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Article

Comparative Analysis of Urban Heat Island Cooling Strategies According to Spatial and Temporal Conditions Using Unmanned Aerial Vehicles(UAV) Observation

by
Young-Il Cho
1,
Donghyeon Yoon
2 and
Moung-Jin Lee
1,*
1
Water and Land Research Group, Division for Environmental Planning, Korea Environment Institute (KEI), 370 Sicheong-daero, Sejong-si 30147, Republic of Korea
2
Satellite Application Research Center, Future Innovation Institute, Seoul National University, Seouldaehak-ro 173, Siheung-si 15011, Gyeonggi-do, Republic of Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(18), 10052; https://doi.org/10.3390/app131810052
Submission received: 17 August 2023 / Revised: 2 September 2023 / Accepted: 4 September 2023 / Published: 6 September 2023
(This article belongs to the Special Issue Geospatial Technologies in Spatial and Environmental Planning)
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Abstract

:
Heat island cooling strategies (HICSs) are used to mitigate urban heat island phenomena and adapt to climate change as proposed by the U.S. Environmental Protection Agency (EPA), the Intergovernmental Panel on Climate Change (IPCC), and the World Health Organization (WHO). This study investigated urban heat island reduction and assessed the cooling effect of HICSs under various temporal and spatial conditions in urban areas. The study area was the Mugye-dong urban area in South Korea. To identify the effectiveness of heat island cooling strategies (HICSs), unmanned aerial vehicle (UAV)-based remote sensing and microclimate sensors were used to generate land cover, sky view factor (SVF) distribution, and land surface temperature (LST) maps of the study area. Differences in cooling effect according to spatial density (SD) were identified by dividing the SVF into five intervals of 0.2. Temporal changes were investigated throughout the day and under cloudiness-based meteorological conditions affected by solar radiation or less affected by solar radiation. Lower SD was associated with a greater cooling effect; meteorological conditions affected by solar radiation had a stronger cooling effect. The variation of the daytime cooling effect increased with decreasing SD. The difference in cooling effect between morning and afternoon was <1 °C under conditions less affected by solar radiation. Under conditions affected by solar radiation, the maximum temperatures were −6.716 °C in urban green spaces and −4.292 °C in shadow zones, whereas the maximum temperature was −6.814 °C in ground-based albedo modification zones; thus, differences were greater under conditions affected by solar radiation than under conditions less affected by solar radiation. As a result, it was found that HICS show a high cooling effect, high diurnal variation, and high morning-afternoon deviation under weather conditions with low SD and under conditions affected by solar radiation. This study quantitatively calculated the cooling effect of HICSs applied in urban areas under various spatiotemporal conditions and compared differences by technology. Accordingly, it is believed that it will serve as a basis for supporting the practical effects of the concepts presented by international organizations for climate change adaptation.

1. Introduction

The urban heat island (UHI) effect is a representative microclimate phenomenon that has resulted from urbanization, leading to higher temperatures in central urban areas than in suburban areas [1]. The effect causes social problems in cities that can be attributed to high temperatures, including increased energy demand and a greater incidence of heat-related illnesses. In particular, heatwaves caused by global warming and subsequent climate change are combining with the UHI effect to exacerbate health risks for urban residents [2]. The United States Environmental Protection Agency has proposed heat island cooling strategies (HICSs) to solve problems caused by the UHI effect [3]. These strategies involve planting trees and vegetation; installing green roofs, cool roofs, and cool pavements; and implementing smart growth [3]. Various national and local governments are currently using HICS to reduce UHIs [3,4,5]. The World Health Organization (WHO) has specified that areas in which the abovementioned techniques are used, including the planting of vegetation and trees and the development of green roofs in cities, should be regarded as urban green spaces (UGSs). These measures will improve the quality of urban environments, which tend to trap more heat than natural environments [6]. The Intergovernmental Panel on Climate Change (IPCC) collectively refers to cool roofs and cool pavement techniques as ground-based albedo modification (GBAM); it promotes GBAM as an important approach for reducing high temperatures in urban areas [7]. Both bodies indicate that these measures are necessary for adaptation to climate change.
The measures described above utilize three mechanisms to lower temperatures. First, UGSs, including trees and vegetation, as well as green roofs, cool the urban surface via evapotranspiration [3]. Second, GBAM, including cool pavements and cool roofs, creates a cooling effect by suppressing overheating through the reflection of radiant energy from the sun using high-albedo materials coated onto surfaces [8,9]. Third, the shadows formed by the leaves and stems of trees and vegetation and green roofs in UGSs generate a cooling effect by providing shielding from solar radiation [10,11].
HICS effectiveness has been quantitatively analyzed indoors and outdoors by observing changes in meteorological factors such as temperature and humidity using meteorological sensors [12]. Laboratory observations/experiments are effective for measuring HICS effectiveness while excluding external influences [13,14,15]. However, there are difficulties in replicating an actual urban environment, such as the challenges involved in creating an artificial climate. In field experiments, HICS effectiveness was analyzed for specimens artificially created with HICS-related techniques in places where the external environment is limited, including rooftops [16,17,18,19,20,21,22]. This observation-based analysis method can verify the effect in the experimental field setting of HICS. However, actual urban environments exhibit distinct thermal environments according to the prevailing spatial and temporal conditions.
Actual urban environments are composed of various structures, including buildings and roadside trees, which have diverse spatial densities (SDs). These diverse SDs in cities make important contributions to the formation of an urban thermal environment [23,24,25]. The sky view factor (SVF) is a key index used to determine the relationship between SDs and urban thermal environments [26]. The SVF, which quantitatively represents the fraction of sky visible from the ground surface, has a value between 0.0 and 1.0 [27]. Many studies have demonstrated a relationship between SVF and the urban thermal environment [28,29]. Kim et al. [29] reported that there are differences in heat environment aspects between cities with high and low SDs [29].
The land surface temperature (LST) distribution in urban thermal environments also differs according to temporal conditions. Wang et al. [30] observed a relative difference in temperature for each land cover type in urban thermal environments, even between day and night [30]. Scarano and Sobrino [28] and Zheng et al. [31] revealed a relationship between the SVF and urban thermal environment, as well as differences in the heat environment according to meteorological conditions [28,31]. Previous studies have shown urban thermal environments exhibit various characteristics according to temporal and SD conditions. Therefore, when using HICS to improve the urban thermal environment, comprehensive considerations of the spatial and temporal aspects of urban heating are needed.
Accordingly, to analyze HICS effectiveness when considering actual urban environments and including the aspects referred to above, Ko et al. [12] used measurement sensors mounted on a mobile platform [12]. However, their research had limitations in the determination of overall urban thermal environment characteristics because it only included meteorological observations along a specific travel route. Although observation-based methods can obtain accurate measurements, the observations are one-dimensional and collected at a single time; thus, HICS effectiveness can only be determined in the dimension of a plane surface [32,33]. In contrast, using unmanned aerial vehicle (UAV)-based remote sensing methods with thermal infrared (TIR) spectral sensors mounted on a UAV, the temperature of an observed area can be rapidly captured in a two-dimensional space for a continuous period, and high-spatial-resolution data can be obtained [34,35,36].
The purpose of this study was to determine the practical effectiveness of HICS for reducing UHI intensity in a study area where concepts specified by the WHO and IPCC can be spatially implemented. Because urban thermal environments exhibit various LST distributions according to temporal and SD characteristics, the difference in HICS effectiveness was also examined over time and according to the level of SD in the area. A study area was selected that featured the three representative cooling mechanisms of HICS. First, it contained zones that were expected to have cooling effects on the ground surface via the evapotranspiration of trees, vegetation, and green roofs within UGSs, as suggested by the WHO [3,6]. Second, it had zones where overheating was expected to be suppressed by high-albedo materials coated on the surfaces of cool roofs and cool pavements, consistent with the GBAM proposed by the IPCC [7,8,9]. Third, it had shaded zones formed by the leaves and stems of trees and vegetation, as well as green roofs, in UGSs [10,11]. To determine HICS effectiveness, TIR sensors were installed on a UAV to measure LSTs in the study area. The cooling effect was defined as a relative value, in which the temperatures of the experimental groups were compared with those of the control groups. The experimental groups included zones affected by the abovementioned three cooling mechanisms; the control groups were general urban zones not affected by HICS.
The detailed workflow of this study was as follows: First, analysis targets were selected, and the HICS effect values to be estimated were defined. Second, a scenario for analyzing HICS effectiveness was established. The analysis scenario was designed as a matrix, with the SVF representing SD and the temporal conditions regarded as the two axes. The temporal conditions included changes in HICS effectiveness with the passage of time during the day and under different meteorological conditions. Third, data for determining HICS effectiveness based on an established scenario were constructed. Data were obtained for zones selected as experimental and control groups; LSTs and SVF distribution maps were constructed for these zones. Fourth, HICS effectiveness was estimated using these data. The findings of this study provide information that can assist decision-making when urban planners and policymakers apply HICS to urban environments (Figure 1).

