Quantifying the effects of spatial patterns of green spaces on urban climate and urban heat island in a semi-arid climate

Abstract

Green spaces in urban areas have a positive effect on the urban climate and microclimate. They help regulate the urban climate and mitigate the urban heat island (UHI) by creating a cooling effect through shade and evapotranspiration. In addition, they release oxygen, absorb carbon dioxide, generate shade, as well as energy usage and pollution emissions. The aim of this study is to assess the impact of spatiotemporal changes in green cover on the urban climate to mitigate the urban heat island (UHI). This is achieved through analyzing their effects on the land surface temperature (LST) due to the change in the spatial configuration of this green cover in the period between 1990 and 2019 in Constantine city. To materialize this effect, Google Earth Pro images, Landsat 5TM and Landsat 8 OLI/TIRS images of multiple years were acquired, processed and analyzed to generate land use maps (LU/LC), the normalized difference vegetation index (NDVI) maps. Those maps were used in order to estimate land surface temperature LST, the green cooling island (GCI) and the urban cooling island (UCI) of vegetation. Many landscape metrics (PLAND, CA, PD, NP, LPI, LSI, MPS, AI, PR, and SHAPE_MN) were chosen for the study at the class and landscape level to analyze the relationship between spatial patterns of vegetation and spatial distribution of LST through the SPSS 26 software. Our results showed that there is a negative relationship between NDVI and LST during the study period. Thus, the increase in NDVI values caused a decrease in LST values. Dense green space with the highest values of NDVI had the highest cooling effect. Therefore, our study confirmed that the type, density, size and the shape of vegetation are important factors in determining its cooling effect. The obtained results showed also that a simple, homogeneous and aggregated green landscape is more effective. The large dominant green patch has the highest impact on LST distribution leading to fragmented green patches with complicated shapes which led to an increase in LST.