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<p>With the continuous evolution of urban surface types, the impact of the urban heat island effect on the human population has intensified. Investigating the factors influencing urban thermal environments is crucial for providing theoretical support to urban planning and decision-making. In this study, Shenyang was selected to comprehensively analyse multiple factors, including topography, human activity, vegetation and landscape. Moreover, we used the random forest algorithm to explore nonlinear factors influencing land surface temperature (LST) over four years in the study area. The results revealed that from 2005 to 2020, the total areas with sub-high and high-temperature zones in northern Shenyang steadily increased. The area ratio of these zones increased from 20.18% in 2005 to 24.86% in 2020. Additionally, significant and strong correlations were observed between LST and variables such as the enhanced vegetation index (EVI), normalised difference vegetation index (NDVI), population density, proportion of cropland and proportion of impervious land. In 2010, proportion of impervious land exhibited the strongest correlation with LST at the 5 km scale, reaching 0.852 (<i>p</i>&lt;0.01). The 4 km grid scale was identified as the optimal grid size for this study, while the 2 km grid performed the worst. In 2020, NDVI emerged as the most significant factor influencing LST. These findings provide valuable guidance for improving urban planning and developing sustainable strategies.</p>

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Investigating nonlinear factors influencing multi-scale urban land surface temperature using machine learning models

  • 期刊论文

  • 发表日期：

    2025年9月23日

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  • Yue Shi,
  • Qiang Fan,
  • Shuang Sun,
  • Xiaonan Song,
  • Bing Zhang

摘要

With the continuous evolution of urban surface types, the impact of the urban heat island effect on the human population has intensified. Investigating the factors influencing urban thermal environments is crucial for providing theoretical support to urban planning and decision-making. In this study, Shenyang was selected to comprehensively analyse multiple factors, including topography, human activity, vegetation and landscape. Moreover, we used the random forest algorithm to explore nonlinear factors influencing land surface temperature (LST) over four years in the study area. The results revealed that from 2005 to 2020, the total areas with sub-high and high-temperature zones in northern Shenyang steadily increased. The area ratio of these zones increased from 20.18% in 2005 to 24.86% in 2020. Additionally, significant and strong correlations were observed between LST and variables such as the enhanced vegetation index (EVI), normalised difference vegetation index (NDVI), population density, proportion of cropland and proportion of impervious land. In 2010, proportion of impervious land exhibited the strongest correlation with LST at the 5 km scale, reaching 0.852 (p<0.01). The 4 km grid scale was identified as the optimal grid size for this study, while the 2 km grid performed the worst. In 2020, NDVI emerged as the most significant factor influencing LST. These findings provide valuable guidance for improving urban planning and developing sustainable strategies.