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Characterization of mass variations in Antarctica in response to climatic fluctuations from space-based gravimetry and radar altimetry data / Athul Kaitheri (2021)
Titre : Characterization of mass variations in Antarctica in response to climatic fluctuations from space-based gravimetry and radar altimetry data Titre original : Caractérisation des variations de masse en Antarctique en réponse aux fluctuations climatiques à partir des données de gravimétrie spatiale et d’altimétrie radar Type de document : Thèse/HDR Auteurs : Athul Kaitheri, Auteur ; Anthony Mémin, Directeur de thèse ; Frédérique Rémy, Directeur de thèse Editeur : Nice : Université Côte d'Azur Année de publication : 2021 Importance : 138 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse présentée en vue de l’obtention du grade de docteur de l'Université de Côte d'Azur, Spécialité Sciences de la Planète et de l'UniversLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] altimétrie satellitaire par radar
[Termes IGN] analyse comparative
[Termes IGN] Antarctique
[Termes IGN] calotte glaciaire
[Termes IGN] changement climatique
[Termes IGN] données altimétriques
[Termes IGN] données GRACE
[Termes IGN] image Envisat
[Termes IGN] levé gravimétrique
[Termes IGN] masse
[Termes IGN] oscillation
[Termes IGN] régressionIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Quantifying the mass balance of the Antarctic Ice Sheet (AIS), and the resulting sea level rise, requires an understanding of inter-annual variability and associated causal mechanisms. This has become more complex and challenging in the backdrop of global climate change. Very few studies have been exploring the influence of climate anomalies on the AIS and only a vague estimate of its impact is available. Usually changes to the ice sheet are quantified using observations from space-borne altimetry and gravimetry missions. In this study, we use data from Envisat (2002 to 2010) and Gravity Recovery and Climate Experiment (GRACE) (2002 to 2016) missions to estimate monthly elevation changes and mass changes, respectively. Similar estimates of the changes are made using weather variables (surface mass balance (SMB) and temperature) from a regional climate model (RACMO2.3p2) as inputs to a firn compaction (FC) model. Using the firn compaction model we were able to model the transformation of snow into glacial ice and hence estimate changes in the elevation of the ice sheet using climate parameters. Elevation changes estimated from different techniques are in good agreement with each other across the AIS especially in West Antarctica, Antarctic Peninsula, and along the coasts of East Antarctica. Inter-annual height change patterns are then extracted using for the first time an empirical mode decomposition followed by a reconstruction of modes. These signal on applying least square method revealed a sub-4-year periodic signal in the all the three distinct height change patterns. This was indicative of the influence of the El Niño Southern Oscillation (ENSO), a climate anomaly that alters, among other parameters, moisture transport, sea surface temperature, precipitation, in and around the AIS at similar frequency by alternating between warm and cold conditions. But there existed altering periodic behavior among inter annual height change patterns in the Antarctic Pacific (AP) sector which was found possibly by the influence of multiple climate drivers, like the Amundsen Sea Low (ASL) and the Southern Annular Mode (SAM). A combined analysis of the three distinct estimates using a PCA (principal component analysis) along the coast revealed similar findings. Height change anomaly also appears to traverse eastwards from Coats Land to Pine Island Glacier (PIG) regions passing through Dronning Maud Land (DML) and Wilkes Land (WL) in 6 to 8 years. This is indicative of climate anomaly traversal due to the Antarctic Circumpolar Wave (ACW) which propagates anomalies through the Southern Ocean in 8 to 10 years. Altogether, inter-annual variability in the SMB of the AIS is found to be modulated by multiple competing climate anomalies. Note de contenu : 1. Introduction
1.1 Climate change scenario
1.2 Antarctica
1.3 Thesis overview
2. Height changes from satellite observations
2.1 Observations
2.2 Satellite gravimetry
2.3 Satellite altimetry
3. Height changes from modelling
3.1 Climate Model
3.2 Height changes from RACMO2.3p2 outputs
3.3 Firn densification model
4. Inter-annual variability
4.1 Comparison between height changes
4.2 Extraction of inter annual signals
4.3 Characterizing inter-annual signals
4.4 Principal component analysis
5. Influence of climate anomalies
5.1 El Ni˜no Southern Oscillation
5.2 Southern Annular Mode
5.3 Amundsen Sea Low
5.4 Antarctic Circumpolar Wave
6. General conclusions
6.1 Conclusions
6.2 Future perspectivesNuméro de notice : 26825 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Thèse française Note de thèse : Thèse de doctorat : Sciences de la Planète et de l'Univers : Côte d'Azur : 2021 Organisme de stage : Géoazur nature-HAL : Thèse DOI : sans Date de publication en ligne : 19/04/2022 En ligne : https://tel.hal.science/tel-03644306/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100655 Development and analysis of land-use/land-cover spatio-temporal metrics in urban environments: Exploring urban growth patterns and linkages to socio-economic factors / Marta Sapena Moll (2021)
