Space In Beijing Research Articles

Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an Example

Urban green spaces constitute a vital component of the ecosystem. This study focused on urban green spaces located within the Fifth Ring Road of Beijing, using Gaofen 7 (GF-7) as the primary data source for analysis. The main objective was to develop a system for extracting and classifying urban green spaces in Beijing by applying deep learning and machine learning algorithms, and further, the results were validated with ground survey samples. This study provides detailed extraction and classification of urban green space coverage by creating a comprehensive evaluation system. The primary findings indicate that the deep learning algorithm enhances the precision of green space information extraction by 10.68% compared to conventional machine learning techniques, effectively suppresses “pretzel noise”, and eventually aids in extracting green space information with complete edges. The thorough assessment of green spaces within the study area indicated favorable outcomes showing the high service capacity of park green spaces. The overall classification accuracy of the final extraction results was 94.31%. Nonetheless, challenges, such as unequal distribution of green zones and a significant fragmentation level throughout the study area, were still encountered. Consequently, the use of GF-7 high-resolution imagery, in conjunction with the collaborative application of deep learning and machine learning techniques, enabled the acquisition of highly accurate information regarding urban green zone coverage. According to the established grading standards of evaluation indices, the landscape pattern of urban green spaces within the study area was comprehensively assessed. This evaluation offers essential data support for monitoring urban green spaces and planning landscape patterns, thereby contributing to the achievement of sustainable development objectives related to urban greening and ecological conservation.

Study On Urban Waterfront Public Space Renewal Along the Grand Canal in Beijing's Urban Sub Center from The Perspective of Place Attachment

Urban renewal is a policy hot spot and a focus of work in the field of urban construction, which is realizing a major transformation from rough development to quality enhancement and organic renewal at the current stage. As an important waterfront space in Beijing, the Grand Canal is the memory and soul of Beijing’s development. The renewal of the public space along the canal play an important role for the overall renewal and development of the urban sub center. ‘Place attachment’ is an important theory to discover the emotional connection between people and places in the discipline of recreation geography. It has been used by relevant scholars at home and abroad to study human-land interaction in communities, urban parks and other places. Based on the problems of the public space such as inefficient use of residents and lack of emotional indentity and the growing needs of people for a better life, this study carries out the research on the renewal of urban waterfront public space along the grand canal based on place attachment, with the hope of addressing the lack of a sense of belonging and identity in the use of waterfront public space and supplement the updated method of public space in human-land interaction.

Optimized green infrastructure planning at the city scale based on an interpretable machine learning model and multi-objective optimization algorithm: A case study of central Beijing, China

Green infrastructure (GI) has developed as a sustainable approach to the mitigation of urban floods. While machine learning (ML) models have exhibited advantages in urban flood simulation, their direct application to support the quantitative planning of GI at the city scale remains a challenge. To address this, an interpretable ML model based on support vector machine (SVM) and the Shapley additive explanations (SHAP) approach is integrated with the non-dominated sorting genetic algorithm-II (NSGA-II) in this study. The model is applied to the case of central Beijing, China, and demonstrates a robust performance with a high area under curve (AUC) value of 0.94. The results of the urban flood susceptibility assessment identify the urban-rural transition zone in the study area as being under a greater flood threat. Via model interpretation with SHAP, the dominant roles of GI and grey infrastructure (GrI) in preventing flood are revealed and the non-linear complementarity between the two is demonstrated to be more significant in study units with a GI proportion of less than 0.45. Supported by the NSGA-II-based optimization framework, optimal GI plans under different total implementations of GI are achieved, among which a solution with a 3.21% increase in the total GI area is selected as that with the best investment efficiency. The pattern of GI implementation is suggested to be dispersed and small-scale by model. This study provides a tool with broad application prospects, effectively integrating GI implementation with urban planning. The findings of this study not only provide important references for the determination of the priority areas of new ecological space in Beijing, but also provide areas that share similar characteristics with new insight into GI planning and the management of urban floods.

