In recent years, there has been an increasing interest in exploring capabilities of remote sensing technologies to monitor and detect marine litter, and in particular, plastic litter floating in our water bodies. Remote sensing is now considered as the solely tool able to provide regular information at global scale to inform about this problem (Maximenko et al, 2019), which is essential to constrain the Lagrangian transport models put in place to understand its dynamics (Maximenko et al, 2012; van Sebille, 2015). In 2020, the European Space Agency (ESA) launched the Discovery Campaign on Remote Sensing of Plastic Marine Litter, funding a total of 26 initiatives across a wide spectrum of areas. The “Windrows As Proxies” project (WASP), included in the initiative, aimed to prototype an operational processor capable of identifying filaments of floating marine debris using Copernicus Sentinel-2/MSI (S-2) images, with a high probability of containing marine litter, based in the existing oceanographic knowledge of their role as accumulating agents of marine debris and plastic litter (Cozar et al, 2021; Ruiz et al, 2020).
The work from Hu (2021) stressed that a direct plastic detection using S-2 data could fall out of the possible or being specially challenging, due to spectral mixing, lack of specific bands, and challenges in both SNR and expected spatial coverage. Moreover, Dr. Hu (personal communication) also raises questions about the potential separability between marine litter and other substances, like sea snot, with similar spectral signature in the range 480-900 nm. Contrary to other initiatives (Biermann et al, 2020; Kikaki et al., 2020), where several attempts have been made to directly classify plastic within the images at pixel level, WASP embraced the problem from a different perspective. Assuming that litter rarely appears as a separate end member of the spectral reflectance measured at each pixel of S-2 data, and considering that S-2 lacks of plastic litter-specific bands (Hu, 2021), the objective is to detect filaments of floating debris, which often contain significantly lager quantities of plastic litter than their surroundings. This detection by proxy also enables the performance of both spectral and contextual (object-based) detection, which leads towards a less ambiguous classification process, with no need of external sources of data.
In a first step, WASP developed a specific spectral index that explores the use of NIR and SWIR bands as main source of information for the classification. This is also a different solution than published, where most of referred algorithms employ techniques closer to Ocean Colour using VIS bands as main source, following the lead of exiting spectral indices like NDVI and FAI (Biermann et al, 2020; Kikaki et al., 2020). The advantage of using NIR/SWIR is that, such spectral region, are containing information relevant for the detection of plastic (Garaba et al, 2020) and floating organic matter, reason why no external data is needed to support the detection.
In this work, we explore the use of such bands in Sentinel-2 for the detection of these filaments of floating marine debris, as well as create a better understanding of the general challenges that detection of plastic litter from space has. Part of the work performed includes the generation of an ad hoc procedure for cloud detection and filtering, and the use of a deterministic contextual classifier for the detection of filaments within the image, using a multiscale approach.
The results of WASP consist on snippets of S-2 analysed data containing the detected filaments. Such snippets are manually supervised by operators, in order to discard known false positives. Validation of the detection method has been carried out with the support of information over artificial targets deployed at sea in Lesbos Island (Topouzelis et al, 2020). The results of the validation show the capability the method has to detect both plastic and floating debris with high organic load.
The work presented here has helped also to understand the roadmap and needs for improvement of the techniques, as well as to advance in the detection principles of plastic litter with its challenges, a cornerstone information for the definition of a specific future EO mission devoted to plastic litter monitoring.
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