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How can Sentinel-2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters? / Katja Kuhwald in Remote sensing in ecology and conservation, vol 8 n° 3 (June 2022)  

Public [article]  inRemote sensing in ecology and conservation > vol 8 n° 3 (June 2022) . - pp 328 - 346 Titre : How can Sentinel-2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters? Type de document : Article/Communication Auteurs : Katja Kuhwald, Auteur ; Jens Schneider Von Deimling, Auteur ; Philipp Schubert, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 328 - 346 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Allemagne [Termes IGN] Baltique, mer [Termes IGN] carte thématique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] eaux côtières [Termes IGN] fond marin [Termes IGN] herbier marin [Termes IGN] image aérienne [Termes IGN] image Sentinel-MSI [Termes IGN] lidar bathymétrique [Termes IGN] turbidité des eaux Résumé : (auteur) Seagrass meadows are one of the most important benthic habitats in the Baltic Sea. Nevertheless, spatially continuous mapping data of Zostera marina, the predominant seagrass species in the Baltic Sea, are lacking in the shallow coastal waters. Sentinel-2 turned out to be valuable for mapping coastal benthic habitats in clear waters, whereas knowledge in turbid waters is rare. Here, we transfer a clear water mapping approach to turbid waters to assess how Sentinel-2 can contribute to seagrass mapping in the Western Baltic Sea. Sentinel-2 data were atmospherically corrected using ACOLITE and subsequently corrected for water column effects. To generate a data basis for training and validating random forest classification models, we developed an upscaling approach using video transect data and aerial imagery. We were able to map five coastal benthic habitats: bare sand (25 km²), sand dominated (16 km²), seagrass dominated (7 km²), dense seagrass (25 km²) and mixed substrates with red/ brown algae (3.5 km²) in a study area along the northern German coastline. Validation with independent data pointed out that water column correction does not significantly improve classification results compared to solely atmospherically corrected data (balanced overall accuracies ~0.92). Within optically shallow waters (0–4 m), per class and overall balanced accuracies (>0.82) differed marginally depending on the water depth. Overall balanced accuracy became worse ( Numéro de notice : A2022-499 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1002/rse2.246 Date de publication en ligne : 07/12/2021 En ligne : https://doi.org/10.1002/rse2.246 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100995 [article]

Decision fusion of deep learning and shallow learning for marine oil spill detection / Junfang Yang in Remote sensing, vol 14 n° 3 (February-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 3 (February-1 2022) . - n° 666 Titre : Decision fusion of deep learning and shallow learning for marine oil spill detection Type de document : Article/Communication Auteurs : Junfang Yang, Auteur ; Yi Ma, Auteur ; Yabin Hu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 666 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de fusion [Termes IGN] analyse multiéchelle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] hydrocarbure [Termes IGN] image hyperspectrale [Termes IGN] marée noire [Termes IGN] milieu marin [Termes IGN] pollution des mers [Termes IGN] précision de la classification [Termes IGN] sous ensemble flou [Termes IGN] surveillance écologique [Termes IGN] transformation en ondelettes Résumé : (auteur) Marine oil spills are an emergency of great harm and have become a hot topic in marine environmental monitoring research. Optical remote sensing is an important means to monitor marine oil spills. Clouds, weather, and light control the amount of available data, which often limit feature characterization using a single classifier and therefore difficult to accurate monitoring of marine oil spills. In this paper, we develop a decision fusion algorithm to integrate deep learning methods and shallow learning methods based on multi-scale features for improving oil spill detection accuracy in the case of limited samples. Based on the multi-scale features after wavelet transform, two deep learning methods and two classical shallow learning algorithms are used to extract oil slick information from hyperspectral oil spill images. The decision fusion algorithm based on fuzzy membership degree is introduced to fuse multi-source oil spill information. The research shows that oil spill detection accuracy using the decision fusion algorithm is higher than that of the single detection algorithms. It is worth noting that oil spill detection accuracy is affected by different scale features. The decision fusion algorithm under the first-level scale features can further improve the accuracy of oil spill detection. The overall classification accuracy of the proposed method is 91.93%, which is 2.03%, 2.15%, 1.32%, and 0.43% higher than that of SVM, DBN, 1D-CNN, and MRF-CNN algorithms, respectively. Numéro de notice : A2022-125 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14030666 Date de publication en ligne : 30/01/2022 En ligne : https://doi.org/10.3390/rs14030666 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99688 [article]

