Recognition and Prediction of Land Dynamics and Its Associated Impacts in Al-Qurayyat City and along Al-Shamal Train Pathway in Saudi Arabia

• Main Authors: Seham S. Al-Alola, Haya M. Alogayell, Ibtesam I. Alkadi, Soha A. Mohamed, Ismail Y. Ismail
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Sustainability
• Subjects: sustainability; land-use change/land cover; change detection; image classification; change prediction; Markov chain model
• Online Access: https://www.mdpi.com/2071-1050/13/17/9913

Saudi Arabia has experienced substantial urban growth over the last few decades, transforming from rural to urban communities due to rapid economic growth. Saudi Arabia is ranked as one of the most urbanized countries, with more than 80% of its population existing in urban centers. Four Landsat imagery datasets acquired in 1989, 2002, 2013, and 2021 were used to estimate the dynamics of land cover and urban growth in Al-Qurayyat City and investigate the relationship between the construction of Al-Shamal train in 2011 and the land dynamics. The results emphasize a strong intercorrelation between the construction of the Al-Shamal train pathway and the land development and the rapid urbanization in Al-Qurayyat City. The results show that the urban and built-up area expanded from 1.96% to 7.25% between 1989 and 2021. Future prediction of land cover dynamics and urban growth in 2030 were estimated using the Markov chain and CA-Markov models. The findings of future prediction show that more than 60% of the total area of Al-Qurayyat City will transform into urban and built-up areas by 2030. The dramatic increase in urban and built-up areas and the subsequent reduction in other land cover types will impact the environmental sustainability of Al-Qurayyat City. The findings in this paper recommend smart growth, which guarantees environmentally friendly development for future land use/land cover planning in Al-Qurayyat City. This study will be beneficial to the urban planner and policymakers for proper sustainable development decisions by exploring the land cover changing pattern and the trends of urban expansion.