The urban heat island (UHI) refers to the phenomenon of urban air temperature higher than that of the surrounding area by 2 °C or more [1
]. This phenomenon is attributable to the difference in the spatial characteristics of land surface between urban and suburban areas. Whereas artificial land cover materials such as concrete and asphalt and high-rise buildings are densely concentrated in urban areas, suburban areas are mostly covered with natural materials such as woodland, grassland, and barren land. Unlike natural surfaces, artificial land cover materials cause solar radiation energy to accumulate on the land surface due to energy exchange characteristics such as emissivity, albedo, and heat capacity [1
], causing the LST to rise. As the amount of energy accumulated on land surface increases, LST increases. The LST thus elevated affects the air temperature, acting as a major factor leading to the temperature rise in urban areas [1
]. It is by this mechanism that the UHI phenomenon is closely associated with the land cover material [9
]. Therefore, it is important to set up a plan to mitigate the UHI effect by understanding the spatial characteristics pertaining to urban and suburban areas and their impact on the LST and air temperature.
Several previous studies have analyzed surface urban heat island (SUHI) or air urban heat island (AUHI) using the LST derived from the satellite imagery and have clarified the relationship with the spatial characteristics. Satellite imagery provides LST over a large area, which allows identifying UHI-related features over the entire surface area of a city without time gap [10
]. Use of geographic information systems (GIS) in conjunction with satellite imagery facilitates the analysis of LST characteristics dependent upon spatial patterns and, consequently, the identification of major causes of the UHI phenomenon [14
]. Previous studies have analyzed the characteristics of SUHI by deriving LST data from a satellite equipped with thermal infrared sensors [14
], and the relationship between spatial patterns and UHI phenomenon using the normalized difference vegetation index (NDVI) or normalized difference built-up index (NDBI) derived from satellite imagery or spatial data such as land-use and land-dover maps [17
]. Benali et al. [24
] and Kloog et al. [25
] analyzed the temperature profiles over urban areas and shed light on AUHI effect by working out the relationship between the in situ air temperature measurements and the surface temperature derived from satellite imagery. Coutts et al. [26
], Wang et al. [27
], Ferreira and Duarte [28
], Hereher [29
], and Pal and Ziaul [30
] analyzed the relationship between the land cover area and the LST of satellite imagery, and quantitatively predicted the LST characteristics according to the spatial pattern change. Zhou et al. [31
] noted that several previous studies analyzed the SUHI-related characteristics using satellite imagery, most frequently Landsat (53%), followed by MODIS (25%) and ASTER (7%).
As examined above, many researchers have studied the UHI phenomenon using satellite imagery. However, to mitigate the UHI effect in a continuous and controlled manner from the perspective of urban planning, it is crucial to understand the effect of urban spatial patterns on LST and air temperature. Although many researchers have identified the LST and air temperature characteristics depending on spatial patterns which vary by season and time of day [32
], the sustainable growth of cities is obstructed by haphazard green space expansion and urban development restrictions under the banner of UHI mitigation. This suggests that the research findings based on satellite imagery can be effective in supporting the decision-making in favor of mitigating the UHI effect [37
], but show insufficiency when it comes to practical application. To address this drawback, it is necessary to obtain quantitative data applicable to practical urban planning rather than simple results about spatial patterns and LST characteristics. On a related note, there is a limitation in directly utilizing the data obtained in previous studies, given the different construction materials and spatial patterns depending on the geographical location and urban environment of each city. For example, European and Asian cities are exposed to different meteorological conditions and thus use different land cover materials for construction, which have different impact on LST depending on period and spatial pattern. Therefore, there is a need to identify the characteristics of spatial patterns and LST of each city, which vary according to the urban environment, compare the results with those of previous research, and then to prepare action plans to mitigate the UHI effect from the perspective of continuous growth of the city.
Therefore, this study analyzed the relationship between spatial patterns and urban temperature considering various periods to provide an effective urban planning for continuously mitigating urban heat island in urban area of Changwon city, located in southeastern South Korea. For this purpose, we collected ASTER LST data of 90-m spatial resolution during the daytime and nighttime from June to September, 2012–2014, a land-cover map, and a digital surface model (DSM), and analyzed spatiotemporal characteristics of urban temperature according to the spatial patterns.
This study analyzed the characteristics of urban temperature depending on spatial patterns using ASTER LST data and spatial data from various time periods to highlight the need to set up an UHI effect mitigation plan taking into account urban environment and meteorological characteristics from the perspective of urban planning. To achieve this end, we analyzed the spatial characteristics of the in situ measurement point in different time periods and times of day and the relationship between LST and air temperature, and derived the characteristics of LST according to the area ratio by land-use type. Then, we compared the analysis results with those of previous research and established a methodology for applying those researches finding to setting up UHI effect mitigation plans.
As a result, the correlation between LST and air temperature during the daytime was found to have an accuracy rate of 60% when checked against the in situ measurements, similar to that of previous research, and 89.4% during the nighttime, which could not be compared with previous research for lack of studies involving nighttime LST. The daytime LST was found to rise in urban land to different extents depending on time of day, which was slightly different from the result of a comparable previous study. The nighttime LST did not show any distinct trend depending on spatial patterns. In addition, because of the lack of previous research data at nighttime because of the collection of satellite image data, we could not directly compare with the results of this study.
This study analyzed urban temperature characteristics for various time and period according to spatial pattern. Its main achievements are twofold: identification of urban temperature characteristics that vary depending on spatial patterns (land-use types) and time of day (daytime vs. nighttime) and determination of the impact of spatial patterns on urban temperature depending on urban environment and meteorological characteristics, differentiating itself from previous research. These results can be used as important source data for decision-making regarding UHI effect reduction from the perspective of urban planning. In particular, to mitigate the current UHI effect, indiscriminately creating green space or imposing restrictions on urban development activities can hamper a sustainable development of cities. The results of this study provide quantitative data on the impact of spatial patterns (land-use types) on the urban temperature in summer, i.e., at the peak of the UHI effect. They are expected to serve as important data that can help decision-making regarding urban planning and policy decision for mitigating the UHI effect at the stages of urban redevelopment and reconstruction.