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Dater les documents cartographiques / Jean-Luc Arnaud in e-Perimetron, vol 17 n° 1 (avril 2022)  

Public [article]  ine-Perimetron > vol 17 n° 1 (avril 2022) . - pp 1 - 18 Titre : Dater les documents cartographiques Type de document : Article/Communication Auteurs : Jean-Luc Arnaud, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 18 Note générale : bibliographie Langues : Français (fre) Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie ancienne [Termes IGN] cartographie militaire [Termes IGN] datation [Termes IGN] dépôt de la guerre [Termes IGN] document cartographique [Termes IGN] échelle cartographique [Termes IGN] édition cartographique [Termes IGN] histoire [Termes IGN] précision [Termes IGN] Service Géographique de l'Armée [Termes IGN] utilisateur Résumé : (auteur) Cet article examine les modes de datation de la production cartographique française depuis la fin du XVIIIe siècle. Il est composé de sept chapitres thématiques qui envisagent la multiplicité des pratiques des éditeurs et montrent qu’en fonction de l’usage envisagé pour chaque document et de son niveau de précision, les enjeux portés par la datation prennent des formes différentes. Numéro de notice : A2022-294 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : sans En ligne : http://www.e-perimetron.org/Vol_17_1/Arnaud.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100346 [article]

Monitoring grassland dynamics by exploiting multi-modal satellite image time series / Anatol Garioud (2022)  

Public Titre : Monitoring grassland dynamics by exploiting multi-modal satellite image time series Titre original : Suivi de la dynamique des prairies permanentes par analyse des séries temporelles multi-modales Type de document : Thèse/HDR Auteurs : Anatol Garioud , Auteur ; Clément Mallet , Directeur de thèse ; Silvia Valero, Directeur de thèse Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2022 Importance : 194 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse présentée et soutenue en vue de l'obtention du Doctorat de l'Université Gustave Eiffel, Spécialité Sciences et Technologies de l'Information Géographique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] analyse multivariée [Termes IGN] apprentissage profond [Termes IGN] classification par Perceptron multicouche [Termes IGN] données auxiliaires [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Mâcon [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] prairie [Termes IGN] régression [Termes IGN] série temporelle [Termes IGN] seuillage d'image [Termes IGN] superpixel [Termes IGN] surveillance agricole [Termes IGN] Toulouse Index. décimale : THESE Thèses et HDR Résumé : (Auteur) The vast grassland surfaces as well as the growing recognition of the ecosystem services thez provide have revealed urgent needs for their conservation and sutainable management. Despite the acknowledged importance of grassland management practices, there are currently no large-scale efforts reporting on their frequency and nature. Satellite remote sensing time series appear to be a suitable tool for efficient grassland monitoring and allow synoptic and regular analysis. The research conducted in this PhD aims to develop methods for the detection of grassland management practices from complementary optical and SAR multivariate time series. Advances in deep learning are employed to regress multivariate SAR time series and contextual knowledge towards optical NDVI. Resulting gap-free time series are used to efficiently explore methods aiming to detect vegetation status changes related to management practices on grasslands. Note de contenu : INTRODUCTION 1. Grasslands and remote sensing: context, diversity and challenges 1.1 Definition, extent and importance of grasslands 1.2 Earth observation from space: principles and applications over grasslands 1.3 Problem statement and objectives 1.4 Outline of the manuscript 2. Study areas and datasets 2.1 Study areas 2.2 Satellite data 2.3 Reference and ancillary datasets 2.4 Feature derived from sentinel images for grassland monitoring 2.5 Description of the feature engineering steps 2.6 Exploring the relationships between derived satellite features 2.7 Concluding remarks HIGH-TEMPORAL SAMPLED TIME-SERIES 3. Sentinels regression for vegetation monitoring 3.1 Monitoring vegetation through optical-SAR synergy 3.2 Retrieving missing data in optical time series 3.3 SenRVM: a deep learning-based regression framework 3.4 Concluding remarks 4. Outcomes of the SenRVM approach 4.1 Experimental design for training and evaluating SenRVM models 4.2 Assessment of SenRVM predictions 4.3 Empirical analysis of the SenRVM results 4.4 Generalization capabilities of single-class grassland SenRVM models 4.5 Further post-processing of SenRVM results 4.6 Concluding remarks MONITORING GRASSLANDS 5. Detecting and quantifying grassland management practices 5.1 Challenges and related work 5.2 The proposed methodology 5.3 Description of validation data 5.4 Experimental setup 5.5 Assessment of the proposed method 5.6 Potential outcomes 5.7 Concluding remarks GENERAL CONCLUSION 6. Conclusion and perspectives 6.1 Summary 6.2 Perspectives Numéro de notice : 26831 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences et Technologies de l'Information Géographique : Gustave Eiffel : 2022 Organisme de stage : LASTIG (IGN) nature-HAL : Thèse DOI : sans En ligne : https://www.theses.fr/s208897 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100728

