Effects of Blue, Green, and Gray Infrastructure Landscape Patterns on Land Surface Temperature: A Case Study of Hue City, Vietnam

Global urbanization and climate change are significant challenges for any country worldwide to achieve sustainable goals. Urban is closely linked to deteriorated environment quality and urban heat islands that are accelerated by greenspace shrinkage. Blue, green, and gray infrastructures (BGGIs) are the leading influential components of urban air temperature. The spatial arrangement of the BGGIs is also considered to optimize their positive and negative effects on urban planning. This chapter applied the combination of remote sensing, GIS, and landscape metrics to analyze the relationship between the BGGI and land surface temperature (LST) of Hue city. The LST was retrieved from the Landsat 8 OLI/TIRS image, while the BGGIs were grouped from land use/land cover classified from the Sentinel-2A image. Ten landscape metrics were calculated to examine the spatial arrangement of the BGGIs landscape pattern associated with the LST using the grid size of 10 x10 pixels. The analysis of variance (ANOVA) test demonstrates that the blue and green infrastructures ease the LST by approximately 2 °C and 3.5 °C compared to the gray infrastructure (p < 0.05). The multiple regression analysis between blue, green, and gray infrastructure landscape pattern metrics and the LST reduces urban heat island with an R-value of all models above 0.5. The effects of landscape patterns on LST depend on the characteristics of components of urban infrastructure. Significant predictors for LST variations in blue, green, and gray infrastructure were the CABlueI, PDGreenI, PLANDGray, and EDGray metrics were determined with the best predictive performance with a significant value (.sig <0.05). The CABlueI and PDGreenI contribute to decreasing LST (β = −0.305 and −0.140, respectively). Conversely, the PLANDGray and EDGray positively contribute to increasing the LST (β = 0.775 and 0.084). This study’s findings will serve as an excellent scientific basis for the authoritarian government in urban green space planning under climate change challenges in recent years.