5-Publications IGN 2019
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Couplings in cell differentiation kinetics mitigate air temperature influence on conifer wood anatomy / Henri E. Cuny in Plant, cell & environment, vol 42 n° 4 (April 2019)
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Titre : Couplings in cell differentiation kinetics mitigate air temperature influence on conifer wood anatomy Type de document : Article/Communication Auteurs : Henri E. Cuny , Auteur ; Patrick Fonti, Auteur ; Cyrille B.K. Rathgeber, Auteur ; Georg von Arx, Auteur ; Richard L. Peters, Auteur ; David Frank, Auteur
Année de publication : 2019 Projets : 3-projet - voir note / Article en page(s) : pp 1222 - 1232 Note générale : bibliographie
The authors acknowledge the Swiss National Science Foundation SNF (projects CLIMWOOD‐160077 and LOTFOR‐150205). G. v. A. was supported by a grant from the Swiss State Secretariat for Education, Research and Innovation SERI (SBFI C14.0104). This research also benefited from the support of the FPS COST Action STReESS (FP1106).Langues : Anglais (eng) Descripteur : [Termes IGN] anatomie du bois
[Termes IGN] cerne
[Termes IGN] Europe centrale
[Termes IGN] Larix decidua
[Termes IGN] Picea abies
[Termes IGN] Pinophyta
[Termes IGN] température de l'air
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Conifer trees possess a typical anatomical tree‐ring structure characterized by a transition from large and thin‐walled earlywood tracheids to narrow and thick‐walled latewood tracheids. However, little is known on how this characteristic structure is maintained across contrasting environmental conditions, due to its crucial role to ensure sap ascent and mechanical support. In this study, we monitored weekly wood cell formation for up to 7 years in two temperate conifer species (i.e., Picea abies (L.) Karst and Larix decidua Mill.) across an 8°C thermal gradient from 800 to 2,200 m a.s.l. in central Europe to investigate the impact of air temperature on rate and duration of wood cell formation. Results indicated that towards colder sites, forming tracheids compensate a decreased rate of differentiation (cell enlarging and wall thickening) by an extended duration, except for the last cells of the latewood in the wall‐thickening phase. This compensation allows conifer trees to mitigate the influence of air temperature on the final tree‐ring structure, with important implications for the functioning and resilience of the xylem to varying environmental conditions. The disappearing compensation in the thickening latewood cells might also explain the higher climatic sensitivity usually found in maximum latewood density. Numéro de notice : A2019-272 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/pce.13464 Date de publication en ligne : 16/10/2018 En ligne : https://doi.org/10.1111/pce.13464 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95323
in Plant, cell & environment > vol 42 n° 4 (April 2019) . - pp 1222 - 1232[article]Improvement of photogrammetric accuracy by modeling and correcting the thermal effect on camera calibration / Mehdi Daakir in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)
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Titre : Improvement of photogrammetric accuracy by modeling and correcting the thermal effect on camera calibration Type de document : Article/Communication Auteurs : Mehdi Daakir , Auteur ; Yilin Zhou
, Auteur ; Marc Pierrot-Deseilligny
, Auteur ; Christian Thom
, Auteur ; Olivier Martin
, Auteur ; Ewelina Rupnik
, Auteur
Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 142 - 156 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] CamLight
[Termes IGN] déformation d'image
[Termes IGN] détecteur CMOS
[Termes IGN] effet thermique
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] image captée par drone
[Termes IGN] modèle géométrique de prise de vue
[Termes IGN] photogrammétrie métrologiqueRésumé : (Auteur) This paper presents a new method for improving the geometric accuracy of photogrammetric reconstruction by modeling and correcting the thermal effect on camera image sensor. The objective is to verify that when the temperature of image sensor varies during the acquisition, image deformation induced by the temperature change is quantifiable, modelisable and correctable. A temperature sensor integrated in the camera enables the measurement of image sensor temperature at exposure. It is therefore natural and appropriate to take this effect into account and to finally model and correct it after a calibration step. Nowadays, in cartography applications performed with UAV, the frame rate of acquisitions is continuously increasing. A high frame rate over a long acquisition time can result in an important temperature increase of the image sensor and thus introduces image deformations. The correction of the above-mentioned effect can improve the measurement accuracy. We present three methods to calibrate the thermal effect and experiments on two datasets are carried out to verify the improvement in terms of the photogrammetric accuracy. Numéro de notice : A2019-072 Affiliation des auteurs : LASTIG LOEMI+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.12.012 Date de publication en ligne : 04/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.12.012 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92159
