Descripteur
Documents disponibles dans cette catégorie (121)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
CAVIAR: an R package for checking, displaying and processing wood-formation-monitoring data / Cyrille B.K. Rathgeber in Tree Physiology, vol 38 n° 8 (August 2018)
[article]
Titre : CAVIAR: an R package for checking, displaying and processing wood-formation-monitoring data Type de document : Article/Communication Auteurs : Cyrille B.K. Rathgeber, Auteur ; Philippe Santenoise, Auteur ; Henri E. Cuny , Auteur Année de publication : 2018 Projets : ARBRE / AgroParisTech (2007 -) Article en page(s) : pp 1246 - 1260 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cerne
[Termes IGN] données allométriques
[Termes IGN] dynamique de la végétation
[Termes IGN] forêt boréale
[Termes IGN] forêt tempérée
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Loi de Gompertz
[Termes IGN] phénologie
[Termes IGN] Pinophyta
[Termes IGN] R (langage)
[Termes IGN] régression logistique
[Termes IGN] visualisation de données
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) In the last decade, the pervasive question of climate change impacts on forests has revived investigations on intra-annual dynamics of wood formation, involving disciplines such as plant ecology, tree physiology and dendrochronology. This resulted in the creation of many research groups working on this topic worldwide and a rapid increase in the number of studies and publications. Wood-formation-monitoring studies are generally based on a common conceptual model describing xylem cell formation as the succession of four differentiation phases (cell division, cell enlargement, cell wall thickening and mature cells). They generally use the same sampling techniques, sample preparation methods and anatomical criteria to separate between differentiation zones and discriminate and count forming xylem cells, resulting in very similar raw data. However, the way these raw data are then processed, producing the elaborated data on which statistical analyses are performed, still remains quite specific to each individual study. Thereby, despite very similar raw data, wood-formation-monitoring studies yield results that are still quite difficult to compare. CAVIAR—an R package specifically dedicated to the verification, visualization and manipulation of wood-formation-monitoring data—can help to improve this situation. Initially, CAVIAR was built to provide efficient algorithms to compute critical dates of wood formation phenology for conifers growing in temperate and cold environments. Recently, we developed it further to check, display and process wood-formation-monitoring data. Thanks to new and upgraded functions, raw data can now be consistently verified, standardized and modelled (using logistic regressions and Gompertz functions), in order to describe wood phenology and intra-annual dynamics of tree-ring formation. We believe that CAVIAR will help strengthening the science of wood formation dynamics by effectively contributing to the standardization of its concepts and methods, making thereby possible the comparison between data and results from different studies. Numéro de notice : A2018-657 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/treephys/tpy054 Date de publication en ligne : 19/05/2018 En ligne : https://doi.org/10.1093/treephys/tpy054 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93813
in Tree Physiology > vol 38 n° 8 (August 2018) . - pp 1246 - 1260[article]Combining land cover products using a minimum divergence and a Bayesian data fusion approach / Sarah Gengler in International journal of geographical information science IJGIS, vol 32 n° 3-4 (March - April 2018)
[article]
Titre : Combining land cover products using a minimum divergence and a Bayesian data fusion approach Type de document : Article/Communication Auteurs : Sarah Gengler, Auteur ; Patrick Bogaert, Auteur Année de publication : 2018 Article en page(s) : pp 806 - 826 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Belgique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification bayesienne
[Termes IGN] distance de Kullback-Leibler
[Termes IGN] entropie maximale
[Termes IGN] entropie relative
[Termes IGN] fusion de données
[Termes IGN] source de donnéesRésumé : (Auteur) Land cover mapping plays an important role for a wide spectrum of applications that are ranging from climate modeling to food security. However, it is a common case that several and partially conflicting land cover products are available at the same time over a same area, where each product suffers from specific limitations and lack of accuracy. In order to take advantage of the best features of each product while at the same time attenuating their respective weaknesses, this paper is proposing a methodology that allows the user to combine these products together based on a general framework involving maximum entropy/minimum divergence principles, Bayesian data fusion and Bayesian updating. First, information brought by each land cover product is coded in terms of inequality constraints so that a first estimation of their quality can be computed based on a maximum entropy/minimum divergence principle. Information from these various land cover products can then be fused afterwards in a Bayesian framework, leading to a single map with an associated measure of uncertainty. Finally, it is shown how the additional information brought by control data can help improving this fused map through a Bayesian updating procedure. The first part of the paper is briefly presenting the most important theoretical results, while the second part is illustrating the use of this suggested approach for a specific area in Belgium, where five different land cover products are at hand. The benefits and limitations of this approach are finally discussed by the light of the results for this case study. Numéro de notice : A2018-045 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1413577 En ligne : https://doi.org/10.1080/13658816.2017.1413577 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89267
