Introduction

Remote sensing has proven in the last years to be a valid complement to standard in-situ methods for the investigation and analysis of geohazards (Tomás and Li 2017). In particular, optical and radar satellite-based imagery provided great advances in the identification, mapping, and quantification of surface changes caused by earthquakes (Jelének and Kopačková-Strnadová 2021), volcanic deformation (Ebmeier et al. 2018), land subsidence (Peng et al. 2022), and slope instabilities (Lissak et al. 2020). A prominent technique to measure surface displacements from space is the synthetic aperture radar differential interferometry (InSAR). This approach relies on the identification of phase differences between multi-temporal SAR acquisitions (Bürgmann et al. 2000). The analysis of multi-temporal SAR datasets with specific algorithms (e.g., persistent scatterer interferometry, PSI, or small-baseline interferometry, SBAS) allows the generating of ground velocity maps and displacement time series, reaching (in ideal cases) sub-centimetric accuracies (Ferretti et al. 2001, 2011; Berardino et al. 2002; Werner et al. 2003; Hooper 2008).

PSI applications revolutionized the investigation of surface deformation associated with landslide processes (Crosetto et al. 2016). The possibility to obtain data of surface deformation at relatively high spatial and temporal resolutions without installing costly instrumentation is an essential monitoring tool to investigate and interpret landslide processes (Casagli et al. 2016). The current availability of regional, country-scale, and even continental-scale PSI datasets (Crosetto et al. 2020; Lanari et al. 2020) changed the perspective not only in research activities but also the daily work of practitioners, as well as civil protection strategies (Dehls et al. 2019; Raspini et al. 2019; Bianchini et al. 2021). Intrinsic limitations, however, might hinder the nominal performance of InSAR and PSI in mountain areas (Wasowski and Bovenga 2014; Manconi et al. 2018). For example, the presence of vegetation and/or snow cover, as well as steep areas affected by geometric distortions in radar geometry cannot be efficiently monitored (Cigna et al. 2014). Additionally, InSAR measurements are in one dimension only, i.e., along the satellite’s line of sight (LOS), and a combination of a minimum of two or more orbits is required to extract displacement in two and three dimensions (Delbridge et al. 2016; Li et al. 2019). Due to typically nearly polar orbits of SAR satellites, however, slope movement along the satellite’s track (i.e., almost in north–south directions) remains unmeasured or severely underestimated (Wasowski and Bovenga 2014). Atmospheric phase screen may also seriously affect the accuracy of measurements if not properly considered and corrected (Dini et al. 2019).

Another important limitation of InSAR is that accurate measurements are prevented when the surface deformation is relatively large, rapid, and/or spatially and temporally heterogeneous (Manconi 2021). This can be of particular importance when analyzing large landslide complexes, which may have a heterogeneous evolution characterized by non-steady velocities, development of different compartments, and generate potentially local failure events (Stead and Eberhardt 2013; Agliardi et al. 2020). Large and/or rapid displacements can still be measured from SAR images with other approaches, as for example the digital image correlation method (DIC, known also as pixel offset, speckle, or feature tracking). This is an efficient workaround to retrieve measurements also where large spatial gradients occur; however, the accuracy of the measurements is related to the ground sampling distance (GSD) of the imagery used, which in the case of SAR satellites is typically on the order of meters (Casu et al. 2011; Manconi et al. 2014). Some authors have shown how joint analysis and integration of standard InSAR, DIC, and PSI can be used to investigate large compound landslides, although such comprehensive studies are unusual (Singleton et al. 2014; Li et al. 2019).

Here we present an extensive analysis of C-band SAR datasets (frequency 5.4 GHz, wavelength ca. 5.6 cm) acquired from the European Space Agency (ESA) radar missions, i.e., ERS-1/2, Envisat ASAR, and Sentinel-1, covering the period 1992–2020. We reconstruct the spatial and temporal evolution of the surface displacements over ca. 30 years at the Brienz/Brinzauls landslide complex (hereafter referred to as Brienz, see Fig. 1), located in canton Graubünden (Switzerland). We compare our results against ground-based GNSS measurements and independent information derived from additional radar sensors (Radarsat-2 and ALOS-2 PALSAR-2). To complement the InSAR analysis, we also considered DIC on multi-temporal digital terrain models (DTM) generated from airborne LiDAR surveys. The latter were combined with InSAR results to compute the 3-D surface deformation field.

