Correlation dispersion as a measure to better estimate uncertainty in remotely sensed glacier displacements

In recent years a vast amount of glacier surface velocity data from satellite imagery has emerged based on correlation between repeat images. Thereby, much emphasis has been put on the fast processing of large data volumes and products with complete spatial coverage. The metadata of such measurements are often highly simplified when the measurement precision is lumped into a single number for the whole dataset, although the error budget of image matching is in reality neither isotropic nor constant over the whole velocity field. The spread of the correlation peak of individual image offset measurements is dependent on the image structure and the non-uniform flow of the ice and is used here to extract a proxy for measurement uncertainty. A quantification of estimation error or dispersion for each individual velocity measurement can be important for the inversion of, for instance, rheology, ice thickness and/or bedrock friction. Errors in the velocity data can propagate into derived results in a complex and exaggerating way, making the outcomes very sensitive to velocity noise and outliers. Here, we present a computationally fast method to estimate the matching precision of individual displacement measurements from repeat imaging data, focusing on satellite data. The approach is based upon Gaussian fitting directly on the correlation peak and is formulated as a linear least-squares estimation, making its implementation into current pipelines straightforward. The methodology is demonstrated for Sermeq Kujalleq (Jakobshavn Isbræ), Greenland, a glacier with regions of strong shear flow and with clearly oriented crevasses, and Malaspina Glacier, Alaska. Directionality within an image seems to be the dominant factor influencing the correlation dispersion. In our cases these are crevasses and moraine bands, while a relation to differential flow, such as shear, is less pronounced on the correlation spread.</p

Improved surface displacement estimation through stacking cross-correlation spectra from multi-channel imagery

Studying sporadic and complex geophysical surface flows, like earthquakes or sea surface circulation, are challenging cases. If a satellite is able to image an event, it becomes essential to pull out as much information as possible. In this contribution we demonstrate a method to increase the coverage and signal-to-noise ratio for displacement estimation, making such surface flow estimates more complete. We leverage upon the redundant offset information acquired by multi-channel push-broom imagery. The individual cross-correlation spectra (cross power spectral density; Fourier transform of the cross-correlation function) of different spectral bands are averaged in the frequency domain before sub-pixel offset-estimation by phase-plane fitting. The method is demonstrated near Kaikōura, where in 2016 a surface rupture occurred. RapidEye data from two different dates were used to reconstruct the displacement. In addition, the circulation along the coast is estimated from data from a single date where multiple spectral bands were acquired within seconds which made stacking of cross-correlation spectra possible. The demonstrated methodology is applied to data from the already decommissioned RapidEye constellation, but can be adopted to other pushbroom systems, such as the Landsat legacy or Sentinel-2

On the possibility of a long subglacial river under the north Greenland ice sheet

The Tenth Symposium on Polar Science/Ordinary sessions: [OM] Polar Meteorology and Glaciology, Wed. 4 Dec. / 2F Auditorium, National Institute of Polar Researc

From high friction zone to frontal collapse: dynamics of an ongoing tidewater glacier surge, Negribreen, Svalbard

Abstract Negribreen, a tidewater glacier located in central eastern Svalbard, began actively surging after it experienced an initial collapse in summer 2016. The surge resulted in horizontal surface velocities of more than 25 m d −1 , making it one of the fastest-flowing glaciers in the archipelago. The last surge of Negribreen likely occurred in the 1930s, but due to a long quiescent phase, investigations of this glacier have been limited. As Negribreen is part of the Negribreen Glacier System, one of the largest glacier systems in Svalbard, investigating its current surge event provides important information on surge behaviour among tidewater glaciers within the region. Here, we demonstrate the surge development and discuss triggering mechanisms using time series of digital elevation models (1969–2018), surface velocities (1995–2018), crevasse patterns and glacier extents from various data sources. We find that the active surge results from a four-stage process. Stage 1 (quiescent phase) involves a long-term, gradual geometry change due to high subglacial friction towards the terminus. These changes allow the onset of Stage 2, an accelerating frontal destabilization, which ultimately results in the collapse (Stage 3) and active surge (Stage 4)

Simulating the roles of crevasse routing of surface water and basal friction on the surge evolution of Basin 3, Austfonna ice cap