in ISPRS Journal of photogrammetry and remote sensing > vol 148 (February 2019) . - pp 142 - 156[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019021 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019023 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019022 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Ship identification and characterization in Sentinel-1 SAR images with multi-task deep learning / Clément Dechesne in Remote sensing, Vol 11 n° 24 (December-2 2019)
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Titre : Ship identification and characterization in Sentinel-1 SAR images with multi-task deep learning Type de document : Article/Communication Auteurs : Clément Dechesne , Auteur ; Sébastien Lefèvre, Auteur ; Rodolphe Vadaine, Auteur ; Guillaume Hajduch, Auteur ; Ronan Fablet, Auteur
Année de publication : 2019 Projets : SESAME / Fablet, Ronan Article en page(s) : n° 2997 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] détection de cible
[Termes IGN] image Sentinel-SAR
[Termes IGN] navire
[Termes IGN] objet mobileRésumé : (auteur) The monitoring and surveillance of maritime activities are critical issues in both military and civilian fields, including among others fisheries’ monitoring, maritime traffic surveillance, coastal and at-sea safety operations, and tactical situations. In operational contexts, ship detection and identification is traditionally performed by a human observer who identifies all kinds of ships from a visual analysis of remotely sensed images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provide a regular and worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as state-of-the-art solutions for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and investigate a deep neural network for the joint classification and characterization of ships from SAR Sentinel-1 data. We benefit from the synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We design a multi-task neural network architecture composed of one joint convolutional network connected to three task specific networks, namely for ship detection, classification, and length estimation. The experimental assessment shows that our network provides promising results, with accurate classification and length performance (classification overall accuracy: 97.25%, mean length error: 4.65 m ± 8.55 m). Numéro de notice : A2019-632 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11242997 Date de publication en ligne : 13/12/2019 En ligne : https://doi.org/10.3390/rs11242997 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95325
in Remote sensing > Vol 11 n° 24 (December-2 2019) . - n° 2997[article]Chilling 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)
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Titre : Chilling and forcing temperatures interact to predict the onset of wood formation in Northern Hemisphere conifers Type de document : Article/Communication Auteurs : Nicolas Delpierre, Auteur ; Ségolène Lireux, Auteur ; Florian Hartig, Auteur ; J. Julio Camarero, Auteur ; Alissar Cheaib, Auteur ; Katarina Čufar, Auteur ; Henri E. Cuny , Auteur ; Annie Deslauriers, Auteur ; Patrick Fonti, Auteur ; et al., Auteur
Année de publication : 2019 Projets : ARBRE / AgroParisTech (2007 -) Article en page(s) : pp 1089 - 1105 Note générale : bibliographie
Funding information : notamment
Agence Nationale de la Recherche. Grant Number: ANR‐11‐LABX‐0002‐01, Lab of Excellence ARBRE
GIP‐ECOFOR. Grant Number: SACROBOQUE 2016.013
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung. Grant Number: INTEGRAL‐121859, LOTFOR‐150205
French National Research Agency. Grant Numbers: ANR‐11‐LABX‐0002‐01, LOTFOR‐150205Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative
[Termes IGN] Canada
[Termes IGN] Europe (géographie politique)
[Termes IGN] forêt boréale
[Termes IGN] forêt tempérée
[Termes IGN] formation du bois
[Termes IGN] hémisphère Nord
[Termes IGN] inférence statistique
[Termes IGN] Larix decidua
[Termes IGN] phénologie
[Termes IGN] Picea abies
[Termes IGN] Picea mariana
[Termes IGN] Pinophyta
[Termes IGN] Pinus sylvestris
[Termes IGN] température au sol
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) The phenology of wood formation is a critical process to consider for predicting how trees from the temperate and boreal zones may react to climate change. Compared to leaf phenology, however, the determinism of wood phenology is still poorly known. Here, we compared for the first time three alternative ecophysiological model classes (threshold models, heat‐sum models and chilling‐influenced heat‐sum models) and an empirical model in their ability to predict the starting date of xylem cell enlargement in spring, for four major Northern Hemisphere conifers (Larix decidua, Pinus sylvestris, Picea abies and Picea mariana). We fitted models with Bayesian inference to wood phenological data collected for 220 site‐years over Europe and Canada. The chilling‐influenced heat‐sum model received most support for all the four studied species, predicting validation data with a 7.7‐day error, which is within one day of the observed data resolution. We conclude that both chilling and forcing temperatures determine the onset of wood formation in Northern Hemisphere conifers. Importantly, the chilling‐influenced heat‐sum model showed virtually no spatial bias whichever the species, despite the large environmental gradients considered. This suggests that the spring onset of wood formation is far less affected by local adaptation than by environmentally driven plasticity. In a context of climate change, we therefore expect rising winter–spring temperature to exert ambivalent effects on the spring onset of wood formation, tending to hasten it through the accumulation of forcing temperature, but imposing a higher forcing temperature requirement through the lower accumulation of chilling. Numéro de notice : A2019-646 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/gcb.14539 Date de publication en ligne : 09/12/2018 En ligne : https://doi.org/10.1111/gcb.14539 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96894
