Deciphering Small-Scale Seasonal Surface Dynamics of Rock Glaciers in the Central European Alps Using DInSAR Time Series
Abstract
:1. Introduction
2. Study Areas
2.1. Kaiserbergtal
2.2. Grison Study Sites
3. Data and Methods
3.1. Differential SAR Interferometry Displacement Time Series Generation
3.2. Displacement Decomposition
3.3. Reference Data Generation Using Feature Tracking and DEM Differencing
3.4. Calculation of Seasonal Movement
3.5. Correlation Analysis of Spatiotemporal Dynamics
4. Results
4.1. Comparison of DInSAR Displacement with Feature Tracking and DEM Differencing
4.1.1. Kaiserbergtal
4.1.2. Grison Sites
4.2. Seasonal Movement
4.3. Correlation Analysis
5. Discussion
5.1. Accuracy of the Yearly DInSAR Displacement Rate and Its Decomposition
- (i)
- The FT product is unable to detect displacement in case of sun shadow (Grischa, Muragl; Figure 5d,g) or snow cover (Davains; Figure 5k). These areas have to be masked out in the FT approach due to a failure in detecting features because of the strong difference between the images used, whereas DInSAR is independent from lighting conditions. Further, at Gianda Grischa we could visually identify a systematic error in the displacement detection of the northern rock glacier, which is caused by different sun illumination angles between the two acquisitions resulting in shifted shadows of the blocks on the surface (Figure 5d). Hence, inaccuracies of the DInSAR product are rather overestimated, as we do not correct for these artefacts induced by the FT product.
- (ii)
- DInSAR shows systematic underestimation of the movement in areas with strong and abrupt changes in displacement values such as at fast moving rock glacier parts near the front (Kaiserbergtal, Davains; Figure 4b and Figure 5l) or adjacent to the lateral boundaries (Muragl; Figure 5h). In these cases, we still observe reduced velocities; it is only at Gianda Grischa that the movement of the middle part of the southern rock glacier is not detected at all (Figure 5e). Such effects are caused by errors in the phase unwrapping process. We observe that the upper detectable boundary of DInSAR displacement is lower than the previously described [34,43] single full phase cycle 2, which represents about a 1.7 m/a displacement. At our study sites, phase unwrapping errors start to occur at already half a phase cycle (0.9 m/a), but can occur at down to even less than 0.4 m/a (Figure 5) in unfavorable conditions (e.g., Gianda Grischa). More unwrapping errors in interferograms with longer temporal baselines, which were used to bridge gaps with low coherence due to snowfall events during summer, could partially explain the reduced sensitivity. This highlights the importance of dense C-Band time series for rock glacier monitoring. Phase unwrapping tends to fail preferably at the margins of rock glaciers, where abrupt phase changes occur along with a decorrelation of the signal due to the instability of the surface. Unwrapping errors occur mostly along East-West boundaries due to the reduced number of adjacent pixels in this direction, which is caused by the uneven pixel size (2.3 m in range, 13.9 m in azimuth). Thereby, distinct boundaries in the north-south direction develop for unwrapping affected areas (e.g., Kaiserbergtal, Gianda Grischa, Muragl; Figure 6). Projection to squared pixels in Cartesian coordinates before unwrapping might reduce this tendency and could improve results, but the interpolation and oversampling in an azimuth direction of the wrapped interferograms might cause new artefacts. Therefore, we did not carry out an adapted workflow. Besides such changes in preprocessing, advanced methods for phase unwrapping such as deep-learning-based approaches (e.g., [86,87]) could improve the successful unwrapping of the interferograms.
- (iii)
- The spatial patterns of the two independent datasets match very well. Even smaller areas and those with weaker displacement rates (e.g., eastern part of Muragl; Figure 5g,h) are congruently detected by both methods. Our results show similar accuracies as other studies with the SD ranging from 0.23 m up to 0.33 m after scaling compared to, e.g., the results of Strozzi et al., 2020 [34] with an SD ranging from 0.20 m to 0.34 m. It is only at Davains that higher inaccuracies are observed because a larger proportion of the investigated area is affected by phase unwrapping issues (Figure 5k–m). However, the scatterplot shows similar good agreement for lower displacement rates (Figure 5m). Hence, the higher spatial sampling does not lead to higher inaccuracies in the aggregated yearly displacement rate and our approach is able to accurately detect movement. Based on an RMSE of around 10 cm (except Davains with more unwrapping issues) and visual inspection of value ranges in stable areas, we state that the lower detectable boundary with 6d interferogram stacks is at around 10 cm/a displacement, which is the limit for active rock glaciers [15]. Our results indicate slightly higher sensitivity than other recent studies [34,43], which might be due to the longer time series used in the stacking procedure [41,42,77,78] which results in a stronger dampening of arbitrary phase contributions from atmosphere or noise. Discriminating areas with displacement rates of less than 10 cm/a need longer temporal baselines [34,43] and more sensitive methods such as small baseline subset (SBAS) InSAR should be used [41,42,45,88].
