49.Assessing urban landscape ecological risk through an adaptive cycle framework
Landscape and Urban Planning，07 September 2018
Fanghan Luo, Yanxu Liu, Jian Peng*, Jiansheng Wu
Abstract:Cities are suffering various ecological risks due to rapid urbanization and global climate change. Urban landscape ecological risk assessment is conducive to identifying high risk areas and guiding risk prevention. However, few studies have characterized the dynamic processes of landscape ecological risk. In this study, taking Beijing City as a case study, the adaptive cycle in resilience theory was incorporated into a risk assessment framework using such three criteria as potential, connectedness, and resilience, together with integrating exposure and disturbance effects of risk sources. This framework contributed to understanding the complex interactions between landscapes and risk effects from a holistic and dynamic view. The results showed that the ecological risk of “potential” and “connectedness” weakened radially from downtowns to outer suburbs. The distributions of “resilience”, “exposure”, “disturbance”, and the final risk, all exhibited a concentric pattern of the higher risk, highest risk, and lowest risk sequentially from downtowns to outer suburbs. The results reflected the facts that residents living in downtowns had taken ecological restoration measures to reduce risk, while continuous urban constructions in outer suburbs increased the risk. In terms of the adaptive cycle phases of ecological risk, Yanqing, Miyun, Huairou, Mentougou, Fangshan and Pinggu districts were in the reorganization α-phase; Daxing, Changping, Shunyi and Tongzhou districts were in the exploitation r-phase; Dongcheng, Xicheng, Fengtai, Haidian, Chaoyang and Shijingshan districts were in the conservation K-phase. The results provided scientifically spatial guidance for implementing resilient urban planning, in order to realize sustainable development of metropolitan areas.
Remote Sensing of Environment，19 June 2018
Jian Peng*, Jinglei Jia, Yanxu Liu, Huilei Li, Jiansheng Wu
Abstract:Urban heat island (UHI) has become an urban eco-environmental problem globally. Land surface temperature (LST) is widely used to quantify UHI. This study used Shenzhen, a southern coastal city in China, as an example to explore the relationship between spatial variation of LST in different seasons and the influencing factors in five dimensions, integrating the methods of ordinary least-squares regression, stepwise regression, all-subsets regression, and hierarchical partitioning analysis. The results showed that the most important factor affecting spatial heterogeneity of LST in summer was the normalized difference build-up index (53.62%, for contributing rate), whereas in the transition season the most important factor was thenormalized difference vegetation index (NDVI) (47.84%). In winter the construction land percentage and NDVI (26.84% and 25.56%, respectively) were the most influential. Artificial surface and green space had a dominant effect on LST spatial differentiation. Landscape configuration and diversity were not the dominant influencing factors in summer or in the transition season. Furthermore, the independent contribution rate of the Shannon diversity index (SHDI) reached 8.79% in the transition season, while in winter, the independent contribution rates of SHDI and the landscape shape index were 8.52% and 3.45%, respectively. The influence of landscape diversity and configuration factors tended to increase as LST reduced, while thecontribution rate of the important factors such as artificial surface and green space decreased significantly. These relationships indicate that theinfluence of landscape configuration and diversity factors on LST is relatively weak, and can be easily concealed by the influence of landscape components, especially when the spatial variation of LST is not strong. These findings can help to develop UHI adaptation strategies based on local conditions.
