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La géostatistique : une vision novatrice au service des géosciences / Bernard Bourgine in Géosciences, n°20 (février 2016)
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Titre : La géostatistique : une vision novatrice au service des géosciences Type de document : Article/Communication Auteurs : Bernard Bourgine, Auteur ; Nicolas Jeannée, Auteur ; Jérémy Rhomer, Auteur Année de publication : 2016 Article en page(s) : pp 22 - 29 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse de données
[Termes IGN] estimation statistique
[Termes IGN] géostatistique
[Termes IGN] incertitude des données
[Termes IGN] modèle numérique
[Termes IGN] risque naturelRésumé : (auteur) La géostatistique est née en France au début des années 1960. Initialement appliquée à l'évaluation de gisements miniers, son utilisation s'est ensuite généralisée : géologie pétrolière, géophysique, géotechnique, géothermie, hydrogéologie, environnement. Le succès de cette discipline est lié à sa capacité à intégrer de nouveaux types de données et à construire des modèles numériques de la variable d'intérêt assortis d'une évaluation de l'incertitude. Un atout majeur pour la prise de décision et l'analyse de risque, notamment en cas de fort enjeu économique ou environnemental. Numéro de notice : A2016-134 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article DOI : sans En ligne : https://www.brgm.fr/fr/actualite/revue/geosciences-ndeg20-geosciences-innover Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80293
in Géosciences > n°20 (février 2016) . - pp 22 - 29[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 076-2016011 RAB Revue Centre de documentation En réserve L003 Disponible Space–time adaptive processing and motion parameter estimation in multistatic passive radar using sparse Bayesian learning / Qisong Wu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)
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Titre : Space–time adaptive processing and motion parameter estimation in multistatic passive radar using sparse Bayesian learning Type de document : Article/Communication Auteurs : Qisong Wu, Auteur ; Yimin D. Zhang, Auteur ; Moeness G. Amin, Auteur ; Brahim Himed, Auteur Année de publication : 2016 Article en page(s) : pp 944 - 957 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage dirigé
[Termes IGN] capteur passif
[Termes IGN] estimation bayesienne
[Termes IGN] estimation des paramètres
[Termes IGN] filtre adaptatif
[Termes IGN] image radar
[Termes IGN] matrice de covarianceMots-clés libres : sparse Bayesian learning Résumé : (Auteur) Conventional space-time adaptive processing suffers from the requirement of a large number of secondary samples. In this paper, a novel method is proposed to accurately estimate the clutter covariance matrix based on a small number of secondary samples, by exploiting the common clutter support across nearby range cells in the angle-Doppler domain. By taking advantage of the intrinsic sparsity of the clutter in the angle-Doppler domain, the recently developed sparse Bayesian learning technique is employed for high-resolution clutter profile estimation. The proposed method does not require the independent and identically distributed secondary sample assumption, and the required number of secondary data samples can be significantly reduced. In addition, we propose a sparse reconstruction-based approach to acquire the 2-D motion parameters of moving targets, by exploiting their group sparsity in the velocity domain in the multistatic passive radar systems. Simulation results verify the effectiveness of the proposed algorithm. Numéro de notice : A2016-118 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2470518 En ligne : https://doi.org/10.1109/TGRS.2015.2470518 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79998
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 944 - 957[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016021 SL Revue Centre de documentation Revues en salle Disponible Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation / Göran Stahl in Forest ecosystems, vol 3 (2016)
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Titre : Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation Type de document : Article/Communication Auteurs : Göran Stahl, Auteur ; Svetlana Saarela, Auteur ; Sebastian Schnell, Auteur ; Sören Holm, Auteur ; et al., Auteur Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] échantillonnage
[Termes IGN] estimation statistique
[Termes IGN] inférence statistique
[Termes IGN] inventaire forestier (techniques et méthodes)
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes design-based and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, model-based, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters. Numéro de notice : A2016--161 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1186/s40663-016-006 En ligne : https://doi.org/10.1186/s40663-016-0064-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87015
in Forest ecosystems > vol 3 (2016)[article]Application of topo-edaphic factors and remotely sensed vegetation indices to enhance biomass estimation in a heterogeneous landscape in the Eastern Arc mountains of Tanzania / Mercy Ojoyi in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)
