Community Managed vs. Protected Forests: A Remote Sensing Workflow for Assessing Forest Conservation in Liberia (2002–2024)

Abstract. This study assesses long-term forest change in Liberia’s Community Forest Management Areas for Conservation (CFMACs) and Protected Areas (PAs) from 2002 to 2024 using an integrated Landsat–Google Earth Engine (GEE) and an ArcGIS Pro workflow. Annual dry-season composites for three time periods were classified using a Random Forest model with 81.7% accuracy (Kappa = 0.781). Results show contrasting governance outcomes: CFMACs experienced modest forest gains from 2002–2014 and localized losses thereafter, while PAs exhibited larger overall gains but also greater cumulative forest loss, particularly along concession boundaries. Stability analysis revealed that PAs retained a higher proportion of Mature Forest, whereas CFMACs showed more dynamic turnover and localized regrowth. The combined GEE/ArcGIS approach provides a scalable, transparent monitoring framework and demonstrates how governance type influences forest persistence, degradation, and recovery across Liberia’s tropical landscapes.