- (iv)
- Visual assessment of the displacement maps shows that DInSAR lacks the ability to detect very high-resolution variabilities (e.g., Kaiserbergtal for the elevation change—Figure 4c,d, Nair for outlines of the smaller active lobes—Figure 5a,b). The coverage of such high-resolution spatial variations is more limited in the north-south direction due to the lower azimuth resolution (13.9 m) compared to the range (2.3 m) of Sentinel-1. However, the extent and variations on the scale of a few decameters are captured well. Moreover, the systematic difference in the distribution of horizontal and vertical displacement at Kaiserbergtal (subsidence in western part; increasing horizontal movement towards southeast) agrees well in both methods (Figure 4). Spatial agreement in E-W displacement is also good in areas where less suitable conditions exist for DInSAR, such as areas adjacent to widespread layover and shadow at Gianda Grischa or strong northern orientation at Nair (Figure 5a,b,d,e). Even active areas with a direct northern orientation are highly detectable due to their vertical displacement contribution (see Appendix A Figure A1), which is evident for nearly all rock glaciers. However, neglecting the N-S displacement component leads to incorrect estimates, especially in the vertical displacement. This is twofold: (1) neglecting N-S displacement leads to a systematic increase in the vertical displacement component due to their similar behavior in LOS. (2) The contribution of slope in N-S direction to the vertical displacement component is neglected and, therefore, leads to an overestimation of elevation change in areas with strong N-S displacement.
5.2. Insights from Spatial Patterns of Movement Direction and Seasonality
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
APS | Atmospheric Phase Screen |
a.s.l. | above sea level |
DEM | Digital Elevation Model |
DInSAR | Differential SAR Interferometry |
ECV | Essential Climate Variable |
EMT | Environmental Motion Tracking |
EOS | end of snow cover |
E-W | East-West |
FT | Feature Tracking |
IPA | International Permafrost Association |
LOS | Line of Sight |
MCF | Minimum Cost Flow |
N-S | North-South |
OTB | Orfeo Toolbox |
RGV | Rock glacier velocity |
RMSE | Root Mean Squared Error |
SAR | Synthetic Aperture Radar |
SBAS | Small Baseline Subset |
sd | Standard Deviation |
SLC | Single Look Complex |
UAV | unpiloted aerial vehicle |
U-D | Up-Down |
Appendix A
Appendix A.1
Appendix A.2
Appendix A.3
Appendix B
Software | Version | Processing Steps |
---|---|---|
SNAP (accessed via snappy) | v8.0.3 | Preprocessing: Apply Orbit File, TOPSAR Split & Deburst Coregistration: Backgeocoding, Enhanced Spectral Diversity Interferogram: Calculation of Phase, Topographic Phase removal, Goldstein Phase Filter Projection to map coordinates |
snaphu | v2.0.4 | Phase unwrapping (MCF algorithm) |
pyrate | v0.5.0 | Reference Phase: Detection of stable area; Calculation of Reference Phase Corrections: Mask low coherence areas; orbit error correction Atmospheric Phase Screen: spatial and temporal low-pass gaussian filter Conversion of phase to displacement |
Agisoft Metashape Professional | 1.7.2 | Structure from motion point cloud generation from UAV imagery Generation of digital elevation model |
Environmental Motion Tracker | 0.9.3 | UAV hillshade/orthophoto image coregistration Feature Tracking in coregistered UAV hillshade/orthophoto |
Orfeo Toolbox | 8.1.0 | Preparation of dataset for segmentation: stacking, normalization Segmentation of rock glacier areas into segments of similar dynamics |
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Buchelt, S.; Blöthe, J.H.; Kuenzer, C.; Schmitt, A.; Ullmann, T.; Philipp, M.; Kneisel, C. Deciphering Small-Scale Seasonal Surface Dynamics of Rock Glaciers in the Central European Alps Using DInSAR Time Series. Remote Sens. 2023, 15, 2982. https://doi.org/10.3390/rs15122982
Buchelt S, Blöthe JH, Kuenzer C, Schmitt A, Ullmann T, Philipp M, Kneisel C. Deciphering Small-Scale Seasonal Surface Dynamics of Rock Glaciers in the Central European Alps Using DInSAR Time Series. Remote Sensing. 2023; 15(12):2982. https://doi.org/10.3390/rs15122982
Chicago/Turabian StyleBuchelt, Sebastian, Jan Henrik Blöthe, Claudia Kuenzer, Andreas Schmitt, Tobias Ullmann, Marius Philipp, and Christof Kneisel. 2023. "Deciphering Small-Scale Seasonal Surface Dynamics of Rock Glaciers in the Central European Alps Using DInSAR Time Series" Remote Sensing 15, no. 12: 2982. https://doi.org/10.3390/rs15122982