ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 6, pp. 210-221

Satellite based night lights data as an indicator of general socio-economic development of regions of Russia

I.Yu. Savin 1, 2 , D. Stathakis 3 , P.A. Dokukin 2 
1 V.V. Dokuchaev Soil Science Institute, Moscow, Russia
2 Agrarian-Technological Institute RUDN, Moscow, Russia
3 University of Thessaly, Volos, Greece
Accepted: 16.05.2017
DOI: 10.21046/2070-7401-2017-14-6-210-221
The possibilities of evaluation of the main socio-economic indicators of Russian regions based on the analysis of night lights satellite data DMSP/OLS were analyzed. We used an archive of satellite data for the period from 1993 to 2013 and statistical data on the number of urban, rural population and the gross regional product of the constituent entities of the Russian Federation. Satellite data were previously smoothed based on a specially developed approach. It was found that DMSP/OLS night lights satellite data correlate with indicators characterizing the social and economic state of Russian regions and can be used as a proxy for monitoring the general state of the regions. The most reliable indicator characterizing the regional gross product is the night lights of urban areas. The average night lights for the regions is well correlated with the total population and the number of urban population in the regions. The ranking of regions of Russia on the specifics of the change in the indicators of night lights has been carried out. A satellite analysis of the night lights of Russian regions showed that from 1993 to 2001–2002, the socio-economic situation in most regions worsened, and since 2002, a positive trend has emerged in most regions.
Keywords: DMSP/OLS, population, regional gross value, satellite monitoring, Russia
Full text


  1. Aerokosmicheskie metody i geoinformatsionnye tekhnologii v lesovedenii, lesnom khozyaistve i ekologii Doklady VI Vserossiiskoi konferentsii (Moskva, 20–22 Aprelya 2016 g.) (Aero-Space methods and geoinformatic technologies in forest study, forestry and ecology), Moscow: TsEPL RAN, 2016, 230 p.
  2. Zhizhin M. N., Elvidzh K., Poida A. A., Godunov A. I., Velikhov V. E., Erokhin G. N., Alsynbaev K. S., Bryksin V. M. Ispol’zovanie dannykh DZZ dlya monitoringa dobychi uglevodorodov (Using ERS data for the hydrocarbons extraction’s monitoring), Informatsionnye tekhnologii i vychislitel’nye sistemy, 2014, No. 3, pp. 97–111.
  3. Kushnyr’ O. V. Razrabotka metodiki opredeleniya plotnosti naseleniya po nochnym snimkam DMSP OLS (Elaboration of methods for population density assessment based on DMSP OLS images), Izvestiya vuzov. Geodeziya i aerofotos»emka, 2014, No. 1, pp. 66–70.
  4. Loupian E. A., Balashov I. V., Burtsev M. A., Savorskii V. P., Karelov A. I., Shcheglov M. A. Razrabotka tekhnologii sputnikovogo monitoringa zheleznodorozhnoi infrastruktury (Elaboration of the technology of real way infrastructure monitoring), IV Intern. Conf. “IntellektTrans-2014”, Book of Abstracts, Saint Petersburg: PGUPS, 2014, p. 7.
  5. Agro-meteorological Monitoring in Russia and Central Asian Countries, Ispra: OPOCE, 2006, 214 p.
  6. Becker-Reshef I., Justice C., Sullivan M., Vermote E., Tucker C., Anyamba A., Small J. Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project, Remote Sensing, 2010, Vol. 2(6), pp. 1589–1609.
  7. Cova T., Sutton P., Theobald D. Exurban change detection in fire prone areas with night time satellite imagery, Photogramm. Eng. Remote Sens., 2004, Vol. 70, No. 11, pp. 1249–1257.
  8. Elvidge C., Baugh K., Dietz J., Bland T., Sutton P., Kroehl H. Radiance Calibration of DMSP-OLS Low-Light Imaging Data of Human Settlements, Remote Sensing of Environment, 1999, Vol. 68, No. 1, pp. 77–88.
  9. Elvidge C., Erwin E., Baugh K., Ziskin D., Tuttle B., Ghosh T., Sutton P. Overview of DMSP night time lights and future possibilities, Urban Remote Sens. Jt. Event, 2009, pp. 1–5.
  10. Fan J., Ma T., Zhou C., Zhou Y., Xu T. Comparative estimation of urban development in China’s cities using socioeconomic and DMSP/OLS night light data, Remote Sensing, 2014, Vol. 6, No. 8, pp. 7840–7856.
  11. Gao B., Huang Q., He C., Ma Q. Dynamics of urbanization levels in China from 1992 to 2012: perspective from DMSP/OLS night time light data, Remote Sensing, 2015, Vol. 7, No. 2, pp. 1721–1735.
  12. Ghosh T., Powell R., Elvidge C., Baugh K., Sutton P., Anderson S. Shedding light on the global distribution of economic activity, Open Geography J., 2010, No. 3, pp. 148–161.
  13. Liu Z., He C., Zhang Q., Huang Q., Yang Y. Extracting the dynamics of urban expansion in China using DMSP–OLS night time light data from 1992 to 2008, Landscape and Urban Planning, 2012, Vol. 106, pp. 62–72.
  14. Ma T., Zhou C., Pei T., Haynie S., Fan J. Quantitative estimation of urbanization dynamics using time series of DMSP/OLS night time light data: a comparative case study from China’s cities, Remote Sens. Environ., 2012, Vol. 124, pp. 99–107.
  15. Rembold F., Atzberger C., Savin I., Rojas O. Using low resolution satellite imagery for yield prediction and yield anomaly detection, Remote Sensing, 2013, Vol. 5, No. 4, pp. 1704–1733.
  16. Small C., Elvidge C. Night of Earth: mapping decadal changes of anthropogenic night light in Asia, Int. J. Applied Earth Observation and Geoinformation, 2013, Vol. 22, pp. 40–52.
  17. Stathakis D., Perakis K., Savin I. Efficient segmentation of urban areas by the VIBI, Intern. J. Remote Sensing, 2012, Vol. 33, No. 20, pp. 6361–6377.
  18. Stathakis D., Tselios V., Faraslis I. Urbanization in European regions based on nightlights, Remote Sensing Applications: Society and Environment, 2015, No. 2, pp. 26–34.
  19. Sutton P. Modeling population density with night-time satellite imagery and GIS, Computers, Environment and Urban Systems, 1997, Vol. 21, pp. 227–244.
  20. White J. C., Wulder M. A., Varhola A., Vastaranta M., Coops N. C., Cook B. D., Pitt D., Woods M. A best practice’s guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach, Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Victoria, BC, Information Report FI-X-010, 2013, 50 p.
  21. Wu B., Meng J., Li Q., Yan N., Du X., Zhang M., Remote sensing-based global crop monitoring: experiences with China’s CropWatch system, International Journal of Digital Earth, 2014, Vol. 7, No. 2, pp. 113–137.