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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)
[article]
Titre : Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plants Type de document : Article/Communication Auteurs : Fangyuan Yu, Auteur ; Thomas A. Groen, Auteur ; Tiejun Wang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 190 - 212 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] aire de répartition
[Termes IGN] changement climatique
[Termes IGN] Chine
[Termes IGN] climat de montagne
[Termes IGN] croissance des arbres
[Termes IGN] distribution spatiale
[Termes IGN] entropie maximale
[Termes IGN] région
[Termes IGN] Rhododendron (genre)
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Understanding the environmental factors determining the distribution of species with different range sizes can provide valuable insights for evolutionary ecology and conservation biology in the face of expected climate change. However, little is known about what determines the variation in geographical and elevational ranges of alpine and subalpine plant species. Here, we examined the relationship between geographical and elevational range sizes for 80 endemic rhododendron species in China using Spearman’s rank-order correlation. We ran the species distribution model – maximum entropy modelling (MaxEnt) – with 27 environmental variables. The importance of each variable to the model prediction was compared for species groups with different geographical and elevational range sizes. Our results showed that the correlation between geographical and elevational range sizes of rhododendron species was not significant. Climate-related variables were found to be the most important factors in shaping the distributional ranges of alpine and subalpine plant species across China. Species with geographically and elevationally narrow ranges had distinct niche requirements. For geographical ranges, the narrow-ranged species showed less tolerance to niche conditions than the wide-ranged species. For elevational ranges, compared with the wide-ranged species, the narrow-ranged species showed an equivalent niche breadth, but occurred at different niche position along the environmental gradient. Our findings suggest that over large spatial extents the elevational range size can be a complementary trait of alpine and subalpine plant species to geographical range size. Climatic niche breadth, especially the range of seasonal variability, can explain species’ geographical range sizes. Changes in climate may influence the distribution of rhododendrons, with the effects likely being felt most by species with either a narrow geographical or narrow elevational range. Numéro de notice : A2017-031 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1195502 En ligne : http://dx.doi.org/10.1080/13658816.2016.1195502 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84024
in International journal of geographical information science IJGIS > vol 31 n° 1-2 (January - February 2017) . - pp 190 - 212[article]Réservation
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Titre : Introduction to Artificial Intelligence Type de document : Monographie Auteurs : Wolfgang Ertel, Auteur Editeur : Springer International Publishing Année de publication : 2017 Importance : 365 p. ISBN/ISSN/EAN : 978-3-319-58487-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification bayesienne
[Termes IGN] entropie maximale
[Termes IGN] exploration de données
[Termes IGN] PROLOG
[Termes IGN] raisonnement sémantique
[Termes IGN] réseau neuronal artificielRésumé : (éditeur) This concise and accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The book presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book; supports the text with highlighted examples, definitions, and theorems; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper reading on further topics; supplies additional teaching resources, including lecture slides and training data for learning algorithms, at an associated website. Note de contenu : 1- Introduction
2- Propositional Logic
3- First-order Predicate Logic
4- Limitations of Logic
5- Logic Programming with PROLOG
6- Search, Games and Problem Solving
7- Reasoning with Uncertainty
8- Machine Learning and Data Mining
9- Neural Networks
10- Reinforcement Learning
11- Solutions for the ExercisesNuméro de notice : 25753 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-58487-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94945
Titre : Surface reconstruction based on forest terrestrial LiDAR data Type de document : Thèse/HDR Auteurs : Jules Morel, Auteur ; Marc Daniel, Directeur de thèse ; Cédric Vega , Directeur de thèse ; Alexandra Bac, Directeur de thèse Editeur : Aix-en-Provence : Université d'Aix-Marseille Année de publication : 2017 Importance : 178 p. Format : 21 x 30 cm Note générale : bibliographie
A dissertation presented to the Department of Mathématique et Informatique in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Computer ScienceLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] distribution de Poisson
[Termes IGN] données lidar
[Termes IGN] données TLS (télémétrie)
[Termes IGN] fonction de base radiale
[Termes IGN] interpolation
[Termes IGN] modélisation de la forêt
[Termes IGN] placette d'échantillonnage
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de points
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] structure de la végétationIndex. décimale : THESE Thèses et HDR Résumé : (auteur) In recent years, the capacity of LiDAR technology to capture detailed information about forests structure has attracted increasing attention in the field of forest science. In particular, the terrestrial LiDAR arises as a promising tool to retrieve geometrical characteristics of trees at a millimeter level. This thesis studies the surface reconstruction problem from scattered and unorganized point
