Artificial intelligence and remote sensing for spatiotemporal analysis and prediction of vegetation changes: a case study of El Oued, northeastern Algeria

Abstract

This study investigates vegetation ecology and rangeland degradation in the arid and desert regions of northeastern Algeria from 2015 to 2023, with predictions extending to 2031. Utilizing remote sensing techniques, specifically Landsat 8 and Landsat 9 imagery, and the Modified Soil Adjusted Vegetation Index (MSAVI), we mapped and analyzed changes in vegetation cover. The QGIS MOLUSCE plugin facilitated spatiotemporal analysis, revealing significant natural and anthropogenic impacts on a study area. Less degraded rangelands have also seen an increase, from 44,110,116 hectares in 2015 to 48,481,851 hectares in 2023, due to drought, overgrazing, and socioeconomic pressures. With a slight decrease to 48,450,495 hectares projected for 2031. This suggests efforts in land rehabilitation and sustainable rangeland management are yielding positive results, though there remains room for improvement. The study recorded that Agricultural croplands have experienced a slight decrease from 1,404,153 hectares in 2015 to 1,350,693 hectares in 2023. However, a modest increase to 1,587,242 hectares is projected by 2031. The findings indicate that Oases date palm areas have increased from 1,287,342 hectares in 2015 to 1,630,197 hectares in 2023, with further growth to 1,816,296 hectares anticipated by 2031. Our research highlights the critical role of remote sensing in monitoring vegetation dynamics within arid and desert ecosystems. The methodology provides a robust framework for similar studies, emphasizing the necessity for ongoing monitoring using advanced télédétection technologies. To mitigate further degradation and preserve these fragile ecosystems, the implementation of sustainable land management practices is essential.