TOWARDS FINE SCALE CHARACTERIZATION OF GLOBAL URBAN EXTENT, CHANGE AND STRUCTURE

Abstract

Urbanization is a global phenomenon with far-reaching environmental impacts. Monitoring, understanding, and modeling its trends and impacts require accurate, spatially detailed and updatable information on urban extent, change, and structure. In this dissertation, new methods have been developed to map urban extent, sub-pixel impervious surface change (ISC), and vertical structure at national to global scales. First, an innovative multi-level object-based texture classification approach was adopted to overcome spectral confusion between urban and nonurban land cover types. It was designed to be robust and computationally affordable. This method was applied to the 2010 Global Land Survey Landsat data archive to produce a global urban extent map. An initial assessment of this product yielded over 90% overall accuracy and good agreement with other global urban products for the European continent. Second, for sub-pixel ISC mapping, the uncertainty caused by seasonal and phenological variations is one of the greatest challenges. To solve this issue, I developed an iterative training and prediction (ITP) approach and used it to map the ISC of entire India between 2000 and 2010. At 95% confidence, the total ISC for India between 2000 and 2010 was estimated to be 2274.62±7.84 km2. Finally, using an object-based feature extraction approach and the synergy of Landsat and freely available elevation datasets, I produced 30m building height and volume maps for England, which for the first time characterized urban vertical structure at the scale of a country. Overall, the height RMSE was only ±1.61 m for average building height at 30m resolution. And the building volume RMSE was ±1142.3 m3. In summary, based on innovative data processing and information extraction methods, this dissertation seeks to fill in the knowledge gaps in urban science by advancing the fine scale characterization of global urban extent, change, and structure. The methods developed in this dissertation have great potentials for automated monitoring of global urbanization and have broad implications for assessing the environmental impact, disaster vulnerability, and long-term sustainability of urbanization.