Evaluating the potential of nighttime satellite imagery to analyze urban dynamics: a case study of the Tashkent Region, Uzbekistan

DOI: 10.35595/2414-9179-2025-1-31-654-670

View or download the article (Eng)

About the Authors

Lola Gulyamova

Tashkent State Technical University named after Islam Karimov,
2, Universitetskaya str., Tashkent, 100174, Uzbekistan,
E-mail: lolagulyam@gmail.com

Nargiza Abdullaeva

Tashkent State Technical University named after Islam Karimov,
2, Universitetskaya str., Tashkent, 100174, Uzbekistan,
E-mail: abdullaeva.nargiza@tdtu.uz

Ilkhomjon Abdullaev

National University of Uzbekistan named after Mirzo Ulugbek,
4, Universitetskaya str., Tashkent, 100174, Uzbekistan,
E-mail: ilkhomjon.abdullaev@gmail.com

Risolat Nizomova

Tashkent State Technical University named after Islam Karimov,
2, Universitetskaya str., Tashkent, 100174, Uzbekistan,
E-mail: risolatnizomovatdtu@gmail.com

Sattarbergan Avezov

Urgench State University named after Abu Rayhan Biruni,
14, Kh. Alimdjana str., Tashkent, 220100, Uzbekistan,
E-mail: avezovsattarbergan@gmail.com

Abstract

Assessing urban dynamics is a critical prerequisite for fostering sustainable regional development, particularly in rapidly urbanizing areas experiencing intense demographic and economic transformation. This study investigates the potential of nighttime satellite imagery—specifically data from the Suomi National Polar-orbiting Partnership (Suomi NPP) and its Visible Infrared Imaging Radiometer Suite (VIIRS)—to serve as a proxy for tracking spatial and temporal patterns of urban growth in the Tashkent Region of Uzbekistan, a key economic hub in Central Asia undergoing significant structural change. Drawing on a comprehensive dataset comprising 11 664 radiance observations across 81 urban settlements between 2012 and 2023, we analyze metrics of radiance growth, intensity distribution, temporal frequency, and variability to characterize urban trajectories. Our findings reveal robust correlations between nighttime light radiance and both economic activity and urban expansion, enabling the classification of settlements into three distinct categories of urban dynamism: low, moderate, and high. Notably, in some cases—such as Bekabad—radiance aligns more closely with industrial output than with population size, underscoring the method’s sensitivity to economic structure. The approach demonstrates high scalability, cost-efficiency, and reliability, especially in contexts where official socio-economic statistics are sparse, inconsistent, or delayed. By validating radiance as a robust indicator of urban vitality, this research establishes a foundation for integrating remote sensing data with conventional planning tools to support evidence-based decision-making in regional policy, infrastructure investment, and sustainable urban management. The developed methodological framework is readily transferable to other regions facing similar data limitations, offering a replicable model for monitoring urban change in the Global South.

Keywords

night-time satellite imagery, VIIRS, Suomi NPP, urban dynamics, Tashkent Region, remote sensing

