scholarly journals Response Patterns of Vegetation Phenology along Urban-Rural Gradients in Urban Areas of Different Sizes

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Xue Luo ◽  
Yuqing Zhang ◽  
Dongqi Sun
Keyword(s):  
Urban Areas ◽  
Growing Season ◽  
Liaoning Province ◽  
Vegetation Phenology ◽  
Coastal Cities ◽  
Nighttime Light Data ◽  
Urban Rural ◽  
Nighttime Light ◽  
Urban Size ◽  
The Difference

On the basis of MODIS Enhanced Vegetation Index time series data and multisource data, such as nighttime light data and China City Statistical Yearbook data, we investigated the differences in vegetation phenology along urban-rural gradients in urban areas of different sizes between coastal and inland cities in Liaoning Province, China. The results showed that the following: (1) the iterative extraction of urban built-up areas using the threshold method based on nighttime light data combined with the definition of urban built-up areas had high accuracy. (2) Additionally, we found that the start of the growing season (SOS) in Liaoning Province occurred between day 100 and day 180, while the end of the growing season (EOS) occurred between days 260 and 330. The difference in the SOS between coastal cities (i.e., Dalian, Yingkou, Panjin, Jinzhou, Huludao, and Dandong) and inland cities (i.e., Chaoyang, Fuxin, Tieling, Shenyang, Fushun, Liaoyang, Benxi, and Anshan) was 1.70 days. However, the difference in the EOS was more significant, i.e., the EOS in coastal cities occurred 4.47 days later than that in the inland cities. (3) In urban areas of different sizes, the ∆SOS and ∆EOS of inland cities had negative correlations with urban size. Specifically, when the urban size increased 10-fold, the ∆SOS and ∆EOS advanced by 10.03 and 5.71 days, respectively. In contrast, the ∆SOS and ∆EOS of coastal cities had positive and negative correlations with the urban size, respectively. Specifically, when urban size increased 10-fold, ∆SOS was delayed by 11.29 days while EOS was advanced by 8.83 days.

scholarly journals Delineating the Urban Areas of a Cross-Boundary City with Open-Access Data: Guangzhou–Foshan, South China

2021 ◽  
Vol 13 (5) ◽  
pp. 2930
Author(s):  
Pengfei Ban ◽  
Wei Zhan ◽  
Qifeng Yuan ◽  
Xiaojian Li
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
City Size ◽  
Border City ◽  
Point Of Interest ◽  
Nighttime Light Data ◽  
Profile Line ◽  
Point Analysis ◽  
Nighttime Light ◽  
Single Data

Cities defined mainly from the administrative aspect can create impact and problems especially in the case of China. However, only a few researchers from China have attempted to identify urban areas from the morphology dimension. In addition, previous studies have been mostly based on the national and regional scales or a single prefecture city and have completely ignored cross-boundary cities. Defining urban areas on the basis of a single data type also has limitations. To address these problems, this study integrates point of interest and nighttime light data, applies the breaking point analysis method to determine the physical geographic scope of the Guangzhou–Foshan cross-border city, and then compares this city with Beijing and Shanghai. Results show that Guangzhou–Foshan comprises one core urban area and six suburban counties, among which the core urban area extends across the administrative boundaries of Guangzhou and Foshan. The urban area and average urban radius of Guangzhou–Foshan are larger than those of Beijing and Shanghai, and this finding contradicts the city size measurements based on the administrative division system of China and those published on traditional official statistical yearbooks. In terms of urban density value, Shanghai has the steepest profile followed by Guangzhou–Foshan and Beijing, and the profile line of Guangzhou–Foshan has a bimodal shape.

scholarly journals Decomposing Urban-rural Differences in Multimorbidity Among Older Adults in India: A Study Based on Lasi Data

2021 ◽  
Author(s):  
Shekhar Chauhan ◽  
Shobhit Srivast ◽  
Pradeep Kumar ◽  
Ratna Patel
Keyword(s):  
Older Adults ◽  
Rural Areas ◽  
Urban Areas ◽  
Decomposition Analysis ◽  
The Public ◽  
Analysis Techniques ◽  
Ageing Study ◽  
Urban Rural ◽  
The Difference ◽  
Urban And Rural Areas

