This page has only limited features, please log in for full access.
Deteriorating air quality is one of the most important environmental factors posing significant health risks to urban dwellers. Therefore, an exploration of the factors influencing air pollution and the formulation of targeted policies to address this issue are critically needed. Although many studies have used semi-parametric geographically weighted regression and geographically weighted regression to study the spatial heterogeneity characteristics of influencing factors of PM2.5 concentration change, due to the fixed bandwidth of these methods and other reasons, those studies still lack the ability to describe and explain cross-scale dynamics. The multi-scale geographically weighted regression (MGWR) method allows different variables to have different bandwidths, which can produce more realistic and useful spatial process models. By applying the MGWR method, this study investigated the spatial heterogeneity and spatial scales of impact of factors influencing PM2.5 concentrations in major Chinese cities during the period 2005–2015. This study showed the following: (1) Factors influencing changes in PM2.5 concentrations, such as technology, foreign investment levels, wind speed, precipitation, and Normalized Difference Vegetation Index (NDVI), evidenced significant spatial heterogeneity. Of these factors, precipitation, NDVI, and wind speed had small-scale regional effects, whose bandwidth ratios are all less than 20%, while foreign investment levels and technologies had medium-scale regional effects, whose bandwidth levels are 23% and 32%, respectively. Population, urbanization rates, and industrial structure demonstrated weak spatial heterogeneity, and the scale of their influence was predominantly global. (2) Overall, the change of NDVI was the most influential factor, which can explain 15.3% of the PM2.5 concentration change. Therefore, an enhanced protection of urban surface vegetation would be of universal significance. In some typical areas, dominant factors influencing pollution were evidently heterogeneous. Change in wind speed is a major factor that can explain 51.6% of the change in PM2.5 concentration in cities in the Central Plains, and change in foreign investment levels is the dominant influencing factor in cities in the Yunnan-Guizhou Plateau and the Sichuan Basin, explaining 30.6% and 44.2% of the PM2.5 concentration change, respectively. In cities located within the lower reaches of the Yangtze River, NDVI is a key factor, reducing PM2.5 concentrations by 9.7%. Those results can facilitate the development of region-specific measures and tailored urban policies to reduce PM2.5 pollution levels in different regions such as Northeast China and the Sichuan Basin.
Feili Wei; Shuang Li; Ze Liang; Aiqiong Huang; Zheng Wang; Jiashu Shen; Fuyue Sun; Yueyao Wang; Huan Wang; Shuangcheng Li. Analysis of Spatial Heterogeneity and the Scale of the Impact of Changes in PM2.5 Concentrations in Major Chinese Cities between 2005 and 2015. Energies 2021, 14, 3232 .
AMA StyleFeili Wei, Shuang Li, Ze Liang, Aiqiong Huang, Zheng Wang, Jiashu Shen, Fuyue Sun, Yueyao Wang, Huan Wang, Shuangcheng Li. Analysis of Spatial Heterogeneity and the Scale of the Impact of Changes in PM2.5 Concentrations in Major Chinese Cities between 2005 and 2015. Energies. 2021; 14 (11):3232.
Chicago/Turabian StyleFeili Wei; Shuang Li; Ze Liang; Aiqiong Huang; Zheng Wang; Jiashu Shen; Fuyue Sun; Yueyao Wang; Huan Wang; Shuangcheng Li. 2021. "Analysis of Spatial Heterogeneity and the Scale of the Impact of Changes in PM2.5 Concentrations in Major Chinese Cities between 2005 and 2015." Energies 14, no. 11: 3232.
