Evaluating Urban Expansion Using Integrated Remote Sensing and GIS technique: A Case Study in Greater Chengdu, China

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Date
2016-04-11Author
Zhang, Sisi
Type
ThesisDegree Level
MastersMetadata
Show full item recordAbstract
The overall goal of this thesis is to better understand changes in the spatial pattern of urban growth and its impact on landscape configuration by conducting a case study in Greater Chengdu, an inland megacity in China. The objectives are as follows: 1) Quantifying changes in the spatial pattern of the study area between 2003 and 2013; 2) Evaluating the degree of urban sprawl over that period; 3) Evaluating urban expansion dynamics; and 4) Examining and defining the types of urban growth. Satellite imagery was employed to distinguish and identify different land surface categories. Integrated remote sensing and GIS (Geographic Information System) technique was used to analyse both qualitative and quantitative perspectives regarding the objectives. The results indicate that the urban area of Greater Chengdu doubled from 525.5 km2 to 1191.85 km2 during 2003 to 2013. The geographic footprint demonstrates that the distribution of the built-up area was dispersed and continues to grow more dispersed. The dominant type of urban growth is outward expansion, by which the city grew within a 10 km to 25 km radius surrounding the city center. A substantial infill phenomenon exists between a 5 km and 10 km radius from the city center. The urban core boundary expanded outward by 5 km, while the fringe of suburban area expanded outward by 10 km during the time period, which both indicate a substantial outward expansion over the city. The significant contribution of this study could benefit to many aspects such as comparative studies between cities or continuous studies relevant to urban growth.
Degree
Master of Science (M.Sc.)Department
Geography and PlanningProgram
GeographySupervisor
Guo, XulinCommittee
Patrick, Robert; Hackett, Paul; Garcea, JosephCopyright Date
February 2016Subject
Urban expansion, remote sensing, GIS, Shannon’s Entropy, Landscape metrics, ULAT