Satellite radar TerraSAR-X data processing

Satellite SAR imagery allows glacier surfaces to be mapped under all weather conditions. Because of the TerraSAR-X/TanDEM-X acquisition plan constraints, only three repeat-pass images were successfully acquired, on 23 October, 3 November and 14 November (after the removal of the other instruments). These images were acquired in ascending passes with a ground resolution of ~2 m in both azimuth and range directions. Due to the glacier surface change, SAR interferometry could not be used to obtain an accurate range displacement measurement. However, with ~1.5 m glacier displacement during the 11 day consecutive acquisitions, amplitude correlation techniques can be applied to measure two-dimensional (2-D) displacement fields in both range and azimuth directions (Reference Luckman, Quincey and BevanLuckman and others, 2007; Reference Ciappa, Pietranera and BattazzaCiappa and others, 2010). During the experiment, three SAR corner reflectors (CR) were installed: a large one (CR1, 2 m side) on the right glacier bank and two small CRs (CR2 and CR3) fixed on the ice close to two Geocubes. Figure 3 presents a colour composition of the three SAR images over the study area after orthorectification. CR1, which was left after the experiment, appears in the three images with a scattering coefficient >30 dB above the surrounding mean value, creating the white point visible in Figure 3. The two other CRs are ~20 dB higher than the surrounding mean value, and appear in orange/yellow, since they were removed before the acquisition of the last image.

Fig. 3.

Colour composition of orthorectified TerraSAR-X amplitude images (red: 23 October 2013; green: 3 November 2013; blue: 14 November 2013) over the part of Glacier d’Argentière where the multi-instrument experiment took place, showing the positions of the three corner reflectors (CR).

In a mountainous area, such as the Mont Blanc massif (where altitude ranges from 1000 to 4800 m), measuring the glacier displacement field from SAR data is not straightforward. It was performed using EFIDIR Tools software (Reference PontonPonton and others, 2014) with the following main steps:

1. 1. An initial rough coregistration (by translation only) was made by cropping the useful part of the image according to a digital elevation model (DEM) and the orbital information provided by the SAR data.

2. 2. A fast-correlation technique (Reference VernierVernier and others, 2011) was applied to the image pairs, with a search window corresponding to a maximum expected displacement and topographic effect (10 pixels in each direction). On the glacier, the measured offset is the sum of the displacement offset and the geometrical offset due to the baseline and the topography.

3. 3. The remaining geometrical offset is subtracted using, in the range direction, the predictions from the DEM and the orbits, and in the azimuth direction the results of the sub-pixel correlation around the glacier. The large CR (CR1) is used in this case to estimate and remove the sub-pixel azimuth offset left by the initial co-registration.

4. 4. The resulting range and azimuth displacement are converted in m d⁻¹ and orthorectified on the DEM grid (4 m spacing, realized in 2008 by IGN (Institut National de l'Information Géographique et Forestière) and provided by the RGD73-74).

The magnitude of the 2-D velocity field measured with the 23 October 2013–3 November 2013 pair is illustrated in Figure 4. Some abnormal values (0.5 m d⁻¹) are obtained in texture-free areas. These could be discarded by introducing a threshold on the correlation peak. The nonzero values on the motion-free areas reveal that the SAR measurements are still affected by processing uncertainty, which comes either from the amplitude correlation function or from the geometric corrections (orbits and DEM). The DEM error over the glacier, due to surface elevation change, may also affect the results. In the study area, the average ablation rate of the glacier is ~4ma⁻¹ (Reference Berthier and VincentBerthier and Vincent, 2012), ~20 m since 2008. The range offsets due to the baseline and the topography (used in step (3)), show that a 100 m variation between 2400 and 2500 m a.s.l. introduces a 0.06 pixel shift (0.12 m) in range direction. So the error induced by a 20 m elevation change is limited to 2.4 cm for the 11 day displacement; <0.0022 m d⁻¹ error in range direction.

Fig. 4.

Magnitude of the 2-D velocity field measured in the range and azimuth direction with TerraSAR-X images (23 October 2013–3 November 2013), normalized on m d⁻¹ and orthorectified.