Spatial Gradient Differences in the Cooling Island Effect and Influencing Factors of Urban Park Green Spaces in Beijing

Urban park green spaces exhibit significant cool island effects, which can effectively mitigate the urban heat environment. Clarifying the characteristics and differences in the cool island effects of urban parks across different spatial gradients within cities is instrumental in identifying potential issues and optimizing the structure and resource allocation of park green spaces in a scientifically rational manner. This study focuses on parks within the central urban area of Beijing, utilizing remote sensing image interpretation and land surface temperature (LST) inversion to extract relevant characteristics of park green spaces and the park cool island intensity (PCI) index. Various mathematical and statistical methods including correlation analysis, regression analysis, and cluster analysis are employed to conduct comparative studies across three gradients: within the 3rd ring road, between the 3rd and 5th ring roads, and outside the 5th ring road. The analysis reveals that both park green space characteristics and urban heat island effects exhibit spatial gradient differences, collectively influencing the cool island effects of urban park green spaces. PCI gradually decreases across the three spatial gradients. Cluster analysis identifies four distinct types of parks with different cool island effect characteristics, highlighting the need for optimization and improvement in over half of the parks. Various indicators of park green space characteristics show different correlations with PCI, with variations in correlation strength and thresholds across gradients. The fitting effects of regression equations for each characteristic indicator and PCI gradually worsen from within the 3rd ring road to outside the 5th ring road, with different factors playing important roles across gradients. This study enhances our understanding of the cool island effects of urban park green spaces and facilitates the proposition of differentiated optimization management strategies for urban park green space planning and system construction in different regions.

Quantification and Proxy Indicators of the Carbon Pool in Urban Tree Litterfall: A Case Study of Urban Green Spaces in Beijing

As major carbon (C) pools in cities, urban green spaces play a crucial role in reducing atmospheric carbon. To determine the importance of litterfall C storage in urban green spaces, we selected the leaf area index (LAI) as a proxy indicator for litterfall C density (LCD), and established a log-linear regression model between LCD and LAI to predict the annual litterfall C pool in large-scale urban green spaces using Sentinel-2 satellite remote sensing data. Forty-five sample units were randomly selected in typical urban green spaces in Beijing, China. A high-temperature combustion method was used to measure the LCD of the sampling units, and stepwise linear regression was used to filter the proxy indicator for LCD. The annual litterfall C pool in regions within the Fifth Ring Road was also estimated with inversion using remote sensing data. From 2015 to 2021, the estimated annual litterfall C pool was in the range of 4.5–5.8 × 1010 g, i.e., approximately 18.9% of the total C storage recorded for the urban green space, which was far greater than that observed in forest ecosystems. We concluded that the litterfall C pool in urban green spaces is seriously underestimated, and that urban tree litterfall has the potential to reduce greenhouse gas emissions if used as a carbon-neutral resource.

Spatiotemporal Analysis of Urban Blue Space in Beijing and the Identification of Multifactor Driving Mechanisms Using Remote Sensing

With rapid urban development in Beijing, there is a critical need to explore urban natural resources and understand their underlying mechanisms. Urban blue space (UBS) has gained increasing attention due to its potential to drive microcirculation, mitigate heat islands, and enhance residents’ well-being. In this study, we used remote sensing data to extract UBS in Beijing and employed exploratory spatial data analysis (ESDA) methods to examine its spatial and temporal development over the past two decades. We adopted a mesoscopic perspective to uncover the full spectrum of landscape patterns and quantitatively simulate the mechanisms influencing the area of UBS and landscape patterns. Our findings are as follows: (1) The UBS area in Beijing exhibited fluctuating growth from 2000 to 2020. (2) Spatial clustering of UBS was stable with subtle changes. (3) The ecological conditions in Beijing improved over the last 21 years, indicated by increased habitat diversity and richness, while notable landscape fragmentation posed significant challenges. (4) Science and technology management-related factors, such as UEM, EDUI, and STI, emerged as the most influential mechanisms for the UBS area. The coefficients for these factors were 0.798, 0.759, and 0.758, respectively. Following closely were vegetation conditions (NDVI) with a coefficient of 0.697 and an annual average temperature (T) with a coefficient of 0.692. (5) Precipitation was identified as the most vital influencing factor for the UBS landscape, with a significant correlation coefficient of 0.732. It was followed by residential population (POP), with a coefficient of 0.692, and economic conditions represented by gross domestic product (GDP), with a coefficient of 0.691.

