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Title: Analysing the Complexity of Urbanization and Land Use/Cover Changes and the Associated Environmental Impacts
Contributor(s): Alqurashi, Abdullah (author); Kumar, Lalit  (supervisor)orcid ; Reid, Nicholas  (supervisor)orcid 
Conferred Date: 2017-10-27
Copyright Date: 2017-01
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Related DOI: 10.1016/j.habitatint.2016.10.001

The present research analysed the expansion of urbanization and land cover changes and the associated environmental impacts in five Saudi Arabian cities: Riyadh, Jeddah, Makkah, AlTaif and Eastern Area using Landsat data. It first started by reviewing the related literature that have utlized the use of remote sensing (RS) data and techniques to quantify land use and land cover (LULC) changes and urban expansion. It was divided into three sections. First, it investigated the most used techniques to detect the LULC changes in the previous studies. Both pixel-based image analysis (PBIA) and object-based image analysis (OBIA) have been reviewed. An evaluation of the strengths, weaknesses and the acurracy of these techniques has been discussed. The second section focused on the spatial and statsitical techniques used to model the future LULC changes. The final section reviewed and discussed the previous studies that used RS data and techniques in Saudi Arabia.

The second research used two sets of Landsat images of 1985 and 2014 to map and monitor the spatial distribution of the urban extent among the five cities. A decision tree classifier was applied using object-based image analysis (OBIA) to analyse urban land cover in the five cities. The accuracy assessment of the urban change detection maps indicated a high overall accuracy and kappa coefficient. The results of this research show a high rate of urbanization and complex dynamics across the five cities. The significant changes were the result of a rapid increase in land development, exhibiting complex patterns in the urbanization process across the five cities. The government’s policy and increased oil revenues significantly contributed to increasing the urban cover in the five selected cities.

The third research investigated the effect of four driving forces, including elevation, slope, distance to drainage and distance to major roads, on urban expansion in the selected five cities. The prediction of urban probabilities in the selected cities based on the four driving forces was generated using a logistic regression model for two time periods of urban change in 1985 and 2014. The validation of the model was tested using two approaches. The first approach was a quantitative analysis by using the Relative Operating Characteristic (ROC) method. The second approach was a qualitative analysis in which the probable urban growth maps based on urban changes in 1985 was used to test the performance of the model to predict the probable urban growth after 2014 by comparing the probable maps of 1985 and the actual urban growth of 2014. The results indicate that the prediction model of 2014 provides a reliable and consistent prediction based on the performance of 1985 model. The analysis of driving forces shows variable effects over time. Variables such as elevation, slope and road distance had significant effects on the selected cities. However, distance to major roads was the factor with the most impact to determine the urban form in all five cites in both 1985 and 2014.

The fourth research quantified LULC changes and the effect of urban expansion in the five cities using Landsat images of 1985, 2000 and 2014. A total of 72 images including Multispectral Scanner (MSS) and Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) were acquired during winter (December to February), spring (March to May), summer (June to August) and autumn (September to November). The seasonal change of vegetation cover was conducted using Normalized Difference Vegetation Index (NDVI). OBIA was used to classify the LULC changes. The overall accuracies of the classified maps ranged from 82% to 96% which indicated sufficiently robust results. Urban area was the most changed land cover, and most of the converted land to urban was from bare soil. The seasonal analysis showed that the change of vegetation cover was not constant due to climatic conditions in these areas. The agricultural lands were significantly decreased between 1985 and 2014, and most of these lands were changed to bare soil. Dwindling groundwater resources has significantly led to a reduction in the agricultural practices, especially in Riyadh.

The fifth research analysed the expansion of urban growth and land cover changes in the five Saudi Arabian cities using Landsat TM, ETM+ and OLI images for the 1985, 1990, 2000, 2007 and 2014 time periods. The classification was carried out using OBIA to create land cover maps. The classified images were used to predict the land cover changes and urban growth for 2024 and 2034. The simulation model integrated the Markov Chain (MC) and Cellular Automata (CA) modelling methods and the simulated maps were compared and validated with the reference maps. The simulation results indicated high accuracy of the MC– CA integrated models. The total agreement between the simulated and the reference maps was > 92% for all the simulation years. The results indicated that all five cities showed a massive urban growth between 1985 and 2014 and the predicted results showed that urban expansion is likely to continue for 2024 and 2034 periods. The transition probabilities of land cover, such as vegetation and water, are most likely to be urban areas, first through conversion to bare soil and then to urban land use. Integrating of time-series satellite images and the MC–CA models provides a better understanding of the past, current and future patterns of land cover changes and urban growth in this region. Simulation of urban growth will help planners to develop sustainable expansion policies that may reduce future environmental impacts.

Publication Type: Thesis Doctoral
Fields of Research (FoR) 2008: 090905 Photogrammetry and Remote Sensing
090903 Geospatial Information Systems
Fields of Research (FoR) 2020: 401304 Photogrammetry and remote sensing
401302 Geospatial information systems and geospatial data modelling
Socio-Economic Objective (SEO) 2008: 960503 Ecosystem Assessment and Management of Coastal and Estuarine Environments
960510 Ecosystem Assessment and Management of Sparseland, Permanent Grassland and Arid Zone Environments
Socio-Economic Objective (SEO) 2020: 180601 Assessment and management of terrestrial ecosystems
HERDC Category Description: T2 Thesis - Doctorate by Research
Description: Please contact if you require access to this thesis for the purpose of research or study.
Appears in Collections:School of Environmental and Rural Science
Thesis Doctoral

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