2. Methods and Materials

2.1. Methodology

2.1.1. Study Area and Analysis Targets

Because this study was performed to analyze HICS effectiveness via actual measurements, it focused on an area in which HICS had been applied. The study area was located in Jangyu Mugye-dong, Gimhae-si, Gyeongsangnam-do, where the Ministry of Environment of the Republic of Korea has implemented a project to prevent heatwaves as a climate change adaptation measure [37]. Trees and vegetation, green roofs, cool pavements, and cool roofs have been established in the region as HICS. Therefore, our selected study area was within a region in which HICS had been applied to a downtown urban area. The HICSs in the study area are set as the analysis targets for this study. The average annual air temperature is 15.1 °C, and the average temperature in summer is 25.3 °C. The average annual relative humidity is 62.1%, and the average humidity in the summer is 80.3%. There are 2462.9 h of sunshine per year and 574.5 h in the summer. As of 2022, the average number of clear days per year is 197, and 24 clear days were recorded during the period from June to August, which corresponds to summer (https://data.kma.go.kr (accessed on 31 August 2023)). The total size of the study area was approximately 0.185 km2 (Figure 2).
HICS effectiveness, in this study, was estimated based on differences in average LSTs between the experimental and control groups. The experimental groups in this study included zones where one of three cooling mechanisms was utilized in HICS. First, they included zones where evapotranspiration occurs from the surfaces of trees, vegetation, and green roofs [3,6]. Zones where the cooling effect was exerted by evapotranspiration were defined as UGSs. Second, they included zones where overheating was suppressed by the high-albedo materials of cool pavements and roofs, corresponding to GBAM [7,8,9]. Zones where the cooling effect was exerted by high-albedo materials were defined as GBAM zones. Third, they included zones that experienced a cooling effect from the shadows formed by trees and vegetation, as well as green roofs, in UGSs [10,11]. The shaded areas formed by plants were defined as shadow zones in UGSs. Therefore, UGSs, GBAM zones, and shadow zones in UGSs were regarded as the experimental groups in this study (Table 1).
The control groups were zones that were unaffected by HICS. The UGSs and GBAM zones generated a cooling effect by modifying the physical properties of artificial cladding surfaces, which contribute to elevated urban temperatures. Therefore, zones with general artificial cladding that lacked cooling measures were regarded as control groups for the UGSs and GBAM zones; they were referred to as “built-up zones.” The control groups for shadow zones in UGSs where the LST is reduced by blocking solar radiation were regarded as unshaded zones in the study area. They had the opposite LST characteristics and were referred to as “unshaded zones” (Table 1).

2.1.2. Scenarios Used to Analyze HICS Effectiveness

The following scenarios were used to analyze HICS’s effectiveness: First, HICS effectiveness was defined. Second, the range of temporal and spatial conditions for the effect analysis was established. Third, a scenario matrix for the effect analysis of HICS was generated based on the temporal and spatial conditions established in the previous step.
HICS effectiveness was determined using the LSTs of the study area collected via TIR sensors installed on a UAV. HICS effectiveness was defined as the difference between average LSTs in the experimental and control groups. An analysis set of representative temperatures for each zone in the experimental and control groups, and the relevant average LSTs were estimated. Average temperatures estimated in this process were represented by the median LST in cells distributed in the experimental and control groups.
The temporal and spatial conditions in this study were as follows: The characteristics of the thermal environment within a city were distributed differently according to weather conditions and the passage of time during the daytime [2,31,38]. Sunshine duration has a large impact on urban temperatures and is controlled by the frequencies of low-level cloudiness and mid-level cloudiness (MLC) [38]. Therefore, HICS effectiveness was assessed according to meteorological conditions (i.e., cloudiness) during the day. The study was conducted on 13 July 2022 and 23 August 2022, with LSTs measured at 09:00, 11:00, 13:00, 15:00, and 17:00. According to observation data from an Automated Surface Observing System (ASOS) adjacent to the study area and operated by the Korea Meteorological Administration, the MLCs between 09:00 and 17:00 on 13 July 2022 and 23 August 2022 were 78.8% and 50%, respectively (https://data.kma.go.kr (accessed on 24 May 2023)). The adjacent ASOS is located about 13 km from the study area (Figure 3). Matuszko compared the sunshine duration according to cloudiness for each type of cloud; if MLC was ≥ 80%, solar radiation was blocked [38]. In the present study, HICS effectiveness according to cloudiness was determined by classifying 13 July 2022 (when the MLC was close to 80%) as less affected by solar radiation and 23 August 2022 as a day affected by solar radiation.
HICS effectiveness was examined at 10 time points (TPs) based on the aforementioned time criteria (Table 2). The spatial conditions in this study were examined using the SVF, an index that has been utilized in various studies to identify the relationship between SD characteristics and the urban thermal environment. The SVF value ranges from 0.0 to 1.0. In this study, scenarios were designed by dividing the SVF scale into intervals of 0.2.
The evaluation metrics, experimental and control groups, and spatial and temporal control conditions were clarified. Based on these conditions, 150 analysis scenarios were developed for use in the study. The 150 scenarios included 50 scenarios for each HICS. Variable parameters for the analysis scenario establishment were as follows (Table 2).

2.2. Materials

In the next step, the necessary data were constructed to estimate the effect values for each developed scenario. Data regarding the study area (experimental and control groups) and the distributions of SD and LSTs in the area were required. These data were obtained from field data and land cover maps of the study area. The land cover map was produced via digitizing by referring to a drone-based orthomosaic of the visible spectral range area and the subdivision land cover map from the Ministry of Environment of the Republic of Korea [37]. The field data included unmanned aerial vehicle (UAV) remote sensing data and micro-meteorological observation data (Table 3). The equipment used to acquire UAV remote sensing data was a DJI-Matrice 300 RTK aircraft equipped with FLIR Vue Pro R TIR sensors and MicaSense RedEdge MX (five bands: blue, green, red, near-IR, and red-edge). FLIR Vue Pro R is used to measure surface temperature by mounting it on a UAV in various research fields (spectral range of 7.5~13.5 μm, frame rate of 30 Hz) [37,39]. Orthomosaic images of UAV footage were constructed using Agisoft Metashape 1.8.1.

