Modeling the luminous intensity of Beijing, China using DMSP-OLS night-time lights series data for estimating population density

https://doi.org/10.1016/j.pce.2018.06.002Get rights and content

Highlights

  • Multitemporal NTL data can serve as an excellent proxy for estimating population density of a geographic region.

  • NTL data better predict population density than optical data.

  • Future research can leverage VIIRS′ superior sensitivity and on-board calibration.

Abstract

Various scientific researches were conducted to monitor human activities and natural phenomena with the availability of various night time satellite data such as Defense Meteorological Satellite Program (DMPS). Population growth especially in a faster growing economy like China is an important indicator for assessing socio-economic development, urban planning and environmental management. Thus, spatial distribution of population is instrumental in assessing growth and developmental activities in Beijing city of China. The satellite observation data derived from Defense Meteorological Satellite Program (DMSP) was utilized to estimate population density through the measurement of light flux with radiometric recording. The data was calibrated using C0, C1, C2 parameters before processing. Population density of Beijing city was estimated using light volume of this calibrated data. Regression analysis between urban population and light volume revealed high correlation (r2=0.89). Thus, population density can effectively be estimated using light intensity. The model used for estimating urban population density can effectively be utilized for other major cities of the world.

Introduction

Population growth has been used by many scholars as a significant parameter forassessing socio-economic development, urban planning, environmental management and sustainable development and management of resources (Zhuo et al., 2009; Zeng et al., 2011; Ma et al., 2012, 2015; Yu et al., 2014; Gao et al., 2015). Urban population in China has increased from 17.9% in 1978 to 57.35% in 2016 (United Nations, 2015). An increase in its population is projected to be 60% by 2020 (National Bureau of Statistics of the People's Republic of China, 2017). This tremendous increase in urban population has increased the demand for energy consumption, (Tripathy et al., 2018; Zhang, 2015; He et al., 2012; Dobson et al., 2000), land transformation (Li et al., 2016; Wang et al., 2017), shortage of food supply (Obienusi et al., 2014, Hutchinson and Hutchinson, 1969), emissions from household garbage (Atasoylu et al., 2007) anddomestic water use (Yigzaw and Hossain, 2016). The exponential increase in population has put stress on food safety (János, 2014), ecological environment (Elvidge et al., 1997a, Elvidge et al., 1997b; Miao et al., 2016) and sustainable development (János, 2014). Thus, scientific assessment of human population distribution in case of developing nation is immensely important (Pan et al., 2016). Urbanization is increasingly gaining interest among policy makers and researchers alike. Urban areas are being included among other indicators for assessing economic development, sources of environmental and climate change pressure and crime hotspots (Pandey et al., 2013; Raupach et al., 2010).

Light at night is like a Miniature Sun that makes everyone and everything appears to be at its best and at the same time enhances workplaces as well as surroundings. Scientists have worked foryears to betray the new prospect of looking at our planet at night (Lu et al., 2008; Jing et al., 2015). A number of satellites across globe are available to observe the earth during day at the cost of light delivered by the sun. Operational Linescan System (OLS) images help in providing night time visualization of the earth surface. United States Air Force Defence Meteorological Satellite Program Operation Line scan System (DMSP-OLS) night-time light images have largely been utilized to monitor urbanization dynamics since two decades (Elvidge et al., 1997a, Elvidge et al., 1997b, Elvidge et al., 2007a, Elvidge et al., 2007b; Lo, 2001; Liu et al., 2012, Witmer and O’Loughlin, 2011). The global composite cloud free images obtained through National Oceanic and Atmospheric Administration (NOAA) enableto have a synoptic view of sparkling light being emitted from the area inhabited by humans. The advent of such satellite has made it possiblescientists to observe the planet Earth even at night and made us to be staunch supporter of “Nothing tells us more about the spread of humans across the Earth than city lights” (Elvidge, 1999). For the lastfour decades, the satellites in the U.S. Defence Meteorological Satellite Program (DMSP) have been making observations with the low light sensors. Assessment of clouds, storms and smoke plumes at each and every hour of a day has been driving force for the introduction of low light earth observations. Night light data is user friendly for studying the various aspects of population for identifying urban and modelling different socio-economic parameters (Kumar et al., 2017; Tripathi et al., 2017; Doll and Muller, 1999; Levin and Duke, 2012; Christopher et al., 2006; Sutton, 1997). Population is unevenly distributed over the earth's surface mainly due to varied climatic and topographical conditions. Elvidge (2009) in late 1960s suggested that settlement size can be helpful in assessing human population in any defined region (Sutton et al., 2009; Elvidge et al., 2009; Jenerette et al., 2007).

