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Artificial intelligence oceanography / Xiaofeng Li (2023)  

Public Titre : Artificial intelligence oceanography Type de document : Monographie Auteurs : Xiaofeng Li, Éditeur scientifique ; Fan Wang, Éditeur scientifique Editeur : Springer Nature Année de publication : 2023 Importance : 346 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-981-19637-5-9 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] algue [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] cyclone [Termes IGN] détection d'objet [Termes IGN] iceberg [Termes IGN] intelligence artificielle [Termes IGN] océanographie [Termes IGN] température de surface de la mer Résumé : (éditeur) This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing. Note de contenu : 1- Artificial Intelligence Foundation of smart ocean 2- Forecasting tropical instability waves based on artificial intelligence 3- Sea surface height anomaly prediction based on artificial intelligence 4- Satellite data-driven internal solitary wave forecast based on machine learning techniques 5- AI-based subsurface thermohaline structure retrieval from remote sensing observations 6- Ocean heat content retrieval from remote sensing data based on machine learning 7- Detecting tropical cyclogenesis using broad learning system from satellite passive microwave observations 8- Tropical cyclone monitoring based on geostationary satellite imagery 9- Reconstruction of pCO2 data in the Southern ocean based on feedforward neural network 10- Detection and analysis of mesoscale eddies based on deep learning 11- Deep convolutional neural networks-based coastal inundation mapping from SAR imagery: with one application case for Bangladesh, a UN-defined least developed country 12- Sea ice detection from SAR images based on deep fully convolutional networks 13- Detection and analysis of marine green algae based on artificial intelligence 14- Automatic waterline extraction of large-scale tidal flats from SAR images based on deep convolutional neural networks 15- Extracting ship’s size from SAR images by deep learning 16- Benthic organism detection, quantification and seamount biology detection based on deep learning Numéro de notice : 24105 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Monographie DOI : 10.1007/978-981-19-6375-9 En ligne : https://link.springer.com/book/10.1007/978-981-19-6375-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103058

Detection and biomass estimation of phaeocystis globosa blooms off Southern China from UAV-based hyperspectral measurements / Xue Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 1 (January 2022) . - n° 4200513 Titre : Detection and biomass estimation of phaeocystis globosa blooms off Southern China from UAV-based hyperspectral measurements Type de document : Article/Communication Auteurs : Xue Li, Auteur ; Shaoling Shang, Auteur ; Zhongping Lee, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4200513 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] algue [Termes IGN] biomasse [Termes IGN] cartographie thématique [Termes IGN] Chine [Termes IGN] chlorophylle [Termes IGN] couleur de l'océan [Termes IGN] espèce exotique envahissante [Termes IGN] image captée par drone [Termes IGN] image hyperspectrale [Termes IGN] plancton [Termes IGN] réflectance Résumé : (auteur) Phaeocystis globosa (P. globosa) is a unique causative species of harmful algal blooms, which can form gelatinous colonies. We, for the first time, used unmanned aerial vehicle (UAV) measurements to identify P. globosa blooms and to quantify the biomass. Based on in situ measured remote sensing reflectance ( Rrs ), it is found that, for P. globosa blooms, the maximum of the second-derivative ( dλ2Rrs ) of Rrs(λ) in the 460–480-nm domain is beyond 466 nm. An analysis of the absorption properties from algal cultures suggested that this feature comes from the absorption of chlorophyll c3 (Chl −/c3 ) around 466 nm, a prominent feature of P. globosa. This position of dλ2Rrs maximum was, thus, selected as the criterion for P. globosa identification. The spatial extent of P. globosa blooms in two bays off southern China was then mapped by applying the criterion to UAV-measured Rrs . Twelve out of 16 UAV and in situ match-up stations were consistently identified as dominated by P. globosa, indicating the accuracy of 75%. Furthermore, using localized empirical models, chlorophyll a (Chl −/a ) concentration and colony numbers of P. globosa were estimated from UAV-derived Rrs , where P. globosa colonies were found in a range of ~3–37 gel matrix/L, indicating the occurrence of weak to moderate P. globosa blooms during the surveys. The promising results suggest a high potential for detection and quantification of P. globosa blooms in near-shore bays or harbors using UAV-based hyperspectral remote sensing, where conventional ocean color satellite remote sensing runs into difficulties. Numéro de notice : A2022-025 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3051466 Date de publication en ligne : 26/01/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3051466 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99254 [article]

Self-attention and generative adversarial networks for algae monitoring / Nhut Hai Huynh in European journal of remote sensing, vol 55 n° 1 (2022)  