Fig. 1
figure 1

(Top) Overview of the Brienz landslide complex (location shown in the inset), background Google Earth imagery. The main morphological distinctions are identified in red (Rutschung Berg, RB) and yellow (Rutschung Dorf, RD), respectively. Two exemplary pictures are shown to highlight the morphological differences between the higher and the lower portions of the slope affected by surface deformation (picture locations and fields of view are shown in the top panel in black)

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We completed our analysis up to 2020, i.e., before a further dramatic increase in landslide velocity led to the evacuation of the village of Brienz at the beginning of May, 2023, culminating with a failure event on June 15, 2023, with a mass wasting of about 1.2 Mm3 (Loew et al. 2023). We deliberately focus on the analysis of the long-term evolution of surface displacements occurring before this major event. The time frame before the event (2020–2023) and additional details on the catastrophic failure are not covered here and will be the subject of future publications, for which specific analyses are presently carried out. The aim of this work is manifold. We target at showing and discussing how spatial and temporal heterogeneities might strongly influence the investigation of displacements in complex landslide scenarios when relying on C-band InSAR satellite measurements only. In most cases only little or no constraints from field data is available; thus, our results in one of the best monitored landslides in the Alps provide important assessments on accuracy and challenges of such analyses in complex scenarios in other regions. In addition, we show how a careful use of InSAR based results, not only on PSI but also on wrapped, unwrapped, and stacked interferograms, can be of great value, but often not sufficient to fully judge spatial and temporal heterogeneities.

Background on the study area

The Brienz Mountain slope deformation, located in canton Graubünden, Switzerland, affects a large portion of the southern flank of Piz Linard and the Albula river, and includes the village of Brienz/Brinzauls (765′048 E, 1′170′830 N, CH1903/LV03). The active parts of this mountain slope deformation occur at the lower slope areas (below about 1800 m a.s.l) and form a very heterogeneous landslide complex. The geomorphological, hydrogeological, and subsurface geological conditions of the active part of this landslide complex have been investigated in detail during the last years (Figi et al. 2022). The currently active upper slope portions, namely the “Rutschung Berg” (i.e., expression to indicate “landslide affecting the mountain” in German language, hereafter referred to as RB), are located between approximately 1770 m and 1150 m a.s.l., with an average slope of 36°. The currently active lower slope portions, namely the “Rutschung Dorf” (i.e., expression to indicate the “landslide affecting the village” in German language, hereafter referred to as RD), extend from about 1150 m a.s.l. to the Albula riverbed located at approx. 870 m a.s.l, with an average slope of 8°. The landslide involves low-grade sedimentary units of the Penninic and Austroalpine nappes with strong differences in mechanical properties. In particular, the RB and RD domains include (from bottom up) located in North Penninic Flysch units, South Penninic Allgäu Schists, Austroalpine Arlberg Dolomite, and Raibler Schists units. The Brienz landslide complex is composed of a series of stacked old rockslides, rock mass fall deposits, and dry/moist granular flows. In the frontal 400–500 m, the RD have been thrusted over Late-glacial and Holocene fluvial deposits of the Albula river. Remarkable morphological and geological heterogeneities in RB lead to a distinction into different landslide compartments having different surface velocity amplitudes and directions, as well as different kinematic and dynamic behaviors. In Fig. 2, we show the approximate delimitation and naming of the main landslide compartments. A full description of the characteristics is beyond the scope of this paper. Details on the geological, structural setting, hydrogeology, and dynamic behavior can be found in Figi et al. (2022).

Fig. 2
figure 2

Approximate delimitation and naming of the active compartments (cf. Figi et al. 2022). Black dots show the location of GNSS stations (full set with coordinates reported in the Supplementary Information, Table S1). Shaded relief generated from the SwissALTI3D digital model (source, swisstopo, 2013, GSD 2 m)

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Large surface displacements and catastrophic failure potential cause major concerns to the authorities because approx. 100 people permanently reside in the Brienz village, with up to 200 people during holiday periods. In addition, several important connecting roads and a railway line are affected by damages. Slope instability and rock falls have been a recurrent problem for the Brienz population. In the eastern side of the slope, a large earthflow (known as “Igl Rutsch”) accelerated in November, 1878, and reached velocities of up to 1 m/day, alarming the inhabitants for several months before resting (Ludwig 2011). Since 1921 the displacements on the village have been periodically measured, and in 2011 a permanent total station was installed to continuously monitor target points in RB and RD. Average surface velocities increased in the entire slope since 2015, and for this reason, the monitoring network has been expanded. This includes a combination of periodic surveys and permanent GNSS stations with currently more than 80 points, as well as more than 40 reflectors. Moreover, surface displacements are also continuously monitored with a permanent ground-based SAR, and the network is complemented with time-lapse cameras acquiring multi-temporal pictures form different locations, as well as with a Doppler radar system aimed at detecting rock falls potentially reaching the road connecting Brienz to Vazerol (Schneider et al. 2023). In addition, the dynamic response of the landslide is monitored by analyzing ambient vibration recorded through a network of broadband seismometers (Häusler et al. 2022). All these data have been extremely important for the implementation of an early warning system, which allowed to recognize timely the critical slope acceleration in spring 2023 and the subsequent evacuation of the population before the slope failure event (Loew et al. 2023).

