The local climate zone (LCZ) classification scheme provides a standardized method to characterize urban morphological characteristics and urban thermal environments. However, its capability to differentiate urban temperatures has not been well examined. This study investigated the LCZ-based land surface temperature (LST) in five megacities, including Shenyang, Beijing, Xi'an, Nanjing, and Nanchang. The results indicate that the LCZ scheme might conceal areas with the most critical heat risks, if the maximum LST was not used. The built-dominated zones often contributed to urban temperature increase, but it was not always true. The non-built-dominated zones, mostly lowered urban temperatures, while they could contribute to urban temperature increase depending on seasonal and urban context. Both hot and cold non-built-dominated zones varied significantly with city and season. Some zones were the hottest in one season, but changed to be the coldest in another season. LCZ scheme showed good capability to differentiate the temperatures of built-dominated zones, while its capability to characterize non-built-dominated zones was weaker. In Beijing, the LCZ capability to characterize the temperature of non-built-dominated zones was below 70%, and was only 16.67% in summer. Therefore, urban planners, designers, and managers should prudently adopt LCZ scheme to rank the priorities for integrating cooling interventions in both built-dominated and non-built-dominated zones. It is important to not copy the LCZ–based LST pattern of other cities or seasons when making decisions. Overall, this study provides a reference to understand LCZ capability and make proper decisions for urban heat mitigation, adaptation, and management.

The urbanization process leads to the enhancement of the urban heat island (UHI) effect, and the high temperature brought by it exacerbates the risk of heat exposure and seriously endangers human health. Analyzing the spatiotemporal characteristics and levels of heat exposure risk is important for formulating heat risk prevention and control measures. Therefore, this study analyzes the spatiotemporal characteristics of heat exposure risk based on the UHI footprint (FP) and explores the relationship between it and urbanization factors in the Beijing–Tianjin–Hebei (BTH) region from 2000 to 2020, and obtains the following conclusions: (1) The BTH region suffers from severe UHI problems, with FP ranging from 6.05 km (Chengde) to 32.51 km (Beijing), and the majority of cities show significant trends of FP increase. (2) With the increase in FP, massive populations are exposed within the heat risk areas, with the average annual population at risk across cities ranging from 269,826 (Chengde) to 166,020,390 (Beijing), with a predominance of people exposed to high risk (more than 65% of the total) and generally showing increasing trends. (3) The population at risk of heat exposure is significantly correlated with urbanization factors, indicating that urbanization is an important reason for the increase in the risk population and the enhancement of the risk level. These results suggest that with the continuous urbanization process, the heat exposure risk problem faced by cities in the BTH region will persist and gradually worsen, which must be paid attention to and effective mitigation measures must be taken.

The precise quantification of surface urban heat island intensity (SUHII) is fundamental for understanding the process, causes, and solutions to thermal environmental change. However, existing methods for SUHII estimation are not uniform in non-urban reference selection, with inconsistent consideration of relevant influencing factors. The associated uncertainty can be further exacerbated under seasonal fluctuations of atmospheric and surface environments. This study concentrated on macro city and intra-urban local scales to examine the variations in SUHII assessment and its seasonal changes using different reference delineation methods. City-scale analysis included eight references based on fixed areas or dynamic buffers, while local-scale analysis took six natural cover types as references under the Local Climate Zone (LCZ) framework, respectively. Results revealed significant differences in SUHII using diverse references, and the inconsistency varied across seasons. On the city scale, the most pronounced inter-method difference occurred in winter, while stronger consistency of spatial patterns was observed in summer. Relatively, higher seasonal SUHIIs and stronger spatial variabilities were generated by methods using fixed areas. On the local scale, strong consistency of spatial patterns was also observed in summer, while the most pronounced difference occurred in spring. Maximum local SUHIIs in all seasons were obtained using LCZ G as a reference. The study further summarized a list of criteria of reference selection for both scales. Overall, this study provides empirical evidence supporting the appropriate reference delineation for reliable SUHII estimate, especially for seasonal analysis. It can facilitate improved understanding of urban thermal variations and benefit effective urban heat mitigation.

Vegetation is an important strategy for mitigating heat island effects, owed to its shading and evaporative cooling functions. However, urbanization has significantly affected regional vegetation phenology and can potentially weaken the cooling potential of vegetation. Previous studies have mainly focused on national and regional vegetation phenology, but local-scale vegetation phenology and dynamic variations in built-up areas remain unclear. Therefore, this study characterized the vegetation phenology in the densely built-up area of Beijing, China over the period of 2000–2020 based on high-resolution NDVI data using Savitzky–Golay filtering and explored its spatiotemporal characteristics and drivers. The results indicate that the vegetation phenology exhibits significant spatial heterogeneity and clustering characteristics. Compared with vegetation in peripheral blocks, vegetation in central urban blocks generally has an earlier start in the growing season (SOS), later end in the growing season (EOS), and a longer growing season length (GSL). However, the overall distribution of these parameters has experienced a process of decentralization along with urbanization. In terms of drivers, vegetation phenology indicators are mainly influenced by background climate. Specifically, SOS and GSL are mainly affected by temperature (TEP), whereas EOS is mainly influenced by annual precipitation (PRE). Additionally, local environmental factors, particularly the percentage of water body (WAP), also have an impact. Notably, the local environment and background climate have a synergistic effect on vegetation phenology, which is greater than their individual effects. Overall, this study extends the current knowledge on the response of vegetation phenology to urbanization by investigating long-term vegetation phenology dynamics in dense urban areas and provides new insights into the complex interactions between vegetation phenology and built environments.