Comprehensive Evaluation Model of Display Brightness in Public Space of Urban Community

With the development of urbanization, the display in urban public environment appears more and more in our life. They not only provide us with convenient means of information exchange, but also provide convenience for merchants and governments to release information and promote their brands. However, at the same time, the display screen also brings certain negative effects such as light pollution and environmental pollution, which makes the community public space deteriorates accordingly. To solve this problem, this study aims to comprehensively and accurately evaluate the brightness of display screens in public spaces of urban communities by constructing an evaluation model and comprehensively considering the influences of light environment, equipment spatial layout, observation height and other factors on display screen brightness. This model is built on the basis of ANP method, and the evaluation system is formed by obtaining expert scores through questionnaire survey, in which the index weight of absolute brightness of the display screen is significantly higher. Based on this evaluation mechanism and evaluation method, this paper is able to evaluate the comprehensive evaluation of display brightness in 26 community public spaces in Beijing, form the evaluation results, and compare them with the visual comfort of residents, tourists, and other respondents collected by questionnaires, so as to verify the effectiveness of the model analysis. The results show that the prediction efficiency of the evaluation model used in this paper reaches a high level, and has the advantages of simple, feasible, accurate and reliable, which provides a certain reference for the improvement of urban public services.

NPP and Vegetation Carbon Sink Capacity Estimation of Urban Green Space Using the Optimized CASA Model: A Case Study of Five Chinese Cities

Urban area is a major source of CO2 and other greenhouse gases. Urban green space (UGS) is an essential element to increase carbon sequestration directly and reduce emission indirectly. In this study, the net primary production (NPP) and net ecosystem productivity (NEP) was monitored in order to enhance the carbon sequestration function of UGS and promote urban low-carbon development. Based on the Sentinel-2 L2A satellite images, meteorological data, and vegetation type data in 2019, we used the optimized Carnegie Ames Stanford Approach (CASA) model to estimate the NPP values of UGS types including attached green space, park green space, protective green space, and regional space in Beijing, Guangzhou, Shanghai, Shenyang, and Xi’an. The NEP values were evaluated based on NPP and soil heterotrophic respiration (RH) to quantify the vegetation carbon sink capacity. The accuracy test shows that the estimated NPP values based on the optimized CASA model are effective. The results indicate that the average NPP values (1008.5 gC·m−2·a−1) and vegetation carbon sink capacity (771.49 gC·m−2·a−1) of UGS in Beijing rank first among the cities, which is followed by the values in Guangzhou. The regional green space and park green space in five cities function as carbon sinks with high NPP values and have vegetation carbon sink capacity, whereas the attached green space in Shanghai and Xi’an as well as the protective green space in Guangzhou and Xi’an function as carbon sources. Moreover, the NEP distribution shows obvious spatial aggregation characteristics, that is, the high NEP values of UGS are clustered in mountainous forest areas in the west and north of Beijing, Northeast Guangzhou, and South Xi’an whereas the low NEP values are mostly concentrated in the urban built-up areas under strong influences of human activities. This research provides a new method for NPP and NEP estimation of UGS at the city scale and the scientific basis for the improvement of the vegetation carbon sink capacity of UGS.

Rainstorm and high‐temperature disaster risk assessment of territorial space in Beijing, China

AbstractIt is important for territorial spatial planning to master meteorological disaster risk under the conditions of climate change and then carry out risk adaptation planning according to local conditions. Taking Beijing, a large city in China, as an example, a meteorological disaster risk assessment model was established based on the framework of hazard factors, disaster‐bearing body exposure, and vulnerability of underlying surface. Combined with 11 years of observation data from 293 high‐density weather stations, the rainstorm and high‐temperature risks of urban, agriculture, and ecological spaces were studied. The results show that (1) rainstorms and high‐temperature are mainly distributed in the built‐up areas of plain towns, which are the climate risk factors that need to be considered. (2) The central city of Beijing is at a high risk of rainstorms and high temperature, indicating that the underlying surface and disaster‐bearing body are highly vulnerable to meteorological disasters. (3) In suburbs with agricultural land, there is a certain rainstorm risk in Fangshan and Daxing districts, and a risk of high temperatures in the southern part of Tongzhou and Daxing. (4) The risk of high temperatures in the ecological space (eco‐zone) is relatively low, but the rainstorm risk is relatively high in Pinggu and Miyun. (5) The strategies of coping with rainstorm and high‐temperature disaster risk in Beijing's territorial space planning were discussed.

