ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W1, 61-66, 2016
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-4-W1/61/2016/
doi:10.5194/isprs-annals-IV-4-W1-61-2016
 
05 Sep 2016
GROWTH SCENARIOS FOR THE CITY OF GUANGZHOU, CHINA: TRANSFERABILITY AND CONFIRMABILITY
A. Lehner1,2, V. Kraus1, C. Wei2, and K. Steinnocher1 1AIT Austrian Institute of Technology, Energy Department, Giefinggasse 2, 1210 Vienna, Austria
2University of Salzburg, Department of Geoinformatics, Schillerstrasse 30, 5020 Salzburg, Austria
Keywords: Urban remote sensing, Urban planning, Urban growth, Pearl River Delta, Guangzhou, China Abstract. This work deals with the development of urban growth scenarios and the prevision of the spatial distribution of built-up area and population for the urban area of the city of Guangzhou in China. Using freely-available data, including remotely sensed data as well as census data from the ground, expenditure of time and costs shall remain low. Guangzhou, one of the biggest cities within the Pearl River Delta, has faced an enormous economic and urban growth during the last three decades. Due to its economical and spatial characteristics it is a promising candidate for urban growth scenarios. The monitoring and prediction of urban growth comprises data of population and give them a spatial representation. The model, originally applied for the Indian city Ahmedabad, is used for urban growth scenarios. Therefore, transferability and confirmability of the model are evaluated. Challenges that may occur by transferring a model for urban growth from one region to another are discussed. With proposing the use of urban remote sensing and freely available data, urban planners shall be fitted with a comprehensible and simple tool to be able to contribute to the future challenge Smart Growth.
Conference paper (PDF, 1796 KB)


Citation: Lehner, A., Kraus, V., Wei, C., and Steinnocher, K.: GROWTH SCENARIOS FOR THE CITY OF GUANGZHOU, CHINA: TRANSFERABILITY AND CONFIRMABILITY, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W1, 61-66, doi:10.5194/isprs-annals-IV-4-W1-61-2016, 2016.

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