Abstract Background: Multimorbidity is defined as the co-occurrence of two or more than two diseases in the same person. With rising longevity, multimorbidity has become a prominent concern among the older population. Evidence from both developed and developing countries shows that older people are at much higher risk of multimorbidity, however, urban-rural differential remained scarce. Therefore, this study examines urban-rural differential in multimorbidity among older adults by decomposing the risk factors of multimorbidity and identifying the covariates that contributed to the change in multimorbidity.Methods: The study utilized information from 31,464 older adults (rural-20,725 and urban-10,739) aged 60 years and above from the recent release of the Longitudinal Ageing Study in India (LASI) wave 1 data. Descriptive, bivariate, and multivariate decomposition analysis techniques were used.Results: Overall, significant urban-rural differences were found in the prevalence of multimorbidity among older adults (difference: 16.3; p<0.001). Moreover, obese/overweight and high-risk waist circumference were found to narrow the difference in the prevalence of multimorbidity among older adults between urban and rural areas by 8% and 9.1%, respectively.Conclusion: There is a need to substantially increase the public sector investment in healthcare to address the multimorbidity among older adults, more so in urban areas, without compromising the needs of older adults in rural areas.

Abstract 13706: Differences in the Distribution of Systolic Blood Pressure by Rural and Urban Residence

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Brittain Heindl ◽  
George Howard ◽  
Elizabeth A Jackson
Keyword(s):  
Blood Pressure ◽  
Systolic Blood Pressure ◽  
Racial Differences ◽  
Rural Areas ◽  
Urban Areas ◽  
Census Bureau ◽  
Urban Group ◽  
Rural And Urban ◽  
Urban Rural ◽  
The Difference

Introduction: The incidence of stroke is higher in rural areas. Hypertension is the leading risk factor for stroke, but the difference in systolic blood pressure (SBP) for those living in rural and urban areas is unknown. Hypothesis: We hypothesized that rural residence is associated with higher SBP levels, and this difference is modified by race, sex, and United States (US) division. Methods: We analyzed 26,113 participants enrolled in the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study, recruited between 2003 and 2007. Participants were grouped based on the Rural-Urban Commuting Area (RUCA) scheme into urban, large-rural, and small-isolated rural groups. Resting SBP was measured during the initial home visit. Differences in percentiles of SBP distribution were compared using multivariate models with adjustment for age, race, sex, and US Census Bureau division. Results: Of the participants, 20,976 (80.3%) were classified as urban, 3,020 (11.6%) as large-rural, and 2,137 (8.2%) as small-isolated rural, reflecting the distribution of the population. The large-rural group had a 0.09 mmHg higher mean SBP compared to the urban group (95% CI, 0.33 to 1.52 mmHg, p = 0.0023), but the difference in SBP at the 95th percentile between these groups was 3.23 mmHg (95% CI, 1.43 to 4.73 mmHg, p = 0.0006). A similar difference was present between the small-isolated rural and urban groups at the highest percentiles. No urban-rural interaction was observed by race, sex, or US division. However, large SBP differences were present between US divisions, especially at the highest percentiles. To illustrate, SBP at the 95th percentile was 9.51 mmHg higher in the East North Central division than in the Pacific (95% CI, 6.41 to 12.61 mmHg, p < 0.0001). Conclusions: Residence in a rural area is associated with higher SBP, with larger differences at the highest percentiles of distribution. SBP differences are present between US divisions, independent of urban-rural status.

Mapping Global Urban Areas From 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products

2019 ◽  
Vol 12 (4) ◽  
pp. 1143-1153 ◽  
Author(s):  
Zuoqi Chen ◽  
Bailang Yu ◽  
Yuyu Zhou ◽  
Hongxing Liu ◽  
Chengshu Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Urban Areas ◽  
Nighttime Light Data ◽  
Nighttime Light

scholarly journals Extracting urban areas in China using DMSP/OLS nighttime light data integrated with biophysical composition information

2016 ◽  
Vol 26 (3) ◽  
pp. 325-338 ◽  
Author(s):  
Yang Cheng ◽  
Limin Zhao ◽  
Wei Wan ◽  
Lingling Li ◽  
Tao Yu ◽  
...  
Keyword(s):  
Urban Areas ◽  
Nighttime Light Data ◽  
Nighttime Light

scholarly journals A Zipf’s Law-Based Method for Mapping Urban Areas Using NPP-VIIRS Nighttime Light Data

2018 ◽  
Vol 10 (1) ◽  
pp. 130 ◽  
Author(s):  
Wenjia Wu ◽  
Hongrui Zhao ◽  
Shulong Jiang
Keyword(s):  
Urban Areas ◽  
Zipf’S Law ◽  
Zipf's Law ◽  
Nighttime Light Data ◽  
Nighttime Light

scholarly journals A model for urban biogenic CO<sub>2</sub> fluxes: Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes (SMUrF v1)

