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<p>Land surface temperature (LST) is substantially influenced by landscape characteristics, as different surface features exhibit distinct thermal responses. The study assesses spatio-temporal dynamics of land use and land cover (LULC), LST, and its association with vegetation, water, built-up, and bare surfaces using spectral indices in the rapidly urbanizing Imphal city, Manipur. Multi-spectral and multi-temporal Landsat imagery (1994, 2004, 2014, and 2024) is used to derive LULC and LST and to analyze the relationship between LST and four spectral indices—the Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Bareness Index (NDBaI). Pearson’s correlation coefficient was applied to evaluate the strength and direction of the relationship. The findings indicate substantial LULC transformation with built-up land increasing from 58% in 1994 to 77.03% in 2024, accompanied by declines in agricultural land and vegetated areas. Seasonal maximum LST increases by 2.81&#xa0;°C in winter and 0.23&#xa0;°C in summer during 1994 to 2024. Correlation analysis demonstrates LST is positively associated with NDBI (r = 0.273 to 0.763, p &lt; 0.001) and NDBaI (r = 0.152 to 0.624, p &lt; 0.001), while showing negative relationships with NDVI (r = − 0.516 to − 0.047), with most associations significant at p &lt; 0.001 except a weak non-significant summer relationship in 2004 (r = − 0.047, p = 0.094) and MNDWI (r = − 0.502 to − 0.173, p &lt; 0.001). These results indicate that built-up and bare surfaces are generally associated with higher LST, whereas vegetation and water bodies are associated with lower LST, with seasonal variability in relationship strength. In addition, long-term trend analysis of annual maximum air temperature using the Mann–Kendall test revealed a statistically significant increasing trend (Z = 3.569, p &lt; 0.001), while Sen’s slope estimator indicated a warming rate of 0.036&#xa0;°C&#xa0;year <sup>−1</sup>. The study provides a scientific basis for planners and policy makers to support urban heat management, sustainable land use planning, and climate-responsive development strategies for Imphal.</p>

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Spatio-temporal variability of land surface temperature and its relationships with spectral indices in Imphal city, Manipur

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Renubala Rajkumari,
  • Mayengbam Binata Devi,
  • Md Hibjur Rahaman,
  • Shabina Hussain

摘要

Land surface temperature (LST) is substantially influenced by landscape characteristics, as different surface features exhibit distinct thermal responses. The study assesses spatio-temporal dynamics of land use and land cover (LULC), LST, and its association with vegetation, water, built-up, and bare surfaces using spectral indices in the rapidly urbanizing Imphal city, Manipur. Multi-spectral and multi-temporal Landsat imagery (1994, 2004, 2014, and 2024) is used to derive LULC and LST and to analyze the relationship between LST and four spectral indices—the Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Bareness Index (NDBaI). Pearson’s correlation coefficient was applied to evaluate the strength and direction of the relationship. The findings indicate substantial LULC transformation with built-up land increasing from 58% in 1994 to 77.03% in 2024, accompanied by declines in agricultural land and vegetated areas. Seasonal maximum LST increases by 2.81 °C in winter and 0.23 °C in summer during 1994 to 2024. Correlation analysis demonstrates LST is positively associated with NDBI (r = 0.273 to 0.763, p < 0.001) and NDBaI (r = 0.152 to 0.624, p < 0.001), while showing negative relationships with NDVI (r = − 0.516 to − 0.047), with most associations significant at p < 0.001 except a weak non-significant summer relationship in 2004 (r = − 0.047, p = 0.094) and MNDWI (r = − 0.502 to − 0.173, p < 0.001). These results indicate that built-up and bare surfaces are generally associated with higher LST, whereas vegetation and water bodies are associated with lower LST, with seasonal variability in relationship strength. In addition, long-term trend analysis of annual maximum air temperature using the Mann–Kendall test revealed a statistically significant increasing trend (Z = 3.569, p < 0.001), while Sen’s slope estimator indicated a warming rate of 0.036 °C year −1. The study provides a scientific basis for planners and policy makers to support urban heat management, sustainable land use planning, and climate-responsive development strategies for Imphal.