Titre : Development and analysis of land-use/land-cover spatio-temporal metrics in urban environments: Exploring urban growth patterns and linkages to socio-economic factors Type de document : Thèse/HDR Auteurs : Marta Sapena Moll, Auteur ; Luis Angel Ruiz Fernandez, Directeur de thèse Editeur : Valencia : Universitat politécnica de Valencia Année de publication : 2021 Importance : 268 p. Format : 21 x 30 cm Note générale : bibliographie
PhD in Geomatics Engineering, Universidad politécnica de ValenciaLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse discriminante
[Termes IGN] analyse spatio-temporelle
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] croissance urbaine
[Termes IGN] données socio-économiques
[Termes IGN] implémentation (informatique)
[Termes IGN] milieu urbain
[Termes IGN] modélisation spatiale
[Termes IGN] occupation du sol
[Termes IGN] population urbaine
[Termes IGN] régression linéaire
[Termes IGN] Rhénanie du Nord-Wesphalie (Allemagne)
[Termes IGN] utilisation du sol
[Termes IGN] ville durableRésumé : (auteur) This thesis addresses the development and analysis of new tools and methods for monitoring and characterizing urban growth using geo-data and land-use/land-cover (LULC) databases, as well as exploring their relationships with socio-economic factors, providing new evidences regarding the use of LULC data for urban characterization at different levels by means of spatial and statistical methods. First, the most common spatio-temporal metrics were compiled and implemented within a software tool, IndiFrag. Then, we present a methodology based on spatio-temporal metrics and propose a new index that quantifies the inequality of growth between population and built-up areas to analyze and compare urban growth patterns at different levels. This allowed for a differentiation of growing patterns, besides, the analysis at various levels contributed to a better understanding of such patterns. Second, we quantified the two-way relationship between the urban structure in cities and their socio-economic status by means of spatial metrics issued from a local climate zone map for 31 cities in North Rhine-Westphalia, Germany. Based on these data, we quantified their relationship with socio-economic indicators by means of multiple linear regression models, explaining a significant part of their variability. The proposed method is transferable to other datasets, levels, and regions. Third, we assessed the use of spatio-temporal metrics derived from LULC maps to identify urban growth spatial patterns. We applied LULC change models to simulate different long-term scenarios of urban growth following various spatial patterns on diverse baseline urban forms. Then, we computed spatio-temporal metrics for the simulated scenarios, selected the most explanatory by applying a discriminant analysis and classified the growth patterns using clustering methods. Finally, we identified empirical relationships between socio-economic indicators and their change over time with the spatial structure of the built and natural elements in up to 600 urban areas from 32 countries. We employed random forest regression models and the spatio-temporal metrics were able to explain substantially the variability of socio-economic variables. This confirms that spatial patterns and their change are linked to socio-economic indicators. This work contributes to a better understanding of urban growth patterns and improves knowledge about the relationships between urban spatial structure and socio-economic factors, providing new methods for monitoring and assessing urban sustainability by means of LULC databases, which could be used by researchers, urban planners and decision-makers to ensure the sustainable future of urban environments. Note de contenu : 1- Introduction
2- Hypotheses and objectives
3- Spatio-temporal analysis of LULC and population in urban areas
4- Relationships between spatial patterns of urban structure and quality of life
5- Spatio-temporal metrics for urban growth spatial pattern categorization
6- Linking spatio-temporal metrics of built-up areas to socio-economic indicators on a semi-global scale
7- ConclusionsNuméro de notice : 28308 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Thèse étrangère Note de thèse : PhD Thesis : Geomatics Engineering : Valencia, Spain : 2021 Organisme de stage : German Aerospace Center DOI : 10.4995/Thesis/10251/158626 En ligne : https://doi.org/10.4995/Thesis/10251/158626 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98112 Développement d’une méthode innovante pour l’ajustement des paramètres internes du système de gravimétrie sous-marine GraviMob / Ossama Kharbou (2021)
Titre : Développement d’une méthode innovante pour l’ajustement des paramètres internes du système de gravimétrie sous-marine GraviMob Type de document : Mémoire Auteurs : Ossama Kharbou, Auteur Editeur : Le Mans : Ecole Supérieure des Géomètres et Topographes ESGT Année de publication : 2021 Importance : 76 p. Format : 21 x 30 cm Note générale : bibliographie
Mémoire présenté en vue d'obtenir le diplôme d'ingénieur ESGT, spécialité Géomètre et TopographeLangues : Français (fre) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] ajustement de paramètres
[Termes IGN] analyse de données
[Termes IGN] gravimétrie en mer
