Segmentation on sentinel-3 data for surface heat Island detection

Abstract

The commonly higher temperatures in urban environment, compared to its surrounding countryside, have been observed and described for a long time. Several studies, focusing on the quantification of this phenomenon, have been carried out. Detecting, understanding and monitoring of heat islands is of utmost importance. This paper presents a methodological framework for a rapid identification of surface heat islands. For this purpose, image pre-processing, image segmentation and image analysis are conducted in SNAP, Orfeo ToolBox (OTB) and QGIS accordingly. Sentinel-3 data were obtained and land surface temperature (LST) product was utilized. This is not equal to air temperature that is presented in the daily weather report; however, it is a quite good and accessible indicator. Specifically, two products were used, one of day observation and one of night observation in order to highlight the differentiation of these two views. In addition, the correlation between NDVI and LST was examined in order to comprehend how land cover affects temperature. The proposed methodology was carried out by obtaining freely-available data that were processed in open-source software.