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Volume 12, Issue 3, pp. 113 - 129
Article Info
Accepted: 11/11/2021
Corresponding Author: * florim.isufi@uni-pr.edu
DOI: https://doi.org/10.48088/ejg.a.ber.12.3.113.129
Mapping summer SUHI and its impact on the environment using
GIS and Remote Sensing techniques: A case study on
Municipality of Prishtina (Kosovo)
Albert Berila1 &
Florim Isufi1*
1 University of Prishtina, Kosovo
Keywords
Summer SUHI,
LST,
GIS,
Remote Sensing,
Prishtina
Abstract
Urban areas, compared to peripheral and rural areas, have higher temperatures
which are caused by a series of unplanned activities that are undertaken by
humans. Such a thing leads to the emergence of the Surface Urban Heat Island
(SUHI) phenomenon. In this paper, summer SUHI is determined through the
calculation of LST for the Municipality of Prishtina using GIS and Remote Sensing
techniques. To make this calculation, the Landsat 8 satellite image with 0% cloud
cover was used. From the calculations made it turns out that the pixels with the
highest value of LST are found in those parts where the urban area appears, where
there are numerous constructions with impermeable materials, as well as in those
areas where there are bare surfaces. Whereas, the pixels with lower values of LST
appear in those parts where there are vegetation and water bodies, making these
areas fresher. The SUHI phenomenon makes the lives of citizens difficult, therefore,
such information is very important for the leaders and urban planners of the city of
Prishtina, so that they take a series of steps towards minimizing such an effect in
order to the life of the citizens to be as healthy as possible.
Highlights:
-Mapping Summer SUHI using GIS and Remote Sensing
-Make connectivity between the atmosphere and LST in order to identify the areas that face this undesirable phenomenon
-Presentation of the methodology for calculating LST in order to identify areas that face the SUHI phenomenon.
Copyright: © 2021 by the authors.
Licensee European Association of Geographers
(EUROGEO). This article is an open access article
distributed under the terms and conditions of the Creative
Commons Attribution (CC BY) license
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1. INTRODUCTION
Earth is the only planet on which life takes place. Throughout the organization of human life,
natural elements have always played a very important role – depending on the degree of
technological and human development. Among the most important/powerful ecological factors
are humans – by constantly acting in the living environment have radically changed the living
space and defining the paths of further development. But, with all these uncontrolled actions,
human is making changes which are going to his detriment. These actions will have great
consequences on the existence of the generations to come because are breaking the laws
which are balanced and which exist millions of years ago.
In addition to the role of other natural sciences, a greater emphasis falls on the science of
Geography. This science should give its contribution regarding the mapping and presentation
of phenomena using conventional signs. This is done in order to illustrate the phenomena in
different spaces – small or large. All this development, which is constantly following humanity,
is seriously questioning the natural balance. It is here that geography (experts in this field)
must intervene in all this disorder that has been made in the space around us, which, once,
was almost free (unoccupied) and empty, while now overloaded and very dense with more
and more inhabitants, construction and waste. At the moment, an approach to geography is
more than necessary in the case of the extension and placement of industrial facilities,
settlements, infrastructure, services, etc., in order to provide the most adequate solutions –
sustainable development.
From all that has been said above about our living environment, it can be concluded quite
clearly that humanity can no longer continue with such steps towards the future – neglect of
the living environment – an integral part of human life. Any thoughtless behavior of the human
factor towards the environment will turn into horrible events for him. Now more than ever it is
necessary to avoid, as much as possible, the destruction of many components of the
environment. The basic factor that determines the level of contemporary and economic
civilization is the use of natural resources by human. Therefore, without a rational and
controlled use of them, the future of humanity can not be imagined.
The great increase of the population has made the process of urbanization take a big boost
of development. Such a process, if it took place incorrectly, the impact it will have on people
and the environment around us will undoubtedly be negative. Therefore, such a process needs
to be developed positively (Morefield et al., 2018; Berila & Isufi, 2021). If we analyze such a
large development and at such a fast pace, we will notice that the effects, of course, will not
be satisfactory. From such processes occur effects which are considered as harmful and
which are: the increase of built-up areas; expansion of impermeable surfaces; reduction of
natural surfaces; damage to forest areas, etc. Such cases, in abundance, we encounter in
cities. These are exactly the reasons why in cities the temperature is higher than its periphery.
