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
  1. Li, X. & Y. Zhou *, 2017. A Stepwise Calibration of Global DMSP/OLS Stable Nighttime Light Data (1992–2013). Remote Sensing, 9, 637.
  2. Li, X. & Y. Zhou *, 2017. Urban mapping using DMSP/OLS stable night-time light: a review. International Journal of Remote Sensing.
  3. 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
  4. 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.
  5. 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
  6. 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.
  7. 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.
  8. Zhou, Y., and Y. Wang, 2007. An Assessment of Impervious Surface Areas in Rhode Island. Northeastern Naturalist. 14(4), 643-650.