Differences in mass concentration and elemental composition of leaf surface particulate matter: Plant species and particle size ranges

Leaf surface particulate matter (PM) is an optimal material for the environmental monitoring of urban green spaces Currently, most studies have looked at leaf surface PM, using only its mass concentration to investigate the dust retention capacity and mechanisms of different plant leaves. The elemental composition of leaf surface PM has been neglected as a reflection of the environment and its sources. Therefore, in this study, ten common plant species were selected from the Olympic Forest Park, an urban green space in Beijing, and the characteristics of leaf surface PM were analysed in depth in terms of mass concentration and elemental composition. The leaf surface PM mass concentration was first obtained by vacuum filtration (VF) method, and then the leaf surface PM attached to the filter membrane obtained from the previous step was used as experimental material to study its elemental composition using cold field emission scanning electron microscopy combined with energy dispersive X-ray spectroscopy (FESEM-EDS). The results show that (1) In terms of mass concentration, were Hemerocallis fulva, Poa pratensis, Acorus calamus, Typha orientalis, and Phragmites communis had better dust retention capacity; (2) The five most important elements deposited on the leaf surface of plants in Beijing were C, O, Mg, Al, and Si, with a total of 26 elements detected; (3) The particulate matter retained on the leaf surface of plants in Beijing was classified as Geogenic particles (57.70%) > Anthropogenic particles (22.30%) > Biogenic particles (20.00%). The results at this stage are expected to promote the in-depth understanding of plant leaf remediation of PM.

Spatiotemporal Evolution of Residential Exposure to Green Space in Beijing

Urban green space has a critical impact on the urban ecological environment, residents’ health, and urban sustainability. Quantifying residential exposure to green space and proposing targeted enhancement strategies in urban areas is helpful to rationally plan urban green space construction, reduce the inequality in residential exposure to green space, and promote environmental equity. However, the long-time evolution analysis of residential exposure to green space at different scales and the influence of green space quality on residential exposure to green space are rarely reported. Here we produced a long-time series dataset of urban green space from 1990 to 2020 based on the 30 m Landsat data and used the Normalized Difference Vegetation Index (NDVI) as a representation of the green space quality to comprehensively analyze residential exposure to green space at the city and block scales within the 5th ring of Beijing, China. We found that the urban green space in Beijing is mainly distributed in urban areas between the 4th and 5th rings (i.e., 153.4 km2 in 2020), and there is little green space within the 2nd ring area (i.e., 12.6 km2 in 2020). There is clear spatial inequality in residential exposure to green space, and about 2.88 million (i.e., ~27%) residents have experienced different degrees of decline in residential exposure to green space from 2015 to 2020. However, the degree of inequality in residential exposure to green space has gradually weakened from a high level (Palma ratio = 2.84) in 1990 to a relatively low level (Palma ratio = 0.81) in 2020. In addition, the spatial-temporal analysis method of residential exposure to green space based on green space quality has certain advantages that can help explore the degraded and lost areas of green space.

Livability Evaluation of Land Ecological Space in Beijing, China Based on Geographical Conditions Census

Livability Evaluation of Land Ecological Space in Beijing, China Based on Geographical Conditions Census

Creative Hubs in Hanoi, Vietnam

Creative Hubs in Hanoi, Vietnam

Structure of an urban green space indirectly affects the distribution of airborne particulate matter: A study based on structural equation modelling

Urban green spaces can effectively regulate microclimate and improve air quality. However, the relationship between the structure of an urban green space, meteorological conditions, and the concentration distribution of different sized particles inside and outside of green spaces is unclear. In this study, a field survey was conducted on the structures of 188 green spaces in Beijing. The temperature and relative humidity were also monitored, and the concentrations of different sized particles were measured both inside and outside of the selected green spaces to comprehensively analyse the relationships between these variables and the structure of a green space. The results of structural equation modelling showed that the structure of a green space indirectly affected the concentration distribution of particulate matter (PM) through the effects of cooling and humidification, while the direct effect was not obvious. In addition, the impact mechanisms of the structure of a green space on the concentration distribution of particles differed between PM1, PM1–5, and PM5–25, primarily due to the influence of particle size and meteorological factors on the movement of PM. This study deepens the understanding of the influencing mechanisms of the structure of a green space on the concentration distribution of PM, thus providing an important reference for subsequent related field and numerical simulation research.

