• Journal of the Korean Regional Science Association
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• v.33 no.2
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• pp.47-59
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• 2017

The purpose of this study is to examine various definitions of urban heat island and to analyze the characteristics and differences of each methodology by applying each methodology for deriving the heat island region to Seoul. The definition of the heat island is divided into the atmospheric heat island and the surface heat island according to the utilized temperature data. The methodology for deriving the heat island area differs depending on the comparison method and the spatial extent of the analysis. As a result of analyzing each methodology for Seoul city, it was confirmed that the heat island area of Seoul is different according to temperature data. Also, it is confirmed that the distribution range of the heat island area is different according to the spatial range of analysis. This shows that even if the heat island area is analyzed for the same space and the same view point, the heat island area is derived differently according to each methodology. This study can be used as a basic study to solve the urban heat island problem in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.133-143
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• 2012

The purpose of this study is to investigate the characteristics of urban heat island considering urban space at nighttime. We used to analyze landuse and landcover data of 1:1,000 scale, DTM, and surface temperature extracted ASTER image satellite of nighttime. According to the analytical results, heat intensity in single-family residential is higher than that in industrial area, public facility area, and commercial area because the anthropogenic heat by energy consumption is released. Likewise, the temperature difference were big in the buildings of industrial area depending on operating hours. Meanwhile, green and river area had cooling impacts mitigating the urban heat island. Therefore, we have to mitigate heat intensity through constructing green space and waterfront area. As mentioned above, we think that the results of this study will be used as base data for effective spatial planning when formulating development planning to mitigate urban heat island at nighttime.

• Korean journal of applied entomology
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• v.63 no.1
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• pp.77-80
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• 2024

Aedes albopictus, a common species in the Republic of Korea, is internationally known as a major vector for various diseases, and it is well-adapted to urban environments. Recent insect outbreaks in urban areas, attributed to climate change and urban heat islands, have increased the necessity of researching the effects on mosquito populations. This study analyzed climate data from 25 Automatic Weather System (AWS) stations in Seoul, identifying urban areas with pronounced heat island effects and suburban areas with milder effects. Nine urban heat island conditions were established based on this analysis, under which the hatching rates of Ae. albopictus were examined. The results revealed an increase in hatching rates correlating with the intensity of the urban heat island effect. Regression analysis further indicated that this trend accelerates as the strength of the heat island effect increases. This study suggests that temperature variations resulting from urban heat island phenomena can significantly influence the hatching rates of Ae. albopictus.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.4
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• pp.305-316
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• 2018

As the urbanization ratio increases, the heat environment in cities is becoming more important due to the urban heat island. In this study, the heat island spatial analysis was calculated and conducted for analysis of urban thermal environment of Sejong city, which was launched in 2012 and has been developed rapidly. To analyze the ratio and change rate of urban area, a multi temporal land cover map (2013 to 2015 and 2017) of study area is generated based on Landsat-8 OLI/TIRS (Operational Land Imager / Thermal Infrared Sensor) satellite imagery. Then, we select an TIR (Thermal Infrared) band from the two TIR bands provided by the Landsat-8, which is used for calculating the heat island potential, through the accuracy evaluation of the brightness temperature and AWS (Automatic Weathering Station) data. Based on the selected band and surface emissivity, land surface temperature is calculated and the estimated heat island potential change is analyzed. As a result, the land surface temperature of the high ratio and change rate of urban area was significantly higher than the surrounding area around $3^{\circ}C$ to $4^{\circ}C$, and the heat island potential was also higher around $4^{\circ}C$ to $5^{\circ}C$. However, the heat island phenomenon was alleviated in urban areas with high rate of change that also show high green area ratio. Therefore, we demonstrated that dense urban area increases the possibility of inducing heat island, but it can mitigate the heat island through green areas.

• Spatial Information Research
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• v.20 no.4
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• pp.1-16
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• 2012

There is no doubt that the urban heat island (UHI) is a mounting problem in built-up environments, due to energy retention by the surface materials of dense buildings, leading to increased temperatures, air pollution, and energy consumption. To investigate the UHI, three-dimensional (3-D) information is necessary to analyze complex sites, including dense building clusters. In this research, 3-D building geometry information is combined with two-dimensional (2-D) urban surface information to examine the relationship between urban characteristics and temperature. In addition, this research introduces spatial regression models to account for the spatial spillover effects of urban temperatures, and includes the following steps: (a) estimating urban temperatures, (b) developing a 3-D city model, (c) generating urban parameters, and (d) conducting statistical analyses using both Ordinary Least-Squares (OLS) and Spatial Regression Models. The results demonstrate that 3-D urban characteristics greatly affect temperatures and that neighborhood effects are critical in explaining temperature variations. Finally, the implications of the results are discussed, providing guidelines for policies to reduce the UHI.