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Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC4A / Olivier Bock in Earth System Science Data, vol 13 n° 5 (May 2021)  

Public [article]  inEarth System Science Data > vol 13 n° 5 (May 2021) . - pp 2407 - 2436 Titre : Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC4A Type de document : Article/Communication Auteurs : Olivier Bock , Auteur ; Pierre Bosser , Auteur ; Cyrille Flamant, Auteur ; Erik Doerflinger, Auteur ; Friedhelm Jansen, Auteur ; Romain Fagès , Auteur ; Sandrine Bony, Auteur ; Sabrina Schnitt, Auteur Année de publication : 2021 Projets : VEGAN / Bock, Olivier, EUREC4A / Bock, Olivier Article en page(s) : pp 2407 - 2436 Note générale : bibliographie This work was supported by the CNRS program LEFE/INSU through the project VEGAN. The EUREC4A project was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 694768). Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] Caraïbes [Termes IGN] données auxiliaires [Termes IGN] données GNSS [Termes IGN] données météorologiques [Termes IGN] erreur systématique [Termes IGN] humidité de l'air [Termes IGN] retard troposphérique zénithal [Termes IGN] teneur intégrée en vapeur d'eau Résumé : (auteur) Ground-based Global Navigation Satellite System (GNSS) measurements from nearly fifty stations distributed over the Caribbean Arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality Integrated Water Vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the Tradewinds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD) to IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams international airport (GAIA). A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (−2.9 kg m−2) and the GNSS results (−1.2 kg m−2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg m−2) where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a colocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS derived IWV peaks. Two successive peaks are observed on 22 January and 23/24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organisation for five representative GNSS stations across the Caribbean Arc and found that the environment of Fish cloud patterns to be moister than that of Flowers cloud patterns which, in turn, is moister than the environment of Gravel cloud patterns. The differences in the IWV means between Fish and Gravel were assessed to be significant. Finally, the Gravel moisture environment was found to be similar to that of clear, cloud-free conditions. The moisture environment associated with the Sugar cloud pattern has not been assessed because it was hardly observed during the first two months of 2020. The reprocessed ZTD and IWV data set from 49 GNSS stations used in this study are available from the AERIS data center (https://doi.org/10.25326/79; Bock (2020b)). Numéro de notice : A2021-172 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/essd-13-2407-2021 Date de publication en ligne : 24/02/2021 En ligne : http://dx.doi.org/10.5194/essd-13-2407-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97213 [article]

