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Les États-Unis remplacent le NAD83 par le NATRF2022 : ce que cela signifie pour le Canada / Caroline Erickson in Geomatica, vol 74 n° 1 (Mars 2020)
[article]
Titre : Les États-Unis remplacent le NAD83 par le NATRF2022 : ce que cela signifie pour le Canada Type de document : Article/Communication Auteurs : Caroline Erickson, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1 - 8 Note générale : bibliographie Langues : Anglais (eng) Français (fre) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] Canada
[Termes IGN] Etats-Unis
[Termes IGN] North American Datum 1983
[Termes IGN] North American Terrestrial Reference Frame 2022Résumé : (auteur) En 2022, les États-Unis, dans le cadre de la modernisation de leur système de référence, remplaceront le Système de référence géodésique nord-américain de 1983 (NAD83) par un nouveau cadre de référence terrestre nord-américain (NATRF2022), ce qui entraînera des différences de coordonnées horizontales de 1,3 à 1,5 mètre à la frontière canado-américaine par rapport au NAD83 (SCRS) canadien. Jamais auparavant des différences aussi importantes n’avaient existé entre les cadres de référence de nos deux pays. Le présent document examine les raisons pour lesquelles les États-Unis apportent ce changement et examine ensuite la situation du Canada en ce qui concerne les cadres de référence. Il y a des raisons impérieuses pour que le Canada emboîte le pas et passe au NATRF2022 d’ici une décennie, mais cela représente aussi des défis majeurs. Que le Canada suive ou non la même voie, il y a beaucoup de travail à accomplir pour préparer le Canada à l’adoption du NATRF2022 par les États-Unis. Le présent document se veut une première étape pour informer la communauté géospatiale canadienne de la décision des États-Unis d’adopter le NATRF2022 et de ce que cela signifie pour le Canada. Numéro de notice : A2020-841 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1139/geomat-2020-0008 En ligne : https://doi.org/10.1139/geomat-2020-0008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98318
in Geomatica > vol 74 n° 1 (Mars 2020) . - pp 1 - 8[article]Tree annotations in LiDAR data using point densities and convolutional neural networks / Ananya Gupta in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)
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Titre : Tree annotations in LiDAR data using point densities and convolutional neural networks Type de document : Article/Communication Auteurs : Ananya Gupta, Auteur ; Jonathan Byrne, Auteur ; David Moloney, Auteur Année de publication : 2020 Article en page(s) : pp 971 - 981 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] Dublin (Irlande ; ville)
[Termes IGN] extraction d'arbres
[Termes IGN] image spectrale
[Termes IGN] Montréal (Québec)
[Termes IGN] segmentation
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] voxel
[Termes IGN] zone urbaineRésumé : (auteur) LiDAR provides highly accurate 3-D point clouds. However, data need to be manually labeled in order to provide subsequent useful information. Manual annotation of such data is time-consuming, tedious, and error prone, and hence, in this article, we present three automatic methods for annotating trees in LiDAR data. The first method requires high-density point clouds and uses certain LiDAR data attributes for the purpose of tree identification, achieving almost 90% accuracy. The second method uses a voxel-based 3-D convolutional neural network on low-density LiDAR data sets and is able to identify most large trees accurately but struggles with smaller ones due to the voxelization process. The third method is a scaled version of the PointNet++ method and works directly on outdoor point clouds and achieves an F score of 82.1% on the ISPRS benchmark data set, comparable to the state-of-the-art methods but with increased efficiency. Numéro de notice : A2020-095 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2942201 Date de publication en ligne : 11/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2942201 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94658
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 971 - 981[article]Photogrammetric Bathymetry for the Canadian Arctic / Matus Hodul in Marine geodesy, Vol 43 n° 1 (January 2020)
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Titre : Photogrammetric Bathymetry for the Canadian Arctic Type de document : Article/Communication Auteurs : Matus Hodul, Auteur ; René Chénier, Auteur ; Marc-André Faucher, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 23 - 43 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bathymétrie
[Termes IGN] Arctique, océan
[Termes IGN] Canada
[Termes IGN] carte marine
[Termes IGN] données hydrographiques
[Termes IGN] fond marin
[Termes IGN] image Worldview
