Quantitative Estimation of the Velocity of Urbanization in China Using Nighttime Luminosity Data

Abstract

Rapid urbanization with sizeable enhancements of urban population and built-up land in China creates challenging planning and management issues due to the complexity of both the urban development and the socioeconomic drivers of environmental change. Improved understanding of spatio-temporal characteristics of urbanization processes are increasingly important for investigating urban expansion and environmental responses to corresponding socioeconomic and landscape dynamics. In this study, we present an artificial luminosity-derived index of the velocity of urbanization, defined as the ratio of temporal trend and spatial gradient of mean annual stable nighttime brightness, to estimate the pace of urbanization and consequent changes in land cover in China for the period of 2000-2010. Using the Defense Meteorological Satellite Program-derived time series of nighttime light data and corresponding satellite-based land cover maps, our results show that the geometric mean velocity of urban dispersal at the country level was 0.21 km¨yr´1 across 88.58ˆ10 3 km 2 urbanizing areas, in which~23% of areas originally made of natural and cultivated lands were converted to artificial surfaces between 2000 and 2010. The speed of urbanization varies among urban agglomerations and cities with different development stages and urban forms. Particularly, the Yangtze River Delta conurbation shows the fastest (0.39 km¨yr´1) and most extensive (16.12ˆ10 3 km 2 ) urban growth in China over the 10-year period. Moreover, if the current velocity holds, our estimates suggest that an additional 13.29ˆ10 3 km 2 in land area will be converted to human-built features while high density socioeconomic activities across the current urbanizing regions and urbanized areas will greatly increase from 52.44ˆ10 3 km 2 in 2010 to 62.73ˆ10 3 km 2 in China's mainland during the next several decades. Our findings may provide potential insights into the pace of urbanization in China, its impacts on land changes, and accompanying alterations in environment and ecosystems in a spatially and temporally explicit manner.

Characterizing the Growth Patterns of 45 Major Metropolitans in Mainland China Using DMSP/OLS Data

“…2017, 9, 571 13 of 17 patterns were examined by employing all lit-pixels instead of the urban-pixels [4,32,38]; (3) growth patterns were modeled by integrating intensification and expansion together instead of concentrating on either urban expansion [30,31,47] or urbanization [25,28].…”

“…At the metropolitan level, this study differs from the previous work in two folds. Firstly, it employs every single lit-pixel to examine the growth patterns instead of placing its emphasis only on the urban lit-pixels as most previous studies; Secondly, the growth patterns are modeled from the aspects of both intensification and expansion, which is different from most previous studies concentrating either on dynamics of urbanization [24,28] or on urban expansion itself [30,31]. In our study, intensification is characterized as the change of DN values, which is related to human activities in terms of demographic dynamics and economic development; expansion is presented as the change of DN extent and its spatial types, which is also related to human activities by consuming unexploited land.…”

“…On one hand, previous findings had suggested strong relationships between digital number (DN) values with urban population density [16], built area density [17], house vacancy rate [18], and other socio-economic indicators [19][20][21][22][23]; On the other hand, they have a relative high temporal frequency of observation. Previous studies mainly focused on the dynamics of urbanization in general [24][25][26][27][28] or urban expansion itself in particular [29][30][31], and they ignored the influence from the non-urban lit-pixels [3,8,32,33]. In addition, there were many other studies focusing on the dynamics of urbanization or urban expansion from the pixel perspective [26], for individual megacities [34,35], at a local or region level [13,36], at a country level [27,37], or even in a long time period [38].…”

Urban Landscape Spatial Pattern Estimation of Cities in Shandong Province Using Nighttime Luminosity Data

“…Among the data, nighttime stable light (NSL) contains lights from cities, towns and other sites with persistent lighting Amaral et al 2005;Pandey et al 2013). The composed stable nighttime light data consists of 30×30 arc-seconds (approximately 1000 meters at the equator) (Ma et al 2016). The range of the nighttime stable light data's digital number (DN) is 0 to 63.…”

“…Remotely sensed nighttime light time series dataset derived from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) provides a straightforward way to analyze the relationship between urbanization and anthropogenic activities and has been extensively used in urban studies (Ma et al 2012b;Fan et al 2014). The DMSP/OLS sensor could detect low-level visible and near-infrared bands radiance signals at night and the composed stable nighttime light imageries have been widely used for urban development mapping Small et al 2005;Zhang et al 2011;Zhou et al 2014, Xu et al 2014, city built-up area expansion trends exploring (Sutton 2003;Ma et al 2016) and socioeconomic activities detecting (Doll et al 2006;Elvidge et al 2009). The nighttime light time series data also used for estimation of regional socioeconomic development inequality (Zhou et al 2015).…”