=Paper=
{{Paper
|id=Vol-3899/paper8
|storemode=property
|title=GIS-based remote sensing identification method of thermal pollution in cities: case study of Lutsk, Ukraine and Lublin, Poland
|pdfUrl=https://ceur-ws.org/Vol-3899/paper8.pdf
|volume=Vol-3899
|authors=Mykola Fedoniuk,Olga Pavlova,Vitalina Fedoniuk,Dominika Riznyk,Ihor Konstankevych
|dblpUrl=https://dblp.org/rec/conf/advait/FedoniukPFRK24
}}
==GIS-based remote sensing identification method of thermal pollution in cities: case study of Lutsk, Ukraine and Lublin, Poland==
https://ceur-ws.org/Vol-3899/paper8.pdf
GIS-based remote sensing identification method of
thermal pollution in cities: case study of Lutsk, Ukraine
and Lublin, Poland⋆
Mykola Fedoniuk1,∗,†, Olga Pavlova2,∗,†, Vitalina Fedoniuk1,†, Dominika Riznyk1,† and Ihor
Konstankevych1,†
1 Lutsk National Technical University, Lvivska str. 75, Lutsk, 43018, Ukraine
2 Khmelnytskyi National University, Instytuts’ka str., 11, Khmelnytskyi, 29016, Ukraine
Abstract
Due to climate change, heat waves are expected to occur more frequently in the future, which might cause
adverse health effects for the urban population. The combination of high temperatures and poor air
quality significantly impinges on the natural environment and could cause degradation. This accentuates
the need to assess residents' health risks regarding air pollutants and anomalously high summer air
temperatures. However, comprehensive information on the spatial and temporal distribution of
temperature and particulate matter concentration in cities is complex to obtain since only a few
measurement sites exist. The text discusses the modern methods for monitoring heat islands in urban areas
using remote sensing data. It focuses on the techniques for monitoring both the quantity and quality of
temperature indicators using various cloud-based web services. It also introduces an algorithm and provides
examples for estimating spatial and temporal surface temperature variations in cities using the EO-Browser
service.
Keywords
land surface temperature (LST), GIS-based web services, EO-Browser, Evalscript, Landsat-8,9 1
1. Introduction
Thermal pollution is a significant environmental issue that impacts the health and comfort of city
residents, as well as energy consumption and society's ecological footprint. It is often categorized as
direct or indirect. Direct thermal pollution involves the release of heated gases and liquid vapors into
the environment, such as emissions from thermal power plants, industrial facilities, and vehicle
engines. Indirect thermal pollution occurs when greenhouse gas emissions, like carbon dioxide and
methane, contribute to the greenhouse effect.
To reduce the impact of direct thermal pollution, cities are developing a combination of
architectural and planning solutions, vertical and horizontal landscaping, changes in traffic flow, and
maximizing the use of "green energy". An accurate assessment of the spatial distribution of heat
islands and their changes is crucial for developing these solutions. Remote sensing methods,
including thermal sensors on satellites, are widely used to gather information about the flow of
infrared radiation from different parts of the Earth to estimate Land Surface Temperature (LST).
However, the quality and accuracy of this data depend largely on the information technology used
for processing.
AdvAIT-2024: 1st International Workshop on Advanced Applied Information Technologies, December 5, 2024, Khmelnytskyi,
Ukraine - Zilina, Slovakia
∗ Corresponding author.
† These authors contributed equally.
m.fedoniuk@lntu.edu.ua (M. Fedoniuk); pavlovao@khmnu.edu.ua (O. Pavlova); riznyk.d1705.073.24@lntu.edu.ua (D.
Riznyk); v.fedoniuk@lutsk-ntu.com.ua (V. Fedoniuk); up.ihor@gmail.com (I. Konstankevych)
0000-0003-3412-1639 (M. Fedoniuk); 0000-0003-2905-0215 (O. Pavlova); 0000-0002-1880-6710 (V. Fedoniuk); 0009-
0001-1017-4544 (D. Riznyk); 0000-0002-8836-4401 (I. Konstankevych)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
2. Related works
During the study, an analysis of the most recent scientific publications on urban heat islands was
conducted. The research [1] helps to choose various mitigation solutions required to address urban
heat across diverse climatic contexts. The purpose of [2] is to highlight the issue of research on
measures involving the use of vegetation has a decisive prevalence over research on other measures.
