm > rainfall ≥ 600 mm; b. 750 mm > rainfall >150 mm. The number of baobab trees in the surrounding areas of the surveyed DHS clusters (5 km radius) and also the total number of trees (including all species extracted from ref. 32) was compared across clusters with high vs low consumption of dark green leafy vegetables. The upper horizontal lines represent error bar and the filled bars denote the mean value. Dots are the original observations for each group. Significant differences from the one-sided t-test are marked with * (p < 0.05).

Extended Data Fig. 6 Examples of Google building data.

Buildings from google building open dataset from ref. 53 are shown as yellow polygons. The basemaps are Skysat satellite images. Inconsistencies with Skysat images may be a result of different image acquisition times. Image © 2023 Planet Labs PBC.

Extended Data Table 1 Baobab density per country
Full size table
Extended Data Table 2 Proportion of baobab trees compared to all trees
Full size table

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, K., Brandt, M., Hiernaux, P. et al. Mapping every adult baobab (Adansonia digitata L.) across the Sahel and relationships to rural livelihoods. Nat Ecol Evol 8, 1632–1640 (2024). https://doi.org/10.1038/s41559-024-02483-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-024-02483-9

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing