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<p>Urban heat stress is an escalating environmental risk in rapidly industrialising regions of India, where land-use transformation and industrial expansion interact with background climatic warming. This study examines the spatio-temporal evolution of land surface temperature (LST) and the Urban Heat Index (UHI_index) across Telangana, India, over the period 2003–2023, with specific emphasis on the thermal influence of major industrial zones. Multi-temporal Landsat imagery, land-use information, and meteorological variables were analysed within a geospatial data analytics framework to quantify UHI intensity, temporal trends, and industrial attribution. Results indicate systematic amplification of UHI_index within industrial areas relative to non-industrial zones. Mean UHI_index in industrial regions increased from approximately 1.35 in 2003 to 2.70 in 2023, whereas non-industrial areas exhibited a lower increase from 1.00 to 1.82. Machine learning models demonstrated strong predictive performance (R2 ≈ 0.87; RMSE ≈ 0.26), identifying industrial land use, built-up density, and near-surface air temperature as dominant drivers. Monte Carlo uncertainty analysis (n = 100) indicates stable regional inference (± 0.18 UHI_index). The findings provide quantitative evidence of industrial contributions to urban thermal intensification and offer a reproducible framework for industrial heat mitigation planning in rapidly developing Indian states.</p>

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Data-driven assessment of industrial influences on urban heat index dynamics across Telangana, India

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月1日

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  • Bhogayya Naidu,
  • Tan Kuan Tak,
  • Sivaneasan Bala Krishnan,
  • Vinay Kumar Gaddam,
  • Shankar Karuppannan

摘要

Urban heat stress is an escalating environmental risk in rapidly industrialising regions of India, where land-use transformation and industrial expansion interact with background climatic warming. This study examines the spatio-temporal evolution of land surface temperature (LST) and the Urban Heat Index (UHI_index) across Telangana, India, over the period 2003–2023, with specific emphasis on the thermal influence of major industrial zones. Multi-temporal Landsat imagery, land-use information, and meteorological variables were analysed within a geospatial data analytics framework to quantify UHI intensity, temporal trends, and industrial attribution. Results indicate systematic amplification of UHI_index within industrial areas relative to non-industrial zones. Mean UHI_index in industrial regions increased from approximately 1.35 in 2003 to 2.70 in 2023, whereas non-industrial areas exhibited a lower increase from 1.00 to 1.82. Machine learning models demonstrated strong predictive performance (R2 ≈ 0.87; RMSE ≈ 0.26), identifying industrial land use, built-up density, and near-surface air temperature as dominant drivers. Monte Carlo uncertainty analysis (n = 100) indicates stable regional inference (± 0.18 UHI_index). The findings provide quantitative evidence of industrial contributions to urban thermal intensification and offer a reproducible framework for industrial heat mitigation planning in rapidly developing Indian states.