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Termes IGN > géomatique > base de données localisées > couche thématique > occupation du sol
occupation du sol
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Employé pour :
Espace, organisation de l' Utilisation du sol Politique foncière Sol, Occupation du Sols -- Utilisation Sols -- Utilisation Terrains -- Utilisation Terrains, Utilisation des Utilisation du sol Espace (économie politique) >> Aménagement du territoire Paysage -- Évaluation Syndrome NIMBY >>Terme(s) spécifique(s) : Améliorations foncières Cadastres Décharges contrôlées Immobilier Photographie aérienne en utilisation du sol Politique forestière Promotion immobilière Propriété foncière Propriété immobilière -- Acquisition par l'Administration Terres publiques Zones d'aménagement différé Equiv. LCSH : Land use Domaine(s) : 330 |
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Adjusting for long term anomalous trends in NOAA's Global Vegetation Index datasets / L. Jiang in IEEE Transactions on geoscience and remote sensing, vol 46 n° 2 (February 2008)
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[article]
Titre : Adjusting for long term anomalous trends in NOAA's Global Vegetation Index datasets Type de document : Article/Communication Auteurs : L. Jiang, Auteur ; D. Tarpley, Auteur ; K. Mitchell, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 409 - 422 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] correction
[Termes IGN] erreur systématique
[Termes IGN] image NOAA-AVHRR
[Termes IGN] modèle météorologique
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] occupation du sol
[Termes IGN] sécheresse
[Termes IGN] stabilitéRésumé : (Auteur) The weekly 0.144° resolution global vegetation index from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) has a long history, starting late 1981, and has included data derived from Advanced Very High Resolution Radiometer (AVHRR) sensors onboard NOAA-7, -9, -11, -14, -16, -17, and -18 satellites. Even after postlaunch calibration and mathematical smoothing and filtering of the normalized difference vegetation index (NDVI) derived from AVHRR visible and near-infrared channels, the time series of global smoothed NDVI (SMN) still has apparent discontinuities and biases due to sensor degradation, orbital drift [equator crossing time (ECT)], and differences from instrument to instrument in band response functions. To meet the needs of the operational weather and climate modeling and monitoring community for a stable long-term global NDVI data set, we investigated adjustments to substantially reduce the bias of the weekly global SMN series by simple and efficient algorithms that require a minimum number of assumptions about the statistical properties of the interannual global vegetation changes. Of the algorithms tested, we found the adjusted cumulative distribution function (ACDF) method to be a well-balanced approach that effectively eliminated most of the long-term global-scale interannual trend of AVHRR NDVI. Improvements to the global and regional NDVI data stability have been demonstrated by the results of ACDF-adjusted data set evaluated at a global scale, on major land classes, with relevance to satellite ECT, at major continental regions, and at regional drought detection applications. Copyright IEEE Numéro de notice : A2008-072 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.902844 En ligne : https://doi.org/10.1109/TGRS.2007.902844 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29067
in IEEE Transactions on geoscience and remote sensing > vol 46 n° 2 (February 2008) . - pp 409 - 422[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-08021 RAB Revue Centre de documentation En réserve L003 Disponible Land-cover change and environmental impact analysis in the Greater Mankato area of Minnesota using remote sensing and GIS modelling / F. Yuan in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)
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Titre : Land-cover change and environmental impact analysis in the Greater Mankato area of Minnesota using remote sensing and GIS modelling Type de document : Article/Communication Auteurs : F. Yuan, Auteur Année de publication : 2008 Article en page(s) : pp 1169 - 1184 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aménagement du territoire
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] carte d'occupation du sol
[Termes IGN] détection de changement
[Termes IGN] fusion d'images
[Termes IGN] image Quickbird
[Termes IGN] impact sur l'environnement
[Termes IGN] Minnesota (Etats-Unis)
[Termes IGN] photographie aérienne
[Termes IGN] planification urbaine
[Termes IGN] surface imperméable
[Termes IGN] système d'information géographique
[Termes IGN] urbanisationRésumé : (Auteur) Land use and land-cover (LULC) data provide essential information for environmental management and planning. This research evaluates the land-cover change dynamics and their effects for the Greater Mankato Area of Minnesota using image classification and Geographic Information Systems (GIS) modelling in high-resolution aerial photography and QuickBird imagery. Results show that from 1971 to 2003, urban impervious surfaces increased from 18.3% to 32.6%, while cropland and grassland decreased from 54.2% to 39.1%. The dramatic urbanization caused evident environmental impacts in terms of runoff and water quality, whereas the annual air pollution removal rate and carbon storage/sequestration remained consistent since urban forests were steady over the 32-year span. The results also indicate that highly accurate land-cover features can be extracted effectively from high-resolution imagery by incorporating both spectral and spatial information, applying an image-fusion technique, and utilizing the hierarchical machine-learning Feature Analyst classifier. This research fills the high-resolution LULC data gap for the Greater Mankato Area. The findings of the study also provide valuable inputs for local decision-makers and urban planners. Copyright Taylor & Francis Numéro de notice : A2008-008 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701294703 En ligne : https://doi.org/10.1080/01431160701294703 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29003
in International Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 1169 - 1184[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-08021 RAB Revue Centre de documentation En réserve L003 Disponible Multisource classification using Support Vector Machines: an empirical comparison with Decision Tree and Neural Network classifiers / P. Watanachaturaporn in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 2 (February 2008)
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Titre : Multisource classification using Support Vector Machines: an empirical comparison with Decision Tree and Neural Network classifiers Type de document : Article/Communication Auteurs : P. Watanachaturaporn, Auteur ; M. Arora, Auteur ; K. Varshney, Auteur Année de publication : 2008 Article en page(s) : pp 239 - 246 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données multisources
