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Article

Use of Satellite Images to Determine the Temperature of Urban Surfaces for Landscape Management Purposes, Case Study Bratislava (Slovak Republic)

by
Martin Šalkovič
1 and
Eva Pauditšová
1,2,*
1
Department of Environmental Ecology and Landscape Management, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovičova 6, 842-15 Bratislava, Slovakia
2
Institute of Management, Slovak University of Technology, Vazovova 5, 812-43 Bratislava, Slovakia
*
Author to whom correspondence should be addressed.
Land 2023, 12(2), 384; https://doi.org/10.3390/land12020384
Submission received: 15 December 2022 / Revised: 12 January 2023 / Accepted: 18 January 2023 / Published: 31 January 2023
(This article belongs to the Section Land–Climate Interactions)
Browse Figures
Versions Notes

Abstract

:
This contribution deals with the use of data obtained from Landsat 8 satellite imaging to identify surface temperature variability in the example of the city of Bratislava, with an emphasis on identifying hotspots outside the built-up area, for example, on agricultural land—locations which are part of the European Network of Protected Areas. Surface temperature variability is presented in two time periods, on the daytime image taken on 26 July 2021 and on the nighttime image from 28 June 2021. Surface temperature is projected in a profile cut of the area. It vertically illustrates the temperatures of individual types of surfaces. Surfaces are classified by Urban Atlas classes. Areas reflecting the spatial distribution of the residential development in the city of Bratislava have been identified by satellite images in the studied area, and they represent a phenomenon of the urban heat island. Such areas were also identified outside the built-up area, in agricultural areas. The results of our research show that it is important to deal with UHI outside the built-up areas of cities and to orient the attention the territory planning and also to the proposal of measures for the management of these areas. Especially if these areas also include territories of the European system of protected areas, as it is in the case of Bratislava city (e.g., SPA029 Sysľovské polia). The results of reducing the impacts of climate change in cities concern not only the residents. In spatial planning, it is also necessary to address the management of non-built-up areas—localities with a quasi-natural character (e.g., areas with diverse vegetation cover). In order to recognize UHI within residential areas, it is essential to identify areas with significant differences between daytime and nighttime surface temperatures. Large differences between night and daytime surface temperatures can be seen in areas outside the built-up area in Bratislava on arable land where the difference is up to 8.0 °C (in the continuous housing class where the proportion of impermeable surfaces is higher than 80% with a temperature difference of 7.6 °C). Identification of overheated surfaces in the territory makes an important basis for modification of the landscape management and management of nature protection areas. It is important to propose measures related to the reduction in the negative impacts of climate change on the landscape and biodiversity.