Such changes have led to the emergence of a phenomenon called UHI (Urban Heat Island).
UHI is a phenomenon in which temperatures in urban areas are higher compared to their
suburbs or rural areas (Wang et al., 2017). As we are aware, there is a constant increase in
the number of population in urban areas. Such an increase means an increase in all human
activities, which, unfortunately, reinforce the UHI phenomenon. Therefore, it can be rightly
said that this phenomenon is seriously threatening humans – especially those living in cities
(Mohajerani et al., 2017) and that there will be many consequences in many sectors of life –
air pollution, human health, energy management, etc. (Peres et al., 2018; Vintar Mally, 2021).
It is precisely human activities that cause the UHI phenomenon to occur. This happens when
the soil cover is transformed – natural surfaces become impermeable (concrete, asphalt, low
albedo materials) (Buyantuyev & Wu, 2009; EPA, 2017; Harlan & Ruddell, 2011; Mavrogianni
et al., 2011; Santamouris et al., 2011). It should be a top priority for city managers and urban
planners to minimize such a harmful phenomenon as much as possible. It remains a task to
find a way to prevent such a phenomenon – at best.
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The UHI phenomenon has its types – depending on how the temperature is measured
(Fabrizi et al., 2010; Sherafati et al., 2018):
a) CLHI (Canopy Layer Heat Island) – this layer lies approximately at the average height
of buildings and is determined by measuring the air temperature at a height of 2 meters above
the ground. The CLHI has usually measured through sensors mounted on fixed meteorological
stations (Clay et al., 2006; Nichol et al., 2009; Schwarz et al., 2012; Smoliak et al., 2015).
b) Boundary Layer Heat Island (BLHI) – lies above the CLHI layer and can reach a
thickness of up to 1 km. It is measured using special platforms, such as radiosondes and
aircraft (Sherafati et al., 2018; Voogt, 2008).
c) SUHI (Surface Urban Heat Island) – difference in radiant temperature between urban
and non-urban surfaces. The measurement/determination of this layer is done using thermal
remote sensors (Voogt & Oke, 2003).
Various ways of measuring the UHI phenomenon have been presented in the scientific
literature. Which way the researcher chooses, depends entirely on the availability of
equipment/data and the accuracy required (Schwarz et al., 2012). Measuring the UHI
phenomenon using geospatial technology represents SUHI (Surface Urban Heat Island)
(Zhou et al., 2018). This is evident in cases where the study area has insufficient data on the
measured temperature – the distribution of measuring equipment is inhomogeneous and rare.
Thus, the use of geospatial technology emerges as a suitable tool to determine such a
phenomenon through the calculation of LST (Land Surface Temperature) (Pour & Voženílek,
2020; Despini et al., 2016; Voogt & Oke, 2003). Atmospheric UHI is called as such by merging
BLHI and CLHI. If we make the comparison between SUHI and atmospheric UHIs then it turns
out that SUHI has a higher value during the day (Roth et al., 1989; Yuan & Bauer, 2007).
Consequently, it falls to SUHI that it will be of higher value during the summer season when
the sunlight falls at a more right angle and the conditions are generally drier (Oke, 1987). For
all the factors we are talking about, which cause the appearance of the SUHI phenomenon, it
is clear that the countries that will be at war with it are those countries that are developing –
the most vulnerable/attacked (Mubea et al., 2011; Lee & Chang, 2011; Kityuttachai et al.,
2013; Berila & Isufi, 2021; Sudhira et al., 2003; Kamusoko & Aniya, 2007; Kamusoko & Aniya,
2009; Liu et al., 2011; Moghadam & Helbich, 2013). Kosovo has not escaped this
development, which also brings negative effects and the appearance of dangerous
phenomena. Major challenges arise in the capital of Kosovo – Prishtina. All this happens due
to the extraordinary changes that this city has experienced.
