Attribution of Urban Diurnal Thermal Environmental Change: Importance of Global–Local Effects

Abstract

Global climate change is causing various negative impacts on urban ecosystems and energy systems. To effectively mitigate and adapt to these changes, it is important to understand the contributions of background climate and local effects to urban thermal environment variation. This study utilized the empirical orthogonal function (EOF) approach to deconstruct long-term MODIS land surface temperature (LST) datasets to obtain the main features of change in daytime and nighttime thermal environments. Local bivariate spatial autocorrelation analysis was used to explore the underlying causes of these changes. The main EOF modes explained 73.14% and 81.33% of daytime and nighttime thermal environment variation, respectively. The correlation coefficient between the time coefficient of the main modes and the average LST was > 0.99, reflecting the role of global effect caused by background climate change. The secondary EOF modes explained 12.51% and 4.12% of daytime and nighttime thermal environment variation, respectively, and were spatially correlated with changes in landscape thermal intensity, reflecting local effect caused by landscape change and anthropogenic heat emissions. In expansion and renewal areas, industrial zones and compact high-rise buildings had the most obvious warming effect on the daytime thermal environment, while mid-to-high-rise buildings had the most obvious warming effect on the nighttime thermal environment. The results of this study provide valuable insights into the mechanisms of background climate and local effects on the urban thermal environment, and provide a reference for formulating effective strategies for mitigating and adapting to change in urban areas, and for promoting sustainable development.

Evolution and Built-Up Age Dependency of Urban Thermal Environment

“…Third, the shape and size of built-up areas and surrounding green patches also have impacts on the LST of different built-up age cohorts. Urban areas exhibit a variable LST pattern that can be linked to albedo variations [85][86][87]; the increase in building density and floor area ratio will also cause LST to rise with urban construction [61,[88][89][90]. In contrast to previous studies, our results show that landscape plays a leading role in determining the LST pattern among built-up age cohorts (Figure 5).…”

XGBoost-Based Analysis of the Relationship Between Urban 2-D/3-D Morphology and Seasonal Gradient Land Surface Temperature

“…Research concerning urban morphology-LST relationships has predominantly centered on their correlation. [37], [25], [38], [39]. Guo et al (2020) investigated indices such as landscape patterns, the proportion of construction land (PCL), the proportion of woodland and grass (PWG), and the 2 > IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing < normalized difference built-up index (NDBI), identifying a strong correlation between NDBI and LST.…”

Urban Trees Expansion and Exposure Improvement in China: Insights From Satellite Observations (1985–2023)

“…By moderating energy and water exchanges between land and the atmosphere, urban trees exert considerable influence on the climate system, operating at both local and regional scales [9], [10]. Extensive research conducted at local and urban scales has consistently demonstrated the diverse environmental benefits of urban trees, including the mitigation of microclimatic conditions [5], [11], [12], [13], reduction of heat island intensity [2], [6], [14], alleviation of air pollution [1], [3], and preservation of biodiversity [15], [16]. Moreover, urban trees have been widely acknowledged for their protective effects on human health and mental well-being, attributed to their facilitation of physical activity, support for social interaction, promotion of community awareness, and stress alleviation capabilities [1], [8], [17], [18].…”