# Practical Estimation of Landslide Kinematics Using PSI Data

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Case Studies

^{2}. Their onset can be linked to the post-LGM destabilization of the valley flanks by means of progressive failure processes [23]. They are characterized by evident superficial morphostructures (e.g., Mt. Farinaccio), are part of ground based monitoring networks and affect critical scenarios (e.g., Mt. Solena impending over Cancano Lake and dam) and provide good examples of complex and segmented phenomena undergoing differential style of deformation (e.g., Corna Rossa) with nested sectors possibly evolving towards catastrophic collapse.

## 3. Workflow and Analyses

- 1-
- PSI data post-processing to extract quantities suitable to describe landslide kinematics;
- 2-
- spatial analysis of (point-like) PSI data to select profile traces representative of landside complexity (swath profiles) and distribute the information over landslide area (interpolation);
- 3-
- extraction of 2D profiles and assessment of the most suitable approach;
- 4-
- interpretation of 2D profiles using non-specific templates derived from simplified 2D FEM models and comparison to field evidence of selected case studies.

#### 3.1. PSI Datasets Post-Processing and Kinematic Descriptors

_{LOS}) is the most straightforward way to investigate the style of activity of a landslide, but for a correct interpretation, it is necessary to take into account the LOS parameters and the slope topography (slope, aspect, etc.) to estimate how much of the true 3D displacement vector can be observed [11,40].

_{slope}[2,40,42]). This facilitates the interpretation of VLOS data and maximizes the data availability, but since it assumes a global translational sliding, it hampers any unconstrained interpretation of the landslide kinematics [11,43]. This is especially true for phenomena such as complex landslides, in which the internal displacement pattern can vary and differ from a simple slope-parallel movement.

_{v}) and horizontal (V

_{e}) components and the 2D total displacement vectors (V

_{T}) [10]:

_{a}and V

_{d}are the ascending and descending LOS velocities (mm/year), and θ

_{a}and θ

_{d}are the incidence LOS angles for the considered satellite platform in the two acquisitions geometries.

_{v}, V

_{e}and τ change too (Figure 6a). Close to the main headscarp, the displacement vectors have a downward movement [11] and the total vector plunges at high angle into the slope (Figure 6b). In the middle and lower part, the horizontal component tends to become dominant and T vector usually becomes parallel to the slope or points upward in response of the toe uplifting (Figure 6b). Therefore, the displacement distributions may be used as a tool for interpreting the different geometry of the sliding surface for landslides of different typology and to identify active structures on the slope.

#### 3.2. Data Spatialization

#### 3.3. Profiles Extraction

_{v}, V

_{e}, V

_{LOS}, τ, Δ) considering both synthetic 2D finite elements (2DFEM) models and real case studies with known kinematics.

#### 3.4. Profiles Interpretation

#### 3.4.1. DFEM Interpretation Templates

_{v}), horizontal (V

_{e}) and 2D total displacement inclination (τ) values were extracted along slope and plotted in normalized distance-displacement graphs (Figure 9).

_{e}(Figure 9) is less meaningful because it is strongly biased by the geometry of the model and does not provide clear signatures of different kinematic styles.

#### 3.4.2. Interpretation Using Geomorphological Mapping

_{LOS}distribution with geometrical information of V

_{v}and τ resulting from the 2DInSAR analysis. We did not consider the horizontal displacement rate V

_{e}since it represents the horizontal movement on a 2D E-W vertical plane and its values can be highly biased on slope with unfavorable orientation (Figure 10). On the contrary the vertical velocity represents the upward/downward displacement rate and gives more significant information on the landslide gravitational movement.

_{v}values (Figure 10g) close to the headscarp, that going downslope tend to stabilize around a steady value of about −3.5 mm/y, suggesting that the entire mass is uniformly moving and there are no active structures that induce important vertical movements. Similarly, τ profile (Figure 10g) confirms this observation with only few local fluctuations in the top slope sector and at the toe. Instead, LOS velocity profile (Figure 10g) sharply decreases in the upper slope portion to then rise towards less negative values towards the toe. The simple interpretation of 1D LOS profile may thus result misleading since the change in LOS velocity is not directly reflected by a change in kinematic style and a rise in V

_{LOS}may be improperly interpreted as signature of a rotational movement, while only corresponding to a faster sliding sector.

