SEASONS IN STUTTGART: DEVELOPING A GOOGLE EARTH ENGINE TOOL FOR HEAT ISLAND MAPPING
Keywords: Remote Sensing, Land Surface Temperature, Landsat, Urban Heat Island, Google Earth Engine, Python
Abstract. This study generates a process in GEE (Google Earth Engine) for SUHI (Surface Urban Heat islands) identification derived from TIRS (Thermal Infrared Sensor) and OLI (Operational Land Imager) sensors of Landsat 8 imagery in the area of Stuttgart, Germany. By comparing the temperature images in winter and summer seasons through a regression model, a relation between the Surface Cover (SC), the Terrain Shape (DEM) and the LST (Land Surface Temperature) is established. A Python code is developed for modelling the data and displaying the results linked to GEE. Three different models are used to establish the relationship between different variables (Temperature, Height, Wind etc.). Accuracy/goodness of fit of these models are measured using R-squared and standard error. Results shows that polynomial regression of 3rd order degree fits best to the dataset used in this study. Moreover, it is found that temperature values are not perfect for this study, as Landsat 8 have been acquired at 10’o clock in the morning (local time), whereas night time acquisition (which was not available for Stuttgart, Germany) would be best suited for the study. The results indicate that urban areas and meadow (open areas without vegetation) get the bigger values of temperature. Terrain Shape with respect to height indicates that the bigger the height, the lower the temperature in most of the regions. This project provides insight into the development of applications using a web-based platform and leads to a fast and accurate result for identifying the SUHI effect. It can contribute to the necessity of planning more vegetation areas in order to reduce hot temperature values in Stuttgart.