in Global change biology > vol 25 n° 3 (March 2019) . - pp 1089 - 1105[article]Documents numériques
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Chilling and forcing temperatures interact - préprintAdobe Acrobat PDFA learning approach to evaluate the quality of 3D city models / Oussama Ennafii in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 12 (December 2019)
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Titre : A learning approach to evaluate the quality of 3D city models Type de document : Article/Communication Auteurs : Oussama Ennafii , Auteur ; Arnaud Le Bris
, Auteur ; Florent Lafarge, Auteur ; Clément Mallet
, Auteur
Année de publication : 2019 Projets : 1-Pas de projet / AgroParisTech (2007 -) Article en page(s) : pp 865 - 878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Bâti-3D
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection d'erreur
[Termes IGN] données localisées
[Termes IGN] France (administrative)
[Termes IGN] image à très haute résolution
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle d'erreur
[Termes IGN] modèle numérique de surface
[Termes IGN] qualité des données
[Termes IGN] taxinomieRésumé : (Auteur) The automatic generation of three-dimensional (3D) building models from geospatial data is now a standard procedure. An abundance of literature covers the last two decades, and several solutions are now available. However, urban areas are very complex environments. Inevitably, practitioners still have to visually assess, at a city-scale, the correctness of these models and detect frequent reconstruction errors. Such a process relies on experts and is highly time-consuming, with approximately two hours/km 2 per expert. This work proposes an approach for automatically evaluating the quality of 3D building models. Potential errors are compiled in a novel hierarchical and versatile taxonomy. This allows, for the first time, to disentangle fidelity and modeling errors, whatever the level of details of the modeled buildings. The quality of models is predicted using the geometric properties of buildings and, when available, Very High Resolution images and Digital Surface Models. A baseline of handcrafted, yet generic, features is fed into a Random Forest classifier. Both multiclass and multilabel cases are considered: due to the interdependence between classes of errors, it is possible to retrieve all errors at the same time while simply predicting correct and erroneous buildings. The proposed framework was tested on three distinct urban areas in France with more than 3000 buildings. 80%–99% F-score values are attained for the most frequent errors. For scalability purposes, the impact of the urban area composition on the error prediction was also studied, in terms of transferability, generalization, and representativeness of the classifiers. It showed the necessity of multimodal remote sensing data and mixing training samples from various cities to ensure a stability of the detection ratios, even with very limited training set sizes. Numéro de notice : A2019-569 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.12.865 Date de publication en ligne : 01/12/2019 En ligne : https://doi.org/10.14358/PERS.85.12.865 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94440
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 12 (December 2019) . - pp 865 - 878[article]Réservation
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A learning approach to evaluate the quality of 3D city models - preprint HALAdobe Acrobat PDFRegisTree: a registration algorithm to enhance forest inventory plot georeferencing / Maryem Fadili in Annals of Forest Science, vol 76 n° 2 (June 2019)
PermalinkTime-lapse photogrammetry of distributed snow depth during snowmelt / Simon Filhol in Water resources research, vol 55 n° 9 (September 2019)
PermalinkThe effect of stumpage prices on large-area forest growth forecasts based on socio-ecological models / Mathieu Fortin in Forestry, an international journal of forest research, vol 92 n° 3 (July 2019)
PermalinkThe AROME-WMED reanalyses of the first special observation period of the Hydrological cycle in the Mediterranean experiment (HyMeX) / Nadia Fourrié in Geoscientific Model Development, vol 12 n° 7 (July 2019)
PermalinkHarmonisation of stem volume estimates in European National Forest Inventories / Thomas Gschwantner in Annals of Forest Science, vol 76 n° 1 (March 2019)
PermalinkUncertainty assessment of optical distance measurements at micrometer level accuracy for long-range applications / Joffray Guillory in IEEE Transactions on Instrumentation and Measurement, vol 68 n° 6 (June 2019)
PermalinkPiecewise-planar approximation of large 3D data as graph-structured optimization / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W5 (May 2019)
PermalinkImpact of thermospheric mass density on the orbit prediction of LEO satellites / Changyong He in Space weather, vol 18 n° 1 (January 2020)
PermalinkNon-stationary response of tree growth to climate trends along the Arctic margin / Annika Hofgaard in Ecosystems, vol 22 n° 2 (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)
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