Effective forest degradation monitoring is crucial for devising targeted interventions to curb carbon emissions and safeguard ecosystem services. In Ethiopia, where coffee farming is intricately tied to forest health, understanding and managing degradation is essential for sustaining both agricultural productivity and environmental integrity. This study rigorously assesses the impact of different management interventions on forest degradation in Ethiopian coffee plots, with a specific focus on quantifying carbon emissions. By integrating field data with freely available high-resolution Sentinel-2 imagery and employing a neural network model to predict NDVI, we achieved a high level of accuracy, as demonstrated by a strong correlation between a predicted greenness indicator (NDVI) and field biomass data (R² = 0.97), while also establishing a robust framework for monitoring forest degradation. Our degradation mapping from 2021 to 2023 demonstrated a notable reduction in degraded areas within managed coffee plots, though baseline plots exhibited a more significant reduction in later years. These findings underscore the transformative potential of combining machine learning with remote sensing to effectively monitor and mitigate forest degradation, enhancing the precision of carbon accounting and promoting sustainable land management practices. This approach holds significant potential for use in company-internal sustainability audits, compliance with the upcoming European Union Deforestation Regulation (EUDR), and the generation of carbon credits for both insetting and offsetting carbon emissions.