Surface deformation from remote sensing

In this work, we considered multiple datasets and methodologies, including the following:

  1. 1.

    Differential interferograms considering all available ESA C-band satellite imagery (993 images in total, see details in Table 1) acquired from ascending and descending orbits of ERS-1/2 (1992–2000), Envisat ASAR (2004–2010), and Sentinel-1 (2015–2020). The InSAR analysis is performed to obtain initial surface velocity maps by considering different time intervals and served to potentially identify the initiation and the variability of surface displacement in the different domains of the Brienz slope, as well as possibly their main periods of activity. Moreover, we stacked selected Sentinel-1 interferograms from three different orbits, one ascending and two descending. This approach has been used to increase the signal-to-noise ratio, mitigate atmospheric disturbances, and to provide averaged, spatially continuous surface velocity maps over the entire Brienz slope.

  2. 2.

    Persistent scatterer interferometry (PSI) on data acquired from ERS-1/2, Envisat ASAR, and Sentinel-1, each on ascending and descending orbit. We considered the interferometric point target analysis (IPTA, Werner et al. 2003), aiming at computing surface velocity maps and displacement time series at point targets maintaining a good coherence (i.e., SAR signal quality).

  3. 3.

    Digital image correlation (DIC) applied to four digital terrain models (DTMs) generated from airborne LiDAR. The data were acquired by helicopter on November 12, 2015, June 15, 2018, December 6, 2019, and September 4, 2020, respectively. The DIC analysis provided measurements over areas affected by large displacements, i.e., where DInSAR usually fails to provide useful results.

  4. 4.

    3-D surface displacements for two selected periods, i.e., 2015–2018 and 2018–2020. Sentinel-1 stacking computed in (1) and the DIC results obtained in (3) have been jointly used to obtain a full description of the displacement field, as well as hints on the temporal evolution of the landslide complex.

Table 1 Summary table of the ESA C-band SAR dataset available. The A in the orbit name stands for ascending tracks, while D for descending. *The Sentinel-1 revisit time of 6 days is available after 2017. Before this date the revisit time is 12 days
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To ease following our workflow and the reading, in the sections below we sequentially describe the methods and directly present the most relevant results. Additional outcomes (in particular, selected wrapped interferograms) are reported in the Supplementary Information (see Figs. S1–S8) and discussed in the “Discussion and conclusions” section.

Differential interferometry and multitemporal stacking

We computed surface displacement maps with differential interferometry by considering pairs of SAR images after alignment (also known as co-registration) and removal of the phase contribution due to topography (Bürgmann et al. 2000). The products obtained (i.e., differential interferograms) were visually inspected in their wrapped phase form, and then unwrapped by considering the MCF algorithm (Costantini 1998). For each satellite and orbit, we stacked selected interferograms with good quality (i.e., spatial coherence). Stacking methodologies are frequently used in geophysical data processing and remote sensing analyses in order to improve the signal-to-noise ratio (Stumpf et al. 2017; Gorelick et al. 2017). Regarding InSAR, stacking is generally performed by combining multiple unwrapped differential interferograms covering a pre-defined time period (e.g., one or multiple years), in order to highlight spatial domains retaining surface velocities within this time frame (Lundgren et al. 2001; Ciuffi et al. 2021). Any artifacts affecting single acquisitions are expected to have random variability over time and thus be mitigated with integration and/or averaging of the signal, provided that enough input images are available.

Results

No clear signs of surface displacements are evident in the period 1992–2010 with standard InSAR analyses on ERS-1/2 and Envisat ASAR (see also Supplementary Information S1–S2), although during this period minor surface activity was already known and measurable in situ. Single interferograms considering temporal baselines ranging from 35 days to 1 year were not successful in retrieving suitable measurements due to the excessive noise levels. Failure to retrieve measurements can be explained mainly due to relatively poor quality (in terms of spatial and temporal baselines) of the data available in this time range in comparison to the rates of motion and landcover. Figure 3 shows the results of annual stacking obtained for the Sentinel-1 orbits, which provide the most valuable results when selecting interferograms with perpendicular baselines (orbit separations) below 150 m and temporal baselines as short as 12 days. Interferograms with longer temporal baselines suffer from a substantial drop in coherence and were not beneficial in our investigation.

