Filtering out false Sargassum detections using context features
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...
Published in: | Frontiers in Marine Science |
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Main Authors: | , , , , , |
Other Authors: | , , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2022
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Subjects: | |
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 |
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ftunivnantes:oai:HAL:hal-03846138v1 |
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openpolar |
institution |
Open Polar |
collection |
Université de Nantes: HAL-UNIV-NANTES |
op_collection_id |
ftunivnantes |
language |
English |
topic |
[SDE]Environmental Sciences |
spellingShingle |
[SDE]Environmental Sciences Podlejski, Witold Descloitres, Jacques Chevalier, Cristèle Minghelli, Audrey Lett, Christophe Berline, Léo Filtering out false Sargassum detections using context features |
topic_facet |
[SDE]Environmental Sciences |
description |
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 ... |
author2 |
Géosciences Environnement Toulouse (GET) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-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 Systèmes (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 (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) |
format |
Article in Journal/Newspaper |
author |
Podlejski, Witold Descloitres, Jacques Chevalier, Cristèle Minghelli, Audrey Lett, Christophe Berline, Léo |
author_facet |
Podlejski, Witold Descloitres, Jacques Chevalier, Cristèle Minghelli, Audrey Lett, Christophe Berline, Léo |
author_sort |
Podlejski, Witold |
title |
Filtering out false Sargassum detections using context features |
title_short |
Filtering out false Sargassum detections using context features |
title_full |
Filtering out false Sargassum detections using context features |
title_fullStr |
Filtering out false Sargassum detections using context features |
title_full_unstemmed |
Filtering out false Sargassum detections using context features |
title_sort |
filtering out false sargassum detections using context features |
publisher |
HAL CCSD |
publishDate |
2022 |
url |
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 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
ISSN: 2296-7745 Frontiers in Marine Science https://hal.science/hal-03846138 Frontiers in Marine Science, 2022, 9, ⟨10.3389/fmars.2022.960939⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.3389/fmars.2022.960939 hal-03846138 https://hal.science/hal-03846138 https://hal.science/hal-03846138/document https://hal.science/hal-03846138/file/fmars-09-960939.pdf doi:10.3389/fmars.2022.960939 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.3389/fmars.2022.960939 |
container_title |
Frontiers in Marine Science |
container_volume |
9 |
_version_ |
1766135618094497792 |
spelling |
ftunivnantes:oai:HAL:hal-03846138v1 2023-05-15T17:36:12+02:00 Filtering out false Sargassum detections using context features Podlejski, Witold Descloitres, Jacques Chevalier, Cristèle Minghelli, Audrey Lett, Christophe Berline, Léo Géosciences Environnement Toulouse (GET) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-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 Systèmes (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 (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) 2022-09-23 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 en eng HAL CCSD Frontiers Media info:eu-repo/semantics/altIdentifier/doi/10.3389/fmars.2022.960939 hal-03846138 https://hal.science/hal-03846138 https://hal.science/hal-03846138/document https://hal.science/hal-03846138/file/fmars-09-960939.pdf doi:10.3389/fmars.2022.960939 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess ISSN: 2296-7745 Frontiers in Marine Science https://hal.science/hal-03846138 Frontiers in Marine Science, 2022, 9, ⟨10.3389/fmars.2022.960939⟩ [SDE]Environmental Sciences info:eu-repo/semantics/article Journal articles 2022 ftunivnantes https://doi.org/10.3389/fmars.2022.960939 2023-02-22T01:20:47Z 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 ... Article in Journal/Newspaper North Atlantic Université de Nantes: HAL-UNIV-NANTES Frontiers in Marine Science 9 |