The purpose of our work is to map the summer SUHI for the entire Municipality of Prishtina
and to make connectivity between the atmosphere and LST in order to identify the areas that
face this undesirable phenomenon and to see if the areas with the greatest impact of this
phenomenon are exactly those in it which human activities are most pronounced. The
identification of these areas will be of great importance because it will enable the city
managers to take the necessary mitigations in those places where the population faces this
phenomenon.
2. STUDY AREA
Prishtina, taking into account its geographical location, occupies a suitable geographical
position because it has a central location in the Balkan Peninsula (Figure 1). Whereas, within
the Republic of Kosovo, this city occupies the northeastern part. Prishtina is surrounded by
the following municipalities: Kastriot (Obiliq) in the west, Podujeva in the north, Graçanicë and
Lipjan in the south, Fushë Kosova in the southwest, Kamenica in the east, Novobërdë in the
southeast. Whereas, with the Republic of Serbia it borders on the northeastern part (Isufi &
Berila, 2021) (Figure 2).
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Figure 1. Location of the study area.
The whole municipality occupies approximately 523 km2. It lies in the morphological plan
of Kosovo and represents an alluvial plan covered with sediments of lakes, and geologically it
is a tectonic depression, which has risen long Oligo-Miocene changes (Municipality of
Prishtina, 2013; Isufi & Berila, 2021). The climate of our study area is continental with hot
summers, cold winters, with 600 mm of rainfall on average per year (Municipality of Prishtina,
2013; Isufi & Berila, 2021). Our study area has hilly-mountainous relief in the southeastern,
northeastern, and eastern parts. Somewhere, approximately, 535-580 meters goes the
altitude of the western part and, if we exclude the eastern part, for the slope of the terrain we
can say that it is low (Municipality of Prishtina, 2013; Berila & Isufi, 2021).
Prishtina, in Kosovo, is the largest cultural, administrative and economic center. It has a
total population of 216,870 (KAS, 2020). In Prishtina, the change that has undergone in many
parts of it is clearly noticeable. These changes, unfortunately, are for the most part negative –
unplanned constructions, occupation of agricultural land with impermeable areas, a large
expansion of the urban area (Isufi & Berila, 2021), etc. The increase of the population that
Prishtina has experienced, and all other socio-economic developments, has made the needs
increase. Consequences of all this are the great increase in population density, the increase
of illegal constructions, the increase of unplanned constructions, etc. (Berila & Isufi, 2021; Isufi
& Berila, 2021).
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Figure 2. Satellite image of Prishtina – Landsat 8.
It is impossible not to mention that all this chaotic situation has been contributed also by
the political aspect – political interference/corruption (Berila & Isufi, 2021; Isufi & Berila, 2021).
In Prishtina, as well as around the world, there have been population movements (migrations).
These movements, towards Prishtina, have occurred especially from rural areas to urban
ones. Such movements have caused radical changes in almost all spheres of life (Isufi &
Berila, 2021; KAS, 2014). All the developments/changes that have taken place in Prishtina,
have made today, the capital, face many challenges and problems. These problems make the
lives of citizens more difficult – heavy vehicle traffic, increase in the density of buildings,
increase in the density of residents, increase in pollution, etc. When the UHI phenomenon is
added to all this, then the situation only gets worse. From what was said above, comes the
importance of mapping this phenomenon for Prishtina, so that when identifying areas facing
high values of this phenomenon, mitigations are taken to facilitate the lives of citizens.