[Termes IGN] gravimétrie mobile
[Termes IGN] instrument de géodésie
[Termes IGN] modèle mathématique
[Termes IGN] régression linéaireIndex. décimale : ESGT Mémoires d'ingénieurs de l'ESGT Résumé : (auteur) Le système de gravimétrie mobile GraviMob, est un système simple et innovant qui permet de mesurer les 3 composantes de l’accélération de la pesanteur, en fond de mer, en utilisant six accéléromètres capacitifs divisés en deux triades. Le traitement des mesures nécessite une connaissance de onze paramètres internes dont la détermination est essentielle pour estimer les trois composantes ou la norme de g. Les valeurs de ces paramètres varient en fonction de la température. L’ajustement de ces paramètres internes dans une enceinte climatique, a permis de tracer leur évolution, et ainsi proposer, et valider statistiquement, des modèles polynomiaux de degré 4, permettant de déterminer la valeur de chaque paramètre en fonction de la température sur une étendue de mesure de 4°C à 18°C. Note de contenu : Introduction
1- Instrumentation
2- Traitement
ConclusionNuméro de notice : 15286 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Mémoire ingénieur ESGT Organisme de stage : Laboratoire Géomatique et Foncier En ligne : https://dumas.ccsd.cnrs.fr/MEMOIRES-CNAM/dumas-03563094 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101470 Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs / Yang Bai in Computers & geosciences, vol 146 (January 2021)
[article]
Titre : Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs Type de document : Article/Communication Auteurs : Yang Bai, Auteur ; Maojin Tan, Auteur Année de publication : 2021 Article en page(s) : n° 104626 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] classification floue
[Termes IGN] classification par réseau neuronal
[Termes IGN] puits de carbone
[Termes IGN] régression linéaire
[Termes IGN] schisteRésumé : (auteur) The total organic carbon (TOC) content is of great significance to reflect the hydrocarbon-generation potential in shale reservoirs. The well logs were always used to predict the TOC content, but some linear regression methods do not match well with complex data. The neural network method can improve prediction accuracy, but it always generates unstable prediction models. A static committee machine can reduce errors and uncertainties by combining multiple learners, but the weight of integrating learners is difficult to determine. Therefore, a dynamic committee machine with fuzzy-c-means clustering (DCMF) was proposed to predict the TOC content. Experts in the DCMF include Elman neural network, extreme learning machine, and generalized regression neural network. The fuzzy-c-means clustering algorithm was used as the gate network to perform subtasks decomposition and weights calculation based on input data. The subtasks were used to train more adaptive TOC content prediction models, and the weights were transferred to the combiner to integrate all experts’ outputs into final results. The DCMF was applied in two wells located in the Jiumenchong formation in the Qiannan depression, China. The TOC prediction results using the DCMF method are more accurate than the linear regression method, three individual intelligent algorithms, and the static committee machine. The DCMF also provides a new method for weight calculation by mining potential information of input data. Numéro de notice : A2021-019 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2020.104626 Date de publication en ligne : 17/10/2020 En ligne : https://doi.org/10.1016/j.cageo.2020.104626 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96512
in Computers & geosciences > vol 146 (January 2021) . - n° 104626[article]
Titre : Dynamic scene understanding using deep neural networks Type de document : Thèse/HDR Auteurs : Ye Lyu, Auteur ; M. George Vosselman, Directeur de thèse ; Michael Ying Yang, Directeur de thèse Editeur : Enschede [Pays-Bas] : International Institute for Geo-Information Science and Earth Observation ITC Année de publication : 2021 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] chaîne de traitement
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] compréhension de l'image
[Termes IGN] détection d'objet
[Termes IGN] image captée par drone
[Termes IGN] image vidéo
[Termes IGN] poursuite de cible
[Termes IGN] régression
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Scene understanding is an important and fundamental research field in computer vision, which is quite useful for many applications in photogrammetry and remote sensing. It focuses on locating and classifying objects in images, understanding the relationships between them. The higher goal is to interpret what event happens in the scene, when it happens and why it happens, and what should we do based on the information. Dynamic scene understanding is to use information from different time to interpret scenes and answer the above related questions. For modern scene understanding technology, deep learning has shown great potential for such task. "Deep" in deep learning refers to the use of multiple layers in the neural networks. Deep neural networks are powerful as they are highly non-linear function that possess the ability to map from one domain to another quite different domain after proper training. It is the best solution for many fundamental research tasks regarding scene understanding. This