Estimating Population Density Using DMSP-OLS Night-Time Imagery and Land Cover Data

Population density is an essential indicator of human society. Night-time light (NTL) data provided by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) has been widely used in estimating population distribution, due to its capability of indicating human activity. The overglow effect of the DMSP-OLS NTL image caused by reflection of light from adjacent areas and the different population distribution patterns between urban and rural areas have limited its application in estimating population density. Therefore, a method was proposed to reduce the overglow effect and to model urban and rural population densities separately. Moderate resolution imaging spectroradiometer (MODIS) land cover product was applied to reduce the overglow effect and to separate urban and rural areas. In urban area, the extracted urban DMSP-OLS NTL image was used to model population density. In rural area, a slope adjusted human settlement index (SAHSI), based on digital elevation model, MODIS enhanced vegetation index (EVI), and the DMSP-OLS NTL data, was proposed to estimate rural population density. Guangdong Province of China was taken as the study area for it has diverse population densities. The estimation in urban area was compared with population densities derived from normalized difference vegetation index adjusted NTL urban index (VANUI) and EVI adjusted NTL urban index (VANUI-EVI). Population density in the rural area was compared with results from EVI adjusted human settlement index (HSI-EVI) and the NTL data. The mean relative error of the proposed method was 55.14% in urban areas, which was better than VANUI (60.10%) and VANUI-EVI (60.16%), and was 71% in rural areas, which was 6% lower than HSI-EVI and 3% lower than NTL data. The result indicates that the proposed method has the ability to reduce the overglow effect of DMSP-OLS NTL image and to correct the impact of terrain on rural population density estimation.

[1]  J. E. Dobson,et al.  LandScan: A Global Population Database for Estimating Populations at Risk , 2000 .

[2]  P. C..  Modeling the Population of China Using DMSP Operational Linescan System Nighttime Data , 2005 .

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

[4]  U. Deichmann,et al.  World population in a grid of spherical quadrilaterals. , 1997, International journal of population geography : IJPG.

[5]  Ramakrishna R. Nemani,et al.  International Journal of Remote Sensing the Nightsat Mission Concept the Nightsat Mission Concept , 2022 .

[6]  P. Sutton,et al.  Building and Evaluating Models to Estimate Ambient Population Density , 2003 .

[7]  Zhang Shuwen,et al.  Simulating Urban-rural Population Distribution Based Remote Sensing:Taking Songyuan City As a Case Study , 2007 .

[8]  M. Friedl,et al.  A new map of global urban extent from MODIS satellite data , 2009 .

[9]  Jean-Michel Guldmann,et al.  Landscape ecology, land-use structure, and population density: Case study of the Columbus Metropolitan Area , 2012 .

[10]  Shixin Wang,et al.  Population spatialization in China based on night-time imagery and land use data , 2011 .

[11]  Paul C. Sutton,et al.  A scale-adjusted measure of Urban sprawl using nighttime satellite imagery , 2003 .

[12]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[13]  Christopher D. Elvidge,et al.  Overview of DMSP nightime lights and future possibilities , 2009, 2009 Joint Urban Remote Sensing Event.

[14]  K. Seto,et al.  The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity , 2013 .

[15]  Yang Yang,et al.  Application of DMSP/OLS Nighttime Light Images: A Meta-Analysis and a Systematic Literature Review , 2014, Remote. Sens..

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

[17]  Dengsheng Lu,et al.  Regional mapping of human settlements in southeastern China with multisensor remotely sensed data , 2008 .

[18]  C. Elvidge,et al.  Spatial analysis of global urban extent from DMSP-OLS night lights , 2005 .

[19]  Jack T. Harvey,et al.  Estimating census district populations from satellite imagery: Some approaches and limitations , 2002 .

[20]  C. Small.  Estimation of urban vegetation abundance by spectral mixture analysis , 2001 .

[21]  E. R. Stoner,et al.  Characteristic variations in reflectance of surface soils , 1981 .

[22]  Norman Kerle,et al.  Quantifying urban land use and impact on soils in Egypt using diurnal satellite imagery of the Earth surface , 2002 .

[23]  Christopher Small,et al.  Analysis of Urban Land Cover and Population Density in the United States , 2005 .

[24]  Jordan Chamberlin,et al.  How does population density influence agricultural intensification and productivity? Evidence from Malawi , 2014 .

[25]  C. Elvidge,et al.  A Technique for Using Composite DMSP/OLS "City Lights"Satellite Data to Map Urban Area , 1997 .

[26]  Haijuan Qiao,et al.  An empirical study on the spatial distribution of the population, economy and water resources in Northeast China , 2015 .

[27]  Brane Širok,et al.  Towards more detailed determination of third party impact on risk on natural gas pipelines: Influence of population density , 2015 .

[28]  Thomas S. Jayne,et al.  Effects of rising rural population density on smallholder agriculture in Kenya , 2014 .

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

[30]  P. Sutton.  Modeling population density with night-time satellite imagery and GIS , 1997 .

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

[32]  Zhang Shuwen,et al.  The effects of population density changes on ecosystem services value: A case study in Western Jilin, China , 2016 .

[33]  Xiangzheng Deng,et al.  Surface modelling of human population distribution in China , 2005 .

[34]  Chong Liu,et al.  The Integrated Use of DMSP-OLS Nighttime Light and MODIS Data for Monitoring Large-Scale Impervious Surface Dynamics: A Case Study in the Yangtze River Delta , 2014, Remote. Sens..

[35]  Patrizia Tenerelli,et al.  Population density modelling in support of disaster risk assessment , 2015 .

[36]  Shushen Zhang,et al.  A dynamic model for population mapping: a methodology integrating a Monte Carlo simulation with vegetation-adjusted night-time light images , 2015 .

[37]  Jin Chen,et al.  Modelling the population density of China at the pixel level based on DMSP/OLS non‐radiance‐calibrated night‐time light images , 2009 .