Public [article]  inEuropean journal of remote sensing > vol 55 n° 1 (2022) . - pp 10 - 22 Titre : Self-attention and generative adversarial networks for algae monitoring Type de document : Article/Communication Auteurs : Nhut Hai Huynh, Auteur ; Gordon Boër, Auteur ; Hauke Schramm, Auteur Année de publication : 2022 Article en page(s) : pp 10 - 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algue [Termes IGN] analyse en composantes principales [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image hyperspectrale [Termes IGN] plancton [Termes IGN] réseau antagoniste génératif [Termes IGN] réseau neuronal artificiel Résumé : (auteur) Water is important for the natural environment and human health. Monitoring algae concentrations yield information on the water quality. Compared with in situ measurements of water quality parameters, which are often complex and expensive, remote sensing techniques, using hyperspectral data analysis, are fast and cost-effective. The objectives of this study are (1) to estimate the algae concentrations from hyperspectral data using deep learning techniques, (2) to investigate the applicability of attention mechanisms in the analysis of hyperspectral data, and (3) to augment the training data using generative adversarial networks (GANs). The results show that the accuracy of deep learning techniques is 7.6% higher than that of simpler artificial neural networks. Compared to noise injection and principal component analysis-based data augmentation, the use of a GAN-based data augmentation method significantly improves the accuracy of algae concentration estimates (>5%). In addition, models with added attention mechanisms yield an on average 3.13% higher accuracy than those without attention techniques. This result demonstrates the improvement of spectral features of artificial hyperspectral data based on the self-attention approach, revealing the potential of attention techniques in hyperspectral remote sensing. Numéro de notice : A2022-097 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2021.2010605 Date de publication en ligne : 02/01/2022 En ligne : https://doi.org/10.1080/22797254.2021.2010605 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99547 [article]

Analysing the impact of climate change on hydrological ecosystem services in Laguna del Sauce (Uruguay) using the SWAT model and remote sensing data / Celina Aznarez in Remote sensing, vol 13 n°10 (May-2 2021)  

Public [article]  inRemote sensing > vol 13 n°10 (May-2 2021) . - n° 2014 Titre : Analysing the impact of climate change on hydrological ecosystem services in Laguna del Sauce (Uruguay) using the SWAT model and remote sensing data Type de document : Article/Communication Auteurs : Celina Aznarez, Auteur ; Patricia Jimeno-Sáez, Auteur ; Adrián López-Ballesteros, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2014 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] algue [Termes IGN] bassin hydrographique [Termes IGN] changement climatique [Termes IGN] eau potable [Termes IGN] érosion [Termes IGN] gestion de l'eau [Termes IGN] image satellite [Termes IGN] modèle hydrographique [Termes IGN] ressources en eau [Termes IGN] risque naturel [Termes IGN] service écosystémique [Termes IGN] Uruguay Résumé : (auteur) Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment. Numéro de notice : A2021-472 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13102014 Date de publication en ligne : 20/05/2021 En ligne : https://doi.org/10.3390/rs13102014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97820 [article]

Using remote sensing and modeling to monitor and understand harmful algal blooms. Application to Karaoun Reservoir (Lebanon) / Najwa Sharaf (2021)  

