## 2 Urban Mapping Applications  
The majority of remote sensing work has been focused on natural environments over the past decades. Applying remote sensing technology to urban areas is relatively new. With the advent of high resolution imagery and more capable techniques, urban remote sensing is rapidly gaining interest in the remote sensing community. Driven by technology advances and societal needs, remote sensing of urban areas has increasing become a new arena of geospatial technology and has applications in all socioeconomic sectors ([PERSON] and [PERSON], 2006). Urban landscapes are typically a complex combination of buildings, roads, parking lots, sidewalks, garden, cemetery, soil, water, and so on. Each of the urban component surfaces exhibits a unique radiative, thermal, moisture, and aerodynamic properties, and relates to their surrounding site environment to create the spatial complexity of ecological systems ([PERSON] 1982). To understand the dynamics of patterns and processes and their interactions in heterogeneous landscapes such as urban areas, one must be able to quantify accurately the spatial pattern of the landscape and its temporal changes ([PERSON] et al. 2000). In order to do so, it is necessary: (1) to have a standardized method to define theses component surfaces, and (2) to detect and map them in repetitive and consistent ways, so that a global model of urban morphology may be developed, and monitoring and modeling their changes over time be possible ([PERSON] 1995). Remote sensing technology has been widely applied in urban land use, land cover classification, and change detection. However, it is rare that the classification accuracy of greater than 80% can be achieved by using per-pixel classification (so called \"hard classification\") algorithms ([PERSON] 1999). The low accuracy of land use/cover (LU/LC) classification in urban areas is largely attributed to the mixed pixel problem, where several types of LU/LC are contained in one pixel. The mixed pixel problem is resulted from the fact that the scale of observation (i.e., pixel resolution) fails to correspondto the spatial characteristics of the target ([PERSON] 1999). Therefore, the \"soft\"/fuzzy approach of LU/LC classifications has been applied, in which each pixel is assigned a class membership of each LU/LC type rather than a single label ([PERSON] 1990). Nevertheless, as [PERSON] (1999) suggested, either \"hard\" or \"soft\" classifications was not an appropriate tool for the analysis of heterogeneous landscapes. Both [PERSON] (1995) and [PERSON] (1999) maintained that identification/description/quantification, rather than classification, should be applied in order to provide a better understanding of the compositions and processes of heterogeneous landscapes such as urban areas. [PERSON] (1995) proposed a major conceptual model for remote sensing analysis of urban landscapes, i.e., the vegetation - impervious surface - soil (V-I-S) model. It assumes that land cover in urban environments is a linear combination of three components, namely, vegetation, impervious surface, and soil. [PERSON] believed that this model can be applied to spatial-temporal analyses of urban morphology, biophysical, and human systems. While urban land use information may be more useful in socioeconomic and planning applications, biophysical information that can be directly derived from satellite data is more suitable for describing and quantifying urban structures and processes ([PERSON] 1995). The V-I-S model was developed for Salt Lake City, Utah, but has been tested in other cities ([PERSON] et al. 2000, [PERSON] et al. 2001, [PERSON] et al. 2006). All of these studies employed the V-I-S model as the conceptual framework to relate urban morphology to medium-resolution satellite imagery, but \"hard classification\" algorithms were applied. Therefore, the problem of mixed pixels cannot be solved, and the analysis of urban landscapes was still based on \"pixels\" or \"pixel groups\".