The final 2-D velocity field measured in SAR images by amplitude correlation corresponds to the projection of the 3-D velocity (east, north, up) in the SAR image plane. The two projection vectors are determined by the incidence angle of the line of sight (LOS) and the azimuth angle of the satellite orbit. For the TerraSAR-X images acquired during this experiment on ascending pass with an incidence angle of 44°, the two unit vectors are

(1)

Ground-based digital camera data acquisition and processing

Terrestrial photogrammetry gives access to a dense surface velocity field over a limited area. Here this technique was used to map the velocity field for all points in the area of interest, and particularly in the cracked area near the Lognan icefall.

To this end, two cameras were installed on the right bank of the glacier at 2631 and 2683 m a.s.l., to monitor the glacier flow. These cameras (Leica DMC-LX 4, 10 megapixels; Fig. 2) (Reference FallourdFallourd and others, 2010) were automated and prepared for prevailing glacier climate conditions. They were programmed to acquire five pictures per day between 09:00 and 21:00. This installation provided acquisitions of stereoscopic time-lapse images (stereoscopic time series) of an area 1 km² and with 158.6 m of baseline between the cameras. The ground pixel resolution is ~50 cm near the glacier. The objective was to compute the displacement of the glacier in two and three dimensions (3-D).

Due to the atmospheric conditions (e.g. wind, humidity, temperature) the positions of the cameras can change. Such motion is highlighted by displacement effects on the motion-free part of the images: the mountains seem to move. Thus, the computation of displacement fields from the images acquired by a camera requires a co-registration step. The objective is to transform all the images of the time series in the geometry of a reference image. Many registration algorithms exist in the literature (Reference Zitová and FlusserZitová and Flusser, 2003). In our approach we used global registration given by

(2)

where p is a pixel in the original image and p _r the same point in the registered image. It should be noted that p and p _r are homogeneous coordinates. To compute the registration matrix, M, a set of points of interest (PoI) was computed and a reference image of the time series was chosen. The choice of the reference image can be arbitrary (e.g. the first image of the time series) or more complex (e.g. an image of the time series that minimizes a distance to the others). The set of PoI can be obtained by SIFT (Reference LoweLowe, 1999), co-registration of image parts (Reference VernierVernier and others, 2011) or other algorithms. The registration matrix, M, was computed for each image, i, using Eqn (2), considering p _r as a PoI on the reference image and p the corresponding point on image i. In our case, six areas (three on each bank of the glacier) on motion-free parts close to the glacier were chosen to compute the PoI using the co-registration method (using the normalized cross-correlation function as similarity measure), and the registration matrix, M, was computed as a homograph.

After registration of the left and right time series, the displacement fields were computed by dense correlation (Reference VernierVernier and others, 2011) on the whole image between two dates. Here left images were used to derive the displacement fields because the LOS is near-perpendicular to the glacier flow in this dataset. The result of this step provides the changes over time and the residual error on motion-free parts of the images. Here the mean residual error reaches 0.3 pixel. Figure 5 illustrates the displacements computed from pictures acquired on 20 and 25 September with arrows drawn with a scale factor.

Fig. 5.

Displacements (in pixels) computed by photogrammetry between 20 and 25 September 2013. The red arrows highlight the residual errors and the black arrows illustrate the glacier flow. Discrepancies on red arrows correspond to correlation failures.

Depth information is required to translate such results in metres and to compute total displacements, taking into account the displacement component parallel to the LOS direction. This information over the whole image was obtained by computing georeferenced DEMs from the stereoscopic installation. MicMac software (Reference Pierrot-Deseilligny, De Luca and RemondinoPierrot-Deseilligny and others, 2011) was used for this step. Previously coregistered pictures were first referenced by bundle adjustment using ground-control points determined by GPS-RTK (real-time kinematic). For technical reasons, only five ground-control points were measured and their repartition in the images is rather poor: they are concentrated in a small area on the right glacier bank. Despite the coarse referencing, a dense correlation algorithm was used to compute a DEM at each date. Next, DEMs and 2-D displacement fields were combined to match 3-D points in DEM computed at different dates (Reference Travelletti, Malet and DelacourtTravelletti and others, 2014). Finally, 3-D displacement and velocity fields were derived from this matching. A mask was used during the processing to reduce the area of interest to the glacier and some stable banks, and thus boost the processing. Figure 6 illustrates the amplitude of the total velocity field computed from pictures acquired on 23 September and 7 October. The result shows good agreement with the crevasse pattern of the glacier. However, some outliers, due to shadow variations, appear on the stable banks. In addition, correlation fails at the right glacier margin due to a high inter-date dissimilarity. The very high velocity and correlation failures at the terminal part of the glacier (right-hand side of the figure) are due to icefalls.