3. DATA AND METHODS
To do the summer SUHI mapping, it is necessary to do the LST calculation. To do this, we
used the Landsat 8 satellite image for July 2, 2019. We downloaded this image from the
United States Geological Survey (USGS) – from Collection 1 and at a Level 1 and, thankfully,
it was without any cloud cover (0%) (Table 1). The selection of this date of the Landsat 8
satellite image, which belongs to the summer season and that is not covered by clouds, was
done on purpose because as we know, the SUHI phenomenon intensifies during the warm
season and when the climatic conditions are drier. As mentioned above, the SUHI
phenomenon becomes more severe and strikes more during the summer season – when the
sunlight falls at a greater angle and when the numerous activities of the population (vehicles,
industrial, commercial, etc.) are more pronounced. All these human activities cause a large
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number of pollutants to be released into the atmosphere. In such a state, sunlight falls on the
surface of the earth but, due to the greenhouse effect created, this long-wave radiation is not
allowed to escape. Therefore, here is the moment when the SUHI phenomenon strikes
causing many problems to the entire population living in that area.
Table 1. Characteristics of the satellite image used in this study
Sensor type
Date
Path/Row
Cloud
cover
Spatial
resolution
Format
Source
Landsat 8
OLI/TIRS
2019/07/02
185/30
0%
30 m
Tiff
https://earthexplorer.usgs.gov/
In calculating the LST, it is necessary to make some preliminary adjustments to the image
– atmospheric correction (Berila and Isufi, 2021). We made this adjustment using the ENVI
software through the DOS (Dark Object Subtraction) method. DOS is an effective and simple
method which is used to make atmospheric correction for satellite images. This method
assumes that an important component of atmospheric scattering is the reflection that occurs
from dark objects. This method works by searching through each band to find the darkest pixel
values. The value found is deducted. In this way the scattering is removed.
It is important to note that the Landsat Database has shown considerable benefit in relation
to environmental and climate issues (Mfondum et al., 2016; Adeyeri et al., 2017; Alavipanah
et al., 2010; Narayan et al., 2016). In our case, the goal was to make a connection between
the atmosphere and the LST. This is because, as mentioned earlier, the highest SUHI values
are reached during the summer season, when the climatic conditions are drier, and when the
sunlight falls perpendicularly. In relation to this issue, LST has been shown to be a key element
in achieving this connectivity (Sobrino et al., 2004b; Sobrino et al., 2003; Sobrino et al., 2004a;
Adeyeri et al., 2017; Dickinson, 1983).
In maps, it is important to pay as much attention as possible to the presentation of the
particular phenomenon. For such a thing, in order to have a better and more accurate picture
of the presentation of the SUHI phenomenon, we have used High Resolution (HR) ALOS-
PALSAR (Advanced Land Observing Satellite-Phased Array-Type L-band Synthetic Aperture
Radar) RTC (Radiometrically Terrain Corrected) elevation model (DEM), as an auxiliary data
source with a 12.5 m spatial resolution. We have downloaded this digital relief model from the
Alaska Satellite Facility website (https://asf.alaska.edu/).
The Landsat 8 satellite was successfully launched on 11 February 2013 and deployed into
orbit with two instruments onboard (Valizadeh Kamran et al., 2015): the Operational Land
Imager (OLI) and the Thermal Infrared Sensor (TIRS), which jointly produce a multispectral
image consisting of 11 spectral bands. OLI collects nine spectral bands including a pan band:
bands 1-4 correspond to the visible part of the electromagnetic spectrum, bands 5-7 and 9 to
near-infrared and shortwave infrared regions of the spectrum (Taloor et al., 2021) (all of these
have a spatial resolution of 30 m) (Jeevalakshmi et al., 2017); band 8 is panchromatic, taking
images in the wide visible wavelength range (0,503-0,676 μm) with 15 m spatial resolution.
These bands allow obtaining various kinds of information on the characteristics of the land
surface, including vegetation cover. Two spectral bands for infrared wavelength are collected
by the TIRS instrument (Kesikoğlu et al., 2020). In previous sensors (TM and ETM +), these
were covered by a single band: band 10 (TIRS 1, 10.6-11.19 μm) and band 11 (TIRS 2, 11.5-
12.51 μm), both with 100 m spatial resolution (Table 2) (NASA, 2018).
In this paper, we have presented and explained in detail the entire methodology used to
arrive at the mapping of the SUHI phenomenon – the compilation of the LST map. To have a
clearer picture, we have prepared the diagram (Figure 3) which shows all the steps taken.
