The climate of a region changes with the reasons such as the shape of the land surface, human activities, atmospheric movements, and latent and sensible heat movements. Reasons such as urbanization, increase in human activities and socio-economic development of the region cause an increase in surface temperature. (Daramola ve ark., 2018). In summer, the climate of big cities causes thermal discomfort and reduces the quality of life. Buildings and urban areas with impervious surfaces covered with asphalt surfaces store heat during the day and release it at night. This in turn affects the energy distribution and causes a temperature concentration called an urban heat island. The UHI effect reasoned by human thermal comfort, air pollution and climate and urban morphologies creates planning concerns in cities. Urban radiation balance and thermal environment are significantly affected by the geometry of cities. (Ooka ve ark, 2011). This change is not only between the rural area and the urban area but also within the urban area itself. This difference within the urban area is greater in areas with changing land surfaces. The change of the land surface properties significantly affects the surface heat flux. For example, factors that make up the physical structure of the city, such as building heights, spaces between buildings, street width, in other words, urban geometry, affect the climate of the city as it affects the temperature distribution of the region, the flow of the wind, and the air quality (Chen ve ark. 2012; Middel ve ark. 2014). Buildings block the clear sky, affecting the cooling of the area at night. SVF, BVF, and TVF values are actively used in the determination of urban geometry. However, studies on how these factors affect the thermal state of the urban environment are insufficient. It is very important to determine the changes in the land surface as a result of urbanization and the surface thermal condition characteristics (LST, NDVI, SHF, and LHF) and to know how these characteristics affect the thermal conditions in the construction of sustainable urban planning. The association between air temperature and SVF has been studied in a number of ways. Oke et al (1991) demonstrated that a difference in SVF between the suburbs and the city might result in a temperature differential of 5–7 0C. Yamashita et al (1986) discovered a strong association between air temperature and SVF in 1986. Karlsson (2000) proposed a link between air temperature, pure sun energy, and SVF in forests. Svensson(2004) demonstrated that air temperature overnight and SVF have a substantial relationship, and that both land use and SVF influence air temperature changes in urban areas. However, Eliasson (1996) and Barring et al. (1985) showed that there is no statistical relationship between the geometry of urban straits and air temperature. According to Blankenstein and Kuttler (2004), SVF cannot characterize thermal qualities, the variety inside the urban heat island cannot be anticipated only by SVF.
The study calculated SVF, BVF, TVF, LST, NDVI, SHF, and LHF values of 55 points determined in three different areas with different urban geometries. How these values affect each other and their situation on urban outdoor thermal comfort is evaluated. According to the study, the highest SVF value was determined in the 1st and 2nd regions (0.53) where the buildings and trees are located. The reason why this value is low in Zone 3 where there are high-rise buildings is the distance between the buildings. However, the BVF value reaches its maximum values in Zone 3 (0.75) as expected (Figure-12). As expected, NDVI (0.22) and TVF (0.80) values are higher in Zone-1, where green areas are present compared to other regions.
The sky view factor (SVF), tree view factor (TVF), and building view factor (BVF) represent the hemispherical ratio calculated by the sky, trees, and buildings when viewed from a location, respectively. To balance urban radiation, these three view factors interact with one another. SVF is a set of structures and trees that influence air temperature. SVF is decreased by street trees because they provide shade to the environment, resulting in less net longwave loss at night. In comparison to cool skies and trees, buildings generate more long-wave radiation. As a result, urban street canyons with a higher BVF will generate more net longwave radiation. The street tree canopy as measured by the TVF serves ecologically because it reduces the urban temperature. The cooling effect of trees occurs due to the cooling of the radiation reaching the ground surface by evaporation from the leaf surfaces and the shadow effect of the trees. The buildings in the study areas have different heights and material variety. This causes buildings to have a higher heat capacity and surface heat compared to other areas. An Urban Heat Island is formed when buildings retain heat during the day and release it at night (UHI).
The view factors caused by urban geometry differ with their contribution to thermal conditions (Mirzaei and Haghighat, 2010). Therefore, this study, it is aimed to determine the effects of changes in land surface properties due to urban growth on the surface thermal condition, especially LST. As a result of the study, it was found that the surface heat flux and surface temperature change in regions with different urban geometries.
Cheung et al. (2016) determined in their study that the lower the sky visibility factor, the greater the effect on the thermal state of an area. Gal et al. (2009), Hungary (Szeged), in their study, found that areas with higher SVF values cooled faster. With their study, they confirmed the thesis of Unger (2009), who argued that urban geometry affects urban climate. It is also known that urban geometry can affect nighttime thermal conditions (Svensson, 2004). Studies have found a link between land surface temperature and sky view factor (SVF) levels ranging from moderate to high (LST)(Kim et al. 2022).
When the correlation of surface heat fluxes with LST was evaluated, the effect degrees of each factor were found to be significant (p < 0.01). NDVI factor affects surface flow values and LST more than SVF. The SVF value varies between 0.1 and 0.8. While this value is between 0.2 and 0.8 in 1 region, this value is between 0.1 and 0.71 in 3 regions. Svensson (2004) emphasized in his studies that the SVF value has low values (below 0.5) of the sky visibility factors in places where the density of the urban tissue is high. Low-rise buildings and green areas in 3 regions confirm this view with an average of 0.4. The average SVF value in the 1st Region, where high-rise buildings are located, is 0.53.
