Applying geographically weighted regression to quantify the impact of impervious surface density on land surface temperature in Ho Chi Minh City, Vietnam

Abstract

Surface characteristics are believed to be the primary factor causing spatial variation in land surface temperature (LST), especially in a large and rapidly growing city like Ho Chi Minh City. The study aims to estimate the spatial relationship between land surface temperature (LST) and impervious surface density (ISA) by using three statistical techniques: 1) Optimized hot spot analysis; 2) Global linear regression (OLS); and 3) Geographically weighted regression (GWR). LST and ISA were extracted from Landsat 8 OLI of 2020. According to the optimized hot spot analysis’s findings, over 80% of the areas of high ISA values (mean ISA: 0.86, SD: 0.13) overlap with areas of high LST values (mean LST: 36.2°C, SD: 2.04°C), with a correlation coefficient of r=0.775. Likewise, Moran’s I index (I=0.82, Z=72, P<0.001) indicates that LST in Ho Chi Minh City has a significantly spatial dependence, so geographically weighted regression (GWR) and global linear (OLS) models need to be taken into account simultaneously. Coefficient of determination R2 and AICc (Akaike information criterion corrected) were used to compare the models. R2 increases from 0.78 to 0.87, whereas AICc decreases from 8047 to 7033 in OLS and GWR, respectively. GWR has a more substantial explanatory power for the association between LST and ISA and offers superior LST prediction accuracy compared to OLS. Potential urban heat island areas can be accurately detected by combining GWR and optimized hot spot analysis.