A multi-criteria analysis of urban characteristics for surface urban heat island mitigation based on remote sensing data

Abstract

The Surface Urban Heat Island (SUHI) effect reduces thermal comfort and increases energy consumption in urban areas, especially in hot and humid climates. In this study, SUHI is examined as an intra-urban phenomenon, where relative land surface temperature differences are evaluated within a morphologically homogeneous residential complex rather than between urban and rural areas. This study investigates the influence of canyon orientation, height-to-width (H/W) ratio, and sky view factor (SVF) on intra-urban land surface temperature (LST) patterns at the micro-scale within a case study area with uniform materials and limited vegetation. LST data derived from 58 Landsat 8 images (2020 and 2024) were used to map persistent thermal patterns, while SVF and H/W calculations were conducted using field observations and modeling software. An Analytical Hierarchy Process (AHP) was applied to reliably evaluate canyon orientations and normalization techniques facilitated comparison of the factors. Spearman rank correlation (n = 10) quantified statistical relationships between LST and morphological parameters. Unlike most previous studies conducted at city or district scales, this research investigates intra-urban surface temperature variability at the level of individual urban canyons within a single residential complex. The results indicate that canyon orientation exhibited the strongest and statistically significant influence on LST (ρ = 0.68, p < 0.05), whereas H/W ratio and SVF showed weaker and non-significant associations under early morning conditions. Comparative analysis between heat and cool points suggests that a 6% reduction in H/W ratio combined with a 3% increase in SVF may correspond to a mean temperature reduction of up to 1.15 °C. When these changes combine with a NW-SE canyon orientation aligned with prevailing cool winds, this reduction can reach by up to 1.5 °C. This study provides a replicable micro-scale analytical framework integrating remote sensing, morphological assessment, and multi-criteria evaluation to support climate-responsive urban design in compact residential contexts.