Improving the synoptic mapping of coral reef geomorphology using object-based image analysis

Leon, Javier and Woodroffe, Colin D. (2011) Improving the synoptic mapping of coral reef geomorphology using object-based image analysis. International Journal of Geographical Information Science, 25 6: 949-969. doi:10.1080/13658816.2010.513980


Author Leon, Javier
Woodroffe, Colin D.
Title Improving the synoptic mapping of coral reef geomorphology using object-based image analysis
Journal name International Journal of Geographical Information Science   Check publisher's open access policy
ISSN 1365-8816
1365-8824
Publication date 2011-06-01
Year available 2011
Sub-type Article (original research)
DOI 10.1080/13658816.2010.513980
Open Access Status Not yet assessed
Volume 25
Issue 6
Start page 949
End page 969
Total pages 21
Place of publication Essex, United Kingdom
Publisher Taylor & Francis
Language eng
Abstract Monitoring coral reefs is of great importance for environmental management of these ecosystems. The use of remote sensing and geographical information systems enables rapid and effective mapping of the geomorphology of reefs that can be used as a basis for biodiversity and habitat assessments. However, pixel-based approaches have not been appropriate for detailed mapping of such complex systems. An object-based image analysis (OBIA) approach was used in this study to map intra-reef geomorphology of coral reefs across the Torres Strait region using Landsat ETM+‚ÄČimagery. By combining image analysis techniques and a non-parametric neural network classifier and incorporating additional spatial information such as context, shape and texture, the accuracy of the segmentation and classification was improved considerably. A large-scale synoptic map of 10 geomorphological classes was produced for Torres Strait with an overall accuracy of 75%. The OBIA approach employed in this research has enabled the geomorphology of reef platforms to be mapped for the first time at such accuracy and descriptive resolution.
Keyword OBIA
Torres Strait
Neural networks
Classification
Landsat ETM+
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status Non-UQ
Additional Notes Special Issue: Object-based Landscape Analysis

Document type: Journal Article
Sub-type: Article (original research)
Collections: Global Change Institute Publications
Non HERDC
 
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