FN Archimer Export Format
PT J
TI Filtering out false Sargassum detections using context features
BT 
AF Podlejski, Witold
   Descloitres, Jacques
   Chevalier, Cristele
   Minghelli, Audrey
   Lett, Christophe
   Berline, Léo
AS 1:1,2;2:3;3:2;4:4;5:1;6:2;
FF 1:;2:;3:;4:;5:;6:;
C1 Marbec, Université de Montpellier, Centre National de la Recherche Scientifique (CNRS), Ifremer, Institut de Recherche pour le Developpement (IRD), Sète, France
   Aix Marseille University, Université de Toulon, Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Developpement (IRD), Mediterranean Institute of Oceanography (MIO), Marseille, France
   AERIS/ICARE Data and Services Center, University of Lille, Centre National de la Recherche Scientifique (CNRS), Centre National d'Etudes Spatiales (CNES), Lille, France
   LIS Laboratory, Université de Toulon, Aix Marseille University, SeaTech, Centre National de la Recherche Scientifique (CNRS), Toulon, France
C2 UNIV MONTPELLIER, FRANCE
   UNIV AIX MARSEILLE, FRANCE
   UNIV LILLE, FRANCE
   UNIV TOULON, FRANCE
UM MARBEC
IN WOS Cotutelle UMR
   DOAJ
   copubli-france
   copubli-univ-france
IF 3.7
TC 2
UR https://archimer.ifremer.fr/doc/00795/90741/96335.pdf
LA English
DT Article
DE ;Sargassum algae;remote sensing;random forest;contextual analysis;Tropical North Atlantic;fractional coverage;time series
AB 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 remote sensing approaches. Our model could easily be adapted to other datasets containing erroneous detections. 
PY 2022
PD SEP
SO Frontiers In Marine Science
SN 2296-7745
PU Frontiers Media SA
VL 9
UT 000873957800001
DI 10.3389/fmars.2022.960939
ID 90741
ER

EF