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A new stochastic simulation algorithm for image-based classification : Feature-space indicator simulation / Qing Wang in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)
[article]
Titre : A new stochastic simulation algorithm for image-based classification : Feature-space indicator simulation Type de document : Article/Communication Auteurs : Qing Wang, Auteur ; Hua Sun, Auteur ; Ruopu Li, Auteur ; Guangxing Wang, Auteur Année de publication : 2019 Article en page(s) : pp 145 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] forêt
[Termes IGN] géostatistique
[Termes IGN] image Landsat-OLI
[Termes IGN] image SPOT 5
[Termes IGN] Mongolie intérieure (Chine)
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] utilisation du sol
[Termes IGN] variogrammeRésumé : (Auteur) Traditional parametric methods for classification of land use and land cover (LULC) types using remote sensing imagery assume a global distribution model and fail to consider local variation of categorical variables. Differently, non-parametric methods do not make any statistical assumptions but are typically sensitive to the sample sizes of training sample data that usually require a high cost to collect in the field. Geostatistical classifiers, such as indicator kriging and simulation, are local variability-based methods that exhibit great potential for image-based classification of LULC types. However, variogram models required are highly sensitive to the spatial configuration of training samples as well as sample size given a study area. Moreover, when a large number of spectral variables are considered into kriging systems, modeling the variograms and cross-variograms would be problematic. To circumvent these issues, this study extended the geostatistical methods from a 2-dimensional geographic space to a m-dimensional image feature space to derive feature-space indicator variograms (FSIVs). Moreover, a novel stochastic simulation classification algorithm, Feature-Space Indicator Simulation (FSIS), was proposed and examined for classification of LULC types in Duolun County located in Inner Mongolia and in Huang-Feng-Qiao (HFQ) forest farm, Hunan of China. In Duolun, six LULC types were involved and in HFQ a complicated forest landscape consisting of nine forest types plus water, built-up area, and agricultural/bare soil, was classified. The classification results of FSIS were compared with another feature-space geostatistical classifier – feature-space indicator kriging (FSIK), a traditional parametric method – maximum likelihood (ML), a widely used nonparametric method – support vector machine (SVM), and a recently popular method – random forest (RF). The results showed that compared with ML, SVM and RF, in both study areas FSIS statistically significantly increased the accuracy of the classifications by 10.0–29.9% for percentage correct and 19.0–47.6% for Kappa statistic. Compared with FSIK, FSIS also improved the classification accuracy but the accuracy increases were relatively smaller with the percentages correct of 3.5% and 7.6% and the Kappa values of 4.6% and 8.6% for Duolun and HFQ, respectively. Moreover, FSIS led to the spatial uncertainties of the classification estimates as the quality measure of the estimates. In addition, the results also demonstrated that FSIVs were sensitive to the within-class heterogeneity but not very much to the size of training samples. Overall, FSIS exhibited the greater potential to improve the classification accuracy of LULC and forest types using remote sensing image. Numéro de notice : A2019-457 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.04.011 Date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.04.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92871
in ISPRS Journal of photogrammetry and remote sensing > vol 152 (June 2019) . - pp 145 - 165[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Embedding road networks and travel time into distance metrics for urban modelling / Henry Crosby in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)
[article]
Titre : Embedding road networks and travel time into distance metrics for urban modelling Type de document : Article/Communication Auteurs : Henry Crosby, Auteur ; theodore Damoulas, Auteur ; Stephen A. Jarvis, Auteur Année de publication : 2019 Article en page(s) : pp 512 - 536 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] covariance
[Termes IGN] distance euclidienne
[Termes IGN] durée de trajet
[Termes IGN] espace-temps
[Termes IGN] géostatistique
[Termes IGN] isométrie
[Termes IGN] krigeage
[Termes IGN] logement
[Termes IGN] modèle de simulation
[Termes IGN] réseau routier
[Termes IGN] trafic routier
[Termes IGN] urbanisme
