Trends of the contributions of biophysical (climate) and socioeconomic elements to regional heat islands

Abstract

The development of urban accumulations in recent decades has led to the transformation of urban heat islands (UHI) into regional heat islands (RHI). The contributions of the biophysical, climate, and socioeconomic factors to RHI in urban agglomeration remain poorly understood. Here Yangtze River Delta urban agglomeration (YRDUA) in eastern China has been selected as a case area to explore the influences trends, of the influencing factors to RHI by using MODIS data from 2003 to 2017. Results showed that, in summer, the area fraction of daytime RHI in YRDUA has increased from 21.74 to 31.03% in 2003 and 2017, respectively. As compared to 2003, the annual nighttime RHI area in 2017 has increased from 7510 to 20,097 km2. The dominant factors of surface RHI intensity (SRHII) showed seasonal variation. Enhanced vegetation index (EVI) (interpretation of 33.27%) was the dominant factor of daytime SRHII in spring. The most important factor was normalized difference build-up density (NDBI) (37.28% and 26.83%, respectively) in summer and autumn. In winter, precipitation (26.16%) was the most influential. At night, Modified Normalized Difference Water Index (MNDWI) had a dominant effect on SRHII in spring (54.12%), autumn (52.62%), and winter (24.19%). The dominant factor of nighttime SRHII in summer was EVI (42%). Moreover, water bodies harm RHI during the day while having a positive effect at night. These findings can provide a theoretical basis for regional environment improvement and regional sustainable development.

An Approach to Characterizing the Spatial Pattern and Scale of Regional Heat Islands Over Urban Agglomerations

“…The regional mean LSTA annual cycle in large cities is thus selected as the reference series having notable SUHI effects in the YRD region and is referred to as the RSUHI-Ref LSTA annual cycle. Zhou et al (2015) found an obvious urban-rural LST cliff approximately 20-25 km from urban centers, but accurately positioning the LST cliff is a great challenge for urban agglomerations due to the strong contributions to SUHIs from the land-cover changes, local climate variations and complex terrain (Chen et al, 2021;Manoli et al, 2019;Sun et al, 2016;Xie et al, 2017;Zhao et al, 2014). We here explore how to reasonably identify the LST cliff using an improved regression model (IRGM).…”

“…If researchers can accurately identify the spatiotemporal variations of RSUHIs, they can use more reasonable measurements to shape rational urban clusters, relieve the adverse effects of RSUHIs and improve our understanding of urban planning policy outcomes on the regional scale (Mohammad et al., 2022). However, in contrast with a SUHI affecting a single city, RSUHIs greatly affect the thermal environment of continuous regions over wider ranges (Yu et al., 2019; Zhou et al., 2018), because the SUHI effects can be observed over from large cities, suburbs to small towns in urban agglomerations (Chen et al., 2021; Deilami et al., 2018; Fluschnik et al., 2016). It is thus difficult to quantify RSUHIs using the urban–rural dichotomy.…”

An Investigation to Identify the Effectiveness of Socioeconomic, Demographic, and Buildings’ Characteristics on Surface Urban Heat Island Patterns

“…The SUHIs are measured from satellites; satellites offer numerous advantages in terms of costs, the robustness of the process, and the convenience of processing [12,28,36]. However, these UHIs mostly indicate daytime intensity to be more severe [6,37], whereas SUHIs measured from air temperature show nighttime UHIs to be hotter [36,38]. Moreover, these SUHIs are seen to be directly driven by vegetation cover, impervious surface cover, and other landscape characteristics; very few have analyzed their relationship with other factors such as socioeconomic variables, demographic variables, and building characteristics [39].…”

“…Reasonable research has been conducted to study the impacts of biophysical factors on SUHI. Previous studies have covered the impacts of urban vegetation/greenness, vegetation gradient between urban to rural, urban trees, impervious surfaces, albedo, urban water bodies, and LULC change to SUHI [16,[27][28][29][30][32][33][34][35][36][37]. However, building attributes (height, roof type, age, and building density) could not obtain an appropriate attraction by the researchers among other biophysical factors [24,42].…”

Understanding the Relationship between Urban Biophysical Composition and Land Surface Temperature in a Hot Desert Megacity (Saudi Arabia)

“…In the context of Saudi cities, unprecedented urban expansion with a rapid increase in impervious surfaces could impact the LST and lead to deterioration of the urban thermal environment [ 25 , 26 , 27 ]. The application of remote sensing indices offers significant opportunities to understand the relationship between biophysical indices and LST [ 28 , 29 ].…”