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Titre : GENESIS: Co-location of Geodetic Techniques in Space Type de document : Article/Communication Auteurs : Pacôme Delva, Auteur ; Zuheir Altamimi , Auteur ; Alejandro Blazquez, Auteur ; Mathis Blossfeld, Auteur ; Johannes Böhm , Auteur ; Pascal Bonnefond, Auteur ; et al., Auteur ; Laurent Métivier , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2022 Projets : 1-Pas de projet / Note générale : bibliographie
auteurs : Pacome Delva, Zuheir Altamimi, Alejandro Blazquez, Mathis Blossfeld, Johannes Böhm, Pascal Bonnefond, Jean-Paul Boy, Sean Bruinsma, Grzegorz Bury, Miltiadis Chatzinikos, Alexandre Couhert, Clement Courde, Rolf Dach, Veronique Dehant, Simone Dell’Agnello, Gunnar Elgered, Werner Enderle, Pierre Exertier, Susanne Glaser, Rudiger Haas, Wen Huang, Urs Hugentobler17, Adrian J¨aggi11, Ozgur Karatekin12, Frank G. Lemoine18, Christophe Le Poncin-Lafitte, Susanne Lunz, Benjamin Mannel, Flavien Mercier, Laurent Metivier, Benoıt Meyssignac, Jurgen Muller, Axel Nothnage, Felix Perosanz, Roelof Rietbroek, Markus Rothacher, Hakan Sert, Krzysztof Sosnica, Paride Testani, Javier Ventura-Traveset, Gilles
Wautelet, and Radoslaw ZajdeLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] co-positionnement
[Termes IGN] état de l'art
[Termes IGN] International Terrestrial Reference Frame
[Termes IGN] mission spatiale
[Termes IGN] station de mesureRésumé : (auteur) Improving and homogenizing time and space reference systems on Earth and, more directly, realizing the Terrestrial Reference Frame (TRF) with an accuracy of 1mm and a long-term stability of 0.1mm/year are relevant for many scientific and societal endeavors. The knowledge of the TRF is fundamental for Earth and navigation sciences. For instance, quantifying sea level change strongly depends on an accurate determination of the geocenter motion but also of the positions of continental and island reference stations, as well as the ground stations of tracking networks. Also, numerous applications in geophysics require absolute millimeter precision from the reference frame, as for example monitoring tectonic motion or crustal deformation for predicting natural hazards. The TRF accuracy to be achieved represents the consensus of various authorities which has enunciated geodesy requirements for Earth sciences.
Today we are still far from these ambitious accuracy and stability goals for the realization of the TRF. However, a combination and co-location of all four space geodetic techniques on one satellite platform can significantly contribute to achieving these goals. This is the purpose of the GENESIS mission, proposed as a component of the FutureNAV program of the European Space Agency. The GENESIS platform will be a dynamic space geodetic observatory carrying all the geodetic instruments referenced to one another through carefully calibrated space ties. The co-location of the techniques in space will solve the inconsistencies and biases between the different geodetic techniques in order to reach the TRF accuracy and stability goals endorsed by the various international authorities and the scientific community. The purpose of this white paper is to review the state-of-the-art and explain the benefits of the GENESIS mission in Earth sciences, navigation sciences and metrology.Numéro de notice : P2022-007 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2209.15298 Date de publication en ligne : 30/09/2022 En ligne : https://doi.org/10.48550/arXiv.2209.15298 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101792 Genetic diversity of sessile oak populations in the Czech Republic / Jakub Dvořák in Journal of forest science, vol 68 n° 1 (January 2022)
[article]
Titre : Genetic diversity of sessile oak populations in the Czech Republic Type de document : Article/Communication Auteurs : Jakub Dvořák, Auteur ; Jiri Korecký, Auteur ; Zuzana Faltinová, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 8 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] diversité génétique
[Termes IGN] génétique forestière
[Termes IGN] optimisation (mathématiques)
[Termes IGN] Picea abies
[Termes IGN] Quercus sessiliflora
[Termes IGN] République Tchèque
[Vedettes matières IGN] SylvicultureRésumé : (auteur) The sessile oak is a broadleaved tree species of great ecological and silvicultural importance. Oaks are the second most widespread deciduous tree species in the Czech Republic, and ongoing climate change negatively affects the abundant and often monocultural Norway spruce. Therefore, a proportional increase of more resilient tree species such as sessile oak has emerged. This study aimed to depict population genetic diversity when analysing 272 individuals from 10 subpopulations selected across the Czech Republic. Targeted populations were chosen based on the minimal expected human impact on the stand (presumably autochthonous stands). All individuals were genotyped using 18 polymorphic microsatellite markers (SSRs) assembled into two amplification multiplexes. The high discriminatory power of SSR markers was tested and confirmed by the probability of identity analysis. The genetic differentiation of the subpopulations was low yet significant, quantified by Wright’s F-statistics within the range from 0.012 to 0.029. Based on discriminant analysis of principal components (DAPC), we detected two populations with geographic genetic correlation (the 15th meridian east being a north-south boundary line) and one with a distinct genetic pattern. We assume that the population might previously be established from seed sources outside the Czech Republic. Moreover, to some extent, our findings advocate the legitimacy of the legislative rules for forest reproductive material (FRM) transfer. Numéro de notice : A2022-116 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.17221/99/2021-JFS Date de publication en ligne : 05/01/2022 En ligne : https://doi.org/10.17221/99/2021-JFS Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99642
