## 2 Urban Mapping Applications
Linear spectral mixture analysis (LSMA) is another approach that can be used to handle the mixed pixel problem, besides the fuzzy classification. Instead of using statistical methods, LSMA is based on physically deterministic modeling to unmix the signal measured at a given pixel into its component parts called endmembers ([PERSON] et al. 1986; [PERSON] 1993; [PERSON] et al. 1995). Endmembers are recognizable surface materials that have homogenous spectral properties all over the image. LSMA assumes that the spectrum measured by a sensor is a linear combination of the spectra of all components within the pixel ([PERSON] 1993). Because of its effectiveness in handling spectral mixture problem and ability to provide continuum-based biophysical variables, LSMA has been widely used in: (1) estimation of vegetation cover ([PERSON] and [PERSON] 2000; [PERSON] et al. 2000; [PERSON] 2001; [PERSON] et al. 2004; [PERSON] and [PERSON] 2005), (2) impervious surface estimation and/or urban morphology analysis ([PERSON] et al. 2002; [PERSON] and [PERSON] 2003; [PERSON] et al. 2003; [PERSON] and [PERSON] 2006a, 2006b; [PERSON] et al. 2005), (3) vegetation or land cover classification ([PERSON] et al. 1995; [PERSON] and [PERSON] 1998; [PERSON] et al. 1999; [PERSON] and [PERSON] 2004), and (4) change detection ([PERSON] et al. 2005; [PERSON] et al. 2007). However, with a few exceptions, these studies have focused on technical specifics and on the examination of the effectiveness of LSMA. Only a few studies have explicitly adopted the V-I-S model as the conceptual model to explain urban land cover patterns ([PERSON] et al. 2002; [PERSON] and [PERSON] 2003; [PERSON] et al. 2005; [PERSON] and [PERSON] 2006a, 2006b; [PERSON] et al. 2007), while others implicitly ([PERSON] et al. 2003, 2005). Figure 1 shows LULC map of Indianapolis, United States, derived from a Landsat ETM+ image of June 22, 2000. LULC classes were formed by using LSMA derived fractions with a hybrid procedure that combined maximum-likelihood and decision-tree algorithms ([PERSON] _et al._ 2004). 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 recent years, impervious surface has emerged not only as an indicator of the degree of urbanization, but also a major indicator of urban environmental quality ([PERSON] and [PERSON], 1996). Various digital remote sensing approaches have been developed to estimate and map impervious surfaces, including mainly: image classification, multiple regression, sub-pixel classification, artificial neural network, classification and regression tree algorithm, and so on. To study further on this topic, please refer to a book by [PERSON] (2007), entitled \"Remote Sensing of Impervious Surfaces\". Through review of basic concepts and methodologies, analysis of case studies, and examination of methods for applying up-to-date techniques to impervious surface estimation and mapping, this book may serve undergraduate and graduate students as a textbook, or be used as a reference book for professionals, researchers, and alike in the academics, government, industries, and beyond. Figure 1: Land-use/land-cover map of Indianapolis, United States, derived from a Landsat ETM+ image of June 22, 2000. Figure 2 shows impervious surface images of Indianapolis derived from a Terra's ASTER image of June 16, 2001. LSMA and an ANN model (Multi-Layer Perceptron feed forward network with the back-propagation learning algorithm) were employed to estimate the impervious surfaces. The root-mean-square-error of the impervious surface map with the ANN model was 12.3%, and that resulted from LSMA was 13.2%.