USING REMOTE SENSING TECHNOLOGIES FOR MONITORING URBAN HEAT ISLANDS
DOI:
https://doi.org/0.17721/1728-2713.106.13Keywords:
urban heat island, surface temperatures, impervious surfaces, green spaces, Landsat 8 OLI/TIRS, GoogleEarthEngine, Remote sensing, GISAbstract
Background. The urbanization process is accelerating every day, which entails significant changes in the natural landscape. This leads to microclimatic changes, air pollution, thermal effect, etc. Due to air pollution by man-made emissions in urbanized areas, the thermal regime is changing; the concentration of carbon dioxide and water vapor has now reached 90 % of the total amount of pollutants. As a result, another problem arises that contributes to global warming – the "greenhouse effect". Elevated air temperatures affect human health leading to breathing problems, seizures, heat and sunstroke, heat stress, and increased mortality. Considering the potential danger of elevated air temperatures caused by urban heat islands affecting the lives of residents, an effective and relevant method for surface temperature analysis and heat island location determination should be developed.
Methods. During urban heat island monitoring, the main factor of analysis is surface temperature, which was determined in the study using indices such as: Normalized Difference Vegetation Index (NDVI), Urban Thermal Field Variance Index (UTVFI) and Normalized Difference Built-up Index (NDBI).
Results. This paper describes a study of the distribution of urban heat islands in three European capitals, including Kyiv, Oslo and Rome, from May 2013 to August 2023. Using the capabilities of the GoogleEarthEngine cloud platform and data from the Landsat 8 OLI/TIRS satellite, the condition of green spaces, the number of impervious surfaces and indices of surface temperatures (LST) were compared, resulting in maps of the distribution of urban heat islands (UHIs) in the areas of selected cities and towns demonstrating quantitative changes.
Conclusions. Thus, the study showed a decrease in the number of urban heat islands along with impervious surfaces in the city of Kyiv by 4 %. At the same time, Rome and Oslo experienced an increase in the number of urban heat islands along with impervious surfaces. The data obtained prove the feasibility of using the chosen research method and can be used to assess the environmental condition, identify risk zones, and develop effective measures to further prevent the spread of UHI in megacities.
References
Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach, D. A., Burrows, M., Chandra, T., Fikes, A., & Gruber, R. E. (2006). Bigtable: A Distributed Storage System for Structured Data. 7th USENIX Symposium on Operating Systems Design and Implementation, (pp. 205–218). Google, Inc. https://research.google.com/archive/bigtable-osdi06.pdf
Debele, G. B., & Beketie, K. T. (2024). Studying the spatial non- stationary relationships of some physical parameters on the Earth's surface temperature using GWR in Upper Awash basin, Ethiopia. Scientific African, 23. https://doi.org/10.1016/j.sciaf.2023.e02052.
Fang, H., Baret, F., Plummer, S., & Schaepman‐Strub, G. (2019). An overview of global leaf area index (LAI): Methods, products, validation, and applications. Reviews of Geophysics, 57. https://doi.org/10.1029/2018RG000608
Ghosh, I. (2019). The Dramatic Global Rise of Urbanization (1950–2020). World Economic Forum. https://www.weforum.org/agenda/2019/09/mapped-the-dramatic-global-rise-of-urbanization-1950-2020
OneSoil Blog. (2018). What the NDVI index is and how it makes a farmer's life easier. https://blog.onesoil.ai/en/what-is-ndvi
Quintano, C., Fernández-Manso, A., Calvo, L., Marcos, E., & Valbuena, L. (2015). Land surface temperature as potential indicator of burn severity in forest Mediterranean ecosystems. International Journal of Applied Earth Observation and Geoinformation, 36, 1–12. https://doi.org/10.1016/j.jag.2014.10.015
Sakhniuk, S., Tovstonoh, D., Monastyrova, O., & Zatserkovnyi, V. (2022). Monitoring of urban heat islands using remote sensing technologies. 16th International Conference Monitoring of Geological Processes and Ecological Condition of the Environment, November 2022 (p. 1–5). https://doi.org/10.3997/2214-4609.2022580055
Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90, 434–440. http://dx.doi.org/10.1016/j.rse.2004.02.003
Solecki, W. D., Rosenzweig, C., Parshall, L., Pope, G., Clark, M., Cox, J., & Wiencke, M. (2005). Mitigation of the heat island effect in urban New Jersey. Global Environmental Change Part B: Environmental Hazards, 6(1), 39–49. https://doi.org/10.1016/j.hazards.2004.12.002
U. S. Geological Survey. (2018). USGS EROS Archive – Landsat Archives – Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) Level-1 Data Products. https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-8-oli-operational-land-imager-and#overview
Zha, Y., Gao, J., & Ni, S. (2003). Use of Normalized Difference Built-Up Index in Automatically Mapping Urban Areas from TM Imagery. International Journal of Remote Sensing, 24, 583–594. https://doi.org/10.1080/01431160304987
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Віталій ЗАЦЕРКОВНИЙ, Мауро ДЕ ДОНАТІС, Людмила ПЛІЧКО, Станіслав CАХНЮК, Наталія ОДАРЧУК, Тетяна МІРОНЧУК
This work is licensed under a Creative Commons Attribution 4.0 International License.
Read the policy here: https://geology.bulletin.knu.ua/licensing
