Remote sensing and local narratives converge and diverge on forest change in West Africa

Integrating satellite observations with community insights to enhance forest monitoring and conservation strategies.

Understanding tropical forest change necessitates integrating satellite observations with insights from forest-dependent communities. In West Africa, deforestation and ecological degradation occur within complex social-ecological systems, yet traditional forest monitoring often neglects community insights. Satellite imagery provides broad spatial and temporal coverage but may overlook fine-scale disturbances such as canopy thinning or understory removal. Meanwhile, forest-dependent communities hold valuable, place-based knowledge of environmental change that is rarely integrated into monitoring frameworks.

In this project, we employed a convergence matrix that pairs satellite-derived trajectories of forest cover from 2000 to 2022 with community-reported perceptions. Our analysis reveals that while these data converge in areas of significant deforestation, they diverge in regions where ecological degradation is more nuanced or historically rooted. By combining spatial and narrative data, we enhance the validity and depth of forest monitoring, underscoring the importance of integrative approaches for equitable and context-sensitive conservation strategies.

Visual Summary

Fig. 1. Spatiotemporal patterns of forest cover change across the study area (2000–2022). a. Regional map classifies each pixel as net deforestation (red), net regrowth (blue), or stable forest (green). b. National-level forest trajectories normalized to 2000. c. Local forest patch trends fitted with linear models.
Fig. 2. Demographic characteristics of household survey respondents across Togo, Benin, Nigeria, and Cameroon. Variables include education, gender, occupation, age, residency duration, and household size.
Fig. 3. Community perceptions of forest change over 5- and 10-year periods, with analysis of variation across socio-demographic attributes. Significant differences identified by Kruskal–Wallis and Fisher’s exact tests.
Fig. 4. Perceived drivers of forest change, categorized by community members as positive, neutral, or negative, and visualized over 5- and 10-year recall periods across forest patches.
Fig. 5. Thematic convergence matrix comparing remote sensing and local narrative insights for each forest patch. Final column indicates level of agreement: full convergence, partial convergence, or dissonance.

Key Takeaways

  • Remote sensing and community narratives converge on major deforestation hotspots.
  • Divergences emerge in ecologically complex or historically degraded landscapes.
  • Mixed-method integration reveals fine-scale changes often missed by satellites.
  • Convergence frameworks support context-sensitive conservation and enhance legitimacy.

Methodological Insights

This work applies a convergence coding matrix to align remote sensing-derived forest cover trajectories with community-reported change. Such integration enables triangulation of quantitative and qualitative data, increasing the robustness and depth of forest change assessments.

Conservation Implications

By combining satellite data with local knowledge, this project enhances forest monitoring practices and supports adaptive, inclusive, and just conservation strategies. It demonstrates the utility of participatory data in informing national and regional forest policies across West Africa.