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Auteur Nicolas Baghdadi |
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Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesComparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands / Mohammad El Hajj in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)
Titre : Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands Type de document : Article/Communication Auteurs : Mohammad El Hajj, Auteur ; Nicolas Baghdadi, Auteur ; Mehrez Zribi, Auteur Année de publication : 2019 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes descripteurs IGN] analyse comparative
[Termes descripteurs IGN] bande C
[Termes descripteurs IGN] bande L
[Termes descripteurs IGN] humidité du sol
[Termes descripteurs IGN] image ALOS-PALSAR
[Termes descripteurs IGN] image radar moirée
[Termes descripteurs IGN] image Sentinel-MSI
[Termes descripteurs IGN] image Sentinel-SAR
[Termes descripteurs IGN] Normalized Difference Vegetation Index
[Termes descripteurs IGN] réseau neuronal artificiel
[Termes descripteurs IGN] surface cultivéeRésumé : (auteur) Surface soil moisture (SSM) estimation is of great importance in several areas, such as hydrology, agriculture and risk assessment. C-band SAR (synthetic aperture radar) data have been widely used to estimate SSM, whereas few studies have been performed using L-band SAR due to the low availability of L-band SAR data. In this context, the objective of the present paper is to compare the SSM estimation potentials of the C- (Sentinel-1) and L-bands (PALSAR) for wheat and grassland plots. The inversion approach developed in this study uses neural networks to invert the SAR signal and estimate the SSM. For each radar frequency, the developed neural networks were trained using the following as an input vector: SAR incidence angle, SAR polarization (VV for the C-band and HH for the L-band), and NDVI from optical images. Artificial Neural networks (ANNs) were developed and validated using synthetic and real databases. The results showed that the L-band provided slightly less accurate SSM estimates than the C-band. Moreover, the results showed that the accuracies of the SSM estimates for both frequencies strongly depended on the soil roughness (Hrms) and SSM values. From the synthetic database at SSM values less than 25 vol.%, the ANNs underestimated the SSM for Hrms values less than 1.5 cm and overestimated the SSM for Hrms values greater than 1.5 cm. In addition, the ANNs underestimated the SSM value regardless of the Hrms value when the SSM value was greater than 25 vol.%. An RMSE analysis of the SSM estimates showed that the highest RMSE values were observed for the L-band regardless of the SSM value, and high RMSE values were observed for the C-band only in very wet soil conditions (SSM>25 vol.%). From the real database at NDVI values less than 0.7, the RMSE (root mean square error) of the SSM estimates was 4.6 vol.% for the C-band and 5.3 vol.% for the L-band. Most importantly, the L-band enabled the estimation of the SSM under a well-developed vegetation cover (NDVI > 0.7) with an RMSE of 6.7 vol.%, whereas the C-band SAR signal became completely attenuated for some crops when the NDVI value was greater than 0.7, and thus the estimation of SSM was impossible using the C-band. Numéro de notice : A2019-473 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern date de publication en ligne : 29/06/2019 En ligne : https://doi.org/10.1016/j.jag.2019.05.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93634
in International journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - 13 p.[article]
Titre : The ‘urban’ component of the French Land Data and Services Centre (Theia) Type de document : Article/Communication Auteurs : Anne Puissant, Auteur ; Arnaud Sellé, Auteur ; Nicolas Baghdadi, Auteur ; Vincent Thierion, Auteur ; Arnaud Le Bris Congrès : JURSE 2019, Joint Urban Remote Sensing Event (22 - 24 mai 2019; Vannes, France) Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Année de publication : 2019 Autre Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes descripteurs IGN] image aérienne