References

  1. Abdullaev I., Nasirov A., Pirimov J., Abdullaeva N. Integrated Information System for Cadastre based on GIS and Web Technologies. 2021 International Conference on Information Science and Communications Technologies (ICISCT), 2022. P. 1–3. DOI: 10.1109/ICISCT55600.2022.10146844.
  2. Bagan H., Yamagata Y. Analysis of Urban Growth and Estimating Population Density using Satellite Images of Nighttime Lights and Land-Use and Population Data. GIScience & Remote Sensing, 2015. V. 52. No. 6. P. 765–780. DOI: 10.1080/15481603.2015.1072400.
  3. Bennett M.M., Smith L.C. Advances in Using Multitemporal Night-Time Lights Satellite Imagery to Detect, Estimate, and Monitor Socioeconomic Dynamics. Remote Sensing of Environment, 2017. V. 192. P. 176–197. DOI: 10.1016/j.rse.2017.01.005.
  4. Chen Z., Yu S., You X., Yang Ch. New Nighttime Light Landscape Metrics for Analyzing Urban-Rural Differentiation in Economic Development at Township: A Case Study of Fujian Province, China. Applied Geography, 2022. V. 150. Art. 102841. DOI: 10.1016/j.apgeog.2022.102841.
  5. Coscieme L., Sutton P.C., Anderson S., Liu Q., Elvidge C.D. Dark Times: Nighttime Satellite Imagery as a Detector of Regional Disparity and the Geography of Conflict. GIScience & Remote Sensing, 2016. V. 54. No. 1. P. 118–139. DOI: 10.1080/15481603.2016.1260676.
  6. Doll C.N.H., Muller J.-P., Morley J.G. Mapping Regional Economic Activity from Night-Time Light Satellite Imagery. Ecological Economics, 2005. V. 57. No. 1. P. 75–92. DOI: 10.1016/j.ecolecon.2005.03.007.
  7. Dong H., Li R., Li J., Li S. Study on Urban Spatiotemporal Expansion Pattern of Three First-class Urban Agglomerations in China Derived from Integrated DMSP-OLS and NPP-VIIRS Nighttime Light Data. Journal of Geo-information Science, 2020. V. 22. No. 5. P. 1161–1174. DOI: 10.12082/dqxxkx.2020.190711.
  8. Elvidge C.D., Baugh K.E., Anderson S.J., Sutton P.C., Ghosh T. The Night Light Development Index (NLDI): A Spatially Explicit Measure of Human Development from Satellite Data. Social Geography, 2012. V. 7. No. 1. P. 23–35. DOI: 10.5194/sg-7-23-2012.
  9. Elvidge C.D., Baugh K.E., Zhizhin M., Hsu F.-C. Why VIIRS Data are Superior to DMSP for Mapping Nighttime Lights. Proceedings of the Asia-Pacific Advanced Network, 2013. V. 35. P. 62–69. DOI: 10.7125/APAN.35.7.
  10. Elvidge C.D., Cinzano P., Pettit D.R., Aversen J., Sutton P., Small C., Nemani R., Longcore T., Rich C., Safran J., Weeks J., Ebener S. The Nightsat Mission Concept. International Journal of Remote Sensing, 2007. V. 28. No. 12. P. 2645–2670. DOI: 10.1080/01431160600981525.
  11. Fonseca Flores A., Oro Boff V.M., Freitas Silveira Netto C., Brei V., Limongi R. Using Nightlight Satellite Imagery to Predict Energy Consumption in Multiple Spatial-Temporal Aggregations with Machine Learning. SSRN, 2023. 38 p. DOI: 10.2139/ssrn.4599953. Web resource: https://ssrn.com/abstract=4599953 (accessed 06.08.2025).
  12. Levin N., Kyba Ch., Zhang Q., Sánchez A., Román M., Li X., Portnov B., Molthan A., Jechow A., Miller S., Wang Z., Shrestha R., Elvidge C. Remote Sensing of Night Lights: A Review and an Outlook for the Future. Remote Sensing of Environment, 2020. V. 237. Art. 111443. DOI: 10.1016/j.rse.2019.111443.
  13. Levin N., Phinn S. Illuminating the Capabilities of Landsat-8 for Mapping Night Lights. Remote Sensing of Environment, 2016. V. 182. P. 27–38. DOI: 10.1016/j.rse.2016.04.021.
  14. Levin N., Zhang Q. A Global Analysis of Factors Controlling VIIRS Nighttime Light Levels from Densely Populated Areas. Remote Sensing of Environment, 2017. V. 190. P. 366–382. DOI: 10.1016/j.rse.2017.01.006.
  15. Li X., Elvidge C.D., Zhou Y., Cao Ch., Warner T. Remote Sensing of Night-Time Light. International Journal of Remote Sensing, 2017. V. 38. No. 21. P. 5855–5859. DOI: 10.1080/01431161.2017.1351784.
  16. Li J., He S., Wang J., Ma W., Ye H. Investigating the Spatiotemporal Changes and Driving Factors of Nighttime Light Patterns in RCEP Countries based on Remote Sensed Satellite Images. Journal of Cleaner Production, 2022. V. 359. Art. 131944. DOI: 10.1016/j.jclepro.2022.131944.
  17. Li Yu, Ye H., Gao X., Sun D., Li Z., Zhang N., Leng X., Meng D., Zheng J. Spatiotemporal Patterns of Urbanization in the Three Most Developed Urban Agglomerations in China based on Continuous Nighttime Light Data (2000–2018). Remote Sensing, 2021. V. 13. No. 12. Art. 2245. DOI: 10.3390/rs13122245.
  18. Liu Q., Sutton P.C., Elvidge C.D. Relationships between Nighttime Imagery and Population Density for Hong Kong. Proceedings of the Asia-Pacific Advanced Network, 2011. V. 31. P. 79. DOI: 10.7125/APAN.31.9.