2021 ◽  
Vol 14 (6) ◽  
pp. 3633-3661
Author(s):  
Dien Wu ◽  
John C. Lin ◽  
Henrique F. Duarte ◽  
Vineet Yadav ◽  
Nicholas C. Parazoo ◽  
...  
Keyword(s):  
Urban Areas ◽  
Fossil Fuel ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Growing Season ◽  
Carbon Fluxes ◽  
Co2 Fluxes ◽  
Biogenic Carbon ◽  
Urban Rural ◽  
Biogenic Fluxes

Abstract. When estimating fossil fuel carbon dioxide (FFCO2) emissions from observed CO2 concentrations, the accuracy can be hampered by biogenic carbon exchanges during the growing season, even for urban areas where strong fossil fuel emissions are found. While biogenic carbon fluxes have been studied extensively across natural vegetation types, biogenic carbon fluxes within an urban area have been challenging to quantify due to limited observations and differences between urban and rural regions. Here we developed a simple model representation, i.e., Solar-Induced Fluorescence (SIF) for Modeling Urban biogenic Fluxes (“SMUrF”), that estimates the gross primary production (GPP) and ecosystem respiration (Reco) over cities around the globe. Specifically, we leveraged space-based SIF, machine learning, eddy-covariance (EC) flux data, and ancillary remote-sensing-based products, and we developed algorithms to gap-fill fluxes for urban areas. Grid-level hourly mean net ecosystem exchange (NEE) fluxes are extracted from SMUrF and evaluated against (1) non-gap-filled measurements at 67 EC sites from FLUXNET during 2010–2014 (r>0.7 for most data-rich biomes), (2) independent observations at two urban vegetation and two crop EC sites over Indianapolis from August 2017 to December 2018 (r=0.75), and (3) an urban biospheric model based on fine-grained land cover classification in Los Angeles (r=0.83). Moreover, we compared SMUrF-based NEE with inventory-based FFCO2 emissions over 40 cities and addressed the urban–rural contrast in both the magnitude and timing of CO2 fluxes. To illustrate the application of SMUrF, we used it to interpret a few summertime satellite tracks over four cities and compared the urban–rural gradient in column CO2 (XCO2) anomalies due to NEE against XCO2 enhancements due to FFCO2 emissions. With rapid advances in space-based measurements and increased sampling of SIF and CO2 measurements over urban areas, SMUrF can be useful to inform the biogenic CO2 fluxes over highly vegetated regions during the growing season.

scholarly journals Research on spatial characteristics of metropolis development using nighttime light data: NTL based spatial characteristics of Beijing

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242663
Author(s):  
Yuli Yang ◽  
Mingguo Ma ◽  
Xiaobo Zhu ◽  
Wei Ge
Keyword(s):  
Urban Areas ◽  
Urban Land Use ◽  
Rapid Urbanization ◽  
Spatial Characteristics ◽  
Spatial And Temporal Scales ◽  
Island Effect ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Urban Heat ◽  
Research Period

As the capital and one of the metropolises in China, Beijing has met with a number of serious so-called "urban diseases" in the process of rapid urbanization such as blind expansion of urban areas, explosion of population and the increase of urban heat island effect. To treat these “urban diseases” and make the metropolis develop healthful and sustainable in Beijing in the future, the spatial characteristics of metropolis developments in Beijing are explored in this paper. The urban built-up areas in Beijing are extracted using the DMSP-OLS nighttime light data from 1992 to 2013. The characteristics of the urban developments of Beijing are studied, including spatial and temporal scales of urban developments, urban barycenter of Beijing and its transfer trajectory, variations of urban spatial forms and the differences of urban internal developments. The results have shown that the built-up areas had been increasing and circling extending from the central urban areas to the outer spaces in the last 21 years. The built-up area had expanded by 878km2 in 1992–2013, and the built-up area in 2013 had expanded to three times comparing to that of 1992. The expanding area of the built-up area in the northeast is the largest. The expansion of the urban had mainly occurred in 1996–2007, and the expanded area had accounted for 92% of the total research period. During the whole research period, the urban barycenter of Beijing had moved 5000.71 meters towards Northeast 28° of its original place from Dongcheng District to Chaoyang District. The development level of each municipal district had been increasing year by year, and the development differences among the municipal districts had been gradually reduced; the spatial forms of Beijing had been alternately changed between extensive and intensive expansion. The results of this study can help to plan urban land use and people migration of Beijing.

Sign in / Sign up

Export Citation Format

Share Document