Predicting spatio-temporal land use / land cover changes and their drivers forces based on a cellular automated Markov model in Ibb City, Yemen

Abstract

Land use / land cover (LULC) change profoundly impacts regional natural, economic, and ecological development; However, no study has been conducted to classify LULC changes in Ibb city using high-resolution satellite images. This study aims to evaluate and predict LULC changes and their driving forces in Ibb city, the tourist capital of Yemen. In this study, an integrated cellular automata and Markov chain (CA-Markov) model were implemented to analyze the spatio-temporal trends of Ibb city. Meanwhile, a socio-economic survey and key informant interviews were conducted to analyze the probable drivers of LULC change. Landsat (5, 7, and 8) data are used to analyze maps of LULC distributions for 1990, 2005, and 2020 at regular intervals. A CA–Markov model was employed to simulate long-term changes to the landscape at 15-year intervals from 2020 to 2050. Results indicate that the vegetation area decreased from 1760.4 km2 (33.2%) to 1371.7 km2 (27.8%). Meanwhile, barren land, grassland, and built-up areas increased from 3190.5 km2 (60.1%) to 3428.6 km2 (64.8%), from 336.0 km2 (6.3%) to 419.3 km2 (7.9%), and from 11.8 km2 (0.38%) to 76.2 km2 (1.42%), respectively. The CA-Markov model’s accuracy was validated by comparing simulated and actual LULC maps for 2020 using the land change modeler (LCM) of IDRISI-TerrSet software. The predicted LULC maps for 2035 and 2050 indicate that the vegetation area, grassland, and barren land showed decreasing trends, while the built-up area and waterbody showed increasing trends. These results provide valuable insights for tracking future LULC changes and are pivotal in guiding sustainable land use practices, striking a balance between conserving natural resources and advancing urban development projects in the future. HIGHLIGHTS Driving factors on land-cover transitions tend to be spatiotemporal scale-dependent. The study analyzed changes in LULC trends and their Proximate and Underlying drivers. Significant changes were observed between 1990 and 2020, and changes are expected by 2035 and 2050. The integration of the CA-Markov Model and GIS techniques successfully simulated spatiotemporal LULC changes. Fine-tuned analysis of driving forces is essential for planning and managing sustainable land use.

Keywords

• lulc change
• ca-markov model
• driving forces
• change detection
• predict lulc change
• ibb city