[Termes IGN] variogrammeRésumé : (auteur) Urban environments are restricted by various physical, regulatory and customary barriers such as buildings, one-way systems and pedestrian crossings. These features create challenges for predictive modelling in urban space, as most proximity-based models rely on Euclidean (straight line) distance metrics which, given restrictions within the urban landscape, do not fully capture spatial urban processes. Here, we argue that road distance and travel time provide effective alternatives, and we develop a new low-dimensional Euclidean distance metric based on these distances using an isomap approach. The purpose of this is to produce a valid covariance matrix for Kriging. Our primary methodological contribution is the derivation of two symmetric dissimilarity matrices (B+ and B2+), with which it is possible to compute low-dimensional Euclidean metrics for the production of a positive definite covariance matrix with commonly utilised kernels. This new method is implemented into a Kriging predictor to estimate house prices on 3,669 properties in Coventry, UK. We find that a metric estimating a combination of road distance and travel time, in both R2 and R3, produces a superior house price predictor compared with alternative state-of-the-art methods, that is, a standard Euclidean metric in RN and a non-restricted road distance metric in R2 and R3. F Numéro de notice : A2019-024 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1547386 Date de publication en ligne : 06/12/2018 En ligne : https://doi.org/10.1080/13658816.2018.1547386 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91952
in International journal of geographical information science IJGIS > Vol 33 n° 3-4 (March - April 2019) . - pp 512 - 536[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2019031 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019032 RAB Revue Centre de documentation En réserve L003 Disponible Evidence of climate effects on the height-diameter relationships of tree species / Mathieu Fortin in Annals of Forest Science, vol 76 n° 1 (March 2019)
[article]
Titre : Evidence of climate effects on the height-diameter relationships of tree species Type de document : Article/Communication Auteurs : Mathieu Fortin, Auteur ; Rosalinde van Couwenberghe, Auteur ; Vincent Perez, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] allométrie
[Termes IGN] changement climatique
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] diamètre des arbres
[Termes IGN] France (végétation)
[Termes IGN] gestion forestière
[Termes IGN] hauteur des arbres
[Termes IGN] modèle de mélange multilinéaire
[Termes IGN] précipitation
[Termes IGN] température
[Termes IGN] variable régionalisée
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Key message : The mean temperature from March to September affects the height-diameter relationship of many tree species in France. For most of these species, the temperature effect is nonlinear, which makes the identification of an optimal temperature possible. Increases in mean temperature could impact the volume supply of commercial species by the end of the twenty-first century.
Context : Height-diameter (HD) relationships are central in forestry since they are essential to estimate tree volume and biomass. Since the late 1960s, efforts have been made to generalize models of HD relationships through the inclusion of plot- and tree-level explanatory variables. In some recent studies, climate variables such as mean annual temperature and precipitation have been found to have a significant effect on HD allometry. However, in these studies, the effects were all considered to be linear or almost linear, which supposes that there is no optimal temperature and no optimal precipitation.
Aims : In this study, we tested the hypothesis that an optimum effect of temperature and precipitation exists on tree heights.
Methods : We fitted generalized models of HD relationships to 44 tree species distributed across France. To make sure that the climate variables would not hide some differences in terms of the local environment, the models included explanatory variables accounting for competition, tree social status and other plot-level factors such as slope inclination and the occurrence of harvesting in the last five years.
Results : It turned out that the temperature effect was significant for 33 out of 44 species and an optimum was found in 26 cases. The precipitation effect was linear and was found to be significant for only seven species. Although the two climate variables did not contribute as much as the competition and the social status indices to the model fit, they were still important contributors. Under the representative concentration pathway (RCP) 2.6 and the assumptions of constant form factors and forest conditions in terms of competition and social statuses, it is expected that approximately two thirds of the species with climate-sensitive HD relationships will generally be shorter. This would induce a decrease in volume ranging from 1 to 5% for most of these species.
