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March 15, 2026Urban Science5 citationsOpen Access

Evaluation of the Land Use Land Cover Impact on Surface Temperature and Urban Thermal Comfort: Insight from Saudi Arabia’s Five Most Populated Cities (2000-2024)

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AAAmal H. Aljaddani

Key Points

  • This research investigates how land use land cover transitions affect surface temperatures and urban thermal comfort in Saudi Arabia's major cities.
  • Analysis of land use land cover using Landsat imagery from 2000 to 2024
  • Random forest machine learning applied to thermal indices
  • Generation of LULC maps categorized by urban area, barren land, vegetation, and water
  • Difference-in-difference method to compute thermal effects
  • Overall accuracy of LULC classifications between 89.00% and 97.00%
  • Urban area increased significantly, particularly in Riyadh by 448.84 km2
  • LST rose by 8.31 °C in Riyadh and 5.24 °C in Jeddah from 2000 to 2024
  • Vegetation cover increased in most cities with varying impacts on LST

Abstract

Since 2025, 45% of the world’s population of 8.2 billion people has lived in cities, and by 2050, that number is expected to increase to 66%. As the number of people living in cities increases, natural landscapes will be transformed into impervious surfaces, leading to serious challenges and resulting in a phenomenon named the urban heat island (UHI) effect. Although urban thermal variation has been studied globally, few studies have examined the impact of land use transitions on local surface temperatures. This study aims to address this gap by investigating the impact of LULC transitions on the land surface temperature (LST) and the urban thermal field variation index (UTFVI) in the five most populated cities in Saudi Arabia between 2000 and 2024: Riyadh, Jeddah, Makkah, Madinah, and Dammam. This study provides not only a comprehensive overview of the cities in Saudi Arabia but also a detailed analysis of each city using a novel approach that integrates thermal land use analysis. In this study, Landsat TM-5, OLI-TIRS-8, and OLI2-TIRS2-9 were used to process the LULC using random forest machine learning and thermal indices. Fifteen LULC maps were generated and assessed based on four classifications across the cities and time periods: urban area, barren land, vegetation, and water. The difference-in-difference (DiD) analytical approach was used to compute the thermal effect size and compare the specified changed pixels (barren-to-urban, vegetation-to-urban) with stable urban. Then, the relationship between the LST and the NDVI–NDBI were investigated. The results show that the overall accuracy of the 15 LULC classifications ranged from 89.00% to 97.00%. The urban area increased across all the cities, with the greatest changes being 448.84, 179.67, 177.96, 126.33, and 95.69 km2 in Riyadh, Jeddah, Dammam, Madinah, and Makkah, respectively. Furthermore, the vegetation cover increased in most of the cities over time. The LST of the urban areas increased by 8.31 °C in Riyadh, 5.24 °C in Jeddah, and 1.41 °C in Makkah in 2024 compared to 2000, while those in Dammam and Madinah decreased by 2.67 °C and 0.60 °C, respectively. This study delivers robust insights into two decades of urban surface temperature dynamics across major Saudi Arabian cities, offering critical evidence to inform UHI mitigation strategies and support the long-term sustainability of urban environments.

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Cite This Study

Amal H. Aljaddani (2026) studied this question.

synapsesocial.com/papers/69b606af83145bc643d1cd55https://doi.org/10.3390/urbansci10030157
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