How to cite: Magyari-Sáska, Z., Dombay, Ș. (2022) Experimental Method to Assess the Looseness or Compactness
in Climate Changing for Several Major Cities of Hungary. 2022 ”Air and Water – Components of the Environment”
Conference Proceedings, Cluj-Napoca, Romania, p. 116-127, DOI: 10.24193/AWC2022_12.
EXPERIMENTAL METHOD TO ASSESS THE LOOSENESS OR COMPACTNESS IN CLIMATE CHANGING FOR SEVERAL MAJOR CITIES OF HUNGARY
Zsolt MAGYARI-SÁSKA, Ștefan DOMBAY
ABSTRACT. – In this paper we tried to study the values of radiant temperatures (Land Surface Temperature) and NDVI (Normalized Difference Vegetation Index) for areas occupied by buildings and green spaces. The area affected by the Urban Heat Island (UHI) was also determined. Study Area, Iasi, the largest city in eastern Romania, is geographically situated on latitude 47°12'N to 47°06'N and longitude 27°32'E to 27°40'E. LST is an estimate of ground temperature and is important to identify change in environment. An important parameter in global climate change is rapid urbanization which leads to an increase in Land Surface Temperature (LST). The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural zones due to urbanization. It also been found that night UHI is more powerful than day. At night the LST values for SUHI varies between 24.5°C-25.9°C, and during the day between 35°C-38.7°C. With the development of remote sensing technology, it has become an important approach to urban heat island research. MODIS and Landsat data were used to estimate the LST and NDVI. From the analysis of the images it can be seen that the temperatures in SUHI are lower where there are green spaces around the buildings, and temperatures are higher in the non-UHI area, where inside or around the green spaces there are surfaces built or covered with concrete. Statistical data show very average temperatures for areas affected by UHI, 37.8°C for daytime and 24.6°C for night.
Keywords: climate, network model, data aggregation, R CRAN, Hungary
Creative Commons Attribution Non-Commercial 3.0 License.