Spatial Process of Surface Urban Heat Island in Rapidly Growing Seoul Metropolitan Area for Sustainable Urban Planning Using Landsat Data (1996–2017)

Abstract

The urban heat island (UHI) phenomenon is an important research topic in the scholarly community. There are only few research studies related to the UHI in the Seoul metropolitan area (SMA). Therefore, this study examined the impact of urbanization on the formation of UHI in the SMA as a geospatial study by using Landsat data from 1996, 2006, and 2017. For this purpose, we analyzed the relative variation of land surface temperature (LST) with changes of land use/land cover (LULC) rather than absolute values of LST using gradient, intensity, and directional analyses. It was observed that the impervious surface (IS) has expanded, and the UHI effect was more penetrating in the study area, with considerable loss of other LULC including green surfaces along with the rapid urbanization of the study area. In this study, we divided the IS into persistent IS (PIS) and newly added IS (NAIS). The spatial distribution of the IS, forest surface (FS), PIS, and NAIS was observed based on gradient zones (GZs). The results show that GZ1 recorded a difference of 6.0 °C when compared with the GZ109 in 2017. The results also show that the city center was warmer than the surrounding areas during the period of study. Results reveal that the mean LST has a strong significant positive relationship with a fraction of IS and PIS in 2006 and 2017. On other hand, the mean LST has a strong negative relationship with a fraction of FS and NAIS in the same time points. Relatively low temperatures were recorded in FS and NAIS in both time points. Further, it was proved that the local climate of the SMA and its surroundings had been affected by the UHI effect. Therefore, urban planners of the SMA should seriously consider the issue and plan to mitigate the effect by improving the green surfaces of the city. More greening-oriented concepts are recommended in both horizontal and vertical directions of the SMA, that can be used to control the negative impact associated with UHI. The overall outputs of the study could be used as a proxy indicator for the sustainability of the SMA and its surroundings.

Spatiotemporal Variation of Urban Heat Islands for Implementing Nature-Based Solutions: A Case Study of Kurunegala, Sri Lanka

“…The observed increasing patterns are different from other cities in Sri Lanka [9]. However, the magnitude of UHI among IS and GS are comparable with previous studies for the other cities in the world, such as Bangkok (Thailand), Manila (Philippines) [26] and Seoul (South Korea) [33].…”

“…The rapid development of RS data and geospatial analysis has significantly enhanced UHI studies during the past three decades [28,29]. Past studies have mainly used the relationship between mean LST and two primary land use/cover (LULC) types (i.e., impervious surfaces (IS) and green spaces (GS)) to understand the UHI effect in several cities such as mountainous cities [9,30], coastal cities [26,31] and inland cities [31][32][33]. There are several approaches for the extraction of land use/land cover (LULC) information such as pixel-and object-based [34,35] and spectral index-based methods.…”

Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data

“…The lowest increase in LST is related to orchard and vegetation cover. This is primarily due to high evapotranspiration from vegetation that decreases temperature (Simwanda et al 2019;Priyankara et al 2019;Balew and Korme 2020). Research has shown that green cover tends to reduce the temperature by absorption and reflection of radiation and adjustment of latent and sensible heat exchange (Yue et al 2007;Lin et al 2017).…”

Analysis of Life Quality in a Tropical Mountain City Using a Multi-Criteria Geospatial Technique: A Case Study of Kandy City, Sri Lanka

“…High temperatures can negatively affect the ability to lead a healthy life. They may lead to a low LQ [39,40]. Due to the absence of continuous air temperature data measured at ground level, remote sensing based LST was derived [5,8] Landsat [41].…”