Nelvinebi/Deforestation-and-Vegetation-Loss-Monitoring-in-Southern-Nigeria-Using-Remote-Sensing-and-ML

AI-driven framework for monitoring deforestation and vegetation loss in Southern Nigeria using synthetic remote sensing data and machine learning. Designed for reproducibility, policy simulation, and capacity building in data-scarce regions, with scalable pathways to real satellite-based forest monitoring systems. # 🌍 Deforestation and Vegetation Loss Monitoring in Southern Nigeria ### Using Remote Sensing and Machine Learning (Synthetic Data Approach) ## 📌 Project Overview Deforestation and vegetation degradation pose significant threats to biodiversity, climate regulation, and livelihoods in Southern Nigeria. However, reliable long-term environmental datasets are often limited or inaccessible. This project presents a **fully reproducible, data-scarce-friendly framework** for monitoring deforestation and vegetation loss using **synthetic but physically realistic remote sensing data** and **machine learning techniques**. The methodology mirrors real-world satellite-based workflows (e.g., Sentinel-2, Landsat) while remaining accessible for research, training, and policy simulation. The project demonstrates how **Environmental AI** can support early warning systems, land-use monitoring, and climate resilience planning in developing regions. --- ## 🎯 Objectives - Simulate realistic remote sensing indicators for Southern Nigeria - Derive vegetation indices commonly used in forest monitoring - Apply machine learning to detect deforestation and vegetation loss - Provide a reproducible pipeline suitable for: - Research and academic use - Capacity building and training - Policy and scenario analysis in data-scarce regions --- ## 🛰️ Simulated Remote Sensing Variables The synthetic dataset represents pixel-level observations similar to satellite imagery products. ### Raw Environmental Features - **Red Band Reflectance** - **Near-Infrared (NIR) Band Reflectance** - **Land Surface Temperature (K)** - **Annual Rainfall (mm)** - **Soil Moisture** - **Elevation (m)** ### Derived Vegetation Indices - **NDVI (Normalized Difference Vegetation Index)** - **EVI (Enhanced Vegetation Index)** - **NBR (Normalized Burn Ratio)** ### Target Variable - **Deforestation Label** - `1` → Vegetation loss / deforestation - `0` → Stable vegetation cover --- ## 🧠 Machine Learning Concept The datase …