The SHF value represents heat loss in the air due to convection and conduction, which is the primary driver of global warming. In contrast, LHF refers to heat loss from the surface as a result of evapotranspiration. The sum of the two components is known as usable energy. Differences in the land surface in urban areas also create differences in heat flow densities. It is important to identify these differences in heat flux as they affect the thermal state of the urban environment. It has been observed that the perceived heat flux is above 300 W/m2. The average SHF value in Zone 1 was 434.76 W/m2, in Zone 2 it was 429.46 W/m2, and in Zone 3 it was 381.89 W/m2. LHF distribution, on the other hand, is in the area with the highest SHF heat flux density, with the lowest average value of 117.51 W/m2 (Figure-13).
As a result of the study, it was found that the surface features of urban areas cause an increase in heat flux and surface temperature densities. Cities are spreading from the city center to their surroundings (Balogun et al. 2011). Ogunjobi et al. (2018) stated that in urban centers with high urban density and high-rise buildings, surface energy forms also change as surface conditions change. Jiang et al. (2014) and Guo and Schuepp (1994) stated that surface heat flux and surface temperature density also depend on the heterogeneity of urbanization. The decrease in the vegetation cover and the increase in the impermeable surface cover change the SHF and LHF densities. These two factors are very important in heat flux variation (Brom et al. 2009).
SHF value was found to be more intense on impermeable surfaces in urban areas and LHF value was found more intense in green areas. Daramola and Balagun (2019) reached the same conclusion in their study. This explains the high SHF heat value in areas with high-rise buildings. Dense structures consist of materials with high thermal storage capacity and prefer sensing heat flux over locations. On the other hand, green areas have higher LHF density compared to the city center. The high SHF density over the city center has an impact on the thermal state of the urban environment as it contributes significantly to the urban heat island effect.
The two indicators of urban land regeneration used in this study are Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI). The LST distribution was highest in Zone 2 (40.9°C), that is, in the densely populated urban area. The lowest intensity (37.9°C) was observed in Zone 3, where high-rise buildings are located. The Normalized Difference Vegetation Index (NDVI) indicates the extent of vegetation in the area. The level of land conversion within the city is also shown by the NDVI value. The highest NDVI values ranging from 0.07 to 0.22 were observed in Zone 1. As the degree of transformation from vegetated surface to urban surface increases, the NDVI value decreases. This explains why the maximum LST values in regions with the lowest NDVI values (Figure-14).
When the values in the study areas are processed on 3D maps, the spatial differences are more clearly understood (Figure-15).
In the study, statistical analysis was performed to evaluate the relationship between surface heat value and flow, different view factors, and vegetation. ANOVA, TUKEY, and PEARSON correlation were used to evaluate the relationship between them. The values obtained according to the ANOVA analysis (Table-2) were found to be very important (p ≤ 0.01) in all regions. This shows how important the values revealed in the study are in determining the urban climate.
According to the results of the analysis performed to determine which values differ between regions according to the multiple comparison test, it was found that the LST value differed for all three study areas, while the SHF, TVF and SVF factors differed for one region and two or three regions. It was concluded that NDVI, LHF, and BVF values differed between one or two regions and three regions (Table-3).
According to the correlation analysis performed to determine how all values affect each other, the LST value has a very negative relationship with LHF, TVF and NDVI values, while it shows a significant positive relationship with SVF and BVF values.
The SVF value, on the other hand, has a very significant negative effect on the LHF, TVF, BVF values. SVF value increases as SHF and LST values increase (Table-4).
The sky view factor affects both sensible and latent heat flux in distinct ways. The sky view factor has a direct relationship with the sensible heat flow. The sky view factor is inversely related to the latent heat flux. In the case of surface heat flux variation about vegetation, the situation is inverted. The NDVI and latent heat flow are inextricably linked. The NDVI and the sensible heat flux are inversely proportional. The temperature of the land surface varies as well, depending on the Sky View Factor and the vegetation. LST has a direct association with the Sky View Factor and an inverse relationship with vegetation, according to research.
The lowest LST (37.8 0C) and SHF (381.89) values in the study areas were observed in the 3rd region where NDVI values are high and vegetation is present. It reduces the surface temperature and SHF density due to the evaporative cooling effect of green areas; however, with the increase in vegetation, the LHF density increases. Similar observations were found in Akure, Nigeria, in areas with high SHF and LST values, high SVF, and Low NDVI values (Daramola and Balagun 2019). According to Sahana et al. (2016), in the Sundarban Biosphere Reserve in India, Sannigrahi et al. (2017) found that green areas have low LST in their study at the Hyderabad Municipal Corporation, Anyanwu and Kanu (2006), Hamada and Ohta (2010) and Adeyeri et al. (2016) emphasized that vegetation decreases both surface and air temperatures by evaporation, which in turn cools the surface. Tan et al. (2016) emphasized that the cooling effects of urban trees are highly correlated with SVF. Tan et al. (2017) determined that the temperature values measured under the trees decreased due to the decrease in SVF values.