  19. Madrimov R., Tashtaeva S., Kasimov Kh., Yusupov H. Modern Features of Urban Development: A Case Study from Uzbekistan. E3S Web of Conferences, 2024. V. 563. Art. 02001. DOI: 10.1051/e3sconf/202456302001.
  20. Manesha E.P.P., Jayasinghe A., Kalpana H.N. Measuring Urban Sprawl of Small and Medium Towns using GIS and Remote Sensing Techniques: A Case Study of Sri Lanka. The Egyptian Journal of Remote Sensing and Space Science, 2021. V. 24. No. 3. P. 1051–1060. DOI: 10.1016/j.ejrs.2021.11.001.
  21. Martínez M.L., Intralawan A., Vázquez G., Pérez-Maqueo O., Sutton P., Landgrave R. The Coasts of Our World: Ecological, Economic and Social Importance. Ecological Economics, 2007. V. 63. No. 2-3. P. 254–272. DOI: 10.1016/j.ecolecon.2006.10.022.
  22. Mellander C., Lobo J., Stolarick K., Matheson Z. Night-Time Light Data: A Good Proxy Measure for Economic Activity? PLoS ONE, 2015. V. 10. Iss. 10. Art. e0139779. DOI: 10.1371/journal.pone.0139779.
  23. Mokhtari Z., Bergantino A.S., Intini M. Nighttime Light Extent and Intensity Explain the Dynamics of Human Activity in Coastal Zones. Scientific Reports, 2025. V. 15. Art. 1663. DOI: 10.1038/s41598-025-85917-z.
  24. Morales-Arilla J., Gadgin Matha S. GLocal: A Global Development Dataset of Subnational Administrative Areas. Scientific Data, 2024. V. 11. Art. 851. DOI: 10.1038/s41597-024-03539-y.
  25. Muxtarov B.A., Murotjonova M.D., Sharafullina R.R. Analysis of Dynamic Population Growth in the Republic of Uzbekistan. Journal of Contemporary World Studies, 2024. V. 2. No. 3. P. 45–52. Web resource: https://bestjournalup.com/index.php/jcws/article/view/muxt (accessed 07.08.2025).
  26. Narziev A. Development of Urban Development in the Territory of Uzbekistan. Central Asian Journal of Theoretical and Applied Sciences, 2021. V. 2. No. 10. P. 24–26. Web resource: https://cajotas.centralasianstudies.org/index.php/CAJOTAS/article/view/238 (accessed 05.07.2025).
  27. Salimova Y. The Concept of Urbanization Development and Regulation in Uzbekistan. Science and Innovations, 2022. No. 11. P. 65–71. DOI: 10.29235/1818-9857-2022-11-65-71.
  28. Sarstedt M., Mooi E. A Concise Guide to Market Research: The Process, Data, and Methods using IBM SPSS Statistics. 2nd ed. Springer, 2014. DOI: 10.1007/978-3-642-53965-7.
  29. Schober P., Boer C., Schwarte L.A. Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia & Analgesia, 2018. V. 126. No. 5. P. 1763–1768. DOI: 10.1213/ANE.0000000000002864.
  30. Sutton P.C. A Scale-Adjusted Measure of “Urban Sprawl” using Nighttime Satellite Imagery. Remote Sensing of Environment, 2003. V. 86. No. 3. P. 353–369. DOI: 10.1016/s0034-4257(03)00078-6.
  31. Sutton P.C., Costanza R. Global Estimates of Market and Non-Market Values Derived from Nighttime Satellite Imagery, Land Cover, and Ecosystem Service Valuation. Ecological Economics, 2002. V. 41. No. 3. P. 509–527. DOI: 10.1016/s0921-8009(02)00097-6.
  32. Sutton P., Roberts D., Elvidge C., Baugh K. Census from Heaven: An Estimate of the Global Human Population using Night-Time Satellite Imagery. International Journal of Remote Sensing, 2001. V. 22. Iss. 16. P. 3061–3076. DOI: 10.1080/01431160010007015.
  33. Tashtaeva S.K., Bakhramova S.Sh., Kosimov Kh.S. Urbanization and Geo-Urban Situation in Uzbekistan. Indiana Journal of Humanities and Social Sciences, 2022. V. 3. No. 6. P. 1–5. Web resource: https://indianapublications.com/journal/IJHSS (accessed 07.07.2025).
  34. Wang X., Rafa M., Moyer J., Li J. Estimation and Mapping of Sub-National GDP in Uganda using NPP-VIIRS Imagery. Remote Sensing, 2019. V. 11. No. 2. Art. 163. DOI: 10.3390/rs11020163.
  35. Wang X., Sutton P.C., Qi B. Global Mapping of GDP at 1 km² using VIIRS Nighttime Satellite Imagery. ISPRS International Journal of Geo-Information, 2019. V. 8. No. 12. P. 580. DOI: 10.3390/ijgi8120580.
  36. Xu J., Song J., Li B., Liu D. Combining Night Time Lights in Prediction of Poverty Incidence at the County Level. Applied Geography, 2021. V. 135. Art. 102552. DOI: 10.1016/j.apgeog.2021.102552.
  37. Yang Z., Chen Y., Guo G., Zheng Z. Using Nighttime Light Data to Identify the Structure of Polycentric Cities and Evaluate Urban Centers. The Science of the Total Environment, 2021. V. 780. Art. 146586. DOI: 10.1016/j.scitotenv.2021.146586.

For citation: Gulyamova L., Abdullaeva N., Abdullaev I., Nizomova R., Avezov S. Evaluating the potential of nighttime satellite imagery to analyze urban dynamics: a case study of the Tashkent Region, Uzbekistan. InterCarto. InterGIS. Moscow: MSU, Faculty of Geography, 2025. V. 31. Part 1. P. 654–670. DOI: 10.35595/2414-9179-2025-1-31-654-670