abdelfatah-chaib/SylvaGuard-Forest-Degradation-Prediction

SylvaGuard is an intelligent system designed to monitor, analyze, and predict forest degradation, using the Maâmora Forest (Morocco) as a case study. ## Project Description SylvaGuard is an intelligent system designed to monitor, analyze, and predict forest degradation, with the Maâmora Forest (Morocco) as the study area. The project leverages satellite imagery and environmental indices to detect early signs of degradation, anticipate risks, and support sustainable forest management and decision-making. ## Objectives Monitor forest vegetation dynamics over time Detect environmental degradation patterns Predict future degradation risks using AI models Support decision-making for sustainable forest conservation ## Data Sources Satellite imagery: Sentinel, Landsat Environmental indices: NDVI (Normalized Difference Vegetation Index) NDMI (Normalized Difference Moisture Index) LST (Land Surface Temperature) Climate, humidity, and vegetation-related data ## Methodology Geospatial Data Collection and Processing Google Earth Engine Rasterio GIS tools Feature Extraction Spectral indices Climatic and environmental variables Predictive Modeling Random Forest Ridge Regression LSTM (Long Short-Term Memory networks) Interactive Visualization Web-based interface developed using Streamlit ## Technologies Used Python Google Earth Engine TensorFlow Scikit-learn Streamlit Rasterio / GIS ## Impact SylvaGuard combines artificial intelligence, remote sensing, and spatial analysis to contribute to the sustainable protection of Moroccan forests, with a particular focus on the Maâmora Forest.