Digital twin and mixed-reality technologies are emerging as transformative tools in ecosystem restoration, offering innovative approaches to monitor, predict, and visualize ecological changes. Digital twins replicate ecosystems in virtual environments by integrating real-time IoT sensor data, satellite imagery, and historical records, enabling dynamic modeling and scenario testing. However, despite their potentials, the application of these technologies in Sub-Saharan Africa’s region facing severe ecosystem degradation remains underexplored. This study focused on addressing ecosystem challenges in two contrasting regions: the Sahel, characterized by semi- arid conditions and land degradation, and the Congo Basin, a biodiversity-rich tropical forest under threat from deforestation. By combining IoT sensor data, and machine learning, a digital twin framework was developed to monitor and forecast biodiversity changes under various climatic scenarios. The framework utilized a Linear Regression model, achieving exceptional predictive accuracy (R = 0.987, R² = 0.975), demonstrating that soil moisture, air temperature, and humidity are strong predictors of vegetation health. Mixed-reality tools were employed to visualize restoration outcomes, bridging communication gaps between scientists, policymakers, and local communities. The findings underscore the framework's potential to inform conservation policies, optimize resource allocation, and enhance climate adaptation strategies. Despite challenges in data integration and stakeholder adoption, the study highlights the scalability and adaptability of digital twin and mixed-reality technologies for resource- constrained environments. Future research should focus on refining predictive models, expanding IoT sensor networks, and fostering collaboration among stakeholders to maximize the impact of these technologies on ecosystem restoration and sustainable development in Sub- Saharan Africa.
Monye et al. (Thu,) studied this question.