Paper
9 October 2009 Monitoring urbanization and land cover change in the Yangtze River Delta: a case study of Pudong New Area, Shanghai
Junshi Xia, Peijun Du, Wen Cao
Author Affiliations +
Proceedings Volume 7471, Second International Conference on Earth Observation for Global Changes; 74710B (2009) https://doi.org/10.1117/12.836311
Event: Second International Conference on Earth Observation for Global Changes, 2009, Chengdu, China
Abstract
In this paper, Pudong New Area in Shanghai was selected as the study area, and medium resolution Landsat TM/ETM+ and CBERS (China-Brazil Earth Resources Satellite) images were used as data source. Two classification methods were applied to generate land cover maps: Maximum Likelihood Classifier (MLC) and a hierarchical method based on the V-I-S model (H-VIS). After comparing the results derived from these two methods, H-VIS model provides more accurate results than MLC. By analyzing the land cover change from 1989 to 2008, it was found that agricultural land has decreased greatly, while impervious surface area (ISA, including residential and commercial/industrial/traffic land) has increased year by year. In order to better monitor urbanization, diversity index, shape index, fractal dimension and isolation were selected to analyze the landscape pattern in the study area. The results show that the complexity of landscape structure and the fragmentation of the landscape increased from 1989 to 2008, however, the intensity and tendency of the landscape changes varied during the two comparative periods: 1989-2001 and 2001-2008. Finally, using data obtained from image interpretation and other data source, land cover change patterns and their driving forces, including economy, population and policies were analyzed.
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Junshi Xia, Peijun Du, and Wen Cao "Monitoring urbanization and land cover change in the Yangtze River Delta: a case study of Pudong New Area, Shanghai", Proc. SPIE 7471, Second International Conference on Earth Observation for Global Changes, 74710B (9 October 2009); https://doi.org/10.1117/12.836311
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KEYWORDS
Agriculture

Earth observing sensors

Landsat

Fractal analysis

Image classification

Image segmentation

Shape analysis

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