The coastal zone is under considerable pressure from development and is subject to change. Consequently, shoreline monitoring has grown in importance. Remotely sensed imagery from satellite sensors has been used as an alternative to conventional methods, such as those based on the interpretation of aerial photography and ground-based surveying, for monitoring shoreline position. However, the accuracy of shoreline mapping from satellite sensor imagery has been limited because of the relatively coarse spatial resolution (>10 m) of the sensors commonly used. Because of major practical and financial constraints, very fine spatial resolution (<5 m) data are often impractical for mapping large stretches of shoreline, so refinement of image analysis methods are needed to extract the desired subpixel-scale information from relatively coarse spatial resolution imagery. In this paper, the potential to map the shoreline at a subpixel scale from a soft classification of relatively coarse spatial resolution satellite sensor imagery was evaluated. Unlike conventional approaches, the methods used allowed the shoreline to be mapped within image pixels and have the potential to yield an accurate and realistic prediction of shoreline location. The approach involved the use of a soft image classification to estimate the subpixel-scale thematic composition of image pixels, which were then located geographically through postclassification analysis. Specifically, a contouring and geostatistical method based on a two-point histogram was used to position geographically the shoreline within image pixels. The approach was applied to differently shaped shoreline extracts in imagery at two spatial resolutions. The most accurate prediction of the shoreline position from images with 16- and 32-m spatial resolutions were typically for a simple linear stretch of coast for which the smallest root mean square error values were ≤1.20 m. The shoreline predictions satisfied the map accuracy standards specified for large-scale maps.
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