Fig. 3
figure 3

Results of the annual Sentinel-1 stacking analysis. Velocities are in satellite LOS. Negative values (blue) mean that the ground moved away from the satellite, while positive values (red) indicate that the ground moved toward the satellite. Black solid lines represent the main connection roads and are shown in all tiles to provide a spatial reference. Missing results are due to layover/shadow masking

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Sentinel stacks were generated considering the same observation periods for each orbit, consistently starting in the beginning of September of each year, highlighting inter-annual variability. We set coherence thresholds aiming at increasing the quality of the final stacking products; however, no significant improvement was observed and thus we decided to present the results here without thresholding. We attempted shorter stacking, covering about 3 months to potentially identify intra-annual and/or seasonal variabilities, but results were inconclusive. We also tried stacking solely Sentinel-1 interferograms with temporal baselines of 6 days, but this approach drastically reduced the number of input images, compromising the effectiveness of the procedure.

A striking observation is the remarkable differences in the spatial distribution of the displacement signal when comparing ascending and the two descending results. Results of T015A highlight mainly LOS movement away from the satellite in RB and the outer extents of RD in a “horseshoe-like” pattern. Distinct signals showing a different LOS sign for RB and RD are instead detected in descending orbits, where the surface displacements change sign even within the RD domain at the landslide toe, in the area close to the Albula river. This spatial variability suggests a strong heterogeneity of the surface displacement directions, combined with the remarkable topographic variations of the area under investigation. Focusing on the temporal behavior, a progressive increase of the surface displacements can be observed at selected locations, although this is not very clear in all considered orbits (Fig. 4). For example, surface velocities close to the Brienz village (GNSS points 11, 134, and 5001) appear very small in the period 2016–2017 compared with the domains located at higher elevations. After 2017, the displacement starts to be more evident and locally of the same order of magnitude. Moreover, the eastern portion (GNSS point 134) of the instability shows a remarkable acceleration from 0.2 m/a in 2016–2017 to about 0.3–0.4 m/a in 2018–2020. On the RB, near the GNSS stations 6006 and 6001 (see locations in Fig. 2), LOS surface velocities were of about 0.2–0.4 m/a in the period 2016–2018 but reached values up to 1 m/a in 2019–2020. Some discrepancies between the results obtained by different orbits can be related to the changes in viewing angles and/or to the impact of random noise on the stacking products. Considering the variability of the results in the vicinity of areas assumed stable over the analyzed period, for example, near GNSS point 42 located in the village of Vazerol, the expected level of accuracy of the stacking results is on the order of 0.1 m/a. This value is higher in areas where the displacements are of several m/a.

Fig. 4
figure 4

Results of the annual stacking at selected locations (see Fig. 2 for the position of points). Velocities are in satellite LOS

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Interferometric point target analysis (IPTA)

PSI is considered an advanced remote sensing method; however, in the last few years this approach has gained more and more popularity and can be considered a standard approach for the investigation of surface displacements on unstable slopes (Wasowski and Bovenga 2014). This is mainly due to the availability of new generation satellites such as the ESA Sentinel-1 constellation, which provides reliable acquisitions at global scales and with unprecedented spatial and temporal resolutions (Torres et al. 2012).

Several projects have been developed to provide wide-area coverage of PSI results (Zinno et al. 2018; Dehls et al. 2019; Crosetto et al. 2020), which directly provide to the end users PSI results in the form of surface velocity maps and displacement time series. Here we performed PSI processing on two relevant Sentinel-1 tracks, by applying the IPTA method on “single-reference” stacks (GAMMA Software, Wegmüller et al. 2016). This is one of the most diffused approaches for multi-temporal DInSAR analyses over unstable slope regions. The reference images for the IPTA processing were selected in the middle of the entire time of analysis (about five years), i.e., July 23, 2018, and July 27, 2018, for T015A and T066D, respectively. This choice helped to balance the temporal baselines of the processed pairs and expected to improve interferometric coherence. Due to the accurate control of the Sentinel-1 orbital parameters, the spatial baseline between all acquisitions and the reference image is generally below 150 m, ensuring a good quality of the SAR phase and thus more reliable displacement measurements.

