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Auteur Mehrez Zribi |
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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 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]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 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
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)
Titre de série : QGIS in Remote Sensing, Volume 3 Titre : QGIS and Applications in Territorial Planning Type de document : Monographie Auteurs : Nicolas Baghdadi, Editeur scientifique ; Clément Mallet Editeur : Londres : ISTE Editions Année de publication : 2018 Importance : 306 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-119-45712-1 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes descripteurs IGN] aménagement du littoral
[Termes descripteurs IGN] aménagement du territoire
[Termes descripteurs IGN] climat urbain
[Termes descripteurs IGN] morphologie urbaine
[Termes descripteurs IGN] QGIS
[Termes descripteurs IGN] service écosystémique
[Termes descripteurs IGN] urbanismeIndex. décimale : 37.40 Applications 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. Over the past 15 years, Geographic Information Systems have become central to applications dealing with territorial planning issues. This third volume of the set is devoted to the presentation and practice in QGIS and its libraries of various applications related to territorial planning. The chapters illustrate the great diversity of case studies, spatial scales and actors involved in this issue: from the world to the city and from objects of special interest, from detection of objects, to studies of location or the impact of works through spatial indicators, urban, agro-forestry or coastal environments. 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. Design and Implementation of Automated Atlas / Boris Mericskay.
2. Estimation of Land Use Efficiency from the Global Human Settlement Layer (GHSL) / Christina Corbane, Panagiotis Politis, Martino Pesaresi, Thomas Kemper and Alice Siragusa.
3. Characterizing Urban Morphology for Urban Climate Simulation Based on a GIS Approach / Justin Emery, Julita Dudek, Ludovic Granjon, Benjamin Pohl, Yves Richard, Thomas Thevenin and Nadège Martiny.
4. Airborne Optical Remote Sensing Potential for Pool Mapping in an Urban Environment / Josselin Aval and Thierry Erudel.
5. Automation of Workflows for the Installation of a Wind Farm / Boris Mericskay.
6. Ecosystemic Services Assessment: Applicationbto Forests for the Preservation of Water Resources in Tropical Islands / Rémi Andreoli and Brice Van Haaren.
7. Assessing the Influence of Landscape on Biodiversity Using the QGIS Plugin LecoS / Sylvie Ladet, David Sheeren, Pierre-Alexis Herrault and Mathieu Fauvel.Numéro de notice : 17567C Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Recueil / ouvrage collectif nature-HAL : DirectOuvrColl/Actes DOI : 10.1002/9781119457121 date de publication en ligne : 16/02/2018 En ligne : http://dx.doi.org/10.1002/9781119457121 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91933
- QGIS in Remote Sensing, Volume 2. QGIS and applications in agriculture and forest / Nicolas Baghdadi (2018)
- QGIS in Remote Sensing, Volume 4. QGIS and Applications in Water and Risks / Nicolas Baghdadi (2018)
- QGIS in Remote Sensing, Volume 1. QGIS and generic tools / Nicolas Baghdadi (2018)
- Utilisation de QGIS en télédétection, Volume 3. QGIS et applications en aménagement du territoire / Nicolas Baghdadi (2018)
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