This review [3] shows an exponentially increasing trend in satellite-based SUHI publications since
2005, with large biases in the geographic areas, study time of day, study seasons, and research foci.
The goal of the studies [4-6] was to determine surface urban heat islands detected by all-weather
satellite land surface temperature and a modeling toolbox designed using a spatial technique. The
web application LST products were evaluated to observe and document the behavior of different
emissivity products on LST [7-9]. Tool for environmental indices computing using Landsat and
remote sensing data/techniques. The research [10,11] uses web service-oriented geoprocessing
system for supporting intelligent land cover change detection. The investigations [12, 13] dedicate
vegetation indexes in the EO-Browser and EOS LandViewer services and cloudiness dynamics in
Lutsk in the context of climate change. In work [14] web GIS was used to promote stakeholder
understanding of scientific results in sustainable urban development. In [15] the authors explore
some of urban heat research, and highlights positive progress which offers grounds for optimism.
The work [16] presents the concept of an information system for predicting the temperature regime
of the earth's surface using machine learning. Forecasting is carried out on the basis of historical data
for a certain territory. To increase the accuracy of forecasting results, an analysis of the features of
climatic zones was carried out to identify patterns. A comparison of the dependencies of the average
monthly temperatures of the earth's surface in countries, depending on their location in climatic
zones, is carried out.
Currently, there are several satellites equipped with a thermal infrared channel that can monitor
temperature differences on the Earth's surface. These satellites include MODIS (Terra/Aqua),
Sentinel-3, NOAA 20 (VIIRS), Landsat-8, Landsat-9, and others. However, most of these satellites
have a low spatial resolution, ranging from 375 meters to 1 kilometer per pixel, which limits their
effectiveness for detailed research [4]. Among open-access civilian satellites, the highest thermal
sensor resolution is 100 meters, found on Landsat-8 (operational since 2013) and Landsat-9
(operational since 2021) operated by the US Geological Survey. Although this resolution is lower
than other multispectral satellite channels, it still allows for detecting and analyzing Land Surface
Temperature (LST) differences at the city level (Figure 1).
Figure 1: Comparison of thermal images with a 1 km resolution (Sentinel-3, left picture) and 100 m
resolution (Landsat-8,9 right picture) for the city of Lutsk on October 12, 2024.
The data from these satellites not only provides a spatial picture of relative LST values, but also
offers information on the quantitative parameters of measured brightness values in the long-wave
infrared range, which can be converted into absolute temperature values.
Numerous studies have focused on automating LST monitoring using Landsat-8 data. These
studies propose implementing these functions using desktop or online GIS, with tools such as
ArcGIS, QGIS, and others being commonly used.
Recently, there have been efforts to integrate LST data into cloud-based GIS web services, such
as Google Earth Engine, EO-Browser, and EOSDA LandViewer, which have generated considerable
interest. Using these services is often more convenient and accessible to more users than working
with desktop software products. However, it is important to note that the display of LST from the
same satellite data set in these services can differ significantly due to the peculiarities of the applied
pre- and post-processing algorithms.
Remember the following text:
For example, the EOSDA LandViewer service (eos.com/products/landviewer) only displays the
Landsat-8 thermal channel in shades of gray. However, it provides increased detail and the option to
use the pansharpening function. You can also upload your own thermal image to a local device with
georeference (kmz file) and create your own overlay with a high-resolution image (Figure 2).
Nevertheless, the LandViewer service does not allow the thermal channel display to be edited or
provide the necessary numerical LST parameters. These advanced capabilities are available in the
EO-Browser service from Copernicus Sentinel Hub (apps.sentinel-hub.com/eo-browser). In the
future, we will describe the main features of thermal pollution assessment based on LST indicators
in this service.
3. Methodology and results
We analyzed the human-induced temperature differences in the cities of Lutsk and Lublin using
LST data over several years. Based on this analysis, we propose conducting similar studies in the
future:
1. Creation of vector files of city contours (KML/KMZ, GPX, JSON) and upload them to the
service
2. The general assessment of Landsat-8,9 images over several years to assess spatial differences
in the distribution of Land Surface Temperature (LST) across the territory and outline the
urban heat island
3. Creation of overlays of thermal and optical images to identify the hottest and coldest areas
of the city. Construction of separate vector contours for representative zones selected on this
basis (industrial areas, green zones, various residential buildings, etc.)