Quantification of Spatial Association between Commercial and Residential Spaces in Beijing Using Urban Big Data

Commercial and residential spaces are two core types of geographical objects in urban areas. However, these two types of spaces are not independent of each other. Spatial associations exist between them, and a thorough understanding of this spatial association is of great significance for improving the efficiency of urban spatial allocation and realizing scientific spatial planning and governance. Thus, in this paper, the spatial association between commercial and residential spaces in Beijing is quantified with GIS spatial analysis of the average nearest neighbor distance, kernel density, spatial correlation, and honeycomb grid analysis. Point-of-interest (POI) big data of the commercial and residential spaces is used in the quantification since this big data represents a comprehensive sampling of these two spaces. The results show that the spatial distributions of commercial and residential spaces are highly correlated, maintaining a relatively close consumption spatial association. However, the degrees of association between different commercial formats and residential spaces vary, presenting the spatial association characteristics of “integration of daily consumption and separation of nondaily consumption”. The commercial formats of catering services, recreation and leisure services, specialty stores, and agricultural markets are strongly associated with the residential spaces. However, the development of frequently used commercial formats of daily consumption such as living services, convenience stores, and supermarkets appears to lag behind the development of residential spaces. In addition, large-scale comprehensive and specialized commercial formats such as shopping malls, home appliances and electronics stores, and home building materials markets are lagging behind the residential spaces over a wide range. This paper is expected to provide development suggestions for the transformation of urban commercial and residential spaces and the construction of “people-oriented” smart cities.

Evaluation of PM2.5 Retention Capacity and Structural Optimization of Urban Park Green Spaces in Beijing

Green space can effectively retain particles and improve air quality. However, most studies have focused on leaf-scale measurements or regional-scale model simulations, and few focus on individual trees. In this study, 176 urban park green spaces were selected within the 5th Ring Road in Beijing, and the i-Tree Eco model was used to estimate the PM2.5 retention levels of individual trees and sample plots. The results show that the retention capacity varied according to tree species. The PM2.5 retention of each sample plot was significantly affected by the tree coverage, species richness, mean tree height, mean crown width, and number of trees. The PM2.5 retention of urban park green spaces in the study area was estimated to be about 6380 t·year−1, and the air quality improvement rate was 1.62%. After structural optimizing, PM2.5 retention of the mixed coniferous and broad-leaved green space was as high as 80,000 g·year−1. This study studied the effects of trees on PM2.5 retention at multiple scales to fill the gaps in existing research at the scales of individual trees and communities, and it can serve as a reliable reference for the design and construction of green spaces aimed at improving air quality.

Recreational Services from Green Space in Beijing: Where Supply and Demand Meet?

Green space, mainly forests, shrubs, and grasslands, provides essential ecosystem services for human well-being. Based on multi-source data and using the Maximum Entropy model and Geographical Information System (GIS) tools, this research comprehensively assesses the supply and demand of recreational services from green space in Beijing. The supply of recreational services in Beijing is influenced by natural and human factors, showing large spatial variability. The supply level of mountainous areas with good natural geographical conditions and intact ecological landscape is significantly higher than that of plain areas with reduced vegetation and overexploitation. Residents have a high demand for recreational services in green space landscape and low demand in non-green space landscape. The quantitative balance pattern of supply and demand varies greatly, and most areas show the state of undersupply. The spatial matching pattern of supply and demand varies significantly too, and the mismatch is apparent. Spatial allocation should be more carefully considered than the aggregated supply and demand. Differentiated development strategies such as ecological reshaping, ecological development, restoration, and protection should be implemented for different areas in the future of planning and management in urban green areas. This will optimize and balance the supply-demand matching pattern for recreational services and promote the effective improvement of ecosystem service functions and residents’ ecological welfare.