Public Titre : Using remote sensing and modeling to monitor and understand harmful algal blooms. Application to Karaoun Reservoir (Lebanon) Type de document : Thèse/HDR Auteurs : Najwa Sharaf, Auteur ; Brigitte Vinçon-Leite, Directeur de thèse ; Kamal Slim, Directeur de thèse Editeur : Paris : Ecole Nationale des Ponts et Chaussées ENPC Année de publication : 2021 Importance : 132 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat Sciences et Techniques de l’environnement Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] algue [Termes IGN] barrage [Termes IGN] chlorophylle [Termes IGN] distribution spatiale [Termes IGN] espèce exotique envahissante [Termes IGN] hydrodynamique [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] lac [Termes IGN] Liban [Termes IGN] modélisation 3D [Termes IGN] plancton [Termes IGN] simulation hydrodynamique [Termes IGN] température de surface Index. décimale : THESE Thèses et HDR Résumé : (auteur) Reservoirs are strategic water resources in particular for drinking water and hydropower production. Nevertheless, their physical and biogeochemical processes have been long influenced by anthropogenic pressures. A complete and regular monitoring of reservoir water quality in the context of current climate change, eutrophication and higher water demand, has become crucial for optimal management strategies. Recent progress in the satellite remote sensing field made it possible to enhance data acquisition on a synoptic scale and to perform retrospective studies. Satellite data can complement measurements however over a limited depth of the water column. In addition, three-dimensional (3D) numerical models which integrate physical, chemical and biological processes can fill temporal gaps and extend the information into the vertical domain.In this context, this PhD thesis focuses on the combined use of techniques and data derived from field monitoring, satellite remote sensing and 3D modeling. The overreaching objective of this work is to propose a combined approach for surveying the water quality of medium-sized reservoirs (~ 14 km2).The study site is Karaoun Reservoir, Lebanon (semi-arid climate, surface 12 km2, capacity 110 hm3). It mainly serves for hydropower however with possibly a future drinking water production. It is eutrophic and has been experiencing regular events of toxic cyanobacterial blooms. The following methodological approach was adopted:i) In situ measurements were regularly collected from spring to fall for the calibration and the validation of remote sensing algorithms and of the model.ii) In order to calibrate and validate remote sensing algorithms, Landsat 8 and Sentinel-2 imagery were atmospherically corrected using a single-channel algorithm and the 6SV code respectively.a. Four algorithms from literature for deriving surface temperature were validated using Landsat 8 thermal data.b. A previously calibrated and validated Sentinel-2 algorithm was applied to retrieve chlorophyll-a concentrations.c. An empirical algorithm was calibrated and validated in order to retrieve transparency from Sentinel-2 data.iii) In order to conduct a retrospective analysis of surface temperature, the validated single channel algorithm was applied to a series of Landsat images from 1984 to 2018.iv) In order to reproduce the hydrodynamics and ecological processes, including cyanobacterial biomass in space and time, the Delft3D model was configured, calibrated and validated for summer and fall. The spatial distribution of surface temperature and chlorophyll-a concentrations from the satellite and the model were investigated.The results of this study revealed that, among the four tested algorithms, the single channel algorithm dependent on atmospheric water vapor content and lake water emissivity yielded the best estimations of surface temperature. Using this validated algorithm, the retrospective analysis of surface temperature did not reveal any warming trend over the 1984-2018 period at the study site. Compared to in situ profiles, the Delft3D model represented well the evolution of the water level fluctuations, and the time and vertical distribution of temperature and phytoplankton biomass. Satellite data and model simulations showed minor spatial heterogeneities of surface temperature ( Note de contenu : General introduction 1- State of the art 2- Materials and methods 3- Field data analysis 4- Lake surface temperature retrieval from Landsat-8 and retrospective analysis 5- Thermal regime of reservoirs: A satellite and 3D modeling approach 6- 3D ecological modeling at Karaoun Reservoir 7- Conclusions and perspectives Numéro de notice : 28499 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences et Techniques de l’environnement : Ponts ParisTech : 2021 Organisme de stage : Laboratoire Eau Environnement et Systèmes Urbains DOI : sans En ligne : https://pastel.archives-ouvertes.fr/tel-03404563 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99311

Unmanned aerial vehicles (UAVs) for monitoring macroalgal biodiversity: comparison of RGB and multispectral imaging sensors for biodiversity assessments / Leigh Tait in Remote sensing, vol 11 n° 19 (October-1 2019)  

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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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Advances in environmental monitoring and assessment / Suriyanarayanan Sarvajayakesavalu (2019)  

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Macroalgues intertidales : Apport de la télédétection hyperspectrale pour le suivi sectoriel dans le cadre de la DCE/DCSMM / Arnaud Le Bris (2019)

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Télédétection multispectrale et hyperspectrale des eaux littorales turbides / Morgane Larnicol (2018)  

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Texture augmented detection of macrophyte species using decision trees / Cameron Proctor in ISPRS Journal of photogrammetry and remote sensing, vol 80 (June 2013)  

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Indicateur CARLIT* = Rapport de stage de fin d'études, cycle des ingénieurs diplômés de l'ENSG 3eme année (IT3), master Carthageo-Pro / Nicolas Gonindard (2008)

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Design and implementation of a distributed GIS portal for oil spill and harmful algal bloom monitoring in the marine environment / E.O. Tuama in Marine geodesy, vol 30 n° 1-2 (March - June 2007)  

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SeaWIFS discrimination of harmful algal bloom evolution / P.I. Miller in International Journal of Remote Sensing IJRS, vol 27 n° 11 (June 2006)  

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Remote sensing techniques to assess water quality / J.C. Ritchie in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 6 (June 2003)  

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