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Effect of climate change on the growth of tree species: Dendroclimatological analysis / Archana Gauli in Forests, vol 13 n° 4 (April 2022)
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Titre : Effect of climate change on the growth of tree species: Dendroclimatological analysis Type de document : Article/Communication Auteurs : Archana Gauli, Auteur ; Prem Raj Neupane, Auteur ; Philip Mundhenk, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 496 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Allemagne
[Termes IGN] analyse diachronique
[Termes IGN] croissance des arbres
[Termes IGN] dendrochronologie
[Termes IGN] dendrologie
[Termes IGN] données météorologiques
[Termes IGN] échantillonnage
[Termes IGN] Picea abies
[Termes IGN] Pinus sylvestris
[Termes IGN] prévision météorologique
[Termes IGN] Pseudotsuga menziesii
[Termes IGN] sécheresse
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Tree ring analyses can assist in revealing the effect of gradual change in climatic variables on tree growth. Dendroclimatic analyses are of particular importance in evaluating the climate variables that affect growth significantly and in determining the relative strength of different climatic factors. In this study, we investigated the growth performance of Pinus sylvestris, Picea abies, and Pseudotsuga menziesii in northern Germany using standard dendrochronological methods. The study further analyzed tree growth responses to different climatic variables over a period of a hundred years. Both response function analysis and moving correlation analysis confirmed that the climate and growth relationship is species-specific and variable and inconsistent over time. Scots pine and Douglas fir growth were stimulated mainly by the increase in winter temperatures, particularly the January, February, and March temperatures of the current year. In contrast, Norway spruce growth was stimulated mainly by the increase in precipitation in May, June, and July and the increase in temperature in March of the current year. Climate projections for central Europe foresee an increase in temperature and a decrease in the amount of summer precipitation. In a future, warmer climate with drier summers, the growth of Norway spruce might be negatively affected. Numéro de notice : A2022-259 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f13040496 Date de publication en ligne : 22/03/2022 En ligne : https://doi.org/10.3390/f13040496 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100237
in Forests > vol 13 n° 4 (April 2022) . - n° 496[article]Effect of PCV and attitude on the precise orbit determination of Jason-3 satellite / Kai Li in Journal of applied geodesy, vol 16 n° 2 (April 2022)
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Titre : Effect of PCV and attitude on the precise orbit determination of Jason-3 satellite Type de document : Article/Communication Auteurs : Kai Li, Auteur ; Xuhua Zhou, Auteur ; Nannan Guo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 143 - 150 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Techniques orbitales
[Termes IGN] capteur d'orientation
[Termes IGN] centre de phase
[Termes IGN] données Jason
[Termes IGN] orbite basse
[Termes IGN] orbitographie
[Termes IGN] perturbation orbitaleRésumé : (auteur) Satellite attitude modes and antenna phase center variations have a great influence on the Precise Orbit Determination (POD) of Low Earth Orbit satellites (LEOs). Inaccurate information about spacecraft attitude, phase center offsets and variations in the POD leads to orbital error. The Jason-3 satellite experienced complex attitude modes which are fixed, sinusoidal, ramp-up/down and yaw-flip. Therefore, it is necessary to properly construct the attitude model in the process of POD especially when there is no external attitude data. For the antenna phase center correction, the PCO which is the deviation between Antenna Reference Point (ARP) and Mean Antenna Phase Center (MAPC) usually can be calibrated on the ground accurately, but the PCV which is the deviation between Instantaneous Antenna Phase Center (IAPC) and Mean Antenna Phase Center (MAPC) will change greatly with the change of space environment. Residual approach can be used to estimate the receiver PCV map. In this paper, we collected the on-board GPS data of Jason-3 satellite from January 2019 and analyzed the impacts of PCV and spacecraft attitude on the orbit accuracy by performing the reduced-dynamic POD. Compared with the reference orbit released by the Centre National d’Études Spatiales (CNES), using the PCV map can reduce the Root Mean Square (RMS) of orbit differences in the Radial (R), Along-track (T), Cross-track (N) and 3D direction about 0.3, 1.0, 0.9, and 1.4 mm. Based on the estimated PCV map, the orbit accuracy in R, T, N and 3D direction is 1.24, 2.81, 1.17, and 3.29 cm respectively by using the measured attitude data. When using the attitude model, the orbit accuracy in R, T, N and 3D directions is 1.60, 3.54, 1.33, and 4.13 cm, respectively. The results showed that the combination of measured attitude data and modeled PCV map can obtain the better orbit solution. It is essential to build a corresponding model in high-precision orbit determination, when there is no attitude data and PCV map. Numéro de notice : A2022-251 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/jag-2021-0052 Date de publication en ligne : 26/01/2022 En ligne : https://doi.org/10.1515/jag-2021-0052 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100204