[Termes descripteurs IGN] image spatialeRésumé : (auteur) The THEIA data and services centre has been created with the objective of increasing the use of space data by the scientific community and the public actors. THEIA structured the French scientific community 1) through a mutualized Service and Data Infrastructure (SDI) distributed between several centers, allowing access to a variety of products; 2) through the setup of Regional Animation Networks (RAN) to federate and animate users (scientists and public / private actors) and 3) through Scientific Expertise Centres (SEC) clustering virtual research groups on a thematic domain. The research works carried out for urban studies in three SEC are presented in this paper. The works are organized around the design and development of value-added products and services. Numéro de notice : C2019-003 Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/JURSE.2019.8808998 date de publication en ligne : 22/08/2019 En ligne : https://doi.org/10.1109/JURSE.2019.8808998 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92208 QGIS in Remote Sensing, ch. 2. Contribution of the Integrated Topo-bathymetric Model for Coastal Wetland Evolution: Case of Geomorphologic and Biological Evolution of Ichkeul Marshes (North Tunisia) / Zeineb Kassouk (2018)
contenu dans QGIS in Remote Sensing, Volume 4. QGIS and Applications in Water and Risks / Nicolas Baghdadi (2018)
Titre de série : QGIS in Remote Sensing, ch. 2 Titre : Contribution of the Integrated Topo-bathymetric Model for Coastal Wetland Evolution: Case of Geomorphologic and Biological Evolution of Ichkeul Marshes (North Tunisia) Type de document : Chapitre/Contribution Auteurs : Zeineb Kassouk, Auteur ; Zohra Lili-Chabaane, Auteur ; Benoit Deffontaines Editeur : Londres : ISTE Editions Année de publication : 2018 Autre Editeur : New York, Londres, Hoboken (New Jersey), ... : John Wiley & Sons Note générale : bibliographie Langues : Anglais (eng) Résumé : (auteur) This chapter focuses on the reliability of remote sensing imagery combined with topographic and bathymetric data to map the coastal wetland communities in Ichkeul marshes. It combines both the object‐oriented classification of high spatial resolution imagery and the integrated topographic‐bathymetric digital terrain model (topo‐bathymetric DTM) in order to characterize and to map vegetation communities across Ichkeul marshes. The chapter settles the integrated topo‐bathymetric DTM by combining three datasets: the lake bathymetry, the marshes and the mountain digital topographies. It classifies Ichkeul marshes, the new topo‐bathymetric DTM is used with multispectral imagery from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images from the Terra satellite. In the chapter, classification rules for distinguishing vegetation communities are created automatically by using attributes (PCA1, topo‐bathymetric DTM and Normalized Vegetation Index (NDVI) value) of training segments. The first rule in the classification tree separates non‐vegetated and vegetated objects using the NDVI attribute. Numéro de notice : H2018-008 Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1002/9781119476726.ch2 date de publication en ligne : 16/02/2018 En ligne : https://doi.org/10.1002/9781119476726.ch2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91930
Titre de série : QGIS in Remote Sensing, Volume 1 Titre : QGIS and generic tools Type de document : Monographie Auteurs : Nicolas Baghdadi, Editeur scientifique ; Clément Mallet Editeur : Londres : ISTE Editions Année de publication : 2018 Importance : 316 p. Format : 16 x 24 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes descripteurs IGN] application web
[Termes descripteurs IGN] Geospatial data abstraction library
[Termes descripteurs IGN] GRASS
[Termes descripteurs IGN] QGISIndex. décimale : 37.35 Logiciels SIG Résumé : (éditeur) Numéro de notice : 17565A Thématique : GEOMATIQUE Nature : Recueil / ouvrage collectif nature-HAL : DirectOuvrColl/Actes DOI : 10.1002/9781119457091 date de publication en ligne : 19/01/2018 En ligne : http://dx.doi.org/10.1002/9781119457091 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91953
- QGIS in Remote Sensing, Volume 2. QGIS and applications in agriculture and forest / Nicolas Baghdadi (2018)
