Nelvinebi/Land-Use-Land-Cover-Change-Analysis-in-the-Niger-Delta-Using-Multi-Temporal-Satellite-Data-and-ML

Machine learning–based land use and land cover change analysis for the Niger Delta using synthetic multi-temporal satellite data. The project simulates real remote-sensing workflows to classify land cover types, detect environmental change, and support sustainable land management and planning decisions. Land Use Land Cover Change Analysis in the Niger Delta Using Multi-Temporal Satellite Data and Machine Learning 📌 Project Overview This project demonstrates a machine learning–driven approach to Land Use / Land Cover (LULC) classification and change detection in the Niger Delta using synthetic multi-temporal satellite data. It simulates real-world remote sensing workflows for environmental monitoring, urban expansion analysis, and ecosystem assessment. The system integrates spectral indices, supervised machine learning, and temporal comparison to identify land cover transitions over time. 🎯 Objectives Simulate multi-temporal satellite observations for the Niger Delta Classify land cover types using machine learning Detect and quantify land cover changes across time periods Provide a reproducible framework for environmental and geospatial research 🛰️ Land Cover Classes The model classifies pixels into the following categories: Water Bodies Vegetation Built-up Areas Bare Land Wetlands 🧠 Methodology Synthetic Data Generation Multi-temporal satellite-like data representing spectral bands (Red, NIR, Green, SWIR) were generated to mimic real satellite observations. Feature Engineering Vegetation and water indices such as NDVI and NDWI were computed to improve class separability. Machine Learning Classification A Random Forest Classifier was trained to perform LULC classification for each time period. Change Detection Analysis Classified maps from different years were compared to identify land cover transitions and spatial trends. Evaluation & Visualization Model performance metrics and feature importance analysis were generated to validate results. 🧪 Technologies Used Python NumPy Pandas Scikit-learn Matplotlib 📂 Project Structure ├── lulc_change_niger_delta_ml.py ├── lulc_change_niger_delta_dataset.xlsx ├── README.md ▶️ How to Run Clone the repository: git clone github.com Install depend …