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<p>Unplanned urban expansion profoundly transforms the thermal landscape of cities, notably intensifying the urban heat island phenomenon. This study systematically examines the spatial and temporal interplay among land surface temperature, land use/land cover transformations, and key geospatial indices NDVI, NDWI, and NDBI in Visakhapatnam, India, over the decade from 2012 to 2022. Leveraging multi-temporal Landsat imagery and employing Support Vector Machine-based supervised classification, four principal LULC categories were delineated and quantified. LST was derived from thermal infrared bands, while its relationships with land surface indices were rigorously assessed using both regression analysis and Pearson’s correlation. The results reveal substantial urban expansion, with built-up areas increasing by 22.67%, accompanied by significant losses in vegetative cover (down by 28.52%) and water bodies (down by 49.71%). These dramatic land cover shifts directly impacted the city’s thermal environment: maximum LST escalated from 35.23&#xa0;°C in 2012 to 40.87&#xa0;°C in 2022, representing an average annual increment of 0.564&#xa0;°C. Mean LST within built-up areas rose sharply by 6.6&#xa0;°C, from 29.3&#xa0;°C to 35.9&#xa0;°C during the study period. Strong positive correlations were found between LST and NDBI (R = 0.95), while NDVI (R = –0.94) and NDWI (R = –0.96) demonstrated robust inverse relationships with LST, underscoring the thermal regulatory roles of vegetative and aquatic surfaces. Regression models yielded high coefficients of determination (R<sup>2</sup> &gt;0.98), affirming the predictive capacity of geospatial indices for LST dynamics. This research delivers novel empirical insights into how patterns of urbanization shape thermal regimes in rapidly growing coastal cities of India. By integrating remote sensing with advanced spatial analytics, it generates actionable knowledge to inform climate-sensitive urban planning. The findings strongly advocate for the preservation and restoration of vegetative and water surfaces as core strategies to mitigate UHI effects and support sustainable, climate-resilient urban development in regions undergoing rapid urban transformation.</p>

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Assessing the Urban thermal dynamics: a geospatial study of land surface temperature, land use/land cover and spectral indices in Visakhapatnam, India

  • 期刊论文

  • 发表日期：

    2025年9月16日

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  • Priyanka Nyayapathi,
  • Sai Santosh Basina

摘要

Unplanned urban expansion profoundly transforms the thermal landscape of cities, notably intensifying the urban heat island phenomenon. This study systematically examines the spatial and temporal interplay among land surface temperature, land use/land cover transformations, and key geospatial indices NDVI, NDWI, and NDBI in Visakhapatnam, India, over the decade from 2012 to 2022. Leveraging multi-temporal Landsat imagery and employing Support Vector Machine-based supervised classification, four principal LULC categories were delineated and quantified. LST was derived from thermal infrared bands, while its relationships with land surface indices were rigorously assessed using both regression analysis and Pearson’s correlation. The results reveal substantial urban expansion, with built-up areas increasing by 22.67%, accompanied by significant losses in vegetative cover (down by 28.52%) and water bodies (down by 49.71%). These dramatic land cover shifts directly impacted the city’s thermal environment: maximum LST escalated from 35.23 °C in 2012 to 40.87 °C in 2022, representing an average annual increment of 0.564 °C. Mean LST within built-up areas rose sharply by 6.6 °C, from 29.3 °C to 35.9 °C during the study period. Strong positive correlations were found between LST and NDBI (R = 0.95), while NDVI (R = –0.94) and NDWI (R = –0.96) demonstrated robust inverse relationships with LST, underscoring the thermal regulatory roles of vegetative and aquatic surfaces. Regression models yielded high coefficients of determination (R2 >0.98), affirming the predictive capacity of geospatial indices for LST dynamics. This research delivers novel empirical insights into how patterns of urbanization shape thermal regimes in rapidly growing coastal cities of India. By integrating remote sensing with advanced spatial analytics, it generates actionable knowledge to inform climate-sensitive urban planning. The findings strongly advocate for the preservation and restoration of vegetative and water surfaces as core strategies to mitigate UHI effects and support sustainable, climate-resilient urban development in regions undergoing rapid urban transformation.