in Journal of applied geodesy > vol 16 n° 2 (April 2022) . - pp 143 - 150[article]Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation / Yingjie Hu in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)
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Titre : Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation Type de document : Article/Communication Auteurs : Yingjie Hu, Auteur ; Zhipeng Gui, Auteur ; Jimin Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 799 - 821 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] descripteur
[Termes IGN] données d'entrainement sans étiquette
[Termes IGN] image cartographique
[Termes IGN] métadonnées
[Termes IGN] projection
[Termes IGN] système d'information géographique
[Termes IGN] Web Map Service
[Termes IGN] web mappingRésumé : (auteur) Maps in the form of digital images are widely available in geoportals, Web pages, and other data sources. The metadata of map images, such as spatial extents and place names, are critical for their indexing and searching. However, many map images have either mismatched metadata or no metadata at all. Recent developments in deep learning offer new possibilities for enriching the metadata of map images via image-based information extraction. One major challenge of using deep learning models is that they often require large amounts of training data that have to be manually labeled. To address this challenge, this paper presents a deep learning approach with GIS-based data augmentation that can automatically generate labeled training map images from shapefiles using GIS operations. We utilize such an approach to enrich the metadata of map images by adding spatial extents and place names extracted from map images. We evaluate this GIS-based data augmentation approach by using it to train multiple deep learning models and testing them on two different datasets: a Web Map Service image dataset at the continental scale and an online map image dataset at the state scale. We then discuss the advantages and limitations of the proposed approach. Numéro de notice : A2022-258 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1080/13658816.2021.1968407 En ligne : https://doi.org/10.1080/13658816.2021.1968407 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100231
in International journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 799 - 821[article]Estimating forest attributes in airborne laser scanning based inventory using calibrated predictions from external models / Ana de Lera Garrido in Silva fennica, vol 56 n° 2 (April 2022)
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Titre : Estimating forest attributes in airborne laser scanning based inventory using calibrated predictions from external models Type de document : Article/Communication Auteurs : Ana de Lera Garrido, Auteur ; Terje Gobakken, Auteur ; Hans Ole Ørka, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 10695 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étalonnage
[Termes IGN] hauteur des arbres
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de simulation
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] Norvège
[Termes IGN] parcelle forestière
[Termes IGN] placette d'échantillonnage
[Termes IGN] semis de points
[Termes IGN] volume en boisRésumé : (auteur) Forest management inventories assisted by airborne laser scanner data rely on predictive models traditionally constructed and applied based on data from the same area of interest. However, forest attributes can also be predicted using models constructed with data external to where the model is applied, both temporal and geographically. When external models are used, many factors influence the predictions’ accuracy and may cause systematic errors. In this study, volume, stem number, and dominant height were estimated using external model predictions calibrated using a reduced number of up-to-date local field plots or using predictions from reparametrized models. We assessed and compared the performance of three different calibration approaches for both temporally and spatially external models. Each of the three approaches was applied with different numbers of calibration plots in a simulation, and the accuracy was assessed using independent validation data. The primary findings were that local calibration reduced the relative mean difference in 89% of the cases, and the relative root mean squared error in 56% of the cases. Differences between application of temporally or spatially external models were minor, and when the number of local plots was small, calibration approaches based on the observed prediction errors on the up-to-date local field plots were better than using the reparametrized models. The results showed that the estimates resulting from calibrating external models with 20 plots were at the same level of accuracy as those resulting from a new inventory. Numéro de notice : A2022-367 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10695 Date de publication en ligne : 25/04/2022 En ligne : https://doi.org/10.14214/sf.10695 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100589
