Post-Classification Urban Heat Island Change Detection Based on Multi-Temporal Satellite Images

Increasing human desire for urbanization and, as a result, urban growth and development has caused the replacement of natural objects such as vegetation and mountains with building structures and roads. This exacerbates the phenomenon of urban heat islands (UHI), one of the main threats to the quality of human life. In this research, a post-classification change detection algorithm is proposed based on UHI classification maps are generated using multi-temporal Landsat images from a rapidly developing urban area, in Iran in 17-years’ time interval. The main research objective is to investigate the effects of constructing new built-up areas on urban heat islands considering the land surface temperature, vegetation coverage, and surface object types. The production of UHI classification maps is based on the probabilistic reasoning rules for integrating the LST, Albedo, and NDVI as lines of evidence considering land use/land cover (LU/LC) object types. The analysis of the UHI change detection results in the rapidly developing urban area in this research indicates that the area percentage of built-up object class has increased by 12.1% in 17 years. This amount of construction led to a 23.7% increase in the high UHI class, which is equivalent to 2931 hectares. Moreover, the regions with medium and low UHI have decreased by − 16.09 and − 9.12%, respectively, which indicates an increase in the intensities of urban heat islands during this period.