Fig. 6.

Velocity field computed by photogrammetry between 23 September and 7 October 2013.

Geocubes data acquisition and processing

Geocubes are multisensor units with networked operation capability developed by IGN for structural and geophysical monitoring (Reference Benoit, Briole, Martin and ThomBenoit and others, 2014). Each receiver includes a single-frequency GPS module for positioning, a radio module for data exchange and a CPU to manage data acquisition. It is designed to be powered by a small solar panel and to support extra sensor modules to supplement the GPS.

During this experiment, Geocubes were used to carry out a GPS survey performed by numerous receivers set up at a large number of accessible points on the central part of the glacier. This leads to an intermediate system between the very punctual conventional geodetic GNSS measurements and the dense remote-sensing results. The low cost of the Geocubes and the ease of setting them up led us to install 11 receivers on the ice and two on the stable bedrock of the glacier banks (Fig. 1). The receivers on the glacier were installed on top of 2 m stems drilled 1.5 m into the ice and powered by a solar panel fixed in the same way (Fig. 2a). Inclinometers included as extra sensors in each Geocube were used to detect stem tilt induced by ice ablation and melting. Thus, it was possible to select only periods with stable GPS receivers for the processing, thereby ensuring that displacements recorded at the GPS antenna phase centre were only due to the flow of the glacier.

GPS data were processed by software developed for Geocubes (Benoit and others, 2014). The processing algorithm is based on L1 carrier-phase double differences, which mitigate dramatically spatially correlated errors if short baselines (<1 km) are used. This makes the L2 frequency data unnecessary. Epoch-by-epoch positions were computed at a 30 s data acquisition sampling rate using an extended Kalman filter. Thus, high-resolution time series of position were computed at each point of the Geocube network (Fig. 7), with a standard deviation of 0.005 and 0.01 m for the horizontal and vertical components, respectively. The main remaining error sources are multipath, i.e. GPS wave reflection and diffusion in the environment surrounding the antenna (Reference Larson, Small, Gutmann, Bilich, Axelrad and BraunLarson and others, 2008). They were mitigated using a time-stacking method (Reference Choi, Bilich, Larson and AxelradChoi and others, 2004) applied when the velocities were derived from the receiver positions. This leads to a velocity time series with a standard deviation of 0.003 and 0.006 m d⁻¹ for the horizontal and vertical components, respectively.

Fig. 7.

Horizontal velocity at the base (cavitometer) and at the surface (Geocube 1006) of the glacier. Date format is mm/dd.

Due to receiver tilt, fully reliable data are not available from all points in the experiment. Usable data were collected from the whole network only over the first 5 days, which is sufficient to allow surface velocity heterogeneities to be measured on the central part of the glacier (Fig. 8), where the ice seemed to be a rigid block in the velocity fields derived from remote sensing (Figs 4 and 6), due to the intrinsic precision of these methods. The precision of the results derived from Geocubes allowed us to record, within this area, a higher velocity at the centre of the glacier than near the banks (Fig. 8). A ~7.5% mean ratio between the fastest and the slowest receiver was recorded. The relative displacements of the ice-fixed Geocubes were then used as input data to derive ice surface deformation. Strain tensors were computed using the method described by Reference NyeNye (1959), applied on an arbitrary triangulation between receivers. Computed horizontal strain tensors were then superimposed on orthophotography derived from Pleiades satellite images acquired on 20 September 2013 (Fig. 8). The results show that the deformations of the rigid part of the glacier are very consistent with the direction of the neighbouring lateral crevasses.

Fig. 8.

Geocube velocities averaged over five days (14–18 September) and related deformation. The black star denotes the position of Geocube 1006.

Basal velocity measured beneath the glacier

Glacier d’Argentière is a unique site, where basal velocity can be measured continuously throughout the year, thanks to the tunnel dug beneath the glacier. The ability to access the glacier base provides a rare opportunity to directly monitor the basal velocity with high precision and at high temporal resolution.

