Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers

Abstract

This study deals with the mixed-pixel problem of detecting benthic habitat class membership and evaluates two soft classifiers for coral habitat mapping on Lang Tengah island (Malaysia). A comparison was made between the Bayesian and Dempster–Shafer (D–S) with a traditional maximum likelihood (ML). The heterogeneous pattern of reef environment, established by field observation, four classes of coral habitats containing various combinations of live coral, dead coral with algae, rubble coral and sand. Posterior probability and belief maps, generated by Bayesian and D–S, respectively, were evaluated by visual inspection and final coral habitat distribution maps were validated via accuracy assessment estimates. The accuracy validation tests agreed with the visual inspection of the probability, uncertainty and coral distribution maps. The Bayesian algorithm performed better, with a 34.7–68.5% improvement in accuracy compared to D–S and ML, respectively. Probability maps demonstrate the advantages of the soft classifier over the hard classifier for coral mapping.

Keywords:

• Sub-pixel classification
• benthic habitat
• remote sensing
• QuickBird
• Malaysia

Acknowledgements

The authors would like to thank the Editor, Professor Lulla and the four anonymous reviewers, whose constructive comments and inputs significantly improved the article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available on request from the corresponding author, Aidy M. Muslim. The satellite data are not publicly available due to nature of satellite data in commercial state and owned by University Malaysia Terengganu. However, it is available from satellite data provider, DigitalGlobe via authorized reseller.

Additional information

Funding

This work was supported by the Higher Institution Centre of Excellence (HICoE) Research Grant (Vote Number: 53209), awarded to the Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT).