Combined Effects of Impervious Surface Change and Large-Scale Afforestation on the Surface Urban Heat Island Intensity of Beijing, China Based on Remote Sensing Analysis

Spatio-Temporal Variation and Its Driving Forces of Soil Organic Carbon along an Urban-Rural Gradient: A Case Study of Beijing

Rapid urbanization has reshaped land cover and the ecological environment, potentially improving or deteriorating soil organic carbon (SOC). However, the response of SOC to urbanization has not yet been fully exploited. Herein, by using the land-use transfer matrix, the Sen & Mann-Kendall tests, the Hurst index, and a geographical and temporal weighted regression (GTWR) model, as well as an urban-rural gradient perspective, we assessed the dynamic response of SOC to Beijing's urbanization from 2001 to2015 and identified the main drivers. The results found that SOC stock decreased by 7651.50 t C during the study period. SOC density varied significantly along an urban-rural gradient, with high value areas mainly being located in remote mountainous rural areas and low value areas mainly being located in urban areas on the plains. There was an uneven variation in SOC density across the urban-rural gradient, with suburban areas (25-40 km away from urban cores) losing the most SOC density while urban areas and rural areas remained relatively unchanged. GTWR model revealed the spatio-temporal non-flat stability of various driving forces. Precipitation, the proportion of forest, the proportion of grassland, the population, distance to the urban center, the slope, and the silt content are the main factors related to SOC stock change. As a result, we suggest policy makers reconceptualize the uneven variation in the SOC between urban and rural areas, emphasize suburban areas as a target for controlling SOC loss, and take into consideration the spatial and temporal heterogeneity of the factors influencing SOC stock when evaluating policies.

Urban warming increases the temperature sensitivity of spring vegetation phenology at 292 cities across China

Urban spring phenology changes governed by multiple biological and environmental factors significantly impact urban ecosystem functions and services. However, the temporal changes in spring phenology (i.e., the start of the vegetation growing season, SOS) and the magnitude of SOS sensitivity to temperature in urban settings are not well understood compared with natural ecosystems. Therefore, we explored warming impacts on SOS across 292 rural and urban areas from 2001 to 2016. We found that warming occurred in 79.9% of urban areas and 61.3% of rural areas. This warming advanced SOS in 78.3% of the urban settings and 72.8% of the rural areas. The accelerated rate of SOS in urban settings was significantly higher (-0.52 ± 0.86 days/year) than in rural areas (-0.09 ± 0.69 days/year). Moreover, SOS was significantly more sensitive to warming in urban areas (-2.86 ± 3.57 days/°C) than in rural areas (-1.57 ± 3.09 days/°C), driven by urban-rural differences in climatic (precipitation, temperature, and warming speed) and vegetation factors. Precipitation contributed the most had the highest relative importance for controlling SOS, at 45% and 63% for urban and rural areas, respectively. These findings provide a new understanding of the impacts of urbanization and climate change on vegetation phenology. Moreover, our results have implications for urban environment impacts on ecosystems and human health.

Does urban climate follow urban form? Analysing intraurban LST trajectories versus urban form trends in 3 cities with different background climates

The current climate change trend urges the application of efficient spatial planning to mitigate the effects of urbanization on local urban warming. Nevertheless, how urban temperatures respond to urban form changes inside cities is still insufficiently understood. In this paper, we explored the relationship between urban form and diurnal space-time land surface temperature (LST) trends (2003-2019) in Beijing (continental climate), Cairo (arid) and Santiago (temperate). We analysed changes in land cover, white sky albedo (WSA), night-time lights (NL) and the enhanced vegetation index (EVI) inside areas representing clustered thermal performance (steady cold and hot spots and warming cold and hot spots). The structure of local climate zones (LCZs) was assessed for each LST trend. To test the relevance of other urban form dimensions, we analysed the hierarchical influence of the employed 2D metrics (i.e., built-up cover, WSA, NL and EVI) and additional 3D indicators (i.e., height and volume) on LST, applying machine learning classification and regression trees (CARTs) to Beijing's data. Despite diverse patterns of urban form change, cities in our sample present common LST trends, with thermal differences as a consequence of local climate. LCZs are composed of highly heterogeneous built-up areas inside LST trend categories. In the case of Beijing, LST is hierarchically driven by footprint, WSA and EVI. Moreover, by adding height and volume, urban form differences between LST trend classes that are not evident with 2D data were found. Our findings suggest that a compact green urban tissue is necessary to cope with the current trends of urban warming, taking into account city-specific measures based on the local background climate.