_{v}and LOS curves because the slope has an unfavorable orientation, almost N-S, with sliding direction towards S and E-W component almost null. As consequence, since in the 2DInSAR approach the N-S component is set to zero and the V

_{e}is very small, LOS velocity and the vertical component tend to coincide. τ profile can be more easily interpreted since it shows dipping displacement vectors in the upper sector (Figure 10h) of the slope, corresponding to the main headscarp, which then decrease downslope as the movement becomes less steep, suggesting sliding on a surface that progressively becomes more parallel to the slope.

## 4. Discussion

_{LOS}profiles is not always straightforward in the assessment of kinematics because they can present peaks and fluctuations linked to heterogeneous velocity and isolated high values more than true kinematic transitions. Negative or positive peaks can outline active structures or nested phenomena that, despite having different displacement rates, keep the same deformation style (e.g., fast or slow sliding sectors).

_{LOS}) is not suitable to unambiguously represent landslide kinematics, especially when with complex phenomena such as slow RSD. These are characterized by sectors with different activity, kinematics and heterogeneous strain fields, and the single use of V

_{LOS}values results less robust and partially ineffective in describing the response of each slope sector. At the same time, the kinematics also influences the percentage of movement that can be sensed along the LOS thus resulting in different representative LOS velocities that only capture part of the total displacement vector thus returning a partial velocity information. Instead, the combination of data from different geometries captured with different LOS increases the sensitivity to displacement and reduces the complexity related to interpretation of InSAR data [10].

