faneleedison-ux/monitoring-mangroves-peatlands-africa

NRF & SARAO research: Monitoring peatlands and mangroves to track evolution, identify unmonitored areas, and help reduce CO₂ levels to protect the Earth. # Monitoring Forests, Peatlands, and Mangroves in Africa This project focuses on monitoring and mapping **forests, peatlands, and mangroves** across Africa using remote sensing data and machine learning methods. These ecosystems are critical for climate regulation, carbon capture, and coastal protection, yet they remain under-monitored on the continent. --- ## ℹ️ About the Project This project is part of research initiatives by **NRF & SARAO**, focusing on: - Tracking changes in mangroves and peatlands over time - Identifying unmonitored areas for conservation - Supporting environmental research and global CO₂ reduction efforts --- ## 🌍 Project Objectives - Monitor the **evolution of peatlands and mangroves** over time. - Develop **machine learning models** (e.g., Random Forest, SVM) for mapping and classification. - Generate a **map inventory** of mangrove forests and peatlands. - Support **real-time monitoring solutions** for conservation efforts. --- ## 🛠️ Methodology - Use **satellite imagery** (Landsat, Sentinel-2, GeoMAD). - Apply **Random Forest classifiers** for peatland and mangrove detection. - Perform **feature importance analysis** and correlation studies. - Improve models by incorporating data from more African countries. --- ## 📂 Project Structure ``` monitoring-forests-peatlands-mangroves-africa/ │ ├── assets/ ├── data/ ├── notebooks/ ├── scripts/ ├── results/ ├── docs/ ├── requirements.txt ├── README.md ``` --- ## 📊 Sample Results --- ## 🚀 Future Work - Expand dataset coverage across more African countries - Collaborate with conservation agencies for validation - Explore **deep learning approaches** for improved classification - Integrate with **real-time monitoring systems** --- ## 📚 Resources & References - Landsat Surface Reflectance - Sentinel-2 - GeoMAD - Digital Earth Africa Documentation - Scalable Machine Learning Examples --- ## 👩‍💻 Team Members - **Fanelesibonge Mbuyazi** - Quinta Otieno - Felicity Musau - …