Johnson, SarahHakami, Thamer2024-03-032024-03-032024-03-01https://hdl.handle.net/20.500.14154/71546This study delves into a comprehensive examination relating to the dynamic Land Use and Land Cover (LULC) patterns of Riyadh Metropolitan, utilizing Landsat 8 and 9 satellite data to conduct a thorough analysis spanning the years 2013, 2018, and 2023. Riyadh, which is the capital city of Saudi Arabia, is emblematic of fast growing urbanization combined with developmental shifts, and this research seeks to unravel the intricacies of its evolving landscape. Urban expansion dynamics, a focal point of the study, are meticulously examined for each five-year interval from 2013 to 2018 and from 2018 to 2023. The study delves into a comprehensive analysis of the Land Use Land Cover (LULC) areas in Riyadh Metropolitan, meticulously examining seven key classes—Water, Built-Up, Roads, Sandy Soil, Bare Soil, Rocky Soil, and Vegetation—over the years 2013, 2018, and 2023. In 2013, the LULC composition revealed distinct areas, with highlights including 21074.76 hectares of Water, 104062.5 hectares of Built-Up, and 24842.61 hectares of Roads, indicative of the city's diverse environmental characteristics. The subsequent year, 2018, witnessed shifts reflective of urban expansion, with 3145.86 hectares of Water, 125339.3 hectares of Built-Up, and 14900.31 hectares of Roads. Fast forward to 2023, further transformations unfolded, exemplified by 4889.16 hectares of Water, 126858.2 hectares of Built-Up, and 19810.35 hectares of Roads, showcasing the city's dynamic growth and changing landscape. Ensuring the reliability of the LULC classification is a critical aspect of the study, and an accuracy assessment approach provides a quantitative measure of the classification model's precision. In 2013, the classification exhibited an impressive overall accuracy of 94.04%, with User's Accuracy ranging from 77.27% (Roads) to 100% (Vegetation). The Kappa Coefficient, a statistical metric for assessing agreement, reached a remarkable value of 0.92, confirming the 3 model's correctness over that specific time frame. The accuracy measures in 2018 showed consistent and improved performance, with a high Kappa Coefficient of 0.94. In 2023, the classification model demonstrated exceptional performance, achieving an overall accuracy of 94.21% and a Kappa Coefficient of 0.9242. The accuracy assessments highlight the dependability and consistency of the classification model, establishing trust in the study's findings and their implications for urban planning and environmental management in Riyadh. Keywords: LULC, Change Detection, Urbanization, LULC Dynamics.93en-USGISlanduseThesis