in International journal of geographical information science IJGIS > vol 32 n° 3-4 (March - April 2018) . - pp 806 - 826[article]Réservation
Réserver ce documentExemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2018022 RAB Revue Centre de documentation En réserve L003 Disponible 079-2018021 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Probability and statistics : A course for physicists and engineers Type de document : Guide/Manuel Auteurs : Arak M. Mathai, Auteur ; Hans J. Haubold, Auteur Editeur : Berlin, New York : Walter de Gruyter Année de publication : 2018 Importance : 582 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-3-11-056253-8 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] densité de probabilité
[Termes IGN] distribution, loi de
[Termes IGN] échantillonnage (statistique)
[Termes IGN] estimation statistique
[Termes IGN] régression
[Termes IGN] théorie des probabilités
[Termes IGN] variable aléatoireRésumé : (éditeur) This textbook offers an introduction to concepts of probability theory, probability distributions relevant in the applied sciences, as well as basics of sampling distributions, estimation and hypothesis testing. As the basis for courses on space and atmospheric science, remote sensing, geographic information systems, meteorology, climate and satellite communications at UN-affiliated regional centers, various applications of the formal theory are discussed as well. These include applied topics such as model building and experiment design. Designed for students in engineering and physics with applications in mind. Note de contenu : Introduction
1- Random phenomena
2- Probability
3- Random variables
4- Expected values
5- Commonly used density functions
6- Commonly used density functions
7- Commonly used density functions
8- Some multivariate distributions
9- Collection of random variables
10- Sampling distributions
11- Estimation
12- Interval estimation
13- Tests of statistical hypotheses
14- Model building and regression
15- Design of experiments and analysis of variance
16- Questions and answersNuméro de notice : 25970 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Manuel de cours DOI : 10.1515/9783110562545 En ligne : https://doi.org/10.1515/9783110562545 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96610 Decomposition of LiDAR waveforms by B-spline-based modeling / Xiang Shen in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
[article]
Titre : Decomposition of LiDAR waveforms by B-spline-based modeling Type de document : Article/Communication Auteurs : Xiang Shen, Auteur ; Qing-Quan Li, Auteur ; Guofeng Wu, Auteur ; Jiasong Zhu, Auteur Année de publication : 2017 Article en page(s) : pp 182 - 191 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] B-Spline
[Termes IGN] décomposition de Gauss
[Termes IGN] distribution, loi de
[Termes IGN] forme d'onde pleine
[Termes IGN] traitement du signal
[Termes IGN] transformation géométrique
[Termes IGN] translationRésumé : (Auteur) Waveform decomposition is a widely used technique for extracting echoes from full-waveform LiDAR data. Most previous studies recommended the Gaussian decomposition approach, which employs the Gaussian function in laser pulse modeling. As the Gaussian-shape assumption is not always satisfied for real LiDAR waveforms, some other probability distributions (e.g., the lognormal distribution, the generalized normal distribution, and the Burr distribution) have also been introduced by researchers to fit sharply-peaked and/or heavy-tailed pulses. However, these models cannot be universally used, because they are only suitable for processing the LiDAR waveforms in particular shapes. In this paper, we present a new waveform decomposition algorithm based on the B-spline modeling technique. LiDAR waveforms are not assumed to have a priori shapes but rather are modeled by B-splines, and the shape of a received waveform is treated as the mixture of finite transmitted pulses after translation and scaling transformation. The performance of the new model was tested using two full-waveform data sets acquired by a Riegl LMS-Q680i laser scanner and an Optech Aquarius laser bathymeter, comparing with three classical waveform decomposition approaches: the Gaussian, generalized normal, and lognormal distribution-based models. The experimental results show that the B-spline model performed the best in terms of waveform fitting accuracy, while the generalized normal model yielded the worst performance in the two test data sets. Riegl waveforms have nearly Gaussian pulse shapes and were well fitted by the Gaussian mixture model, while the B-spline-based modeling algorithm produced a slightly better result by further reducing 6.4% of fitting residuals, largely benefiting from alleviating the adverse impact of the ringing effect. The pulse shapes of Optech waveforms, on the other hand, are noticeably right-skewed. The Gaussian modeling results deviated significantly from original signals, and the extracted echo parameters were clearly inaccurate and unreliable. The B-spline-based method performed significantly better than the Gaussian and lognormal models by reducing 45.5% and 11.5% of their fitting errors, respectively. Much more precise echo properties can accordingly be retrieved with a high probability. Benefiting from the flexibility of B-splines on fitting arbitrary curves, the new method has the potentiality for accurately modeling various full-waveform LiDAR data, whether they are nearly Gaussian or non-Gaussian in shape. Numéro de notice : A2017-334 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.03.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.03.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85487
