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Spectrally unmixed percent of impervious surface, soil, and vegetation cover in central Arizona-Phoenix, year 2000

Urban land covers (e.g., cement parking lots, asphalt roads, shingle rooftops, grass, tress, exposed soil) can only be recorded as either present or absent in each pixel when using traditional per-pixel classifiers. Sub-pixel analysis approaches that can provide the relative fraction of surface covers within a pixel may be a potential solution to effectively identifying urban impervious areas. Spectral mixture analysis approach is probably the most commonly used approach that models image spectra as spatial average of spectral signatures from two or more surface features. However, spectral mixture analysis does not account for the absence of one of the surface features or spectral variation within pure materials since it utilizes an invariable set of surface features. Multiple endmember spectral mixture analysis (MESMA) approach addresses these issues by allowing endmembers to vary on a per pixel basis. The MESMA technique was employed in this study to model Landsat ETM+ reflectance in the Phoenix metropolitan area. Field spectra of vegetation, soil, and impervious surface areas collected with the use of a fine resolution Quickbird image and pixel purity index tool in ENVI software were modeled as reference endmembers in addition to photometric shade that was incorporated in every model. This study employs thirty endmembers and six hundred and sixty spectral models to identify soil, impervious, vegetation, and shade in the Phoenix metropolitan area. The mean RMS error for the selected land use land cover classes range from 0.003 to 0.018. The Pearson correlation between the fraction outputs from MESMA and reference data from Quickbird 60 cm resolution data for soil, impervious, and vegetation were 0.7052, 0.7249, and 0.8184 respectively.