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Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission / Nicolas Gasnier (2022)  

Public Titre : Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission Type de document : Thèse/HDR Auteurs : Nicolas Gasnier, Auteur ; Florence Tupin, Directeur de thèse ; Loïc Denis, Directeur de thèse Editeur : Paris : Institut Polytechnique de Paris Année de publication : 2022 Importance : 213 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse de doctorat présentée à l’Institut Polytechnique de Paris, spécialité Images Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] base de données localisées [Termes IGN] détection d'objet [Termes IGN] détection de changement [Termes IGN] données hydrographiques [Termes IGN] hauteurs de mer [Termes IGN] image multitemporelle [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] image SWOT [Termes IGN] lac [Termes IGN] rivière [Termes IGN] série temporelle [Termes IGN] télédétection en hyperfréquence Index. décimale : THESE Thèses et HDR Résumé : (Auteur) Spaceborne remote sensing provides hydrologists and decision-makers with data that are essential for understanding the water cycle and managing the associated resources and risks. The SWOT satellite, which is a collaboration between the French (CNES) and American (NASA, JPL) space agencies, is scheduled for launch in 2022 and will measure the height of lakes, rivers, and oceans with high spatial resolution. It will complement existing sensors, such as the SAR and optical constellations Sentinel-1 and 2, and in situ measurements. SWOT represents a technological breakthrough as it is the first satellite to carry a near-nadir swath altimeter. The estimation of water levels is done by interferometry on the SAR images acquired by SWOT. Detecting water in these images is therefore an essential step in processing SWOT data, but it can be very difficult, especially with low signal-to-noise ratios, or in the presence of unusual radiometries. In this thesis, we seek to develop new methods to make water detection more robust. To this end, we focus on the use of exogenous data to guide detection, the combination of multi-temporal and multi-sensor data and denoising approaches. The first proposed method exploits information from the river database used by SWOT (derived from GRWL) to detect narrow rivers in the image in a way that is robust to both noise in the image, potential errors in the database, and temporal changes. This method relies on a new linear structure detector, a least-cost path algorithm, and a new Conditional Random Field segmentation method that combines data attachment and regularization terms adapted to the problem. We also proposed a method derived from GrabCut that uses an a priori polygon containing a lake to detect it on a SAR image or a time series of SAR images. Within this framework, we also studied the use of a multi-temporal and multi-sensor combination between Sentinel-1 SAR and Sentinel-2 optical images. Finally, as part of a preliminary study on denoising methods applied to water detection, we studied the statistical properties of the geometric temporal mean and proposed an adaptation of the variational method MuLoG to denoise it. Note de contenu : 1. Introduction 1.1 Context 1.2 Contributions 1.3 Organization of the manuscript I BACKGROUND ON SAR REMOTE SENSING AND WATER SURFACE MONITORING WITH SAR IMAGES 2. SAR images 2.1 Physics and statistics of SAR images 2.2 The SWOT mission 2.3 Sentinel-1 3. SAR water detection and hydrological prior 3.1 Water detection in SAR images 3.2 SWOT processing and products 3.3 Prior water masks and databases 4. Methodological background 4.1 Markov random fields 4.2 Variational methods for image denoising PROPOSED APPROACHES 5. Guided extraction of narrow rivers on SAR images using an exogenous river database 5.1 Introduction 5.2 Proposed river segmentation pipeline 5.3 Experimental results 5.4 Conclusion 6. Adaptation of the GrabCut method to SAR images: lake detection from a priori polygon 6.1 Single-date GrabCut method for lake detection from a priori polygon 6.2 Multitemporal and multi-sensor adaptations of the method 6.3 2D+T GrabCut of SAR images with temporal regularization for lake detection within an a priori mask 6.4 Joint 2D+T segmentation of SAR and optical images 7. Denoising of the temporal geometric mean 7.1 Introduction 7.2 Statistics of the temporal geometric mean of SAR intensities 7.3 Denoising method 7.4 Experiments 7.5 Application to change detection 7.6 Application to ratio-based denoising of single SAR images within a time series 7.7 Conclusion 8 Conclusion and perspectives Numéro de notice : 26762 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Images : Palaiseau : 2022 Organisme de stage : Télécom Paris nature-HAL : Thèse DOI : sans Date de publication en ligne : 17/02/2022 En ligne : https://tel.archives-ouvertes.fr/tel-03578831/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99823

La modélisation des eaux / Michel Kasser in Géomètre, n° 2197 (décembre 2021)

