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Inside the ice shelf: using augmented reality to visualise 3D lidar and radar data of Antarctica / Alexandra L. Boghosian in Photogrammetric record, vol 34 n° 168 (December 2019)  

Public [article]  inPhotogrammetric record > vol 34 n° 168 (December 2019) . - pp 346 - 364 Titre : Inside the ice shelf: using augmented reality to visualise 3D lidar and radar data of Antarctica Type de document : Article/Communication Auteurs : Alexandra L. Boghosian, Auteur ; Martin J. Pratt, Auteur ; Maya A. Becker, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 346 - 364 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] Antarctique [Termes descripteurs IGN] banquise [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] glace de mer [Termes descripteurs IGN] GNSS-INS [Termes descripteurs IGN] image radar [Termes descripteurs IGN] Matlab [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] pas d'échantillonnage au sol [Termes descripteurs IGN] réalité augmentée [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] travail coopératif [Termes descripteurs IGN] VRML Résumé : (auteur) From 2015 to 2017, the ROSETTA‐Ice project comprehensively mapped Antarctica's Ross Ice Shelf using IcePod, a newly developed aerogeophysical platform. The campaign imaged the ice‐shelf surface with lidar and its internal structure with ice‐penetrating radar. The ROSETTA‐Ice data was combined with pre‐existing ice surface and bed topography digital elevation models to create the first augmented reality (AR) visualisation of the Antarctic Ice Sheet, using the Microsoft HoloLens. The ROSETTA‐Ice datasets support cross‐disciplinary science that aims to understand 4D processes, namely the change of 3D ice‐shelf structures over time. The work presented here uses AR to visualise this dataset in 3D and highlights how AR can be simultaneously a useful research tool for interdisciplinary geoscience as well as an effective device for science communication education. Numéro de notice : A2019-575 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12298 date de publication en ligne : 23/12/2019 En ligne : https://doi.org/10.1111/phor.12298 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94455 [article]

Sea ice extent detection in the Bohai Sea using Sentinel-3 OLCI data / Hua Su in Remote sensing, Vol 11 n° 20 (2 October 2019)  

Public [article]  inRemote sensing > Vol 11 n° 20 (2 October 2019) . - 17 p. Titre : Sea ice extent detection in the Bohai Sea using Sentinel-3 OLCI data Type de document : Article/Communication Auteurs : Hua Su, Auteur ; Bowen Ji, Auteur ; Yunpeng Wang, Auteur Année de publication : 2019 Article en page(s) : 17 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bande infrarouge [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] Chine, mer de [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] glace de mer [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-OLCI [Termes descripteurs IGN] Normalized Difference Snow Index [Termes descripteurs IGN] réflectance Résumé : (auteur) Sea ice distribution is an important indicator of ice conditions and regional climate change in the Bohai Sea (China). In this study, we monitored the spatiotemporal distribution of the Bohai Sea ice in the winter of 2017–2018 by developing sea ice information indexes using 300 m resolution Sentinel-3 Ocean and Land Color Instrument (OLCI) images. We assessed and validated the index performance using Sentinel-2 MultiSpectral Instrument (MSI) images with higher spatial resolution. The results indicate that the proposed Normalized Difference Sea Ice Information Index (NDSIIIOLCI), which is based on OLCI Bands 20 and 21, can be used to rapidly and effectively detect sea ice but is somewhat affected by the turbidity of the seawater in the southern Bohai Sea. The novel Enhanced Normalized Difference Sea Ice Information Index (ENDSIIIOLCI), which builds on NDSIIIOLCI by also considering OLCI Bands 12 and 16, can monitor sea ice more accurately and effectively than NDSIIIOLCI and suffers less from interference from turbidity. The spatiotemporal evolution of the Bohai Sea ice in the winter of 2017–2018 was successfully monitored by ENDSIIIOLCI. The results show that this sea ice information index based on OLCI data can effectively extract sea ice extent for sediment-laden water and is well suited for monitoring the evolution of Bohai Sea ice in winter. Numéro de notice : A2019-557 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11202436 date de publication en ligne : 29/10/2019 En ligne : https://doi.org/10.3390/rs11202436 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94214 [article]

Real-time sea-level monitoring using Kalman filtering of GNSS-R data / Joakim Strandberg in GPS solutions, vol 23 n° 3 (July 2019)  

Public [article]  inGPS solutions > vol 23 n° 3 (July 2019) Titre : Real-time sea-level monitoring using Kalman filtering of GNSS-R data Type de document : Article/Communication Auteurs : Joakim Strandberg, Auteur ; Thomas Hobiger, Auteur ; Rüdiger Haas, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Nivellement [Termes descripteurs IGN] B-Spline [Termes descripteurs IGN] déformation de la croute terrestre [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] glace de mer [Termes descripteurs IGN] marée terrestre [Termes descripteurs IGN] niveau de la mer [Termes descripteurs IGN] rapport signal sur bruit [Termes descripteurs IGN] réflectométrie par GNSS [Termes descripteurs IGN] temps réel Résumé : (auteur) Current GNSS-R (GNSS reflectometry) techniques for sea surface measurements require data collection over longer periods, limiting their usability for real-time applications. In this work, we present a new, alternative GNSS-R approach based on the unscented Kalman filter and the so-called inverse modeling approach. The new method makes use of a mathematical description that relates SNR (signal-to-noise ratio) variations to multipath effects and uses a B-spline formalism to obtain time series of reflector height. The presented algorithm can provide results in real time with a precision that is significantly better than spectral inversion methods and almost comparable to results from inverse modeling in post-processing mode. To verify the performance, the method has been tested at station GTGU at the Onsala Space Observatory, Sweden, and at the station SPBY in Spring Bay, Australia. The RMS (root mean square) error with respect to nearby tide gauge data was found to be 2.0 cm at GTGU and 4.8 cm at SPBY when evaluating the output corresponding to real-time analysis. The method can also be applied in post-processing, resulting in RMS errors of 1.5 cm and 3.3 cm for GTGU and SPBY, respectively. Finally, based on SNR data from GTGU, it is also shown that the Kalman filter approach is able to detect the presence of sea ice with a higher temporal resolution than the previous methods and traditional remote sensing techniques which monitor ice in coastal regions. Numéro de notice : A2019-198 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-019-0851-1 En ligne : https://doi.org/10.1007/s10291-019-0851-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92653 [article]

