The Multi-Pairwise Image Correlation (MPIC) processing chain, an end-to-end online service for ice motion monitoring using optical imagery
- 1Ecole et Observatoire des Sciences de la Terre, Université de Strasbourg, Strasbourg, France (michea@unistra.fr)
- 2Application Satellite Survey, A2S - CNRS, Université de Strasbourg, Strasbourg, France
- 3European Space Agency, ESA/ESRIN, Frascati, Italia (floriane.provost@laposte.net)
- 4Institut de Physique du Globe de Strasbourg, Université de Strasbourg, Strasbourg, France
- 5Institut des Sciences de la Terre, Université Grenoble-Alpes, Grenoble, France
- 6Laboratory of Hydraulics,Hydrology and Glaciology (VAW), ETH Zürich, Zürich, Switzerland
- 7Terradue, Roma, Italy
- 8Institut de Physique du Globe de Paris, Université Paris Diderot, Paris, France
Documenting ground deformation is important for a range of areas in Earth and environmental sci-
ences (such as earthquake, volcanoes, landslides and glaciers/ice sheets monitoring). In particular
monitoring the deformation of the cryosphere is key to understand its evolution in a context of
global changes, through the creation of long-term ice velocity datasets, but also possibly detect
failure onsets. The availability of optical satellite constellations with a frequent revisit time at medi-
um to high spatial resolution and an open access policy (e.g. Sentinel 2, Landsat 7/8) provides the
potential to contribute to ice monitoring on a global basis. However, this observational capability
also represents a challenge in term of storage capacity and computing resources which together
with the complexity of the tuning of the different parameters, may prevent users from exploiting the
data.
Here we propose a new version of the Multi-Pairwise Image Correlation for OPTical images
(MPIC-OPT) algorithm. The new version of the algorithm offers a complete chain to process optical
images including data download, image pairs creation and advanced analysis of the displacement
field. It offers the choice to compute the ground displacement associated to image pairs with two
correlation techniques (MicMac, developed by IGN; GéFolki developed by ONERA). Finally, the
Time-Series Inversion for Opical image (TIO) algorithm is integrated to provide displacement time
series.
The processing chain is accessible through the Geohazards Exploitation Platform (GEP) in the
framework of the Thematic Exploitation Platform initiative of the European Space Agency and the
runs are performed using the High Performance Computing facility at the A2S/Mesocentre of Uni-
versity of Strasbourg.
We present the results of the chain in various cryospheric areas: the European Alps glaciers
(France, Italy, Switzerland), the Astrolabe ice shelf (Antartica) and the Gangotri glacier (India). We
define some relevant strategies for an operational use of the service for regional monitoring of
land-ice from satellite images. We compare the results of the MPIC-OPT-ICE service to in-situ
dataset and/or results obtained with similar strategies (e.g. GoLive or ITS-LIVE products, etc.). We
discuss the influence of the pair network and the inversion strategy to retrieve short-term to long-
term kinematic regimes.
How to cite: Michea, D., Provost, F., Malet, J.-P., Doin, M.-P., Lacroix, P., Dehecq, A., Boissier, E., Pointal, E., and Bally, P.: The Multi-Pairwise Image Correlation (MPIC) processing chain, an end-to-end online service for ice motion monitoring using optical imagery, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15876, https://doi.org/10.5194/egusphere-egu21-15876, 2021.
