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Spectral Mixture Analysis (SMA) Model for Extracting Urban Fractions from Landsat and Sentinel-2A Images in the Al-Ahsa Oasis, Eastern Region of Saudi Arabia

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  • Abdelrahim Salih

    (Department of Geography, Faculty of Arts, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

Abstract

The rapid expansion of urban areas is a major driver of deforestation and other associated damage to the local ecosystem and environment in arid and semi-arid oases, especially in the eastern region of Saudi Arabia. It is therefore necessary to accurately map and monitor urban areas to maintain the ecosystem services in these oases. In this study, built-up areas were mapped using a spectral mixture analysis (SMA) model in the Al-Ahsa Oasis in the eastern region of Saudi Arabia by analyzing Landsat images, including Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Operational Land Imager (OLI), and Sentinel-2A images, acquired between 1990 and 2020. Principle component analysis (PCA) was used to build and select endmembers, and then SMA was applied to each image to extract urban/built-up fractions. In addition, this study also discusses the possible driving forces of the urban dynamics. SMA classification performance was assessed using fraction error maps and a confusion matrix. The results show that the Al-Ahsa Oasis’ urban area had been rapidly expanding during 2010–2020 with an expansion rate of nearly 9%. The results also indicated that the SMA model provides high precisions (overall accuracy = ~95% to 100%) for an oasis urban mapping in an arid and semi-arid region that is disturbed by the mixed-pixel problem, such as the Al-Ahsa Oasis in eastern Saudi Arabia.

Suggested Citation

  • Abdelrahim Salih, 2023. "Spectral Mixture Analysis (SMA) Model for Extracting Urban Fractions from Landsat and Sentinel-2A Images in the Al-Ahsa Oasis, Eastern Region of Saudi Arabia," Land, MDPI, vol. 12(10), pages 1-17, September.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:10:p:1842-:d:1248603
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    References listed on IDEAS

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    1. Jinghu Pan & Tianyu Li, 2013. "Extracting desertification from Landsat TM imagery based on spectral mixture analysis and Albedo-Vegetation feature space," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 915-927, September.
    2. Abdulrahman Mohamed Almadini & Abdalhaleem Abdalla Hassaballa, 2019. "Depicting changes in land surface cover at Al-Hassa oasis of Saudi Arabia using remote sensing and GIS techniques," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-19, November.
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