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. 2022 Dec 22;13(1):91–97. doi: 10.1038/s41558-022-01544-w

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 8 — a, We took 150 (n = 150) random 1 × 1 km samples and aggregated the predicted tree-level carbon stocks and compared the results to previously published maps using two-tailored Pearson correlation. We did not include ref. ⁶¹ because of the coarse native resolution which did not guarantee that the same areas were compared. Note that ref. ⁴⁵ does not report values above 85 Mg biomass per hectare, explaining the lower correlation values. P < 0.001 for all correlations. b, The model trained on the Rwanda aerial images was directly applied on aerial images from France (20 cm) and c, NAIP aerial images at 60 cm. In boxplots in a, lines from top to bottom represent the maximum, third quartile, median, first quartile and minimum values. Credit: photographs in b, USGS NAIP; c, IGN France.