in Silva fennica > vol 56 n° 2 (April 2022) . - n° 10695[article]Exploring scientific literature by textual and image content using DRIFT / Ximena Pocco in Computers and graphics, vol 103 (April 2022)
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Titre : Exploring scientific literature by textual and image content using DRIFT Type de document : Article/Communication Auteurs : Ximena Pocco, Auteur ; Tiago da Silva, Auteur ; Jorge Poco, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 140 - 152 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse visuelle
[Termes IGN] bibliothèque numérique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] corpus
[Termes IGN] exploration de données
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] recherche d'image basée sur le contenu
[Termes IGN] recherche scientifique
[Termes IGN] similitude sémantiqueRésumé : (auteur) Digital libraries represent the most valuable resource for storing, querying, and retrieving scientific literature. Traditionally, the reader/analyst aims to compose a set of articles based on keywords, according to his/her preferences, and manually inspect the resulting list of documents. Except for the articles which share citations or common keywords, the results retrieved will be limited to those which fulfill a syntactic match. Besides, if instead of having an article as a reference, the user has an image, the process of finding and exploring articles with similar content becomes infeasible. This paper proposes a visual analytic methodology for exploring and analyzing scientific document collections that consider both textual and image content. The proposed technique relies on combining multiple Content-Based Image Retrieval (CBIR) components and multidimensional projection to map the documents to a visual space based on their similarity, thus enabling an interactive exploration. Moreover, we extend its analytical capabilities with visual resources to display complementary information on selected documents that uncover hidden patterns and semantic relations. We evidence the effectiveness of our methodology through three case studies and a user evaluation, which attest to its usefulness during the process of scientific collections exploration. Numéro de notice : A2022-289 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cag.2022.02.005 Date de publication en ligne : 11/02/2022 En ligne : https://doi.org/10.1016/j.cag.2022.02.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100332
in Computers and graphics > vol 103 (April 2022) . - pp 140 - 152[article]A graph attention network for road marking classification from mobile LiDAR point clouds / Lina Fang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)
PermalinkGraph learning based on signal smoothness representation for homogeneous and heterogeneous change detection / David Alejandro Jimenez-Sierra in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)
PermalinkHybrid georeferencing of images and LiDAR data for UAV-based point cloud collection at millimetre accuracy / Norbert Haala in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 4 (April 2022)
PermalinkIdentification and classification of routine locations using anonymized mobile communication data / Gonçalo Ferreira in ISPRS International journal of geo-information, vol 11 n° 4 (April 2022)
PermalinkImproving the (re-)convergence of multi-GNSS real-time precise point positioning through regional between-satellite single-differenced ionospheric augmentation / Ahao Wang in GPS solutions, vol 26 n° 2 (April 2022)
PermalinkA knowledge representation model based on the geographic spatiotemporal process / Kun Zheng in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)
PermalinkMeta-learning based hyperspectral target detection using siamese network / Yulei Wang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)
PermalinkMining crowdsourced trajectory and geo-tagged data for spatial-semantic road map construction / Jincai Huang in Transactions in GIS, vol 26 n° 2 (April 2022)
PermalinkNatural disturbances risks in European boreal and temperate forests and their links to climate change : A review of modelling approaches / Joyce Machado Nunes Romeiro in Forest ecology and management, vol 509 (April-1 2022)
PermalinkOn enhanced PPP with single difference between-satellite ionospheric constraints / Yan Xiang in Navigation : journal of the Institute of navigation, vol 69 n° 1 (Spring 2022)
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