An Application of the LCZ Approach in Surface Urban Heat Island Mapping in Sofia, Bulgaria

Abstract

This article presents the results of the thermal survey of the capital of Bulgaria (Sofia) carried out in August 2019, with the application of an unmanned aerial system (UAS). The study is based on the concept of local climate zones (LCZs), taking into account the influence of the features of land use/land cover and urban morphology on the urban climate. The basic spatial units used in the study are presented in the form of a regular grid consisting of 3299 cells with sides of 250 ´ 250 m. A total of 13 types of LCZs were identified, of which LCZs 6, 5, 8, 4, D, and A form the largest share. In the thermal imaging of the surface, a stratified sampling scheme was applied, which allowed us to select 74 cells, which are interpreted as representative of all cells belonging to the corresponding LCZ in the urban space. The performed statistical analysis of the thermal data allowed us to identify both the most thermally loaded zones (LCZs 9, 4, and 5) and the cells forming Urban Cool Islands (mainly in LCZs D and C). The average surface temperature in Sofia during the study period (in the time interval between 8:00 PM and 10:00 PM) was estimated at 20.9 °C, and between the different zones it varied in the range 17.2–25.1 °C. The highest maximum values of LST (27.9–30.6 °C) were registered in LCZ 4 and LCZ 5. The relation between the spatial structure of the urban thermal patterns and urban surface characteristics was also analyzed. Regression analysis confirmed the hypothesis that as the proportion of green areas increases, surface temperatures decrease, and, vice versa, as the proportion of built-up and impermeable areas increases, surface temperatures increase. A heat load map (via applying a z-transformation to standardize the temperature values), a map of the average surface temperature, and a map of the average intensity of the heat island on the surface were generated in the GIS environment. The results of the study adequately reflect the complex spatial model of the studied phenomenon, which gives grounds to conclude that the research approach used is applicable to similar studies in other cities.

Assessment of Urban Heat Island in the Northeastern State Capitals in Brazil Using Sentinel Satellite Data – 3 SLSTR

“…Therefore, considering that areas with vegetation and bodies of water mitigate the accumulated heat, while areas with exposed soil and certain construction materials increase the temperature [35][36][37][38][39][40][41], the hypotheses were raised to explain the results obtained in the Northeastern capitals, with the support of LULC images from MapBiomas [42], Figure 7. Analyzing the Fortaleza maximum SUHI, a high temperature may have occurred in the furthest region of the urban area, at PUa, with a value of 2.31, because the hottest pixel of this area had a very high temperature of approximately 30.96 °C.…”

Local Climate Zones to Identify Urban Surface Heat Islands: a Systematic Review

“…Their main advantage is the high spatial and temporal resolution, which is very useful for obtaining land surface temperature and their analysis in specific local climate zones. However, the operational cost of these tools is still relatively high [31].…”

“…The surface urban heat islands (SUHI) are commonly calculated as the difference between the LST in urban and rural areas, according to [31], as illustrated in Figure 8. However, there are several proposals for calculating SUHI because not every urban cluster will be the same, and the urban and rural border areas are sometimes difficult to discriminate [30].…”

Investigating the Spatial Heterogeneity of Urban Heat Island Responses to Climate Change Based on Local Climate Zones

“…Li distinguished the cooling and heating effects of LCZ [25]. Researchers have found that land cover, solar radiation, surface albedo, urban form, human activities, and climate change can all have significant effects on LST [22,[25][26][27][28][29][30][31][32]. Studies have shown that UHI is strongest in the summer [25].…”

“…The urban and rural temperature difference, the temperature grade difference, the Gaussian surface height, and the temperature attenuation amplitude methods are used mainly to quantify the urban heat island intensity [25,[36][37][38][39][40][41][42][43][44][45]. Remote-sensing inversion or field measurements are usually used to obtain LST data [21,26]. Pearson's correlation coefficient is commonly used to study the strength of the linear correlation between two variables-in this case, LCZ and the LST [7,23,46].…”