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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 Water 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 Affiliation des auteurs : non IGN 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 : Innovative Methods and Products of the " Urbanization and Artificialization" Scientific Expertise Centre Type de document : Article/Communication Auteurs : Anne Puissant, Auteur ; Arnaud Le Bris Congrès : Congrès: Living Planet Symposium LPS 2019 (13 - 17 mai 2019; Milan, Italie), Commanditaire Editeur : Paris : HAL Année de publication : 2019 Importance : 2 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes descripteurs IGN] données localisées
[Termes descripteurs IGN] empreinte écologique
[Termes descripteurs IGN] information scientifique et technique
[Termes descripteurs IGN] télédétection
[Termes descripteurs IGN] urbanisation
[Termes descripteurs IGN] utilisation du solRésumé : (auteur) he THEIA Land Data and Services Centre (www.theia-land.fr) is a consortium of 12 French public institutions involved in Earth observation and environmental sciences (CEA, CEREMA, CIRAD, CNES, IGN, INRA, CNRS, IRD, Irstea, Météo France, AgroParisTech, and ONERA). THEIA has been initiated in 2012 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. One of this SEC is the "Urbanization and Artificialization” Centre clustering experts in multi-sensor urban remote sensing. THEIA in collaboration with ODATIS (Data and Service for the Ocean), ForM@Ter (Data and Service for the Solid Earth), and AERIS (Data and Service for the Atmosphere) form the "Earth System" Research Infrastructure. The objective of this poster is to present recent (>2016) innovations of the URBAN SEC in terms of (1) development of algorithms useful for urban remote sensing using optical and SAR sensors, (2) validation of the urban products provided by the THEIA Land Service and Data Infrastructure, and (3) demonstration of user-tailored applications for urban studies. The Urban Expert Centre brings together researchers and engineers from several institutes: LIVE - Strasbourg, IGN-LaSTIG - Univ. Paris Est, CESBIO – Toulouse, LETG - Rennes, Irstea – Montpellier, TETIS - Montpellier, INP Bordeaux, IRD, ESPACE-DEV - Montpellier, ESPACE - Nice, ONERA. Research results and methods linked to (1) the detection and mapping of the urban footprint at an annual frequency; (2) the identification of urban fabrics, (3) the mapping of green networks within the cities. In parallel, the group proposes to summarize and identify relevant indicators (parameters) dedicated to urban planning and management. Numéro de notice : C2019-046 Affiliation des auteurs : LaSTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Poster nature-HAL : ComSansActesPubliés-Unpublished DOI : sans En ligne : https://hal.archives-ouvertes.fr/hal-02135846 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95429
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 : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2019 Projets : 1-Pas de projet / Importance : 4 p. 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 Affiliation des auteurs : LaSTIG+Ext (2020- ) 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 Estimation of surface roughness over bare agricultural soil from Sentinel-1 data / Mohammad Choker (2018)
contenu dans HAL Hyper articles en ligne / Centre pour la Communication Scientifique Directe CCSD (2000)
Titre : Estimation of surface roughness over bare agricultural soil from Sentinel-1 data Type de document : Thèse/HDR Auteurs : Mohammad Choker, Auteur ; Nicolas Baghdadi, Directeur de thèse ; Mehrez Zribi, Directeur de thèse Editeur : AgroParisTech Année de publication : 2018 Importance : 214 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse pour obtenir le grade de Docteur de l'Institut des Sciences et Industries du Vivant et de l'Environnement, AgroParisTech, GéomatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] bande C
[Termes descripteurs IGN] écho radar
[Termes descripteurs IGN] état de surface du sol
[Termes descripteurs IGN] humidité du sol
[Termes descripteurs IGN] image Cosmo-Skymed
[Termes descripteurs IGN] image Sentinel-SAR
[Termes descripteurs IGN] image TerraSAR-X
[Termes descripteurs IGN] modèle de rétrodiffusion
[Termes descripteurs IGN] polarisation
[Termes descripteurs IGN] rugosité du sol
[Termes descripteurs IGN] surface cultivée
[Termes descripteurs IGN] télédétection en hyperfréquenceRésumé : (auteur) Spatial remote sensing is of paramount importance for mapping and monitoring environmental problems. Its interest lies in the ability of space satellite sensors in providing permanent information of the planet, at local, regional and global scales. Also, it provides spatial and repetitive territories visions and ecosystem views. Radar remote sensing has shown great potential in recent years for the characterization of soil surface conditions. The state of the soil surface, in particular moisture and roughness, has a fundamental influence on the distribution of rainfall between infiltration, surface retention and runoff. In addition, it plays an essential role in surface hydrological processes and those associated with erosion and evapotranspiration processes. Characterization and consideration of these surface conditions have been recently considered as an important issue for physically based modeling of hydrological processes or for surface-atmosphere coupling. In this context and for several years, several scientific studies have shown the potential of active microwave data for estimation of the soil moisture and the surface roughness.New SAR (Synthetic Aperture Radar) systems have opened new perspectives for earth observation through improved spatial resolution (metric on TerraSAR-X and COSMO-SkyMed) and temporal resolution (TerraSAR-X, COSMO-SkyMed, Sentinel-1) . The recent availability of new Sentinel-1 C-band radar sensors (free and open access) makes it essential to evaluate the potential of Sentinel-1 data for the characterization of soil surface conditions and in particular surface roughness.The work revolves around three parts. The first part consist of evaluation of the most used radar backscattering models (IEM, Oh, Dubois, and AIEM) using a wide dataset of SAR data and experimental soil measurements. This evaluation gives the ability to find the most robust backscattering model that simulates the radar signal with good agreement in order to use later in the inversion procedure of the radar signal for estimating the soil roughness. The second research axe of this thesis consists of proposing an empirical radar backscattering model for HH, HV and VV polarizations. This new model will be developed using a large real dataset. This new model also will be used in the inversion procedure of the radar signal for estimating the soil roughness. The last axe of this thesis consists of producing a method to invert the radar signal using neural networks. The objective is to evaluate the potential of Sentinel-1 data for estimating surface roughness. These neural networks will be trained using wide synthetic dataset produced from the radar backscattering models chosen (IEM calibrated by Baghdadi and the new proposed model) and validated using two datasets: one synthetic dataset and one real (Sentinel 1 images and in-situ measurements). The real datasets are collected from Tunisia (Kairouan) and France (Versailles). Note de contenu : 1- Introduction
2- Generalities
3- Evaluation of radar backscattering models
4- A new empirical model for radar scattering from bare soil surfaces
5- Estimation of soil roughness using neural networks from sentinel-1 SAR data
6- General conclusion and perspectivesNuméro de notice : 25595 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Géomatique : Paris : 2018 Organisme de stage : TETIS (Montpellier) En ligne : https://tel.archives-ouvertes.fr/tel-02293194/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95218 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 Affiliation des auteurs : UPEM-LaSTIG+Ext (2016-2019) 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 PermalinkQGIS in Remote Sensing, Volume 2. QGIS and applications in agriculture and forest / Nicolas Baghdadi (2018)
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