The marine-terminating outlet in Basin 3, Austfonna ice cap, has been accelerating since the mid-1990s. Stepwise multi-annual acceleration associated with seasonal summer speed-up events was observed before the outlet entered the basin-wide surge in autumn 2012. We used multiple numerical models to explore hydrologic activation mechanisms for the surge behaviour. A continuum ice dynamic model was used to invert basal friction coefficient distributions using the control method and observed surface velocity data between April 2012 and July 2014. This has provided input to a discrete element model capable of simulating individual crevasses, with the aim of finding locations where meltwater entered the glacier during the summer and reached the bed. The possible flow paths of surface meltwater reaching the glacier bed as well as those of meltwater produced at the bed were calculated according to the gradient of the hydraulic potential. The inverted friction coefficients show the "unplugging" of the stagnant ice front and expansion of low-friction regions before the surge reached its peak velocity in January 2013. Crevasse distribution reflects the basal friction pattern to a high degree. The meltwater reaches the bed through the crevasses located above the margins of the subglacial valley and the basal melt that is generated mainly by frictional heating flows either to the fast-flowing units or potentially accumulates in an overdeepened region. Based on these results, the mechanisms facilitated by basal meltwater production, crevasse opening and the routing of meltwater to the bed are discussed for the surge in Basin 3.Peer reviewe

Observing change in glacier flow by using optical satellites

In the last couple of years many Earth observation satellites with optical instruments have been set in space. These satellites generate an enormous amount of data and give us a image of different landforms on Earth. The data are available for researchers in Earth Science, though efficiently transforming this imagery data to glaciological information has been a challenge. The work in this dissertation presents modern day techniques to extract glacier velocity information from the satellite imagery. Now it is possible to extract reliable displacement measurements from any satellite independent of its flight path. In this way extracting reliable decadal changes of glacier velocity is finally possible. Moreover, by recent development in technology and clever algorithms developed in this PhD work, extracting short term velocity changes are one of the possibilities. So the timing of sliding of a glacier due to melt water can be observed and located. Lastly, methods for data reduction of big data volumes are exploited to develop a discovery tool that is able to observe glacier dynamics over several large mountain ranges. This research might be the first step towards transforming large data volumes into useful information for worldwide glacier monitoring

Elevation Change and Improved Velocity Retrieval Using Orthorectified Optical Satellite Data from Different Orbits

Optical satellite products are available at different processing levels. Of these products, terrain corrected (i.e., orthorectified) products are the ones mostly used for glacier displacement estimation. For terrain correction, a digital elevation model (DEM) is used that typically stems from various data sources with variable qualities, from dispersed time instances, or with different spatial resolutions. Consequently, terrain representation used for orthorectifying satellite images is often in disagreement with reality at image acquisition. Normally, the lateral orthoprojection offsets resulting from vertical DEM errors are taken into account in the geolocation error budget of the corrected images, or may even be neglected. The largest offsets of this type are often found over glaciers, as these may show strong elevation changes over time and thus large elevation errors in the reference DEM with respect to image acquisition. The detection and correction of such orthorectification offsets is further complicated by ice flow which adds a second offset component to the displacement vectors between orthorectified data. Vice versa, measurement of glacier flow is complicated by the inherent superposition of ice movement vectors and orthorectification offset vectors. In this study, we try to estimate these orthorectification offsets in the presence of terrain movement and translate them to elevation biases in the reference surface. We demonstrate our method using three different sites which include very dynamic glaciers. For the Oriental Glacier, an outlet of the Southern Patagonian icefield, Landsat 7 and 8 data from different orbits enabled the identification of trends related to elevation change. For the Aletsch Glacier, Swiss Alps, we assess the terrain offsets of both Landsat 8 and Sentinel-2A: a superior DEM appears to be used for Landsat in comparison to Sentinel-2, however a systematic bias is observed in the snow covered areas. Lastly, we demonstrate our methodology in a pipeline structure; displacement estimates for the Helheim-glacier, in Greenland, are mapped and corrected for orthorectification offsets between data from different orbits, which enables a twice as dense a temporal resolution of velocity data, as compared to the standard method of measuring velocities from repeat-orbit data only. In addition, we introduce and implement a novel matching method which uses image triplets. By formulating the three image displacements as a convolution, a geometric constraint can be exploited. Such a constraint enhances the reliability of the displacement estimations. Furthermore the implementation is simple and computationally swift