Results

Figure 5 shows the Sentinel-1 surface velocities over the Brienz slope complex achieved with IPTA processing, compared with previous results obtained by considering ERS and Envisat ASAR sensors in the ESA GMES TERRAFIRMA project (Raetzo et al. 2007), for ascending and descending orbits. The relatively poor spatial coverage of point measurements is as expected. Some signs of surface displacements on the order of 0.01 cm/a can be seen in the village of Brienz starting from the period 2002 to 2010 (Fig. 5c); however, as already evidenced in the standard InSAR and stacking analyses, the surface deformation starts to be relevant only when considering the Sentinel-1 results. The measurement points are located mainly at buildings and/or other anthropic infrastructures, as well as some rock faces maintaining a relatively good temporal coherence over the period of analysis. While no information is generally available from the IPTA on the RB portions, measurement points located at the Brienz village are of positive sign in descending orbit and negative on the ascending orbit. Different signs on displacement results from ascending and descending orbits occur only in case of dominant slope-parallel motion, as the ground moves toward the sensor LOS with respect to one orbit and away from the sensor LOS in the other. On the contrary, in case of dominant vertical motion on a relatively flat area as the Brienz village, the sign of LOS velocity would be the same. This result suggests that a relevant component of the surface displacement affecting the Brienz village is slope parallel and/or sub-horizontal. In the Sentionel-1 results (Fig. 5e, f), some additional isolated points can be seen southwest of Brienz village that show an opposite sign of surface velocity. This suggests the presence of local areas with different displacement directions; however, the overall number of IPTA points is too small to perform a comprehensive interpretation. Snow cover in winter periods and rapid seasonal changes deeply affect the phase correlation in alpine regions (Wasowski and Bovenga 2014). This cannot be avoided despite performing local investigations on the slope of interest, and thus, with all processing parameters calibrated to obtain the best solution over the study area. The time series mostly show a linear displacement trend, with some minor oscillations that might be related to seasonal variations and/or uncompensated atmospheric artifacts (see Supplementary Information, Fig. S9). The ground-based measurements from 2015 to 2020 show a substantial acceleration in the same time period (Figi et al. 2022). Discrepancies between IPTA results and in-situ observations are likely caused by the large and rapid displacements occurring over the Brienz slope complex (Manconi 2021).

Fig. 5
figure 5

Surface velocity map over the Brienz slope obtained by applying the IPTA approach on different C-band satellite sensors. Positive values (red) mean that the ground moved toward the satellite LOS, while negative values (blue) indicate that the ground moved away from the satellite LOS. a ERS ascending, 1992–2000, note the poor spatial coverage due to reduced amount of imagery in comparison with the descending orbit; b ERS descending, 1992–2000; c Envisat ASAR ascending, 2002–2010; d Envisat ASAR descending, 2002–2010; e Sentinel-1 ascending, 2015–2020; f Sentinel-1 descending, 2015–2020. Selected Sentinel-1 PS time series are presented in the Supplementary Information, Fig. S9

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Digital image correlation analysis

Digital image correlation (DIC) is a method allowing to track displacements by comparing pixel groups in multi-temporal digital imagery acquired from different sensors, mainly optical and radar, and from different platforms (ground based, airborne, and/or spaceborne). The DIC strategy has been initially adopted on remote sensing datasets to study rapid flow of glaciers (Strozzi et al. 2002; Kääb et al. 2009). More and more often, however, this technique is used for the investigation of slope instabilities and landslide events, despite a reduced accuracy compared to standard InSAR approaches (Manconi et al. 2014). The theoretical accuracy of slope displacements measured by DIC strongly depends on the signal-to-noise ratio of the imagery, as well as on the GSD of the input data (Bickel et al. 2018; Bontemps et al. 2018; Stumpf et al. 2017). For this reason, Sentinel-1 imagery (with GSD of about 3 m in range and 15 m in azimuth direction) is often not suitable to obtain the desired accuracy levels at the slope scale.

Here we considered multi-temporal DTMs generated from airborne LiDAR surveys over the Brienz slope in the period between 2015 and 2020. First, we resampled the input data to a common grid of 1 m, as they initially entailed different GSDs. Then, we generated shaded relief images from the DTMs by considering multi-directional sun azimuth and elevation (Fey et al. 2015). The shaded relief data is projected coordinates in map coordinates (CH1903, EPSG 21781); thus, DIC results provide measurements of the displacement components occurring in the north–south and east–west directions. The DIC processing was performed using the software presented in Bickel et al. (2018), which has already demonstrated good performances for large slope deformation related to other alpine mass movements (Manconi et al. 2018; Glueer et al. 2019; Storni et al. 2020; Aaron et al. 2021). We have done several tests to identify the parameters providing reliable results. The final DIC parameters used in this analysis are reported in the Supplementary Information, Table S2.

