The remote sensing-based monitoring reported here has revealed that the seagrass meadow at Pulau Semakau declined drastically from 2001 to 2013. The small size of the seagrass beds in Singapore relative to those previously studied in temperate and less stressed regions makes this loss more serious. For example, by building a model to estimate seagrass biomass directly and incorporating ancillary information on sediment deposition, strong evidence was found for rapid, sedimentation-driven declines in seagrass bed extent and changes in the seagrass community assemblage. Previous studies have also provided evidence that these effects are exacerbated by poor ambient water quality. This ambient water quality is unlikely to improve without direct
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intervention, and such rapid declines are likely to occur in the future and may be currently occurring elsewhere in Singapore’s waters. Unfortunately, this study is one of very few focused on Singapore’s seagrass assets. For these reasons, it is imperative that more research be directed towards monitoring Singapore’s remaining seagrass habitats and other coastal ecosystems.
Although a considerable amount of effort was dedicated to quantifying error in this analysis, future studies should improve upon these methods. Non-governmental organizations and research institutions should consider including periodic GCP surveys in their monitoring regimes to provide researchers with the validation data necessary for accurate remote monitoring campaigns. With such validation data, more precise estimates of sensor-specific bias and tidally-linked error could be quantified. With error quantification and contemporary satellite imaging technology, deriving higher order information from satellite imagery, such as biomass and species identification, would be less uncertain and prone to error. Future research can also more closely examine the extent of macroalgae blooms in Singapore and their contribution to misclassification error. In addition to determining misclassification error, quantification of macroalgal bloom biomass alone would be useful in modelling water quality, primary production, and damage to ecosystems and infrastructure associated with macroalgae. As with seagrass, however, monitoring macroalgae requires extensive validation data, which is even more difficult to collect for macroalgae than for seagrass due to the depth at which macroalgae grows and the unpredictability of blooms.
The methods developed here have important implications for future remote sensing studies, especially along the populated coastline of Southeast Asia. Measuring trends in
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seagrass bed extent provides insight into the state of colonization occurring and the future viability of a meadow, but does not necessarily provide a measure of the abundance of seagrass or seagrass health. Trends in above-ground biomass provide more information on the internal health of a seagrass bed, and coupled with bed extent can provide inferences on complex processes such as species transitions and meadow responses to punctuated disturbances. The complex water quality and seagrass communities in this region restrict the ability of conventional techniques applied in clearer waters and at temperate latitudes. Turbidity in coastal regions with large anthropogenic disturbance limits the spectrum of useful wavelengths of light. Research conducted in Southeast Asia requires the analysis of non-linear relationships between remote sensing indices and seagrass density, because of the complex communities and wide range of biomass present in seagrass beds in the region. Moderate resolution, freely available satellite imagery produce maps of seagrass bed extent on par with very- high resolution imagery. ALI imagery may underestimate overall extent and OLI/ETM+ imagery may not effectively detect small seagrass patches, but they produce classification products suitable for long term trend analysis.
The results presented here also support the field application of normalised canopy volume as an index of seagrass abundance using remote sensing methods. The use of NCI offers a multitude of benefits compared with conventional means of estimating seagrass standing crop: e.g. visual density index or aboveground biomass. Being non- destructive, determination of NCI in the field provides a quick, efficient, non-biased and sustainable method of documenting seagrass abundance. As the technique involved in assessing NCI is relatively fast, field sampling can effectively cover a wider spatial scale within a given period of time, improving the variability captured in training and
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validation of empirical models. In addition, NCI can possibly be translated for use in studies of photosynthetic productivity, nutrient uptake, sediment accretion, and local- scale hydrodynamics. To substantiate the application of NCI as an abundance index, further research is required to fully understand the degree of correspondence between NCI and above-ground biomass and how much the superior performance of NCI is due to sampling design. Additional research is also required to validate the use of NCI in meadows with different community structure than that encountered in this study.
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