Estimating Above-Ground Biomass Using Remote Sensing and Machine Learning in Kwahu South, Ghana

The RF model with Landsat 8 data outperformed the Linear Regression model, achieving a 20% lower root mean square error (RMSE = 37.09 t/ha) and explaining 85% of the variance in AGB measurements (R² = 0.85). Spatial analysis revealed higher AGB (up to 475.3 t/ha) in dense forest zones in the southwest and lower values (down to 4.23 t/ha) in sparsely vegetated eastern areas, aligning with ecological patterns. These findings highlight that non-parametric machine learning, paired with Landsat 8 data, provides a reliable and cost-effective method for AGB estimation in tropical settings.

This approach enhances carbon stock assessments and informs forest management strategies, offering a practical tool for researchers and policymakers working on climate resilience in regions facing environmental pressures.