2.2.1. Areas of Experimental and Control Groups

In this step, spatial data for the experimental and control groups were constructed. The data for the experimental groups comprising the different zones corresponded to the UGSs, GBAM zones, and shadow zones in the UGSs defined in the previous step (Table 3). The UGSs and GBAM zones required the operation of techniques that involve changing the physical properties of urban surfaces, such as artificial cladding; therefore, they represent land cover characteristics. The UGSs and GBAM zones in the study area were extracted as experimental groups, and built-up zones were established as control groups (Figure 4), using the land cover map developed by Cho et al. [37] to determine object-based HICS effectiveness [37].
The shadow zones in UGSs represent shaded areas, and their shape changes over time with alterations in solar altitude and azimuth [40]. Thus, shadow zones formed in the UGSs of the study area were estimated via the hillshade tool in the ArcGIS Spatial Analyst toolbox [41,42]. The input data required for hillshade analysis included a digital surface model (DSM) of the study area, along with solar altitude and azimuth data for each measurement TP in the study area. The DSM uses data constructed by Cho et al. [39] for this study area (Table 3). The DSM was constructed using Agisoft Metashape 1.8.1 using images acquired through a visible light sensor mounted on a UAV [39]. In the process, pre-collected ground control points were used to improve the positional accuracy of geospatial data in the images taken [37]. The DSM used in this study was delivered with a resolution of 0.16 m (Figure 5). For solar altitude and azimuth data, relevant data for the study area were obtained from the Korea Astronomy and Space Science Institute (https://www.kasi.re.kr (accessed on 31 August 2023)). The date 13 July 2022 marks a solar altitude of up to 75 degrees and a solar azimuth of about 67–291 degrees. The date 23 August 2022 has a maximum solar altitude of 56 degrees and a solar azimuth range of 76–283 degrees.
Hillshade analysis of the study area was performed to estimate the entire shaded area. This study aimed to determine the effect of shadow zones in UGSs on the cooling of LSTs. Therefore, only shadow zones in UGSs in the study area were selected, and the following process was implemented: First, a DSM of the virtual space was produced, with the exception of UGSs, from which all natural and man-made features in the study area, including buildings, were removed. Second, hillshade analysis was performed to extract the shadow zones in the UGSs in the area. The extracted zones were regarded as experimental groups for analyses of the effect of shadow zones in UGSs. Third, based on the results of the hillshade analysis using the DSM of the study area, unshaded zones were re-extracted and regarded as control groups (Figure 6).

2.2.2. Construction of Urban Morphological Parameters

In this step, SVFs were calculated to map the SD (Figure 7). The SVFs were estimated using the Urban Multi-Scale Environmental Predictor (UMEP), open-source software designed as a plugin for the QGIS software. The UMEP enables the calculation of urban morphological variables related to various urban climates, including SVFs. The SVF estimation model of the UMEP was based on the shadow casting algorithm developed using a DSM by Lindberg et al. [43].

2.2.3. LST Retrieval

In this step, LSTs were estimated to determine HICS effectiveness. The LSTs were determined based on values measured via TIR sensors installed on a UAV. The TIR sensors measured the temperature of the target object based on the fixed emissivity of all objects. Accordingly, the temperature of the target object was overestimated or underestimated. For this reason, in the present study, bright temperature values acquired via TIR sensors were calculated as corrected LSTs that reflected the emissivity of each object. LSTs were estimated using a formula based on TIR remote sensing, as suggested by Maes and Steppe [44]. This estimation requires bright temperature values of the study area (acquired via TIR sensors), along with the land surface emissivity and background temperature (Tbkg) of the study area.
The bright temperature values in this study were acquired using an FLIR Vue Pro R TIR sensor. The values observed via the TIR sensor are recorded in degrees Celsius. The land surface emissivity estimation algorithm utilized in this study was developed by Heinemann et al. [45] using UAVs, with data obtained from the red and near-IR range bands of MicaSense RedEdge-MX [45]. The distribution of surface emissivity throughout the study area was determined. The Tbkg, a value required for atmospheric correction, was estimated based on downward longwave radiation according to the Stefan-Boltzmann law [43]. The estimation algorithm proposed by Aimi et al. [46] was used to obtain downward longwave radiation [46]. Atmospheric temperature and relative humidity are input data required for value estimation; measurements were made via automatic weather station sensors installed in the study area (Figure 8).

3. Results

This study analyzed differences in HICS effectiveness according to temporal and spatial conditions. Thus, the effect value was estimated for each scenario established in the previous step. For the spatial condition, changes in HICS effectiveness were compared according to SD. Temporal conditions were the passage of time during the day, and meteorological conditions were low-level cloudiness or MLC; the effects of both conditions on sunshine duration were compared. Observation times during the day were 09:00, 11:00, 13:00, 15:00, and 17:00. The meteorological conditions were classified as affected by solar radiation or less affected by solar radiation. The SD was divided into five levels in increments of 0.2 based on the SVF: 0.0–0.2, 0.2–0.4, 0.4–0.6, 0.6–0.8, and 0.8–1.0 (Figure 9). As SVF approached 1.0, fewer obstructions covered the sky, resulting in an environment with a low SD [47]. HICS effectiveness was estimated for each of the five levels of SD.
HICS effectiveness for each temporal and spatial condition was analyzed to determine: (1) differences in HICS effectiveness for each SD at the same TP; (2) the range of variation in cooling effect during the day by referring to the difference between maximum and minimum effect values; and (3) differences in HICS effectiveness between morning and afternoon TPs for each SD. Using 13:00 as the time when the sun was highest, 09:00 and 11:00 were defined as morning TPs; 15:00 and 17:00 were defined as afternoon TPs. When performing these analyses, the differences in effect for each meteorological condition were compared.

3.1. Effectiveness of Urban Green Spaces

3.1.1. Cooling Effects of UGSs for Each Level of SD Based on the Same TP

Considering the SD based on the same TP, when meteorological conditions were less affected by solar radiation, the effects of UGSs were as follows. In all observed TPs, the cooling effect was smallest in the SVF range of 0.0–0.2 with high SD: −0.836 °C (at 9:00), −0.661 °C (at 11:00), −0.505 °C (at 13:00), −0.936 °C (at 15:00), and 0.312 °C (at 17:00). On the contrary, the cooling effect is highest in the SVF range of 0.8–1.0, which is the range with low SD: −4.208 °C (at 9:00), −7.426 °C (at 11:00), −9.749 °C (at 13:00), −6.863 °C (at 15:00), and −3.833 °C (at 17:00). Therefore, as the SVF increased at all TPs, HICS effectiveness also tended to increase (Table 4). On the same note, when meteorological conditions were affected by solar radiation, the cooling effects of UGSs were as follows. In all observed TPs, the cooling effect was smallest in the SVF range of 0.0–0.2 with high SD: −0.025 °C (at 9:00), −1.208 °C (at 11:00), −1.678 °C (at 13:00), −0.775 °C (at 15:00), and −1.072 °C (at 17:00). Conversely, the cooling effect is highest in the SVF 0.8–1.0 range, which is the range with low SD. However, at 17:00, the highest cooling effect is shown in the SVF range of 0.6–0.8: −11.839 °C (at 9:00), −13.704 °C (at 11:00), −13.328 °C (at 13:00), −8.224 °C (at 15:00), and −5.050 °C (at 17:00). Therefore, at all TPs except 17:00, a larger SVF was associated with a larger cooling effect (Table 4).