1. Introduction

The global trend of urban population growth is contributing to an increase in urban built-up areas. Conversion of land surface types often results in a reduction in vegetation areas and an increase in artificial impermeable surfaces. Built-up areas in the urban environment are the primary source of urban heat islands (UHI). The urban heat island is defined as an area of increased air temperature in the boundary and ground layer of the atmosphere over the city or industrial agglomeration compared to the surrounding area [1]. Oke [2] defines UHI as an industrialized area of the city differing from the surrounding rural area by the higher temperature, which arises as a consequence of human activity and an increased degree of urbanization. According to Voogt [3], conditions for the development of UHIs occur virtually in every city. UHIs are formed when artificial and not water-permeable surfaces (a consequence of water impermeability: rapid outflow of water in the area, low air humidity, lack of water for evaporation, increased amount of aerosols in the atmosphere) with high thermal capacity (asphalt, concrete) prevail. The problem of UHI occurrence increases significantly if large areas in cities are turned into impermeable surfaces. In such areas, solar energy is converted into non-utilizable heat [4]. The degree of the UHI effect and its changeability over time is related to geographical characteristics such as the urban and terrain morphology, type of relief, altitude, regional climate, etc. The UHI is also directly related to the spatial size of the city as well as to the type of surfaces. The UHI parameter is not stable. UHI area changes during the year, mainly due to changes in the intensity of sunlight, as a consequence of the changeability of weather conditions and changes in surface properties [5]. This is also confirmed by Oke’s findings [6], according to which atmospheric layers are affected by the properties of surfaces. According to this author, factors affecting mainly lower layers of the atmosphere include thermal and radiation properties of active surfaces, impermeability of active surfaces, the geometric arrangement of active surfaces, atmospheric pollution, and production of excess heat.
It is essential to determine the energetic balance of surfaces in order to examine the consequences and effects of urban heat islands. In the urban environment, short-wave electromagnetic radiation is lower due to the layer of aerosols in the atmosphere [7]. Materials used in the urban environment affect the percentage of reflected radiation in relation to the total incident radiation (albedo). Many building materials have a lower albedo than vegetation-covered areas. The lower the albedo, the faster the surface temperature increases. Artificial surfaces are also characterized by specific thermal properties that contribute to the increase in air temperature in cities [8]. These properties include high thermal capacity and thermal conductivity. High levels of these properties cause higher accumulation of heat, which in turn determines surfaces to be in negative intervals in terms of energetic balance. Not only the type of surfaces but also the geometry of the residential development plays an important role in the reduction in albedo. The method of construction leads to an increase in the amount of absorbed sunlight during the day and a reduction in long-wave radiation during night-time [9].
Observation of the Earth’s surface and processes taking place on it is key to the understanding of the dynamics of processes occurring at the interface between surfaces and the atmosphere. According to Kidd et al. [10], surface temperature information is one of the most important quantitative characteristics of surfaces. Authors Wan and Snyder [11] characterize land surface temperature (LST) as a combination of the results of all interactions between the surface and atmosphere, and the energy flows between them. This is the reason why LST is an appropriate indicator of the energy balance occurring on the Earth’s surface. Wan [12] mentions LST as a good indicator of energy balance on the Earth’s surface because it is an important parameter of physical processes occurring on the Earth’s surface, both on a regional and global scale.
The examination of surface temperature is directly related to the types of media (e.g., satellites, aircraft, drones), sensors, and other areas for which research results are used. Remote surface temperature imaging can be performed using satellite imagery, aerial imagery, or imagery using an unmanned aerial vehicle, i.e., a remotely controlled drone. Satellite imagery of the Earth’s surface by means of thermal sensors is provided, among others, by the Landsat 8 and Landsat 9 satellites equipped with Operational Land Imager (OLI) and Infrared Sensor (TIRS). Many authors have dealt with the examination of the outputs obtained when approaching land by these satellites. In their published works, they most often dealt with UHI. These, for example, include works by [13,14,15,16] and others. A study by the authors Darettamarlan et al. [17] examines the correlation analysis of the surface temperature in the city of Surabaya (Indonesia) measured by the DJI Mavic Enterprise Dual Thermal drone and Landsat 8 satellite images.
Climate change and adaptation to new climate conditions pose a challenge for almost all cities. One of the initiatives to motivate cities towards the improvement in adaptation to changing climate conditions is the Climate Neutral and Smart Cities Mission [18] which aims to achieve carbon neutrality through the implementation of intelligent and green solutions by 2030. In 2022, 100 cities (Bratislava and Košice from the Slovak Republic) have been selected from each EU Member State, including 12 other cities from countries associated with Horizon Europa or from countries that could potentially join the program. Cities shall draw up action plans in accordance with the support initiatives and commitments resulting from the strategy papers. Many parameters need to be known to draw correct proposals of measures for action plans. Primary information includes data on urban surface temperatures and the identification of UHI.
There are many works dealing with the identification of UHI or the investigation of LST in the surrounding cities, either in the territory of Slovakia or in neighboring countries. The paper capturing the surface temperature of Bratislava [19] in two cases, July and December, shows average values for selected elements of land use. Another Slovak city for which the LST was processed is the city of Košice [20]. In which the authors discuss combining Landsat 8 and Sentinel-2 data in Google Earth Engine to derive higher-resolution land surface temperature maps in urban environments (Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment). In the immediate vicinity of Bratislava is the capital of Austria, Vienna. The authors of Han et al. [21] investigate the response of urban land surface temperature on land cover, using the two cities of Vienna and Madrid as an example (Responses of Urban Land Surface Temperature on Land Cover: A Comparative Study of Vienna and Madrid). In the territory of the Czech Republic, Poland, and Hungary, several works were carried out for cities such as Prague, Brno, Olomouc, Krakow, and Budapest [22,23,24,25].
The paper focuses on the investigation of the surface temperature in Bratislava, with an emphasis on identifying hot spots outside the built-up city center. In the last decades, this city has been attacked by very intensive construction activity. The type and quality of urban surfaces and the height horizons of urban development are fundamentally changing, which has a fundamental impact on the emergence of UHI. However, quasi-natural landscape elements that are part of the city are also affected by the impacts of climate change. Apart from the urban structure of surfaces, other factors in the natural landscape are also responsible for the emergence of UHI, the identification of which we set as a research goal.

