Outward urban growth, driven by increasing population, economic development, and technological advancement, has become a worldwide phenomenon. Such growth is often viewed as the vitality of a regional economy. But it has brought negative impacts on the environment such as biodiversity loss, soil erosion, hydrological perturbation, water and solid pollution, and global warming. Monitoring and modeling urban spatial growth are important for environmental sustainability and urban planning. This dissertation research has aimed at the investigation of urban growth patterns, urban growth processes, and their relevance through the lens of complexity theory to improve our understanding of the spatial and temporal dynamics of urban growth in a rapidly growing metropolitan area. Central to this research effort is the development of a technological framework that tightly integrates satellite imagery processing, artificial intelligence, and geographic information systems (GIS). Specifically, this project includes two principle components. One is to examine the use of artificial neural networks for improving urban land cover change detection from remote sensor data. Due to their capability of dealing with nonlinear and complex phenomena, integrating artificial neural networks with remote sensing has improved the performance of image classification for the fragmented and heterogeneous landscape in an urban environment. The other component is to characterize urban spatial growth at the metropolitan, functional zone, and cell levels by using three approaches: urban land change mapping, landscape metrics analysis, and moving windows analysis. This part of the research has provided insights into urban growth dynamics in urban societies that are not comparable to either industrial or post-industrial cities in the United States through measuring the spatial and temporal variations of urban patterns and processes at different scales. These societies have unique urban forms and development trajectories due to technological robustness and contemporary international and domestic socio-economic conditions. / A Dissertation submitted to the Department of Geography in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester, 2012. / March 14, 2012. / artificial neural networks, GIS, landscape metrics, remote sensing, urban growth pattern / Includes bibliographical references. / Xiaojun Yang, Professor Directing Dissertation; Timothy S. Chapin, University Representative; James B. Elsner, Committee Member; Jon A. Stallins, Committee Member; Trajco V. Mesev, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_183213 |
| Contributors | Zhou, Libin (authoraut), Yang, Xiaojun (professor directing dissertation), Chapin, Timothy S. (university representative), Elsner, James B. (committee member), Stallins, Jon A. (committee member), Mesev, Trajco V. (committee member), Department of Geography (degree granting department), Florida State University (degree granting institution) |
| Publisher | Florida State University, Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text |
| Format | 1 online resource, computer, application/pdf |
| Rights | This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them. |
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