3.1.2. Range of Variation in Cooling Effects of UGSs for Each Level of SD during the Daytime

For each level of SD and measurement TP during the daytime, the daily variation in cooling effects of UGSs with meteorological conditions less affected by solar radiation was as follows. The daily variation in cooling effect increased with increasing SVF value: −0.624 °C (SVF 0.0–0.2), −1.604 °C (SVF 0.2–0.4), −2.748 °C (SVF 0.4–0.6), −4.943 °C (SVF 0.6–0.8), and −5.916 °C (SVF 0.8–1.0). Therefore, a larger SVF value was associated with greater diurnal variation in cooling effects of UGSs during the daytime (Table 4). From the same perspective, the diurnal variation in the cooling effects of UGSs with meteorological conditions affected by solar radiation was as follows. The daily variation in cooling effect increased with increasing SVF value: −1.653 °C (SVF 0.0–0.2), −2.639 °C (SVF 0.2–0.4), −3.572 °C (SVF 0.4–0.6), −4.965 °C (SVF 0.6–0.8), and −9.817 °C (SVF 0.8–1.0). Therefore, a larger SVF value was associated with greater diurnal variation in the cooling effects of UGSs during the daytime under both meteorological conditions (Table 4).

3.1.3. Differences in Cooling Effects of UGSs between Morning and Afternoon for Each Level of SD

At each level of SD, the difference in cooling effects of UGSs between average effects (AEs) in the morning and afternoon with meteorological conditions less affected by solar radiation was as follows. In all levels of SD, the AEs difference between morning and afternoon were less than about 1 °C: −0.125 °C (SVF 0.0–0.2), 0.381 °C (SVF 0.2–0.4), 0.527 °C (SVF 0.4–0.6), 1.033 °C (SVF 0.6–0.8), and −0.469 °C (SVF 0.8–1.0). From the same perspective, the difference in cooling effects of UGSs between morning and afternoon with meteorological conditions affected by solar radiation was as follows. As the SVF increased, the difference in cooling effects between morning and afternoon also tended to increase: 0.307 °C (SVF 0.0–0.2), −0.305 °C (SVF 0.2–0.4), −1.262 °C (SVF 0.4–0.6), −1.826 °C (SVF 0.6–0.8), and −6.716 °C (SVF 0.8–1.0). With an SVF of < 0.8, the difference in cooling effects was ~1 °C; in the SVF range of 0.8–1.0, the difference in cooling effects was −6.716 °C (Table 4).

3.2. Effectiveness of Ground-Based Albedo Modification

3.2.1. Cooling Effects of GBAM for Each Level of SD Based on the Same TP

Considering the SD based on the same TP, when meteorological conditions were less affected by solar radiation, the effects of GBAM were as follows. In all observed TPs, the cooling effect was smallest in the SVF range of 0.0–0.2 with high SD: 0.018 °C (at 9:00), 0.701 °C (at 11:00), 1.025 °C (at 13:00), 1.167 °C (at 15:00), and 0.971 °C (at 17:00). On the contrary, the cooling effect is highest in the SVF range of 0.8–1.0, which is the range with low SD: −4.620 °C (at 9:00), −6.494 °C (at 11:00), −7.900 °C (at 13:00), −4.263 °C (at 15:00), and −2.803 °C (at 17:00). Therefore, as the SVF increased at all TPs, HICS effectiveness also tended to increase (Table 4). On the same note, when meteorological conditions were affected by solar radiation, the cooling effects of GBAM were as follows. In all observed TPs, the cooling effect was smallest in the SVF range of 0.0–0.2 with high SD: 0.003 °C (at 11:00), 0.817 °C (at 13:00), 0.927 °C (at 15:00), and 0.530 °C (at 17:00) except for 0.180 at 09:00 with the SVF range of 0.2–0.4. Conversely, the cooling effect is highest in the SVF 0.8–1.0 range, which is the range with low SD. However, at 17:00, the highest cooling effect is shown in the SVF range of 0.6–0.8: −9.462 °C (at 9:00), −10.827 °C (at 11:00), −9.943 °C (at 13:00), −5.159 °C (at 15:00), and −2.110 °C (at 17:00). Therefore, a larger SVF tends to be associated with a larger cooling effect (Table 5).

3.2.2. Range of Variation in Cooling Effects of GBAM for Each Level of SD during the Daytime

For each level of SD and measurement TP during the daytime, the daily variation in cooling effects of GBAM with meteorological conditions less affected by solar radiation was as follows. The daily variation in cooling effect increased with increasing SVF value: −1.149 °C (SVF 0.0–0.2), −1.554 °C (SVF 0.2–0.4), −1.820 °C (SVF 0.4–0.6), −2.167 °C (SVF 0.6–0.8), and −5.097 °C (SVF 0.8–1.0). As the SVF value increased, the diurnal variation in cooling effects during the daytime tended to increase (Table 5). From the same perspective, the diurnal variation in cooling effects of GBAM with meteorological conditions affected by solar radiation was as follows. The daily variation in the cooling effect increased with increasing SVF value: −0.924 °C (SVF 0.0–0.2), −1.205 °C (SVF 0.2–0.4), −2.031 °C (SVF 0.4–0.6), −2.994 °C (SVF 0.6–0.8), and −9.325 °C (SVF 0.8–1.0). As the SVF value increased, the diurnal variation in cooling effects during the daytime tended to increase (Table 5).

3.2.3. Differences in Cooling Effects of GBAM between Morning and Afternoon for Each Level of SD

For each level of SD, the difference in cooling effects of GBAM between AEs in the morning and afternoon with meteorological conditions less affected by solar radiation was as follows. In all levels of SD, the AEs difference between morning and afternoon was less than about 1 °C, excluding the range of SVF 0.8–1.0: −0.710 °C (SVF 0.0–0.2), −0.553 °C (SVF 0.2–0.4), −0.603 °C (SVF 0.4–0.6), −0.345 °C (SVF 0.6–0.8), and −2.024 °C (SVF 0.8–1.0). From the same perspective, the difference in cooling effects of GBAM between morning and afternoon with meteorological conditions affected by solar radiation was as follows. As the SVF increased, the difference in cooling effects between morning and afternoon also tended to increase: −0.306 °C (SVF 0.0–0.2), −0.184 °C (SVF 0.2–0.4), −0.771 °C (SVF 0.4–0.6), −1.586 °C (SVF 0.6–0.8), and −6.814 °C (SVF 0.8–1.0). With an SVF of < 0.8, the difference in cooling effects was ~1 °C; in the SVF range of 0.8–1.0, the difference in cooling effects was −6.814 °C (Table 5).

