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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)
[article]
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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Titre : Excel data analysis : modeling and simulation Type de document : Guide/Manuel Auteurs : Hector Guerrero, Auteur Mention d'édition : 2ème édition Editeur : Springer Nature Année de publication : 2019 Importance : 358 p. ISBN/ISSN/EAN : 978-3-030-01279-3 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse de données
[Termes IGN] données qualitatives
[Termes IGN] Excel (logiciel)
[Termes IGN] inférence statistique
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] programmation linéaireRésumé : (Editeur) This book offers a comprehensive and readable introduction to modern business and data analytics. It is based on the use of Excel, a tool that virtually all students and professionals have access to. The explanations are focused on understanding the techniques and their proper application, and are supplemented by a wealth of in-chapter and end-of-chapter exercises. In addition to the general statistical methods, the book also includes Monte Carlo simulation and optimization. The second edition has been thoroughly revised: new topics, exercises and examples have been added, and the readability has been further improved. The book is primarily intended for students in business, economics and government, as well as professionals, who need a more rigorous introduction to business and data analytics – yet also need to learn the topic quickly and without overly academic explanations. Note de contenu :
1. Introduction to Spreadsheet Modeling
2. Presentation of Quantitative Data: Data Visualization
3. Analysis of Quantitative Data
4. Presentation of Qualitative Data—Data Visualization
5. Analysis of Qualitative Data
6. Inferential Statistical Analysis of Data
7. Modeling and Simulation: Part 1
8. Modeling and Simulation: Part 2
9. Solver, Scenarios, and Goal Seek ToolsNuméro de notice : 26280 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Manuel informatique DOI : 10.1007/978-3-030-01279-3 En ligne : https://doi.org/10.1007/978-3-030-01279-3 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94931
Titre : Uncertainty in radar emitter classification and clustering Titre original : Gestion des incertitudes en identification des modes radar Type de document : Thèse/HDR Auteurs : Guillaume Revillon, Auteur ; Charles Soussen, Directeur de thèse ; A. Mohammad-Djafari, Directeur de thèse Editeur : Paris-Orsay : Université de Paris 11 Paris-Sud Centre d'Orsay Année de publication : 2019 Importance : 181 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l’Université Paris-Saclay préparée à l’Université Paris-Sud Sciences et Technologies de l’Information et de la Communication (STIC) Spécialité : Traitement du signal et des imagesLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] approximation
[Termes IGN] détection du signal
[Termes IGN] écho radar
[Termes IGN] émetteur
[Termes IGN] estimation bayesienne
[Termes IGN] inférence statistique
[Termes IGN] modèle de mélange multilinéaire
[Termes IGN] modulation du signal
[Termes IGN] probabilités
[Termes IGN] valeur aberranteIndex. décimale : THESE Thèses et HDR Résumé : (auteur) In Electronic Warfare, radar signals identification is a supreme asset for decision making in military tactical situations. By providing information about the presence of threats, classification and clustering of radar signals have a significant role ensuring that countermeasures against enemies are well-chosen and enabling detection of unknown radar signals to update databases. Most of the time, Electronic Support Measures systems receive mixtures of signals from different radar emitters in the electromagnetic environment. Hence a radar signal, described by a pulse-to-pulse modulation pattern, is often partially observed due to missing measurements and measurement errors. The identification process relies on statistical analysis of basic measurable parameters of a radar signal which constitute both quantitative and qualitative data. Many general and practical approaches based on data fusion and machine learning have been developed and traditionally proceed to feature extraction, dimensionality reduction and classification or clustering. However, these algorithms cannot handle missing data and imputation methods are required to generate data to use them. Hence, the main objective of this work is to define a classification/clustering framework that handles both outliers and missing values for any types of data. Here, an approach based on mixture models is developed since mixture models provide a mathematically based, flexible and meaningful framework for the wide variety of classification and clustering requirements. The proposed approach focuses on the introduction of latent variables that give us the possibility to handle sensitivity of the model to outliers and to allow a less restrictive modelling of missing data. A Bayesian treatment is adopted for model learning, supervised classification and clustering and inference is processed through a variational Bayesian approximation since the joint posterior distribution of latent variables and parameters is untractable. Some numerical experiments on synthetic and real data show that the proposed method provides more accurate results than standard algorithms. Note de contenu : Introduction
1- State of the art and the selected approach
2- Continuous data
3- Mixed data
4- Temporal evolution data