2. Materials and Methods

The area of interest is the city of Bratislava, situated in western Slovakia, bordering Austria and Hungary (Figure 1). Bratislava is the capital city of the Slovak Republic, the largest city of Slovakia in respect of its area (367.66 km2) and population. According to the data of the Statistical Office of the Slovak Republic, there are 475 thousand of inhabitants in Bratislava (as of 31 December 2021). The Danube River flows through the city, and the western border of the city is formed by the Morava River. Bratislava lies at the foot of the Small Carpathian Mountains, which largely determines its settlement differentiation. On the southern slopes of the Small Carpathians, there is a flat area of the Danube Lowland. On the north, there is the Záhorská Lowland. The highest point of the town is Devínska Kobyla, with an altitude of 514 m above sea level. The lowest point has an altitude of 126 m above sea level.
According to Konček’s climate classification, the area of interest is classified as a warm, even dry, to a very dry area with a mild winter [26]. The long-term climatic characteristics within the responsibility of the Slovak Hydrometeorological Institute indicate the average annual temperature of 10.6 °C and the average annual sum of atmospheric precipitation of 561 mm for the reference period of 1981–2010. For the reference period 1981–2010, the average annual duration of sunshine is 1955 h. Annual statistics on climate data show an increasing trend of annual temperature growth in the territory of Bratislava.
By means of the Urban Atlas project, the Copernicus program of the European Space Agency (ESA) provides Europe-wide comparable data on land cover and land use for functional urban areas (FUA). The product line Urban Atlas 2018 covers 27 European countries and includes 788 FUA. The area of the Bratislava region and the bordering part of the Vienna area from the Urban Atlas served to create a complex picture of the landscape cover of the city of Bratislava and its surroundings. Individual FUA data packages contain (1) OGC GeoPackage SQLite (ETRS89-LAEA, EPSG:3035) vector data; (2) a high-resolution map of the area; (3) a delivery message; (4) .lyr, .qml and .sld symbology files; and (5) an xml document with metadata. The number of objects in the area of interest includes 4542 polygons classified in 25 object classes. With a share of 26.6% of the total area of the territory, the most represented is the arable land class with annual crops (Urban Atlas land use class code: 21000), representing 97.8 km2 of the area located at the periphery of the territory. The forests (31000) account for 25.9% of the total area and represent 95.2 km2 of the area. The forest class is located mainly in the area of the Small Carpathians and at the Danube river bed. The class of Industrial, commercial, public, military, and private units (12100) represents a non-natural class. Its share is 10.5% (38.5 km2) of the total area of the city. The most widely represented settlement class is discontinuous dense urban development, with a percentage of impermeable surfaces reaching from 50 to 80% (11210). The share of this class in the total area of the territory is 6.4%, and the area of the territory is 23.5 km2. A complete list of object classes with their shares and area representation within the city of Bratislava is presented in Table 1. Figure 2 shows the spatial arrangement of the Urban Atlas object classes.
Authors Deilami et al. [27] addressed the topic of correlation or causality between landscape cover patterns and urban thermal island effects. These authors have developed a methodological framework to calculate UHI indicators. Using the product to search satellite images, aerial images, and cartographic products at the United States Geological Service (USGS), we searched satellite images capturing the territory of the city of Bratislava. In the first step of the selection, we entered localization criteria corresponding to the location of the city of Bratislava on the map in the form of a polygon. Subsequently, the selection of data to be displayed was carried out. The Landsat—Landsat Collection 1 (Level-1) products were selected, within which we selected Landsat 8-9 OLI/tires C2 L1 for 2021. We have obtained all data sets containing images of flights acquired by the selected satellite product in TIF format, arranged by a timeline from the latest to the oldest. Each obtained data package contained 11 spectral bands with a spatial resolution of 30 m (except for the panchromatic band 8, which has a spatial resolution of 15 m). The data packages also contained a text document with metadata and data for individual bands as well as satellite sensor settings.
A raster for band 10, representing thermal infrared radiation, was used to calculate the temperature of the surfaces in Bratislava. The first step in terms of the methodological procedure for using USGS products [28] was the conversion of raster cell values. We used the ArcCatalog spatial analysis tool—Raster Calculator. The OLI and TIRS band data can be converted to upper atmosphere spectral radiation (TOA) by using radiation scaling factors listed in the metadata set:
L λ = M L Q c a l + A L
where:
Lλ is the upper part of the spectral radiation of the sensor [W/(m2sr·µm)],
ML is the specific value of the spectral band of the multiplicative conversion given in the metadata (RADIANCE_mult_BAND_x, where x is the spectral band), Qcal stands for digital values of the thermal image, AL is the specific value of the spectral band of the multiplicative conversion given in the metadata (RADIANCE_mult_BAND_x, where x is the spectral band).
In our case, the ML band specification was 0.0003342, and the second AL band specification was 0.1. Qcal represents a raster of values of band 10.
The next step focused on the conversion of the temperature band data from the spectral radiation to the upper luminance temperature of the atmosphere using the thermal constants listed in the metadata set. The following mathematical relationship was used for the given conversion [28]:
T = K 2 ln K 1 L λ + 1 − 273.15
where:
T = upper luminance temperature of the atmosphere (K)
Lλ = spectral emission of the upper atmosphere (Watt/(m2*srad*μm))
K1 = the band-specific thermal conversion constant given in the metadata (K1_CONSTANT_BAND_x, where x is the temperature band number)
K2 = the band-specific thermal conversion constant given in the metadata (K2_CONSTANT_BAND_x, where x is the temperature band number).
The values of the constants from the metadata are K1 = 774.8853 and K2 = 1321.0789. As the resulting value of the upper brightness temperature of the atmosphere is given in Kelvin, we added the conversion of the value to degrees Celsius to the formula. After the above data transformation, we calculated the value of the Normalized Difference Vegetation Index (NDVI), which is used in the quantification of vegetation and is useful when determining vegetation density and used in evaluating plant health changes. The NDVI is calculated as the ratio between the red (R) and near-infrared (NIR) values according to the following mathematical relationship [29,30]:
(NIR − R)/(NIR + R)
In the case of Landsat 8, it is a calculation of NDVI = (band 5 − band 4)/(band 5 + band 4).
With the use of the NDVI value, we calculated the share of vegetation within surfaces in Bratislava. The following mathematical relationship was applied [28]:
P v = N D V I − N D V I m i n N D V I m a x − N D V I m i n 2
where NDVImax and NDVImin are the maximum and minimum values we obtained when calculating the NDVI. The calculation of vegetation share served as an input to the next step, which was the calculation of emissivity according to the relationship [28]:
L S E = m * P v + n
where,
m = Standard deviation of surface emissivity (0.004)
n = Subtracted value of vegetation emissivity and value ‘m’ (0.986) where for the actual derivation of the value of m, the relation m =εV − εS − (1− εS)FεV applies. A for the value n, the relation n = εS + (1 +εS)FεV applies. Where εV the vegetation emissivity (0.99), εS is the soil emissivity (0.97), and F represents the value of the different geometric distribution form factors (0.55).
In the last step, we calculated the temperature of the surfaces using the following equation [28]:
L S T = T 1 + λ * T α * ln L S E
where,
λ = wavelength of the emitted radiation. In the case of Landsat 8, it is the value of 10.8 µm
α = h ∗ c / s = 1.4388 ∗ 10−2 mK = 14388 µmK where h is the Planck constant of 6.626 ∗ 10−34Js, c is the speed of light of 2.998 ∗ 103m/s, and s is the Boltzmann constant of 1.38 ∗ 10−23J/K. The upper luminance raster (T), Equation (2), and the emissivity raster (LSE), Equation (5), are further included in the calculation.

