In this study, 7 images of OLI and EMT sensors of Landsat 7 and 8 satellites and multi-time images of TM sensor satellite 5 have been used. These images cover the period from 1987 to 2018 (31-year period); after applying processing to satellite imagery, land use layers were prepared by supervised classification method. Then, using the thermal equations and the center algorithm, the similarity of the surface temperature of the earth for the periods of 1978, 2000, and 2018 was calculated for the study area in four stages. Results indicate that barren lands in 1987 had the largest area with 437 km and the use of irrigated land had the least area with. Studies have also shown that from 2000 to 2018, due to the increase in urban population and migration to this city and the expansion of housing construction, residential use has gradually increased to an area of 446 and the amount of land use in barren lands has decreased to 431 square kilometers. Survey of temperature with temperature changes showed that in 1987, the temperature in barren land uses reached a maximum temperature of 48 outside the suburbs, and from 2000 to 2018, land use in the northwest and west of the metropolis reached 56 to 70. Examination of the results shows that during the study year, due to population growth and the growing trend of residential use in the metropolis due to the replacement of buildings, the Cement and Asphalt Organization will absorb these levels more than they reflect the sun and has increased the temperature in urban areas. This application is considered as the creator of the thermal island for the metropolis.
Gallo, K.P., McNab, A.L., Karl, T.R., Brown, J.F., Hood, J.J. & Tarpley, J.D., 1993. The use of NOAA AVHRR data for assessment of the urban heat island effect. Journal of Applied Meteorology, 32(5), p. 899-908.
Jiang, J. & Tian, G., 2010. Analysis of the impact of land use/land cover change on land surface temperature with remote sensing. Procedia Environmental Sciences, 2, p. 571-575.
Khandelwal, S., Goyal, R., Kaul, N. & Mathew, A., 2017. Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. The Egyptian. Journal of Remote Sensing and Space Science, 21(1), p.87-94.
Liu, L. & Zhang, Y., 2011. Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sensing, 3(7), p. 1535-1552.
Matson, M., Mcclain, E.P., McGinnis Jr, D.F. & Pritchard, J.A., 1978. Satellite detection of urban heat islands. Monthly Weather Review, 106(12), p. 1725-1734.
Ning, J., 2011. Influence of coastal land use change to land surface temperature. Energy Procedia, (11), p.3999-4004.
Rozenstein, O., Qin, Z., Derimian, Y. and Karnieli, A., 2014. Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm. Sensors, 14(4), p.5768-5780.
Roth, M., Oke, T.R. & Emery, W.J., 1989. Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology. International Journal of Remote Sensing, 10(11), p. 1699-1720.
Song, Y. & Wu, C., 2016. Examining the impact of urban biophysical composition and neighboring environment on surface urban heat island effect. Advances in Space Research, 57(1), p. 96-109.
Tan, J., Zheng, Y., Tang, X., Guo, C., Li, L., Song, G., Zhen, X., Yuan, D., Kalkstein, A.J., Li, F. & Chen, H., 2010. The urban heat island and its impact on heat waves and human health in Shanghai. International Journal of Biometeorology, 54(1), p. 75-84.
Yuan, F. and Bauer, M.E., 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of environment, 106(3), p.375-386.
Weng, Q. & Yang, S., 2004. Managing the adverse thermal effects of urban development in a densely populated Chinese city. Journal of Environmental Management, 70(2), p. 145-156.
Weng, Q., 2009. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), p. 335-344.