3.3. Effectiveness of Shadow Zones in UGSs

3.3.1. Cooling Effects of Shadow Zones in UGSs for Each Level of SD Based on the Same TP

Considering the SD based on the same TP, when meteorological conditions were less affected by solar radiation, the cooling effects of shadow zones in UGSs were as follows. At 9:00, the cooling effect was smallest (0.206 °C) in the SVF range of 0.4–0.6 and largest (−1.104 °C) in the SVF range of 0.0–0.2. At 11:00, the cooling effect was smallest (0.448 °C) in the SVF range of 0.4–0.6 and largest (2.939 °C) in the SVF range of 0.8–1.0. At 13:00, the cooling effect was smallest (−0.002 °C) in the SVF range of 0.0–0.2 and largest (−2.214 °C) in the SVF range of 0.8–1.0. At 15:00, the cooling effect was smallest (0.136 °C) in the SVF range of 0.4–0.6 and largest at −1.825 °C in the SVF range of 0.8–1.0. At 17:00, the cooling effect was smallest (−0.099 °C) in the SVF range of 0.2–0.4 and largest (−0.888 °C) in the SVF range of 0.0–0.2. Therefore, the cooling effect was < 1 °C at all levels of SD, excluding the SVF range of 0.8–1.0 at 11:00, 13:00, and 15:00, indicating that the cooling effect was insignificant in shadow zones in UGSs other than shadow zones with the lowest level of SD. This finding suggests that shadow zone effects differ from the effects of UGSs and GBAM, where the extent of cooling increased in parallel with the SVF (Table 6).
From the same perspective, when meteorological conditions were affected by solar radiation, the effects of shadow zones in UGSs were as follows. At 9:00, the cooling effect was smallest (−2.535 °C) in the SVF range of 0.6–0.8 and largest (−5.319 °C) in the SVF range of 0.8–1.0. At 11:00, the cooling effect was smallest (−1.852 °C) in the SVF range of 0.4–0.6 and largest at −4.479 °C in the SVF range of 0.8–1.0. At 13:00, the cooling effect was the smallest (−0.913 °C) in the SVF range of 0.0–0.2 and largest (−3.751 °C) in the SVF range of 0.8–1.0. At 15:00, the cooling effect was smallest (0.807 °C) in the SVF range of 0.0–0.2 and largest (−1.520 °C) in the SVF range of 0.8–1.0. At 17:00, the cooling effect was smallest (0.530 °C) in the SVF range of 0.6–0.8 and largest (−0.425 °C) in the SVF range of 0.0–0.2. Therefore, the cooling effect was greatest in the SVF range of 0.8–1.0 at all TPs, excluding 17:00. However, under the SD condition, where the SVF value was < 0.8, the cooling effect did not increase in parallel with the SVF, in contrast to UGSs and GBAM (Table 6). This finding suggests that shadow zone effects differ from the effects of UGSs and GBAM, where the extent of cooling increased in parallel with the SVF.

3.3.2. Range of Variation in Cooling Effects of Shadow Zones in UGSs for Each Level of SD during the Daytime

For each level of SD during the daytime, the daily variation in the cooling effects of shadow zones in UGSs with meteorological conditions less affected by solar radiation was as follows. The daily variation in cooling effect between levels of SD was largest (−2.472 °C) in the SVF range of 0.8–1.0, followed by variations of −1.102 °C (SVF 0.0–0.2), −0.791 °C (SVF 0.2–0.4), −1.041 °C (SVF 0.4–0.6), and −0.780 °C (SVF 0.6–0.8). Under the SD condition with an SVF range of < 0.8, the diurnal variation in cooling effect was approximately ≤ 1 °C. In contrast to UGSs and GBAM zones, shadow zones in UGSs did not display a tendency for the diurnal variation of the cooling effect to increase with increasing SVF. From the same perspective, the daily variation in cooling effects of shadow zones in UGSs with meteorological conditions affected by solar radiation was as follows. The daily variation in cooling effect between levels of SD was largest (−5.625 °C) in the SVF range of 0.8–1.0, followed by variations of −4.741 °C (SVF 0.0–0.2), −3.204 °C (SVF 0.2–0.4), −3.501 °C (SVF 0.4–0.6), and −3.065 °C (SVF 0.6–0.8). Under the SD condition with an SVF range of < 0.8, the diurnal variation in cooling effect was −3 to −4 °C (Table 6). In contrast to UGSs and GBAM zones, shadow zones in UGSs did not display a tendency for the diurnal variation of the cooling effect to increase with increasing SVF.

3.3.3. Differences in Cooling Effects of Shadow Zones in UGSs between Morning and Afternoon for Each Level of SD

For each SD level, the difference in the cooling effects of shadow zones in UGSs between AEs in the morning and afternoon with meteorological conditions less affected by solar radiation was as follows. In all levels of SD, the AEs difference between morning and afternoon was less than about 1 °C: −0.228 °C (SVF 0.0–0.2), −0.352 °C (SVF 0.2–0.4), 0.556 °C (SVF 0.4–0.6), 0.359 °C (SVF 0.6–0.8), and −0.495 °C (SVF 0.8–1.0). From the same perspective, the difference in the cooling effects of shadow zones in UGSs between morning and afternoon with meteorological conditions affected by solar radiation was as follows. In all levels of SD, the AE difference between morning and afternoon was higher than −2 °C: −3.629 °C (SVF 0.0–0.2), −2.926 °C (SVF 0.2–0.4), −2.930 °C (SVF 0.4–0.6), −2.148 °C (SVF 0.6–0.8), and −4.292 °C (SVF 0.8–1.0). It means that the AEs in morning times are higher than afternoon (at least 2 °C (Table 6)).

