The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels

Nighttime light data offer a unique view of the Earth’s surface and can be used to estimate the spatial distribution of gross domestic product (GDP). Historically, using a simple regression function, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) has been used to correlate regional and global GDP values. In early 2013, the first global Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light data were released. Compared with DMSP/OLS, they have a higher spatial resolution and a wider radiometric detection range. This paper aims to study the suitability of the two nighttime light data sources for estimating the GDP relationship between the provincial and city levels in Mainland China, as well as of different regression functions. First, NPP/VIIRS nighttime light data for 2014 are corrected with DMSP/OLS data for 2013 to reduce the background noise in the original data. Subsequently, three regression functions are used to estimate the relationship between nighttime light data and GDP statistical data at the provincial and city levels in Mainland China. Then, through the comparison of the relative residual error (RE) and the relative root mean square error (RRMSE) parameters, a systematical assessment of the suitability of the GDP estimation is provided. The results show that the NPP/VIIRS nighttime light data are better than the DMSP/OLS data for GDP estimation, whether at the provincial or city level, and that the power function and polynomial models are better for GDP estimation than the linear regression model. This study reveals that the accuracy of GDP estimation based on nighttime light data is affected by the resolution of the data and the spatial scale of the study area, as well as by the land cover types and industrial structures of the study area.

[1]  Paul A. Zandbergen,et al.  Comparison of Dasymetric Mapping Techniques for Small-Area Population Estimates , 2010 .

[2]  Mikhail Zhizhin,et al.  A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data , 2009 .

[3]  Dolores Jane Forbes.  Multi-scale analysis of the relationship between economic statistics and DMSP-OLS night light images , 2013 .

[4]  Jianping Wu,et al.  Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data , 2014, Remote. Sens..

[5]  Zhuolun Li,et al.  Identification and Characteristics Analysis of Urban Land Expansion Types in China in the 1990s Using DMSP/OLS Data , 2006 .

[6]  XU Zhong-min.  Modeling the Spatial Distribution of GDP Based on Night Light Radiation: A Case Study in Ganzhou District,Zhangye Municipality , 2013 .

[7]  Xi Li,et al.  Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China , 2013, Remote. Sens..

[8]  Jinpei Ou,et al.  Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data , 2015, PloS one.

[9]  J. Henderson,et al.  Measuring Economic Growth from Outer Space , 2009, The American economic review.

[10]  L. Rui.  GDP Spatialization in China Based on Nighttime Imagery , 2012 .

[11]  Li Xi,et al.  An Overview on Data Mining of Nighttime Light Remote Sensing , 2015 .

[12]  Zhuolun Li,et al.  The Urbanization Model and Process in Bohai Sea Surrounding Area in the 1990s by Using DMSP/OLS Data , 2005 .

[13]  J. Muller,et al.  Mapping regional economic activity from night-time light satellite imagery , 2006 .

[14]  P. Aldhous.  Energy: China's burning ambition , 2005, Nature.

[15]  Diana Sommer,et al.  Observation Of The Earth And Its Environment Survey Of Missions And Sensors , 2016 .

[16]  Zhou YiWang ShixinLiu RuiYao Yao Han Xiangdi.  GDP Spatialization in China based on DMSP/OLS Data and Land Use Data , 2013 .

[17]  Zhifeng Liu,et al.  Modeling the spatiotemporal dynamics of electric power consumption in Mainland China using saturation-corrected DMSP/OLS nighttime stable light data , 2014, Int. J. Digit. Earth.

[18]  L. Zhifeng.  Understanding Electricity Consumption Changes in Chinese Mainland from 1995 to 2008 by Using DMSP/OLS Stable Nighttime Light Time Series Data , 2011 .

[19]  中華人民共和国国家統計局.  China statistical yearbook , 1988 .

[20]  C. Elvidge,et al.  Nighttime Lights Compositing Using the VIIRS Day-Night Band: Preliminary Results , 2013 .

[21]  C. P. Lo.  Modeling the population of China using DMSP operational linescan system nighttime data , 2001 .

[22]  T. Pei,et al.  Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities , 2012 .

[23]  Zheng Wang,et al.  Exploring factors affecting the relationship between light consumption and GDP based on DMSP/OLS nighttime satellite imagery , 2013 .

[24]  Jürgen Fischer,et al.  High-Resolution Imagery of Earth at Night: New Sources, Opportunities and Challenges , 2014, Remote. Sens..

[25]  Fang Yida,et al.  Modeling Population Density of China in 1998 Based on DMSP/OLS Nighttime Light Image , 2005 .

[26]  Wenze Yue,et al.  Spatial improvement of human population distribution based on multi-sensor remote-sensing data: an input for exposure assessment , 2013 .

[27]  N. Clinton,et al.  Modeling population density using land cover data , 2005 .

[28]  W. Nordhaus,et al.  Using luminosity data as a proxy for economic statistics , 2011, Proceedings of the National Academy of Sciences.

[29]  K. Seto,et al.  Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data , 2011 .

[30]  Su Yongxia.  The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries , 2013 .

[31]  G. Tana,et al.  Estimating energy consumption from night-time DMPS/OLS imagery after correcting for saturation effects , 2010 .

[32]  P. Sutton,et al.  Shedding Light on the Global Distribution of Economic Activity , 2010 .

[33]  Wenze Yue,et al.  Estimation of Gross Domestic Product Using Multi-Sensor Remote Sensing Data: A Case Study in Zhejiang Province, East China , 2014, Remote. Sens..

[34]  Ren Wangbing.  Index System and Transferring Methods to Build the National Society and Economy Grid Database , 2011 .

[35]  C. Elvidge,et al.  Development of a 2009 Stable Lights Product using DMSP-OLS data , 2010 .

[36]  V. Lakshmi.  Observation of the Earth and Its Environment—Survey of Missions and Sensors , 2003 .

[37]  GDP Spatialization in China Based on Nighttime Imagery: GDP Spatialization in China Based on Nighttime Imagery , 2012 .

[38]  Wu Jianshen.  Research on energy consumption dynamic among prefecture-level cities in China based on DMSP/OLS Nighttime Light , 2014 .

[39]  C. Elvidge,et al.  Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption , 1997 .