Monitoring Desertification in Tunisia Using Modis Ecological Indicators and Machine Learning

This study aims to assess the severity of desertification in Tunisia by using machine learning and deep learning models to analyze remote sensing data. By employing MODIS ecological indicators such as Modified Soil-Adjusted Vegetation Index (MSAVI), Normalized Difference Vegetation Index (NDVI), Albedo and Topsoil Grain Size Index (TGSI), different models including eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), LGBM, Convolutional Neural Networks (CNN), Variational Autoencoder (VAE) and Recurrent Neural Networks (RNN) will be trained and compared to effectively monitor desertification trends in Tunisia from 2016 to 2022. The evaluation using metrics such as accuracy, F1 score, recall, and precision showed that XG-Boost performed best with an accuracy of 92.35%. The study also identified a negative trend of desertification affecting the entire country. This research highlights the importance of using computer vision to monitor and address the challenges of desertification and suggests potential applications for environmental management.