Filtering out false Sargassum detections using context features

• Published in: Frontiers in Marine Science
• Main Authors: Podlejski, Witold, Descloitres, Jacques, Chevalier, Cristèle, Minghelli, Audrey, Lett, Christophe, Berline, Léo
• Other Authors: Géosciences Environnement Toulouse (GET), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS), interaction Clouds Aerosols Radiations - ICARE/AERIS Data and Services Center - UMS 2877 (ICARE), Centre National d'Études Spatiales Toulouse (CNES)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Institut méditerranéen d'océanologie (MIO), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon) (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Signal et Image (SIIM), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), MARine Biodiversity Exploitation and Conservation - MARBEC (UMR MARBEC ), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), ANR-19-SARG-0007,FORESEA,Prévision des échouages de sargasses dans l'Atlantique Tropical(2019)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2022
• Subjects: [SDE]Environmental Sciences; North Atlantic
• Online Access: https://hal.science/hal-03846138; https://hal.science/hal-03846138/document; https://hal.science/hal-03846138/file/fmars-09-960939.pdf; https://doi.org/10.3389/fmars.2022.960939

International audience Since 2011, the distribution extent of pelagic Sargassum algae has substantially increased and now covers the whole Tropical North Atlantic Ocean, with significant inter-annual variability. The ocean colour imagery has been used as the only way to monitor regularly such a vast area. However, the detection is hampered by cloud masking, sunglint, coastal contamination and other phenomena. All together, they lead to false detections that can hardly be discriminated by classic radiometric analysis, but may be overcome by considering the shape and the context of the detections. Here, we built a machine learning model base exclusively on spatial features to filter out false detections after the detection process. Moderate-Resolution Imaging Spectroradiometer (MODIS, 1 km) data from Aqua and Terra satellites were used to generate daily map of Alternative Floating Algae Index (AFAI). Based on this radiometric index, Sargassum presence in the Tropical Atlantic North Ocean was inferred. For every Sargassum aggregations, five contextual indices were extracted (number of neighbours, surface of neighbours, temporal persistence, distance to the coast and aggregation texture) then used by a random forest binary classifier. Contextual features at large-scale were most important in the classifier. Trained with a multi-annual (2016-2020) learning set, the model performs the filtering of daily false detections with an accuracy of ~ 90%. This leads to a reduction of detected Sargassum pixels of ~ 50% over the domain. The method provides reliable data while preserving high spatial and temporal resolutions (1 km, daily). The resulting distribution is consistent with the literature for seasonal and inter-annual fluctuations, with maximum coverage in 2018 and minimum in 2016. This dataset will be useful for understanding the drivers of Sargassum dynamics at fine and large scale and validate future models. The methodology used here demonstrates the usefulness of contextual features for complementing classical ...