Object-oriented classification

Today, global warming, increasing the Land Surface Temperature, especially in big cities, is one of the environmental problems. The purpose of this article is to estimate the ability of Sebal, improved single-channel and Split window algorithms in estimating Land Surface Temperature on Landsat 8 image data in July 2019 and extracting Land Use maps in 7 classes for Tabriz County using an object-oriented classification method. Matching the temperature maps obtained by using these three mentioned algorithms with the Land Use map and comparing the algorithms with each other in terms of the proximity of their temperature to the Tabriz station is another goal of this article. To estimate the accuracy of the measured temperature, the data of the measured temperature of the Tabriz station at two centimeters above the land (as a representative of Tabriz County) has been used. The results of Sebal, improved single-channel, and Split window algorithms showed the highest temperature for dry pasture use and the lowest temperature for vegetation use, which indicates the importance of vegetation cover in the temperature changes of the studied area. A comparison of the studied algorithms with the temperature measured in the Tabriz station and their adaptation to different uses showed that the improved single-channel algorithm is more consistent with the actual temperature of the actual Land Surface Temperature in Tabriz County. The results of this study can help environmental planners concerned about the increase in air temperature in cities to make more appropriate decisions regarding the control of this phenomenon.