Effects of topographic and meteorological parameters on the surface area loss of ice aprons in the Mont Blanc massif (European Alps)

Ice aprons (IAs) are part of the critical components of the Alpine cryosphere. As a result of the changing climate over the past few decades, deglaciation has resulted in a surface decrease of IAs, which has not yet been documented out of a few specific examples. In this study, we quantify the effects of climate change on IAs since the mid-20th Century in the Mont-Blanc massif (western European Alps). We then evaluate the role of climate forcing parameters and the local topography in the behaviour of IAs. For this, we precisely mapped the surface areas of 200 IAs using high-resolution aerial and satellite photographs from 1952, 2001, 2012 and 2019. From the latter inventory, the surface area of the present individual IAs ranges from 0.001 to 0.04 km2. IAs have lost their surface area over the past 70 years, with an alarming increase since the early 2000s. The total area, from 7.93 km2 in 1952, was reduced to 5.91 km2 in 2001 (-25.5 %) before collapsing to 4.21 km2 in 2019 (-47 % since 1952). We performed a regression analysis using temperature and precipitation proxies to understand better the effects of climate forcing parameters on IA surface area variations. We found a strong correlation between both proxies and the relative area loss of IAs, indicating the significant influence of the changing climate on the evolution of IAs. We also evaluated the role of the local topographic factors in the IAs area loss. At a regional scale, factors like direct solar radiation and elevation have an important influence on the behaviour of IAs, while others like curvature, slope, and size of the IAs seem to be rather important on a local scale.</p

Elevation Changes Inferred From TanDEM-X Data Over the Mont-Blanc Area: Impact of the X-Band Interferometric Bias

International audienceThe TanDEM-X mission allows generation of Digital Elevation Models (DEM) with high potential for glacier monitoring, but the radar penetration into snow and ice remains a main source of uncertainty. In this study, we generate 5 new DEMs of the Mont-Blanc area from [...

Fusion of remotely sensed displacement measurements: current status and challenges

International audienc

A pattern-based method for handling confidence measures while mining satellite displacement field time series. Application to Greenland ice sheet and Alpine glaciers

International audienceFor more than 40 years, Earth observation satellites have been regularly providing images of glaciers that can be used to derive surface displacement fields and study their dynamics. In the context of global warming, the analysis of Displacement Field Time Series (DFTS) can provide useful information. Efficient data mining techniques are thus required to extract meaningful displacement evolutions from such large and complex datasets. In this paper, a pattern-based data mining approach which handles confidence measures is proposed to analyze DFTS. In order to focus on the most reliable measurements, a displacement evolution reliability measure is defined. It is aimed at assessing the quality of each evolution and pruning the search space. Experiments on two different DFTS (annual displacement fields derived from optical data over Greenland ice sheet and 11-day displacement fields derived from SAR data over Alpine glaciers) show the potential of the proposed approach

Fusion de mesures de déplacement issues d'imagerie SAR (application aux modélisations séismo-volcaniques)

Suite aux lancements successifs de satellites pour l'observation de la Terre dotés de capteur SAR (Synthetic Aperture Radar), la masse de données SAR disponible est considérable. Dans ce contexte, la fusion des mesures de déplacement issues de l'imagerie SAR est prometteuse à la fois dans la communauté de la télédétection et dans le domaine géophysique. Dans cette optique, cette thèse propose d'élargir les approches conventionnelles en combinant les techniques de traitement des images SAR, les méthodes de fusion d'informations et la connaissance géophysique. Dans un premier temps, cette thèse a pour objectif d'étudier plusieurs stratégies de fusion, l'inversion jointe, la pré-fusion et la post-fusion, afin de réduire l'incertitude associée d'une part à l'estimation du déplacement en 3 dimensions (3D) à la surface de la Terre, d'autre part à la modélisation physique qui décrit la source en profondeur du déplacement observé en surface. Nous évaluons les avantages et les inconvénients de chacune des stratégies en ce qui concerne la réduction de l'incertitude et la robustesse vis à vis du bruit. Dans un second temps, nous visons à prendre en compte les incertitudes épistémiques, en plus des incertitudes aléatoires, présentes dans les mesures et proposons les approches classiques et floues basées sur la théorie des probabilités et la théorie des possibilités pour modéliser ces incertitudes. Nous analysons et mettons en évidence l'efficacité de chaque approche dans le cadre de chaque stratégie de fusion. La première application consiste à estimer les champs du déplacement 3D à la surface de la Terre dus au séisme du Cachemire en octobre 2005 et à l'éruption du Piton de la Fournaise en janvier 2004 sur l'île de la Réunion. La deuxième application porte sur la modélisation de la rupture de la faille en profondeur liée au séisme du Cachemire. Les principales avancées sont évaluées d'un point de vue méthodologique en traitement de l'information et d'un point de vue géophysique. Au niveau méthodologique, afin de lever les principales difficultées rencontrées pour l'application de l'interférométrie différentielle à la mesure du déplacement induit par le séisme du Cachemire, une stratégie de multi-échelles basée sur l'information a priori en utilisant les fréquences locales de phase interférométrique est adoptée avec succès. En ce qui concerne la gestion de l'incertitude, les incertitudes aléatoires et épistémiques sont analysées et identifiées dans les mesures du déplacement. La théorie des probabilités et la théorie des possibilités sont utilisées afin de modéliser et de gérer les propagations des incertitudes au cours de la fusion. En outre, les comparaisons entre les distributions de possibilité enrichissent les comparaisons faites simplement entre les valeurs et indiquent la pertinence des distributions de possibilité dans le contexte étudié. Par ailleurs, la pré-fusion et la post-fusion, 2 stratégies de fusion différentes de la stratégie d'inversion jointe couramment utilisée, sont proposées afin de réduire autant que possible les incertitudes hétérogènes présentes en pratique dans les mesures et pour contourner les principales limitations de la stratégie d'inversion jointe. Les bons cadres d'application de chaque approche de la gestion de l'incertitude sont mis en évidence dans le contexte de ces stratégies de fusion. Au niveau géophysique, l'application de l'interférométrie différentielle à l'étude du séisme du Cachemire est réalisée pour la première fois et compléte les études antérieures basées sur les mesures issues de la corrélation des images SAR et optiques, les mesures télésismiques et les mesures de terrain. L'interférométrie différentielle apporte une information précise sur le déplacement en champ lointain par rapport à la position de la faille. Ceci permet d'une part de réduire les incertitudes associées aux mesures de déplacement en surface et aux paramètres du modèle, et d'autre part de détecter les déplacements post-sismiques qui existent potentiellement dans les mesures cosismiques qui couvrent la période de mouvement post-sismique. Par ailleurs, la prise en compte de l'incertitude épistémique et la proposition de l'approche floue pour gérer ce type d'incertitude, fournissent une vision différente de l'incertitude de mesure connue par la plupart des géophysiciens et complétent la connaissance de l'incertitude aléatoire et l'application de la théorie des probabilités dans ce domaine. En particulier, la gestion de l'incertitude par la théorie des possibilités permet de contourner le problème de sous-estimation d'incertitude par la théorie des probabilités. Enfin, la comparaison du déplacement mesuré par les images SAR avec le déplacement mesuré par les images optiques et le déplacement issu des mesures sur le terrain révèle toute la difficulté d'interpréter différentes sources de données plus ou moins compatibles entre elles. Les outils développés dans le cadre de cette thèse sont intégrés dans le package MDIFF (Methods of Displacement Information Fuzzy Fusion) dans l'ensemble des "EFIDIR Tools" distribués sous licence GPL.Following the successive launches of satellites for Earth observation with SAR (Synthetic Aperture Radar) sensor, the volume of available radar data is increasing considerably. In this context, fusion of displacement measurements from SAR imagery is promising both in the community of remote sensing and in geophysics. With this in mind, this Ph.D thesis proposes to extend conventional approaches by combining SAR image processing techniques, information fusion methods and the knowledge on geophysics. First, this Ph.D thesis aims to explore several fusion strategies, joint inversion, pre-fusion and post-fusion, to reduce the uncertainty associated on the one hand to the estimation of the 3-dimensional (3D) displacement at the Earth's surface, on the other hand to physical modeling that describes the source in depth of the displacement observed at the Earth's surface. We evaluate advantages and disadvantages of each fusion strategy in terms of reducing uncertainty and of robustness against noise. Second, we aim to take account of epistemic uncertainty, in addition to the random uncertainty present in the measurements and propose the conventional and fuzzy approaches based on probability theory and possibility theory respectively to model these uncertainties. We analyze and highlight the efficiency of each approach in context of each fusion strategy. The first application consists of estimating the 3D displacement fields at the Earth's surface due to the Kashmir earthquake in October 2005 and the eruption of Piton de la Fournaise in January 2004 on Reunion Island. The second application involves the modeling of the fault rupture in depth related to the Kashmir earthquake. The main achievements and contributions are evaluated from a methodological point of view in information processing and from a geophysical point of view. In the methodological view, in order to address the major difficulties encountered in the application of differential interferometry for measuring the displacement induced by the Kashmir earthquake, a multi-scale strategy based on prior information issued from a deformation model using local frequencies of interferometric phase is adopted successfully. Regarding the measurement uncertainty management, both random and epistemic uncertainties are analyzed and identified in the displacement measurements. The conventional approach and a fuzzy approach based on respectively probability theory and possibility theory are proposed to model uncertainties and manage the uncertainty propagation in the fusion system. In addition, comparisons between possibility distributions enrich the comparisons made simply between displacement values and indicate the relevance of possibility distributions in the considered context. Furthermore, pre-fusion and post-fusion, two fusion strategies different from the commonly used fusion strategy of joint inversion, are proposed to reduce heterogeous uncertainties present in practice in the measurements and to get around the main limitations of joint inversion. Appropriated conditions of the application of each uncertainty management approach are highlighted in the context of these fusion strategies. In the geophysical view, the application of differential interferometry to the Kashmir earthquake is performed successfully for the first time and it completes previous studies based on measurements from the correlation of SAR and optical images, teleseismic measurements and in situ field measurements. Differential interferometry provides accurate displacement information in the far field relative to the fault position. This allows on the one hand reducing uncertainties associated with surface displacement measurements and with model parameters, on the other hand detecting post-seismic movements that exist potentially in the used coseismic measurements covering the post-seismic period. Moreover, taking into consideration of epistemic uncertainty and the proposition of a fuzzy approach for its management, provide a different view of the measurement uncertainty known by most geophysicists and complete the knowledge of the random uncertainty and the application of probability theory in this domain. In particular, the management of uncertainty by possibility theory allows overcoming the problem of under-estimation of uncertainty by probability theory. Finally, comparisons of the displacement measured by SAR images with the displacement measured by optical images and the displacement from in situ field measurements reveal the difficulty to interpret different data sources more or less compatible among them. The tools developed during this Ph.D thesis are included in the MDIFF (Methods of Displacement Information Fuzzy Fusion) package in "EFIDIR Tools" distributed under the GPL lisence.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