4. Discussion

This study explored the extent to which HICS effectiveness is influenced by various temporal and spatial conditions. Here, temporal conditions refer to different TPs during the daytime and the relative influence of solar radiation according to cloudiness conditions. For spatial conditions, the analysis targets were classified according to SVF to determine changes in cooling effect depending on SD. Under these conditions, this study analyzed the following: first, differences in HICS effectiveness for each level of SD based on the same TPs; second, the range of variation in cooling effects of HICS for each level of SD during the daytime; and third, differences in cooling effects between morning and afternoon TPs for each level of SD.
The cooling effects of UGSs were determined as follows. First, analysis of the cooling effects of UGSs by SD at the same TP revealed that cooling effects increased as the SVF approached 1.0, corresponding to a low-SD environment (Table 4). Assessment of meteorological differences showed that cooling effects were stronger when the weather was affected by solar radiation. Cooling effects in the SVF range of 0.0–0.2 were −0.936 to −0.312 °C (less affected by solar radiation) and −1.678 to −0.025 °C (affected by solar radiation), with differences of < 1 °C. Cooling effects in the SVF range of 0.8–1.0 were −9.749 to −3.833 °C (less affected by solar radiation) and −13.704 to −3.887 °C (affected by solar radiation); the affected by solar radiation conditions produced a cooling effect that was approximately 4 °C larger than the effect of the less affected by solar radiation conditions. This analysis revealed that strong cooling effects of UGSs can be expected in a low-SD environment when meteorological conditions are affected by solar radiation.
Second, analysis of the daily variation in cooling effects of UGSs for each level of SD demonstrated that diurnal variation in the cooling effect increased as the SVF approached 1.0 (Table 4). When meteorological conditions were affected by solar radiation, there was greater diurnal variation in the cooling effect. In the SVF range of 0.0–0.2, which corresponded to a high-SD environment, the diurnal variations in cooling effect were −0.624 °C (less affected by solar radiation) and −1.653 °C (affected by solar radiation). In the SVF range of 0.8–1.0, corresponding to a low-SD environment, the diurnal variations in cooling effects were −5.916 °C (less affected by solar radiation) and −9.817 °C (affected by solar radiation). This analysis revealed that diurnal variation in cooling effects was high when meteorological conditions were affected by solar radiation with a low SD.
Third, analysis of the difference in cooling effects of UGSs between morning and afternoon indicated similar cooling effects, regardless of SD, when meteorological conditions were less affected by solar radiation. However, when meteorological conditions were affected by solar radiation, the cooling effect was greater in the morning than in the afternoon as the SD increased (Table 4). When meteorological conditions were less affected by solar radiation, the cooling effect was greater in the morning (−0.469 °C) and afternoon (−1.033 °C) under high-SD conditions. When meteorological conditions were affected by solar radiation, the cooling effects were 0.307 °C in the SVF ranged from 0.0–0.2 and −6.716 °C in the SVF range of 0.8–1.0; the cooling effect tended to be larger in the morning than in the afternoon because morning conditions more closely resembled a low-SD environment (Table 4). The difference in cooling effect between morning and afternoon was greater in a low-SD environment; it was largest when meteorological conditions were affected by solar radiation.
The cooling effects of GBAM zones were characterized as follows. First, analysis of the cooling effects of GBAM for each SD at the same TP revealed that cooling effects increased as the SVF approached 1.0, which corresponded to a low-SD environment (Table 5). There was a stronger heat mitigation effect when meteorological conditions were affected by solar radiation overall. Cooling effects in the SVF range of 0.0–0.2 were 0.018 to 1.167 °C (less affected by solar radiation) and 0.003 to 0.927 °C (affected by solar radiation), with differences of < 1 °C. Cooling effects in the SVF range of 0.8–1.0 were −7.900 to −2.803 °C (less affected by solar radiation) and −10.827 to −1.502 °C (affected by solar radiation); the affected by solar radiation conditions produced a cooling effect that was approximately 3 °C larger than the effect of the less affected by solar radiation conditions. This analysis revealed that strong cooling effects of GBAM can be expected in a low-SD environment when meteorological conditions are affected by solar radiation.
Second, analysis of the daily variation in cooling effects of GBAM zones for each level of SD showed that a larger SVF was associated with greater variation in cooling effects during the daytime (Table 5). Exploration of differences relative to weather conditions revealed greater diurnal variation when meteorological conditions were affected by solar radiation. Diurnal variations in cooling effects were −1.149 °C (less affected by solar radiation) and −0.924 °C (affected by solar radiation) in the SVF range of 0.0–0.2; the variations were −5.079 °C (less affected by solar radiation) and 9.325 °C (affected by solar radiation) in the SVF range of 0.8–1.0. Therefore, in a low-SD environment, diurnal variation in cooling effects tends to increase when meteorological conditions are affected by solar radiation.
Third, analysis of the difference in cooling effects of GBAM zones between morning and afternoon revealed similar cooling effects, regardless of SD, when meteorological conditions were less affected by solar radiation. Conversely, when meteorological conditions were affected by solar radiation, the cooling effect tended to differ between morning and afternoon with increasing SD (Table 5). When meteorological conditions were less affected by solar radiation, the cooling effect was −0.710 to −0.345 °C at an SVF of ≤ 0.8; the difference between morning and afternoon was ~1 °C. However, this difference substantially changed to −2.024 °C in the SVF range of 0.8–1.0. When meteorological conditions were affected by solar radiation, the cooling effect was ~1 °C (−1.586 to −0.184 °C) at an SVF of ≤ 0.8. However, in the SVF range of 0.8–1.0, the cooling effect was −6.814 °C larger in the morning than in the afternoon; this represented the largest difference between TPs (Table 4). Overall, in a low-SD environment, the difference in cooling effect between morning and afternoon increased when meteorological conditions were affected by solar radiation.
Analysis of the cooling effects of shadow zones in UGSs revealed the following. First, analysis of the cooling effects of the shadow zones in UGSs for each SD at the same TP showed that the cooling effect increased as the SVF approached 1.0, which corresponded to a low-SD environment (Table 6). Additionally, differences according to weather conditions indicated a stronger cooling effect when meteorological conditions were affected by solar radiation. Cooling effects in the SVF range of 0.0–0.2 were −1.104 °C to −0.002 °C (less affected by solar radiation) and 0.807 °C to −3.934 °C (affected by solar radiation). Cooling effects in the SVF range of 0.8–1.0 were −2.939 °C to −0.467 °C (less affected by solar radiation) and −5.319 °C to 0.306 °C (affected by solar radiation). Therefore, strong cooling effects of shadow zones in UGSs can be expected in a low-SD environment when meteorological conditions are affected by solar radiation.
Second, analysis of the daily variation in cooling effects of shadow zones in UGSs for each level of SD revealed that a larger SVF was associated with greater variation in cooling effects during the daytime (Table 6). Furthermore, exploration of differences according to weather conditions revealed greater diurnal variation when meteorological conditions were affected by solar radiation. In the SVF range of 0.0–0.2, cooling effects were −1.102 °C (less affected by solar radiation) and −4.741 °C (affected by solar radiation), indicating a wide range of diurnal fluctuation; in the SVF range of 0.8–1.0, cooling effects were −2.472 °C (less affected by solar radiation) and −5.625 °C (affected by solar radiation). Accordingly, in a low-SD environment, diurnal variation in cooling effects tended to increase when meteorological conditions were affected by solar radiation.
Third, analysis of the difference in cooling effects of shadow zones in UGSs between morning and afternoon revealed similar cooling effects, regardless of SD, when meteorological conditions were less affected by solar radiation. Conversely, when meteorological conditions were affected by solar radiation, the cooling effect was greater in the morning than in the afternoon, with increasing SD (Table 6). When meteorological conditions were less affected by solar radiation, the cooling effect was −0.495–0.556 °C; the difference between morning and afternoon was ~1.0 °C. When meteorological conditions were affected by solar radiation, the cooling effect was −4.292 to −2.148 °C (Table 6). Collectively, these findings indicated that shadow zones in UGSs are low-SD environments; differences between morning and afternoon heat mitigation tended to increase when meteorological conditions were affected by solar radiation.

5. Conclusions

5.1. Cooling Characteristics of HICSs

In this study, the cooling characteristics of HICS were explored under different spatial and temporal conditions. Under low SD and with meteorological conditions affected by solar radiation, there was a stronger cooling effect and greater diurnal variation in cooling. Additionally, the cooling effect was stronger in the morning than in the afternoon.
UGSs and GBAM zones reduced the UHI by transforming urban land cover features into components that create low temperatures. The cooling effect of UGSs in the SVF range of 0.0–0.2 (i.e., the highest SD range) was −1.678 to −0.025 °C (Table 4). In GBAM zones, the cooling effect in the SVF range of 0.0–0.2 was 0.003 to 1.167 °C at all TPs (Table 5). An effect value of ≥ 0 °C or larger implies no effect compared with non-HICS conditions. This finding indicates that GBAM zones had no cooling effect under high-SD conditions. The basic GBAM mechanism involves suppression of temperature elevation by reducing the amount of solar radiation absorbed through a coating of high-albedo materials [8]. However, a lower SVF causes a smaller amount of solar radiation to reach the floor surface because of the effects of surrounding terrain features [50]. With respect to GBAM zones, where solar radiation is blocked, it is likely that no cooling effect occurred because a small amount of solar radiation reached the covering [39]. Conversely, in UGSs, there was a lower cooling effect in low-SD environments; in contrast to GBAM zones, UGSs produced an effect value < 0 °C. UGSs were characterized by the effect of blocking solar radiation as well as the cooling effect of vegetation cover via evapotranspiration [3]. Thus, UGSs had a temperature-reducing effect under high-SD conditions, whereas GBAM zones did not.
The basic principle of shadow zones in UGSs is to reduce the surface temperature by blocking solar radiation [10,11]. When meteorological conditions were less affected by solar radiation, there was minimal need to block solar radiation; a cooling effect value of ~1 °C was achieved under all SD conditions and times except for 11:00 and 13:00 in the SVF range of 0.8–1.0. In contrast to UGSs and GBAM zones, the cooling effect was weak. When meteorological conditions were affected by solar radiation, a cooling effect of −5.319 to −0.913 °C was observed under all SD conditions at 9:00, 11:00, and 13:00, indicating a stronger cooling effect than when meteorological conditions were less affected by solar radiation (Table 6). When morning and afternoon were compared, the cooling effect was greater in the morning than in the afternoon, with a minimum value of −3.065 °C (SVF 0.6–0.8) and a maximum value of −5.625 °C (SVF 0.8–1.0) under all SD conditions (Table 6). Thus, shadow zones in UGSs were more strongly affected by diurnal changes and weather conditions (i.e., temporal conditions) than by SD (i.e., spatial conditions).

