An optimization method for surface urban heat island footprint extraction based on anisotropy assumption

Abstract

The footprint (FP) is the main indicator used to quantitatively analyse the surface urban heat island (SUHI) effect. Currently, SUHI FP extraction methods ignore the interference caused by the city shape and are mostly based on the isotropic expansion assumption, resulting in an exaggerated range. Therefore, a new approach for an optimization schema that leverages the assumptions of angle segmentation and anisotropy is proposed herein. This method divides an urban area and its buffer rings into 16 sectors with equal central angles and selects appropriate land surface temperature (LST) curves for an exponential decay fit. The final FP was determined using the SUHI extension distance and the reference rural LST. To verify the effectiveness of this method, SUHI FPs of 27 cities in the central region of the Yangtze River Delta (YRD) were extracted during the summer of 2019, and the results were compared with those derived from traditional methods. The results showed that the FPs calculated using this method were more detailed because they minimized the influence of urban shape, topography, and the surrounding landscape. Moreover, this method played a vital role in revealing the effect of the land use allocation outside urban area on the SUHI effect. This state-of-the-art method is useful for unveiling SUHI spatial distribution patterns and for providing scientific support for land planning for sustainable urban development.