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To control the growth of CO2 emissions and achieve the goal of carbon peaking, this study carried out a detailed spatio-temporal analysis of carbon emissions in major cities of China on a city-wide and seasonal scale, used carbon emissions as an indicator to explore the impact of COVID-19 on human activities, and thereby studied the urban resilience of different cities. Our research re-vealed that (i) the seasonal patterns of CO2 emissions in major cities of China could be divided into four types: Long High, Summer High, Winter High, and Fluctuations, which was highly related to the power and industrial sectors. (ii) The annual trends, which were strongly affected by the pan-demic, could be divided into four types: Little Impact, First Impact, Second Impact, and Both Impact. (iii) The recovery speed of CO2 emissions reflected urban resilience. Cities with higher levels of de-velopment had a stronger resistance to the pandemic, but a slower recovery speed. Studying the changes in CO2 emissions and their causes can help to make timely policy adjustments during the economic recovery period after the end of the pandemic, provide more references to urban resilience construction, and provide experience for future responses to large-scale emergencies. [ABSTRACT FROM AUTHOR]

As an essential structural indicator of buildings, sky view factor (SVF) is one of the most critical factors affecting the urban thermal environment. However, the relationship between SVF and the thermal environment at the neighborhood scale has not been adequately studied. Therefore, this paper investigates the relationship between SVF and air temperature in different building scenarios based on the Local Climate Zone (LCZ) classification framework. Firstly, the study is based on multi-source urban data and the Open Street Map (OSM) to map the local climate zones in Beijing. Then, a simulation model with different LCZs was constructed based on realistic scenarios using the microclimate simulation software ENVI-met, and the thermal environment was simulated in 24 h on a single day in summer. Finally, the SVF and air temperature relationship under different LCZ scenarios was calculated and analyzed. The results show that (1) the SVF values of LCZ 1, LCZ 2, and LCZ 5 show a more apparent positive correlation with air temperature than other categories, and the SVF values of LCZ 6–9 show a negative and then positive correlation with air temperature; (2) in the morning, the dense building areas show a weak correlation with air temperature, and the differences in air temperature corresponding to the SVF values in different zones are greater; (3) in the morning, the air temperature in the dense building areas showed a weak correlation, the difference between the SVF values and the air temperature in different intervals was different, and when the SVF was larger or smaller, the air temperature change was smaller and concentrated, and the correlation between the air temperature and the SVF in the open building areas was not obvious; (4) with 12:00 as the dividing line, the SVF and the air temperature in all categories showed a weak positive correlation after this time. This study can provide guidance on optimizing building layouts and mitigating the impacts of urban heat on human health. [ABSTRACT FROM AUTHOR]

The tremendous advancement of cities has caused changes to the urban subsurface. Urban climate problems have become increasingly prominent, especially with regard to the intensification of the urban heat island (UHI) effect. The local climate zone (LCZ) is a new quantitative method for analyzing urban climate that is based on the kind of urban surface and can effectively deal with the problem of the hazy distinction between urban and rural areas in UHI effect research. LCZs are widely used in regional climate modeling, urban planning, and thermal comfort surveys. Existing large-scale LCZ classification methods usually use visual features of optical images, such as spectral and textural features. There are many problems with hyperspectral LCZ extraction over large areas. LCZ is an integrated concept that includes features of the geography, society, and economy. Consequently, it makes sense to consider the characteristics of human activity and the visual features of the images to interpret them accurately. ALOS_DEM data can depict the city's physical characteristics; however, images of nighttime lights are crucial indicators of human activity. These three datasets can be used in combination to portray the urban environment. Therefore, this study proposes a method for fusing daytime and nighttime data for LCZ mapping, i.e., fusing daytime Zhuhai-1 hyperspectral images and their derived feature indices, ALOS_DEM data, and nighttime light data from Luojia-1. By combining daytime and nighttime information, the proposed approach captures the temporal dynamics of urban areas, providing a more complete representation of their characteristics. The integration of the data allows for a more refined identification and characterization of urban land cover. It comprehensively integrates daytime and nighttime data, exploits synergistic information from multiple sources, and provides higher accuracy and resolution for LCZ mapping. First, we extracted various features, namely spectral, red-edge, and textural features, from the Zhuhai-1 images, ALOS_DEM data, and nighttime light data from Luojia-1. Random forest (RF) and XGBoost classifiers were used, and the average impurity reduction method was employed to assess the significance of the variables. All the input variables were optimized to select the best combination of variables. The results from a study of the 5th ring road area of Beijing, China, revealed that the technique achieved LCZ mapping with good precision, with a total accuracy of 87.34%. In addition, to examine and contrast the effects of various feature indices on the LCZ classification accuracy, feature combination methods were used. The results of the study showed that the accuracies of LCZ classification in terms of spectral and textural were improved by 2.33% and 2.19% using the RF classifier, respectively. The radiation brightness value (RBV) (GI value = 0.0212) attained the classification's highest variable importance value; the DEM also produced a high GI value (0.0159), indicating that night lighting and landform features strongly influence LCZ classification. [ABSTRACT FROM AUTHOR]