Spatial Analysis of the Effects of Green Infrastructure on Surface Urban Heat Island Intensity at the Neighborhood Scale in Tehran During the Period 2015-2025

Document Type : Research Paper

Authors

1 Department of Geography, Faculty of Humanities, Islamic Azad University, Research and Science Branch, Tehran, Iran.

2 Department of Urban Planning, Faculty of Architecture and Urban Planning, Islamic Azad University, Science and Technology Pardis Branch, Pardis, Iran.

10.22059/jes.2026.408985.1008667

Abstract

Objective: This study aimed to spatially investigate the intensity of the surface urban heat island in 352 neighborhoods of the Tehran metropolis during the summers of 2015-2025. The emphasis was on the role of vegetation cover, percentage of tree cover, impervious surfaces, surface albedo, and topographic features (elevation, slope) in order to identify spatial heterogeneity patterns, calculate local cooling potential, and provide a prioritization framework for urban green infrastructure interventions.
Method: Satellite remote sensing data from Landsat 8/9 (OLI/TIRS thermal bands) and Sentinel‑2 (MSI Level‑2A) on the Google Earth Engine platform were used to derive land surface temperature, the normalized difference vegetation index, tree cover percentage (WorldCover 2021), impervious surfaces, surface albedo, mean elevation (SRTM), and slope. Surface urban heat island intensity was calculated as the difference between the mean neighborhood land surface temperature and the median temperature of the entire city. Exploratory analyses included descriptive statistics, global and local Moran’s I (spatial autocorrelation), hot spot analysis (Getis‑Ord Gi* statistic), Pearson correlation, and simple regression. modeling comprised ordinary least squares regression, Lagrange Multiplier tests (for selecting spatial error/lag/Durbin models), multiscale geographically weighted regression, and calculation of a cooling potential index (combining the absolute values of significant local coefficients) implemented in ArcGIS Pro and Python.
Results: According to the results, the mean land surface temperature was 44.21 ± 2.56, and the surface urban heat island intensity was 2.56 ± 0.10 degrees Celsius. Strong spatial autocorrelation was detected (global Moran’s I for heat island intensity = 0.7245, p < 0.001), and hot clusters of impervious surfaces were observed in the southern and central parts of the city. Strong correlations were found for elevation (r = −0.45), impervious surfaces (r = 0.38), and vegetation cover (r = −0.28). Multiscale geographically weighted regression (adjusted R² = 0.9224, adjusted Akaike information criterion = 2127.36) showed that vegetation cover (coefficient = −1.52), tree cover (coefficient = −0.11), and albedo (coefficient = −6.71) exerted significant cooling effects (p < 0.001 in 50–94% of neighborhoods), whereas impervious surfaces had a warming effect (in 100% of neighborhoods) with pronounced heterogeneity (local R² = 0.62–0.88). The mean cooling potential index was 3.97 (range: 2.28–6.38).
Conclusions: The results indicate that tree and vegetation cover in a substantial proportion of Tehran’s neighborhoods have high cooling potential and play a key role in moderating the surface urban heat island. The multiscale geographically weighted regression (MGWR) model, which clearly outperforms global models (ordinary least squares regression with R² = 0.62 and the spatial error model with R² = 0.75), accurately reveals the spatial heterogeneity and location dependence of the relationships between variables. From a policy perspective, the findings underscore the need to prioritize green interventions in southern neighborhoods and parts of the city center characterized by high imperviousness and low cooling potential, a strategy that can promote thermal justice and enhance urban climate resilience.

Keywords

Main Subjects

References

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Journal of Environmental Studies
Volume 51, Issue 4
March 2026
Pages 511-534

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