2.1

Study area

The present study is focused on the urbanized area of Sofia - the capital of Bulgaria. The city is located in the largest valley in Bulgaria - Sofia (about 1200 sq. Km) at 550 meters above sea level. The relief of the valley floor is flat, with a slight slope up to the north. To the north, it is surrounded by the Balkan Mountains and to the south-by the Vitosha Mountain. The climate is temperate continental with clearly distinguished four seasons. According to the Sofia Station, which operates from 1881 to the present, the average annual temperature is 10.3 °C, and the average annual rainfall is 612 mm, with a maximum in May-June. The average temperature of the coldest month is -1.7 °C and the warmest month is 21.2 °C. The measured absolute minimum air temperature is -27.5 °C and the absolute maximum temperature is 37.4 °C. Sofia’s urban area is a complex combination of different functional zones (administrative, residential, industrial, transport, commercial, parks, etc.), that have been developed at different times, over a very long period of time.

2.2

Methodological framework

The present study is based on an integrated scheme of application of different methods for collecting, analyzing and processing geospatial data and generating geo-referenced information, which allows characterizing, mapping and evaluating the basic parameters of the formed surface urban heat island (SUHI) within the urbanized area of Sofia. In order to achieve this, the following key research activities have been undertaken, organized in 4 logically related research phases:

2.3

Local climate zones (LCZ)

For the purposes of the study, a classification scheme for the LCZ for the territory of Sofia was developed, by using the fundamentals of the proposed and widely applied LCZ scheme, suggested by Oke and Stewart [8]. It has been successfully used in various as structure and genesis urban areas in recent years and has gradually established itself as a leading classification scheme for this type of research. What is specific in this case, is that the scheme is implemented in the form of a structured GRID, formed by cells of uniform size and geometry, which cover the territory of the urban space. This geospatial structuring approach allows a better adaptation of the spatial data, resulting from LCZ, for the purposes of stratified sampling and subsequent geostatistical processing. Each cell is 250x250 m in size, covering an area of 6.25 ha, divided into 13 types (classes) of zones (Fig. 1).

Figure 1.

LCZ in Sofia as grid 250x250m

The developed LCZ classification for the city of Sofia does not identify three types of zones from the original scheme, proposed by Oke and Stewart: LCZ 1. Compact high-rise building, LCZ 2. Compact medium-high build-up and LCZ 7. Unstable low build-up. The reason for this missing is either the three types of LCZ are too small and fragmented, or because the source data for their identification (orthophoto plans and cadastral data) is not with the appropriate resolution for their spatial recognition. The share distribution for each type of LCZ is presented in Table 1.

Table 1.

Number of cells per LCZ, area and relative share of identified LCZ

2.4

Remote sensing techniques and methods

In this study, two sets of remote sensing data are used: a) remote sensing data from satellite platforms (in this case, Landsat) and b) collected data through the use of Unmanned aerial system (UAS) platform.

2.4.1.

Satellite remote sensing data

Thermal (thermographic) satellite images have been used to provide a general mapping of the SUHI effect, providing a dense network of time-synchronized temperature data on the earth, including urban areas [9]. In this case, the thermal channel (TIRS) of the Landsat 8 platform is used, where the temperature of the earth’s surface was derived by a calculation algorithm based on the following channels: 10 thermal, 4 RED, and 5 NIR [10].

The result of the above calculation is a digital geospatial data representation of the urbanized teritory of Sofia and the surrounding area, delivering information about the surface temperature at the time of image capture, as well as about the nature of the landcover (in terms of vegetation distribution and impervious areas).

The main problem and significant disadvantages of the applied methodology are related to the restrictions that objectively exist regarding the period during the actual satellite “snapshots” are taken. In this case, the images from Landsat 8 platform are taken at noon local time, when the earth’s surface has been ov erheated. This is resulting in serious distortions of the parametrization and specifications of the UHI effects within the study area. Additionally, that problem leads to other limitations, including the inability to the intensity of UHI to be calculated correctly.

Regardless of this disadvantage of the Landsat satellite data for the present study, the results clearly show that urbanized territories are characterized with significantly higher surface temperatures than areas dominated by natural landscapes. For example, in the created geospatial model of the surface temperature based on the noon image from 12.08.2019 (Fig. 2), we can see the significant heterogeneity of the temperature of the surface of Sofia and the adjacent territories, with a temperature variation of more than 26°C between the most and the least heated surfaces.

Figure 2.

Temperature profile across Sofia on Landsat thermal channel map from 12.08.2019 (noon)

2.4.2.