To this end, a cavitometer is fixed in a natural cavity accessible from this tunnel under ~60 m of ice. It was installed by R. Vivian in 1971 (Reference Vivian and BocquetVivian and Bocquet, 1973), re-established by L. Moreau in 1986 (Reference MoreauMoreau, 1995) and is still used today. It consists of a bicycle wheel rolling on the glacier base (Figs 2 and 9).

Fig. 9.

Schematic view of the cavitometer.

After 12 days, only half of the ice-fixed receivers were working properly, and only one receiver (Geocube 1006) acquired reliable data over the whole experiment. However, this single ice-fixed Geocube processed with the fixed one (set up on the right glacier bank) allowed us to characterize the ice flow during the whole period with a high time resolution (Fig. 7).

Slip-free rolling is ensured by the impurities embedded in the basal ice (sand, small rock fragments) and by a counterweight fixed on the arm of the cavitometer which secures the ice/wheel contact. In order to take into account that the rolling surface is not flat, mainly due to rock fragments embedded in the ice or vertical variations of the ice roof, the velocity is computed as (Reference MoreauMoreau, 1995)

(3)

The variables used in this equation are shown in Figure 9. The wheel rotation, ω, and the arm tilt, φ, were recorded by analogue potentiometers, and the data were manually digitized for selected periods. During this experiment, data were recorded for the whole session but they were digitized only over the first month (Fig. 7), when they were used for comparison with surface velocity measurements. Figure 7 presents daily basal velocity recorded by the cavitometer from 14 September to 17 October 2013. The resolution of the result, derived from the angular measurement resolution, is theoretically 0.003 m d⁻¹, but its real precision is difficult to establish due to the rolling surface irregularities.

Result evaluation by inter-method comparison

Results from the Geocubes present the highest precision (~0.005 m d⁻¹) of the three methods used during this experiment, as shown by the possibility of detecting deformation over the central part of Glacier d’Argentière (Fig. 8). Thus, Geocube data were used as ground truth to evaluate remote-sensing results. To this end, Geocube results were averaged to match the temporal resolution of remote-sensing techniques.

The comparison between SAR and Geocubes was made by projecting the Geocube 3-D velocity, V _GPS, in the SAR image plane for three Geocubes set up near the three corner reflectors: two moving with the glacier (CR2 and CR3) and one installed on a stable bank (CR1). Table 1 shows very good agreement between SAR and the Geocubes. This highlights the quality of SAR results for places where the correlation works well (e.g. near corner reflectors, which generate a very strong response into SAR images, or in textured areas (rocks, crevasses, etc.)). In addition, the standard deviation computed on the textured areas of the stable glacier banks reaches 0.026 m d⁻¹. Finally, the precision of the surface velocity field derived from SAR images using an amplitude correlation technique can be estimated at a level of 0.03 m d⁻¹ for most of the study area. However this precision is reached only where well-defined structures exist in both images used for the correlation. For texture-free areas, the correlation technique is no longer valid because of the speckle phenomenon, which is decorrelated after 11 days and produces a high noise level in the amplitude data.

Table 1.

Two-dimensional velocity measured at the three corner reflectors by amplitude correlation in the TerraSAR-X images (23 October 2013–3 November 2013 pair) and corresponding GPS measurements

The comparison between photogrammetry and Geocubes was made by comparing the total velocity amplitude at the location of three Geocubes situated on the glacier and operating when pictures were acquired (23 September–7 October). Table 2 shows photogrammetry underestimates the velocity. This is probably due to the coarse image referencing inducing a scale factor, as well as the ground pixel resolution limiting the accuracy of the correlation process. Except for this small bias, the results show good internal consistency and are consistent with the main glacier patterns. Finally, the precision of the velocity field derived from photogrammetry with the ground-based cameras used for this experiment can be estimated at the 0.02–0.05 m d⁻¹ level.

Table 2.

Three-dimensional velocity measured at three points by photogrammetry (23 September 2013–7 October 2013 pair) and corresponding GPS measurements

Assessment of basal velocity measurements

Results from the three surface velocity monitoring techniques can then be used simultaneously to check the reliability of the basal velocity measurements carried out by the cavitometer. As shown in Figure 7, the basal velocity at the cavitometer is always ~25% higher than the surface velocity at Geocube 1006. This difference was not, however, due to an instrumental bias, but can be fully explained by the positions of the sensing points along the glacier flow. Indeed, the cavitometer is situated near the Lognan icefall, where the flow velocity increased, even at the surface, as shown by remote-sensing surface velocity measurements (Figs 4 and 6). Except for this velocity ratio, induced by the location of the instruments, the basal and surface velocities present very consistent time variations (Fig. 7).