A Geometrical Wavelet Framework for the Time-Series Analysis of Full-Polarimetric Features

International audiencePolarimetric SAR (PolSAR) image time series have been employed for the analysis of temporal patterns of natural features in terms of the extended polarimetric scattering properties. However, the time series provide a rich scattering information that can be used for tracking and analyzing the evolution of targets, individuating smooth and/or abrupt changes. In this work we propose a wavelet framework that exploits the information from polarimetric features and analyze them to both mitigate the speckle effect on the multi-temporal information and improve the targets homogeneity using the multi-temporal information. The framework combines the powerful description from the main polarimetric decomposition features and the temporal analysis using geometrical wavelet transform. The analysis is applied on a multi-temporal polarimetric dataset of Radarsat-2 images acquired over the Argentière glacier site

Deep Learning of Radiometrical and Geometrical Sar Distorsions for Image Modality translations

International audienceMultimodal approaches for Earth Observations suffer from both the lack of interpretability of SAR images and the high sensitivity to meteorological conditions of optical images. Translation methods were implemented to solve them for specific tasks and areas. But these implementations lack of generalizability as they do not include samples with challenging characteristics. Firstly, this paper sums up the main problems that a general SAR to optical image translator should overcome. Then, a SAR Distorted Image to optical translator Network (SARDINet) alternating knowledgeable channel-wise spatial convolutions and cross-channel convolutions is implemented. It aims at solving a problem of major concern in remote sensing: translating layover disturbed SAR images into disturbance-free optical ones. SARDINet is trained through a classical and an adversarial framework and compared to cGAN and cycleGAN from the literature. Experimental results prove that adversarial approaches are more qualitative but worsen quantitative results

Détection de failles géologiques par traitement morphologique multi-spectral

- Ce travail présente une méthode de détection automatique de failles dans des images SPOT multi-spectrales, dans le rift Est-Africain du Kenya. Le principe de détection consiste dans la définition et l'utilisation d'opérateurs morphologiques adaptés à la détection automatique de failles. Ces opérateurs sont appliqués séparément sur chaque composante de l'image multispectrale et les résultats sont ensuite agrégés pour prendre la décision finale

Wet Snow Detection From Satellite SAR Images by Machine Learning With Physical Snowpack Model Labeling

The detection of wet snow by satellite imaging is currently done in an unsupervised way and lacks quantitative evaluation due to the difficulty of collecting ground truths in extreme environments. In this article, we propose to take into account information associated with a physical model to label satellite data for the purpose of supervised learning of snow properties using synthetic aperture radar (SAR) imagery. This dataset is constructed from Sentinel-1 SAR images, augmented with topographic information obtained from a digital elevation model. The labeling of this data is done at the scale of the Northern Alps using the CROCUS physical snow model. Then, we trained, over 13 combinations of labeled dataset, a wide range of machine learning models to quantitatively identify the most relevant learners for the wet snow detection task. The results demonstrate consistency among the different algorithms, with significant improvement observed when incorporating polarimetric combinations and topographic orientation data in the input of the model. The best algorithmic solution trained on this dataset is evaluated by comparing the obtained wet snow map over a validation area in the French massif of the Grandes Rousses with the existing Copernicus products, fractional snow cover, and SAR wet snow. We also compare the temporal results obtained at one meteorological station located in the test area. The results show a better representation of wet snow during the melting period using the supervised learning approach, as well as a reduction in areas classified as wet during the winter season