Random Forest and Sentinel-2 Based Approach for Evaluating Climate Change on Olive Trees

The olive industry in Morocco's Meknes region plays a vital role in the economy, creating employment and enhancing GDP. However, climate change has led to a slight decline in olive yields. This study utilizes Sentinel-2 satellite imagery data to analyze physical changes in olive orchards from 2019 to 2023. Through the application of a random forest algorithm for classification, the study evaluates changes in land cover and performance metrics such as confusion matrix, overall accuracy, and Kappa coefficient. Findings indicate a significant decrease (29.13%) in olive-covered land area by 2023 compared to 2019, attributed to climate-related factors including rising temperatures, irregular rainfall, and water stress. To address these challenges, optimized irrigation and pest management strategies are recommended. Leveraging remote sensing and Artificial Intelligence tools is essential for analyzing the complex dynamics between plants, climate, and the environment. Hence, proactive measures for climate change mitigation and adaptation are imperative to ensure the long-term sustainability of olive cultivation in the Meknes region.