DEVELOPMENT OF THE METHODS FOR CALCULATING THE MAIN INDICATORS OF HEAT ISLANDS IN URBAN AREAS
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Abstract
The article is dedicated to the methods developed to calculate the main parameters of “heat islands” that appear in densely built-up urban area. Although remote sensing imaging is ideally used to track and detect frequent land cover changes in urban and surrounding areas as a result of sustainable urbanization and to calculate key parameters of "heat islands" seen in densely populated urban areas, satellite imagery is digitally manually The transformation of a parametric image into a land cover map using existing methods of classification is a long process, and therefore methods are proposed to determine the main indicators of the impact of "heat islands" in urban areas. A modified building density index has been formulated, which is highly informative, involving: (a) the proposed index reaches an extremum when the known building density index BDI and NDVI are equalized, (b) when the specified maximum is reached, it is easy to calculate the LST indicator using the known regression dependences of BDI and NDVI from LST. The method has been developed to calculate the area of “heat islands” on the base of the equivalent radius calculation using the known dependence function of the building density on the distance to the center of the urban area. Our study shows that the distribution of buildings and the slope of the relief affect the surface temperature (LST), in addition, the ratios of different LSTs vary in cities of different sizes, and each city has a temperature difference in LSTs, so the urban heating island To reduce the impact, it is important to identify the characteristics of the thermal environment in cities of different sizes. Urban greening is increasingly valued by cities around the world as an effective measure to reduce the negative effects of the urban heating islands, with different numbers and types of landscape dimensions, different statistical methods used in different surveys, as well as metric scale dependence and contextual differences between cities and we can overcome these challenges by conducting comparative research on time and space using a consistent methodology.
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References
Masoudi, M. and Tan, P. (2019), “Multi-year comparison of the effects of spatial pattern of urban green spaces on urban land surface temperature”, Lands and. Urban Planning, Vol. 184, pp. 44-58, DOI: https://doi.org/10.1016/j.landurbplan.2018.10.023.
Zhang. Y. and Sun, L. (2019), “Spatial-temporal impacts of urban land use land cover on land surface temperature: Case studies of two Canadian urban areas”, Int. J. Appl. Earth Obs. Geoinf, Vol. 75, pp. 171-181.
Yang, J., Zhan, Yi, Xi,Xiaoc, Jianhong Cecilia, Xiad, Sune W. and Li, X., (2020), “Investigation the diversity of land surface temperature characteristics in different scale cities based on local climate zones”, Urban Climate, Vol. 34, 100700, DOI: https://doi.org/10.1016/j.uclim.2020.100700.
Oke, T. R. (1982), “The energetic basis of the urban heat island”, Q. J. R. Meteorol. Soc., Vol. 108, Is. 465, pp. 1-24.
Ayanlade, A. (2017), “Variations in urban surface temperature: An assessment of land use change impacts over Lagos metropolis”, Weather, Vol. 72, Is. 10, pp. 315-319, DOI: https://doi.org/10.1002/wea.2925.
Maishella, A., Dewantoro, B. and Aji, M. (2020), “Correlation Analysis of Urban Development and Land Surface Temperature Using Google Earth Engine in Sleman Regency, Indonesia”, IOP Conference Series: Earth and Environmental Science, 540, 012018, DOI: https://doi.org/10.1088/1755-1315/540/1/012018.
Tuholske, C., Caylor, K., Funk, C., Verdin, A., Sweeney, S., Grace, K., Peterson, P. and Evans, T. (2021), “Global urban population exposure to extreme heat”, PNAS, Vol. 118. No. 41, DOI: https://doi.org/10.1073/pnas.2024792118.
Zha, Y., Gao, J. and Ni, S. (2003), “Use of normalized difference built-up index in automatically mapping urban aeras from TM imagery”, International Journal of Remote Sensing, Vol. 24, No. 3, pp. 583-594.
Mwangi, P. W., Karanja, F. N. and Kamau, P. K. (2018), “Analysis of the relationship between land surface temperature and vegetation and built-up indices in upper-hill, Nairobi”, Journal of Geoscience and Environment Protection, Vol. 6, No. 1,
pp. 1-16, DOI: https://doi.org/10.4236/gep.2018.61001.
Bonafoni, S. and Keeratikasikorn, C. (2018), “Land surface temperature and urban density: multiyear modeling and relationship analysis using MODIS and landsat data”, Remote Sens, Vol. 10, Is. 9, 1471, DOI: https://doi.org/10.3390/rs10091471.