• Journal of the Korean Regional Science Association
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• v.35 no.4
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• pp.61-73
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• 2019

Urban development and densification have led to the Urban Heat Island Effect, in which the temperature of urban space is higher than the surrounding areas, and the intensity is increasing with climate change. In addition, when the city's air temperature rises in summer, low-income, elderly population, and socially vulnerable people who have health problems lack the ability to cope with the elevated heat environment. Therefore, this study aimed to identify the urban heat island area of Seoul through Hotspot analysis, which is a spatial statistics technique, and explored physical environments, demographic and socioeconomic characteristics of urban heat island effect areas using logistic regression models. This study performed urban heat island hotspot analysis using the average air temperatures of the 423 administrative dongs in Seoul. Analysis results identified that the urban heat islands were concentrated in Jung-gu, Jongno-gu, Yongsan-gu, and Yeongdeungpo-gu. Logistic regression analysis results indicated that urban heat island areas of Seoul were affected by residential floor area ratio, commercial facility floor area ratio, overall floor area ratio, impervious surface ratio, and normalized difference vegetation index(NDVI). In addition, as a result of analyzing the vulnerable area of thermal environment considering the demographic and socioeconomic characteristics of the heat island area, urban heat island areas of Seoul were significantly associated with the proportion of low-income elderly living alone. The result of this study provided useful insights for urban thermal environmental design and policy development that could improve the thermal environment for the socially disadvantaged urban population.

• Journal of the Korean earth science society
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• v.42 no.2
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• pp.135-148
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• 2021

The purpose of this study was to evaluate the urban heat island (UHI) intensity and the corresponding surface temperature forecast obtained using the local data assimilation and prediction system (LDAPS) of the Korea Meteorological Administration (KMA) against the AWS observation. The observed UHI intensity in Seoul increases during spring and winter, while it decreases during summer. It is found that the diurnal variability of the UHI intensity peaks at dawn but reaches a minimum in the afternoon. The LDAPS overestimates the UHI intensity in summer but underestimates it in winter. In particular, the model tends to overestimate the UHI intensity during the daytime in summer but underestimate it during the nighttime in winter. Moreover, surface temperature errors decrease in summer but increase in winter. The underestimation of the winter UHI intensity appears to be associated with weak forecasting of urban temperature in winter. However, the overestimated summer UHI intensity results from the underestimation of the suburban temperature forecast in summer. In order to improve the predictability of the UHI intensity, an urban canopy model (MORUSES) that considers urban effects was combined with LDAPS and used for simulation for the summer of 2017. The surface temperature forecast for the city was improved significantly by adopting MORUSES, and there were remarkable improvements in urban surface temperature morning forecasts. The urban canopy model produced an improvement effect that weakened the intensity of the UHI, which showed an overestimation during summer.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.3
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• pp.35-45
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• 1999

The research is to examine urban heat island effects which is resulted from urbanization using thermal infrared band of Landsat TM data and to demonstrate heat island alleviation effects of green spaces through correlation analysis of NDVI(Normalized Difference Vegetation Index) and surface temperature. According to the results, forests which are covered with natural vegetation have a high NDVI digital values, but surface temperature is very low, and urban areas which is composed of artificial paving materials have a low NDVI, surface temperature increases gradually. In summary, the analysis of relationship between NDVI and surface temperature, used in this study, is regarded as one of effective methodologies for proving heat island alleviation effects of vegetation.

• Journal of the Korean earth science society
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• v.43 no.2
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• pp.253-264
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• 2022

The effect of decreased human activity on the urban heat island intensity (UHII) was analyzed using the observed temperature data of six sites (including one reference area) in Daejeon Metropolitan City from February to May of 2019 to 2021. Depending on the observation site, UHII decreased by approximately 20% in 2020 and 2021 compared to 2019 before COVID-19. The decrease in human activity increased UHII at night and decreased it during the daytime. Consequently, UHII diurnal amplitude increased by approximately 20% in 2020 and 2021 compared to 2019, irrespective of location. The decrease in UHII did not appear to be significantly correlated with natural factors such as wind speed and social distancing steps. In contrast, UHII was correlated with social distancing and significantly reduced air pollutants after COVID-19, with the most significant correlation observed for NO₂.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.2
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• pp.1-16
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• 2017

This study was conducted to analyze the urban heat island(UHI) intensity of South Korea by using Moderate Resolution Imaging Spectroradiometer(MODIS) satellite imagery. For this purpose, the metropolitan area was spatially divided according to land cover classification into urban and non-urban land. From the analysis of land surface temperature(LST) in South Korea in the summer of 2009 which was calculated from MODIS satellite imagery it was determined that the highest temperature recorded nationwide was $36.0^{\circ}C$, lowest $16.2^{\circ}C$, and that the mean was $24.3^{\circ}C$, with a standard deviation of $2.4^{\circ}C$. In order to analyze UHI by cities and counties, UHI intensity was defined as the difference in average temperature between urban and non-urban land, and was calculated through RST1 and RST2. The RST1 calculation showed scattered distribution in areas of high UHI intensity, whereas the RST2 calculation showed that areas of high UHI intensity were concentrated around major cities. In order to find an effective method for analyzing UHI by cities and counties, analysis was conducted of the correlation between the urbanization ratio, number of tropical heat nights, and number of heat-wave days. Although UHI intensity derived through RST1 showed barely any correlation, that derived through RST2 showed significant correlation. The RST2 method is deemed as a more suitable analytical method for measuring the UHI of urban land in cities and counties across the country. In cities and counties with an urbanization ratio of < 20%, the rate of increase for UHI intensity in proportion to increases in urbanization ratio, was very high; whereas this rate gradually declined when the urbanization ratio was > 20%. With an increase of $1^{\circ}C$ in RST2 UHI intensity, the number of tropical heat nights and heat wave days was predicted to increase by approximately five and 0.5, respectively. These results can be used for reference when predicting the effects of increased urbanization on UHI intensity.