5- Conclusion and perspectivesNuméro de notice : 25703 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Traitement du signal et des images : Paris 11 : 2019 Organisme de stage : Thales, GPI nature-HAL : Thèse DOI : sans Date de publication en ligne : 02/09/2019 En ligne : https://hal.science/tel-02275817 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94829 How to calibrate historical aerial photographs : a change analysis of naturally dynamic boreal forest landscapes / Niko Kulha in Forests, vol 9 n° 10 (October 2018)
[article]
Titre : How to calibrate historical aerial photographs : a change analysis of naturally dynamic boreal forest landscapes Type de document : Article/Communication Auteurs : Niko Kulha, Auteur ; Leena Pasanen, Auteur ; Tuomas Aakala, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] biome
[Termes IGN] canopée
[Termes IGN] composition floristique
[Termes IGN] dendrochronologie
[Termes IGN] détection de changement
[Termes IGN] erreur systématique
[Termes IGN] étalonnage des données
[Termes IGN] forêt boréale
[Termes IGN] inférence statistique
[Termes IGN] photographie aérienneRésumé : (Auteur) Time series of repeat aerial photographs currently span decades in many regions. However, the lack of calibration data limits their use in forest change analysis. We propose an approach where we combine repeat aerial photography, tree-ring reconstructions, and Bayesian inference to study changes in forests. Using stereopairs of aerial photographs from five boreal forest landscapes, we visually interpreted canopy cover in contiguous 0.1-ha cells at three time points during 1959–2011. We used tree-ring measurements to produce calibration data for the interpretation, and to quantify the bias and error associated with the interpretation. Then, we discerned credible canopy cover changes from the interpretation error noise using Bayesian inference. We underestimated canopy cover using the historical low-quality photographs, and overestimated it using the recent high-quality photographs. Further, due to differences in tree species composition and canopy cover in the cells, the interpretation bias varied between the landscapes. In addition, the random interpretation error varied between and within the landscapes. Due to the varying bias and error, the magnitude of credibly detectable canopy cover change in the 0.1-ha cells depended on the studied time interval and landscape, ranging from −10 to −18 percentage points (decrease), and from +10 to +19 percentage points (increase). Hence, changes occurring at stand scales were detectable, but smaller scale changes could not be separated from the error noise. Besides the abrupt changes, also slow continuous canopy cover changes could be detected with the proposed approach. Given the wide availability of historical aerial photographs, the proposed approach can be applied for forest change analysis in biomes where tree-rings form, while accounting for the bias and error in aerial photo interpretation. Numéro de notice : A2018-475 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9100631 Date de publication en ligne : 11/10/2018 En ligne : https://doi.org/10.3390/f9100631 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91174
in Forests > vol 9 n° 10 (October 2018)[article]A two-stage estimation method with bootstrap inference for semi-parametric geographically weighted generalized linear models / Dengkui Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)
[article]
Titre : A two-stage estimation method with bootstrap inference for semi-parametric geographically weighted generalized linear models Type de document : Article/Communication Auteurs : Dengkui Li, Auteur ; Chang-Lin Mei, Auteur Année de publication : 2018 Article en page(s) : pp 1860 - 1883 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] estimation statistique
[Termes IGN] inférence statistique
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] modèle linéaire
[Termes IGN] population urbaine
[Termes IGN] régression géographiquement pondérée
[Termes IGN] simulation
[Termes IGN] Tokyo (Japon)Résumé : (Auteur) Semi-parametric geographically weighted generalized linear models (S-GWGLMs) are a useful tool in modeling a regression relationship where the impact of certain explanatory variables on a non-Gaussian distributed response variable is global while that of others is spatially varying. In this article, we propose for S-GWGLMs a new estimation method, called two-stage geographically weighted maximum likelihood estimation, and further develop a likelihood ratio statistic-based bootstrap test to determine constant coefficients in the models. The performance of the estimation and test methods is then evaluated by simulations. The results show that the proposed estimation method performs as well as the existing method in estimating both constant and spatially varying coefficients but it is more efficient in terms of computation time; the bootstrap test is of accurate size under the null hypothesis and satisfactory power in identifying spatially varying coefficients. A real-world data set is finally analyzed to demonstrate the application of the proposed estimation and test methods. Numéro de notice : A2018-306 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1463443 Date de publication en ligne : 03/05/2018 En ligne : https://doi.org/10.1080/13658816.2018.1463443 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90449
in International journal of geographical information science IJGIS > vol 32 n° 9-10 (September - October 2018) . - pp 1860 - 1883[article]Réservation
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