Above-Ground Biomass (AGB) is a crucial forest biophysical property, serving as a key indicator of carbon storage and sequestration in forested ecosystems. It plays a fundamental role in global climate change mitigation, particularly in tropical regions, where forests act as vital carbon sinks.
A recent study (here) by Melitha et al. (2024), published in the International Journal of Earth Science and Informatics, leveraged machine learning and UAV-RGB data to enhance AGB estimation in Miombo woodlands. The research applied four machine learning models; Support Vector Machine with Radial Basis Function (SVM-RBF), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Gradient Boosting Machine (GBM)—to assess AGB.
The study was conducted across five Miombo woodland sites in the Morogoro Region, Tanzania—four within Village Land Forest Reserves in Kilosa District and one in Kitulang’h’alo Forest, owned by Sokoine University of Agriculture (SUA).
Key Findings
The results demonstrated that Random Forest (RF) was the most effective model, explaining 77% of the variance (R² = 0.77) with an RMSE of 48.7 Mg/ha. XGBoost followed, achieving R² = 0.65 and RMSE = 52.9 Mg/ha. In contrast, GBM and SVM underperformed (R² = 0.28 and 0.29, respectively), likely due to their limitations in handling small, complex datasets.