Quantifying river ice movement through a combination of European satellite monitoring services

Every spring the mechanical river ice break-up and associated ice-runs or flooding pose a threat to communities at Northern latitudes. Monitoring and mitigation efforts along remote Arctic rivers are possible but logistically complex. In recent years, Earth observation programs have emerged based on spaceborne sensors that record large parts of the Earth’s surface at a regular interval and with fast downlink. Most optical satellites have a similar sun-synchronous orbit, and have thus an akin ground track. When different sun-synchronous missions are combined this results in near-simultaneous acquisitions, which make it possible to monitor fast displacements that occur at or near the Earth’s surface over large scales. Hence, it becomes possible to generate a new monitoring system; one of observing river ice movement. In this study we demonstrate the feasibility of a multi-satellite monitoring system by combining data from freely available medium- and coarse-resolution satellites, in this study that is Sentinel-2 and PROBA-V. Velocities of floating river ice during the spring of 2016 are estimated over a more than 700 km long reach of the Lena River in Russia. In order to achieve automatic velocity estimates at such scales, efficient and river-ice specific processing steps are included. Entropy filters are used to detect regions of high contrast and neglects open water or an intact ice cover, and also help the image matching. Post-processing is done through filtering on the general flow direction, stemming from a global river mask dataset. In all, this study shows the potential of extracting river ice movement from a combination of low and medium resolution satellite sensors in sun-synchronous orbit

Improved surface displacement estimation through stacking cross-correlation spectra from multi-channel imagery

Studying sporadic and complex geophysical surface flows, like earthquakes or sea surface circulation, are challenging cases. If a satellite is able to image an event, it becomes essential to pull out as much information as possible. In this contribution we demonstrate a method to increase the coverage and signal-to-noise ratio for displacement estimation, making such surface flow estimates more complete. We leverage upon the redundant offset information acquired by multi-channel push-broom imagery. The individual cross-correlation spectra (cross power spectral density; Fourier transform of the cross-correlation function) of different spectral bands are averaged in the frequency domain before sub-pixel offset-estimation by phase-plane fitting. The method is demonstrated near Kaikōura, where in 2016 a surface rupture occurred. RapidEye data from two different dates were used to reconstruct the displacement. In addition, the circulation along the coast is estimated from data from a single date where multiple spectral bands were acquired within seconds which made stacking of cross-correlation spectra possible. The demonstrated methodology is applied to data from the already decommissioned RapidEye constellation, but can be adopted to other pushbroom systems, such as the Landsat legacy or Sentinel-2.ISSN:2666-017

Weekly glacier flow estimation from dense satellite time series using adapted optical flow technology

Contemporary optical remote sensing satellites or constellations of satellites can acquire imagery at sub-weekly or even daily timescales. These systems have the potential to facilitate intra-seasonal, short-term surface velocity variations across a range of ice masses. Current techniques for displacement estimation are based on matching image pairs with sufficient displacement and/or preservation of the surface over time and consequently, do not benefit from an increase in satellite revisit times. Here, we explore an approach that is fundamentally different from image correlation or similar approaches and engages the concept of optical flow. Our goal is to assess whether this technique could overcome the limitations of image matching and yield new insights in glacier flow dynamics. We implement two different methods of optical flow, and test these implementations utilizing the SPOT5 Take5 dataset at two glaciers: Kronebreen, Svalbard and Kaskawulsh Glacier, Yukon. At Kaskawulsh Glacier, we extract intra-seasonal velocity variations that are synchronous with episodes of increased air temperature. Moreover, even for the cloudy dataset of Kronebreen, we can extract spatio-temporal trajectories that correlate well with measured GPS flow paths. Since the underlying concept is simple and computationally efficient due to data-reduction, our optical flow methodology can be rapidly adapted for a range of studies from the investigation of large scale ice sheet dynamics down to the estimation of displacements over small and slow flowing glaciers