The Assessment of Coral Reefs Mapping Methodology: An Integrated Method Approach

Abstract

Indonesian waters hold the world’s mega biodiversity of coral reefs. However, a range of anthropogenic pressures are threatening the coral reefs persistence. Since the early 20th century, remote sensing data has been assessed to map and monitor coral reefs. The reef habitats are monitored at various hierarchical spatial scales using integrated remote sensing and field data, but the level of detail and accuracy at a single point still questionable. Therefore, this study aims to assess the coral reefs methodology based on an integrated digital image processing approach. The method will employ a multi-pair and a single pair or an initial pair of Depth Invariant Index (DII) transformation bands, pixel-based Isodata and K-Means algorithm, and supervised classification method based on maximum likelihood and nearest neighbor algorithms. Object-based classification images, training areas, and data references were supported by previous research. The findings indicate that the maximum likelihood algorithm is better to apply for supervised classification for a single transformation band, while the K-Means algorithm is better for pixel-based classification since better accuracy can be obtained. However, various remote sensing data, band combinations, and clusters may affect the difference in results.