Analysis focused on sub-regional differentiation of vegetation greenness and their dominant drivers are needed to properly develop targeted strategies for sustainable management. In this study, we took China as a case study area, and analyzed the spatiotemporal heterogeneity of vegetation greenness and its strength of association with both environmental (topographical factors and hydrothermal conditions) and anthropogenic factors (land use type and population density) across six eco-geographic regions during 1982–2015. The whole period was divided into two periods by the turning point of 1998, after which China has implemented numerous forest protection projects. The attribution results based on the Geodetector method show the followings: (1) In China, precipitation is the dominant factor in landscape variation of Normalized Difference Vegetation Index (NDVI) with a strength of association of 85%. Additionally, precipitation is also the dominant factor in arid and semi-arid regions, including Northern semiarid (NS) region, Northwestern arid (NWA) region and Qinghai-Tibet Plateau (QTP) region. The dominant factors differ across diverse eco-geographic regions; for example, slope dominates in sub-tropical/tropical humid (STH) and middle temperate humid/sub-humid (MTH) regions. (2) Generally, the strength of association between vegetation and temperature decreases across China over the past 34 years, meaning that the limiting effect of temperature on the NDVI is weakened, similarly, the controlling effect of water conditions is also weakened. In contrast, the spatial association between anthropogenic factors and NDVI is enhanced. (3) The temporal dynamics of strength of association between factors and the NDVI differ in diverse periods and regions; for example, the strength of association between wind speed and NDVI decreased during 1982–1998, but increased during 1999–2015 in temperate humid/sub-humid (WTH) region; however, decreasing trends were revealed in the QTP region in both periods. Our study highlights that variation of NDVI is mainly attributed to climate change and land cover change. Generally, the limiting impact of hydrothermal conditions on NDVI weakens, and the controlling effect of human activity increases over time.
Huan Wang; Shijie Yan; Ze Liang; Kewei Jiao; Delong Li; Feili Wei; Shuangcheng Li. Strength of association between vegetation greenness and its drivers across China between 1982 and 2015: Regional differences and temporal variations. Ecological Indicators 2021, 128, 107831 .
AMA StyleHuan Wang, Shijie Yan, Ze Liang, Kewei Jiao, Delong Li, Feili Wei, Shuangcheng Li. Strength of association between vegetation greenness and its drivers across China between 1982 and 2015: Regional differences and temporal variations. Ecological Indicators. 2021; 128 ():107831.
Chicago/Turabian StyleHuan Wang; Shijie Yan; Ze Liang; Kewei Jiao; Delong Li; Feili Wei; Shuangcheng Li. 2021. "Strength of association between vegetation greenness and its drivers across China between 1982 and 2015: Regional differences and temporal variations." Ecological Indicators 128, no. : 107831.
The impact of extreme climate on natural ecosystems and socioeconomic systems is more serious than that of the climate’s mean state. Based on the data of 1698 meteorological stations in China from 2001 to 2018, this study calculated the 27 extreme climate indices of the Expert Team on Climate Change Detection and Indices (ETCCDI). Through correlation analysis and collinearity diagnostics, we selected two representative extreme temperature indices and three extreme precipitation indices. The spatial scale of the impact of extreme climate on Normalized Difference Vegetation Index (NDVI) in China during the growing season from 2001 to 2018 was quantitatively analyzed, and the complexity of the dominant factors in different regions was discussed via clustering analysis. The research results show that extreme climate indices have a scale effect on vegetation. There are spatial heterogeneities in the impacts of different extreme climate indices on vegetation, and these impacts varied between the local, regional and national scales. The relationship between the maximum length of a dry spell (CDD) and NDVI was the most spatially nonstationary, and mostly occurred on the local scale, while the effect of annual total precipitation when the daily precipitation amount was more than the 95th percentile (R95pTOT) showed the greatest spatial stability, and mainly manifested at the national scale. Under the current extreme climate conditions, extreme precipitation promotes vegetation growth, while the influence of extreme temperature is more complicated. As regards intensity and range, the impact of extreme climate on NDVI in China over the past 18 years can be categorized into five types: the humidity-promoting type, the cold-promoting and drought-inhibiting compound type, the drought-inhibiting type, the heat-promoting and drought-inhibiting compound type, and the heat-promoting and humidity-promoting compound type. Drought is the greatest threat to vegetation associated with extreme climate in China.
Shuang Li; Feili Wei; Zheng Wang; Jiashu Shen; Ze Liang; Huan Wang; Shuangcheng Li. Spatial Heterogeneity and Complexity of the Impact of Extreme Climate on Vegetation in China. Sustainability 2021, 13, 5748 .
AMA StyleShuang Li, Feili Wei, Zheng Wang, Jiashu Shen, Ze Liang, Huan Wang, Shuangcheng Li. Spatial Heterogeneity and Complexity of the Impact of Extreme Climate on Vegetation in China. Sustainability. 2021; 13 (10):5748.
Chicago/Turabian StyleShuang Li; Feili Wei; Zheng Wang; Jiashu Shen; Ze Liang; Huan Wang; Shuangcheng Li. 2021. "Spatial Heterogeneity and Complexity of the Impact of Extreme Climate on Vegetation in China." Sustainability 13, no. 10: 5748.
