Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning

The argan tree (Argania spinosa) is a rare species native to southwestern Morocco, valued for its fruit, which produces argan oil, a highly prized natural product with nutritional, health, and cosmetic benefits. However, increasing deforestation poses a significant threat to its survival. This study monitors changes in an argan forest near Agadir, Morocco, from 2017 to 2023 using Sentinel-2 satellite imagery and advanced image processing algorithms. Various machine learning models were evaluated for argan tree detection, with LightGBM achieving the highest accuracy when trained on a dataset integrating spectral bands, temporal features, and vegetation indices information. The model achieved 100% accuracy on tabular test data and 85% on image-based test data. The generated deforestation maps estimated an approximate forest loss of 2.86% over six years. This study explores methods to enhance detection accuracy, provides valuable statistical data for deforestation mitigation, and highlights the critical role of remote sensing, advanced image processing, and artificial intelligence in environmental monitoring and conservation, particularly in argan forests.