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

## References

- Crippa, C.; Valbuzzi, E.; Frattini, P.; Crosta, G.B.; Spreafico, M.C.; Agliardi, F. Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data. Landslides
**2021**. [Google Scholar] [CrossRef] - Aslan, G.; Foumelis, M.; Raucoules, D.; De Michele, M.; Bernardie, S.; Cakir, Z. Landslide mapping and monitoring using persistent scatterer interferometry (PSI) technique in the French alps. Remote Sens.
**2020**, 12, 1305. [Google Scholar] [CrossRef] [Green Version] - Gullà, G.; Peduto, D.; Borrelli, L.; Antronico, L.; Fornaro, G. Geometric and kinematic characterization of landslides affecting urban areas: The Lungro case study (Calabria, Southern Italy). Landslides
**2017**, 14, 171–188. [Google Scholar] [CrossRef] - Brückl, E.; Brunner, F.K.; Kraus, K. Kinematics of a deep-seated landslide derived from photogrammetric, GPS and geophysical data. Eng. Geol.
**2006**, 88, 149–159. [Google Scholar] [CrossRef] - Travelletti, J.; Malet, J.-P. Characterization of the 3D geometry of flow-like landslides: A methodology based on the integration of heterogeneous multi-source data. Eng. Geol.
**2012**, 128, 30–48. [Google Scholar] [CrossRef] - Wasowski, J.; Pisano, L. Long-term InSAR, borehole inclinometer, and rainfall records provide insight into the mechanism and activity patterns of an extremely slow urbanized landslide. Landslides
**2020**, 17, 445–457. [Google Scholar] [CrossRef] - Wasowski, J.; Bovenga, F. Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives. Eng. Geol.
**2014**, 174, 103–138. [Google Scholar] [CrossRef] - Ferretti, A.; Prati, C.; Rocca, F. Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Remote Sens.
**2001**, 39, 8–20. [Google Scholar] [CrossRef] - Schlögel, R.; Doubre, C.; Malet, J.P.; Masson, F. Landslide deformation monitoring with ALOS/PALSAR imagery: A D-InSAR geomorphological interpretation method. Geomorphology
**2015**, 231, 314–330. [Google Scholar] [CrossRef] - Eriksen, H.Ø.; Lauknes, T.R.; Larsen, Y.; Corner, G.D.; Bergh, S.G.; Dehls, J.; Kierulf, H.P. Visualizing and interpreting surface displacement patterns on unstable slopes using multi-geometry satellite SAR interferometry (2D InSAR). Remote Sens. Environ.
**2017**, 191, 297–312. [Google Scholar] [CrossRef] [Green Version] - Frattini, P.; Crosta, G.B.; Rossini, M.; Allievi, J. Activity and kinematic behaviour of deep-seated landslides from PS-InSAR displacement rate measurements. Landslides
**2018**, 15, 1053–1070. [Google Scholar] [CrossRef] - Agliardi, F.; Riva, F.; Barbarano, M.; Zanchetta, S.; Scotti, R.; Zanchi, A. Effects of tectonic structures and long-term seismicity on paraglacial giant slope deformations: Piz Dora (Switzerland). Eng. Geol.
**2019**, 263, 105353. [Google Scholar] [CrossRef] - Agliardi, F.; Crosta, G.B.; Frattini, P. Slow rock-slope deformation. In Landslides: Types, Mechanisms and Modeling; Cambridge University Press: Cambridge, UK, 2012; p. 207. [Google Scholar]
- Crosta, G.B.; Frattini, P.; Agliardi, F. Deep seated gravitational slope deformations in the European Alps. Tectonophysics
**2013**, 605, 13–33. [Google Scholar] [CrossRef] - Chigira, M. September 2005 rain-induced catastrophic rockslides on slopes affected by deep-seated gravitational deformations, Kyushu, southern Japan. Eng. Geol.
**2009**, 108, 1–15. [Google Scholar] [CrossRef] - Audemard, F.A.; Beck, C.; Carrillo, E. Deep-seated gravitational slope deformations along the active Boconó Fault in the central portion of the Mérida Andes, western Venezuela. Geomorphology
**2010**, 124, 164–177. [Google Scholar] [CrossRef] - Lin, C.W.; Tseng, C.M.; Tseng, Y.H.; Fei, L.Y.; Hsieh, Y.C.; Tarolli, P. Recognition of large scale deep-seated landslides in forest areas of Taiwan using high resolution topography. J. Asian Earth Sci.
**2013**, 62, 389–400. [Google Scholar] [CrossRef] - Agliardi, F.; Crosta, G.B.; Frattini, P.; Malusà, M.G. Giant non-catastrophic landslides and the long-term exhumation of the European Alps. Earth Planet. Sci. Lett.
**2013**, 365, 263–274. [Google Scholar] [CrossRef] - Bovis, M.J.; Evans, S.G. Extensive deformations of rock slopes in southern Coast Mountains, southwest British Columbia, Canada. Eng. Geol.
**1996**, 44, 163–182. [Google Scholar] [CrossRef] - Agliardi, F.; Crosta, G.B.; Zanchi, A. Structural constraints on deep-seated slope deformation kinematics. Eng. Geol.
**2001**, 59, 83–102. [Google Scholar] [CrossRef] - Crosta, G.B.; di Prisco, C.; Frattini, P.; Frigerio, G.; Castellanza, R.; Agliardi, F. Chasing a complete understanding of the triggering mechanisms of a large rapidly evolving rockslide. Landslides
**2013**, 11, 747–764. [Google Scholar] [CrossRef] - Grämiger, L.M.; Moore, J.R.; Gischig, V.S.; Ivy-Ochs, S.; Loew, S. Beyond debuttressing: Mechanics of paraglacial rock slope damage during repeat glacial cycles. J. Geophys. Res. Earth Surf.
