The land use history, economic drivers, and future trends of urban growth in Saudi Arabia



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This dissertation investigates the land use history, economic drivers, and future trends of urban growth in Saudi Arabia. It consists of five chapters, including Introduction and Conclusions as the first chapter and last chapter, respectively. Chapter two aimed to use satellite observations to produce a long-term dataset at 30-meter spatial resolution for the 13 capital cities in Saudi Arabia between 1985 and 2019. This chapter has been published as a peer-reviewed article in the journal of Remote Sensing. In this chapter, I downloaded all available Landsat data, including Thematic Mapper (TM 4-5), Enhanced Thematic Mapper Plus (ETM+ 7), and Operational Land Imager (OLI 8). After that, urban and non-urban training samples were collected and fed into a Change detection and classification algorithm (CCDC) that used a random forest classifier (RFC). The CCDC algorithm was used to produce the annual classification maps for the 13 capital cities in the first month of July of each year. Then, historical maps of urban growth were compiled between 1985 and 2019 to monitor the changes in urban growth. I implemented a stratified random sampling design to assess the annual classification maps and multi-temporal urban change maps, which also provided the area estimation and uncertainties. Higher overall accuracy was found in the annual classification maps and the multi-temporal urban change maps. In 1985, 2000, and 2019, the urban area occupied 13.23, 14.96, and 27.43% of the total area, respectively. Chapter three examined the Granger-causality relationship between urban growth and economic variables in Saudi Arabia, such as real GDP, inflation, merchandise imports and exports, and oil rents. First, I implemented the Augmented-Dickey-Fuller (ADF), Phillips & Perron (PP), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) on the initial time series. Then, the first difference of the log was implemented when the time series confirmed non-stationarity. After that, the bivariate Granger-causality was performed on the stationary urban economic time series. The results showed one bidirectional relationship between the urban growth and the real GDP growth, a unidirectional relationship between the urban growth and merchandise imports growth, and a unidirectional relationship between merchandise exports growth and urban growth. There is no Granger causality between urban growth and inflation and between the urban growth and oil rents growth. Chapter four aimed to predict the future of urban growth in Riyadh, Saudi Arabia. In this chapter, the prediction models were based on the driving forces that produced the land suitability maps under three different scenarios: business as usual (BAU), rapid economic growth (REG), and Integrated environmental sustainability (IES). I used cellular automata, Markov chain (CA-Markov), and a multilayer perceptron (MLP) neural network that integrated the driving forces of each scenario. Next, the validation process was implemented for each scenario between the prediction maps of 2019 and the actual classification maps of 2019. The results showed that the Kappa standard of CA-Markov and MLP neural networks was moderate and above 65%. The urban area of the CA-Markov showed a substantial increase over 2019, 2030, and 2050 compared with MLP neural network. Overall, studies included in this dissertation provided a comprehensive understanding of the land use history, the economic drivers, and the likely future scenarios of urban growth in Saudi Arabia. The findings of the dissertation provide the scientific basis for public policy-making to improve urban planning design and environmental sustainability.

Embargo status: Restricted until 09/2023. To request the author grant access, click on the PDF link to the left.



CCDC, Urban Growth, Time Series, Landsat, Stratified Sampling, Land Use and Land Cover Change, Saudi Arabia