3. Results

Two satellite images were selected to determine and subsequently evaluate the temperature of the surfaces in Bratislava—day and night-time satellite images. The night-time image from 28 June 2021, with a scan time of 10:29 p.m. CEST and the daytime image from 26 July 2021, with a scan time of 11:45 a.m. CEST, were selected. In terms of the period, a warm period was selected when the maximum air temperatures ranged above 25 °C for several subsequent days. With the maximum air temperature reaching above 25 °C, we are talking about summer days. The night surface temperature image (Figure 3) is between 10.2 and 23.0 °C. According to the surface temperatures, the coldest areas are located in the valleys of the Small Carpathians, water areas, and agricultural parts of the territory. The warmest locations are in residential parts of the city. The spatial variability of night surface temperatures shows increased temperatures representing road radials in Bratislava and mainly surfaces in industrial areas. Locations with higher temperatures contain impermeable surfaces accumulating solar energy during the day. At night, this energy radiates back to the surroundings in the form of heat. Surface temperature at night is a good example showing the phenomenon of an urban heat island. The daily surface temperature (Figure 4) reaches between 14.3 and 48.3 °C. The temperature range is 34 °C. The highest surface temperature is in the production halls of the automotive plant Volkswagen a.s. located in Devínska Nová Ves. The lowest surface temperatures are on the water surface of the river Danube.
With spatial variability parameters on a daytime image, cooler surface temperature values at the watercourse (Danube), forest areas of the Small Carpathians, and lakes have been observed. Larger areas with a hot surface can be seen on arable land outside the built-up area of the city, except for the area of Volkswagen a.s. It is either soil without vegetation cover (after harvest) or a specific type of crop. In urban areas, especially in industrial areas in the eastern part of the city, there are several areas with high levels of surface temperature. Parts with high surface temperatures identified in the area of interest clearly correspond with the built-up areas of the city, which, depending on the type of landscape cover, have impermeable paved surfaces. Surface temperature is also affected by the materials from which anthropogenic objects are constructed.
To illustrate the variability of surface temperatures confronted with Urban Atlas classes, we created a profile section through the city territory with a length of 20 km (Figure 5). Since the satellite images we used extended into the Vienna area, the profile cut starts in Austria, east of the city of Hainburg a der Donau, continues east through the city ercenter of Bratislava, and ends beyond the city border.
The profile section of the territory is divided into 204 parts as it classifies into land cover categories in accordance with the Urban Atlas. According to the Urban Atlas, the profile section captures 16 types of landscape classes, while 81 sections (a total of 1243 m) represent the most common class of other roads and their adjacent areas (12220). The largest proportion of the length in the entire profile section is represented by the arable land (annual crops) class (21000) with a length of 3835 m. These sections are located at the beginning and the end of the profile section, i.e., these are the peripheral parts of the city, virtually the territory beyond the city boundary. The arable land (annual crops) class (21000) forms eight sections on the profile cut. The second class with the longest share of landscape cover within the profile section is a discontinuous dense residential development with the share of impermeable areas reaching 50—80% (11210).
In the profile section, this class is divided into 31 sections with a total length of 3603 m. Other classification classes of surfaces and their length distribution within the profile section are represented in Table 2.
Relevant surface temperature values were assigned to each classification class within the profile section for both day and night-time imaging. The conversion of surface temperature values into a graph, and its connection with land cover classes made it possible to confront surface temperatures shown in individual territorial classes provided in the Urban Atlas.
