Sub-continental-scale carbon stocks of individual trees in African drylands

Abstract

The distribution of dryland trees and their density, cover, size, mass and carbon content are not well known at sub-continental to continental scales1–14. This information is important for ecological protection, carbon accounting, climate mitigation and restoration efforts of dryland ecosystems15–18. We assessed more than 9.9 billion trees derived from more than 300,000 satellite images, covering semi-arid sub-Saharan Africa north of the Equator. We attributed wood, foliage and root carbon to every tree in the 0–1,000 mm year−1 rainfall zone by coupling field data19, machine learning20–22, satellite data and high-performance computing. Average carbon stocks of individual trees ranged from 0.54 Mg C ha−1 and 63 kg C tree−1 in the arid zone to 3.7 Mg C ha−1 and 98 kg tree−1 in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (±19.8%) Pg C. Comparisons with 14 previous TRENDY numerical simulation studies23 for our area found that the density and carbon stocks of scattered trees have been underestimated by three models and overestimated by 11 models, respectively. This benchmarking can help understand the carbon cycle and address concerns about land degradation24–29. We make available a linked database of wood mass, foliage mass, root mass and carbon stock of each tree for scientists, policymakers, dryland-restoration practitioners and farmers, who can use it to estimate farmland tree carbon stocks from tablets or laptops.

Dynamic carbon allocation trade‐off: A robust approach to model tree biomass allometry

“…A comparison of field data confirmed that conventional models could underestimate the AGB of the plots in tropical large trees by approximately 18%. Our results concerning the underestimates agree with those of a recent study using the predictor of the crown area (Tucker et al, 2023) for African dryland forests and isolated trees outside forests.…”

“…Currently, the application of new technologies such as laser scanning and artificial intelligence is fundamentally improving the observational capabilities for large tree sizes (Brandt et al, 2020;Calders et al, 2022;Jucker et al, 2017;Yang et al, 2022). A recent study using a predictor of the crown area indicated the great necessity of determining the effects of crowns on allometric scaling (Tucker et al, 2023). Therefore, addressing the…”

Mapping every adult baobab (Adansonia digitata L.) across the Sahel to uncover the co-existence with rural livelihoods

“…Images were obtained by NASA through the NextView License from the National Geospatial Intelligence Agency. More information on the data source, such as image dates, can be found in 29,32 .…”

“…However, such maps do not distinguish between an invasive shrub or a baobab, and merge every woody plant under the variable tree cover. Such tree cover maps are often used to proxy ecosystem services in a given area 30 , although they are better suited to assess carbon stocks, climate change mitigation and wood products 31,32 . The creation of maps that include information on speci c tree species and their distribution represent a promising avenue for improving assessments of the ecosystem services provided by various trees as well as their contributions to local livelihoods 33 .…”

“…For this study we acquired satellite images covering the entire Sahel at 50 cm resolution during the early dry season 32 , a period where only Adansonia digitata, Bombax costatum, Ceiba pentandra and Sterculia setigera are large trees which have no or only few leaves. To avoid confusion between these species, we included the shadow of the distinctive branches in the training samples (Fig.…”