5.2. Implications of This Study

For climate change adaptation, a study area was selected with the ability to spatially implement concepts recommended by the IPCC and WHO. An empirical analysis of the actual cooling effects intended to lower LST was performed. Both UGSs and GBAM zones have been proposed as climate change adaptation methods by the IPCC and WHO. The IPCC described GBAM zones as a solar radiation modification technique that can cool urban areas on a regional scale; therefore, it provides the capacity to mitigate solar radiation in response to global warming. Techniques that affect temperature cooling on a regional scale are effective for cooling extreme high-temperature environments [7]. In this study, the cooling effect of GBAM zones on urban areas impacted by UHI was quantitatively examined with respect to LST. Therefore, this study yielded insights that can support the implementation of GBAM zones as proposed by the IPCC. Because of urbanization, the number of UGSs in cities is insufficient, although this method has been highlighted by the WHO as an important approach for climate change adaptation. Both the IPCC and WHO also recommend close monitoring and evaluation of the effects of such measures on actual urban areas; there is a critical need to reduce temperatures within cities. In the present study, the cooling effects of UGSs were examined under various temporal and spatial conditions. Strong cooling effects of UGSs were identified under various conditions. Therefore, this study provides useful physical information regarding the effective deployment of UGSs.
Despite its useful research outcomes, this study had several limitations. First, only LST was used to determine HICS effectiveness. The various metrics for evaluating urban thermal environments include LST (°C), energy flux (W/m2), atmospheric temperature (°C), and physiological equivalent temperature, as well as the predicted mean vote, which reflects the sensory temperature experienced by humans [5,51]. The measurement of UHIs considers both surface heat islands (based on LST) and atmospheric heat islands (based on atmospheric temperature) [3,52]. The LST and atmospheric temperature have similar characteristics, but their reported correlations vary according to measurement time [53]. In contrast to the present study, previous work using physiological equivalent temperature and predicted mean vote (corresponding to sensory temperature) to determine the effect of GBAM zones on pedestrian comfort revealed a negative effect on thermal comfort among urban residents [54]. Therefore, even when the same technique is used in similar studies, results can be conflicting depending on the type of evaluation metrics used. To fully determine HICS effectiveness, future studies should evaluate them using suitable metrics for targets affected by the particular technique implemented.

