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Télédétection pour l'observation des surfaces continentales, Volume 3. Observation des surfaces continentales par télédétection 1 / Nicolas Baghdadi (2017)
Titre de série : Télédétection pour l'observation des surfaces continentales, Volume 3 Titre : Observation des surfaces continentales par télédétection 1 : agriculture et forêt Type de document : Monographie Auteurs : Nicolas Baghdadi, Éditeur scientifique ; Mehrez Zribi, Éditeur scientifique Editeur : Londres : ISTE Editions Année de publication : 2017 Collection : Collection système Terre - Environnement Importance : 455 p. Format : 15 x 23 cm ISBN/ISSN/EAN : 978-1-78405-158-7 Note générale : Bibliographie et glossaire Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] agriculture
[Termes IGN] agriculture de précision
[Termes IGN] biomasse forestière
[Termes IGN] caractérisation
[Termes IGN] carte agricole
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte pédologique
[Termes IGN] couvert forestier
[Termes IGN] couvert végétal
[Termes IGN] cultures
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation statistique
[Termes IGN] forêt
[Termes IGN] image optique
[Termes IGN] image radar
[Termes IGN] image radar moirée
[Termes IGN] indice foliaire
[Termes IGN] modèle de transfert radiatif
[Termes IGN] observation de la Terre
[Termes IGN] phénologie
[Termes IGN] problème inverse
[Termes IGN] production agricole
[Termes IGN] rayonnement lumineux
[Termes IGN] rayonnement proche infrarouge
[Termes IGN] ressources forestières
[Termes IGN] signature spectrale
[Termes IGN] surface cultivée
[Termes IGN] zone intertropicaleIndex. décimale : 35.41 Applications de télédétection - végétation Résumé : (Editeur) Cet ouvrage présente les principales applications de la télédétection pour l’agriculture et la forêt. Les enjeux actuels et futurs en matière de recherche et de développement sont particulièrement analysés : cartographie des propriétés primaires de sol, estimation des paramètres biophysiques du couvert végétal, cartographie de l’occupation du sol, développement d’indicateurs pour la gestion et la modélisation du fonctionnement des cultures et estimation des propriétés du couvert forestier (dynamique du couvert, hauteur, biomasse). Note de contenu : 1. Cartographie de propriétés primaires de sol par télédétection optique visible-proche infra-rouge (Vis-PIR)
2. Méthodes d’estimation des variables biophysiques de la végétation à partir d’observations satellitaires
3. Cartographie de l’occupation des sols à partir d’images optiques
4. Fonctionnement des surfaces agricoles : apport de la télédétection
5. Apport de la télédétection pour le suivi des cultures en zones tropicales
6. Etude des paysages agricoles par télédétection radar
7. Imagerie optique multispectrale satellitaire pour des applications forestières
8. Caractérisation des forêts à l’aide de la technologie lidar
9. Biomasse des forêts par télédétection radarNuméro de notice : 22753C Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Recueil / ouvrage collectif Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86431 Voir aussi
- Télédétection pour l'observation des surfaces continentales, Volume 1. Observation des surfaces continentales par télédétection optique / Nicolas Baghdadi (2017)
- Télédétection pour l'observation des surfaces continentales, Volume 4. Observation des surfaces continentales par télédétection 2 / Nicolas Baghdadi (2017)
- Télédétection pour l'observation des surfaces continentales, Volume 5. Observation des surfaces continentales par télédétection 3 / Nicolas Baghdadi (2017)
- Télédétection pour l'observation des surfaces continentales, Volume 2. Observation des surfaces continentales par télédétection micro-onde / Nicolas Baghdadi (2017)
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Code-barres Cote Support Localisation Section Disponibilité 22753-01C 35.41 Livre Centre de documentation Télédétection Disponible Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas / Charlotte Pelletier in Remote sensing of environment, vol 187 (15 December 2016)
[article]
Titre : Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas Type de document : Article/Communication Auteurs : Charlotte Pelletier, Auteur ; Silvia Valero, Auteur ; Jordi Inglada, Auteur ; Nicolas Champion , Auteur ; Gérard Dedieu, Auteur Année de publication : 2016 Article en page(s) : pp 156 - 168 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] caractérisation
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] France (administrative)
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel
[Termes IGN] image SPOT 4
[Termes IGN] méthode robuste
[Termes IGN] précision de la classification