4. Downloading data on LST numerical parameters for the selected period from EO-Browser
(requires authorization on the service). A CSV file is generated with the following data set:
thermal-C0/date, thermal-C0/min, thermal-C0/max, thermal-C0/mean, thermal-C0/stDev,
thermal-C0/sampleCount, thermal-C0/noDataCount, thermal-C0/median, thermal-C0/p10,
thermal-C0/p90, thermal-C0/cloudCoveragePercent.
5. Filtering of the selected numerical data by a percentage of cloud cover (thermal-
C0/cloudCoveragePercent), cutting off false data [13]. It should be noted that sometimes there
were unrealistic minus values in warm weather, due to obstructions from clouds or fog, even
with the declared zero cloudiness of the image. Such data were screened out in the
subsequent analysis
Figure 2: An example of overlaying thermal and optical images in Google Earth (territory of the city
of Lublin). The hottest and coldest areas are identified and well-aligned with the city’s corresponding
industrial or green areas.
Statistical analysis and construction of generalized LST dynamics graphs for each selected area.
Comparison of the obtained values with the averages or background. (Table 1)
Table 1
Example comparative results of surface temperature averages determination in different parts of
Lutsk and Lublin cities using Landsat-8,9 data in EO-Browser
t, °C
max min avg avg max avg min
Lublin industrial zone 57,0 -14,7 18,99 23,91 14,5
Lutsk Industrial zone 64,7 -20,3 20,89 32,5 13,9
Green areas Lublin 49,97 -15,59 16,9 21,84 13,88
Green areas Lutsk 41,3 -14,63 14,2 18,7 11,46
Lublin high-rise 50,5 - 15,7 18,82 22,54 13,9
buildings
Lutsk high-rise 46,17 -17,59 19,32 23,2 13,8
buildings
Manor building in 48,8 -14,78 18,3 20,1 16,13
Lublin
Manor building in 41,3 -17,75 14,2 18,8 11,47
Lutsk
Improving the visualization of temperature differences by editing the Evalscript display. Since the
default temperature scale in EO-Browser is quite unclear in the range of moderate temperatures, it
is often necessary to modify it to better represent the detected temperature differences (Figure 3).
There is an option to adjust the color for one or more specific LST values (in Kelvin) and add the
colors for the required intermediate values. Also, the script based on the season of the year, the
prevailing temperatures in the image and highlighting the range of critical temperatures can be
modified.
4. Conclusions
The main reason this study is essential is the impact of climate change and rising temperatures. The
use of LST satellite data offers many opportunities for monitoring thermal pollution in urban areas.
This data can help identify critical areas that require management solutions to minimize the negative
impact. GIS-based web services facilitate access, processing, and visualization of this data [14],
including the capability to edit or load custom display scripts. Therefore, it is important to identify
the factors contributing to the temperature rise, apart from industrialization, within the climate
change phenomenon. Additionally, time-series Landsat-8 satellite images were used to explore
temporal shifts across Lutsk (Ukraine) and Lublin (Poland).
Understanding the urban heat island effect in Lutsk, Ukraine, and Lublin, Poland is crucial for
designing methods to reduce its harmful effects. This may involve implementing techniques to
decrease the use of heat-absorbing materials in the city, such as installing green walls and roofs.
Modeling the urban heat island effect in Lutsk can provide valuable guidance to city planners and
decision-makers on the most effective methods for controlling the temperature and preventing
adverse effects on the local environment and population. Ultimately, this can contribute to the
development of a more livable and sustainable city for everyone.
In the future, we plan to conduct further research to improve the accuracy of this method, perform
experiments with different urban settlements, and consider various heatwave scenarios.
Figure 3: Comparison of the LST display in EO-Browser using the standard temperature scale (top)
and with Evalscript's custom modification (Landsat-8,9 image from January 1, 2024).
Declaration on Generative AI
The authors have not employed any Generative AI tools.
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