Quantification of Carbon Stocks at the Individual Tree Level in Semiarid Regions in Africa

Quantifying tree resources is essential for effectively implementing climate adaptation strategies and supporting local communities. In the Sahel, where tree presence is scattered, measuring carbon becomes challenging. We present an approach to estimating aboveground carbon (AGC) at the individual tree level using a combination of very high-resolution imagery, field-collected data, and machine learning algorithms. We populated an AGC database from in situ measurements using allometric equations and carbon conversion factors. We extracted satellite spectral information and tree crown area upon segmenting each tree crown. We then trained and validated an artificial neural network to predict AGC from these variables. The validation at the tree level resulted in an R 2 of 0.66, a root mean square error (RMSE) of 373.85 kg, a relative RMSE of 78.6%, and an overestimation bias of 47 kg. When aggregating results at coarser spatial resolutions, the relative RMSE decreased for all areas, with the median value at the plot level being under 30% in all cases. Within our areas of study, we obtained a total of 3,900 Mg, with an average carbon content per tree of 330 kg. A benchmarking analysis against published carbon maps showed that 9 out of 10 underestimate AGC stocks, in comparison to our results, in the areas of study. An additional comparison against a method using only crown area to determine AGC showed an improved performance, including spectral signature. This study improves crown-based biomass estimations for areas where unmanned aerial vehicle or height data are not available and validates at the individual tree level using solely satellite imagery.