Coral reefs play significant roles in marine ecosystems, and recently, they have been experiencing degradation primarily due to global warming. Monitoring the coral reef ecosystem is crucial to rehabilitation and preventing further degradation. Here, we used high spatial resolution multispectral image data from the QuickBird sensor and in-situ measurements acquired around 2011 to derive a benthic habitat map around the coral reef ecosystem in Weno Island, Micronesia. Water column correction was performed to eliminate the effect of the light attenuation within the water column from the satellite image, employing a band combination approach known as the depth invariant index (DII) transformation. The combination of the new images generated by the DII transformation was used for image segmentation. This approach was conducted to apply object-based image classification. To determine the accuracy of the classification, we separate the in-situ data into 174 training data (70% of the data) and 75 testing data (30% of the data). This study produced classification results with an overall accuracy of 84% and a kappa value of 0.77, using a scale parameter of 5 for the object-based classification, which supported the reliability of the resultant coral reef habitat map. The findings of this study demonstrate that applying the depth invariant algorithm for water column correction on Weno Island is an appropriate step before conducting benthic habitat classification.



Keywords : Coral reef habitat mapping, QuickBird, Water column correction, Object-based image classification, Weno Island