Use of Drones for Estimating Biodiversity, Structural Parameters, and Woody Carbon Storage in Sahelian Ecosystems of Senegal

Abstract

Sahelian ecosystems play a crucial role in climate regulation, particularly through their capacity to store atmospheric carbon. However, their carbon sequestration potential remains poorly quantified due to the high cost and logistical constraints of conventional field-based methods. This study investigates the use of low-cost unmanned aerial vehicles (UAVs) to assess dendrometric parameters and estimate the carbon storage capacity of woody vegetation within a 10-hectare Sahelian plot in Senegal. High-resolution aerial imagery was acquired using a DJI Phantom 4 Pro+ drone and processed with photogrammetric software. Tree height and crown diameter were extracted and validated using field measurements, showing a strong correlation for tree height (R² = 0.94) and a moderate correlation for crown diameter (R² = 0.74). Spectral index analysis enabled the identification of dominant woody species, including Balanites aegyptiaca, Sclerocarya birrea, Acacia raddiana, and Acacia senegal.The results indicate that Balanites aegyptiaca contributes the highest amount of carbon storage per hectare (1520.76 ± 185.5 kg ha⁻¹), mainly due to its high stem density (23.3 individuals ha⁻¹), whereas Sclerocarya birrea exhibits the highest average carbon stock per individual tree (206.8 ± 107.8 kg). These findings highlight the significant role of these species in climate change mitigation strategies. By demonstrating the effectiveness of low-cost UAVs for rapid and non-destructive assessment of Sahelian ecosystems, this study provides a promising approach for large-scale ecological monitoring and sustainable natural resource management. It also identifies methodological improvements required to enhance crown dimension estimates. Overall, this UAVbased approach represents a reliable alternative to destructive methods and is well suited to the ecological and operational challenges of Sahelian environments, with strong potential for application in similar regions.

Plant phenotyping relevance

UAV画像から樹高・樹冠径という植物形態形質を抽出し、地上測定で検証する手法が研究の中心であるため、植物フェノタイピング手法として含める。

Code and data availability

The supplied article blocks describe UAV imagery acquisition, photogrammetric processing (Agisoft Metashape, Pix4Dcapture, ArcGIS, SAGA GIS), Random Forest species classification, and field dendrometric measurements, but contain no data availability statement, no public dataset or image deposit, and no author code or模型

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