Visualizing and interpreting surface displacement patterns on unstable slopes using multi-geometry satellite SAR interferometry (2D InSAR)

Abstract

It is well known that satellite radar interferometry (InSAR) is capable of measuring surface displacement with a typical accuracy on the order of millimeters to centimeters. However, when the true deformation vector differs from the satellite line-of-sight (LOS), the sensitivity decreases and interpretation of InSAR deformation measurements becomes challenging. By combining displacement data from extensive ascending and descending TerraSAR-X datasets collected during the summer seasons of 2009-2014, we estimate two-dimensional (2D) InSAR surface displacement. Displacement data are decomposed into vertical and west/east deformation, dip and combined deformation vector, and validated using Global Navigation Satellite System (GNSS) data. We use the decomposed dataset to visualize variations in surface velocity and direction on unstable slopes in a periglacial environment with sporadic permafrost in northern Norway. By identifying areas with uplift and subsidence, and detecting velocity changes (downslope acceleration/deceleration) and related areas of extension and compression, we are able to explain driving and controlling mechanisms and geomorphology in two rockslides and one area with solifluction landforms.

Monitoring Inter- and Intra-Seasonal Dynamics of Rapidly Degrading Ice-Rich Permafrost Riverbanks in the Lena Delta with TerraSAR-X Time Series

“…The sensitivity of radar to non-line-of-sight features is known, terrain features with steep scarps not aligned to orbit direction have been shown to introduce illumination error [58] and shallower slopes impact backscatter angle, both reducing backscatter intensity and the foreshortening effect [59]. These complex non-linear features can introduce errors to the extracted cliff-top line position and should be taken into consideration when monitoring at intra-annual timescales.…”

Relating 3D surface displacement from satellite- and ground-based InSAR to structures and geomorphology of the Jettan rockslide, northern Norway

“…Scenes were processed to two stacks of interferograms, ~160 each (see Table 1 in Eriksen et al (2017), having a temporal baseline of less than 55 days.…”

“…We validate 3D vectors using GNSS data from four stations at the Jettan rockslide. By using a stable reference frame, data from the Norwegian Permanent GNSS network (Kierulf et al, 2014) were combined with GNSS data from Jettan, as described in Eriksen et al (2017). We computed the mean annual velocity vectors for GNSS stations based on data from the same time interval (snow-free season from June to October 2009-2014) as covered by the interferograms in the TSX ascending and descending dataset (see table 1 in Eriksen et al, 2017).…”

“…However, we were unable to find a common stable area covered by all radar datasets. We therefore carried out a rough calibration of input data to the overall trend of the GNSS network using the two-step calibration routine described in Eriksen et al (2017), before processing 3D displacement vectors. Lastly, we fine-tuned the calibrated InSAR data using an iterative workflow including (1) comparing GNSS displacement from the period covered by InSAR data to averaged velocity, azimuth and plunge from displacement vectors originating from areas close to GNSS-stations 3, 5, 6 and 9, (2) recalibrating InSAR input data and (3) 3D processing using recalibrated input data.…”

Multi-Temporal DInSAR to Characterise Landslide Ground Deformations in a Tropical Urban Environment: Focus on Bukavu (DR Congo)

“…Deformation rate maps in the radar line-of-sight (LOS) for the ascending (a) and descending (b) CSK datasets. InSAR measures displacements along LOS direction, those have to be interpreted as a one-dimensional component of the full 3-D displacement vector (e.g., [40]). Combining at least two different looking geometry vectors allows to evaluate the vertical and E-W component of the displacement.…”