This simultaneous top and bottom study of the ice velocity allows us to precisely connect the basal velocity measurements carried out by the cavitometer to the ice surface velocity field with an unprecedented time resolution. In addition, this survey leads, for the first time, to monitoring the ice flow velocity of Glacier d’Argentière at its base and surface at hourly resolution (Fig. 10), paving the way for an investigation of its acceleration/deceleration heterogeneities, as shown below.

Fig. 10.

Heterogeneities of the acceleration/deceleration pattern at the surface and at the base of Glacier d’Argentière. (a) Long-period surface velocity evolution (14 September–14 October 2013) recorded by Geocube 1006. Rainy periods are highlighted in light blue. (b) Deviation from the mean velocity during two speed-up events (16–21 September and 3–7 October). Time series are shifted (0.03md.. 1) to improve legibility. Green: basal velocity recorded by the cavitometer. Black: surface velocity recorded by Geocubes set up outside the medial moraines. Red: surface velocity recorded by Geocubes set up on the medial moraines. Dashed lines: unavailable data. (c) Precipitation records during speed-up events. (d) Location of Geocube receivers. Date format is mm/dd.

Contribution of precise in situ measurements to rainfall-induced flow heterogeneity monitoring

The Glacier d’Argentière flow velocity was mostly constant during this experiment, but abrupt accelerations followed by a fast return to the mean velocity appear on 19 September and 5 October (Fig. 7). They lead to a velocity peak on 5 October, reaching 160% of the magnitude of the velocity before the event. These accelerations are preceded by heavy rainfall (Fig. 10), which appears to be the major parameter influencing the ice flow velocity variation during the monitored period, probably through increased basal water pressure at the ice/rock interface of the glacier (Reference IkenIken, 1981; Reference Iken and BindschadlerIken and Bindschadler, 1986; Reference Fischer and ClarkeFischer and Clarke, 1997).

However, these acceleration/deceleration events do not have exactly the same pattern throughout the glacier, particularly in a complex and fractured area, as monitored here (Fig. 10). The high precision and the high temporal resolution of the in situ measurements allow analysis of the various glacier responses to rain as a function of the sensingpoint location.

Focusing on surface measurements carried out by the Geocubes, the majority of the receivers present very similar acceleration/deceleration patterns, but two receivers (Geocubes 1010 and 1018) have a different response (Fig. 10b). Their acceleration is delayed compared with the rest of the glacier, and the duration of the speed-up event is longer than for other receivers. A photograph of the area (Fig. 10d) shows that the receivers with the delayed acceleration are both situated on debris-covered medial moraines, while the others are distributed around them but always outside of such moraines. Thus, medial moraines seem to modify slightly and very locally the response of the ice flow to glacier accelerations induced by rainfall. These structures are then presumed to have an effect on basal processes which control the glacier velocity, perhaps due to their excess weight or to a slightly different ice structure beneath them.

The comparison of the Geocube and cavitometer velocities (Fig. 10) highlights that basal and surface accelerations are simultaneous and appear ~1.5–2 hours after the rainfall begins. However, basal velocity seems more sensitive to rainfall intensity, with a pattern directly controlled by rainfall events. The response of the surface is smoother. This higher sensitivity of basal velocity to rainfall events may be due to the local structure of the glacier (thickness of ~60 m of cracked ice above the cavitometer, thickness of ~200 m of compact ice under Geocube 1006) or due to the proximity of subglacial water channels.

Finally, precise in situ measurements carried out at high temporal resolution allowed the monitoring of the heterogeneities of the acceleration/deceleration patterns following rainfall events. Results show that in the study area the glacier does not accelerate totally uniformly. This suggests that the local structure, thickness and topography of the ice locally influence basal processes and then slightly modify the ice flow. The heterogeneous acceleration/deceleration patterns we have documented could be used in further work, together with additional data, such as water run-off downstream or glacier bedrock topography, to model the ice flow of the terminal part of Glacier d’Argentière, which presents a complex ice structure.