Journal of Urban Planning and Development,2018, 144(3)
Jiansheng Wu, Luyi Wang, Weifeng Li
Abstract:With the increasing importance of low-carbon cities, public bicycle systems (PBSs) have become more popular worldwide in recent years. This study proposes a study pipeline framework to explore the usage patterns of the PBSs and to infer critical impact factors leading to different situations. It applies time series analysis on station-based data, and then compares the two systems by using a multinomial logistic regression model to better understand the relationship between public bicycle usage daily changing patterns and underlying spatial and cultural characteristics, which is investigated in a few previous studies. Based on data for the number of available bicycles and slots across all stations, the authors identify general activity patterns and derive different station clusters. For the PBS in city center (Luohu), the general usage depicts commute patterns on working days, and stations are grouped into four clusters. The usage intensity in the nearby suburban district (Longgang) is much lower than that in the city center. Stations are also grouped into four clusters on working days, and all clusters have a smaller range of variation as compared with those in city center. According to the results of multinomial logistic regression, in Luohu, public bicycles play roles of both short distance trips and a complementary tool for vehicle transit facilities. While in Longgang the PBS only serves as a mode to get access to other public transit. The job/housing imbalance and lag in urban infrastructure have been the primary reason for the inefficient use of public bicycles in Longgang. This study provides a research framework that could be reproducible in other problems, benefit optimizing PBSs, and provide guidance for sustainable urban management.
PLOS one,13 February , 2018
Jianzheng Liu, Weifeng Li*, Jiansheng Wu, Yonghong Liu
Abstract:The Beijing-Tianjin-Hebei area faces a severe fine particulate matter (PM2.5) problem. To date, considerable progress has been made toward understanding the PM2.5 problem, including spatial-temporal characterization, driving factors, and health effects. However, little research has been done on the dynamic interactions and relationships between PM2.5 concentrations in different cities in this area. To address the research gap, this study discovered a phenomenon of time-lagged intercity correlations of PM2.5 time series and proposed a visualization framework based on this phenomenon to visualize the interaction in PM2.5 concentrations between cities. The visualizations produced using the framework show that there are significant time-lagged correlations between the PM2.5 time series in different cities in this area. The visualizations also show that the correlations are more significant in colder months and between cities that are closer, and that there are seasonal changes in the temporal order of the correlated PM2.5 time series. Further analysis suggests that the time-lagged intercity correlations of PM2.5 time series are most likely due to synoptic meteorological variations. We argue that the visualizations demonstrate the interactions of air pollution between cities in the Beijing-Tianjin-Hebei area and the significant effect of synoptic meteorological conditions on PM2.5 pollution. The visualization framework could help determine the pathway of regional transportation of air pollution and may also be useful in delineating the area of interaction of PM2.5 pollution for impact analysis.
Science of the Total Environment, 8 July 2018
Jiansheng Wu , Bikai Chen , Jiaying Mao , Zhe Feng*
Abstract:Carbon sequestration plays a vital role in maintaining the stability of global climate and the carbon cycle, but is undergoing significant changes due to urbanization. This study proposes the concept of carbon sequestration vulnerability (CSV), and explores the spatiotemporal evolution of CSV and its relationship between urbanization in China's coastal zone from 2000 to 2010. The study results provide a scientific basis for government management and policy-making. The results showed that the average amount of CSV in 2000 and 2010 was 0.301 and 0.279, respectively, in China's coastal zone and exhibited obvious spatial heterogeneity. Land urbanization had better interpretation strength for CSV than population and economic urbanization indexes, and could explain the 10-year change in CSV well in China's coastal zone. In China's coastal zone from 2000 to 2010, CSV response to land urbanization was proven to be positive and linearly increasing, and the slope of the linear relationship was 0.4214, cities with high land urbanization level have higher CSV; likewise, the change in land urbanization level had a significant positive and linear relationship with the change in CSV, and the slope of the linear relationship was 0.5031.When the city's land urbanization level increased by b6.8% over ten years, the CSV declined, and
conversely, the CSV rose. For the goal of reduce CSV of cities, government and policy-makers should focus on land urbanization and it is possible to realize the goal by controlling land urbanization below 6.8% every ten years.