UAS remote sensing data

To compensate the shortcomings of the satellite data, in the present research has been used UAS system with installed Thermal Infrared (TIR) sensor. In this case, was used the Albris platform of the Swiss company Sensefly, which is an inspection V-shaped quadcopter with 5 sensors and 3 camcorders, allowing 3 types of imaging with image detail below 1 mm, depending on the subject of study. The system is equipped with a TIR camera with parameters 80x60 pixels, with a 50-degree angle and a fixed measuring point, positioned on a 3-axis head (gimble). The unmanned aerial system enables remote capture of thermal images and video with dynamically adaptable scale in real time (Fig. 3).

Figure 3.

Terrain sampling data collection through UAS platform

Due to the constraints of the used UAS platform for data collection, related to its objective limitations in terms of operational time and spatial cover, for the present study, has been developed and applied a stratified sampling scheme (STR) approach. Regarding this, for each LCZ has been determinated a specific number of cells to be observed through i-situ observations, through the use of the UAS platform with TIR sensor on it. The total number of observed grid-cells is 74, which number is determined by the method of the non-return selection[11,12,13]. The most appropriate study period of the year is August, which is the month with the highest average temperature of the earth’s surface, which is a fundamental prerequisite for the most visible signs of the UHI phenomenon. Its effect is most clearly observed during the early evening hours, immediately after the active sunshine [14], with relative windiness and cloudless sky. In this part of the day, there is the best opportunity to maximize the potential for precise identification of micro and local climatic differences between urbanized territories and their response to UHI. Regarding this, the time interval between 8:00h, and 10:00 PM was selected for thermal imaging observations for the predefined grid cells (Fig. 4).

Figure 4.

Thermal images examples from UAS platform

2.5.

Spatial interpretation and analysis of field research data. Determination of SUHI intensity

The results in-situ UAS based thermal surveys are used to construct linear regression dependencies through the least-squares method, between the areas of the main land-use types (as an independent quantity) and the average measured temperatures by land-use types (as a dependent value). The general conclusions confirm the hypothesis, that the greater the proportion of green space, the lower are the measured temperature values, and vice versa - the higher the proportion of built-up and impervious areas, the higher are the surface temperatures:

Figure 5.

Linear regression dependence - green areas

Figure 6.

Linear regression dependence - impervious areas

Figure 7.

Linear regression dependence -build-up areas

Using the created regression dependencies, and the spatial distribution of the measurements made by UAS, a geostatistical model of the spatial distribution of the surface temperature within the city of Sofia was created. For this purpose, an approach for deterministic local interpolation is applied whereby the predicted values are generated within local groups of adjacent points based on the network of measured values and their locations via UAS.

The result of spatial interpolation clearly shows that within the urban space in Sofia a poly-structural (polycentric) urban heat island has been formed (Fig. 8).

Figure 8.

Map of spatial distribution of calculated and interpolated land surface temperatures (LSTs) in Sofia between 8:00 and 10:00 PM

The great diversity of the territories involved in the different LCZs, as well as the high spatial variability of their spatial structure, determines to a large extent the complex and poly-structural nature of the UHI phenomenon. For example, only one section of 1000 square meters exhibits significant temperature contrasts. The figure below shows a fragment of a field measurement of the surface temperature in 22 hours, carried out in the area of a large hypermarket in Sofia. The image clearly shows that within this confined space, extremely large differences in surface temperature are observed - in the grassland it is within 12.9-13.1°C, in the parking places made of concrete rosette panels it is 17.1°C respectively and on the asphalt road 23.6 °C (Fig. 9).

Figure 9.

Thermal image example of the differences between land surface temperatures of different land cover types (hypermarket “HIT” in Lyulin district, Sofia (9:30 PM, 07.08.2019)

These temperature differences are characterizing the complexity of the UHI and represent one of its most important of its core parameters - the intensity (magnitude) of the phenomenon. The intensity of the LST for each LCZ objectively characterizes the power of manifestation of the LST in a spatial aspect. The intensity is calculated as follows [15]:

where ΔT is the maximum difference from the expression LCZ X - Y, in which X = the temperature of the given LCZ, and Y- the temperature of the other LCZs for comparison. The data on the temperature differences between all LCZs in Sofia are presented in Table. 2.

Table 2.

Temperature differences between LCZ, necessary for calculation and mapping of SUHI magnitude

Based on the data from the matrix in the table, a statistical surface was created using a local polynomial interpolation, representing in a continuous form the UHI magnitude in the urban space of Sofia (Fig. 10).

Figure 10.

Map of average magnitude of SUHI in Sofia for August 2019 г. (8:00-10:00 PM)