Results

DIC results are presented for two selected time windows considered important for the interpretation of the spatial and temporal evolution of the Brienz slope complex. The periods range between November 12, 2015, and June 15, 2018 (period 1), and between June 15, 2018, and September 4, 2020 (period 2). These time windows have similar durations (2.6 vs. 2.2 years) and are characterized by the transition from slow to moderate surface velocities (i.e., period 1) and then a period of sustained high velocities (i.e., period 2). The DIC results show the dominating displacement direction toward south, with velocity values exceeding 2 m/a especially in the upper RB portions as well as along the western extents of RB (see Fig. S10). The east–west displacement component is lower in magnitude, and for this reason the DIC results include more noise. Another important observation from the DIC results is that the spatial distribution of the surface displacements is very similar in the two considered periods; however, an overall increase in surface velocities is noted in the 2018–2020 period, indicating an acceleration compared to the 2015–2018 period. This agrees with the ground-based observations. One of the main issues in the DIC processing was related to boundary effects associated with poor coverage of the DTMs around the Brienz slope, i.e., in areas expected to have minor or no displacements and that can be used for the evaluation of the DIC accuracy.

Reconstruction of 3-D displacement field

Due to the increased data availability, the integration of InSAR results from multiple satellite orbits, as well as with DIC products, is increasingly popular in the analysis of slope deformation.

Using this approach it is possible to derive a 2-D and/or 3-D representations of the displacement field and better study the kinematic behavior of landslides (Elefante et al. 2014; Delbridge et al. 2016; Frattini et al. 2018; Crippa et al. 2020). In the specific case of Brienz, a straightforward application of the classical approach combining the Sentinel-1 ascending and descending orbits does not provide substantial benefits. The main reason is that the SAR satellites fly on a near-polar orbit (~ 11° from north), and thus all surface displacements oriented along the satellite’s track are not measurable because they produce only very small (if any) changes in the LOS. Indeed, the displacement components that can be retrieved from two or more orbits are 2-D, i.e. the vertical and east–west directions, assuming that the north–south component is negligible (Manzo et al. 2006). From the DIC results, as well as from the ground-based monitoring, we know that in Brienz the displacement amplitudes show relevant spatial variations, and that the main component of slope movement is oriented toward south. To reconstruct the 3-D displacement components, an integration of all the available results is necessary. We computed the 3-D deformation considering a least-squares estimation of the displacement components starting from multiple observations. The least-squares solution is constructed as an over-determined set of linear equations, with the design matrix solved into the singular value decomposition (see, for example, Casu and Manconi (2016)). We exploited in total five observations (three LOS measurements from the Sentinel-1 orbits and two DIC results, in north–south and east–west directions, respectively). To this end, we have resampled the input datasets to a common grid (5 × 5 m) and computed stacking products to align the temporal observation of the Sentinel-1 data with the LiDAR surveys. The calculation was thus done on the two reference periods 2015–2018 and 2018–2020.

Results

Figure 6 shows the 3-D surface velocities for the time periods 2015–2018 and 2018–2020. The results confirm the dominance of the south-oriented motion component. The increase of surface velocity in the period 2015–2018 can be recognized in all components; however, the changes are more remarkable in the north–south and up-down directions, where the velocity locally exceeds 2 m/a in RB. Uplift of few cm/a was identified west of the Brienz village, with an increasing trend in the 2018–2020 period (~ 6 cm/a) compared to 2015–2018 (~ 2 cm/a). Unfortunately, no monitoring points were installed in this area during the period of observation; thus, we could not compare these results with external data. Additional GNSS survey and leveling measurements were started in summer 2021 to better understand the behavior of this zone. The source of this uplift is not clear with the information currently available.

Fig. 6
figure 6

3-D surface velocities in m/a over the Brienz landslide complex, obtained by combining the Sentinel-1 stacking and the DIC results on the LiDAR DTMs. Median filtering (kernel 9 × 9 pixels) is applied to improve the signal-to-noise ratio on the results. The black dashed polygon shows the area where minor uplift has been identified (see text for more details)

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Comparison with GNSS measurements

The reliability and accuracy of the surface deformation obtained with the combination of InSAR and DIC analyses have been validated with the available GNSS in-situ measurements. The extensive network of stations across the Brienz landslide complex allows for validation of the deformation results obtained in remote sensing analyses. We extracted the surface velocities for each direction at the location of GNSS stations and compared them with the velocities recorded by GNSS over the same time periods. We considered that GNSS station 42 is our stable reference (see location in Fig. 2). Figure 7 shows the scatterplots comparing the stacking results obtained for the three different Sentinel-1 orbits with the GNSS measurements projected along the satellite LOS. The agreement is better in the 2015–2018 period, when surface velocities were relatively lower than during 2018–2020. However, the inaccuracy of the InSAR measurements increases as the surface velocity overcomes the temporal phase aliasing thresholds at 0.85 m/a, considering 6-day revisit time (0.425 m/a with 12-day revisit time). This is more pronounced for the points located in the RB and leads to an underestimation of surface velocities up to several m/a in comparison with GNSS measurements.

