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Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison

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Abstract:

Remote sensing estimation of impervious surfaces is significant in monitoring urban development and determining the overall environmental health of a watershed, and has therefore recently attracted increasing interest. The main objective of this study was to develop a general approach to estimating and mapping impervious surfaces by using medium spatial resolution satellite imagery. We have applied spectral mixture analysis (SMA) to Earth Observing 1 (EO-1) Advanced Land Imager (ALI) (multispectral) and Hyperion (hyperspectral) imagery in Marion County, Indiana, USA, to calculate the fraction images of vegetation, soil, high albedo and low albedo. The effectiveness of the two images was compared according to three criteria: (1) high-quality fraction images for the urban landscape, (2) relatively low error, and (3) the distinction among typical land use and land cover (LULC) types in the study area. The fraction images were further used to estimate and map impervious surfaces. The accuracy of the estimated impervious surface was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. The results indicate that both ALI and Hyperion sensors were effective in deriving the fraction images with SMA and in computing impervious surfaces. The SMA results for both ALI and Hyperion images using four endmembers were excellent, with a mean root mean square error (RMSE) less than 0.04 in both cases. The ALI-derived impervious surface image yielded an RMSE of 15.3%, and the Hyperion-derived impervious surface image yielded an RMSE of 17.5%. However, the Hyperion image was more powerful in discerning low-albedo surface materials, which has been a major obstacle for impervious surface estimation with medium resolution multispectral images. A sensitivity analysis of the mapping of impervious surfaces using different scenarios of Hyperion band combinations suggests that the improvement in mapping accuracy in general and the better ability in discriminating low-albedo surfaces came mainly from additional bands in the mid-infrared region.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01431160701469024

Affiliations: 1: Department of Geography, Geology, and Anthropology, Indiana State University, Terre Haute, IN 47809, USA 2: School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849, USA

Publication date: June 1, 2008

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