However, the presentation and calculation in detail of each step will be presented in the
following sections.
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In Landsat 8 sensor satellite images, thermal data is stored in the form of digital numbers
(DN). These numbers represent cells (pixels) that have not yet been calibrated into units (Käfer
et al., 2020) that make sense (meaningful units). After taking satellite images, the first process
or task is to return the digital numbers to radiance. The following equation was used to convert
DNs to spectral radiance (USGS, 2019) in the Landsat 8 TIRS sensor (Isaya Ndossi et al.,
2016; Mejbel Salih et al., 2018; Guha & Govil, 2020; Berila & Isufi, 2021):
(1)
where:
is TOA spectral radiance , is Band-specific multiplicative
rescaling factor from the metadata, is Quantized and calibrated standard product pixel
values (DN), is Band-specific additive rescaling factor from the metadata (Isaya Ndossi et
al., 2016; Mejbel Salih et al., 2018; Guha & Govil, 2020; Berila & Isufi, 2021). The metadata
of the satellite image is presented in Table 3 (Jeevalakshmi et al., 2017).
Table 2. OLI and TIRS – Landsat 8 spectral bands
Spectral Band
Wavelength (μm)
Spatial Resolution (m)
Band 1 – Coastal/Aerosol
0.435-0.451
30
Band 2 – Blue
0.452-0.512
30
Band 3 – Green
0.533-0.590
30
Band 4 – Red
0.636-0.673
30
Band 5 – NIR
0.851-0.879
30
Band 6 – SWIR 1
1.566-1.651
30
Band 7 – SWIR 2
2.107-2.294
30
Band 8 – Panchromatic
0.503-0.676
15
Band 9 – Cirrus
1.363-1.384
30
Band 10 – TIRS 1
10.60-11.19
100
Band 11 – TIRS 2
11.50-12.51
100
Table 3. Metadata of the satellite image
Band
Variable
Description
Value
10
Thermal band
Thermal constant
774.8853
1321.0789
Band-specific
multiplicative rescaling
factor
3.3420E-04
Band-specific additive
rescaling factor
0.10000
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Figure 3. Flowchart depicting methodology
Then, in the Landsat 8 satellite image MTL file, the reflectance rescaling coefficients
were obtained. This was done in order to transform the OLI band into TOA planetary reflection
(Adeyeri et al., 2017). For such a thing, the following equation was used (Adeyeri et al., 2017):
(2)
Where:
– TOA planetary reflectance, – Band-specific multiplicative rescaling factor, – Band-
specific additive rescaling factor.
Making the correction of the TOA reflection with the angle of the sun, then is (USGS, 2019):
(3)
Where:
– TOA planetary reflectance; – Local solar zenit angle, ; – Local sun
elevation angle (USGS, 2019).
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The DOS (Dark Object Subtraction) method was used to remove small reflection values
(due to air diffusion) (Chavez, 1996; Adeyeri et al., 2017). We used this method through the
use of ENVI 5.3 software as we did not have at our disposal alternatives for atmospheric
measurements (Adeyeri et al., 2017). The equation of an image corrected with DOS is
(Adeyeri et al., 2017):
(4)
Where:
– radiance corresponding to the minimum DN from the sum of all the pixels from the
image, – radiance of Dark Object assumed to have a reflectance of 0.01 (Adeyeri
et al., 2017).
Although from the beginning of its operation TIRS has proven to be stable, still there were
some elements (objects) that are hung in the image like errors in calibration and banding
(Montanaro et al., 2014). Such a thing is caused by stray light effects. The impacts of these 2
effects are smaller in band 10 compared to band 11. Based on the numerous analyzes and
studies that have been done, it has been concluded that band 10 of TIRS data is better and
should be used instead of band 11. This, due to the good performance shown by band 10
(Adeyeri et al., 2017; Montanaro et al., 2014). For this study, TIRS band 10 was used.
