Selected Driver Variables for the Simulation of Land-Use and Land-Cover Change for the Republic of Djibouti: A Study from Semi-Arid Region
Keywords: climate change impacts, Djibouti, Land cover dynamics, LULC stimulation, machine learnings, arid landscapes
Abstract. This study aims to integrate driver variables with a land use change model (LCM) to explore their impact on the natural environment within the context of land-use changes in the Republic of Djibouti, considering possible Business-as-usual scenarios. Secondary data from 1990 and 2012 on land use land cover (LULC) were analyzed, with a 2022 map generated by adopting the same method of secondary data used (random forest classification in Google Earth Engine (GEE)) for validation. Eight key driver variables were utilized to model plausible future land cover (2035) for Djibouti. Statistical outputs and change maps from the LCM were compared to gauge historical change estimates and simulated scenarios. Analysis from 1990 to 2022 highlights significant land use and cover changes spurred by urbanization, environmental factors, and economic development. Barren land and bushland dominated, while built-up areas and water bodies expanded notably. Urbanization, agriculture, and climate change contributed to vegetation degradation, with declines in mangroves and increases in built-up areas. Water bodies also expanded during this period. Projections from the 2035 LULC map anticipate further urban expansion, underscoring the need for sustainable land management practices. In conclusion, comprehensive land-use planning, interdisciplinary approaches, and stakeholder engagement are deemed critical for addressing Djibouti's socio-economic and environmental challenges and steering towards a sustainable future. These simulated results offer valuable insights for regional governments to frame strategic policies and assess management actions for resource utilization amidst urbanization and population growth trends.