A new small area estimation algorithm to balance between statistical precision and scale / Cédric Vega in International journal of applied Earth observation and geoinformation, vol 97 (May 2021)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 97 (May 2021) . - n° 102303 Titre : A new small area estimation algorithm to balance between statistical precision and scale Type de document : Article/Communication Auteurs : Cédric Vega , Auteur ; Jean-Pierre Renaud, Auteur ; Ankit Sagar , Auteur ; Olivier Bouriaud , Auteur Année de publication : 2021 Projets : LUE / Université de Lorraine, DIABOLO / Packalen, Tuula, ARBRE/CHM-era / Jolly, Anne Article en page(s) : n° 102303 Note générale : bibliographie This research was funded by The French Environmental Management Agency (ADEME), grant number 16-60-C0007. The methods and algorithms for processing photogrammetric data were supported by DIABOLO project from the European Union’s Horizon 2020 research and innovation program under grant agreement No 633464, as well as CHM-ERA project from the French National Research Agency (ANR) as part of the “Investissements d’Avenir” program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE). Ankit Sagar received the financial support of the French PIA project “Lorraine Université d’Excellence”, reference ANR-15-IDEX-04-LUE, through the project Impact DeepSurf. Langues : Anglais (eng) Descripteur : [Termes IGN] arbre BSP [Termes IGN] capital sur pied [Termes IGN] données auxiliaires [Termes IGN] données de terrain [Termes IGN] estimation bayesienne [Termes IGN] inventaire forestier national (données France) [Termes IGN] réduction d'échelle [Termes IGN] seuillage [Termes IGN] surface terrière [Vedettes matières IGN] Sylviculture Résumé : (auteur) Combining national forest inventory (NFI) data with auxiliary information allows downscaling and improving the precision of NFI estimates for small domains, where normally too few field plots are available to produce reliable estimates. In most situations, small domains represent administrative units that could greatly vary in size and forested area. In small and poorly sampled domains, the precision of estimates often drop below expected standards. To tackle this issue, we introduce a downscaling algorithm generating the smallest possible groups of domains satisfying prescribed sampling density and estimation error. The binary space partitioning algorithm recursively divides the population of domains in two groups while the prescribed precision conditions are fulfilled. The algorithm was tested on two major forest attributes (i.e. growing stock and basal area) in an area of 7,500 km2 dominated by hardwood forests in the centre of France. The estimation domains consisted in 157 municipalities. The field data included 819 NFI plots surveyed during a 5 years period. The auxiliary data consisted in 48 metrics derived from a forest map, photogrammetric models and Landsat images. A model-assisted framework was used for estimation. For each forest attribute, the best model was selected using a best-subset approach using a Bayesian Information Criteria. The retained models explained 58% and 41% of the observed variance for the growing stocks and basal areas respectively. The performance of the algorithm was evaluated using a minimum of 3 NFI points per domain and estimation errors varying from 10 to 50%. For a target estimation error set to 10%, the algorithm led to a limited number of estimation domains ( The algorithm provides a flexible estimation framework for small area estimation. The key advantages of the approach are relying on its capacity to produce estimations based on a preselected precision threshold and to produce results over the whole area of interest, avoiding areas without any estimates. The algorithm could also be used on any kind of polygon layers (not only administrative ones), provided that the field sampling design enable estimation. This makes the proposed algorithm a convenient tool notably for decision makers and forest managers. Numéro de notice : A2021-067 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2021.102303 Date de publication en ligne : 25/01/2021 En ligne : https://doi.org/10.1016/j.jag.2021.102303 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96992 [article]

Estimation et cartographie d’attributs forestiers haute résolution : Le potentiel des approches multisource / Cédric Vega (2021) 

Public Titre : Estimation et cartographie d’attributs forestiers haute résolution : Le potentiel des approches multisource Type de document : Article/Communication Auteurs : Cédric Vega , Auteur ; Milena Planells, Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Année de publication : 2021 Conférence : Atelier Theia 2021, Les utilisations de la télédétection pour la forêt 11/10/2021 Montpellier France slides & videos Langues : Français (fre) Descripteur : [Termes IGN] données auxiliaires [Termes IGN] données multisources [Termes IGN] image optique [Termes IGN] image radar [Termes IGN] modèle numérique [Termes IGN] prédiction [Termes IGN] segmentation en régions [Termes IGN] série temporelle [Termes IGN] surveillance forestière [Vedettes matières IGN] Inventaire forestier Numéro de notice : C2021-041 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE/INFORMATIQUE/MATHEMATIQUE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99239

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