Selection of Water-Saving Plants and Annual Water Consumption Estimation for Garden Green Spaces in Beijing

Currently, the number of urban garden green spaces (GGSs) being constructed in Beijing is increasing, and their high water resource demands contradict the severe water shortage situation in Beijing that is restricting urban construction and economic development. This has created an urgent need to build water-saving GGSs. This study analyzed and compared the daily/annual water consumption of 79 common plants in Beijing, and low-water-consumption (LWC) trees, shrubs and herbs were selected; additionally, the total annual water consumption (TAWC) of all plants in the built-up areas of all 16 districts in Beijing was calculated according to the result of the eighth general survey of landscaping resources in Beijing. The results are as follows: (1) fifteen LWC tree species were selected from among 25 species, and the average daily water consumption (DWC) was <1.09 kg·m−2; (2) nineteen LWC shrubs were selected from among 35 shrubs, and the average DWC was <1.17 kg·m−2; (3) eleven LWC herbs were selected from among 19 herbs, and the AWC was <460.3 kg·m−2; (4) the TAWC of all trees, shrubs and herb plants in the Beijing GGSs was 1.104 × 109, 0.139 × 109, and 0.16 × 109 m3, respectively. Based on the above results, it was estimated that the TAWC of all plants in the built-up areas of all 16 districts in Beijing is approximately 1.403 × 109 m3. These findings provide a better understanding of the water consumption of GGS plants in cities in semiarid and semihumid climates and can be used to help select LWC greening plants that can reduce water consumption when expanding green areas in cities.

Integrating multiple semantics data to assess the dynamic change of urban green space in Beijing, China

Information regarding the dynamic change of urban green space (UGS) at fine spatiotemporal resolutions is important for developing sustainable urban environments and understanding urban ecosystems in the future. As the capital of China, due to population explosion, economic growth, and redevelopment of inner-city areas, Beijing underwent extremely rapid urbanization in the past few years, which is intensifying the contradiction between human activities and UGS. Timely monitoring of Beijing’s dynamic change of UGS is very important for the planning of the capital’s living environment. Hence, a novel framework was proposed to map the dynamic change of UGS from 2000 to 2018 by integrating social sensing data and time series remote sensing data. First, we generated a dynamic mapping of the vegetation and a mapping of the annual maximum value of the Nominalized Difference Vegetation Index (NDVImax), using time series Landsat images on a Google Earth Engine platform. Then, a temporal segmentation approach was implemented to identify the turning years and using a decision tree approach determine the historical time of social sensing data. Finally, the UGS mapping and its dynamics were generated according to the identified time parameter of social sensing data, as well as remote sensing data. Our results showed that the temporal segmentation approach is reliable for detecting urban changes and identifying the historical time of social sensing data, with an overall accuracy of 90%. The overall classification accuracies of UGS are 0.87 (both Level 1 and Level 2). Moreover, the UGS area continuously increased from 2000 to 2018, of which the dominant types are residential green space and theme parks. The proposed framework provides an effective tool for better understanding the dynamic change of UGS, and the results from this work present vital insights for planners and government departments to develop targeted measures and protect UGS effectively.

Dynamic Cooling Effects of Permanent Urban Green Spaces in Beijing, China

Urban green spaces (UGSs) play a critical role in human thermal comfort, energy consumption and urban ecology. Although the heat mitigation capability of UGSs has been frequently reported, many of the current understandings are based on short-term observations, and the long-term temporal dynamics of UGS cooling effects are still lacking. This gap may cause over- or underestimation and largely ignores how the cooling effects change with climate change and urban growth. Accordingly, we used Landsat-based time series data to analyze the changes in permanent UGS greenness, surface-cooling effects and their biophysical responses in Beijing in the past 40 years (1984–2020). The results demonstrate segmented changes in UGS surface cooling that were mainly linked to the responses of canopy transpiration and albedo to vegetation conditions. During a rapid greening of UGSs in the recent two decades, transpiration cooling dominated albedo-induced warming to provide a discernable cooling enhancement. In addition, such enhancement showed seasonal differences ranging from less than 1 °C to more than 2 °C, and the most evident enhancement occurred on summer days (~2.4 °C) when vegetation is most needed to provide cooling. The highlighted dynamics of UGSs help urban planners better balance the maintenance costs and the environmental gains for UGS management.