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Urban expansion is a phenomenon of urban space increase, and an important measuring index of the process of urbanization. Taking Shanghai as an example, the changes of urban average height and built-up area were studied to represent city＇s vertical and horizontal increases respectively, and statistical methods were used to analyze the driving forces of urban expansion. The research drew following conclusions： 1） The urban expansion process of Shanghai from 1985 to 2006 had a clear periodic feature, and could be divided into three stages： vertical expansion in dominance, coordinated vertical and horizontal expansion, and horizontal expansion in dominance. 2） The average height and quantity of buildings in core city were significantly bigger than those in suburbs, but the changing speed of the latter was faster. And 3） urbanization process was the major driving force for the city＇s horizontal expansion, while industrial structure improvement was the key driving factor for the vertical expansion. Those two driving forces were simultaneously affected by city＇s political factors.

We describe how the Unit-Feature Spatial Classification Method（UFSCM） can be used operationally to classify cloud types in satellite imagery efficiently and conveniently.By using a combination of Interactive Data Language（IDL） and Visual C＋＋（VC） code in combination to extend the technique in three dimensions（3-D）,this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification,so as to deal with the bi-spectral limitations of traditional two dimensional（2-D） UFSCM.The case study of cloud-type classification based on FY-2C satellite data （0600 UTC 18 and 0000 UTC 10 September 2007） is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery,resulting in more reasonable results and an improvement over the 2-D method.