<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 &#8220;Ocean Colour&#8221; 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 &#8216;a-priori&#8217; 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 &#8216;detector&#8217; 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 &#8216;a priori&#8217; 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&#8217; 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>
The Fate of Marine Litter in Semi-Enclosed Seas: A Case Study of the Black Sea
