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<p>Urban Heat Islands (UHIs) pose a significant challenge to sustainable urban planning, impacting energy consumption, air quality, and public health due to elevated temperatures from dense urban development, impermeable surfaces, and reduced green spaces. This study evaluates the accuracy of surface temperature analyses using unmanned aerial vehicle (UAV)-based thermal imagery in the Konak district of İzmir, Turkey, employing the Local Climate Zone (LCZ) classification system to compare analyses based on assigning maximum and minimum temperature values (where pixel-based direct temperature data are unavailable) with those using images with direct pixel-based actual degree values. Key findings reveal a strong correlation between the two methods, with Spearman’s rank correlation coefficient (ρ) approximating 0.99 (<i>p</i> ≈ 1.34e-11), and heat load maps showing an R² of 0.97, while average land surface temperature (LST) maps exhibit an R² of 0.89, indicating that estimated temperature values derived with appropriate methods serve as a reliable alternative in data-constrained environments. These results provide valuable insights for urban planners, environmental scientists, and climate researchers, demonstrating the potential of simplified analytical approaches to mitigate UHI effects effectively.</p>

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Planning cooler cities: evaluating the accuracy of temperature assignments versus thermal imaging in LCZ-Based urban climate analysis

  • 期刊论文

  • 发表日期：

    2025年11月11日

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  • Ayşe Kalayci,
  • Gökçe Gönüllü Sütçüoğlu

摘要

Urban Heat Islands (UHIs) pose a significant challenge to sustainable urban planning, impacting energy consumption, air quality, and public health due to elevated temperatures from dense urban development, impermeable surfaces, and reduced green spaces. This study evaluates the accuracy of surface temperature analyses using unmanned aerial vehicle (UAV)-based thermal imagery in the Konak district of İzmir, Turkey, employing the Local Climate Zone (LCZ) classification system to compare analyses based on assigning maximum and minimum temperature values (where pixel-based direct temperature data are unavailable) with those using images with direct pixel-based actual degree values. Key findings reveal a strong correlation between the two methods, with Spearman’s rank correlation coefficient (ρ) approximating 0.99 (p ≈ 1.34e-11), and heat load maps showing an R² of 0.97, while average land surface temperature (LST) maps exhibit an R² of 0.89, indicating that estimated temperature values derived with appropriate methods serve as a reliable alternative in data-constrained environments. These results provide valuable insights for urban planners, environmental scientists, and climate researchers, demonstrating the potential of simplified analytical approaches to mitigate UHI effects effectively.