Urban form studies on urban heat island (UHI) are mostly from the microscale perspective of thermodynamics or fluid mechanics, lacking of consideration of the impact of urban form on traffic behavior or air pollution, which is also proved to be influential on nighttime urban thermal environment. The objective of this paper is to quantitatively reveal the mediating role of air pollution in the impacts of urban form on nighttime UHI intensity based upon an empirical research of 150 cities of China. We constructed multivariate regression models, structural equation models and scenario simulations to verify and predict the mediating effect of air pollution. Results showed that the fractal dimension of urban planar shape almost all influences UHI intensity through air pollution. The mediating effect related to urban continuity and urban elongation also accounts for 25-28% and 33-40% of the total effect, respectively. Controlling urban population density can play an important role on UHI mitigation, but optimization of urban geometry can bring co-benefits on urban air and thermal environments. For the purpose of improving urban thermal environment, we advocate the proper elongation and decentralization of urban form, and control urban sprawl disorderly, especially in cities with severe air pollution.
Ze Liang; Jiao Huang; Yueyao Wang; Feili Wei; Shuyao Wu; Hong Jiang; Xuliang Zhang; Shuangcheng Li. The Mediating Effect of Air Pollution in the Impacts of Urban Form on Nighttime Urban Heat Island Intensity. Sustainable Cities and Society 2021, 102985 .
AMA StyleZe Liang, Jiao Huang, Yueyao Wang, Feili Wei, Shuyao Wu, Hong Jiang, Xuliang Zhang, Shuangcheng Li. The Mediating Effect of Air Pollution in the Impacts of Urban Form on Nighttime Urban Heat Island Intensity. Sustainable Cities and Society. 2021; ():102985.
Chicago/Turabian StyleZe Liang; Jiao Huang; Yueyao Wang; Feili Wei; Shuyao Wu; Hong Jiang; Xuliang Zhang; Shuangcheng Li. 2021. "The Mediating Effect of Air Pollution in the Impacts of Urban Form on Nighttime Urban Heat Island Intensity." Sustainable Cities and Society , no. : 102985.
Based on the indicators of more than 3000 cities in China, this study shows that the relationship between the urban form and surface urban heat island intensity (SUHII) demonstrates seasonal and diurnal variations, and also changes along urban development and elevation gradients. SUHIIs show seasonal and diurnal change patterns along urban development and elevation gradients, but there is no obvious change trend along temperature and humidity gradients. Among them, the seasonal variation of the SUHII went up about 0.4 ℃ from the first level of urban development to the highest level, while the diurnal variation of the SUHII decreased by 0.4 °C. With urban development, the correlations between the anthropogenic heat flux (AHF), population density (POPDEN) and morphological continuity (CONTIG) with the SUHII of summer days, summer nights and winter nights continued to be enhanced, with the correlation coefficients (β) increased by about 0.3. The effect of area size (AREA) became more influential on the SUHII of summer days and nights, but its effect on the SUHII of winter nights increased first and then decreased along the urban development gradient. With the increase of elevation, the correlations of the AHF, POPDEN, AREA, CONTIG and summer day and night SUHII were gradually reduced (β decreased by about 0.4), but their impact on the SUHII of winter nights was gradually enhanced (β increased by about 0.2 to 0.3). Along temperature and humidity gradients, the positive effect of POPDEN on the summer SUHII decreased gradually (β decreased by about 0.3). However, the enhancement effects of the AHF, AREA, CONTIG and POPDEN on the SUHII of winter nights increased generally (β increased by about 0.2). According to the Random Forest model, for the SUHIIs at night, the relative importance (RI) of urban form factors was greater, while for the SUHIIs of daytime, the RIs of natural factors were greater. The contribution of the urban form to the seasonal variation of the SUHII is similar to that of natural factors, but their contribution to diurnal variation is lower. Our results suggest that it is more necessary to control the urban scale, avoid excessive urban agglomeration and reasonably control the anthropogenic heat emission in more developed and low altitude cities to reduce their summer heat exposure.
Lin Ma; Yueyao Wang; Ze Liang; Jiaqi Ding; Jiashu Shen; Feili Wei; Shuangcheng Li. Changing Effect of Urban Form on the Seasonal and Diurnal Variations of Surface Urban Heat Island Intensities (SUHIIs) in More Than 3000 Cities in China. Sustainability 2021, 13, 2877 .