3.1 Calculation of brightness temperature
The next step is to use the constant values given in the metadata in order to convert the
spectral radiation to brightness temperature (). In order to convert the radiance to brightness
temperature, equation (5) has been used in the study (Isaya Ndossi et al., 2016; USGS, 2019):
(5)
Where:
is the at-sensor brightness temperature [K], is the spectral radiance of thermal band 10
, is the band-specific thermal conversion constant from the metadata and
band-specific thermal conversion constant from the metadata. The same equation is used
for all sensors. The and values may vary depending on the sensor and the wavelengths
by which the thermal bands operate (Isaya Ndossi et al., 2016; Yakar & Bilgi, 2019). The
values of and can be obtained from the metadata file of the scene.
3.2 Estimation of land surface emissivity
To calculate the LST, it is necessary to evaluate the emissivity of the land surface (LSE)
through the NDVI method (Jiménez-Muñoz et al., 2016; Valizadeh Kamran et al., 2015;
Chakraborty et al., 2021). dε is the effect of the geometrical distribution of natural surfaces
and internal reflections (Tan et al., 2010; Igun & Williams, 2018; Carrasco et al., 2020). To
calculate the emissivity, we rely on the following equation (Guha & Govil, 2020):
(6)
Where:
is vegetation emissivity, is soil emissivity, is fractional vegetation and is a shape
factor whose mean is 0.55 (Igun & Williams, 2018; Sobrino et al., 2004a; Guha & Govil, 2020).
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(7)
Where:
is emissivity. From Equations (6) and (7), may be determined by the following equation
(Yuvaraj, 2020):
(8)
The proportion of vegetation () is calculated based on the following equation (Wang et al.,
2015; Yuvaraj, 2020):
(9)
The following equation is used to calculate NDVI with the help of Landsat visible (band 4) and
NIR (band 5) images (Yuvaraj, 2020):
(10)
Where:
NIR and RED are the near-infrared and red band pixel values respectively (Alemu, 2019). The
value of NDVI ranges between -1.0 and 1.0 (Alemu, 2019; Yuvaraj, 2020). High NDVI values
indicate healthy vegetation while low values indicate less or no vegetation.
3.3 Calculation of LST
The final step of estimating the LST is as follows (Yuvaraj, 2020; Alemu, 2019; Weng et al.,
2004):
(11)
Where:
λ is the wavelength of emitted radiance by Landsat 8 which is 10.8 (given by NASA), is the
land surface emissivity, and is given by the following equation (Yuvaraj, 2020):
(12)
Where:
is Planck’s constant (6.626 ×10-34 Js), σ is the Boltzmann constant (1.38 ×10-23 J/K), and is
the velocity of light (2.988 ×108 m/s) (Alemu, 2019; Yuvaraj, 2020).
4. RESULTS AND DISCUSSION
The importance of the natural soil cover, namely the vegetation, is extraordinary. Such a thing
can be easily noticed as soon as we enter an urban area. During the summer season, when
temperatures are high, as soon as we enter urban areas there is difficulty in carrying out our
daily activities. This comes from the impermeable materials which are most abundant in these
areas. These materials, as they have small albedo values, have the ability to absorb heat
causing the temperature to rise. Due to such increased heat, the most exposed are children,
the elderly, and all those who have breathing problems and similar diseases. Whereas, the
complete opposite occurs in those areas where vegetation and water bodies predominate.
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These areas, even during the summer season, are fresh, full of greenery, and minimize the
SUHI effect. People, in these areas, freely carry out all their activities.
According to our LST map, compiled for our study area, we have noticed that the highest
LST values are concentrated in those areas where impermeable materials predominate, as
well as in all those areas where there is a lack of vegetation. Such areas are bare surfaces.
Whereas, the complete opposite occurs in areas where there are vegetation and water bodies.
In these areas, we have noticed the pixels with the lowest LST values. This is best seen in
Figure 4. Here, LST values range from 296,485 K (lowest value) to 317,848 K (highest value).
The pixels with the lowest LST values are located mainly in the parts where vegetation and
water are located. Whereas, the pixels with the highest LST values lie mainly in the urban area
(western part), in the southern part, and in those areas where there is vegetation damage. In
the south there are bare surfaces and, as such, the layer without vegetation heats up quickly
and easily, strengthening the SUHI effect.
