Current and Potential Land Use/Land Cover (LULC) Scenarios in Dry Lands Using a CA-Markov Simulation Model and the Classification and Regression Tree (CART) Method: A Cloud-Based Google Earth Engine (GEE) Approach

Abstract

Rapid population growth accelerates changes in land use and land cover (LULC), straining natural resource availability. Monitoring LULC changes is essential for managing resources and assessing climate change impacts. This study focused on extracting LULC data from 1993 to 2024 using the classification and regression tree (CART) method on the Google Earth Engine (GEE) platform in Qena Governorate, Egypt. Moreover, the cellular automata (CA) Markov model was used to anticipate the future changes in LULC for the research area in 2040 and 2050. Three multispectral satellite images—Landsat thematic mapper (TM), enhanced thematic mapper (ETM+), and operational land imager (OLI)—were analyzed and verified using the GEE code editor. The CART classifier, integrated into GEE, identified four major LULC categories: urban areas, water bodies, cultivated soils, and bare areas. From 1993 to 2008, urban areas expanded by 57 km2, while bare and cultivated soils decreased by 12.4 km2 and 42.7 km2, respectively. Between 2008 and 2024, water bodies increased by 24.4 km2, urban areas gained 24.2 km2, and cultivated and bare soils declined by 22.2 km2 and 26.4 km2, respectively. The CA-Markov model’s thematic maps highlighted the spatial distribution of forecasted LULC changes for 2040 and 2050. The results indicated that the urban areas, agricultural land, and water bodies will all increase. However, as anticipated, the areas of bare lands shrank during the years under study. These findings provide valuable insights for decision makers, aiding in improved land-use management, strategic planning for land reclamation, and sustainable agricultural production programs