RT Journal Article
T1 A Multidimensional Approach to Mapping Urban Heat Vulnerability: Integrating Remote Sensing and Spatial Configuration
A1 Alnajjar, Sonia
A1 García Martínez, Antonio
A1 López Cabeza, Victoria Patricia
A1 Al-Azhari, Wael
AB This study investigates urban heat vulnerabilities in Seville, Spain, using a multidimensional framework that integrates remote sensing, Space Syntax, and social vulnerability metrics. This research identifies Heat Boundaries (HBs), which are critical urban entities with elevated Land Surface Temperatures (LSTs) that act as barriers to adjacent vulnerable neighbourhoods, disrupting both physical and social continuity and environmental equity, and examines their relationship with the urban syntax and social vulnerability. The analysis spans two temporal scenarios: a Category 3 heatwave on 26 June 2023 and a normal summer day on 14 July 2024, incorporating both daytime and nighttime satellite-derived LST data (Landsat 9 and ECOSTRESS). The results reveal pronounced spatial disparities in thermal exposure. During the heatwave, peripheral zones recorded extreme LSTs exceeding 53 °C, while river-adjacent neighbourhoods recorded up to 7.28 °C less LST averages. In the non-heatwave scenario, LSTs for advantaged neighbourhoods close to the Guadalquivir River were 2.55 °C lower than vulnerable high-density zones and 3.77 °C lower than the peripheries. Nocturnal patterns showed a reversal, with central high-density districts retaining more heat than the peripheries. Correlation analyses indicate strong associations between LST and built-up intensity (NDBI) and a significant inverse correlation with vegetation cover (NDVI). Syntactic indicators revealed that higher Mean Depth values—indicative of spatial segregation—correspond with elevated thermal stress, particularly during nighttime and heatwave scenarios. HBs occupy 17% of the city, predominantly composed of barren land (42%), industrial zones (30%), and transportation infrastructure (28%), and often border areas with high social vulnerability. This study underscores the critical role of spatial configuration in shaping heat exposure and advocates for targeted climate adaptation measures, such as HB rehabilitation, greening interventions, and Connectivity-based design. It also presents preliminary insights for future deep learning applications to automate HB detection and support predictive urban heat resilience planning.
PB MDPI
SN 2624-6511 (electrónico)
YR 2025
FD 2025
LK https://hdl.handle.net/10272/27409
UL https://hdl.handle.net/10272/27409
LA eng
NO Alnajjar, S., García-Martínez, A., López-Cabeza, V. P., & Al-Azhari, W. (2025). A Multidimensional Approach to Mapping Urban Heat Vulnerability: Integrating Remote Sensing and Spatial Configuration. Smart Cities, 8(4), 137. https://doi.org/10.3390/smartcities8040137
DS Repositorio Institucional de la Universidad de Huelva
RD 31 may 2026