PhD Student, Polytech Annecy-Chambéry.
Courriel : firstname.lastname@example.org
Office : A109
Adress : LISTIC – Polytech Annecy-Chambéry, BP 80439 – Annecy le Vieux, 74944 ANNECY Cedex, France
Topic : Estimation, fusion and analysis of multi-sensor / multi-temporal glaciers velocity observations derived from remote sensing images
Ice velocity maps are necessary to evaluate glacier contributions to sea level rise and to infer subglacial processes such as ocean forcing or subglacial hydrology. Since the 1970’s, the temporal and spatial sampling of SAR and optical images have been considerably improved. It becomes possible to measure ice velocities with a spatial and temporal sampling up to 50 m and 2 days respectively. However, these ice velocity maps still contain noise, artefacts, and gaps when outliers have been filtered out. Moreover, they are difficult to analyze since they are measured by different methods using SAR and/or optical images spanning different temporal baselines and from different sensors. These are major limitations to the understanding of glacier dynamics, especially intra-annual dynamics. Therefore, methodological developments are still strongly needed in order to extract velocity information easily interpretable with a low uncertainty, by taking advantage of all the available images and ice velocity maps. In this context, this PhD focuses on the estimation and fusion of multi-temporal and multi-sensor velocities.
Since all the operational methods used for ice velocity estimation rely on image matching, we explore the application of two dense and fast estimators coming from computer vision: the optical flow algorithm GeFolki and the deep learning algorithm PWCNet multi-modal. GeFolki has a similar precision than the state-of-the-art image matching algorithms while being faster [Charrier et al., 2020].
Then, we mainly focus on the fusion of the available ice velocity maps. In the state-of-art, authors used to select small temporal baseline velocities to study intra-annual glacier dynamics or to carry on strong assumptions on the displacement behavior (e.g., assuming a sinusoidal behavior). To fuse multi-temporal ice velocities without strong assumption, we propose a new formulation of the temporal closure of the displacement measurement network in order to obtain velocity time series with an optimal temporal sampling [Charrier et al. 2022a]. This temporal sampling can be chosen using two criteria to reach a compromise between uncertainty and temporal sampling. The inversion is done using an iterative reweighted least square to take into account the data uncertainties which are not necessarily known. Then, we extend this method to velocities measured by different sensors and containing gaps. One challenge is that the velocities are observed at different dates for each pixel. The first proposed approach establishes linear equations between combinations of displacement observations and fractioned displacement [Charrier et al. 2022d]. The second one combines the temporal closure and a temporal interpolation. The second approach is more adapted to extract velocity time series with temporal sampling longer than one month [Charrier et al. 2022c].
Study sites with different glacier dynamics are considered: the Totten glacier in Antarctica, the Kyagar glacier in the Karakoram and the Fox glacier in New Zealand. The obtained velocity time series has a reduced uncertainty, a better spatial and temporal coverage, no more redundancy and a regular temporal sampling. The proposed methods could be, in the future, applied over a global scale to study glacier dynamics by taking advantage of all the available data, regardless of the sensor and the temporal baseline.
Supervisors : Emmanuel Trouvé, Elise Colin Koeniguer, Yajing Yan
Beginning of the thesis October 2019
Doctoral school: SIE » Sciences Ingénierie Environnement »
Weissgerber F, Charrier L, Thomas C, Nicolas J-M and Trouvé E (2022e) LabSAR, a one-GCP coregistration tool for SAR–InSAR local analysis in high-mountain regions. Front. Remote Sens. 3:935137. doi: 10.3389/frsen.2022.935137 (pdf accessible here)
Charrier, L., Yan, Y., Koeniguer, E. C., Mouginot J., Millan R. & Trouvé, E. (2022d). Fusion of multi-temporal multi-sensor velocities using temporal closure of fractions of displacements. Geoscience and Remote Sensing Letters (under review)
Charrier, L., Yan, Y., Koeniguer, E. C., Mouginot J., Millan R. & Trouvé, E. (2022c). Fusion of multi-temporal and multi-sensor ice velocity observations ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, Copernicus Publ., 2022, ⟨10.5194/isprs-annals-V-3-2022-311-2022⟩. ⟨hal-03602685⟩ (pdf accessible here)
Charrier, L., Di Martino, T., Colin-Koeniguer, E., Weissgerber, F., Plyer, A. (2022b). Extraction relevance from SAR temporal profiles on cryosphere by a deep autoencoder Iternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH (Copernicus Publications), 2022, XLIII-B3-2022, pp.1309-1316. ⟨10.5194/isprs-archives-XLIII-B3-2022-1309-2022⟩. ⟨hal-03765708⟩ (pdf accessible here)
Charrier, L., Yan, Y., Colin-Koeniguer, E., & Trouvé, E. (2021). Fusion of Glacier Displacement Observations with Different Temporal Baselines. IGARSS 2021, Jul 2021, Bruxelles, Belgium. ⟨10.1109/IGARSS47720.2021.9553831⟩. ⟨hal-03340445⟩ (pdf accesible here)
Charrier, L., Godet, P., Rambour, C., Weissgerber, F., Erdmann, S., & Koeniguer, E. C. (2020). Analysis of dense coregistration methods applied to optical and SAR time-series for ice flow estimations. In 2020 IEEE Radar Conference (RadarConf20) (pp. 1-6). IEEE., doi: 10.1109/RadarConf2043947.2020.9266643. (pdf accesible here)
Charrier, L., Yan, Y., Trouvé, E., Colin Koeniguer, E., Leinss, S., Mouginot, J., and Millan, R.: Fusion of multi-sensor, multi-temporal velocity observations to study intra-annual glacier dynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10033, https://doi.org/10.5194/egusphere-egu22-10033, 2022.
Charrier, L., Yan, Y., Koeniguer, E., Trouvé, E., Millan, R., Mouginot, J., and Derkacheva, A.: Fusion and mining of glacier surface flow velocity time series, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20335, https://doi.org/10.5194/egusphere-egu2020-20335, 2020.
Scheuchl, B., Rignot, E. J., Brancato, V., Mouginot, J., Jeong, S., Milillo, P., & Charrier, L. (2019). Tidally influenced, short term grounding line variations measured with Sentinel-1, RADARSAT-2, and Cosmo Skymed. In AGU Fall Meeting Abstracts (Vol. 2019, pp. C21D-1474).
Charrier, L., Scheuchl, B., Mouginot, J., Jeong, S., Brancato, V., Rignot, E. Grounding zone mapping in Antarctica using radar interferometry MDIS 2019 https://eost.unistra.fr/en/research/ipgs/da/mdis/
2019 – today Teaching assistant at CentraleSupélec – Earth Observation (71.5 hours)
2020 – today Teaching assistant at Polytech Annecy – Informatic and Image analysis (39.3 hours)
In 2021, we wrote a blog paper on remote sensing and glaciers about the project we are supervising with my colleague at CentralSupelec: click here to discover it ! The 2022 edition is available here: it is a nice comparison between ice velocities from ITS_LIVE and RETREAT website, radiometric changes and snow cover.