Global Urbanization Modeling Supported by Remote Sensing

Wednesday, 17 December 2014: 2:10 PM
Yuyu Zhou1, Steven Smith1, Kaiguang Zhao2, Marc Lee Imhoff3, Allison M Thomson4, Ben P Bond-Lamberty4 and Christopher Elvidge5, (1)Joint Global Change Research Institute, College Park, MD, United States, (2)Ohio State University Main Campus, Columbus, OH, United States, (3)Pacific Northwest National Lab, College Park, MD, United States, (4)Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, United States, (5)NOAA Boulder, Boulder, CO, United States
Urbanization, one of the major human induced land cover and land use change, has profound impacts on the Earth system, and plays important roles in a variety of processes such as biodiversity loss, water and carbon cycle, and climate change. Accurate information on urban areas and their spatial distribution at the regional and global scales is important in both scientific and policy-making communities. The Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime stable light data (NTL) provide a potential way to map urban area and its dynamics economically and timely. In this study, we developed a cluster-based method to estimate the optimal thresholds and map urban extents from the DMSP/OLS NTL data. The sensitivity analysis demonstrates the robustness of the derived optimal thresholds and the reliability of the cluster-based method. Compared to existing threshold techniques, our method reduces the over- and under-estimation issue, when mapping urban extent over a large area. Using this cluster-based method, we built new global maps of 1-km urban extent from the NTL data (Figure 1) and evaluated its temporal dynamics from 1992 to 2013. Supported by the derived global urban maps and socio-economic drivers, we developed an integrated modeling framework by integrating a top-down macro-scale statistical model with a bottom-up urban growth model and projected future urban expansion.