- QGIS in Remote Sensing, Volume 3. QGIS and Applications in Territorial Planning / Nicolas Baghdadi (2018)
- QGIS in Remote Sensing, Volume 4. QGIS and Applications in Water and Risks / Nicolas Baghdadi (2018)
- Utilisation de QGIS en télédétection, Volume 1. QGIS et outils génériques / Nicolas Baghdadi (2018)
QGIS in Remote Sensing, Volume 2. QGIS and applications in agriculture and forest / Nicolas Baghdadi (2018)
Titre de série : QGIS in Remote Sensing, Volume 2 Titre : QGIS and applications in agriculture and forest Type de document : Monographie Auteurs : Nicolas Baghdadi, Editeur scientifique ; Clément Mallet Editeur : Londres : ISTE Editions Année de publication : 2018 Importance : 374 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-119-45710-7 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes descripteurs IGN] cartographie thématique
[Termes descripteurs IGN] classification dirigée
[Termes descripteurs IGN] image optique
[Termes descripteurs IGN] image Sentinel-MSI
[Termes descripteurs IGN] image Sentinel-SAR
[Termes descripteurs IGN] QGIS
[Termes descripteurs IGN] télédétection spatialeIndex. décimale : 37.35 Logiciels SIG Résumé : (Editeur) The QGIS in Remote Sensing Set aims to facilitate the appropriation and operational use of the Quantum Geographic Information System (QGIS) software in the field of remote sensing. This second volume of the set presents different applications of QGIS and its libraries for agriculture and forestry. A thorough knowledge of agriculture and forest areas is fundamental from both an economic and an environmental point of view. These environments are strongly involved in the use of spatial data, which are essential for restoring the spatio-temporal variability of surface conditions. In this context, GIS tools have long been used to support the exploitation of spatial imagery. This work is carried out by scientists who are proficient to a high level of technicality. The book is targeted at students (Masters, engineering students, PhDs), engineers and researchers who have already adopted geographic information systems. In addition to the text, readers will have access to data and tools allowing the integral realization of the scientific procedures described in each chapter, as well as screenshots of all the windows which illustrate the manipulations necessary for the realization of each application. Note de contenu : 1. Coupling Radar and Optical Data for Soil Moisture Retrieval over Agricultural Areas / Mohammad El Hajj, Nicolas Baghdadi, Mehrez Zribi and Hassan Bazzi.
2. Disaggregation of Thermal Images / Mar Bisquert and Juan Manuel Sánchez.
3. Automatic Extraction of Agricultural Parcels from Remote Sensing Images and the RPG Database with QGIS/OTB / Jean-Marc Gilliot, Camille Le Priol, Emmanuelle Vaudour and Philippe Martin.
4. Land Cover Mapping Using Sentinel-2 Images and the Semi-Automatic Classification Plugin: A Northern Burkina Faso Case Study / Louise Leroux, Luca Congedo, Beatriz Bellón, Raffaele Gaetano and Agnès Bégué.
5. Detection and Mapping of Clear-Cuts with Optical Satellite Images / Kenji Ose.
6. Vegetation Cartography from Sentinel-1 Radar Images / Pierre-Louis Frison and Cédric Lardeux.
7. Remote Sensing of Distinctive Vegetation in Guiana Amazonian Park / Nicolas Karasiak and Pauline Perbet.
8. Physiognomic Map of Natural Vegetation / Samuel Alleaume and Sylvio Laventure.
9. Object-Based Classification for Mountainous Vegetation Physiognomy Mapping / Vincent Thierion and Marc Lang.Numéro de notice : 17567B Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Recueil / ouvrage collectif nature-HAL : DirectOuvrColl/Actes DOI : 10.1002/9781119457107 date de publication en ligne : 19/01/2018 En ligne : http://dx.doi.org/10.1002/9781119457107 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91932
- Utilisation de QGIS en télédétection, Volume 2. QGIS et applications en agriculture et forêt / Nicolas Baghdadi (2018)
- QGIS in Remote Sensing, Volume 4. QGIS and Applications in Water and Risks / Nicolas Baghdadi (2018)
- QGIS in Remote Sensing, Volume 3. QGIS and Applications in Territorial Planning / Nicolas Baghdadi (2018)
- QGIS in Remote Sensing, Volume 1. QGIS and generic tools / Nicolas Baghdadi (2018)
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