Settlement modelling as circular areas can be beneficial in evaluating population by establishing empirical relationship. The night light imageryhas become an important source forestimating the population of an area and is largely used for studying socio-economic properties, population distribution and density. (Sutton et al., 1997; Ghosh et al., 2009; Elvidge et al., 1997a, Elvidge et al., 1997b; Zhang and Hao, 2007; Small et al., 2005; Milesi et al., 2003; Bennett and Smith, 2017). The night time data has overcome the problem of using high spatial resolution data for identifying human settlement and has made possible to retrieve information during nighttime which definitely reflects more human activities (Sullivan, 1989; Doll et al., 2000, Ebener et al., 2000). Hence night-time satellite data can help in detecting the population of an area.

Supremely the population censuses are held regularly in five or ten year'sintervalsin a country. An acquaintance of the size and dissemination of human population is important for empathetic and retorting to many social, political, economic and environmental problems (Li et al., 2013a, Li et al., 2013b). Remote sensing and GIS have been used since many years to assess population for different areas or regions. Interpolation method and geo-statistical modelling have largely been used for assessing population (Hu, 2016; Xu et al., 2014; Ma et al., 2014). Night time light satellite data can detect presence of human on the earth. The data is used after the calibration of satellite data for each year. This paper makes an attempt to estimate urban population using night time light data in Beijing, China.

The aim of the paper is to quantify the pixel level spatio-temporal pattern and trend of urbanization in Beijing by analysing the night-time light data during 1992–2013 derived from DMSP-OLS. Urbanization was viewed as comprised of urban land cover including the urban population activities as represented by elevated night-time light brightness.

Section snippets

Study area

Beijing, the capital of the People's Republic of China having an area about 16,400 km2 is situated in north China plain between 39°28′N to 41°05′N latitudes and from 115°25′E to 117°30′E longitudes (Fig. 1). Most of the area of the city (62%) is under mountain and nearly 32% area is under plain topography. Beijing has experienced an increase of about 44% in population during the last decade (Sumita, 2011). The total population of Beijing is 2.1 million with a population density of 1525

Determination of inter calibration

The data during 1992–2013 were calibrated using linear regression with the help of parameters provided by National Centres for Environmental Information (NCEI). The coefficient of determination and roo tmean square error (RMSE) were considered to reduce the effect of the outlier most effectively. Fig. 3 shows the DN numbers before and after the calibration process. RMSE were observed to be very negligible suggesting the linear regression models much accurate (see Fig. 4).

Luminous intensity

Luminous intensity maps

Conclusion

This study used DMSP-OLS night time satellite data during 1992–2013 to characterize urbanization dynamics for Beijing, China. The human activities observed during night helped in assessing concentration of human population in the city. Luminous intesity maps revealed continuous increase in urban population. The estimation of urban population was verified by analyzing the relationship among urban population, light area and light volume. The analysis revealed that high urban population density

Acknowledgement

The authors are incredibly thankful to the anonymous reviewers for their constructive comments and sugestions to improve the overall quality of the manuscript.We express deep gratitude and indebtedness to National Ocean Technology Center, China for providing necessary data required for the study. The authors are also grateful to Ou Ling, Sea and Islands Office, National Ocean Technology Center, China for help and support during the study.

References (62)

  • C.L. Miao et al.

    The studies of ecological environmental quality assessment in Anhui Province based on ecological footprint

    Ecol. Indicat.

    (2016)
  • C. Milesi et al.

    Assessing the impact of urban land development on net primary productivity in the southeastern United States

    Remote Sens. Environ.

    (2003)
  • B. Pandey et al.

    Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data

    Int. J. Appl. Earth Obs. Geoinf.

    (2013)
  • M.R. Raupach et al.

    Regional variations in spatial structure of nightlights, population density and fossil-fuel CO2 emissions

    Energy Pol.

    (2010)
  • C. Small et al.

    Spatial analysis of global urban extent from DMSP-OLS night lights

    Remote Sens. Environ.

    (2005)
  • P. Sutton

    Modeling population density with night-time satellite imagery and GIS

    Comput. Environ. Urban Syst.

    (1997)
  • G. Atasoylu et al.

    The household garbage in the western coast region of Turkey and its relationship with the socio-economic characteristics

    J. Environ. Biol.

    (2007)
  • Beijing Statistical Yearbook. Available online: http://www.bjstats.gov.cn/nj/main/2016-tjnj/zk/indexch.htm (Accessed on...
  • N.H. Doll Christopher et al.

    Mapping regional economic activity from night-time light satellite imagery

    Ecol. Econ.

    (2006)
  • J.E. Dobson et al.

    Landscan: a global population database for estimating populations at risk

    Photogramm. Eng. Rem. Sens.

    (2000)
  • C.N.H. Doll et al.

    The use of radiance calibrated night-time imagery to improve remotely sensed population estimation

  • C.N.H. Doll et al.

    Night-time imagery as a tool for global mapping of socio-economic parameters and green house gas emission

    Ambio

    (2000)
  • S. Ebener et al.

    From welth to helth: modelling the distribution of income per capita at the sub-national level using night time lights imagery

    (2000)
  • C.D. Elvidge et al.

    Mapping city lights with nighttime data from the DMSP operational linescan system

    Photogramm. Eng. Rem. Sens.

    (1997)
  • C. Elvidge et al.

    Satellite inventory of human settlements using nocturnal radiation emissions: a contribution for the global toolchest

    Global Change Biol.

    (1997)
  • C.D. Elvidge et al.

    The Nightsat mission concept

    Int. J. Rem. Sens.

    (2007)
  • C.D. Elvidge et al.

    Global distribution and density of constructed impervious Surfaces

    Sensors

    (2007)
  • B. Gao et al.

    Dynamics of urbanization levels in China from 1992 to 2012: perspective from DMSP/OLS nighttime light data

    Rem. Sens.

    (2015)
  • T. Ghosh et al.

    Estimation of Maxico's informal economy and remittances using night time imgery

    Rem. Sens.

    (2009)
  • C.Y. He et al.

    Spatiotemporal dynamics of electric power consumption in Chinese mainland from 1995 to 2008 modeled using dmsp/ols stable nighttime lights data

    J. Geogr. Sci.

    (2012)
  • R.J. Hijmans et al.

    raster: Geographic Analysis and Modeling with Raster Data

    (2011)
  • Cited by (40)

    • Examining human disturbances and inundation dynamics in China's marsh wetlands by using time series remote sensing data

      2023, Science of the Total Environment
      Citation Excerpt :

      An increasing number of scholars have applied nighttime light data to study the long-term urbanization process on regional, zonal, or national scales (Aguilera and González, 2023; Liu et al., 2012; Wei et al., 2014). Compared with urban extractions from image classifications at specific years, the urban expansion derived from nighttime light requires less time but provides reliable results with higher spatial and temporal continuity (Kumar et al., 2019; Liu et al., 2012). Therefore, the time-series nighttime light information could be incorporated with other socioeconomic statistics and meteorological records to evaluate the environmental or ecological consequences evoked by anthropogenic activities.

    • Decoupling relationship analysis between urbanization and carbon emissions in 33 African countries

      2022, Heliyon
      Citation Excerpt :

      As an important branch of remote sensing, luminous remote sensing has attracted more and more attention in the fields of natural science and social science research in recent decades (Zhou et al., 2021). It plays an important role in population density estimation (Kumar et al., 2019), economic development assessment (Keola et al., 2015) and power consumption estimation (Tripathy et al., 2018). Therefore, the night time light data can be used as one of the quantitative basis to measure the level of urban development.

    View all citing articles on Scopus
    View full text