Mapping Development Pattern in Beijing-Tianjin-Hebei Urban Agglomeration Using DMSP/OLS Nighttime Light Data

Hu, Yi’na; Peng, Jian; Liu, Yanxu; Du, Yanjun; Li, Huilei; Wu, J.

doi:10.3390/rs9070760

Cited by 30 publications

(17 citation statements)

References 38 publications

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“…To address inconsistencies among image datasets, Landsat TM images were geo-rectified against the Landsat OLI images, and then all images were projected onto the Albers equal-area conic coordinate system to accurately The area has fertile land, being one of three world-famous black soil belts, and is the main grain-producing area in China, so the area is vital for the country's food security. However, many studies have demonstrated that urban land has encroached considerably on farmland [23,32], threatening agricultural productivity in China [36]. The 40 prefecture-level cities are governed within the study area.…”

Section: Data Sources and Preprocessingmentioning

confidence: 99%

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Quantifying Urban Land Sprawl and its Driving Forces in Northeast China from 1990 to 2015

et al. 2018

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Drastic urbanization has resulted in numerous problems worldwide, and many studies were devoted to individual cities. There is an urgent need to quantify urbanization patterns and illustrate their driving forces in the regional area on a large scale over a longer time period. This study produced a land cover dataset to characterize sequential urban land expansion in Northeast China from 1990 to 2015 using object-based backdating classification and calculating the expansion index. The drivers were investigated using Pearson correlation analysis and the multiple linear stepwise regression model. The results revealed that the total area of urban land increased by 43.15% from 1990 to 2015, mainly in the middle part of the study area, and especially in the coastal area of Liaoning. Liaoning had the fastest growth rate, while Heilongjiang showed a decrease in growth rate rankings. Urban land expanded northward and southward within Harbin City, towards the west and east within Changchun City, and relatively equally in all directions within Shenyang City. Expansion patterns changed from edge expansion (42%) to outlying expansion (47%). Urban land of Liaoning and the Eastern Inner Mongolia Autonomous region expanded with similar patterns as the entire area, but that of Heilongjiang grew in an opposite pattern, and Jilin maintained outlying major expansion. The influence of factors on urban land sprawl varied temporally. Tertiary industry product, gross domestic product, secondary industry product, total population and urban population were driving factors of urban land sprawl in Northeast China from 1990 to 2015. This research provides quantitative methods for better understanding urban land dynamics and devising feasible strategies for sustainable urban development.

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Section: Data Sources and Preprocessingmentioning

confidence: 99%

“…However, many studies have demonstrated that urban land has encroached considerably on farmland [23,32], threatening agricultural productivity in China [36]. The 40 prefecture-level cities are governed within the study area.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Urban Land Sprawl and its Driving Forces in Northeast China from 1990 to 2015

et al. 2018

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“…The December night-time light data can reflect the final expansion of urban built-up areas and population distribution in 2013. Compared to another commonly-used stable night-time light dataset, the DMSP-OLS [35][36][37], the NPP-VIIRS DNB composite imagery has been calibrated in orbit, providing a higher spatial accuracy and solving the brightness saturation problem [34,38]. However, these data contain not only the brightness from urban areas but also abnormal values such as fire points, volcanoes and background noise.…”

Section: Datamentioning

confidence: 99%

Ghost City Extraction and Rate Estimation in China Based on NPP-VIIRS Night-Time Light Data

Yang

Zhu

et al. 2018

IJGI

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Abstract:The ghost city phenomenon is a serious problem resulting from the rapid urbanization process in China. Estimation of the ghost city rate (GCR) can provide information about vacant dwellings. This paper developed a methodology to quantitatively evaluate GCR values at the national scale using multi-resource remote sensing data. The Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer (NPP-VIIRS) night-time light data and moderate resolution imaging spectroradiometer (MODIS) land cover data were used in the evaluation of the GCR values in China. The average ghost city rate (AGCR) was 35.1% in China in 2013. Shanghai had the smallest AGCR of 21.7%, while Jilin has the largest AGCR of 47.27%. There is a significant negative correlation between both the provincial AGCR and the per capita disposable income of urban households (R = −0.659, p < 0.01) and the average selling prices of commercial buildings (R = −0.637, p < 0.01). In total, 31 ghost cities are mainly concentrated in the economically underdeveloped inland provinces. Ghost city areas are mainly located on the edge of urban built-up areas, and the spatial pattern of ghost city areas changed in different regions. This approach combines statistical data with the distribution of vacant urban areas, which is an effective method to capture ghost city information.

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“…To date, scholars have carried out a lot of research based on DMSP/OLS data, mainly in urban expansion [54][55][56], saturation method research [57][58][59], gross domestic product (GDP) estimation [60][61][62], carbon emissions [63][64][65], and power consumption estimation [66][67][68]. In addition, the existing research that applies DMSP/OLS data to describe the evolution of urban land use is mainly based on China at the national level [69,70] and the Beijing-Tianjin-Hebei urban agglomeration [71,72].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics and Driving Forces of Urban Land-Use Expansion: A Case Study of the Yangtze River Economic Belt, China

Zhong

Lin

et al. 2020

Remote Sensing

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It is important to analyze the expansion of an urban area and the factors that drive its expansion. Therefore, this study is based on Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) night lighting data, using the landscape index, spatial expansion strength index, compactness index, urban land fractal index, elasticity coefficient, the standard deviation ellipse, spatial correlation analysis, and partial least squares regression to analyze the spatial and temporal evolution of urban land expansion and its driving factors in the Yangtze River Economic Belt (YREB) over a long period of time. The results show the following: Through the calculation of the eight landscape pattern indicators, we found that during the study period, the number of cities and towns and the area of urban built-up areas in the YREB are generally increasing. Furthermore, the variations in these landscape pattern indicators not only show more frequent exchanges and interactions between the cities and towns of the YREB, but also reflect significant instability and irregularity of the urbanization development in the YREB. The spatial expansion intensity indices of 1992–1999, 1999–2006, and 2006–2013 were 0.03, 0.16, and 0.34, respectively. On the whole, the urban compactness of the YREB decreased with time, and the fractal dimension increased slowly with time. Moreover, the long axis and the short axis of the standard deviation ellipse of the YREB underwent a small change during the inspection period. The spatial distribution generally showed the pattern of “southwest-north”. In terms of gravity shift, during the study period, the center of gravity moved from northeast to southwest. In addition, the Moran's I values for the four years of 1992, 1999, 2006, and 2013 were 0.451, 0.495, 0.506, and 0.424, respectively. Furthermore, by using correlation analysis, we find that the correlation coefficients between these four driving indicators and the urban expansion of the YREB were: 0.963, 0.998, 0.990 and 0.994, respectively. Through the use of partial least squares regression, we found that in 1992-2013, the four drivers of urban land expansion in the YREB were ranked as follows: gross domestic product (GDP), total fixed asset investment, urban population, total retail sales of consumer goods.

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Mapping Development Pattern in Beijing-Tianjin-Hebei Urban Agglomeration Using DMSP/OLS Nighttime Light Data

Cited by 30 publications

References 38 publications

Quantifying Urban Land Sprawl and its Driving Forces in Northeast China from 1990 to 2015

Quantifying Urban Land Sprawl and its Driving Forces in Northeast China from 1990 to 2015

Ghost City Extraction and Rate Estimation in China Based on NPP-VIIRS Night-Time Light Data

Spatiotemporal Dynamics and Driving Forces of Urban Land-Use Expansion: A Case Study of the Yangtze River Economic Belt, China

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