Conclusion : Forest practitioners should be aware that the volume supply of some commercial species could decrease by the end of the twenty-first century. However, these losses could be partly compensated for by changes in the form factors and the species distributions.Numéro de notice : A2019-045 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-018-0784-9 Date de publication en ligne : 19/12/2018 En ligne : https://doi.org/10.1007/s13595-018-0784-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92051
in Annals of Forest Science > vol 76 n° 1 (March 2019)[article]Exercices corrigés de géostatistique / Chantal de Fouquet (2019)
Titre : Exercices corrigés de géostatistique Type de document : Guide/Manuel Auteurs : Chantal de Fouquet, Auteur Editeur : Paris : Presses de l'Ecole des Mines Année de publication : 2019 Collection : Les cours de l'École des mines Importance : 320 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-2-35671-539-5 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] coefficient de corrélation
[Termes IGN] covariance
[Termes IGN] géostatistique
[Termes IGN] krigeage
[Termes IGN] variance
[Termes IGN] variogrammeIndex. décimale : 23.60 Statistiques et probabilités Résumé : (Editeur) Ce recueil d'exercices vise à faire le lien entre la théorie de la géostatistique et la pratique. Savoir poser les équations simples de la géostatistique linéaire permet en effet de comprendre la variation du coefficient de corrélation de deux concentrations avec le support (valeurs horaires, journalières ou hebdomadaires, par exemple), de raisonner le choix entre des échantillons simples ou composites pour établir un schéma de reconnaissance, ou encore de caractériser l'évolution temporelle d'une concentration en estimant la différence de deux moyennes annuelles et en calculant la variance d'erreur associée.
De nombreux exercices sont directement inspirés de cas réels. Ce livre s'adresse aux praticiens désireux d'approfondir leurs connaissances en géostatistique et d'en diversifier les applications, ainsi qu'aux étudiants (niveau école d'ingénieur ou master) et doctorants, soucieux d'en maîtriser les concepts. Principalement issus des applications environnementales de la géostatistique, les exemples se réfèrent à la pollution de l'air, des cours d'eau ou des sols ; beaucoup sont transposables à d'autres contextes, en particulier à l'estimation minière.Note de contenu : 1. Etude exploratoire et variographie
2. Régularisation, variance d'estimation, variance de dispersion
3. Krigeage
4. Introduction aux simulations et à la géostatistique non linéaireNuméro de notice : 26031 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Manuel de cours Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92858 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 26031-01 23.60 Livre Centre de documentation Mathématiques Disponible Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing / Minrui Zheng in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)
[article]
Titre : Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing Type de document : Article/Communication Auteurs : Minrui Zheng, Auteur ; Wenwu Tang, Auteur ; Xiang Zhao, Auteur Année de publication : 2019 Article en page(s) : pp 314 - 345 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] Caroline du Nord (Etats-Unis)
[Termes IGN] données spatiotemporelles
[Termes IGN] géostatistique
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle empirique
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation spatiale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal artificiel
[Termes IGN] système d'information foncièreRésumé : (auteur) Artificial neural networks (ANNs) have been extensively used for the spatially explicit modeling of complex geographic phenomena. However, because of the complexity of the computational process, there has been an inadequate investigation on the parameter configuration of neural networks. Most studies in the literature from GIScience rely on a trial-and-error approach to select the parameter setting for ANN-driven spatial models. Hyperparameter optimization provides support for selecting the optimal architectures of ANNs. Thus, in this study, we develop an automated hyperparameter selection approach to identify optimal neural networks for spatial modeling. Further, the use of hyperparameter optimization is challenging because hyperparameter space is often large and the associated computational demand is heavy. Therefore, we utilize high-performance computing to accelerate the model selection process. Furthermore, we involve spatial statistics approaches to improve the efficiency of hyperparameter optimization. The spatial model used in our case study is a land price evaluation model in Mecklenburg County, North Carolina, USA. Our results demonstrate that the automated selection approach improves the model-level performance compared with linear regression, and the high-performance computing and spatial statistics approaches are of great help for accelerating and enhancing the selection of optimal neural networks for spatial modeling. Numéro de notice : A2019-022 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1530355 Date de publication en ligne : 12/10/2018 En ligne : https://doi.org/10.1080/13658816.2018.1530355 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91689
in International journal of geographical information science IJGIS > Vol 33 n° 1-2 (January - February 2019) . - pp 314 - 345[article]Réservation
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