Assessment of Outdoor Thermal Comfort Using Landsat 8 Imageries with Machine Learning Tools over a Metropolitan City of India

Rapid urbanization causes potential changes in the urban landscape, resulting in significant changes in land surface temperature and outdoor thermal comfort. This urban growth has a detrimental impact on the health and comfort of residents. The comfort level experienced in any given region depends on various parameters, including atmospheric temperature, relative humidity, land use and land cover (LULC). In this study, we aim to examine the spatial variation of outdoor thermal comfort in the city of Hyderabad. To achieve this, we utilize medium-resolution Landsat 8 imageries along with in situ meteorological data. The classification of LULC is carried out using the maximum likelihood method. A machine learning tool known as Support Vector Machine (SVM) is implemented, with seven environmental indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), new built-up index (NBI), LST, brightness, greenness, and wetness to predict outdoor thermal comfort (THI). The study reveals significant variations in THI across different land covers. Barren lands exhibit the highest mean THI values (27.3), followed by built-up areas (26.9), vegetation (24.1), and water bodies (20.7). These findings indicate that barren and built-up areas are associated with higher levels of discomfort, while vegetated regions and water bodies provide more neutral to moderate comfort conditions. These results also highlight distinct spatial variations in THI across different regions of the city, demonstrating the influence of the urban landscape on outdoor thermal comfort. This research is vital for identifying specific areas within cities that require targeted mitigation strategies to enhance outdoor comfort.