Local temperature minima and maxima have been identified during the detailed examination of the sample profile section through the territory. In the daytime image of the surface temperature (yellow line in the temperature graph in Figure 5), there is a significantly low value of the surface temperature in the Danube flow (A in Figure 5), which corresponds to the water surface class (50000) of the Urban Atlas. Another identified site with a significantly low surface temperature value is the Cemetery at Kozia Gate (B in Figure 5), which, according to the Urban Atlas land cover classes, represents the residential green areas (14100). The location with the highest surface temperatures is the Mlynské nivy area, where civic amenities, multifunctional areas, and transport areas are concentrated, which defines the concentration of impermeable surfaces (D in Figure 5). According to Urban Atlas, the given locality corresponds to the class of industrial and commercial areas, civic amenities areas, and military areas (12100). In location E (Figure 5), lower temperatures were recorded within the monitored profile section. There is a forest park in Vrakuňa, which, according to the Urban Atlas, classifies as an area of residential greenery (14100) and water surface (50000). An increase in surface temperature values at the F site (Figure 5) corresponds to residential development in the urban part of Vrakuňa. The maximum nighttime surface temperature value is seen in the city center located at Suché mýto (C in Figure 5).
In order to recognize UHI within residential areas, it is essential to identify areas with significant differences between daytime and nighttime surface temperatures. Large differences between night and daytime surface temperatures can be seen in areas outside the built-up area on arable land, as shown in the graphs in Figure 5. Daytime surface temperature values are significantly higher compared to nighttime surface temperatures. The average values of surface temperatures within each surface cover class identified in the profile section are given in Table 2. Landscape classes reaching the highest average surface temperature are industrial and commercial areas, amenity and military areas (12100), and continuous residential development class (11100), with a share of impermeable surfaces reaching more than 80%. The average temperature of these surfaces on the profile section reaches 28 °C in the daytime image. In the nighttime image, the surface temperature differs in the class of industrial, commercial, public, military, and private units (12100). It reaches 20.8 °C. In the area classified as continuous urban fabric (11100) with a percentage of impermeable surfaces of more than 80%, the average surface temperature is 20.4 °C. The country class with the lowest average surface temperature on the profile section at the daily image is represented by water areas (50000) with a temperature of 19.2 °C. The area of grassland (23000) with an average temperature of 17.1 °C was identified as the coldest class in the night average temperature image of the surfaces on the profile section. The largest differences between the average daily and nighttime surface temperatures are in the arable land class (annual crops) (21000), where the difference is up to 8.0 °C, and in the continuous housing class (11100), where the proportion of impermeable surfaces is higher than 80%, with a temperature difference of 7.6 °C. The least significant difference between night and day surface temperatures of 0.9 °C was recorded in the water surface class (50000).
On the daytime surface temperature scan, sites with significantly higher values compared to the surroundings have been identified. These were areas of arable land (annual crops) (21000) in the southern part of the city. It is a territory with high added value, as it belongs to the European network of protected areas—Special Protection Area Sysľovské polia (SPA029, Figure 6).
Locations with increased surface temperatures (Figure 7) on arable land were compared with the image of the city surface in visible wavelengths (blue, green, and red stripes) from satellite images in the corresponding period. This comparison is illustrated in Figure 8. Based on the identification of satellite image surfaces in visible wavelengths, it may be deduced that arable land surfaces depicted in the daytime image of surface temperature correspond to the exposed soil surface without vegetation cover. The temperature of such an exposed surface was 37.5 °C. Fields with vegetation cover achieve significantly lower surface temperature values.
This observation provides key information for setting up management in a protected bird area, which is very closely related to the types of land cover, the types of vegetation, and agricultural crops. The Great bustard (Otis tarda), which is, among other species, subject to protection in the area, is very sensitive to the character of landscape cover. The most prominent causes of bird threats in Sysľovské polia include the intensification of agriculture and the expansion of recreational tourism and sports activities. Within the long-term objectives of the program designed to care for the Protected Bird Area Sysľovské polia for the years 2020–2049 [31], weather extremes (sudden temperature fluctuations, a long period of rainfall associated with floods or, conversely, extremely dry and hot weather) and lack of food are listed as limiting and modifying factors that may negatively affect populations of selected species. Global climate change is a crucial external natural factor. This has already led to a shift in the area of the distribution of some bird species and also affects the species composition of habitats, which may have a negative impact on the achievement of long-term conservation objectives in the area.