[Termes IGN] extraction automatique
[Termes IGN] Himalaya
[Termes IGN] image IRS-LISS
[Termes IGN] Kappa de Cohen
[Termes IGN] modèle numérique de surface
[Termes IGN] occupation du solRésumé : (Auteur) Remote sensing image classification has proven to be attractive for extracting useful thematic information such as landcover. However, often for a given application, spectral information acquired by a remote sensing sensor may not be sufficient to derive accurate information. Incorporation of data from other sources such as a digital elevation model (DEM), and geophysical and geological data may assist in achieving more accurate land-cover classification from remote sensing images. Recently, support vector machines (SVM) have been proposed as an alternative for classification of remote sensing data, and the results are promising. In this paper, we employ the SVM algorithm to perform multisource classification. An IRS–1C LISS III image along with normalized differenced vegetation index (NDVI) image and DEM are used to produce a land-cover classification for a region in the Himalayas. The accuracy of SVM-based multisource classification is compared with several other nonparametric algorithms namely a decision tree classifier, and back propagation and radial basis function neural network classifiers. The well-known kappa coefficient of agreement is used to assess classification accuracy. The differences in the kappa coefficient of classifiers have been statistically evaluated using a pairwise Z-test. The results show a significant increase in the accuracy of the SVM based classifier on incorporation of ancillary data over classification performed solely on the basis of spectral data from remote sensing sensors. Copyright ASPRS Numéro de notice : A2008-048 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.2.239 En ligne : https://doi.org/10.14358/PERS.74.2.239 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29043
in Photogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 2 (February 2008) . - pp 239 - 246[article]Multispectral land use classification using neural networks and support vector machines: one or the other, or both? / B. Dixon in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)
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[article]
Titre : Multispectral land use classification using neural networks and support vector machines: one or the other, or both? Type de document : Article/Communication Auteurs : B. Dixon, Auteur ; N. Candade, Auteur Année de publication : 2008 Article en page(s) : pp 1185 - 1206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] occupation du solRésumé : (Auteur) Land use classification is an important part of many remote sensing applications. A lot of research has gone into the application of statistical and neural network classifiers to remote-sensing images. This research involves the study and implementation of a new pattern recognition technique introduced within the framework of statistical learning theory called Support Vector Machines (SVMs), and its application to remote-sensing image classification. Standard classifiers such as Artificial Neural Network (ANN) need a number of training samples that exponentially increase with the dimension of the input feature space. With a limited number of training samples, the classification rate thus decreases as the dimensionality increases. SVMs are independent of the dimensionality of feature space as the main idea behind this classification technique is to separate the classes with a surface that maximizes the margin between them, using boundary pixels to create the decision surface. Results from SVMs are compared with traditional Maximum Likelihood Classification (MLC) and an ANN classifier. The findings suggest that the ANN and SVM classifiers perform better than the traditional MLC. The SVM and the ANN show comparable results. However, accuracy is dependent on factors such as the number of hidden nodes (in the case of ANN) and kernel parameters (in the case of SVM). The training time taken by the SVM is several magnitudes less. Copyright Taylor & Francis Numéro de notice : A2008-009 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701294661 En ligne : https://doi.org/10.1080/01431160701294661 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29004
in International Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 1185 - 1206[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-08021 RAB Revue Centre de documentation En réserve L003 Disponible Restructuration foncière : le Doubs poursuit l'opération / André Choby in Le Bois International : l'officiel du bois [édition verte], vol 2008 n° 3 (19 janvier 2008)
[article]
Titre : Restructuration foncière : le Doubs poursuit l'opération Type de document : Article/Communication Auteurs : André Choby, Auteur Année de publication : 2008 Article en page(s) : p. 7 Langues : Français (fre) Descripteur : [Termes IGN] Doubs (25)
[Termes IGN] occupation du solNuméro de notice : IFN_4336 Affiliation des auteurs : non IGN Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=72609
in Le Bois International : l'officiel du bois [édition verte] > vol 2008 n° 3 (19 janvier 2008) . - p. 7[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité IFN-001-P000438 PER Revue Nogent-sur-Vernisson Archives périodiques Exclu du prêt Cadastral mapping of forestlands in Greece : current and future challenges / M. Vogiatzis in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 1 (January 2008)
PermalinkPermalinkPermalinkSupport à la cartographie diachronique des groupes élitaires dans la ville de Bruxelles / P. Collin (2008)
PermalinkContribution à l'étude de la désertification dans le sud oranais / A. Hirche in Revue Française de Photogrammétrie et de Télédétection, n° 187 -188 (Décembre 2007)
PermalinkDynamique spatio-temporelle de l'écosystème du site Ramsar du moyen Niger 1 : cas de la mare de Albarïze / Mahamane Ali in Revue Française de Photogrammétrie et de Télédétection, n° 187 -188 (Décembre 2007)
PermalinkImproving river flood extent delineation from synthetic aperture radar using airborne laser altimetry / D.C. Mason in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)
PermalinkLand-cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data / Dong Lu in International Journal of Remote Sensing IJRS, vol 28 n°23-24 (December 2007)
PermalinkProjet Life : suivi de la désertification dans les pays de la rive sud de la Méditerranée. Application au cas du Maroc / Noureddine Bijaber in Revue Française de Photogrammétrie et de Télédétection, n° 187 -188 (Décembre 2007)
PermalinkSeasonal sensitivity analysis of impervious surface estimation with satellite imagery / C. Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 12 (December 2007)
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