AMA StyleLin Ma, Yueyao Wang, Ze Liang, Jiaqi Ding, Jiashu Shen, Feili Wei, Shuangcheng Li. Changing Effect of Urban Form on the Seasonal and Diurnal Variations of Surface Urban Heat Island Intensities (SUHIIs) in More Than 3000 Cities in China. Sustainability. 2021; 13 (5):2877.
Chicago/Turabian StyleLin Ma; Yueyao Wang; Ze Liang; Jiaqi Ding; Jiashu Shen; Feili Wei; Shuangcheng Li. 2021. "Changing Effect of Urban Form on the Seasonal and Diurnal Variations of Surface Urban Heat Island Intensities (SUHIIs) in More Than 3000 Cities in China." Sustainability 13, no. 5: 2877.
Despite the awareness that green water is the main source of water to produce food, studies on green water use in cropland ecosystems are still rather limited, and almost no research has so far explored its driving factors. In this study, with the help of CropWat 8.0, the green water footprint (GWF) of main crops in China was estimated for the period from 1979 to 2016. On this basis, a novel concept, i.e., green water appropriation rate (GWar) was introduced to reveal the relationship between GWF and precipitation. Then, for the first time, the center of gravity trajectory and the driving factors of the GWar were further investigated. The results show that the total GWF in China has continuously increased from 312,915 million m3 in 1979 to 397,207 million m3 in 2016, an increase of 27%. The provinces with the largest increases were Inner Mongolia (223%), Xinjiang (127%), and Ningxia (123%). Meanwhile, the GWFs of 11 provinces have decreased, and 9 of them were municipalities or coastal areas. The GWar first rose from 30% in 1979 to 38% in 2009 in China, and then dropped to 30% in 2016. Generally, the GWar in the eastern and central provinces was greater than that in the western provinces. The center of gravity of the GWar has always been in Henan Province, but it has moved westward from Kaifeng City in 1979 to Sanmenxia City in 2016 and may further move to Shanxi Province soon.The total power of agricultural machinery and the effective irrigation rate had positive effects on the GWar, while the agricultural GDP was negatively correlated with the GWar. It is expected that the results will explicitly provide a scientific basis for the development of water-appropriate agriculture and the full utilization of rainwater.
Weijing Ma; Feili Wei; Jianpeng Zhang; Daniel Karthe; Christian Opp. Green Water Appropriation of the Cropland Ecosystem in China. 2021, 1 .
AMA StyleWeijing Ma, Feili Wei, Jianpeng Zhang, Daniel Karthe, Christian Opp. Green Water Appropriation of the Cropland Ecosystem in China. . 2021; ():1.
Chicago/Turabian StyleWeijing Ma; Feili Wei; Jianpeng Zhang; Daniel Karthe; Christian Opp. 2021. "Green Water Appropriation of the Cropland Ecosystem in China." , no. : 1.
At the city scale, the diurnal and seasonal variations in the relationship between urban form and the urban heat island effect remains poorly understood. To address this deficiency, we conducted an empirical study based on data from 150 cities in the Jing-Jin-Ji region of China from 2000 to 2015. The results derived from multiple regression models show that the effects of urban geometric complexity, elongation, and vegetation on urban heat island effect differ among different seasons and between day and night. The impacts of urban geometric factors and population density in summer, particularly those during the daytime, are significantly larger than those in winter. The influence of urban area and night light intensity is greater in winter than in summer and is greater during the day than at night. The effect of NDVI is greater in summer during the daytime. Urban vegetation is the factor with the greatest relative contribution during the daytime, and urban size is the dominant factor at night. Urban geometry is the secondary dominant factor in summer, although its contribution in winter is small. The relative contribution of urban geometry shows an upward trend at a decadal time scale, while that of vegetation decreases correspondingly. The results provide a valuable reference for top-level sustainable urban planning.
Ze Liang; Yueyao Wang; Jiao Huang; Feili Wei; Shuyao Wu; Jiashu Shen; Fuyue Sun; Shuangcheng Li. Seasonal and Diurnal Variations in the Relationships between Urban Form and the Urban Heat Island Effect. Energies 2020, 13, 5909 .
AMA StyleZe Liang, Yueyao Wang, Jiao Huang, Feili Wei, Shuyao Wu, Jiashu Shen, Fuyue Sun, Shuangcheng Li. Seasonal and Diurnal Variations in the Relationships between Urban Form and the Urban Heat Island Effect. Energies. 2020; 13 (22):5909.
