The coast of French Guiana is characterised by the northwestward migration of large mud banks alongshore and by high concentrations of suspended particulate matter (SPM) resulting from the strong influence of the Amazon River outflow. Surface OLI SPM concentration, linked to the footprint of the subtidal part of mud banks due to resuspension and migration processes, was used to develop a method to estimate the location of this footprint. A comparison of the results from this method with those obtained by locating the limit of the wave damping, which characterises muddy coasts, revealed good performance of the method based on recurring SPM values. The migration rates of the mud banks in French Guiana were calculated according to the delimitation of their subtidal parts, and showed slightly higher values (2.31 km/year) than suggested by earlier studies. In comparison with other methods, the migration rate estimated using the method proposed within the framework of this study takes into account the variability of the shape of the subtidal part for the first time. It was also shown that the mud banks existing on the coastal area of French Guiana present two different shapes. Our results clearly demonstrate the advantage of ocean colour data to describe mud banks according to their subtidal part, delimited using the assessment of SPM temporal variability.

Decadal-scale morphological evolution of a muddy open coast. Marine Geology, Elsevier, 2020, 420, pp.

The optimal exploitation of the oceanic information provided by recent high spatial resolution sensors such as Landsat 8-OLI is strongly conditioned by the quality of the water reflectance signal retrieval. One main issue stands in the ability to correct water pixels for the contamination of the sun glint, which might induce a seasonal or permanent loss of data according to the latitude. The SWIR information now provided for the most recent high spatial resolution sensors was used for evaluating the sun glint level and correcting the radiative signal for its effect. This has been performed transposing historical empirical formalisms based on the NIR signal. An automated SWIR-based sun glint correction procedure was then developed using a 4-year OLI archive gathered over very turbid waters of French Guiana (227 scenes). This procedure allows the practical limitations associated with past similar empirical methods (sensitivity to water turbidity and manual image per image correction) to be overcome. While a satisfactory preservation of the information over sun glint free pixels was observed, comparison exercises based on in situ R rs data gathered in sun glint affected areas emphasize the relevance of the proposed methodology (correction by a factor of 14 of the averaged bias in the R rs values after removing sun glint effects). Current limitations in the applicability of this SWIR-based empirical automated method are mainly associated with the presence of high cloud coverage, thin clouds in the OLI scene or highly spatially variable marine or atmospheric signal (around 47%, 42% and 11%, respectively, of the total of 12% of failure over French Guiana OLI archive). The potential large applicability of the procedure developed in this work was eventually demonstrated over contrasted coastal environments.

<p>Plastic pollution is widely recognised to be an emerging ecological disaster (Eriksen et al., 2014). While a steady increase in the amount of marine litter is being observed, plastics constitute some 60 to 80% of the total waste (Miladinova et al., 2020), which drift and settle through sinking and beaching. The Black Sea, a semi-enclosed basin with numerous litter inflows by huge watershed rivers, and with only one spillway at the Bosporus, is an ideal test area for the development of litter detection and tracking technologies. Although the occurrence of marine litter in the Black Sea is poorly known, with lack of data in the abundance of floating debris (Miladinova et al., 2020), remote sensing from space (RSS) is considered a promising tool for the observation of floating marine plastics because of its wide observation cover. However, success was only obtained i.in areas with huge accumulations of litter (canals, harbours and estuaries, e.g. rows of litter in the sea after flooding), and ii.with applying “Ocean Colour” RSS methods designed for the assessment of concentration of phytoplankton or other particulates, which are far-off fitting the needs of detecting and tracking scattered macro-litter patches or rows, though they could apply to micro-plastics.</p><p>Within the conventional framework of DCRIT (detection-classification-recognition-identification-tracking and targeting) and based on the classic methodologies derived from Multidimensional Signal Detection Theory (MSDT), we are currently developing a scheme to address the issue of recognising faint signatures of marine litter in Earth Observation (EO) data sets. Most of the RSS studies are focused on the detection of plastic using (a) its spectral signature over water through applying indices such as Normalized Difference Vegetation Index (NDVI) or Floating Debris Index (FDI) owing to the issue of EO pixel size greater than litter accumulation width, with (b) universal thresholds. In our case, we adjust the detection thresholds to the ‘a-priori’ information on litter presence, provided by a model, to the environmental andthe RSS observation conditions, balancing the probability of detection and false alarms using a Bayesian approach.The ‘detector’ is the heir of the binary classification algorithm developed by ARGANS Ltd on a grant by European Space Agency (ESA), which is abinary detector followed by a multi-label classification using a deterministic decision tree to distinguish natural from anthropogenic debris. The ‘a priori’ information is provided by a marine litter model deployed in the Black Sea, locating the main litter accumulation areas. Then, the posterior probability of the uncertain classification of pixels as plastic is the conditional probability that it is assigned considering the observation conditions and the plastics’ presence information coming from the model. To assess the confidence of detection, the Bayes theorem is combined with Receiver Operating Characteristic (ROC) curves. The latter ones can be used to assign higher probabilities to observations with a positive classification and lower probabilities to observations that do not. A further analysis combining both tools allows to improve the thresholds selection to classify pixels as plastic as a function of the background information.</p>