Tshwane is one of the major metropolitan in Gauteng Province. This metropolitan continues to experience rapid urbanization as a result of population growth, leading to the conversion of natural lands into impervious surface area (ISA) i.e., constructed surfaces – sidewalks, roads rooftops, parkinglots covered by impenetrable materials such as asphalt, concrete and stones which prevent water from infiltrating into the soil. Such landscapes influence the climate of the Metropolitan as evidenced by the recent heat wave characterized by high temperature. Therefore, the consistent information about these changes will play an important role in city planning and environmental management. In this study, seven land use/cover types were delineated from the cloud free Landsat images using maximum likelihood (ML) and random forest (RF) classifiers to map the Tshwane metropolis. The overall accuracies for classifying the seven land cover types were 88.63% and 80.13% (Landsat 7 ETM+, 2003) and 88.82% and 82.03% (Landsat 8 LCDM) for both ML and RF, respectively. In addition, based on the pairwise comparison of error matrix the two algorithms were found to produce approximately identical classification errors. Furthermore, the remote sensing data was also used to assess the relationship between LULC changes and LST estimation. Mean near surface temperature from the weather stations was used as a point of reference to verify the accuracy of the final retrieved LST images. From Landsat 7 ETM+ (2003), the mean pixel temperature for Pretoria Eendracht and Irene Wo weather station when compared the mean near surface temperature produced a LST retrieval error of 3.3OC and 1OC respectively. Similarly, Landsat 8 LCDM data (2013) mean pixel temperature for Pretoria UNISA weather station and Pretoria National Botanical Institute when compared the mean near surface temperature produced a LST retrieval error of 0.38OC and 1.3OC for the two stations. Finally, the remote sensing data showed the quantitative effect of impervious surface area changes on mean LSTs, through the distribution of urban heat island within Tshwane metropolitan.