clouds, captured in forested environment by a terrestrial LiDAR. We propose a sequence of algorithms dedicated to the reconstruction of forests plot attributes model: the ground and the woody structure of trees (i.e. the trunk and the main branches). In practice, our approaches model the surface with implicit function build with radial basis functions to manage the homogeneity and handle the noise of the sample data points. Our first focus is on the reconstruction of the ground surface whose level of detail is based on local complexity, through alternation between scale refinement, filtering and reconstruction. The result arises from the polygonization of the implicit function expressed as the merging of local approximations by compactly supported radial basis function used as partition of unity. Once the ground is modeled, the topology effects can be ignored in the following computation steps that focus on the modeling of trees. Traditionally, the processing of the woody part is achieved by a discrete reconstruction in the form of a stack of independent building blocks. From such a model, our approach developed for the ground is adapted to approximate the woody part of the tree by a more flexible continuous surface. Expressed as an implicit function, the tree model can be refined by an additional computational step in order to describe precisely the geometry. With this in mind, we propose a method dedicated to the fine reconstruction of occluded objects: from 3D samples presenting occlusions,
we use the previously described continuous model to guide a Poisson surface reconstruction. Thus, we guarantee the production of a watertight surface that approximates sharply the point cloud in the visible areas and extrapolates consistently the tree shape in the occlusions.Note de contenu : 1- Introduction
2- Terrestrial LiDAR scanning in forests
3- Survey on surface reconstruction
4- Reconstruction of open surface
5- Geometric model of trees
6- Reconstruction of partially occluded objects
7- Conclusion and perspectivesNuméro de notice : 25855 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : IMAGERIE Nature : Thèse française Note de thèse : PhD Thesis: Computer Science : Marseille : 2017 Organisme de stage : Institut Français de Pondichéri (Inde) nature-HAL : Thèse DOI : sans En ligne : http://www.theses.fr/2017AIXM0039 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95472 Systematic effects in laser scanning and visualization by confidence regions / Karl Rudolf Koch in Journal of applied geodesy, vol 10 n° 4 (December 2016)
[article]
Titre : Systematic effects in laser scanning and visualization by confidence regions Type de document : Article/Communication Auteurs : Karl Rudolf Koch, Auteur ; Jan Martin Brockmann, Auteur Année de publication : 2016 Article en page(s) : pp 247 – 257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] carte de confiance
[Termes IGN] covariance
[Termes IGN] densité de probabilité
[Termes IGN] distribution de Gauss
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] ellipsoïde (géodésie)
[Termes IGN] itération
[Termes IGN] matrice de covariance
[Termes IGN] mesure géométrique
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] série temporelle
[Termes IGN] visualisationRésumé : (auteur) A new method for dealing with systematic effects in laser scanning and visualizing them by confidence regions is derived. The standard deviations of the systematic effects are obtained by repeatedly measuring three-dimensional coordinates by the laser scanner. In addition, autocovariance and cross-covariance functions are computed by the repeated measurements and give the correlations of the systematic effects. The normal distribution for the measurements and the multivariate uniform distribution for the systematic effects are applied to generate random variates for the measurements and random variates for the measurements plus systematic effects. Monte Carlo estimates of the expectations and the covariance matrix of the measurements with systematic effects are computed. The densities for the confidence ellipsoid for the measurements and the confidence region for the measurements with systematic effects are obtained by relative frequencies. They only depend on the size of the rectangular volume elements for which the densities are determined. The problem of sorting the densities is solved by sorting distances together with the densities. This allows a visualization of the confidence ellipsoid for the measurements and the confidence region for the measurements with systematic effects. Numéro de notice : A2016-975 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1515/jag-2016-0012 En ligne : https://doi.org/10.1515/jag-2016-0012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83682
in Journal of applied geodesy > vol 10 n° 4 (December 2016) . - pp 247 – 257[article]A 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)
[article]
Titre : A joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing Type de document : Article/Communication Auteurs : Chengjiang Long, Auteur ; Gang Hua, Auteur ; Ashish Kapoor, Auteur Année de publication : 2016 Article en page(s) : pp 136 - 160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification bayesienne
[Termes IGN] classification dirigée
[Termes IGN] distribution de Gauss
[Termes IGN] inférence
[Termes IGN] production participative
[Termes IGN] reconnaissance d'objetsRésumé : (auteur) We present a noise resilient probabilistic model for active learning of a Gaussian process classifier from crowds, i.e., a set of noisy labelers. It explicitly models both the overall label noise and the expertise level of each individual labeler with two levels of flip models. Expectation propagation is adopted for efficient approximate Bayesian inference of our probabilistic model for classification, based on which, a generalized EM algorithm is derived to estimate both the global label noise and the expertise of each individual labeler. The probabilistic nature of our model immediately allows the adoption of the prediction entropy for active selection of data samples to be labeled, and active selection of high quality labelers based on their estimated expertise to label the data. We apply the proposed model for four visual recognition tasks, i.e., object category recognition, multi-modal activity recognition, gender recognition, and fine-grained classification, on four datasets with real crowd-sourced labels from the Amazon Mechanical Turk. The experiments clearly demonstrate the efficacy of the proposed model. In addition, we extend the proposed model with the Predictive Active Set Selection Method to speed up the active learning system, whose efficacy is verified by conducting experiments on the first three datasets. The results show our extended model can not only preserve a higher accuracy, but also achieve a higher efficiency. Numéro de notice : A2016--137 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007%2Fs11263-015-0834-9 En ligne : https://doi.org/10.1007/s11263-015-0834-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85903
in International journal of computer vision > vol 116 n° 2 (15th January 2016) . - pp 136 - 160[article]Convex 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)Permalink