Land Use Change in Errachidia Oases Morocco using GEE and machine learning

Abstract. Classifying land use and land cover (LULC) is essential for monitoring change and protecting the environment, especially in arid regions where ecosystems are fragile and vulnerable to climate change. This research focuses on Errachidia, an arid region in Morocco known for its natural oases, which are vital to the ecological ecosystems and the living conditions of the local population.To study changes in land use and land cover (LULC), 10-metre resolution Sentinel 2 satellite imagery was used from 2017 to 2023. Based on ground truth samples, classification was made by using Google Earth classifiers, namely Random Forest (RF), Support Vector Machine (SVM) and Cartesian Regression Trees (CART). Among these models, the Random Forest (RF) classifier outperformed the others, achieving an impressive accuracy of 94% and a kappa coefficient of 0.89, proving its strength in dealing with the challenges associated with arid environments. Looking to the future, we wanted to predict how land use would change between 2023 and 2030. Using the geemap Python package with GEE, RF and Cellular Automata-Markov (CA-Markov) were compared to predict future trends. While RF excelled in categorizing current land use with an accuracy of 93.17% and a kappa coefficient of 0.71, CA-Markov proved to have an accuracy of 93.82% and a kappa coefficient of 0.75 for long-term predictions. The predictions revealed alarming trends: The risk of desertification in Errachidia is increasing as agricultural areas decrease and desert-like sandy areas increase. The ability of the CA-Markov system to model how land changes over space and time made it particularly effective in capturing these changes. This study highlights the strengths of using GEE-RF classification and CA-Markov prediction for long-term future changes. This study underscores the efficacy of RF classification and CA-Markov in predicting long-term future changes. By identifying these changes, policymakers and land managers can take steps to combat land degradation and ensure the long-term health of these fragile ecosystems. By detecting these changes, policymakers and land managers can take steps to address land changes and ensure the long-term health of these fragile ecosystems.