[Termes IGN] télédétection spatialeRésumé : (auteur) Remote sensing is becoming common in the estimation of bathymetry for navigational charting through a process known as Satellite Derived Bathymetry (SDB). Most SDB techniques currently used by hydrographic offices employ an empirical approach, requiring the use of in-situ data to calibrate a relationship between spectral information and coincident depths. This article reports on a multi-site test of an alternative SDB method which uses photogrammetry to extract depths from stereo WorldView-2 imagery. In areas with heterogeneous seafloors, the empirical approach faces difficulties in establishing the relationship between colour and depth, while the photogrammetric approach uses the contrasting seafloor features for triangulation. Additionally, the photogrammetric method may be applied in areas lacking previous survey data. Five study areas in Nunavut, Canada were selected to test the robustness of the method in different environments and under different imaging conditions. Study areas were (with resulting RMSE/Bias given in metres) Coral Harbour (0.84/−0.47), Cambridge Bay (1.16/−0.15), Queen Maud Gulf (0.97/0.06), Arviat (0.99/−0.009), and Frobisher Bay, where extraction largely failed due to environmental conditions. Accuracies demonstrated here are similar to those seen using the empirical approach, suggesting that these two methods may be used in conjunction, each applied to regions where they are better suited. Numéro de notice : A2020-052 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01490419.2019.1685030 Date de publication en ligne : 22/11/2019 En ligne : https://doi.org/10.1080/01490419.2019.1685030 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94920
in Marine geodesy > Vol 43 n° 1 (January 2020) . - pp 23 - 43[article]Predicting carbon accumulation in temperate forests of Ontario, Canada using a LiDAR-initialized growth-and-yield model / Paulina T. Marczak in Remote sensing, vol 12 n° 1 (January 2020)
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Titre : Predicting carbon accumulation in temperate forests of Ontario, Canada using a LiDAR-initialized growth-and-yield model Type de document : Article/Communication Auteurs : Paulina T. Marczak, Auteur ; Karin Y. Van Ewijk, Auteur ; Paul M. Treitz, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 29 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] changement climatique
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] diamètre des arbres
[Termes IGN] données lidar
[Termes IGN] forêt tempérée
[Termes IGN] modèle de croissance végétale
[Termes IGN] Ontario (Canada)
[Termes IGN] peuplement forestier
[Termes IGN] photo-interprétation
[Termes IGN] puits de carbone
[Termes IGN] rendement
[Termes IGN] semis de pointsRésumé : (auteur) Climate warming has led to an urgent need for improved estimates of carbon accumulation in uneven-aged, mixed temperate forests, where high uncertainty remains. We investigated the feasibility of using LiDAR-derived forest attributes to initialize a growth and yield (G&Y) model in complex stands at the Petawawa Research Forest (PRF) in eastern Ontario, Canada; i.e., can G&Y models based on LiDAR provide accurate predictions of aboveground carbon accumulation in complex forests compared to traditional inventory-based estimates? Applying a local G&Y model, we forecasted aboveground carbon stock (tons/ha) and accumulation (tons/ha/yr) using recurring plot measurements from 2012–2016, FVS1. We applied statistical predictors derived from LiDAR to predict stem density (SD), stem diameter distribution (SDD), and basal area distribution (BA_dist). These data, along with measured species abundance, were used to initialize a second model (FVS2). A third model was tested using LiDAR-initialized tree lists and photo-interpreted estimates of species abundance (i.e., FVS3). The carbon stock projections for 2016 from the inventory-based G&Y model) were equivalent to validation carbon stocks measured in 2016 at all size-class levels (p 0.05). At the plot level, LiDAR-based predictions of carbon accumulation over a nine-year period did not differ when using either inventory or photo-interpreted species (p Numéro de notice : A2020-222 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs12010201 Date de publication en ligne : 06/01/2020 En ligne : https://doi.org/10.3390/rs12010201 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94934
in Remote sensing > vol 12 n° 1 (January 2020) . - 29 p.[article]A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems / Dong Chen in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)
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Titre : A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems Type de document : Article/Communication Auteurs : Dong Chen, Auteur ; Tatiana V. Loboda, Auteur ; Joanne V. Hall, Auteur Année de publication : 2020 Article en page(s) : pp 63 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Alaska (Etats-Unis)
[Termes IGN] Canada
[Termes IGN] changement climatique
[Termes IGN] écosystème forestier
[Termes IGN] forêt boréale
[Termes IGN] image Landsat
[Termes IGN] incendie de forêt
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] perturbation écologique
[Termes IGN] Short Waves InfraRed