4. Discussion

In work, we discovered the creation of hotspots in locations outside the built-up area. The emergence of hotspots in open countries is linked to a certain type of land cover, which becomes overheated due to agrotechnical intervention, and their occurrence is also linked to territories with higher levels of nature protection. Authors from many countries around the world use Landsat 8 images as input for their LST evaluation, especially in urban and suburban areas. E.g., Aryal et al. [32] examined the Kathmandu Valley using satellite images. The results of the research showed where the urban islands of heat are located. The authors found that the administrative units of Kathmandu, Bhaktapur, and Lalitpur have a higher LST diameter than other administrative units (ranging from 22.1 to 36.9 °C in March and June). On the basis of the landscape cover analysis, they concluded that a change in the functional use of the territory was necessary. It is important to increase the share of vegetation areas in order to avoid overheating of surfaces in specific parts of the studied area. Compared to our work, the researched area also focuses on the surroundings of the capital city of Kathmandu. UHI manifestations are observable in the surrounding agglomeration. Hotspots in the territory are manifested in the built-up area.
From the application point of view, the work of Padmanaban et al. [33] is thematically and conceptually well presented. In their work, these authors pointed out that it is advantageous to combine satellite images from multiple sensors when evaluating changes in land cover, surface permeability, and surface temperature and to identify UHI in the settlement environment. In the research, the authors used and merged IRS-LISSIII and Landsat-7 ETM+ images from 2007 and 2017. Thematic raster inputs used the Soil-Adjusted Vegetation Index (SAVI) and the Land Surface Temperature Index (LST) to determine the Soil-Adjusted Vegetation Index (Savi). They evaluated the investigated territory (the city of Tirunelveli, Tamilnadu in India) according to the relationship between these indexes. At the same time, they based identification of UHI on the combination of raster presenting the territory through SAVI and LST indices, but not on the interpretation of surface temperatures from primary satellite images. What turned out to be interesting was that the raster images created in this way showed a higher accuracy of the classification of the territory compared to the classification according to separate satellite images evaluated in our study in the territory of Bratislava. Based on the combined [33] rasters, researchers evaluated and balanced the studied area in terms of surface permeability, UHI, and surface temperature and thus created an important basis for zoning in spatial planning when considering threats resulting from the changing climate.
The authors Yamamoto [34], Avdan, Jovanovska [35], and Ward et al. [36] also dealt with a similar method for the use of information obtained from satellite images, which were applied in our study in the territory of Bratislava, Yang et al. [37], Estoque, Murayama [38], Ravanelli, et al. [39], Zhao et al. [40], Najafzadeh et al. [41] and others. The objective of this work was to identify UHI and classify areas according to surface types in order to identify threatened areas in terms of changing climate and to create a basis for spatial planning.
Compared to other works, such as the work of the authors Steigerwald et al. [42], which deal with the Delimitation of Urban Hot Spots and Rural Cold Air Formation Areas for Nocturnal Ventilation Studies Using Urban Climate Simulations. In their work, they identify urban thermal hot spots and areas of rural cold air formation from thermodynamic urban climate model simulations. For work, they chose the city of Aschaffenburg, located in the hilly terrain of south-central Germany. The flow of cold air in the night hours from higher forest areas above the city can also be identified in the territory of the city of Bratislava. On the contrary, large areas of agricultural arable land without vegetation cover represent the areas behind the emergence of UHI in the peripheral parts of the city. In another work by Waffle et al. [43], they consider Urban Heat Islands as agricultural opportunities in the form of an innovative approach. The authors look at plant growth in Toronto, Canada, noting that given the suitable microclimate combined with the effects of the UHI, Toronto could likely support the growth of warmer-climate crops that would not otherwise grow here. Compared to our territory, we see a similar global trend of an increase in warm days and periods of drought. Therefore, a different type of innovative approach would be required in the form of land segmentation and especially large-block arable land, and we also see the transition from deep plowing to another method that is less harmful to nature as very important.
An important factor that impact the formation of urban thermal islands, especially at night, is the geometry of the city. Researchers in this study did not work with this factor. They did not classify territories in terms of dimensions and spacing of buildings within built-up areas of the territory. Urban geometry affects wind flow, energy absorption, and the ability of surfaces to emit long-wave radiation back [44]. In intensively built-up parts of residential areas, buildings and paved impermeable surfaces create a heat mass from which the heat cannot be easily released. At night, therefore, the air above city centers is usually warmer than the air above the outskirts of cities or above rural areas. However, our study identified overheated areas on the outskirts of the city and in rural agricultural landscapes too. These are attractive areas in terms of nature conservation interests, and, based on our research conclusions, they are vulnerable to climate change. The location in question is the area of Sysľovské pole, a protected bird area. From a European point of view, the area plays an important role as a regular wintering site for approximately 10% of the Central European population of the species Great Bustard (Otis tarda) and more than 1% of Central European geese. The survival of the Great Bustard in Slovakia is directly related to the creation of conditions to protect this area, which is the species’ important historical reproductive habitat. The significance of the location is proven by the regular winter occurrence of a relatively high number of Great Bustards (160–200 individuals). The territory is also the last regular breeding ground of the red-footed falcon (Falco vespertinus) in Slovakia. Other rare steppe bird species have also been found to nest in the area, namely the common falcon (F. cherrug) and the Common buzzard (Circus cyaneus) [45].
Authors of the study carried out by Zemko et al. [46] focused on the use of agricultural landscapes and the potential suitability of the SPA Sysľovské polia to meet the habitat requirements of the falcon (Falco vespertinus). Their objective was to assess the structure of the country (in the period 2004–2017) and the appropriateness of agrotechnical practices in relation to the habitat requirements of this species. The conclusions of the study show that there are areas with negative assessments in all the criteria studied in Sysľovské polia. A change in the crop rotation procedure is therefore necessary for terms of species protection. It is necessary to increase the diversity of so-called positive crops and share permanent grassland. These changes are directly related to the type of land cover, which is a basic parameter to identify UHI via satellite imagery. Since we have identified overheated parts of large-block arable land in Sysľovské polia after the harvest, it is obvious that information on UHI needs to be considered when determining the management of the protected area and designing the type of crops included in the rotation procedure. Figure 8 shows a comparison of the image with the surface temperature and the visible spectrum image. The processed image of the surface temperature dated 9 September 2020 shows significant overheated surfaces at Sysľovské polia. Blocks of fields without vegetation cover, causing the surface to overheat, are to be seen in the image with a visible spectrum.

