Aboveground biomass estimations in Africa: A systematic review and meta-analysis

Abstract

Accurate estimation of Aboveground Biomass (AGB) is essential for climate change mitigation, sustainable forest management, carbon accounting, and the implementation of REDD + and Paris Agreement commitments. Across Africa, particularly in Ghana, AGB estimation is constrained by limited field data, methodological inconsistencies, and reliance on generalized allometric models, often resulting in errors exceeding 20–35%. Although remote sensing technologies, including optical, radar, and LiDAR systems, provide scalable alternatives for biomass monitoring, their effectiveness is often limited by sensor saturation, persistent cloud cover, calibration uncertainties, and inadequate ground validation data. Ghana faces additional challenges due to its heterogeneous forest–savanna mosaic, rapid land-use conversion driven by cocoa expansion, and a scarcity of locally calibrated allometric equations and high-resolution LiDAR datasets. This study employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to systematically review and meta-analyse 74 peer-reviewed studies published between 2000 and 2025. Field-based, remote sensing, and integrated approaches for AGB estimation across African biomes were evaluated, with particular emphasis on Ghana. Accuracy indicators, including coefficient of determination (R²), root mean square error (RMSE), and relative error, were synthesized using a random-effects meta-analytic model. Results indicate that field-based methods provide high precision but limited spatial scalability. LiDAR and L-band Synthetic Aperture Radar (SAR) improve large-scale biomass estimation when combined with field calibration. Integrated approaches achieved the highest accuracy (R² = 0.78); among Ghana-specific studies, mean accuracy was R² = 0.74 (RMSE = 27.1 Mg ha⁻¹). The findings highlight the need for standardized methodologies, regionally calibrated allometric models, robust ground-truth networks, and multi-sensor integration to improve AGB estimation and support evidence-based climate and forest management policies across Africa.