[Termes IGN] toundraRésumé : (Auteur) Satellite imagery has been widely used for the assessment of wildfire burn severity within the scientific community and fire management agencies. Multiple indices have been proposed to assess burn severity, among which the differenced Normalized Burn Ratio (dNBR) is arguably the most commonly used index that is expected to provide an objective and consistent assessment. However, although evidence of variability in the dNBR-based assessment of burn severity driven by image pair selection has been shown in many studies, the comprehensive examination of the extent of the bias resulting from the image selection has been lacking. In this study, we focus on three factors of the image selection process which are encountered by most Landsat-derived dNBR applications, including the sensor combination and the difference in timing of image acquisition (for both the year and seasonality) of pre- and post-fire image pairs. Through separate analyses, each targeting a single factor, we show that Landsat sensor combination between the pre- and post-fire images has a limited impact on the dNBR values. The difference in the year of acquisition between the images in the image pairs is shown to influence dNBR assessment with a noticeable increase in mean dNBR (>0.1) with only a single year difference between images compared to multi-year differences. However, differences in the image acquisition seasons and the resulting phenological differences is shown to impact dNBR values most considerably. Based on our results, we warn against the calculation of dNBR when the images are acquired in different seasons. We believe that despite the existence of multiple derivatives of dNBR, there remains a need for an improved version; one that is less susceptible to the phenological impacts introduced by the selected images. Numéro de notice : A2020-012 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.011 Date de publication en ligne : 19/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.011 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94400
in ISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 63 - 77[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020011 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020013 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020012 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Combining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data / Emanuele Santi in Remote sensing, Vol 11 n° 20 (October-2 2019)PermalinkMapping dead forest cover using a deep convolutional neural network and digital aerial photography / Jean-Daniel Sylvain in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)PermalinkThe utility of terrestrial photogrammetry for assessment of tree volume and taper in boreal mixedwood forests / Christopher Mulverhill in Annals of Forest Science, Vol 76 n° 3 (September 2019)PermalinkComparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data / Joris Ravaglia in Forests, vol 10 n° 7 (July 2019)PermalinkExploitation of deep learning in the automatic detection of cracks on paved roads / Won Mo Jung in Geomatica, vol 73 n° 2 (June 2019)PermalinkA four‐dimensional agent‐based model: A case study of forest‐fire smoke propagation / Alex Smith in Transactions in GIS, vol 23 n° 3 (June 2019)PermalinkWood quality of black spruce and balsam fir trees defoliated by spruce budworm: A case study in the boreal forest of Quebec, Canada / Carlos Paixao in Forest ecology and management, vol 437 (1 April 2019)PermalinkChilling and forcing temperatures interact to predict the onset of wood formation in Northern Hemisphere conifers / Nicolas Delpierre in Global change biology, vol 25 n° 3 (March 2019)PermalinkDeep mapping gentrification in a large Canadian city using deep learning and Google Street View / Lazar Ilic in Plos one, vol 14 n° 3 (March 2019)PermalinkDigital preservation, social history, and the Quon Sang Lung Laundry building : a case study from Fort Macleod, Alberta, Canada / Peter Dawson in Applied geomatics, vol 10 n° 4 (December 2018)PermalinkMapping experience: Age and indigeneity as mediating factors in users’ experiences with the Algonquian linguistic atlas / Adam Stone in Cartographica, vol 53 n° 4 (Winter 2018)PermalinkTowards operational marker-free registration of terrestrial lidar data in forests / Jean-François Tremblay in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkUsing Network Segments in the Visualization of Urban Isochrones / Jeff Allen in Cartographica, vol 53 n° 4 (Winter 2018)PermalinkA new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index / Huanhuan Yuan in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)PermalinkA new method for 3D individual tree extraction using multispectral airborne LiDAR point clouds / Wenxia Dai in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)PermalinkObject-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier / Huanxue Zhang in Geocarto international, vol 33 n° 10 (October 2018)PermalinkDigital aerial photogrammetry for assessing cumulative spruce budworm defoliation and enhancing forest inventories at a landscape-level / Tristan R.H. Goodbody in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)PermalinkLa propriété en 3D : état des lieux / Anonyme in Géomatique expert, n° 123 (juillet - août 2018)PermalinkParametric bootstrap estimators for hybrid inference in forest inventories / Mathieu Fortin in Forestry, an international journal of forest research, vol 91 n° 3 (July 2018)PermalinkPredicting hardwood quality and its evolution over time in Quebec's forests / Hughes Power in Forestry, an international journal of forest research, vol 91 n° 3 (July 2018)Permalink