CORAL REEF HEALTH INDEX CALCULATION FROM REMOTE SENSING DATA: A REVIEW

Abstract

The coral reef ecosystem plays an important role as a provider of ecosystem services and has various economic benefits to the coastal community. However, the coral reefs ecosystem continues to degrade by 19% globally. This degradation caused some coastal and archipelagic countries have developed methods of calculating coral reef health index, including Indonesia. However, some literature shows that there is no common standard method for coral reef health index as the analysis depends on the data availability and the purpose of the study. Remote sensing technology that currently provides much open-source data is a potential method /tool to calculate the health index of coral reefs if the required parameters are met. This article review aims to identify remote sensing data used in the existing coral health indexes and then analyze the integration of all spatial data for coral reef health index calculation. Reference searches are sourced from the SCOPUS database combined with search engines Harzing and Mendeley. There are five coral reef health index calculation models from 25 references consisting of 19 publications and six reports out of 209 filtered references using keywords of Coral Reef Health Index. As a result, coral reef cover and algae are commonly used data obtained from remote sensing imagery. However, remote sensing technology cannot estimate other important parameters such as fish biomass. In addition, physical information of the waters, such as sea surface temperature (SST) and water clarity indicators (turbidity and diffused attenuation coefficient), are parameters contained in the five indexes that can be obtained from remote sensing data. In general, the literature review shows that coral reef health indicators (e.g. index or individual benthic cover) are significantly related to the various parameters affecting coral reef degradation such as algae cover, rubble cover, SST and river plump either as an individual or multivariate factors.