Public [article]  inGéomètre > n° 2197 (décembre 2021) . - pp 41 - 41 Titre : La modélisation des eaux Type de document : Article/Communication Auteurs : Michel Kasser, Auteur Année de publication : 2021 Article en page(s) : pp 41 - 41 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] altimétrie par radar [Termes IGN] image SWOT [Termes IGN] océanographie dynamique [Termes IGN] océanographie spatiale [Termes IGN] précision centimétrique [Termes IGN] précision de localisation [Termes IGN] salinité [Termes IGN] surface de la mer [Termes IGN] vague [Termes IGN] variation temporelle Résumé : (Auteur) Grâce à l’altimétrie radar, il est possible de mesurer la hauteur de la surface des mers, avec des applications fortes pour la connaissance de la Terre. Numéro de notice : A2021-893 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99250 [article]

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Advances in forest management under global change / Ling Zhang (2020)  

Public Titre : Advances in forest management under global change Type de document : Monographie Auteurs : Ling Zhang, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 180 p. ISBN/ISSN/EAN : 978-1-83968-307-7 Note générale : Bibliographie Print ISBN: 978-1-83968-306-0 eBook (PDF) ISBN: 978-1-83968-308-4 Langues : Anglais (eng) Descripteur : [Termes IGN] aire protégée [Termes IGN] analyse fractale [Termes IGN] apprentissage profond [Termes IGN] azote [Termes IGN] conservation des ressources forestières [Termes IGN] déboisement [Termes IGN] gestion forestière [Termes IGN] image SWOT [Termes IGN] incendie de forêt [Termes IGN] maladie phytosanitaire [Termes IGN] risque naturel [Vedettes matières IGN] Végétation et changement climatique Index. décimale : 48.30 Végétation et changement climatique Résumé : (Editeur) Advances in forest management will enhance the sustainable development of human society, and should be focused on. Under the context of global change, soil nutrients, especially nitrogen, should be carefully managed and monitored in plantations experiencing intensive nitrogen input, and forests with exotic plant invasion disturbance, considering its substantial contribution to global nitrous oxide. One negative effect of global change could be loss of biodiversity, which could be maintained by forest management. In addition, advanced technologies should also be developed to prevent fire in forests considering its increased frequency. Importantly, policies and technologies should also be developed for advanced forest management, such as deep learning in plant disease prevention, and quantitative strategic planning matrix in management of forest conservation. Note de contenu : 1. Nitrogen Cycling and Soil Amelioration in Camellia oleifera Plantations / Bangliang Deng and Ling Zhang 2. Research Progress of Forest Land Nutrient Management in China / Zhi Li, Yanmei Wang, Xiaodong Geng, Qifei Cai and Xiaoyan Xue 3. Plant Invasion and N2O Emission in Forest Ecosystems / Nasir Shad, Ling Zhang, Ghulam Mujtaba Shah, Fang Haifu, Muhammad Ilyas, Abbas Ali and Salman Ali Khan 4. Increasing Biodiversity of Russian Taiga Forests by Creating Mixed Forest Cultures of Scots Pine and Siberian Larch / Elena Runova 5. Sustainable Management of National Parks and Protected Areas for Conserving Biodiversity in India / Abhishek Kumar, Rajni Yadav, Meenu Patil, Pardeep Kumar, Ling Zhang, Amandeep Kaur, Sheenu Sharma, Sabir Hussain, Diksha Tokas and Anand Narain Singh 6. Gypsum/Desulfurization Fly Ash/Activated Shale Char/Claystone of Şırnak with Popped Biochar Composite Granules as Fire Inhibitor for Fire Hazard Risk in Forest Management / Yıldırım Ismail Tosun 7. Use of Fractal Analysis in the Evaluation of Deforested Areas in Romania / Daniel Constantin Diaconu, Răzvan Mihail Papuc, Daniel Peptenatu, Ion Andronache, Marian Marin, Răzvan Cătălin Dobrea, Cristian Constantin Drăghici, Radu-Daniel Pintilii and Alexandra Grecu 8. Automatic Recognition of Tea Diseases Based on Deep Learning / Jing Chen and Junying Jia 9. Forest Conservation Management Using SWOT Analysis and QSPM Matrix (Case Study in the Baluran National Park, East Java, Indonesia) / Adil Siswanto Numéro de notice : 26540 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE/INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87525 En ligne : http://doi.org/10.5772/intechopen.87525 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97758