Urban LULC and structure extraction for the City of Beijing, China

As a part of the Urban Environmental Monitoring Program at Arizona State University together with the University of Beijing and the Austrian Academy of Sciences a climate model for the Beijing area should be established. This specific model will then be used for the computation of climate values especially for the Olympic Games in 2008.
Remote sensing imagery can be used for the acquisition of data about the actual land use land cover type. In this study we could obtain a recent ASTER scene acquired in July 2006 and classified the imagery into several urban and sub-urban classes. Due to the lack of a standardized classification schema valid for China, we adopted the U.S. national land cover data schema (NLCD), established for the U.S. The classification into NLCD classes was performed based on an object based image analysis (OBIA) approach and lead to acceptable results in terms of classification accuracy higher than 80%.
Second task in the project was the development of a method for the estimation of urban structure/urban roughness parameters. The urban roughness is an important factor influencing wind wave pattern, wind speed and finally the urban climate. Urban roughness is strongly correlated with building heights and the variations in building heights. And the variations in building heights are finally a function of the texture homogeneity over a specific area. Texture is an equivalent to that value. Based on a panchromatic SPOT image we could define some textural classes matching specific building roughness classes as input parameters for the urban climate model.