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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)
[article]
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] réflectanceRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 1 (January 2022) . - n° 4200513[article]Self-attention and generative adversarial networks for algae monitoring / Nhut Hai Huynh in European journal of remote sensing, vol 55 n° 1 (2022)
[article]
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 artificielRésumé : (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
in European journal of remote sensing > vol 55 n° 1 (2022) . - pp 10 - 22[article]Using remote sensing and modeling to monitor and understand harmful algal blooms. Application to Karaoun Reservoir (Lebanon) / Najwa Sharaf (2021)
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’environnementLangues : 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 surfaceIndex. 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 perspectivesNuméro 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 A novel algorithm to estimate phytoplankton carbon concentration in inland lakes using Sentinel-3 OLCI images / Heng Lyu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
[article]
Titre : A novel algorithm to estimate phytoplankton carbon concentration in inland lakes using Sentinel-3 OLCI images Type de document : Article/Communication Auteurs : Heng Lyu, Auteur ; Zhiqian Yang, Auteur ; Lei Shi, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6512 - 6523 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] changement climatique
[Termes IGN] Chine
[Termes IGN] chlorophylle
[Termes IGN] corrélation
[Termes IGN] image Sentinel-OLCI
[Termes IGN] lac
[Termes IGN] plancton
[Termes IGN] réflectance
[Termes IGN] série temporelle
[Termes IGN] teneur en carboneRésumé : (auteur) Phytoplankton carbon, an important biogeochemical and ecological parameter, plays a critical role in the carbon cycle and in global warming reduction. Estimation of phytoplankton carbon in inland waters on a large scale using remote sensing is useful for understanding, evaluating, and monitoring the carbon dynamics, and, in particular, for determining the spatial–temporal variation of primary production in inland waters. In a correlation analysis of the phytoplankton carbon concentration and water components, the result revealed no significant correlation between the chlorophyll-a concentration and phytoplankton carbon concentration in inland waters. However, the absorption peak height of particles at 675 nm, which is defined as the absorption at 675 nm subtracted by that at 660 nm, was found to be closely correlated with the phytoplankton carbon concentration. Thus, the absorption peak height of particles at 675 nm could be used as an indicator of the phytoplankton carbon concentration. A semianalytical method based on the remote-sensing reflectance in Sentinel-3 Ocean and Land Color Instrument (OLCI) bands 8, 9, and 17 was developed to derive the absorption peak of particles at a wavelength of 675 nm. Finally, an algorithm for estimating the phytoplankton carbon concentration in inland waters using OLCI bands 8, 9, and 17 was constructed. From 2013 to 2018, eight field campaigns were conducted in inland lakes in different seasons, and the optical properties, optically active water components, and phytoplankton carbon concentrations were obtained. An assessment of its accuracy using an independent data set demonstrated that the algorithm performance is acceptable (mean absolute percentage error, 48.6%, and root mean square error, 0.36 mg/L). As a demonstration, the algorithm was successfully applied to map the phytoplankton carbon concentration in Taihu Lake and Chaohu Lake, China, using OLCI images acquired on December 5, 2017, and August 5, 2018 and December 8, 2... Numéro de notice : A2020-531 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2977080 Date de publication en ligne : 12/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2977080 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95714
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6512 - 6523[article]
Titre : Advances in environmental monitoring and assessment Type de document : Monographie Auteurs : Suriyanarayanan Sarvajayakesavalu, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2019 Importance : 108 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 978-1-83881-010-8 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algue
[Termes IGN] apprentissage automatique
[Termes IGN] capteur spatial
[Termes IGN] changement climatique
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MISR
[Termes IGN] mangrove
[Termes IGN] phénomène climatique extrême
[Termes IGN] plancton
[Termes IGN] stress hydrique
[Termes IGN] surveillance de la végétationRésumé : (éditeur) The book Advances in Environmental Monitoring and Assessment is a collection of the latest research techniques on environmental monitoring and assessments. I believe that the information contained in this book will enhance the skills of environmental scientists and decision makers and contribute to the exchange of best practices for developing and implementing optimum methods for environmental assessment and management. Note de contenu : 1- Hydrological stress and climate change impact in arid regions with agricultural valleys in Northern Mexico
2- Evaluation of water quality indices: Use, evolution and future perspectives
3- A survey of satellite biological sensor application for terrestrial and aquatic ecosystems
4- Atmospheric aerosols monitoring: Ground and satellite-based instruments
5- Extreme value analysis and risk communication for a changing climateNuméro de notice : 25963 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.75847 En ligne : https://doi.org/10.5772/intechopen.75847 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96540 Apport des mesures directionnelles et polarisées aux corrections atmosphériques au-dessus des océans ouverts. Application à la mission PARASOL / Tristan Harmel (2009)PermalinkA new algorithm for estimating chlorophyll-a concentration from multi-spectral satellite data in case 2 waters: a simulation based on a controlled laboratory experiment / Y. Oyoma in International Journal of Remote Sensing IJRS, vol 28 n°7-8 (April 2007)Permalink3rd Earsel workshop on LIDAR remote sensing of land and sea, Tallinn, Estonia, 17-19 July 1997 / S. Babichenko (1997)PermalinkMapping of phytoplankton by solar-simulated fluorescence using an imaging spectrometer / J.F.R. Gower in International Journal of Remote Sensing IJRS, vol 11 n° 2 (February 1990)PermalinkPassive and active optical remote sensing of the inland water phytoplankton / K.Y. Kondratyev in ISPRS Journal of photogrammetry and remote sensing, vol 44 n° 5 (February 1990)PermalinkAirborne discrimination between ice and water : application to the laser measurement of chlorophyll-in-water in a marginal ice zone / F.E. Hoge in Remote sensing of environment, vol 30 n° 1 (01/10/1989)PermalinkA three-component model of ocean colour and its application to remote sensing of phytoplankton pigments in coastal waters / S. Sathyendranath in International Journal of Remote Sensing IJRS, vol 10 n° 8 (August 1989)PermalinkThe plume of the Yukon river in relation to the oceanography of the Bering sea / K.G. Dean in Remote sensing of environment, vol 28 n° 1 (April - June 1989)PermalinkFeasibility of using satellites for detection of kinetics of small phytoplancton blooms in estuaries : tidal and migrational effects / M.A. Tyler in Remote sensing of environment, vol 27 n° 3 (01/03/1989)PermalinkSea surface parameters inferred from meteorological satellite data at CMS, Lannion : new products and projects / P. Le Borgne in International Journal of Remote Sensing IJRS, vol 9 n°10-11 (October-November 1988)Permalink