in Journal of forest science > vol 68 n° 1 (January 2022) . - pp 8 - 18[article]A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods / Pengxiang Zhao in Remote sensing, vol 14 n° 1 (January-1 2022)
[article]
Titre : A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods Type de document : Article/Communication Auteurs : Pengxiang Zhao, Auteur ; Zohreh Masoumi, Auteur ; Maryam Kalantari, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aléa
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] cartographie des risques
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] effondrement de terrain
[Termes IGN] Iran
[Termes IGN] modèle numérique de surface
[Termes IGN] régression logistique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] risque naturel
[Termes IGN] système d'information géographiqueRésumé : (auteur) Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). To do so, sixteen landslide causative factors have been extracted and their related spatial layers have been prepared. Then, the algorithms were trained with related landslide and non-landslide points. The results illustrate that the five ML algorithms performed suitably (precision = 82.43–85.6%, AUC = 0.934–0.967). The RF algorithm achieves the best result, while the CNN, SVM, the ANN, and the LR have the best results after RF, respectively, in this case study. Moreover, variable importance analysis results indicate that slope and topographic curvature contribute more to the prediction. The results would be beneficial to planning strategies for landslide risk management. Numéro de notice : A2022-056 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/rs14010211 Date de publication en ligne : 04/01/2022 En ligne : https://doi.org/10.3390/rs14010211 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99459
in Remote sensing > vol 14 n° 1 (January-1 2022) . - n° 211[article]Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)
[article]
Titre : Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles Type de document : Article/Communication Auteurs : Nico Lang, Auteur ; Nicolai Kalischek, Auteur ; John Armston, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n* 112760 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] biomasse aérienne
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation bayesienne
[Termes IGN] forme d'onde
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de pointsRésumé : (auteur) NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias. Numéro de notice : A2022-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112760 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112760 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99495
in Remote sensing of environment > vol 268 (January 2022) . - n* 112760[article]Histograms of oriented mosaic gradients for snapshot spectral image description / Lulu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)
[article]
Titre : Histograms of oriented mosaic gradients for snapshot spectral image description Type de document : Article/Communication Auteurs : Lulu Chen, Auteur ; Yong-Qiang Zhao, Auteur ; Jonathan Cheung-Wai Chan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 79 - 93 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] capteur multibande
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre spectral
[Termes IGN] histogramme
[Termes IGN] image proche infrarouge
[Termes IGN] image spectrale
[Termes IGN] mosaïque d'images
[Termes IGN] poursuite de cible
[Termes IGN] temps instantanéRésumé : (auteur) This paper presents a feature descriptor using Histogram of Oriented Mosaic Gradient (HOMG) that extracts spatial-spectral features directly from mosaic spectral images. Spectral imaging utilizes unique spectral signatures to distinguish objects of interest in the scene more discriminatively. Snapshot spectral cameras equipped with spectral filter arrays (SFAs) capture spectral videos in real time, making it possible to detect/track fast moving targets based on spectral imaging. How to effectively extract the spatial-spectral feature directly from the mosaic spectral images acquired by snapshot spectral cameras is a core issue for detection/tracking. So far, there is a lack of comprehensive and in-depth research on this issue. To this end, this paper proposed a new spatial-spectral feature extractor for mosaic spectral images. The proposed scheme finds two forms of SFA neighborhood (SFAN) to construct a feature extractor suitable for any SFA structure. Exploiting the spatial-spectral correlation in two SFANs, we design six mosaic spatial-spectral gradient operators to compute spatial-spectral gradient maps (SGMs). HOMG descriptors are constructed using the magnitude and orientation of SGMs. The effectiveness and generalizability of the proposed method have been verified with object tracking experiments. Compared to the state-of-the-art feature descriptors, HOMG ranked first on two datasets captured with snapshot spectral camera with different SFAs, achieving a gain of 3.9% and 5.9% in average success rate over the second-ranked feature. Numéro de notice : A2022-010 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.10.018 Date de publication en ligne : 12/11/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.10.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99058
in ISPRS Journal of photogrammetry and remote sensing > vol 183 (January 2022) . - pp 79 - 93[article]Réservation
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