Abstract
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.
Highlights
1.1 Surface heat islandsIt is a fact that many people live in urban areas, with their number estimated to rapidly escalate in the near future (Sobrino and Irakulis, 2020)
Impervious surfaces and heat from human activities cause the increase of urban heat island that is the difference between the air temperature within the urban environment and the air temperature of its surroundings (Amorim, 2018)
The primary objective of this study is to present an efficient methodological framework for the rapid identification of surface heat islands in an on administrative entity including the urban environment and the suburban environment and the provinces
Summary
It is a fact that many people live in urban areas, with their number estimated to rapidly escalate in the near future (Sobrino and Irakulis, 2020). The temperature is higher in urban areas than in rural areas, at night. This circumstance plays an important role in people’s psychology and economic activities. Monitoring heat islands is of great importance (Sobrino and Irakulis, 2020). When monitoring is carried out through remote sensing TIR data, the parameter that is analyzed is the land surface temperature (Weng, 2009; Sobrino and Irakulis, 2020)
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