[Termes IGN] série temporelleRésumé : (Auteur) New remote sensing sensors will acquire High spectral, spatial and temporal Resolution Satellite Image Time Series (HR-SITS). These new data are of great interest to map land cover thanks to the combination of the three high resolutions that will allow a depiction of scene dynamics. However, their efficient exploitation involves new challenges, especially for adapting traditional classification schemes to data complexity. More specifically, it requires: (1) to determine which classifier algorithms can handle the amount and the variability of data; (2) to evaluate the stability of classifier parameters; (3) to select the best feature set used as input data in order to find the good trade-off between classification accuracy and computational time; and (4) to establish the classifier accuracy over large areas. This work aims at studying these different issues, and more especially at demonstrating the ability of state-of-the-art classifiers, such as Random Forests (RF) or Support Vector Machines (SVM), to classify HR-SITS. For this purpose, several studies are carried out by using SPOT-4 and Landsat-8 HR-SITS in the south of France. Firstly, the choice of the classifier is discussed by comparing RF and SVM algorithms on HR-SITS. Both classifiers show their ability to tackle the classification problem with an Overall Accuracy (OA) of 83.3 % for RF and 77.1 % for SVM. But RF have some advantages such as a small training time, and an easy parameterization. Secondly, the stability of RF parameters is appraised. RF parameters appear to cause little influence on the classification accuracy, about 1% OA difference between the worst and the best parameter configuration. Thirdly, different input data – composed of spectral bands with or without spectral and/or temporal features – are proposed in order to enhance the characterization of land cover. The addition of features improves the classification accuracy, but the gain in OA is weak compared with the increase in the computational cost. Eventually, the classifier accuracy is assessed on a larger area where the landscape variabilities affect the classification performances. Numéro de notice : A2016--109 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2016.10.010 Date de publication en ligne : 15/10/2016 En ligne : http://doi.org/10.1016/j.rse.2016.10.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84726
in Remote sensing of environment > vol 187 (15 December 2016) . - pp 156 - 168[article]An iterative interpolation deconvolution algorithm for superresolution land cover mapping / Feng Ling in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
[article]
Titre : An iterative interpolation deconvolution algorithm for superresolution land cover mapping Type de document : Article/Communication Auteurs : Feng Ling, Auteur ; Giles M. Foody, Auteur ; Yong Ge, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7210 - 7222 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification du maximum a posteriori
[Termes IGN] déconvolution
[Termes IGN] image à ultra haute résolution
[Termes IGN] itérationRésumé : (Auteur) Superresolution mapping (SRM) is a method to produce a fine-spatial-resolution land cover map from coarse-spatial-resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation and then determines class labels of fine-resolution pixels using the maximum a posteriori (MAP) principle. By constructing a new image formation process that establishes the relationship between the observed coarse-resolution fraction images and the latent fine-resolution land cover map, it is found that the MAP principle only matches with area-to-point interpolation algorithms and should be replaced by deconvolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation deconvolution (IID) SRM algorithm is proposed. The IID algorithm first interpolates coarse-resolution fraction images with an area-to-area interpolation algorithm and produces an initial fine-resolution land cover map by deconvolution. The fine-spatial-resolution land cover map is then updated by reconvolution, back-projection, and deconvolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multispectral images, and degraded Landsat images, including comparison against three widely used SRM algorithms: pixel swapping, bilinear interpolation, and Hopfield neural network. Results show that the IID algorithm can reduce the impact of fraction errors and can preserve the patch continuity and the patch boundary smoothness simultaneously. Moreover, the IID algorithm produced fine-resolution land cover maps with higher accuracies than those produced by other SRM algorithms. Numéro de notice : A2016-928 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2598534 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2598534 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83342
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7210 - 7222[article]Mapping of land cover in northern California with simulated hyperspectral satellite imagery / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
[article]
Titre : Mapping of land cover in northern California with simulated hyperspectral satellite imagery Type de document : Article/Communication Auteurs : Matthew L. Clark, Auteur ; Nina E. Kilham, Auteur Année de publication : 2016 Article en page(s) : pp 228 - 245 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] base de données d'occupation du sol
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image hyperspectrale
[Termes IGN] interprétation automatique
[Termes IGN] occupation du sol