Chicago/Turabian StyleZe Liang; Yueyao Wang; Jiao Huang; Feili Wei; Shuyao Wu; Jiashu Shen; Fuyue Sun; Shuangcheng Li. 2020. "Seasonal and Diurnal Variations in the Relationships between Urban Form and the Urban Heat Island Effect." Energies 13, no. 22: 5909.
Urbanization has a significant impact on urban precipitation. Existing studies on precipitation pay more attention to the impact of natural and meteorological factors, and the research on the impact of urbanization on the spatial patterns of precipitation is still very deficient. Based on geographic detection, this study quantitatively analyzed the dominant, interaction, and sensitivity factors that affect precipitation changes in more than 150 urban units in Jing–Jin–Ji (Beijing–Tianjin–Hebei) during the process of urbanization. The research findings show the following: ① The dominant factors have seasonal differences in terms of the precipitation variation in Jing–Jin–Ji. The leading factors in summer were the change of radiation and relative humidity. The dominant factors in winter were the changes in radiation, relative humidity, and wind speed. On the annual scale, the dominant factors were the changes in relative humidity, aerosol optical depth, radiation, and wind speed. ② Whether in summer, in winter, or on the annual scale, urbanization can enhance the explanatory power of spatial variation of urban precipitation through interaction with natural/meteorological factors, and all the dominant interaction factors show a nonlinear enhancement trend. ③ The night light intensity and urban heat island can greatly amplify the explanatory power of other factors, thus becoming the most sensitive factor in urbanization precipitation changes. The above research can provide a theoretical basis for the formulation of urban climate policies and urban planning.
Feili Wei; Ze Liang; Yueyao Wang; Zhibin Huang; Huan Wang; Fuyue Sun; Shuangcheng Li. Exploring the Driving Factors of the Spatiotemporal Variation of Precipitation in the Jing–Jin–Ji Urban Agglomeration from 2000 to 2015. Sustainability 2020, 12, 7426 .
AMA StyleFeili Wei, Ze Liang, Yueyao Wang, Zhibin Huang, Huan Wang, Fuyue Sun, Shuangcheng Li. Exploring the Driving Factors of the Spatiotemporal Variation of Precipitation in the Jing–Jin–Ji Urban Agglomeration from 2000 to 2015. Sustainability. 2020; 12 (18):7426.
Chicago/Turabian StyleFeili Wei; Ze Liang; Yueyao Wang; Zhibin Huang; Huan Wang; Fuyue Sun; Shuangcheng Li. 2020. "Exploring the Driving Factors of the Spatiotemporal Variation of Precipitation in the Jing–Jin–Ji Urban Agglomeration from 2000 to 2015." Sustainability 12, no. 18: 7426.
There have been debates and a lack of understanding about the complex effects of urban-scale urban form on air pollution. Based on the remotely sensed data of 150 cities in the Beijing-Tianjin-Hebei agglomeration in China from 2000 to 2015, we studied the effects of urban form on fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations from multiple perspectives. The panel models show that the elastic coefficients of aggregation index and fractal dimension are the highest among all factors for the whole region. Population density, aggregation index, and fractal dimension have stronger influences on air pollution in small cities, while area size demonstrates the opposite effect. Population density has a stronger impact on medium/high-elevation cities, while night light intensity (NLI), fractal dimension, and area size show the opposite effect. Low road network density can enlarge the influence magnitude of NLI and population density. The results of the linear regression model with multiplicative interactions provide evidence of interactions between population density and NLI or aggregation index. The slope of the line that captures the relationship between NLI on PM2.5 is positive at low levels of population density, flat at medium levels of population density, and negative at high levels of population density. The study results also show that when increasing the population density, the air pollution in a city with low economic and low morphological aggregation degrees will be impacted more greatly.
Ze Liang; Feili Wei; Yueyao Wang; Jiao Huang; Hong Jiang; Fuyue Sun; Shuangcheng Li. The Context-Dependent Effect of Urban Form on Air Pollution: A Panel Data Analysis. Remote Sensing 2020, 12, 1793 .
AMA StyleZe Liang, Feili Wei, Yueyao Wang, Jiao Huang, Hong Jiang, Fuyue Sun, Shuangcheng Li. The Context-Dependent Effect of Urban Form on Air Pollution: A Panel Data Analysis. Remote Sensing. 2020; 12 (11):1793.
