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Journal of Applied Remote Sensing • Open Access

Spatiotemporal analysis of urban environment based on the vegetation–impervious surface–soil model
Author(s): Huadong Guo; Qingni Huang; Xinwu Li; Zhongchang Sun; Ying Zhang

Paper Abstract

This study explores a spatiotemporal comparative analysis of urban agglomeration, comparing the Greater Toronto and Hamilton Area (GTHA) of Canada and the city of Tianjin in China. The vegetation–impervious surface–soil (V–I–S) model is used to quantify the ecological composition of urban/peri-urban environments with multitemporal Landsat images (3 stages, 18 scenes) and LULC data from 1985 to 2005. The support vector machine algorithm and several knowledge-based methods are applied to get the V–I–S component fractions at high accuracies. The statistical results show that the urban expansion in the GTHA occurred mainly between 1985 and 1999, and only two districts revealed increasing trends for impervious surfaces for the period from 1999 to 2005. In contrast, Tianjin has been experiencing rapid urban sprawl at all stages and this has been accelerating since 1999. The urban growth patterns in the GTHA evolved from a monocentric and dispersed pattern to a polycentric and aggregated pattern, while in Tianjin it changed from monocentric to polycentric. Central Tianjin has become more centralized, while most other municipal areas have developed dispersed patterns. The GTHA also has a higher level of greenery and a more balanced ecological environment than Tianjin. These differences in the two areas may play an important role in urban planning and decision-making in developing countries.

Paper Details

Date Published: 20 November 2013
PDF: 17 pages
J. Appl. Rem. Sens. 8(1) 084597 doi: 10.1117/1.JRS.8.084597
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Huadong Guo, Institute of Remote Sensing and Digital Earth (China)
Qingni Huang, China Meteorological Administration (China)
Xinwu Li, Institute of Remote Sensing and Digital Earth (China)
Zhongchang Sun, Institute of Remote Sensing and Digital Earth (China)
Ying Zhang, Natural Resources Canada (Canada)

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