Measuring the Physical Composition of Urban Morphology Using Multiple Endmember Spectral Mixture Models

Abstract

The application of multiple endmember spectral mixture analysis (MESMA) to map the physical composition of urban morphology using Landsat Thematic Mapper (TM) data is evaluated and tested. MESMA models mixed pixels as linear combinations of pure spectra, called endmembers, while allowing the types and number of endmembers to vary on a per-pixel basis. A total of 63 two-, three-, and four-endmember models were applied to a Landsat TM image for Los Angeles County, and a smaller subset of these models was chosen based on fraction and root-mean-squared error (RMSE) criteria. From this subset, an optimal model was selected for each pixel based on optimization for maximum area coverage. The resultant endmember fractions were then mapped into four main components of urban land cover: Vegetation, Impervious surfaces, Soil, and Water/Shade. The mapped fractions were validated using aerial photos. The results showed that a majority of the image could be modeled successfully with two-or three-endmember models. The validation results indicated the robustness of MESMA for deriving spatially continuous variables quantified at the sub-pixel level. These parameters can be readily integrated into a wide range of applications and models concerned with physical, economic, and/or sociodemographic phenomena that influence the morphological patterns of the city.

Visualization of a City Sustainability Index (CSI): Towards Transdisciplinary Approaches Involving Multiple Stakeholders

“…In fact, various definitions and methods exist for delineating urban boundaries of cities, including administrative boundaries [53], functional boundaries [54,55], and morphological boundaries [56,57]. In this paper, we defined the spatial extent of urban boundaries based on population density data.…”

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

“…Several methods for retrieval of FVC using remote sensing have been developed including spectral mixture analysis (SMA) [2][3][4], artificial neural networks [5][6][7], fuzzy classifiers [8], maximum likelihood classifiers [9], regression trees [10][11][12], and simple regression based on the Normalized Difference Vegetation Index (NDVI) [13]. In particular, SMA has often been used to estimate FVC from multi-spectral remote sensing data [2,[14][15][16][17][18].…”

Quantification of impervious surface in the Snohomish Water Resources Inventory Area of Western Washington from 1972-2006

“…Wu and Murray (2003), and Wu (2004) compared several spectral unmixing models for estimation of impervious surface distribution in Columbus, OH, and found that the three end-member V-I-S model (including brightness normalization) was the most effective. Other studies, however, have demonstrated the effectiveness of a variety of different end-member combinations, such as high-albedo, low-albedo, and vegetation (Small, 2002), the inclusion of shade as an end-member (Alberti et al, 2004;Lu & Weng, 2004), and allowance for variable numbers and types of end-members (Dennison & Roberts, 2003;Rashed et al, 2003). Lu and Weng (2004) demonstrated that green vegetation, shade, and soil or impervious surface were the most effective end-members for spectral unmixing in Indianapolis, IN.…”