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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. [ABSTRACT FROM AUTHOR]

The Java Sea acts as a carbon source that contributes to the release of carbon dioxide into the atmosphere. The blue carbon ecosystems in the Karimunjawa Islands may absorb and store the carbon released from the Java Sea. The carbon stock in Kemujan Island (Karimunjawa Islands), one of these blue carbon ecosystems, was previously estimated using a remote sensing approach. In the present study, we investigated the carbon stock in the seagrass bed at Kemujan Island by conducting field observations and laboratory analysis. The result shows that there are only two seagrass species in the study area--'Enhalus acoroides' and 'Thalassia hemprichii'--which are categorized as nearly dense and very dense, respectively. Regarding the carbon biomass, the carbon stock below the substrate is more than that above the substrate. However, the carbon stock in the seagrass biomass is less than in the sediment. The average carbon stock in the seagrass bed at Kemujan Island is 4901.91 g C/m2, which is much more than the amount estimated by means of remote sensing. Furthermore, this amount is much larger compared with the average carbon stock of other areas in the Karimunjawa Islands. The large amount of carbon stock stored in the seagrass bed at Kemujan Island denotes the role of this seagrass bed as a carbon sink area. [ABSTRACT FROM AUTHOR]

Mangroves aboveground biomass (ABG) estimation at a large scale is a crucial to understand their role in mitigating climate change. The large-scale estimation of AGB is generally conducted with remote sensing techniques using stand characteristics such as canopy height and species composition. In the present study, we employed Lorey's mean canopy height (H m) and basal area weighted mean wood density (ρ m) to develop models for estimating AGB of mangroves in Asian regions such as Indonesia, Philippines and Japan, considering its application toward large-scale AGB estimation covering different climatic zones of subtropical zone in the Eastern Asia and tropical zone in the Southeastern Asia. The best power function model for AGB estimation with H m as a single explanatory variable was selected based on AIC ranks. The H m – AGB relationship model varied significantly among study sites. We successfully developed common allometric model to estimate AGB for closed-canopy mangroves in the Asian regions. The common allometric model of the H m – AGB relationship tended to show underestimation for old growth mangroves having AGB > ca. 400 Mg ha−1. The common allometric model showed a similar trend with a previously developed model for mangroves in Bangladesh. On the other hand, the common allometric model for mangroves showed distinct difference from the model for terrestrial tropical forests, i.e. the range of H m was narrower in mangroves than in terrestrial tropical forests, and AGB was quite higher in mangroves than in terrestrial tropical forests at a same range of H m. The difference of AGB specific to H m was explained by higher ρ m and cumulative basal area (BA) in mangroves than that in terrestrial tropical forests. In particular, mangrove showed quite high BA in a comparison with terrestrial tropical forests. This study confirmed that developing mangrove specific biomass model is important since mangroves showed its unique characteristics through comparisons with terrestrial tropical forests, and limitations of the use of H m as a single variable. It should be noted that old growth mangrove AGB is underestimated with the developed common allometric models. For old growth mangroves, carbon stock should be fairly evaluated with more accurate models otherwise their contribution to blue carbon would be overlooked, which could lead insufficient efforts for conservation of old growth mangroves. Further accurate models for estimating mangrove AGB at large scale are urgently required. • Model development for biomass estimation using Lorey's height in the Asian regions. • Detection of underestimation of biomass for old growth mangroves. • High cumulative basal area in mangroves compared with terrestrial tropical forests. [ABSTRACT FROM AUTHOR]