Accurate quantification of carbon stocks at the individual tree level in semi-arid regions in Africa

Restoration and conservation efforts in critical regions affecting large populations with adverse climatic conditions, such as the Sahel, in Africa, also provide the grounds for ecosystem services in these areas. Accurate quantification and monitoring of trees in this context are essential for effectively implementing climate mitigation strategies and supporting local communities. Satellite technologies have emerged as powerful tools to obtain carbon stock estimates. However, tree count and coverage are underestimated in these semi-arid and dryland regions, and fine-grained estimates of carbon stocks can unlock tailored management and action and generate a deeper understanding of the distribution of these stocks. We present the first high-resolution, tree-level validated approach to estimate Above Ground Carbon stock leveraging Very High-Resolution imagery (0.5m), field-collected data, and Machine Learning algorithms. Local experts and youth and women communities participating in the Great Green Wall Initiative collected individual tree geolocation in 8 sites within the drylands of the Sahel region (Burkina Faso and Niger). We built a database of tree-level aboveground carbon (AGC) based on field measurements by using allometric equations and carbon conversion factors, and we trained and validated an Artificial Neural Network to predict AGC based on remote sensing imagery variables processed on individual segmented tree crowns. The validation resulted in a R² of 0.69, a Root Mean Square Error (RMSE) of 355.6 kg and a relative RMSE of 51%. When aggregating results at coarser spatial resolutions (plot and site), the relative RMSE decreased below 20% for all areas. AGC density (AGCd) errors remained under 6 Mg ha^-1 on ranges of AGCd up to 26 Mg ha^-1, reaching errors of less than a ton of carbon per hectare for half the study sites. A comparison with other methodologies in the recent literature was carried out and showed a competitive performance of this approach in these regions, with R² of other similar studies being between 0.6 and 0.95, and RMSE ranging from 0.25 to 100 Mg ha^-1. Model results confirm the current trend of underestimating the AGC stocks in drylands using coarser resolution data. Most of the available data in the region estimated the total AGC stocks of the 8 study sites to be less than half compared to the validated model results. The only map that predicted an overshot AGC stock compared to our study was a SAR-based approach at 25-meter resolution by Bouvet et al. 2018, in which the authors claimed more significant relative errors in dry regions. Our results confirm that most previous approaches implemented in drylands produce biased estimations of carbon. Our model exploiting VHR imagery offers the possibility to remedy the lack of resolution and then aggregate at the desired level of granularity. This first-of-its-kind validation at the individual tree level demonstrates the capability of very high-resolution models to correctly assess carbon stocks in the now underestimated drylands and semi-arid areas.