Journal of Cleaner Production, 1 May 2018
Jiansheng Wu ,Hongqian Zheng ,Feng Zhe ,Wudan Xie ,Jing Song
Abstract:Correlation between urbanization and environmental pollution is a major focus of study in geography, environmental science, and economics. Particulate matter is the primary pollutant of air pollution and made up of heavy metal, organic carbon and aromatic hydrocarbon and complicated chemicals. PM2.5 are fine particulate matter with diameters that are less than 2.5 μm. The aim of this study on the relationship between urbanization and PM2.5 concentration is to achieve a win-win situation of both economic development and environmental protection, which is of great significance to sustainable development in China. This paper uses PM2.5 remote sensing data and statistical yearbook data from 2000 to 2011 to build four panel data models within the urbanization-PM2.5 concentration Environmental Kuznets Curve (EKC) framework. The goal is to find out the correlations between PM2.5 concentration and economic urbanization, population urbanization, and space urbanization. Furthermore, scenario simulations are set to predict when China will reach inflection point and achieve its target concentration. Results show that the relationship between economic urbanization and PM2.5 concentration is an inverted N-shaped or inverted U-shaped curve. Most cities in East China have reached the second inflection point of inverted-N curve to step into the win-win stage while many cities in Middle China still need 10–15 years to arrive at the inflection point of the inverted-U curve. Therefore, China is under great pressure to prevent PM2.5 pollution and pursue more targeted PM2.5-reduction policies for air quality improvement.
Science of The Total Environment, 15 March 2018
Fei Yao, Menglin Si, Weifeng Li, Jiansheng Wu*
Abstract: Satellite-derived aerosol optical depth (AOD) has been proven effective for estimating ground-level particles with an aerodynamic diameter < 2.5 μm (PM2.5) concentrations. Using a time fixed effects regression model, we compared the capacity of two AOD sources, Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), to estimate ground-level PM2.5 concentrations over a heavily polluted region in China. Regarding high-quality AOD data, the results show that the VIIRS model performs better than the MODIS model with respect to all model accuracy evaluation indexes (e.g., the coefficient of determination, R2, of the VIIRS and MODIS models are 0.76 and 0.71 during model fitting and 0.72 and 0.66 in cross validation, respectively), the potential for capturing high PM2.5 concentrations, and the precision of annual and seasonal PM2.5 estimates. However, the spatiotemporal coverage of the high-quality VIIRS AOD is inferior to that of the MODIS AOD. We attempted to include medium-quality VIIRS AOD data to eliminate this, while exploring its influence on the performance of the VIIRS model. The results show that it improves the spatiotemporal coverage of the VIIRS AOD dramatically especially in winter, although a decline in model accuracy occurred. Compared to the MODIS model, the VIIRS model with both high-quality and medium-quality AOD data performs comparably or even better with respect to some model accuracy evaluation indexes (e.g., the model overfitting degree of the VIIRS and MODIS models are 7.46% and 5.82%, respectively), the potential for capturing high PM2.5 concentrations, and the precision of annual and seasonal PM2.5 estimates. Nevertheless, the VIIRS models did not perform as well as the MODIS model in summer. This study reveals the advantages and disadvantages of the MODIS and VIIRS AOD in simulating ground-level PM2.5 concentrations, promoting research on satellite-based PM2.5 estimates.
Remote Sensing, 2017, 9(8)
Xiwen Zhang, Jiansheng Wu*, Jian Peng, Qiwen Cao
Abstract: Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO2) emissions are closely related to the scope and intensity of human activities. In this study, we assess the utility of nighttime light data as a powerful tool to reflect CO2 emissions from energy consumption, analyze the uncertainty associated with different nighttime light data for modeling CO2 emissions, and provide guidance and a reference for modeling CO2 emissions based on nighttime light data. In this paper, Mainland China was taken as a case study, and nighttime light datasets (the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime light data and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data) as well as a global gridded CO2 emissions dataset (PKU-CO2) were used to perform simple regressions at provincial, prefectural and 0.1° × 0.1° grid levels, respectively. The analyses are aimed at exploring the accuracy and uncertainty of DMSP-OLS and NPP-VIIRS nighttime light data in modeling CO2 emissions at different spatial scales. The improvement of nighttime light index and the potential factors influencing the effects of modeling CO2emissions based on nighttime light datasets were also explored. The results show that DMSP-OLS is superior to NPP-VIIRS in modeling CO2 emissions at all spatial scales, and the bigger the scale, the more evident the advantages of DMSP-OLS. When modeling CO2 emissions with nighttime light datasets, not only the total amount of lights within a given statistical unit but also the agglomeration degree of lights should be taken into account. Furthermore, the geographical location and socio-economic conditions at the study site, such as gross regional product per capita (GRP per capita), population, and urbanization were shown to have an impact on the regression effect of the nighttime lights-CO2 emissions model. The regression effect was found to be better at higher latitude and longitude areas with higher GRP per capita and higher urbanization, while population showed little effect on the regression effect of the nighttime lights - CO2 emissions model. The limitation of this study is that the thresholds of potential factors are unclear and the quantitative guidance is insufficient.
