Assessing land surface temperature dynamics and urban heat island effects in Delhi: a multi-temporal analysis and future projections.

Understanding the intricate relationship between land use/land cover (LULC) transformations and land surface temperature (LST) is critical for sustainable urban planning. This study investigates the spatiotemporal dynamics of LULC and LST across Delhi, India, using thermal data from Landsat 7 (2001), Landsat 5 (2011) and Landsat 8 (2021) resampled to 30-m spatial resolution, during the peak summer month of May. The study aims to target three significant aspects: (i) to analyse and present LULC-LST dynamics across Delhi, (ii) to evaluate the implications of LST effects at the district level and (iii) to predict seasonal LST trends in 2041 for North Delhi district using the seasonal auto-regressive integrated moving average (SARIMA) time series model. LULC classification is performed using the random forest (RF) approach, and the LULC-LST relationships are statistically examined using a one-way ANOVA paired with Tukey's HSD post hoc test, Pearson's correlation and simple linear regression analysis. The SARIMA model is employed to predict district-level LST for North Delhi in 2041. The results highlight significant LULC-driven LST variations across Delhi, with urban expansion and post-harvest agriculture lands contributing to the temperature increase. Bare land and urban areas exhibited the highest LST, while vegetation and waterbodies consistently recorded lower temperatures. The surface urban heat island intensity was predominantly pronounced in densely built-up areas including the Aerocity region. The district-level analysis reveals substantial spatial heterogeneity, with western districts predominated by agriculture and major urban expansion recording the highest LST, while the southern-central districts experienced lower temperatures due to influence of floodplains, vegetation and ridges. The correlation analysis demonstrated a strong positive association between urban expansion and LST (r = 0.98), while the one-way ANOVA test indicated significant differences in LST across LULC classes (F(4, 148,238) = 9646, p < 0.05, n = 3). The SARIMA-based projections for North Delhi predict escalating temperatures in all seasons by 2041, with a root mean squared error of 2.1. The finding emphasises the need for adaptive urban planning, advocating for strategic integration of vegetation buffers around industrial zones, landfills and along agriculture-urban interfaces to mitigate heat and inform future urban development policies such as the Master Plan Delhi 2041.