Author Contributions

Conceptualization, Y.-I.C. and M.-J.L.; Data curation, Y.-I.C. and D.Y.; Methodology, Y.-I.C. and M.-J.L.; Resources, Y.-I.C. and D.Y.; Visualization, Y.-I.C.; Writing—original draft, Y.-I.C.; Writing—review and editing, M.-J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This paper is based on the findings of the research project Development of Optimization Techniques for Reducing Heat Wave Considering Urban Environment (2023-014(R)), which was conducted by the Korea Environment Institute (KEI). The study was supported by the grant (2020-MOIS35-001(RS-2020-ND629021)) from the Policy-linked Technology Development Program on Natural Disaster Prevention and Mitigation, funded by Ministry of Interior and Safety (MOIS, Korea).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank the anonymous reviewers for their very competent comments and helpful suggestions. The English in this document has been checked by at least two professional editors, both native speakers of English. For a certificate, please see: http://www.textcheck.com/certificate/kjDwNc (accessed on 18 August 2023).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Study workflow.
Figure 1. Study workflow.
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Figure 2. Study area and heat island cooling strategies (HICS): (a) vegetation and trees, (b) cool pavement, (c) green roof, (d) shadow zone provided by shrubs, (e) cool roof, and (f) shadow zone provided by trees.
Figure 2. Study area and heat island cooling strategies (HICS): (a) vegetation and trees, (b) cool pavement, (c) green roof, (d) shadow zone provided by shrubs, (e) cool roof, and (f) shadow zone provided by trees.
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Figure 3. The location of ASOS and study area.
Figure 3. The location of ASOS and study area.
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Figure 4. Urban green spaces (UGSs), ground-based albedo modification (GBAM) zones, and built-up areas; The spatial extent of the study is inside the red line.
Figure 4. Urban green spaces (UGSs), ground-based albedo modification (GBAM) zones, and built-up areas; The spatial extent of the study is inside the red line.
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Figure 5. Digital surface model (DSM).
Figure 5. Digital surface model (DSM).
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Figure 6. Extraction of the shadow zones in urban green spaces (UGSs).
Figure 6. Extraction of the shadow zones in urban green spaces (UGSs).
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Figure 7. Sky view factor map. The spatial extent of the study is inside the red line.
Figure 7. Sky view factor map. The spatial extent of the study is inside the red line.
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Figure 8. Land surface temperature (LST) maps: (a) 2022.07.13. at 13:00 and (b) 2022.08.23. at 13:00; The spatial extent of the study is inside the red line.
Figure 8. Land surface temperature (LST) maps: (a) 2022.07.13. at 13:00 and (b) 2022.08.23. at 13:00; The spatial extent of the study is inside the red line.
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Figure 9. Examples of fisheye photographs and sky view factors (SVFs) for different locations in the study area: (a) SVF = 0.19 [26]; (b) SVF = 0.36 [48]; (c) SVF = 0.48 [48]; (d) SVF = 0.7 [49]; and (e) SVF = 0.97 [48].
Figure 9. Examples of fisheye photographs and sky view factors (SVFs) for different locations in the study area: (a) SVF = 0.19 [26]; (b) SVF = 0.36 [48]; (c) SVF = 0.48 [48]; (d) SVF = 0.7 [49]; and (e) SVF = 0.97 [48].
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Table 1. Experimental and control groups.
Table 1. Experimental and control groups.
Heat Island Cooling StrategiesExperimental GroupsControl Groups
AbbreviationDescription
VegetationUGSs [6]Urban green spaces (UGSs) [6]Built-up zone
Trees
Green roofs
Cool pavementsGBAMGround-based albedo modification (GBAM) zones [7]
Cool roofs
Shadow ofVegetationShadow zones in UGSsShadow zones
in urban green spaces		Unshaded zone
Tree
Green roof
Table 2. Settings used in the analysis scenarios.
Table 2. Settings used in the analysis scenarios.
HICSUrban Geography
Condition (SVF)		Daytime Condition
(Time Points)		Meteorological Condition
Urban green spaces (UGSs)0.0–0.2
0.2–0.4
0.4–0.6
0.6–0.8
0.8–1.0		09:00
11:00
13:00
15:00
17:00		Less affected
by solar radiation
Affected
by solar radiation
Ground-based albedo modification (GBAM) zones0.0–0.2
0.2–0.4
0.4–0.6
0.6–0.8
0.8–1.0		09:00
11:00
13:00
15:00
17:00		Less affected
by solar radiation
Affected
by solar radiation
Shadow zones in UGSs0.0–0.2
0.2–0.4
0.4–0.6
0.6–0.8
0.8–1.0		09:00
11:00
13:00
15:00
17:00		Less affected
by solar radiation
Affected
by solar radiation
Table 3. Data acquired for use in this study.
Table 3. Data acquired for use in this study.
GroupsNameDefinitionDataType
Areas of research targetsExperimental groupsUrban green spaces (UGSs)Areas covered by vegetation, trees, and green roofsLand cover map [37]shp
(polygon)
Ground-based albedo modification (GBAM) zonesAreas covered by cool pavements and cool roofsLand cover map [37]shp
(polygon)
Shadow zones in UGSs *Areas covered by shadows from vegetation, trees, and green roofsDSM [39]tiff
(raster)
Control groupsBuilt-up zonesControl group for UGSs and GBAM zonesLand cover map [37]shp
(polygon)
Unshaded zonesControl group for shadow zones in UGSs *DSM [39]tiff
(raster)
Spatial density (SD)
control		Sky view factor (SVF)Proportion of sky hemisphere visible from the groundDSM [39]tiff
(raster)
TemperatureLand surface temperature (LST) *Surface temperature data for study areaBright temperature, emissivity, Tbkgtiff
(raster)
* Data were constructed 10 times (2022.07.13. 09:00, 11:00, 13:00, 15:00, 17:00, and 2022.08.23. 09:00, 11:00, 13:00, 15:00, 17:00).
Table 4. Cooling effect (°C) of urban green space for each spatial density (SD) condition.
Table 4. Cooling effect (°C) of urban green space for each spatial density (SD) condition.
SVF0.0–0.20.2–0.40.4–0.60.6–0.80.8–1.0
Time
Less
affected by
solar radiation		9:00−0.836 −1.754 −2.449 −2.111 −4.208
11:00−0.661 −2.722 −4.313 −5.348 −7.426
13:00−0.505 −3.358 −5.197 −7.054 −9.749
15:00−0.936 −2.971 −4.570 −6.143 −6.863
17:00−0.312 −2.267 −3.245 −3.381 −3.833
Tmin–Tmax−0.624−1.604−2.748−4.943−5.916
A.M.−0.749 −2.238 −3.381 −3.730 −5.817
P.M.−0.624 −2.619 −3.908 −4.762 −5.348
TA.M.–TP.M.−0.125 0.381 0.527 1.033 −0.469
Affected by
solar radiation		9:00−0.025 −2.091 −4.642 −6.426 −11.839
11:00−1.208 −3.819 −6.757 −9.018 −13.704
13:00−1.678 −4.730 −7.510 −10.015 −13.328
15:00−0.775 −2.856 −4.938 −6.743 −8.224
17:00−1.072 −2.444 −3.938 −5.050 −3.887
Tmin–Tmax−1.653−2.639−3.572−4.965−9.817
A.M.−0.617 −2.955 −5.700 −7.722 −12.772
P.M.−0.924 −2.650 −4.438 −5.897 −6.056
TA.M.–TP.M.0.307 −0.305 −1.262 −1.826 −6.716
Table 5. Cooling effect (°C) of ground-based albedo modification (GBAM) for each spatial density (SD) condition.
Table 5. Cooling effect (°C) of ground-based albedo modification (GBAM) for each spatial density (SD) condition.
SVF0.0–0.20.2–0.40.4–0.60.6–0.80.8–1.0
Time
Less
affected by
solar radiation		9:000.018 −0.650 −1.922 −2.386 −4.620
11:000.701 −0.250 −1.918 −3.379 −6.494
13:001.025 −1.102 −2.750 −4.277 −7.900
15:001.167 0.452 −0.930 −2.966 −4.263
17:000.971 −0.246 −1.704 −2.110 −2.803
Tmin–Tmax−1.149−1.554−1.820−2.167−5.097
A.M.0.360 −0.450 −1.920 −2.883 −5.557
P.M.1.069 0.103 −1.317 −2.538 −3.533
TA.M.–TP.M.−0.710 −0.553 −0.603 −0.345 −2.024
Affected by
solar radiation		9:000.842 0.180 −1.605 −3.101 −9.462
11:000.003 −0.981 −3.015 −4.989 −10.827
13:000.817 −0.220 −2.262 −5.104 −9.943
15:000.927 −0.657 −2.095 −2.809 −5.159
17:000.530 0.224 −0.984 −2.110 −1.502
Tmin–Tmax−0.924−1.205−2.031−2.994−9.325
A.M.0.423−0.401−2.310−4.045−10.145
P.M.0.729−0.217−1.540−2.460−3.331
TA.M.–TP.M.−0.306−0.184−0.771−1.586−6.814
Table 6. Cooling effect (°C) of shadow zones in urban green spaces (UGSs) in each spatial density (SD) condition.
Table 6. Cooling effect (°C) of shadow zones in urban green spaces (UGSs) in each spatial density (SD) condition.
SVF0.0–0.20.2–0.40.4–0.60.6–0.80.8–1.0
Time
Less
affected by
solar radiation		9:00−1.104 −0.865 0.206 −0.042 −0.467
11:00−0.937 −0.326 0.448 −0.174 −2.939
13:00−0.002 −0.890 −0.313 −0.482 −2.214
15:00−0.698 −0.389 0.136 −0.112 −1.825
17:00−0.888 −0.099 −0.593 −0.822 −0.591
Tmin–Tmax−1.102−0.791−1.041−0.780−2.472
A.M.−1.021 −0.596 0.327 −0.108 −1.703
P.M.−0.793 −0.244 −0.229 −0.467 −1.208
TA.M.–TP.M.−0.228 −0.352 0.556 0.359 −0.495
Affected by
solar radiation		9:00−2.941 −3.250 −2.973 −2.535 −5.319
11:00−3.934 −3.260 −1.852 −1.868 −4.479
13:00−0.913 −1.966 −1.628 −1.756 −3.751
15:000.807 −0.602 0.506 −0.638 −1.520
17:00−0.425 −0.056 0.528 0.530 0.306
Tmin–Tmax−4.741−3.204−3.501−3.065−5.625
A.M.−3.438 −3.255 −2.413 −2.202 −4.899
P.M.0.191 −0.329 0.517 −0.054 −0.607
TA.M.–TP.M.−3.629 −2.926 −2.930 −2.148 −4.292
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Cho, Y.-I.; Yoon, D.; Lee, M.-J. Comparative Analysis of Urban Heat Island Cooling Strategies According to Spatial and Temporal Conditions Using Unmanned Aerial Vehicles(UAV) Observation. Appl. Sci. 2023, 13, 10052. https://doi.org/10.3390/app131810052

AMA Style

Cho Y-I, Yoon D, Lee M-J. Comparative Analysis of Urban Heat Island Cooling Strategies According to Spatial and Temporal Conditions Using Unmanned Aerial Vehicles(UAV) Observation. Applied Sciences. 2023; 13(18):10052. https://doi.org/10.3390/app131810052

Chicago/Turabian Style

Cho, Young-Il, Donghyeon Yoon, and Moung-Jin Lee. 2023. "Comparative Analysis of Urban Heat Island Cooling Strategies According to Spatial and Temporal Conditions Using Unmanned Aerial Vehicles(UAV) Observation" Applied Sciences 13, no. 18: 10052. https://doi.org/10.3390/app131810052

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