The study evaluates the extent of land use, land cover, and land surface temperature change between August 2000 and August 2019 in the Al ‘Ain region in the southeast of the United Arab Emirates using Landsat satellite images. The satellite imageries have been classified by both unsupervised and supervised classification methods using ENVI software. In an unsupervised technique, the ISODATA clustering algorithm will be used for the classification. The resulting image will be used as a reference and for understanding the distribution of pixels with different digital numbers. In the supervised classification method, the maximum likelihood algorithm will classify the image based on the region of interest (training sets) provided by the user based on the field knowledge. Changes in land use/land cover between 2000 and 2019 were quantified using post-classification analysis in a geographic information system. Followed by atmospheric correction and LST retrieval. The results have shown a dramatic change in land cover and an obvious increase in land surface temperature over the 19 years’ study period. The composition of land use/landcover features significantly influences the magnitude of land surface temperature, and the percent cover of the urban area had an unexpected inverse effect. In contrast, the percent of vegetation is the most fundamental factor in reducing land surface temperature. Using the topical approach, the researchers suggest that the leadership can directly minimize the urban heat island effect in Al ‘Ain city by keeping the cooling effects of urban greenery.

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