Modelling scale-dependent urban thermal dynamics through landscape structure: A multiscale spatial analysis of Lucknow, India
- 1Department of Continuing Education and Extension, Faculty of Social Sciences, University of Delhi, India
- 2Corresponding author.
Abstract
Rapid urbanization across the Global South is reshaping urban thermal environments, yet the scale-dependent mechanisms linking landscape composition and configuration to land surface temperature (LST) remain poorly quantified in rapidly growing Tier-2 cities. This study aims to evaluate the multiscale spatial relationships between urban green spaces (UGS), urban built-up surfaces (UBS), landscape configuration metrics, and LST across the Lucknow Municipal Corporation (LMC), India, from 2003 to 2025. Using multi-temporal Landsat Collection 2 Level-2 imagery, landscape metrics, spatial statistics, and directional transect profiling, we tracked multi-decadal landscape shifts and calibrated spatial regression models (OLS, GWR, and MGWR) on the contemporary 2025 baseline ( N = 366 ). Between 2003 and 2025, built-up surfaces expanded from 21.62 % to 42.88 % , while vegetation cover fragmented, accompanied by a 1.96 ∘ C increase in mean surface temperature across the pre-monsoon observation dates ( 42.68 ∘ C to 44.64 ∘ C ). Among the evaluated models, the Multiscale Geographically Weighted Regression (MGWR) model provided the best empirical fit ( R 2 = 0.322 , Adjusted R 2 = 0.261 , AICc = 1886.33 ) compared to global OLS ( R 2 = 0.032 , Adjusted R 2 = 0.021 ) and standard GWR ( R 2 = 0.145 , Adjusted R 2 = 0.112 ). MGWR revealed distinct scale dependencies: green space configuration (UGS Edge Density) operated at a localized neighborhood scale (bandwidth = 46 ), whereas built-up land-cover variables operated across broad municipal scales (bandwidths ≥ 357 ). Directional transects confirmed the outward migration of thermal anomalies and intensified peri-urban heating. While based on instantaneous pre-monsoon satellite snapshots and two-dimensional landscape metrics, these findings demonstrate that urban thermal mitigation requires scale-tailored planning: preserving compact, connected green patches at the neighborhood level while managing impervious expansion through municipal-wide master planning.
Related
- Green space dynamics in a rapidly urbanizing landscape: A multi-temporal remote sensing assessment and CA-Markov modelling of Jashore, Bangladesh
- Landscape fragmentation and carbon storage dynamics under co-evolving governance in the Shaanxi Yellow River wetland
- Modelling domoic acid retention in Argopecten purpuratus and Pecten maximus: Comparison of dynamics in fast and slow depurators
- Multiscale Dynamics of Bathymetry-Driven Extreme Wave Breaking
- On the Multiscale Dynamics of the Arctic Sea-Ice Pack
BibTeX
@article{gupta2026modelling,
title = {{Modelling scale-dependent urban thermal dynamics through landscape structure: A multiscale spatial analysis of Lucknow, India}},
author = {Rupesh Kumar Gupta and Arpit Gupta and Grinedge Yadav and Yash Kumar Tiwari},
journal = {Environmental and Sustainability Indicators},
year = {2026},
month = oct,
doi = {10.1016/j.indic.2026.101582},
url = {https://doi.org/10.1016/j.indic.2026.101582},
note = {CC BY},
}
RIS
TY - JOUR
TI - Modelling scale-dependent urban thermal dynamics through landscape structure: A multiscale spatial analysis of Lucknow, India
AU - Rupesh Kumar Gupta
AU - Arpit Gupta
AU - Grinedge Yadav
AU - Yash Kumar Tiwari
PY - 2026
DA - 2026/10/30
JO - Environmental and Sustainability Indicators
DO - 10.1016/j.indic.2026.101582
UR - https://doi.org/10.1016/j.indic.2026.101582
AB - Rapid urbanization across the Global South is reshaping urban thermal environments, yet the scale-dependent mechanisms linking landscape composition and configuration to land surface temperature (LST) remain poorly quantified in rapidly growing Tier-2 cities. This study aims to evaluate the multiscale spatial relationships between urban green spaces (UGS), urban built-up surfaces (UBS), landscape configuration metrics, and LST across the Lucknow Municipal Corporation (LMC), India, from 2003 to 2025. Using multi-temporal Landsat Collection 2 Level-2 imagery, landscape metrics, spatial statistics, and directional transect profiling, we tracked multi-decadal landscape shifts and calibrated spatial regression models (OLS, GWR, and MGWR) on the contemporary 2025 baseline ( N = 366 ). Between 2003 and 2025, built-up surfaces expanded from 21.62 % to 42.88 % , while vegetation cover fragmented, accompanied by a 1.96 ∘ C increase in mean surface temperature across the pre-monsoon observation dates ( 42.68 ∘ C to 44.64 ∘ C ). Among the evaluated models, the Multiscale Geographically Weighted Regression (MGWR) model provided the best empirical fit ( R 2 = 0.322 , Adjusted R 2 = 0.261 , AICc = 1886.33 ) compared to global OLS ( R 2 = 0.032 , Adjusted R 2 = 0.021 ) and standard GWR ( R 2 = 0.145 , Adjusted R 2 = 0.112 ). MGWR revealed distinct scale dependencies: green space configuration (UGS Edge Density) operated at a localized neighborhood scale (bandwidth = 46 ), whereas built-up land-cover variables operated across broad municipal scales (bandwidths ≥ 357 ). Directional transects confirmed the outward migration of thermal anomalies and intensified peri-urban heating. While based on instantaneous pre-monsoon satellite snapshots and two-dimensional landscape metrics, these findings demonstrate that urban thermal mitigation requires scale-tailored planning: preserving compact, connected green patches at the neighborhood level while managing impervious expansion through municipal-wide master planning.
SN - 2665-9727
ER -