How Data-Poor Countries Remain Data Poor: Underestimation of Human Settlements in Burkina Faso as Observed from Nighttime Light Data

The traditional ways of measuring global sustainable development and economic development schemes and their progress suffer from a number of serious shortcomings. Remote sensing and specifically nighttime light has become a popular supplement to official statistics by providing an objective measure of human settlement that can be used as a proxy for population and economic development measures. With the increased availability and use of the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and data in social science, it has played an important role in data collection, including measuring human development and economic growth. Numerous studies are using nighttime light data to analyze dynamic regions such as expansions of urban areas and rapid industrialization often highlight the problem of saturation in urban centers with high light intensity. However, the quality of nighttime light data and its appropriateness for analyzing areas and regions with low and fluctuating levels of light have rarely been questioned or studied. This study examines the accuracy of DMSP-OLS and VIIRS-DNB by analyzing 147 communities in Burkina Faso to provide insights about problems related to the study of areas with a low intensity of nighttime light during the studied period from 1992 to 2012. It found that up to 57% of the communities studied were undetectable throughout the period, and only 9% of communities studied had a 100% detection rate. Unsurprisingly, the result provides evidence that detection rates in both datasets are particularly low (3%) for settlements with 0–9999 inhabitants, as well as for larger settlements with population of 10,000–24,999 (28%). Cross-checking with VIIRS-DNB for the year 2012 shows similar results. These findings suggest that careful consideration must be given to the use of nighttime light data in research and global comparisons to monitor the progress of the United Nation’s Sustainable Development Goals, especially when including developing countries with areas containing low electrification rates and low population density.

[1]  Ramakrishna R. Nemani,et al.  The Nightsat mission concept , 2007, International Journal of Remote Sensing.

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

[3]  F. Pozzi,et al.  MODELING THE DISTRIBUTION OF HUMAN POPULATION WITH NIGHT-TIME SATELLITE IMAGERY AND GRIDDED POPULATION OF THE WORLD , 2002 .

[4]  Souknilanh Keola,et al.  Shedding Light on the Shadow Economy: A Nighttime Light Approach , 2017 .

[5]  Budhendra L. Bhaduri,et al.  A global poverty map derived from satellite data , 2009, Comput. Geosci..

[6]  William J. Volchok,et al.  Radiometric scene normalization using pseudoinvariant features , 1988 .

[7]  M. Bennett,et al.  Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics , 2017 .

[8]  Baghdad Nights: Evaluating the US Military ‘Surge’ Using Nighttime Light Signatures , 2008 .

[9]  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..

[10]  Ola Hall,et al.  Monitoring economic development from space : using nighttime light and land cover data to measure economic growth , 2015 .

[11]  Christian Lessmann,et al.  Regional Inequality, Convergence, and its Determinants - A View from Outer Space , 2015, SSRN Electronic Journal.

[12]  Christopher N.H. Doll,et al.  Population detection profiles of DMSP-OLS night-time imagery by regions of the world , 2010 .

[13]  Sang Michael Xie,et al.  Combining satellite imagery and machine learning to predict poverty , 2016, Science.

[14]  D. Roberts,et al.  Census from Heaven: An estimate of the global human population using night-time satellite imagery , 2001 .

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

[16]  C. Murray,et al.  From wealth to health: modelling the distribution of income per capita at the sub-national level using night-time light imagery , 2005, International journal of health geographics.

[17]  Maria Francisca Archila Bustos,et al.  Population centroids of the world administrative units from nighttime lights 1992-2013 , 2019, Scientific Data.

[18]  Lu Zhang,et al.  Regional Inequality in China Based on NPP-VIIRS Night-Time Light Imagery , 2018, Remote. Sens..

[19]  C. Mellander,et al.  Night-Time Light Data: A Good Proxy Measure for Economic Activity? , 2015, PloS one.

[20]  Roland Hodler,et al.  Nighttime lights as a proxy for human development at the local level , 2017, PloS one.

[21]  Chenghu Zhou,et al.  Diverse relationships between Suomi-NPP VIIRS night-time light and multi-scale socioeconomic activity , 2014 .