Chicago/Turabian StyleZe Liang; Feili Wei; Yueyao Wang; Jiao Huang; Hong Jiang; Fuyue Sun; Shuangcheng Li. 2020. "The Context-Dependent Effect of Urban Form on Air Pollution: A Panel Data Analysis." Remote Sensing 12, no. 11: 1793.
Water scarcity has seriously threatened the sustainable development of Zhangjiakou City, an arid agricultural area in North China, and the ecological security of its neighboring areas. In this study, a system dynamics model is established based on variable sensitivity analysis and is employed to simulate water demand (2015–2035) in four designed alternative development scenarios in Zhangjiakou City. The results show that: (1) the variables related to irrigation farmland are the main driving factors of water demand, especially the area and water use quota. (2) The total water demand will rise continually in the current development scenario and economic priority scenario, and the proportion of agricultural water demand will drop to 67% and 63%, respectively. It will decline continually in the water-saving priority scenario and balanced development scenario, and the proportion of agricultural water demand will drop to 56% and 57%, respectively. (3) Water consumption per ten thousand yuan of GDP will fall to around 20 m3 in 2035 in each scenario, indicating that the reduction of water demand only by slowing down economic growth cannot improve the efficiency of water use. The research results will be beneficial to extract feasible strategies and policies for balancing economic development and water conservation.
Weijing Ma; Lihong Meng; Feili Wei; Christian Opp; Dewei Yang. Sensitive Factors Identification and Scenario Simulation of Water Demand in the Arid Agricultural Area Based on the Socio-Economic-Environment Nexus. Sustainability 2020, 12, 3996 .
AMA StyleWeijing Ma, Lihong Meng, Feili Wei, Christian Opp, Dewei Yang. Sensitive Factors Identification and Scenario Simulation of Water Demand in the Arid Agricultural Area Based on the Socio-Economic-Environment Nexus. Sustainability. 2020; 12 (10):3996.
Chicago/Turabian StyleWeijing Ma; Lihong Meng; Feili Wei; Christian Opp; Dewei Yang. 2020. "Sensitive Factors Identification and Scenario Simulation of Water Demand in the Arid Agricultural Area Based on the Socio-Economic-Environment Nexus." Sustainability 12, no. 10: 3996.
More than 3000 cities in China were used to study the effect of urbanization and local climate variability on urban vegetation across different geographical and urbanization conditions. The national scale estimation shows that China’s urban vegetation depicts a trend of degradation from 2000 to 2015, especially in developed areas such as the Yangtze River Delta. According to the panel models, the increase of precipitation (PREC), solar radiation (SRAD), air temperature (TEMP), and specific humidity (SHUM) all enhance urban vegetation, while nighttime light intensity (NLI), population density (POPDEN), and fractal dimension (FRAC) do the opposite. The effects change along the East–West gradient; the influences of PREC and SHUM become greater, while those of TEMP, SRAD, NLI, AREA, and FRAC become smaller. PREC, SHUM, and SRAD play the most important roles in Northeast, Central, and North China, respectively. The role of FRAC and NLI in East China is much greater than in other regions. POPDEN remains influential across all altitudes, while FRAC affects only low-altitude cities. NLI plays a greater role in larger cities, while FRAC and POPDEN are the opposite. In cities outside of the five major urban agglomerations, PREC has a great influence while the key factors are more diversified inside.
Ze Liang; Yueyao Wang; Fuyue Sun; Hong Jiang; Jiao Huang; Jiashu Shen; Feili Wei; Shuangcheng Li. Exploring the Combined Effect of Urbanization and Climate Variability on Urban Vegetation: A Multi-Perspective Study Based on More than 3000 Cities in China. Remote Sensing 2020, 12, 1328 .
AMA StyleZe Liang, Yueyao Wang, Fuyue Sun, Hong Jiang, Jiao Huang, Jiashu Shen, Feili Wei, Shuangcheng Li. Exploring the Combined Effect of Urbanization and Climate Variability on Urban Vegetation: A Multi-Perspective Study Based on More than 3000 Cities in China. Remote Sensing. 2020; 12 (8):1328.
Chicago/Turabian StyleZe Liang; Yueyao Wang; Fuyue Sun; Hong Jiang; Jiao Huang; Jiashu Shen; Feili Wei; Shuangcheng Li. 2020. "Exploring the Combined Effect of Urbanization and Climate Variability on Urban Vegetation: A Multi-Perspective Study Based on More than 3000 Cities in China." Remote Sensing 12, no. 8: 1328.