5. Conclusions

The results of our research show how it is important to deal with UHI also outside the built-up areas of cities and to orient the attention to the proposals of measures in the territorial planning process that are needed for the management of these parts of cities. Especially if these areas also include territories of the European system of protected areas, as is in the case of Bratislava city. We focused our interest on Special Protection Area (Protected bird area) Sysľovské polia (SPA029). From a European point of view, the area plays an important role for approximately 10% of the Central European population of the species Great Bustard (Otis tarda) and more than 1% of Central European geese. This territory is important historical reproductive habitat for the species Otis tarda. The type of land cover (which affects the surface temperature) and its properties are key parameters for the sustainability and development of the management of this Protected bird area. The survival of the Great Bustard in Slovakia is directly related to the creation of conditions to protect this area. The significance of the location is proven by the regular winter occurrence of a relatively high number of Great Bustards and the historical nesting success of the species in this area in 2022 after several decades.
The differences found between night and daytime surface temperatures are up to 8.0 °C in the protected bird area on arable land, indicating changes in the conditions of existence of the species. Changes in the temperature regime of the territory have a direct impact on the food resources. In addition, Otis tarda is a kind of bird that adapts very poorly to new conditions.
Therefore the reduction in impacts of climate change in cities concerns not only the residents. In spatial planning, it is also necessary to address the management of non-built-up areas—localities with a quasi-natural character (e.g., areas with diverse vegetation cover). Identification of overheated surfaces in the territory makes an important basis for modification of the landscape management and management of nature protection areas. It is important to propose measures related to the reduction in the negative impacts of climate change on the landscape and biodiversity.
Locations with critical surface warming in cities represent areas for the implementation of climate change mitigation measures that can be transformed up to the level of spatial planning regulations. The relationship between the increasing temperature of urban surfaces and the number of tropical days or heat waves in cities has been proven. Reduced temperature comfort in settlements also has a great impact on residents’ health. Detailed observation and assessment of surface temperatures using satellite imagery is, therefore, an important tool for planning processes.

