Vegetation Cover as a Predictor of Land Surface Temperature in Urban Environments: A Remote-Sensing-Based Analysis
Keywords:
Land surface temperature, NDVI, Normalized difference vegetation index, Thermal remote sensing, Urban vegetationAbstract
Urban expansion substantially modifies the composition and physical properties of land surfaces. The progressive replacement of vegetation and permeable land with buildings, roads, parking lots, and other impervious materials alters surface energy exchange, moisture availability, heat storage, and radiative properties, frequently contributing to elevated urban temperatures relative to surrounding less-developed areas. Land surface temperature (LST) derived from thermal infrared remote sensing provides an effective, spatially continuous means of examining spatial variations in urban thermal conditions, while the Normalized Difference Vegetation Index (NDVI) remains one of the most widely applied and accessible measures of vegetation abundance and vigor. This study investigates the statistical relationship between vegetation cover and land surface temperature using a quantitative remote-sensing research framework. Forty urban observation zones were analyzed with NDVI as the principal explanatory variable and LST as the dependent variable. Descriptive statistics, Pearson product-moment correlation analysis, and ordinary least-squares regression were employed. The results indicate a strong inverse relationship between vegetation abundance and surface temperature. Pearson’s correlation coefficient was −0.924, with a significance level of p < .001. Regression analysis produced an R-squared value of 0.855, indicating that NDVI accounted for approximately 85.5% of the observed variation in LST. Mean LST declined progressively from 41.11 °C in areas with sparse vegetation to 32.93 °C in areas with very dense vegetation. The regression model was LST = 42.275 − 11.194 × NDVI. The findings demonstrate the important statistical association between vegetation abundance and urban surface temperature and support the use of vegetation-related indicators in urban thermal-environment research. The study further demonstrates how remote sensing and statistical analysis can be productively combined to investigate spatial patterns of urban environmental change and to inform evidence-based approaches to urban climate mitigation.
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Copyright (c) 2026 Amina K. Diallo (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
