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Urban Mapping & Modeling

Our goal of this research is to improve the understanding of global historical urban expansion, its socioeconomic drivers, and potential future urban expansion. We propose an interdisciplinary research program to achieve our research goal through four objectives:

  1. Building a consistent global urban map series.
  2. Analyzing global urbanization and its driving forces and developing a region-specific macro-scale statistical model.
  3. Developing an integrated framework to project future urban expansion.
  4. Exploring scenarios of urbanization projection and its implications.

Understanding historical global urban dynamics and future urban expansion, especially its spatial dynamic, will enable land managers and decision makers to explore future urban dynamics under certain scenarios, and therefore direct urban development under the framework of global climate change mitigation. This research aims to answer several key science questions identified in the LCLUC research program, including: Where is urban growth, what is the extent and over what time scale and how do the changes vary from year to year, and what are the causes? What are the projected urbanization and its potential impacts?

Relevant Publications (* Corresponding author)

  1. He, W., X. Li, Y. Zhou, X. Liu, P. Gong, T. Hu, P. Yin, J. Huang, J. Yang, S. Miao, X. Wang & T. Wu (2023) Modeling gridded urban fractional change using the temporal context information in the urban cellular automata model. Cities, 133, 104146.
  2. Zhou, Y.*, X. Li, W. Chen, L. Meng, Q. Wu, P. Gong & K. C. Seto (2022) Satellite mapping of urban built-up heights reveals extreme infrastructure gaps and inequalities in the Global South. Proceedings of the National Academy of Sciences, 119, e2214813119.
  3. Zhang, X., S. Du, Y. Zhou & Y. Xu (2022) Extracting physical urban areas of 81 major Chinese cities from high-resolution land uses. Cities, 131, 104061.
  4. Mu, H., C. Xu, D. Liu, X. Li, Y. Zhou, P. Gong, J. Huang, X. Du, J. Guo, W. Cao & Z. Sun (2022) Identifying discrepant regions in urban mapping from historical and projected global urban extents. All Earth
  5. Zhang, Y., G. Chen, S. W. Myint, Y. Zhou, G. J. Hay, J. Vukomanovic & R. K. Meentemeyer (2022) UrbanWatch: A 1-meter resolution land cover and land use database for 22 major cities in the United States. Remote Sensing of Environment, 278, 113106.
  6. Li, X., Y. Zhou*, P. Gong (2022). Diversity in global urban sprawl patterns revealed by Zipfian dynamics. Doi: 10.1080/2150704X.2022.2073794
  7. Zheng, Q., Q. Weng, Y. Zhou & B. Dong (2022) Impact of temporal compositing on nighttime light data and its applications. Remote Sensing of Environment, 274, 113016.
  8. Zhao, M., C. Cheng, Y. Zhou*, X. Li, S. Shen & C. Song (2022). A global dataset of annual urban extents (1992-2020) from harmonized nighttime lights. Earth Syst. Sci. Data. 14, 517–534.
  9. Li, X., Y. Zhou*, M. Hejazi, M. Wise, C. Vernon, G. Iyer & W. Chen (2021) Global urban growth between 1870 and 2100 from integrated high resolution mapped data and urban dynamic modeling. Communications Earth & Environment, 2, 201.
  10. Li, X., J. Zhang, Z. Li, T. Hu, Q. Wu, J. Yang, J. Huang, W. Su, Y. Zhao, Y. Zhou, X. Liu, P. Gong & X. Wang (2021) Critical role of temporal contexts in evaluating urban cellular automata models. GIScience & Remote Sensing, 1-13.
  11. Zhao, X., Y. Zhou*, W. Chen, X. Li, X. Li, & D. Li. (2021) Mapping hourly population dynamics using remotely sensed and geo-spatial data: a case study in Beijing, China. GIScience & Remote Sensing, doi:10.1080/15481603.2021.1935128.
  12. Chen, Z., B. Yu, C. Yang, Y. Zhou, S. Yao, X. Qian, C. Wang, B. Wu & J. Wu (2021) An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth Syst. Sci. Data, 13, 889-906.
  13. Cao, W., Y. Zhou*, R. Li*, X. Li & H. Zhang (2021) Monitoring long-term annual urban expansion (1986–2017) in the largest archipelago of China. Science of The Total Environment, 776, 146015.
  14. Li, X., Y. Zhou*, M. Zhao & X. Zhao (2020) A harmonized global nighttime light dataset 1992–2018. Scientific Data, 7, 168.
