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Shoreline Mapping from Coarse–Spatial Resolution Remote Sensing Imagery of Seberang Takir, Malaysia

Shoreline Mapping from Coarse–Spatial Resolution Remote Sensing Imagery of Seberang Takir, Malaysia
Shoreline Mapping from Coarse–Spatial Resolution Remote Sensing Imagery of Seberang Takir, Malaysia
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.
Remote sensing, subpixel, soft classification, super-resolution, contouring, two-point histogram
0749-0208
Muslim, A.M.
628ed733-f4bc-4afc-a83f-053249b5bf52
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Muslim, A.M.
628ed733-f4bc-4afc-a83f-053249b5bf52
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b

Muslim, A.M., Foody, G.M. and Atkinson, P.M. (2007) Shoreline Mapping from Coarse–Spatial Resolution Remote Sensing Imagery of Seberang Takir, Malaysia. Journal of Coastal Research, 23 (6). (doi:10.2112/04-0421.1).

Record type: Article

Abstract

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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More information

Submitted date: 3 December 2004
Published date: November 2007
Keywords: Remote sensing, subpixel, soft classification, super-resolution, contouring, two-point histogram

Identifiers

Local EPrints ID: 54991
URI: http://eprints.soton.ac.uk/id/eprint/54991
ISSN: 0749-0208
PURE UUID: 664634a8-36d8-4b53-b18a-ed5883235800
ORCID for P.M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

Catalogue record

Date deposited: 01 Aug 2008
Last modified: 16 Mar 2024 02:46

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Contributors

Author: A.M. Muslim
Author: G.M. Foody
Author: P.M. Atkinson ORCID iD

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