The Impact of Central Heating on the Urban Thermal Environment Based on Multi-Temporal Remote Sensing Images

Research on the impact of anthropogenic heat discharge in a thermal environment is significant in climate change research. Central heating is more common in the winter in Northeast China as an anthropogenic heat. This study investigates the impact of central heating on the thermal environment in Shenyang, Changchun, and Harbin based on multi-temporal land surface temperature retrieval from remote sensing. An equivalent heat island index method was proposed to overcome the problem of the method based on a single-phase image, which cannot evaluate all the central heating season changes. The method improves the comprehensiveness of a thermal environment evaluation by considering the long-term heat accumulation. The results indicated a significant increase in equivalent heat island areas at night with 22.1%, 17.3%, and 19.5% over Shenyang, Changchun, and Harbin. The increase was significantly positively correlated with the central heating supply (with an R-value of 0.89 for Shenyang, 0.93 for Changchun, and 0.86 for Harbin; p < 0.05). The impact of central heating has a more significant effect than the air temperature. The results provide a reference for future studies of urban thermal environment changes.

Determinizing the contributions of human activities and climate change on greening in the Beijing-Tianjin-Hebei Region, China

China accounts for 25% of the global greening. There are temporal and spatial differences of China's greening and intrinsic driving forces. Thus, it is crucial to determinize the contributions of human activities and climate change on greening at region scale. The Beijing-Tianjin-Hebei Region (BTHR) is one of the most active areas with human activities in China. It is necessary to explore negative or positive impacts of human activities on the regional greening or browning under climate change. A time series of annual vegetation coverage from satellite data was selected to quantify regional greening in the BTHR from 2000 to 2019 and their responses to climate change and human activities. Results showed generally widespread greening over the last 20 years at an average increased rate of 0.036 decade^-1 in vegetation coverage (P < 0.01). Overall warmer and wetter climate across the BTHR were positively correlated with regional greening. The positive effects of human activities on greening accounted for 48.4% of the BTHR, especially the benefits of ecological restoration projects and the agricultural activities. Increases in vegetation coverage had resulted from the combined effects of climate change and human activities. Climate change had a stronger influence on vegetation coverage than human activities. Contributions of climate change to greening and browning was about 74.1% and < 20%, respectively. The decrease in vegetation coverage was mainly the results of the inhibition of human activities. More detailed socioeconomic and anthropogenic datasets are required for further analysis. Further research consideration would focus on the nonlinear responses of vegetation to climate change.

Perceived urban green and residents' health in Beijing

Green space serves urban residents in various functions including promoting health, but the roles of different types of green space are unclear. A survey titled "Healthy Neighborhood" was conducted in Beijing from May to July 2019 to examine and compare the associations between three types of perceived green space (park green, public-square green and utility green) and three aspects of residents' health (physical health, mental health and social health). Results from the multilevel modeling (MLM) analysis show that the perceived park green has a positive correlation with mental health, and all three types of perceived green space correlate with social health positively. No significant correlation of any type of green space is detected on participants' physical health, nor any relation of public-square green or utility green to their mental health. Overall the role of urban green space is stronger on social health than physical and mental health. The findings support the complementary roles of different types of green space, and suggest that expansion in utility greens could be as effective as investing in more costly park and public-square greens, especially in their benefit in promoting social health.