in ISPRS Journal of photogrammetry and remote sensing > vol 128 (June 2017) . - pp 182 - 191[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017063 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017062 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Development and Comparison of Species Distribution Models for Forest Inventories / Óscar Rodríguez de Rivera in ISPRS International journal of geo-information, vol 6 n° 6 (June 2017)
[article]
Titre : Development and Comparison of Species Distribution Models for Forest Inventories Type de document : Article/Communication Auteurs : Óscar Rodríguez de Rivera, Auteur ; Antonio López-Quílez, Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative
[Termes IGN] analyse de données
[Termes IGN] arbre (flore)
[Termes IGN] classification et arbre de régression
[Termes IGN] distribution spatiale
[Termes IGN] entropie maximale
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modèle mathématique
[Termes IGN] régression multivariée par spline adaptative
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) A comparison of several statistical techniques common in species distribution modeling was developed during this study to evaluate and obtain the statistical model most accurate to predict the distribution of different forest tree species (in our case presence/absence data) according environmental variables. During the process we have developed maximum entropy (MaxEnt), classification and regression trees (CART), multivariate adaptive regression splines (MARS), showing the statistical basis of each model and, at the same time, we have developed a specific additive model to compare and validate their capability. To compare different results, the area under the receiver operating characteristic (ROC) function (AUC) was used. Every AUC value obtained with those models is significant and all of the models could be useful to represent the distribution of each species. Moreover, the additive model with thin plate splines gave the best results. The worst capability was obtained with MARS. This model’s performance was below average for several species. The additive model developed obtained better results because it allowed for changes and calibrations. In this case we were aware of all of the processes that occurred during the modeling. By contrast, models obtained using specific software, in general, perform like “hermetic machines”, because it could sometimes be impossible to understand the stages that led to the final results. Numéro de notice : A2017-810 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi6060176 En ligne : https://doi.org/10.3390/ijgi6060176 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89250
in ISPRS International journal of geo-information > vol 6 n° 6 (June 2017)[article]Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plants / Fangyuan Yu in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)PermalinkPermalinkPermalinkSystematic effects in laser scanning and visualization by confidence regions / Karl Rudolf Koch in Journal of applied geodesy, vol 10 n° 4 (December 2016)PermalinkA joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing / Chengjiang Long in International journal of computer vision, vol 116 n° 2 (15th January 2016)PermalinkConvex programming approach to robust estimation of a multivariate Gaussian model / Samuel Balmand (2016)PermalinkImpacts of species misidentification on species distribution modeling with presence-only data / Hugo Costa in ISPRS International journal of geo-information, vol 4 n°4 (December 2015)PermalinkExtension of the linear chromodynamics model for spectral change detection in the presence of residual spatial misregistration / Karmon Vongsy in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)PermalinkPermalinkA student's guide to Python for physical modeling / Jesse M. Kinder (2015)PermalinkAssessment of crop foliar nitrogen using a novel dual-wavelength laser system and implications for conducting laser-based plant physiology / Jan U.H. Eitel in ISPRS Journal of photogrammetry and remote sensing, vol 97 (November 2014)PermalinkProbabilités pour les sciences de l'ingénieur / Manuel Samuelides (2014)PermalinkAn entropy-based multispectral image classification algorithm / Di Long in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)PermalinkIntroduction au calcul des probabilités et à la statistique / Jean-François Delmas (2013)PermalinkMathématiques programme 2012 Term. [terminale] STI2D, Term. STL / Jean-Denis Astier (2012)PermalinkMathématiques Tle [terminale] ES-L / Eric Sigward (2012)PermalinkMathématiques Tle [terminale] S / Eric Sigward (2012)PermalinkThe elements of probabilistic time geography / Stephan Winter in Geoinformatica, vol 15 n° 3 (July 2011)PermalinkDirected movements in probabilistic time geography / Stephan Winter in International journal of geographical information science IJGIS, vol 24 n° 9 (september 2010)PermalinkGeometric feature extraction by a multimarked point process / Florent Lafarge in IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, vol 32 n° 9 (September 2010)Permalink