  15. Li, X., Y. Zhou*, & W. Chen (2020) An improved urban cellular automata model by using the trend-adjusted neighborhood. Ecological Processes, 9, 28.
  16. Zhao, M., Y. Zhou*., X. Li, W. Cheng*, C. Zhou, T. Ma, M. Li & K. Huang (2020) Mapping urban dynamics (1992–2018) in Southeast Asia using consistent nighttime light data from DMSP and VIIRS. Remote Sensing of Environment, 248, 111980.
  17. Li, X.C., Gong, P*., Y. Zhou*.Y., Wang, J., Bai, Y.Q., Chen, B., Hu, T.Y., Xiao, Y.X., Xu, B., Yang, J., Liu, X.P., Cai, W.J., Huang, H.B., Wu, T.H., Wang, X., Lin, P., Li, X., Chen, J., He, C.Y., Li, X., Yu, L., Clinton, N., Zhu, Z.L (2020). Mapping global urban boundaries from the global artificial impervious area (GAIA) data. Environmental Research Letters. Doi: 10.1088/1748-9326/ab9be3
  18. Li, X., Y. Zhou*, Z. Zhu & W. Cao, 2020. A national dataset of 30m annual urban extent dynamics (1985–2015) in the conterminous United States. Earth Syst. Sci. Data, 12, 357-371.
  19. Li, X., Y. Zhou*, P. Gong, K. C. Seto & N. Clinton, 2020. Developing a method to estimate building height from Sentinel-1 data. Remote Sensing of Environment, 240, 111705.
  20. Gong, P., X. Li, J. Wang, Y. Bai, B. Chen, T. Hu, X. Liu, B. Xu, J. Yang, W. Zhang & Y. Zhou, 2020. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236, 111510.
  21. Zhao, M., Y. Zhou*, X. Li, C. Zhou, W. Cheng, M. Li & K. Huang, 2019. Building a Series of Consistent Night-Time Light Data (1992-2018) in Southeast Asia by Integrating DMSP-OLS and NPP-VIIRS. IEEE Transactions on Geoscience and Remote Sensing, 1-14.
  22. Zhao, M., Y. Zhou *, X. Li, W. Cao, C. He, B. Yu, X. Li, C. D. Elvidge, W. Cheng & C. Zhou, 2019. Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives. Remote Sensing, 11, 1971.
  23. Li, X., Y. Zhou *, J. Eom, S. Yu & G. R. Asrar, 2019. Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Shared Socioeconomic Pathways. Earth's Future, 7, 351-362
  24. Zhu, Z., Y. Zhou, K. C. Seto, E. C. Stokes, C. Deng, S. T. A. Pickett & H. Taubenböck, 2019. Understanding an urbanizing planet: Strategic directions for remote sensing. Remote Sensing of Environment. 228, 164-182.
  25. Chen, Z., B. Yu, Y. Zhou*, H. Liu, C. Yang, K. Shi & J. Wu,2019. Mapping Global Urban Areas From 2000 to 2012 Using Time-Series Nighttime Light Data and MODIS Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1143-1153
  26. Zhou, Y., X. Li, G. R. Asrar, S. J. Smith and M. Imhoff, 2018. A global record of annual urban dynamics (1992–2013) from nighttime lights. Remote Sensing of Environment 219: 206-220.
  27. Li, X. & Y. Zhou *, Z. Zhu, L. Liang, B. Yu, W. Cao, 2018. Mapping annual urban dynamics (1985–2015) using time series of Landsat data. Remote Sensing of Environment, 216, 674-683.
  28. Li, X. & Y. Zhou *, 2017. A Stepwise Calibration of Global DMSP/OLS Stable Nighttime Light Data (1992–2013). Remote Sensing, 9, 637.
  29. Li, X. & Y. Zhou *, 2017. Urban mapping using DMSP/OLS stable night-time light: a review. International Journal of Remote Sensing.
  30. Zhou, Y., S. J. Smith, K. Zhao, M. Imhoff, A. Thomson, B. Bond-Lamberty, G. R. Asrar, X. Zhang, C. He & C. D. Elvidge (2015) A global map of urban extent from nightlights. Environmental Research Letters, 10, 054011
  31. Zhao, N., Y. Zhou* , & E. L. Samson, 2015, Correcting Incompatible DN Values and Geometric Errors in Nighttime Lights Time-Series Images. IEEE Transactions on Geoscience and Remote Sensing, 53, 2039 - 2049.
  32. Zhou, Y., SJ Smith, CD Elvidge, K Zhao, A Thomson, M Imhoff, 2014. A Cluster-based Method to Map Urban Area from DMSP/OLS Nightlights. Remote Sensing of Environment. 147, 173-185
  33. Liu, Z., C. He, Y. Zhou, J. Wu, 2014. How much of the world's land has been urbanized, really? A hierarchical framework for avoiding confusion. Landscape Ecology, 29, 763-771.
  34. Zhou, Y., and Y. Wang, 2008. Extraction of Impervious Surface Areas from High Spatial Resolution Imageries by Multiple Agent Segmentation and Classification. Photogrammetric Engineering and Remote Sensing. 74(7), 857-868.
  35. Zhou, Y., and Y. Wang, 2007. An Assessment of Impervious Surface Areas in Rhode Island. Northeastern Naturalist. 14(4), 643-650.

urban built-up heights