ph.D. research also takes advantage of deep learning for dynamic scene understanding. Temporal information plays an important role for dynamic scene understanding. Compared with static scene understanding from images, information distilled from the time dimension provides values in many different ways. Images across consecutive frames have very high correlation, i.e., objects observed in one frame have very high chance to be observed and identified in nearby frames as well. Such redundancy in observation could potentially reduce the uncertainty for object recognition with deep learning based methods, resulting in more consistent inference. High correlation across frames could also improve the chance for recognizing objects correctly. If the camera or the object moves, the object could be observed in multiple different views with different poses and appearance. The information captured for object recognition would be more diverse and complementary, which could be aggregated to jointly inference the categories and the properties of objects. This ph.D. research involves several tasks related to the dynamic scene understanding in computer vision, including semantic segmentation for aerial platform images (chapter 2, 3), video object segmentation and video object detection for common objects in natural scenes (chapter 4, 5), and multi-object tracking and segmentation for cars and pedestrians in driving scenes (chapter 6). Chapter2 investigates how to establish the semantic segmentation benchmark for the UAV images, which includes data collection, data labeling, dataset construction, and performance evaluation with baseline deep neural networks and the proposed multi-scale dilation net. Conditional random field with feature space optimization is used to achieve consistent semantic segmentation prediction in videos. Chapter3 investigates how to better extract the scene context information for etter object recognition performance by proposing the novel bidirectional multiscale attention networks. It achieves better performance by inferring features and attention weights for feature fusing from both higher level and lower level branches. Chapter4 investigates how to simultaneously segment multiple objects across multiple frames by combining memory modules with instance segmentation networks. Our method learns to propagate the target object labels without auxiliary data, such as optical flow, which simplifies the model. Chapter5 investigates how to improve the performance of well-trained object detectors with a light weighted and efficient plug&play tracker for object detection in video. This chapter also investigates how the proposed model performs when lacking video training data. Chapter6 investigates how to improve the performance of detection, segmentation, and tracking by jointly considering top-down and bottom-up inference. The whole pipeline follows the multi-task design, i.e., a single feature extraction backbone with multiple heads for different sub-tasks. Overall, this manuscript has delved into several different computer vision tasks, which share fundamental research problems, including detection, segmentation, and tracking. Based on the research experiments and knowledge from literature review, several reflections regarding dynamic scene understanding have been discussed: The range of object context influence the quality for object recognition; The quality of video data affect the method choice for specific computer vision task; Detection and tracking are complementary for each other. For future work, unified dynamic scene understanding task could be a trend, and transformer plus self-supervised learning is one promising research direction. Real-time processing for dynamic scene understanding requires further researches in order to put the methods into usage for real-world applications. Numéro de notice : 12984 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Geo-Information Science and Earth Observation : Enschede, university of Twente : 2021 DOI : 10.3990/1.9789036552233 Date de publication en ligne : 08/09/2021 En ligne : https://library.itc.utwente.nl/papers_2021/phd/lyu.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100962 Evaluation of a neural network with uncertainty for detection of ice and water in SAR imagery / Nazanin Asadi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkEvaluation du stock de carbone aérien dans la végétation à partir de multiples observations satellites micro-ondes / Martin Cubaud (2021)PermalinkPermalinkModel based signal processing techniques for nonconventional optical imaging systems / Daniele Picone (2021)PermalinkRegNet: a neural network model for predicting regional desirability with VGI data / Wenzhong Shi in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)PermalinkRetrieving surface soil water content using a soil texture adjusted vegetation index and unmanned aerial system images / Haibin Gu in Remote sensing, vol 13 n° 1 (January-1 2021)PermalinkPermalinkTime-series analysis of massive satellite images : Application to earth observation / Alexandre Constantin (2021)PermalinkVolumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements / Mikko Kukkonen in Silva fennica, vol 55 n° 1 (January 2021)PermalinkApplication of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy / Vasil Yordanov in Applied geomatics, vol 12 n° 4 (December 2020)Permalink