Abstract
Particle transport by erosion from ultramafic lands in pristine tropical lagoons is a crucial problem, especially for the benthic and pelagic biodiversity associated with coral reefs. Satellite imagery is useful for assessing particle transport from land to sea. However, in the oligotrophic and shallow waters of tropical lagoons, the bottom reflection of downwelling light usually hampers the use of classical optical algorithms. In order to address this issue, a Support Vector Regression (SVR) model was developed and tested. The proposed application concerns the lagoon of New Caledonia—the second longest continuous coral reef in the world—which is frequently exposed to river plumes from ultramafic watersheds. The SVR model is based on a large training sample of in-situ turbidity values representative of the annual variability in the Voh-Koné-Pouembout lagoon (Western Coast of New Caledonia) during the 2014–2015 period and on coincident satellite reflectance values from MODerate Resolution Imaging Spectroradiometer (MODIS). It was trained with reflectance and two other explanatory parameters—bathymetry and bottom colour. This approach significantly improved the model’s capacity for retrieving the in-situ turbidity range from MODIS images, as compared with algorithms dedicated to deep oligotrophic or turbid waters, which were shown to be inadequate. This SVR model is applicable to the whole shallow lagoon waters from the Western Coast of New Caledonia and it is now ready to be tested over other oligotrophic shallow lagoon waters worldwide.
Highlights
In numerous tropical Pacific islands, the clarity of coastal lagoon waters is an essential parameter allowing the development of massive coral reefs and numerous benthic living species of prime importance for ecology and for fishing
Due tothe possible strong influence study, we propose a trained algorithm based on support vector regression [51] to get more accurate bathymetry and bottom colour, our support vector regression (SVR)
Satellite values were assigned according to three different methods as preconized for the research of coincident pixels [58], i.e., with the closest neighbour method (CL), the weighted mean method (WMM) and the filtered mean method (FMM)
Summary
In numerous tropical Pacific islands, the clarity of coastal lagoon waters is an essential parameter allowing the development of massive coral reefs and numerous benthic living species of prime importance for ecology and for fishing. This is probably because the effect of bottom reflectance over the coral reefs ecosystems for the retrieval of water quality parameters such as chlorophyll-a concentration ([chl-a]) [40] or turbidity [41] is strong in this context. Such a contribution of both bathymetry and bottom colour in oligotrophic waters has already been shown to impact the detection of [chl-a]. Model considers bathymetry and bottom colour, our support vector regression model considers assessments of remote sensing turbidity [52] in the oligotrophic shallow waters of the Western lagoon reflectance channels and these two physical parameters asasindependent variables. Comparison bathymetry and bottom colour, our support vector regression (SVR) model considers of the results of our approach with published algorithms for estimation of turbidity from in-situ
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