Fig. 7
figure 7

Scatterplots comparing surface velocities measured with Sentinel-1 InSAR stacking and in-situ GNSS (projected along satellite LOS). Perfect agreement is indicated when points lie on the plots’ diagonal. Red dashed lines show the theoretical phase aliasing limits for 6 and 12 days (see Manconi 2021)

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Figure 8 shows the comparison of GNSS with the 3-D surface velocities obtained by combining the Sentinel-1 stacking and the DIC results. The agreement improves compared to the results of the InSAR stacking only; however, large discrepancies can still be observed. This is more evident in the vertical components, as the DIC results do not contribute to the least squares solution, and in the east–west components, as motion in these directions is relatively small compared to the north–south component and mostly below the DIC detection thresholds (see also Supplementary Information, Fig. S10). In general, the main discrepancies with the GNSS measurements are related to the underestimation of the vertical component in the upper portions of the RB. The north–south component gains better agreement to the GNSS measurements; however, in the 2018–2020 period, there is still a clear underestimation of the surface velocities.

Fig. 8
figure 8

Scatterplots comparing 3-D surface velocities obtained with the combination of Sentinel-1 InSAR stacking and DIC versus in-situ GNSS. Perfect agreement is indicated when points lie on the plots’ diagonal. Red dashed lines show the theoretical phase aliasing limits for 6 and 12 days (see Manconi 2021)

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Discussion and conclusions

The results attained with extensive investigations of remote sensing datasets can be of great value for the evaluation of the state of activity of landslide complexes. However, we have shown that several problems can be encountered. The main difficulties arise during processing of the critical stage from slow to fast surface displacement observed at large deep-seated slope instabilities (Agliardi et al. 2020). This period can last for several years or rapidly evolve on the order of few weeks and potentially increase the probability of catastrophic slope failures.

We provide numerous indications of the spatial and temporal evolution of surface displacement occurring at the Brienz slope complex in the period 1992–2020, ca. 30 years. First, we revealed that PSI analyses over heterogeneous, complex landslide scenarios such as Brienz, affected by large and/or rapid surface velocities, are not suitable for a comprehensive investigation of the spatial and temporal kinematic evolution. The main problems are related to the reduced coherence of the SAR phase, which leads to inaccurate displacement measurements and poor PS spatial coverage. The analysis of selected Sentinel-1 interferograms indicates that the RB domain in the period 2015–2018 has constant high levels of surface displacements, which can hardly be accurately detected with InSAR of image pairs acquired more than 18 days apart due to phase decorrelation. By stacking Sentinel-1 interferograms, it has been possible to reconstruct at least the yearly trends of surface displacements on one ascending and two descending orbits. These results provide a fair overview of the spatial distribution of surface deformation over the last 5 years, as well as information on their temporal evolution. In the RB domain the displacement amplitudes have increased between 2015 and 2020, while the RD domain shows an overall similar displacement behavior throughout the analyzed period. The only area with a relevant increase in surface displacement within this time frame is located to the west of the Brienz village.

We have also used additional satellite sensors to cross-validate the InSAR results. The analysis of the Radarsat-2 dataset has provided suitable results for a limited number of interferograms covering the period 2014–2019 despite the very-high spatial resolution of 5 m (see also Supplementary Information S3–S4). Moreover, the information is available only for summer periods. This period is already covered by the Sentinel-1 imagery, which has a more frequent revisit time compared to Radarsat-2 (6 to 12 days vs. 24 days) and thus provided better results (see also Supplementary Information S5–S7). ALOS-2 PALSAR-2 satellite data (L-band SAR imagery) for selected dates can retrieve more deformation than C-band in the RB area (see also Supplementary Information S8). Due to its longer wavelength (ca. 23 cm), L-band is less impacted by phase decorrelation caused by large movement and potentially local failure, and/or can follow displacements occurring in areas with vegetation (Aoki et al. 2021). Additional indications are provided on the surface deformation affecting the areas not covered by GNSS or total station monitoring (eastern slope portions). Unfortunately, the L-band data were very limited over our area of interest. Additional datasets acquired from SAOCOM, as well as future satellite missions planned with L-band SAR such as the NISAR, ALOS-3, PALSAR-4, and ROSE-L, will be an important source of information for landslide analyses in complex scenarios such as the Brienz case (Rosen et al. 2017).