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Titre : Application of topo-edaphic factors and remotely sensed vegetation indices to enhance biomass estimation in a heterogeneous landscape in the Eastern Arc mountains of Tanzania Type de document : Article/Communication Auteurs : Mercy Ojoyi, Auteur ; Onisimo Mutanga, Auteur ; John Olindi, Auteur ; Elfatih M. Abdel-Rahman, Auteur Année de publication : 2016 Article en page(s) : pp 1 - 21 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse
[Termes IGN] données topographiques
[Termes IGN] estimation statistique
[Termes IGN] facteur édaphique
[Termes IGN] forêt tropicale
[Termes IGN] image RapidEye
[Termes IGN] indice de végétation
[Termes IGN] montagne
[Termes IGN] surveillance écologique
[Termes IGN] TanzanieRésumé : (Auteur) Estimating tropical biomass is critical for establishment of conservation inventories and landscape monitoring. However, monitoring biomass in a complex and dynamic environment using traditional methods is challenging. Recently, biomass estimates based on remotely sensed data and ecological variables have shown great potential. The present study explored the utility of remotely sensed data and topo-edaphic factors to improve biomass estimation in the Eastern Arc Mountains of Tanzania. Twenty-nine vegetation indices were calculated from RapidEye data, while topo-edaphic factors were taken from field measurements. Results showed that using topo-edaphic variables or vegetation indices, biomass could be predicted with an R2 of 0.4. A combination of topo-edaphic variables and vegetation indices improved the prediction accuracy to an R2 of 0.6. Results further showed a decrease in biomass estimates from 1162 ton ha−1 in 1980 to 285.38 ton ha−1 in 2012. This study demonstrates the value of combining remotely sensed data with topo-edaphic variables in biomass estimation. Numéro de notice : A2016-079 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1041557 Date de publication en ligne : 20/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1041557 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79865
in Geocarto international > vol 31 n° 1 - 2 (January - February 2016) . - pp 1 - 21[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2016011 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Brownian motion, martingales, and stochastic calculus Type de document : Guide/Manuel Auteurs : Jean-François Le Gall, Auteur Editeur : Springer International Publishing Année de publication : 2016 Importance : 273 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-319-31089-3 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Mathématique
[Termes IGN] chaîne de Markov
[Termes IGN] fonction harmonique
[Termes IGN] mouvement brownien
[Termes IGN] probabilités
[Termes IGN] stochastiqueRésumé : (éditeur) This book offers a rigorous and self-contained presentation of stochastic integration and stochastic calculus within the general framework of continuous semimartingales. The main tools of stochastic calculus, including Itô's formula, the optional stepping theorem and Girsanov's theorem, are treated in detail alongside many illustrative examples. The book also contains an introduction to Markov processes, with applications to solutions of stochastic differential equations and to connections between Brownian motion and partial differential equations. The theory of local times of semimartingales is discussed in the last chapter. Since its invention by Itô, stochastic calculus has proven to be one of the most important techniques of modern probability theory, and has been used in the most recent theoretical advances as well as in applications to other fields such as mathematical finance. Brownian Motion, Martingales, and Stochastic Calculus provides a strong theoretical background to the reader interested in such developments. Beginning graduate or advanced undergraduate students will benefit from this detailed approach to an essential area of probability theory. The emphasis is on concise and efficient presentation, without any concession to mathematical rigor. The material has been taught by the author for several years in graduate courses at two of the most prestigious French universities. The fact that proofs are given with full details makes the book particularly suitable for self-study. The numerous exercises help the reader to get acquainted with the tools of stochastic calculus. Numéro de notice : 26586 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Manuel En ligne : https://doi.org/10.1007/978-3-319-31089-3 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95220 Caractérisation des signaux et des bruits des séries temporelles du géocentre et des paramètres de rotation de la Terre (EOP) / Bachir Gourine in Bulletin des sciences géographiques, n° 30 (2015 - 2016)
PermalinkConvex programming approach to robust estimation of a multivariate Gaussian model / Samuel Balmand (2016)
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PermalinkPermalinkEstimation of forest biomass using multivariate relevance vector regression / Alireza Sharifi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)
PermalinkPermalinkPermalinkPermalinkInvestigating efficacy of robust M-estimation of deformation from observation differences / Krzysztof Nowel in Survey review, vol 48 n° 346 (January 2016)
PermalinkLandmark based localization: LBA refinement using MCMC-optimized projections of RJMCMC-extracted road marks / Bahman Soheilian (2016)
PermalinkLocalisation à base d’amers visuels : Cartographie et mise en correspondance de marquages au sol et intégration dans LBA / Bahman Soheilian (2016)
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