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<p>Urban spatial form or urban spatial morphology significantly influence land surface temperature (LST) across various urban functional zones (UFZs). Understanding these impacts is crucial for mitigating the urban heat island (UHI) effect, enhancing climate resilience. This study explores the impact of the urban spatial forms on LST across different UFZs in Kolkata megacity region using a Bayesian approach. The analysis revealed distinct LST patterns across UFZs. Compact high-rise zones exhibited the highest mean LST i.e., 46 .25&#xa0;°C, and recreational zone recorded the lowest mean LST i.e., 34.43&#xa0;°C, likely due to substantial vegetation cover. The compact low-rise zone showed a high LST with mean LST of 41.69°C, reinforcing the UHI effect while open low-rise areas maintained cooler LST due to the presence of vegetation. The Bayesian linear regression analysis identified the combination of the normalized difference built-up index (NDBI), aggregation index (AI), and largest pattern index (LPI) as the best-supported model for explaining variations in LST. This model achieved the highest posterior probability (P(M|data) = 0.291) and a Bayes Factor (BF) of 206.587, providing strong evidence for the model's superior explanatory power. The Bayesian approach also revealed that alternative models, such as NDBI + AI + Building density and NDVI + NDBI + Building density, although these models also exhibited high R² values R² values of 0.998 and 0.99, respectively, had lower posterior probabilities (P(M|data) = 0.021 and 0.016). The findings provide valuable insights for developing targeted strategies to mitigate UHI effect, and promote sustainable urban development in rapidly growing megacities across the world.</p>

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Spatial configuration and its influence on land surface temperature across urban zones in Kolkata, India

  • 期刊论文

  • 发表日期：

    2026年7月25日

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  • Manob Das,
  • Arijit Das,
  • Suman Singha,
  • Ankita Roy

摘要

Urban spatial form or urban spatial morphology significantly influence land surface temperature (LST) across various urban functional zones (UFZs). Understanding these impacts is crucial for mitigating the urban heat island (UHI) effect, enhancing climate resilience. This study explores the impact of the urban spatial forms on LST across different UFZs in Kolkata megacity region using a Bayesian approach. The analysis revealed distinct LST patterns across UFZs. Compact high-rise zones exhibited the highest mean LST i.e., 46 .25 °C, and recreational zone recorded the lowest mean LST i.e., 34.43 °C, likely due to substantial vegetation cover. The compact low-rise zone showed a high LST with mean LST of 41.69°C, reinforcing the UHI effect while open low-rise areas maintained cooler LST due to the presence of vegetation. The Bayesian linear regression analysis identified the combination of the normalized difference built-up index (NDBI), aggregation index (AI), and largest pattern index (LPI) as the best-supported model for explaining variations in LST. This model achieved the highest posterior probability (P(M|data) = 0.291) and a Bayes Factor (BF) of 206.587, providing strong evidence for the model's superior explanatory power. The Bayesian approach also revealed that alternative models, such as NDBI + AI + Building density and NDVI + NDBI + Building density, although these models also exhibited high R² values R² values of 0.998 and 0.99, respectively, had lower posterior probabilities (P(M|data) = 0.021 and 0.016). The findings provide valuable insights for developing targeted strategies to mitigate UHI effect, and promote sustainable urban development in rapidly growing megacities across the world.