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

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+ imagery. 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