[Termes IGN] simulation d'imageRésumé : (Auteur) Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Analysis of hyperspectral, or imaging spectrometer, imagery has shown an impressive capacity to map a wide range of natural and anthropogenic land cover. Applications have been mostly with single-date imagery from relatively small spatial extents. Future hyperspectral satellites will provide imagery at greater spatial and temporal scales, and there is a need to assess techniques for mapping land cover with these data. Here we used simulated multi-temporal HyspIRI satellite imagery over a 30,000 km2 area in the San Francisco Bay Area, California to assess its capabilities for mapping classes defined by the international Land Cover Classification System (LCCS). We employed a mapping methodology and analysis framework that is applicable to regional and global scales. We used the Random Forests classifier with three sets of predictor variables (reflectance, MNF, hyperspectral metrics), two temporal resolutions (summer, spring-summer-fall), two sample scales (pixel, polygon) and two levels of classification complexity (12, 20 classes). Hyperspectral metrics provided a 16.4–21.8% and 3.1–6.7% increase in overall accuracy relative to MNF and reflectance bands, respectively, depending on pixel or polygon scales of analysis. Multi-temporal metrics improved overall accuracy by 0.9–3.1% over summer metrics, yet increases were only significant at the pixel scale of analysis. Overall accuracy at pixel scales was 72.2% (Kappa 0.70) with three seasons of metrics. Anthropogenic and homogenous natural vegetation classes had relatively high confidence and producer and user accuracies were over 70%; in comparison, woodland and forest classes had considerable confusion. We next focused on plant functional types with relatively pure spectra by removing open-canopy shrublands, woodlands and mixed forests from the classification. This 12-class map had significantly improved accuracy of 85.1% (Kappa 0.83) and most classes had over 70% producer and user accuracies. Finally, we summarized important metrics from the multi-temporal Random Forests to infer the underlying chemical and structural properties that best discriminated our land-cover classes across seasons. Numéro de notice : A2016-783 Affiliation des auteurs : non IGN Autre URL associée : Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.06.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82480
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 228 - 245[article]An impressionistic cartographic solution for base map land cover with coarse pixel data / Paulo Raposo in Cartographic perspectives, n° 83 (2016)
[article]
Titre : An impressionistic cartographic solution for base map land cover with coarse pixel data Type de document : Article/Communication Auteurs : Paulo Raposo, Auteur ; Cynthia A. Brewer, Auteur ; Kevin Sparks, Auteur Année de publication : 2016 Article en page(s) : pp 5 - 21 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] ArcGIS
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte topographique
[Termes IGN] données maillées
[Termes IGN] échantillonnage de données
[Termes IGN] généralisation cartographique
[Termes IGN] généralisation de base de données
[Termes IGN] incertitude géométrique
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Several everyday cartography applications do not require sharply precise base maps, and in fact benefit from their generalization or deliberate obscuration, such as tourist or transit maps. Additionally, raster data fine enough for a given map scale are not always available. We present a method of creating an impressionistic land cover base map for topographic mapping in which the above two conditions are true, using the National Land Cover Database (NLCD) of the US Geological Survey (USGS). The method is based on reclassification, upsampling, constrained randomization at class boundary edges, and deliberate use of colors with very similar lightness values. The method spans both scientific geospatial data treatment and artistic cartographic design, and both generalizes and enhances the data. The processing, automated in ArcGIS™, is detailed, and examples of the product are provided. Numéro de notice : A2016--124 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.14714/CP83.1351 En ligne : http://dx.doi.org/10.14714/CP83.1351 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84968
in Cartographic perspectives > n° 83 (2016) . - pp 5 - 21[article]Markov random field-based method for super-resolution mapping of forest encroachment from remotely sensed ASTER image / L. K. Tiwari in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)PermalinkAn assessment of image features and random forest for land cover mapping over large areas using high resolution Satellite Image Time Series / Charlotte Pelletier (2016)PermalinkPermalinkAutomated annual cropland mapping using knowledge-based temporal features / François Waldner in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)PermalinkCompilation de données radar et optiques pour la cartographie des classes d'occupation du sol aux environs du système lacustre de Bizerte (Tunisie du Nord) / Ibtissem Amri in Photo interprétation, European journal of applied remote sensing, vol 51 n° 2 (juin 2015)PermalinkA fully-automated approach to land cover mapping with airborne LiDAR and high resolution multispectral imagery in a forested suburban landscape / Jason R. Parent in ISPRS Journal of photogrammetry and remote sensing, vol 104 (June 2015)PermalinkBuilding a hybrid land cover map with crowdsourcing and geographically weighted regression / Linda M. See in ISPRS Journal of photogrammetry and remote sensing, vol 103 (May 2015)PermalinkUse of Landsat and Corona data for mapping forest cover change from the mid-1960s to 2000s: Case studies from the Eastern United States and Central Brazil / Dan-Xia Song in ISPRS Journal of photogrammetry and remote sensing, vol 103 (May 2015)PermalinkImproved land cover mapping using aerial photographs and satellite images / Katalin Varga in Open geosciences, vol 7 n° 1 (January 2015)PermalinkMéthode de cartographie de la consommation de sol agricole dans le Grand Genève / Marie-Laure Halle (2015)Permalink