Assessing Baseline and Monitoring Change in Forest Carbon Projects

This study evaluates carbon stock changes in the Northern Scarp West Forest Reserve (project site) and Bumfuom Forest Reserve (control site) in Ghana, using remote sensing techniques and biomass estimation methodologies. The aim is to compare two carbon crediting frameworks Gold Standard V2.1 and Verra VM0047 in terms of their effectiveness in monitoring carbon sequestration in afforestation and reforestation projects. These frameworks are fundamental for ensuring accurate carbon credit issuance, but differ in their baseline approaches: the static Gold Standard provides a conservative, fixed baseline, while the dynamic Verra framework adapts to real-time sequestration trends, allowing for more flexible monitoring. Remote sensing is critical in this context, particularly for regions like Ghana where limited ground-based data and large, inaccessible forested areas make traditional monitoring methods challenging. The study integrates NDVI (Normalized Difference Vegetation Index) a satellite-derived metric that quantifies vegetation health by comparing red and near-infrared reflectance values along with performance benchmarks and Chloris above-ground biomass (AGB) products to estimate carbon stocks and vegetation cover changes. Results show a significant increase in carbon stocks at the project site, with a baseline carbon stock of 36.04 tCO₂/ha. Verra VM0047’s dynamic baseline approach outperforms Gold Standard V2.1 in reflecting real-time sequestration trends. This study highlights the importance of incorporating dynamic baselines in carbon crediting methodologies, especially in ecosystems undergoing frequent land-use changes and recovery, while emphasizing the role of remote sensing in scalable, cost-effective monitoring. The findings contribute to a deeper understanding of forest carbon accounting methodologies and their application in tropical regions for climate change mitigation Google Earth Engine, 2024 QGIS, 3.28 ArcGIS Pro, 3.1 R, 4.3.2