[22]  T. Croft.  Nighttime Images of the Earth from Space , 1978 .

[23]  Cristina Milesi,et al.  U.S. constructed area approaches the size of Ohio , 2004 .

[24]  R. F. Grais,et al.  Explaining Seasonal Fluctuations of Measles in Niger Using Nighttime Lights Imagery , 2011, Science.

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

[26]  John Luke Gallup,et al.  Geography and Economic Development , 1998 .

[27]  Dar A. Roberts,et al.  A Comparison of Nighttime Satellite Imagery and Population Density for the Continental United States , 1997 .

[28]  W. Nordhaus.  Geography and macroeconomics: new data and new findings. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Christopher Doll,et al.  Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery , 2010 .

[30]  Xiaohua Tong,et al.  An Improved Index for Desaturation of DMSP Nighttime Light Data , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[31]  J. O’Loughlin,et al.  Detecting the Effects of Wars in the Caucasus Regions of Russia and Georgia Using Radiometrically Normalized DMSP-OLS Nighttime Lights Imagery , 2011 .

[32]  Shaohua Chen,et al.  When Economic Reform is Faster than Statistical Reform: Measuring and Explaining Inequality in Rural China , 1998 .

[33]  Weiwei Sun,et al.  Expansion Analysis of Yangtze River Delta Urban Agglomeration Using DMSP/OLS Nighttime Light Imagery for 1993 to 2012 , 2018, ISPRS Int. J. Geo Inf..

[34]  Maria Francisca Archila Bustos,et al.  Nighttime lights and population changes in Europe 1992–2012 , 2015, Ambio.

[35]  Nils B. Weidmann,et al.  Using night light emissions for the prediction of local wealth , 2017 .

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

[37]  C. Schueler,et al.  The NPOESS VIIRS Day/Night Visible Sensor , 2006 .

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

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

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

[41]  A. Dabalen,et al.  Mining in Africa: Are Local Communities Better Off? , 2017 .

[42]  T. Pei,et al.  Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in China’s cities , 2014 .

[43]  Qihao Weng,et al.  Global Urban Monitoring and Assessment through Earth Observation , 2014 .

[44]  Ola Hall,et al.  Remote Sensing in Social Science Research , 2010 .

[45]  J. Qu,et al.  Spatiotemporal characterization of the urban sprawl and its impacts on urban island in China with DMSP/OLS and MODIS measurements , 2019, Theoretical and Applied Climatology.

[46]  I. Kloog,et al.  Light at Night Co‐distributes with Incident Breast but not Lung Cancer in the Female Population of Israel , 2008, Chronobiology international.

[47]  P. Gong,et al.  Validation of urban boundaries derived from global night-time satellite imagery , 2003 .

[48]  A. Tatem,et al.  Using remotely sensed night-time light as a proxy for poverty in Africa , 2008, Population health metrics.

[49]  Noam Levin,et al.  A global analysis of factors controlling VIIRS nighttime light levels from densely populated areas , 2017 .

[50]  P. Sutton,et al.  SPECIAL ISSUE: The Dynamics and Value of Ecosystem Services: Integrating Economic and Ecological Perspectives Global estimates of market and non-market values derived from nighttime satellite imagery, land cover, and ecosystem service valuation , 2002 .

[51]  A. Maddison,et al.  Monitoring the world economy, 1820-1992 , 1995 .

[52]  C. Elvidge,et al.  National Trends in Satellite-Observed Lighting: 1992–2012 , 2011 .

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

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

[55]  G. Sun,et al.  The footprint of urban heat island effect in China , 2015, Scientific Reports.

[56]  Simon Johnson,et al.  provided that full credit, including © notice, is given to the source. Is Newer Better? Penn World Table Revisions and Their Impact on Growth Estimates , 2009 .

[57]  Noam Levin,et al.  High spatial resolution night-time light images for demographic and socio-economic studies , 2012 .

[58]  Christopher D. Elvidge,et al.  Sociodemographic Characterization of Urban Areas Using Nighttime Imagery,Google Earth, Landsat,and “Social” Ground Truthing , 2006 .