Due to the intrinsic limitations in spatial resolution and in retrieving north–south displacement components with InSAR, the results obtained from the Sentinel-1 stacking analysis alone cannot be conclusive. Specific approaches have been proposed to combine ascending and descending Sentinel-1 orbits, also including constraints to retrieve the north–south displacement component. These assume that displacements at landslides are generally occurring in the direction of the slope aspect (Isya et al. 2019; Dai et al. 2022). Such assumption can provide misleading results, especially when the north–south components are dominant as in the case of Brienz (see also Supplementary Information, S11). We performed a DIC analysis by considering other remote sensing data, i.e., the DTMs obtained from airborne LiDAR surveys. This investigation is complementary to InSAR, as it provides information on displacements along the east–west and especially north–south directions. Considering the ground resolution of the available DTMs, the accuracy of the DIC analysis is expected to be on the order of ±0.1 m/a (Bickel et al. 2018), which is suitable to provide information in the specific case of Brienz, where the surface velocities are of m/a. The comparison between two selected sub-periods, i.e., 2015–2018 and 2018–2020, shows a clear increase in surface velocities mainly in the RB domain and toward the south. However, slight accelerations are also observed in the RD domain, especially in the westernmost portions at the elevations of the Brienz village. This is one of the areas where major damage is witnessed on the roads and infrastructures. Another area where we observe a slight increase in surface velocities is on the southeast sectors toward the Albula river. DIC approaches are a valid complementary method for the analysis of surface displacement components; however, datasets with suitable GSD are necessary to retrieve accurate measurements. The case of Brienz is rather unique, as it is unusual to have multi-temporal LiDAR surveys within 5 years. In the future, very high-resolution imagery available from new generation satellites (optical and SAR) with a spatial resolution on the order of 1 m will be an important additional source of information to be considered in such investigations.

We have combined the results of DInSAR stacking and the DIC analysis to reconstruct a full, 3-D picture of the surface deformation affecting the Brienz landslide complex. The combination of multiple Sentinel-1 orbits and DIC results has provided important information on the spatial distribution of the surface deformation. This approach allowed us to retrieve a more accurate representation of the displacement field and improved the agreement with the GNSS measurements. The surface deformation is characterized by a “patchy” pattern, which is representative of internal landslide compartments. This is in agreement with detailed evaluations of the 3-D displacements performed for the RB area and based on high-resolution terrestrial LiDAR datasets (Kenner et al. 2022). The most relevant observations on the 3-D surface velocities are related to the upper reaches of the RB domain (i.e., north of GNSS station 6001), where the surface velocities have substantially increased during the period 2018–2020 compared with 2015–2018. The obtained 3-D results show that intrinsic limitations associated with phase aliasing can be only partially overcome. In the north–south direction, where the displacements are of several m/a, we could retrieve a better agreement between with the GNSS measurements. This is because the DIC analysis provides a good complement to the InSAR measurements. East–west components, however, are smaller in amplitude and thus suffer of accuracy problems also with DIC. On the other end, up-down components are exclusively measured through InSAR and cannot be mitigated with this analysis. Higher-resolution datasets or constellation of satellite SAR sensors with more frequent acquisitions can help in retrieving a better representation of the 3-D behavior and allow for a more detailed kinematic interpretation of landslides based on the analysis of the displacement vector orientations (Kenner et al. 2022).

Among the discussed limitations, we note that a substantial underestimation of the landslide acceleration can occur if only spaceborne C-band (i.e., currently Sentinel-1) interferometry is applied. Indeed, phase decorrelation might affect the spatial sampling of measurement points, while phase aliasing can cause large deviations from the correct velocity values. This has to be sensibly considered in systematic monitoring programs aimed at identifying surface displacement anomalies (Dehls et al. 2019; Crosetto et al. 2020; Bianchini et al. 2021). The parameters determining the accuracy of the results when using spaceborne and aerial imagery are the spatial and temporal resolution, as well as their intrinsic detection thresholds and geometric constraints. This seems to be a trivial concept; however, it is not always carefully considered, and interpretations of surface displacements can be misleading. The amount of information available at the Brienz site is exceptional, and only in few alpine site remote sensing investigations can be complemented with detailed surface geology and structural data, and/or validated with in-situ measurements. Especially in inaccessible regions, where remote sensing is often the only available data source to define active landslide domains and possibly build evolutionary scenarios and/or hazard assessments, we suggest a cautious evaluation of the InSAR results obtained with C-band sensors only.