Mapping and quantification of the dwarf eelgrass Zostera noltii using a random forest algorithm on a SPOT 7 satellite image / Salma Benmokhtar in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 5 (May 2021) . - n° 313 Titre : Mapping and quantification of the dwarf eelgrass Zostera noltii using a random forest algorithm on a SPOT 7 satellite image Type de document : Article/Communication Auteurs : Salma Benmokhtar, Auteur ; Marc Robin, Auteur ; Mohamed Maanan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 313 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] biomasse [Termes IGN] cartographie hydrographique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] fond marin [Termes IGN] herbier marin [Termes IGN] image SPOT 7 [Termes IGN] Maroc [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] plante aquatique d'eau salée [Termes IGN] réflectance spectrale [Termes IGN] typologie [Termes IGN] Zostera noltii Résumé : (auteur) The dwarf eelgrass Zostera noltei Hornemann (Z. noltei) is the most dominant seagrass in semi-enclosed coastal systems of the Atlantic coast of Morocco. The species is experiencing a worldwide decline and monitoring the extent of its meadows would be a useful approach to estimate the impacts of natural and anthropogenic stressors. Here, we aimed to map the Z. noltei meadows in the Merja Zerga coastal lagoon (Atlantic coast of Morocco) using remote sensing. We used a random forest algorithm combined with field data to classify a SPOT 7 satellite image. Despite the difficulties related to the non-synchronization of the satellite images with the high tide coefficient, our results revealed, with an accuracy of 95%, that dwarf eelgrass beds can be discriminated successfully from other habitats in the lagoon. The estimated area was 160.76 ha when considering mixed beds (Z. noltei-associated macroalgae). The use of SPOT 7 satellite images seems to be satisfactory for long-term monitoring of Z. noltei meadows in the Merja Zerga lagoon and for biomass estimation using an NDVI–biomass quantitative relationship. Nevertheless, using this method of biomass estimation for dwarf eelgrass meadows could be unsuccessful when it comes to areas where the NDVI is saturated due to the stacking of many layers. Numéro de notice : A2021-393 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10050313 Date de publication en ligne : 07/05/2021 En ligne : https://doi.org/10.3390/ijgi10050313 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97679 [article]

The Realization and evaluation of PPP ambiguity resolution with INS aiding in marine survey / Zhenqiang Du in Marine geodesy, vol 44 n° 2 (March 2021)  

Public [article]  inMarine geodesy > vol 44 n° 2 (March 2021) . - pp 136 - 156 Titre : The Realization and evaluation of PPP ambiguity resolution with INS aiding in marine survey Type de document : Article/Communication Auteurs : Zhenqiang Du, Auteur ; Hongzhou Chai, Auteur ; Guorui Xiao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 136 - 156 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] ambiguïté entière [Termes IGN] filtre de Kalman [Termes IGN] fractional cycle bias [Termes IGN] milieu marin [Termes IGN] positionnement inertiel [Termes IGN] positionnement ponctuel précis [Termes IGN] précision du positionnement [Termes IGN] qualité des données [Termes IGN] récepteur GNSS [Termes IGN] résolution d'ambiguïté [Termes IGN] trajet multiple Résumé : (auteur) The tightly coupled global navigation satellite system (GNSS) precise point positioning (PPP) and inertial navigation system (INS) can provide high-precision position, velocity and attitude information. The coupled system utilizes single receiver, which is particularly suitable for the environment without reference station, such as marine survey. In the former works, the integer ambiguity resolution of PPP/INS in terrestrial environment is researched. However, the GNSS observation is severely affected by the multipath effect in marine environment. In addition, the sideslip caused by wind and sea wave also impact float ambiguity estimation, consequently introducing difficulty for PPP ambiguity fixing. Therefore, the PPP/INS tightly coupled model with fixed ambiguity is proposed for marine survey. The correction model of INS gyroscope bias in closed-loop is deduced in detail. The influence of ship motion noise and multipath in marine environment is reduced by introducing the robust factor to the Kalman filter. The feasibility of the method is verified in a real marine experiment, with a detail evaluation of the data quality and positioning accuracy. The results show that the accuracy of PPP/INS can reach centimeter level after fixing the ambiguity in marine environment. Furthermore, the precise INS-predicted position can significantly shorten the re-fixed time of PPP/INS, which proves the efficiency of the proposed approach. Numéro de notice : A2021-267 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/01490419.2020.1852986 Date de publication en ligne : 07/12/2020 En ligne : https://doi.org/10.1080/01490419.2020.1852986 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97321 [article]