Figure 4. Spatial distribution of LST for Prishtina
The connection between the development of human society and the development of
settlements is inseparable. This is because, the more civilized a society is, the more extensive
the settlements will be. But, unfortunately, human chose exactly the best and most suitable
places for their construction, causing radical changes in the environment. In other words, the
human with his uncontrolled activity constantly destroys and damages the natural cover,
aggravating living conditions such as air, water, and land. The air in cities differs from the
natural one because the amount of oxygen is reduced. While, on the other hand, the amount
of harmful gases is high. The number of cities in the world is ever-increasing, and even cities
with millions of inhabitants are constantly growing. Such growth is permanent, and the world
is plunged into crisis. The rapid population growth in cities creates comprehensive problems.
Wild constructions are becoming more and more of a problem. People, with the changes they
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are making due to increased demand, are losing contact with the natural environment. Various
pollutants that come from human activities, heavy traffic, industry, fossil fuels, etc., are
reducing the process of photosynthesis and, consequently, are reducing the amount of
oxygen. As a result of these processes, climatic conditions are being created which are
different from the surroundings. It is necessary to control the development processes in these
cities, otherwise, they will be plunged into urban chaos.
From the compilation of the LST map, we made a comparison between rural and urban
areas. Regarding the values of LST, we found that the pixels with the highest value of LST
are found exactly in urban spaces (317,848 K), while, on the other hand, the presence of green
areas and water surfaces made the pixels with values lower (296,485 K) of LST to be
distributed in rural areas.
Similar results as in our paper were observed in the work of Li et al. (2014). In this paper, the
pixels with the highest LST values were distributed in both residential and industrial areas in
which vegetated areas were scarce or nonexistent. The same results were found by Gang et
al. (2019). They found that the intensity of the SUHI phenomenon is related to different types
of land use. According to them, the greater the conversion of natural surfaces into artificial
surfaces, the greater the intensity of the SUHI phenomenon and vice versa.
From all the above it follows that a very big challenge in the Municipality of Prishtina is the
unplanned use of natural resources – causing pollution and degradation of the environment to
increase. Unplanned use of land with natural cover and its covering with impermeable
materials will result in very big consequences – any change and transformation of natural
areas into artificial ones will affect that the situation with the SUHI phenomenon worsens even
more.
5. CONCLUSIONS
Our study includes GIS and Remote Sensing techniques to measure the SUHI phenomenon
during the summer season. Such a measurement was made using the free Landsat 8 satellite
image provided by USGS. The Landsat 8 TIRS (10) band is considered to be the most
appropriate and effective way to calculate LST.
Areas that most strongly face the SUHI phenomenon were identified through LST
calculation. Pixels with high LST values were investigated in the western part, where the urban
area stands out, as well as to a large extent in the southern part, exactly where there is a bare
surface. Whereas, low LST pixels were investigated in all areas where vegetation and water
bodies predominated. The problematic issue with the SUHI effect is an issue that requires
great attention because of the concerns it causes to citizens. So it's not a simple matter.
Countries around the world face this problem – some more, some less.
From what was discussed above, we conclude that the greener areas they are, the better.
City leaders need to take a series of steps that will minimize the SUHI effect, such as creating
green areas in the city, using as little material as possible that has low albedo values, using
green roofs on buildings, and so on. All these actions make the life of citizens better, healthier
and promote sustainable development.
The high level of development with which Prishtina is being characterized, such as the large
increase of population density, rapid urban expansion, increased demands for energy,
industrial activities, etc., will make the peripheral areas affected by changes by causing the
green space to be reduced. Based on this, the authors expect that policymakers and urban
planners of the city of Prishtina take this information seriously in order for all future activities
to be planned.
ACKNOWLEDGEMENTS
The authors wish to acknowledge the United States Geological Survey (USGS) for the
provision of data used for this study.
A.Berila & F.Isufi, Vol.12(3), pp.113-078, 2021
© European Association of Geographers
European Journal of Geography - ISSN 1792-1341 125
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