Land Surface Temperature (LST) is an important factor in several climatological and thermo physical studies. It has a profound effect on human health and comfort at the earth’s surface. In recent years, remote sensing studies using satellite imagery have proved to be an effective method for determining LST. Our study uses Landsat Enhanced Thematic Mapper (ETM+) images to obtain LST distribution over the Indian town of Neyveli, home to one of the largest coal based thermal power plants in Asia. Massive afforestation has been undertaken as a suitable countermeasure against the discomfort experienced by residents in this region due to high temperature. We have determined LST using both constant emissivity and pixel-wise emissivity methods, where normalized difference vegetation index and fractional vegetation cover have been used as important parameters. A Supervised classification process based on the known land cover types of the region was used to confirm the most suitable method for obtaining LST at Neyveli. Results show a significant drop of up to six degrees Celsius around afforested areas as compared to barren regions. The Human Comfort Index also shows an improvement of ten percent for areas with green cover. This first study, demonstrates the use of satellite image processing to obtain temperature differences over varying topography over a heavily afforested industrial township in southern India. © 2015 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.