1.1 Introduction
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 possesses unique biophysical properties and relates to their surrounding environment to create the spatial complexity of urban ecological systems and landscape patterns. 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 (Wu et al. 2000). In order to do so, it is necessary (i) to have a standardized method to define theses component surfaces and (ii) 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 (Ridd 1995).
Impervious surfaces are anthropogenic features through which water cannot infiltrate into the soil, such as roads, driveways, sidewalks, parking lots, rooftops, and so on. In the past two decades, impervious surface has emerged not only as an indicator of the degree of urbanization, but also a major indicator of environmental quality (Arnold and Gibbons 1996). Impervious surface is a unifying theme for all participants at all watershed scales, including planners, engineers, landscape architects, scientists, social scientists, local officials, and others (Schueler 1994). The magnitude, location, geometry, and spatial pattern of impervious surfaces, and the perviousâimpervious ratio in a watershed have hydrological impacts. Although landâuse zoning emphasizes roofârelated impervious surfaces, transportârelated impervious surfaces could have a greater impact. The increase of impervious cover would lead to the increase in the volume, duration, and intensity of urban runoff (Weng 2001), and an overall decrease of groundwater recharge and baseflow but an increase of stormflow and flood frequency (Brun and Band 2000). Furthermore, imperviousness is related to the water quality of a drainage basin and itâs receiving streams, lakes, and ponds (Hurd and Civco 2004). In addition, the areal extent and spatial occurrence of impervious surfaces may significantly influence urban climate by altering sensible and latent heat fluxes within the urban canopy and boundary layers (Yang et al. 2003). Therefore, estimating and mapping impervious surface is significant to a range of issues and themes in environmental science central to global environmental change and humanâenvironment interactions and has been regarded as a key variable in urban remote sensing studies. The data sets of impervious surfaces are valuable not only for environmental management, e.g. water quality assessment and storm water taxation, but also for urban planning, e.g. building infrastructure and sustainable urban development.
Remote sensing technology has been widely applied in urban landâuse and landâcover (LULC) classification and change detection. However, it is rare that the classification accuracy of greater than 80% can be achieved by using perâpixel classification (soâcalled âhard classificationâ) algorithms (Mather 1999, p. 10). Therefore, the âsoftâ/fuzzy approach of LULC classifications has been applied, in which each pixel is assigned a class membership of each LULC type rather than a single label (Wang 1990). Nevertheless, as Mather (1999) suggested, either âhardâ or âsoftâ classifications was not an appropriate tool for the analysis of heterogeneous landscapes. Mather (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. Ridd (1995) proposed a major conceptual model for remote sensing analysis of urban landscapes, i.e. the vegetationâimpervious surfaceâsoil (VâIâS) model. It assumes that land cover in urban environments is a linear combination of three components, namely, vegetation, impervious surface, and soil. Ridd believed that this model can be applied to spatialâtemporal analyses of urban morphology, biophysical, and human systems. Having realized that the VâIâS model may be used as a method to define standardized urban landscape components, this chapter employs linear spectral mixture analysis (LSMA) as a remote sensing technique to estimate and map VâIâS components in order to analyze urban pattern and dynamics. The case study will be conducted in Indianapolis, United States, from 1991 to 2000, by using multiâtemporal satellite images, i.e. Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) imagery of 1991, 1995, and 2000. Because of the significance of impervious surface as an urban land cover, land use, or material, this chapter will start with examining data requirements for remote sensing of impervious surfaces, with a particular interest in the impacts of remotely sensed data characteristics (i.e. spectral, temporal, and spatial resolutions).