41.Saturation Correction for Nighttime Lights Data Based on the Relative NDVI
Remote Sensing, 2017, 9(7)
Zheng Wang, Fei Yao, Weifeng Li, Jiansheng Wu*
Abstract: DMSP/OLS images are widely used as data sources in various domains of study. However, these images have some deficiencies, one of which is digital number (DN) saturation in urban areas, which leads to significant underestimation of light intensity. We propose a new method to correct the saturation. With China as the study area, the threshold value of the saturation DN is screened out first. A series of regression analyses are then carried out for the 2006 radiance calibrated nighttime lights (RCNL) image and relative NDVI (RNDVI) to determine a formula for saturation correction. The 2006 stable nighttime lights (SNL) image (F162006) is finally corrected and evaluated. It is concluded that pixels are saturated when the DN is larger than 50, and that the saturation is more serious when the DN is larger. RNDVI, which was derived by subtracting the interpolated NDVI from the real NDVI, is significantly better than the real NDVI for reflecting the degree of human activity. Quadratic functions describe the relationship between DN and RNDVI well. The 2006 SNL image presented more variation within urban cores and stronger correlations with the 2006 RCNL image and Gross Domestic Product after correction. However, RNDVI may also suffer “saturation” when it is lower than −0.4, at which point it is no longer effective at correcting DN saturation. In general, RNDVI is effective, although far from perfect, for saturation correction of the 2006 SNL image, and could be applied to the SNL images for other years.
Remote Sensing, 2017, 9(7)
Yi'na Hu, Jian Peng*, Yanxu Liu, Yueyue Du, Huilei Li, Jiansheng Wu
Abstract: Spatial inequality of urban development may cause problems like inequality of living conditions and the lack of sustainability, drawing increasing academic interests and societal concerns. Previous studies based on statistical data can hardly reveal the interior mechanism of spatial inequality due to the limitation of statistical units, while the application of remote sensing data, such as nighttime light (NTL) data, provides an effective solution. In this study, based on the DMSP/OLS NTL data, the urbanization type of all towns in the Beijing-Tianjin-Hebei urban agglomeration was analyzed from the aspects of development level and speed. Meanwhile, spatial cluster analysis of development level by local Moran’s I was used to explore spatial inequality, and the trend was discussed by comparing the development characteristics on both sides of the transition line of different development levels (inequality boundary). The results showed that the development level of the whole region increased dramatically as the mean DN value increased by 65.99%, and 83.72% of the towns showed a positive development during 2000–2012. The spatial distribution of urbanization types showed that Beijing and Tianjin were at a high urbanization level with rapid speed of development, with the southern region having a medium development level and the northwestern region lagging behind. The spatial cluster analysis also revealed a gradually intensifying trend of inequality as the number of towns with balanced development reduced by 319 during 2000–2012, while the towns in the high-high areas increased by 99 and those in the low-low areas increased by 229. Moreover, the development speed inside the inequality boundary was obviously higher than that outside, indicating an increasingly serious situation for spatial inequality of urban development in the whole region.
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