Combining remote sensing with local knowledge is vital for understanding forest change in West Africa

Understanding tropical forest change requires integrating satellite observations with insights from forest-dependent communities. In West Africa, where deforestation and degradation unfold within complex social-ecological systems, conventional monitoring often overlooks community insights. We combined Landsat-derived forest cover data (2000-2022) with household surveys from 2,621 respondents across nine forest patches, applying a convergence matrix to compare satellite trajectories with local knowledge of forest change. Two themes were analyzed: forest cover loss and forest regrowth. Sites were classified as full convergence, partial convergence, or dissonance based on directional agreement and the proportion of community responses. Full convergence occurred in four sites where > 65% of respondents reported forest loss consistent with satellite-detected declines (- 2.8% to - 13.9% cover). Partial convergence characterized the two sites with mixed local responses and modest net satellite-image changes (< 5%). Dissonance emerged in three sites where satellite-detected stability (< 2% net change, p ≥ 0.10) contrasted with > 65% of respondents reporting degradation. Across all sites, satellite-detected regrowth was minimal, though some communities described localized recovery. These findings show that convergence is strongest for deforestation, while divergences are concentrated around degradation, underscoring the diagnostic value of local knowledge and the limitations of medium-resolution imagery. Embedding such knowledge in monitoring frameworks is therefore both an ethical imperative and a strategic necessity for adaptive forest governance.