Can more accurate night-time remote sensing data simulate a more detailed population distribution?

Nannan Gao, Fen Li*, Hui Zeng, Daniël van Bilsen, WM (Martin) de Jong

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)


Aging, shrinking cities, urban agglomerations and other new key terms continue to emerge when describing the large-scale population changes in various cities in mainland China. It is important to simulate the distribution of residential populations at a coarse scale to manage cities as a whole, and at a fine scale for policy making in infrastructure development. This paper analyzes the relationship between the DN (Digital number, value assigned to a pixel in a digital image) value of NPP-VIIRS (the Suomi National Polar-orbiting Partnership satellite's Visible Infrared Imaging Radiometer Suite) and LuoJia1-01 and the residential populations of urban areas at a district, sub-district, community and court level, to compare the influence of resolution of remote sensing data by taking urban land use to map out auxiliary data in which first-class (R1), second-class (R2) and third-class residential areas (R3) are distinguished by house price. The results show that LuoJia1-01 more accurately analyzes population distributions at a court level for second- and third-class residential areas, which account for over 85% of the total population. The accuracy of the LuoJia1-01 simulation data is higher than that of Landscan and GHS (European Commission Global Human Settlement) population. This can be used as an important tool for refining the simulation of residential population distributions. In the future, higher-resolution night-time light data could be used for research on accurate simulation analysis that scales down large-scale populations.

Original languageEnglish
Article number4488
JournalSustainability (Switzerland)
Issue number16
Publication statusPublished - 19 Aug 2019

Bibliographical note

Funding Information:
We thank the anonymous reviewers for their valuable comments. We further express our gratitude to the Wuhan University, NOAA and Oak Ridge National Laboratory for providing the supporting data from LuoJia1-01, NPP-VIIRS and LandScan Global, respectively. We would also like to thank the European Commission EU Science Hub for the GHS Population Grid data.

Funding Information:
Funding: This research was funded by International S&T Cooperation Projects of Inter-government: Technologies and demonstration of integrated energy system & carbon emission research on city level grant number 2017YFE0101700 and Research on Urban Livable Space Smart Model & Individual Decision Support System, grant number JSGG20170413173425899.

Publisher Copyright:
© 2019 by the authors.

Research programs

  • SAI 2008-06 BACT


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