Spatiotemporal Dynamics of Surface Urban Heat Islands Across Divergent Planning Paradigms: A 25-Year Multi-Temporal Assessment of the Chandigarh Tricity Agglomeration Region (2000–2024)
Manisha Hooda *
Motilal Nehru School of Sports, Rai, Sonipat, Haryana, 131029, India.
Shubham Mor
Rajiv Gandhi Government College, Saha, Ambala, Haryana, India.
Abhishek Malik
Department of Geography, Panjab University, Chandigarh, India.
*Author to whom correspondence should be addressed.
Abstract
Aims: To examine the spatiotemporal dynamics of Land Surface Temperature (LST), biophysical metrics (NDVI, NDBI), and urban heat stress (UTFVI) over multi-decadal timescales across contiguous regions governed by differing urban planning systems.
Study Design: A multi-temporal geospatial study employing remote sensing technology.
Place and Duration of Study: Chandigarh Tricity Agglomeration Region (Chandigarh Union Territory, SAS Nagar [Mohali] district, and Panchkula district), north-western India; pre-monsoon summer periods (1 March to 31 May) were examined over the 25-year period from 2000 to 2024.
Methodology: Surface reflectance and surface temperature datasets from the USGS Harmonized Landsat Collection 2 Level-2 products for Landsat 5 TM, 7 ETM+, 8 OLI/TIRS, and 9 OLI-2/TIRS-2 were obtained and processed using Google Earth Engine. After applying the CFMask method for cloud filtering, pre-monsoon median composite images were produced to estimate LST, NDVI, NDBI, and UTFVI. Twenty-five-year trend lines, stratified random pixel sampling (n=10,000), and bivariate linear regressions (LST~NDVI and LST~NDBI) were conducted using Google Colab (Python).
Results: Thermal characteristics diverged markedly over the 25-year study period within the agglomeration. Mohali recorded the largest surface-temperature increase, rising by \(5.46^{\circ} \mathrm{C}\) (32.91 °C to \(38.37^{\circ} \mathrm{C}\) ) at a rate of \(1.63^{\circ} \mathrm{C} \cdot\) decade \(^{-1}\), alongside rapid horizontal growth. Chandigarh warmed by \(4.10^{\circ} \mathrm{C}\left(33.16^{\circ} \mathrm{C}\right.\) to \(\left.37.26^{\circ} \mathrm{C}\right)\), while topographically diverse Panchkula warmed by 3.15 °C (33.33 °C to 36.48 °C). Bivariate regression results showed that the thermal forcing associated with built-up land steepened from \(+13.69^{\circ} \mathrm{C} / \mathrm{NDBI}\left(R^2=0.373, p<0.001\right)\) in 2000 to \(+17.43^{\circ} \mathrm{C} / \mathrm{NDBI}\left(R^2=0.264, p<0.001\right)\) in 2024 based on stratified random sampling ( \(n=10,000\) ). In 2024, UTFVI zoning revealed pronounced microclimatic polarisation across the \(1,723 \mathrm{~km}^2\) region: \(43.08 \%\left(742.27 \mathrm{~km}^2\right)\) exhibited excellent thermal comfort, whereas \(43.06 \%\) ( \(741.92 \mathrm{~km}^2\) ) experienced extreme heat stress.
Conclusion: The master-planned green corridors and preserved canopy buffers reduce surface heat build-up relative to unregulated suburban growth on the periphery. Harmonised planning across administrative boundaries, mandatory canopy-cover requirements, and reflective surfaces are important measures for controlling increasing heat stress in the region.
Keywords: Land surface temperature (LST), surface urban heat island (SUHI), urban thermal field variance index (UTFVI), google earth engine, Landsat collection 2, spatial autocorrelation, urban microclimate