Author Contributions

Conceptualization, E.P. and M.Š.; methodology, M.Š. and E.P.; software, M.Š.; validation, M.Š.; formal analysis, M.Š. and E.P.; investigation, E.P.; resources, M.Š. and E.P.; data curation, M.Š.; writing—original draft preparation M.Š. and E. P.; visualization, M. Š.; supervision, E.P.; project administration, E.P.; funding acquisition, E.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the project Horizon 2020 “ARCH—Advancing Resilience of historic areas against Climate-related and other Hazards” (June 2019—August 2022). This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 820999. The sole responsibility for the content of this publication lies with the authors. It does not necessarily represent the opinion of the European Union. The research was also funded through the EEA Grant from Iceland, Liechtenstein, and Norway, No. ACC01P03: Climate resilient Bratislava—pilot project for decarbonization, energy effectiveness of buildings and sustainable rainwater management in urban space and the research was funded also by the project VEGA (Slovak Na-tional Grant Agency) No. 1/0658/19 Ecosystem approaches to assessing of anthropogenic changed territories according to selected indicating groups of species.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data used in this contribution are from public resources cited in the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Study area—Bratislava, the capital of the Slovak Republic.
Figure 1. Study area—Bratislava, the capital of the Slovak Republic.
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Figure 2. The land cover of Bratislava and its surroundings by Urban Atlas classes.
Figure 2. The land cover of Bratislava and its surroundings by Urban Atlas classes.
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Figure 3. The night-time surface temperature of Bratislava and its surroundings.
Figure 3. The night-time surface temperature of Bratislava and its surroundings.
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Figure 4. The daytime surface temperature of Bratislava and its surroundings.
Figure 4. The daytime surface temperature of Bratislava and its surroundings.
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Figure 5. Profile section of Bratislava (20 km) divided by Urban Atlas category classes and respective day and night time temperatures of surface types. Subfigures (A–F) represent selected locations in the city.
Figure 5. Profile section of Bratislava (20 km) divided by Urban Atlas category classes and respective day and night time temperatures of surface types. Subfigures (A–F) represent selected locations in the city.
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Figure 6. Special Protection Area Sysľovské polia (© NLC, Zvolen, GKÚ, Bratislava, Slovakia, Ortophotomosaic).
Figure 6. Special Protection Area Sysľovské polia (© NLC, Zvolen, GKÚ, Bratislava, Slovakia, Ortophotomosaic).
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Figure 7. The temperature of surfaces in the Special Protection Area Sysľovské polia, 26 July 2021, about 11:45CEST (on the left: thermal infrared spectrum, on the right: Visible spectrum) (Landsat-8 image courtesy of the U.S. Geological Survey).
Figure 7. The temperature of surfaces in the Special Protection Area Sysľovské polia, 26 July 2021, about 11:45CEST (on the left: thermal infrared spectrum, on the right: Visible spectrum) (Landsat-8 image courtesy of the U.S. Geological Survey).
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Figure 8. Surface temperatures in the area of Sysľovské polia (after the harvest) on the satellite pictures, 9 September 2021, about 11:45CEST (on the left: thermal infrared spectrum, on the right: Visible spectrum) (Landsat-8 image courtesy of the U.S. Geological Survey).
Figure 8. Surface temperatures in the area of Sysľovské polia (after the harvest) on the satellite pictures, 9 September 2021, about 11:45CEST (on the left: thermal infrared spectrum, on the right: Visible spectrum) (Landsat-8 image courtesy of the U.S. Geological Survey).
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Table
Table 1. Representation of Urban Atlas Land Cover Classes in the City of Bratislava.
Table 1. Representation of Urban Atlas Land Cover Classes in the City of Bratislava.
Classes UANomenclatureShare of Area (%)Area (km2)
21000Arable land (annual crops)26.6197.83062
31000Forests25.9195.23761
12100Industrial, commercial, public, military and private units10.4638.46075
11210Discontinuous Dense Urban Fabric (S.D.: 50–80%)6.3923.49307
50000Water bodies4.5116.5618
23000Pastures3.9514.50195
12220Other roads and associated land3.0211.10079
22000Permanent crops (vineyards, fruit trees, olive groves)3.0211.0853
11100Continuous Urban Fabric (Sealing Degree (S.D.): >80%)2.8810.56862
14200Sports and leisure facilities2.188.022471
11220Discontinuous Medium Density Urban Fabric (S.D.: 30–50%)1.686.162269
14100Green urban areas1.545.676118
12400Airports1.094.002781
13300Construction sites1.043.802687
32000Herbaceous vegetation associations (natural grassland, moors...)1.003.682915
12230Railways and associated land0.923.382558
12210Fast transit roads and associated land0.913.346458
13400Land without current use0.843.07096
13100Mineral extraction and dump sites0.632.301383
11230Discontinuous Low Density Urban Fabric (S.D.: 10–30%)0.592.16154
12300Port areas0.351.299934
24000Complex and mixed cultivation patterns0.210.760673
11240Discontinuous Very Low Density Urban Fabric (S.D. < 10%)0.150.533318
11300Isolated Structures0.130.475034
40000Wetland0.020.083483
Table
Table 2. Urban Atlas Landscape Cover Classes in the profile section of Bratislava and average day and night-time surface temperatures.
Table 2. Urban Atlas Landscape Cover Classes in the profile section of Bratislava and average day and night-time surface temperatures.
Classes UALength [m]Number of PartsAvg.
LST Day [°C]		Avg.
LST Night [°C]
1110021802928.020.4
1121036033126.420.0
11220690424.219.7
1210027742028.020.8
12210181226.120.4
1222012438127.020.4
1223032226.419.1
13300434327.520.2
13400333124.719.6
1410011201024.719.8
14200139226.919.8
210003835825.317.3
23000224121.517.1
24000346125.017.7
310002281319.718.6
50000585619.218.3
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Šalkovič, M.; Pauditšová, E. Use of Satellite Images to Determine the Temperature of Urban Surfaces for Landscape Management Purposes, Case Study Bratislava (Slovak Republic). Land 2023, 12, 384. https://doi.org/10.3390/land12020384

AMA Style

Šalkovič M, Pauditšová E. Use of Satellite Images to Determine the Temperature of Urban Surfaces for Landscape Management Purposes, Case Study Bratislava (Slovak Republic). Land. 2023; 12(2):384. https://doi.org/10.3390/land12020384

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Šalkovič, Martin, and Eva Pauditšová. 2023. "Use of Satellite Images to Determine the Temperature of Urban Surfaces for Landscape Management Purposes, Case Study Bratislava (Slovak Republic)" Land 12, no. 2: 384. https://doi.org/10.3390/land12020384

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