Baltic sea ice concentration estimation using SENTINEL-1 SAR and AMSR2 microwave radiometer data / Juha Karvonen in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 5 (May 2017) . - pp 2871 - 2883 Titre : Baltic sea ice concentration estimation using SENTINEL-1 SAR and AMSR2 microwave radiometer data Type de document : Article/Communication Auteurs : Juha Karvonen, Auteur Année de publication : 2017 Article en page(s) : pp 2871 - 2883 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] Baltique, mer [Termes descripteurs IGN] épaisseur de la glace [Termes descripteurs IGN] glace de mer [Termes descripteurs IGN] image Aqua-AMSR [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] navigation maritime [Termes descripteurs IGN] Sentinel-1 [Termes descripteurs IGN] télédétection en hyperfréquence Résumé : (Auteur) Sea ice concentration (SIC) is an important sea ice parameter for sea ice navigation, environmental research, and weather and ice forecasting. We have developed and tested a method for estimation of the Baltic Sea SIC using SENTINEL-1 synthetic aperture radar (SAR) and Advanced Microwave Scanning Radiometer 2 passive microwave radiometer (MWR) data. Here, we present the method and results for January 2016. Ice concentration grids of Finnish Meteorological Institute daily ice charts have been used as reference data in this paper. We present a comparison of four SIC estimation methods with our reference data. In addition to the combined SAR/MWR SIC estimation method, we also compare SIC estimates produced using SAR alone and two MWR-based methods. The main target of this paper was to develop and test a high-resolution SIC estimation method suitable for operational use. Numéro de notice : A2017-470 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2017.2655567 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86393 [article]

Sea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: a case study / Lei Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4524 - 4533 Titre : Sea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: a case study Type de document : Article/Communication Auteurs : Lei Wang, Auteur ; K. Andrea Scott, Auteur ; Linlin Xu, Auteur ; David A. Clausi, Auteur Année de publication : 2016 Article en page(s) : pp 4524 - 4533 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] eau de fonte [Termes descripteurs IGN] glace de mer [Termes descripteurs IGN] iceberg [Termes descripteurs IGN] image Radarsat [Termes descripteurs IGN] navigation maritime [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (Auteur) High-resolution ice concentration maps are of great interest for ship navigation and ice hazard forecasting. In this case study, a convolutional neural network (CNN) has been used to estimate ice concentration using synthetic aperture radar (SAR) scenes captured during the melt season. These dual-pol RADARSAT-2 satellite images are used as input, and the ice concentration is the direct output from the CNN. With no feature extraction or segmentation postprocessing, the absolute mean errors of the generated ice concentration maps are less than 10% on average when compared with manual interpretation of the ice state by ice experts. The CNN is demonstrated to produce ice concentration maps with more detail than produced operationally. Reasonable ice concentration estimations are made in melt regions and in regions of low ice concentration. Numéro de notice : A2016-886 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2016.2543660 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83066 [article]

Characterization of Arctic sea ice thickness using high-resolution spaceborne polarimetric SAR data / J.W. Kim in IEEE Transactions on geoscience and remote sensing, vol 50 n° 1 (January 2012)

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A web GIS for sea ice information and an ice service archive / S. Li in Transactions in GIS, vol 15 n° 2 (April 2011)  

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Sea ice deformation state from Synthetic Aperture Radar Imagery: part 2 Effects of spatial resolution and noise level / W. Dierking in IEEE Transactions on geoscience and remote sensing, vol 46 n° 8 (August 2008)

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Geoid, sea level and vertical datum of the Arctic improved by ICESAT and GRACE / H. Skourup in Geomatica, vol 62 n° 2 (June 2008)

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Télédétection et changement climatique : l'exemple de l'Arctique / Claude Kergomard in Photo interprétation, vol 43 n° 4 (Décembre 2007)

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Using remote sensing data to develop seasonal outlooks for Arctic regional sea-ice minimum extent / S. Drobot in Remote sensing of environment, vol 111 n° 2-3 (30 November 2007)

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Sea-ice deformation state from synthetic aperture radar imagery: Part 1 comparison of C- and L-band and different polarization / W. Dierking in IEEE Transactions on geoscience and remote sensing, vol 45 n° 11 Tome 2 (November 2007)

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Assessment of long-range kinematic GPS positioning errors by comparison with airborne laser altimetry and satellite altimetry / X. Zhang in Journal of geodesy, vol 81 n° 3 (March 2007)

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Assessment of EOS aqua AMSR-E artic sea ice concentrations using Landsat-7 and airborne microwave imagery / D.J. Cavalieri in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 1 (November 2006)

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March2003 EOS aqua AMSR-E sea ice field campaign / D.J. Cavalieri in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 1 (November 2006)

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