The construction of sound speed field based on back propagation neural network in the global ocean / Junting Wang in Marine geodesy, vol 43 n° 6 (November 2020)  

Public [article]  inMarine geodesy > vol 43 n° 6 (November 2020) . - pp 621 - 642 Titre : The construction of sound speed field based on back propagation neural network in the global ocean Type de document : Article/Communication Auteurs : Junting Wang, Auteur ; Tianhe Xu, Auteur ; Wenfeng Nie, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 621 - 642 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] fonction orthogonale [Termes IGN] interpolation spatiale [Termes IGN] milieu marin [Termes IGN] onde acoustique [Termes IGN] propagation du son [Termes IGN] réseau neuronal artificiel [Termes IGN] salinité [Termes IGN] sondage acoustique [Termes IGN] température [Termes IGN] vitesse Résumé : (auteur) The sound speed is a key parameter that affects the underwater acoustic positioning and navigation. Aiming at the high-precision construction of sound speed field in the complex marine environment, this paper proposes a sound speed field model based on back propagation neural network (BPNN) by considering the correlation of learning samples. The method firstly uses measured ocean parameters to construct the temperature and salinity field. Then the spatial position, the temperature and the salinity information are used to construct the global ocean sound speed field based on the back propagation neural network algorithm. During the processing, the learning samples of back propagation neural network are selected based on the correlation between sound speed and distance. The proposed algorithm is validated by the global Argo data as well as compared with the spatial interpolation and the empirical orthogonal function (EOF) algorithm. The results demonstrate that the average root mean squares of the BPNN considering the correlation of learning samples is 0.352 m/s compared to the 1.527 m/s of EOF construction and the 2.661 m/s of spatial interpolation, with an improvement of 76.9% and 86.8%. Therefore, the proposed algorithm can improve the construction accuracy of sound speed field in the complex marine environment. Numéro de notice : A2020-694 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2020.1815912 Date de publication en ligne : 14/09/2020 En ligne : https://doi.org/10.1080/01490419.2020.1815912 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96242 [article]

Advancements in web‐mapping tools for land use and marine spatial planning / Ainhoa González in Transactions in GIS, Vol 24 n° 2 (April 2020)  

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Applications of remote sensing in coastal areas / Konstantinos Topouzelis (2020)  

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Développement de la photogrammétrie et d'analyses d'images pour l'étude et le suivi d'habitats marins / Guilhem Marre (2020)  

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On the use of Sentinel-2 for coastal habitat mapping and satellite-derived bathymetry estimation using downscaled coastal aerosol band / Dimitris Poursanidis in International journal of applied Earth observation and geoinformation, vol 80 (August 2019)  

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Monitoring of marine pollution / Houma Bachari Fouzia (2019)  

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Geovisualization of the impact of ship traffic on marine mammals / Nolwenn Le Poulain (2017) 

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Utilisation d’une source laser pulsée à haute energie comme source acoustique large bande en milieu liquide / Jean-Pierre Sessarego in Traitement du signal, vol 33 n° 1 (2016-1)

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État de conservation des habitats : propositions de définitions et de concepts pour l’évaluation à l’échelle d’un site Natura 2000 / Lise Maciejewski in Revue d'écologie, vol 71 n° 1 (janvier - mars 2016) 

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Coastal and marine ecological changes and fish cage culture development in Phu Quoc, Vietnam (2001 to 2011) / Diep Thi Hong Nguyen in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)  

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The sights below / Kevin Corbley in GEO: Geoconnexion international, vol 12 n° 9 (october 2013)

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