**2017**, 122, 1004–1036. [Google Scholar] [CrossRef] - Riva, F.; Agliardi, F.; Amitrano, D.; Crosta, G.B. Damage-Based Time-Dependent Modeling of Paraglacial to Postglacial Progressive Failure of Large Rock Slopes. J. Geophys. Res. Earth Surf.
**2018**, 123, 124–141. [Google Scholar] [CrossRef] [Green Version] - Ambrosi, C.; Crosta, G.B. Large sackung along major tectonic features in the Central Italian Alps. Eng. Geol.
**2006**, 83, 183–200. [Google Scholar] [CrossRef] - Frattini, P.; Crosta, G.B.; Allievi, J. Damage to buildings in large slope rock instabilities monitored with the PSinSAR™ technique. Remote Sens.
**2013**, 5, 4753–4773. [Google Scholar] [CrossRef] [Green Version] - Crosta, G.B.; Imposimato, S.; Roddeman, D.G. Numerical modelling of large landslides stability and runout. Nat. Hazards Earth Syst. Sci.
**2003**, 3, 523–538. [Google Scholar] [CrossRef] - Eberhardt, E.; Stead, D.; Coggan, J.S. Numerical analysis of initiation and progressive failure in natural rock slopes-the 1991 Randa rockslide. Int. J. Rock Mech. Min. Sci.
**2004**, 41, 69–87. [Google Scholar] [CrossRef] - Rott, H.; Scheuchl, B.; Siegel, A.; Grasemann, B. Monitoring very slow slope movements by means of SAR interferometry: A case study from a mass waste above a reservoir in the Otztal Alps, Austria. Geophys. Res. Lett.
**1999**, 26, 1629–1632. [Google Scholar] [CrossRef] - Agliardi, F.; Crippa, C.; Spreafico, M.C.; Manconi, A.; Bourlès, D.; Braucher, R.; Cola, G.; Zanchetta, S. Strain partitioning and heterogeneous evolution in a giant slope deformation revealed by InSAR, dating and modelling. In Proceedings of the Geophysical Research Abstracts, Vienna, Austria, 7–12 April 2019; Volume 21. [Google Scholar]
- Jongmans, D.; Garambois, S. Geophysical investigation of landslides: A review. Bull. Soc. Géol. Fr.
**2007**, 178, 101–112. [Google Scholar] [CrossRef] [Green Version] - Bovis, M.J. Rock-slope deformation at Affliction Creek, southern Coast Mountains, British Columbia. Can. J. Earth Sci.
**1990**, 27, 243–254. [Google Scholar] [CrossRef] - Ferretti, A.; Fumagalli, A.; Novali, F.; Prati, C.; Rocca, F.; Rucci, A. A new algorithm for processing interferometric data-stacks: SqueeSAR. In IEEE Transactions on Geoscience and Remote Sensing; IEEE: Piscataway, NJ, USA, 2011. [Google Scholar]
- Colesanti, C.; Ferretti, A.; Prati, C.; Rocca, F. Monitoring landslides and tectonic motions with the Permanent Scatterers Technique. Eng. Geol.
**2003**, 68, 3–14. [Google Scholar] [CrossRef] - Colesanti, C.; Wasowski, J. Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry. Eng. Geol.
**2006**, 88, 173–199. [Google Scholar] [CrossRef] - Rosi, A.; Agostini, A.; Tofani, V.; Casagli, N. A Procedure to map subsidence at the regional scale using the persistent scatterer interferometry (PSI) technique. Remote Sens.
**2014**, 6, 10510–10522. [Google Scholar] [CrossRef] - Agliardi, F.; Spreafico, M.C.; Zanchetta, S.; Castellanza, R.; Asnaghi, R.; Paternoster, J.; Crippa, C.; Crosta, G. Gravitational transfer zones influence DSGSD mechanisms and activity. In Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria, 4–13 April 2018; Volume 20, p. 13819. [Google Scholar]
- Schmid, S.M.; Fügenschuh, B.; Kissling, E.; Schuster, R. Tectonic map and overall architecture of the Alpine orogen. Eclogae Geol. Helv.
**2004**, 97, 93–117. [Google Scholar] [CrossRef] - Ferrari, F.; Giani, G.P.; Apuani, T. Towards the comprehension of rockfall motion, with the aid of in situ tests. In Proceedings of the Vajont Thoughts and Analyses after 50 Years Since the Catastrophic Landslide, Padua, Italy, 8–10 October 2013; Sapienza Università Editrice: Roma, Italy; pp. 163–171. [Google Scholar]
- Henderson, I.H.C.; Lauknes, T.R.; Osmundsen, P.T.; Dehls, J.; Larsen, Y.; Redfield, T.F. A structural, geomorphological and InSAR study of an active rock slope failure development. Geol. Soc. London, Spec. Publ.
**2011**, 351, 185–199. [Google Scholar] [CrossRef] - Notti, D.; Herrera, G.; Bianchini, S.; Meisina, C.; García-Davalillo, J.C.; Zucca, F. A methodology for improving landslide PSI data analysis. Int. J. Remote Sens.
**2014**, 35, 2186–2214. [Google Scholar] [CrossRef] - Joughin, L.R.; Kwok, R.; Fahnestock, M.A. Interferometric estimation of three-dimensional ice-flow using ascending and descending passes. IEEE Trans. Geosci. Remote Sens.
**1998**, 36, 25–37. [Google Scholar] [CrossRef] [Green Version] - Notti, D.; Meisina, C.; Zucca, F.; Colombo, A. Models To Predict Persistent Scatterers Data Distribution and Their Capacity To Register Movement Along the Slope. In Proceedings of the Fringe 2011, Frascati, Italy, 19–23 September 2011; pp. 19–23. [Google Scholar]
- Meisina, C.; Zucca, F.; Notti, D.; Colombo, A.; Cucchi, A.; Savio, G.; Giannico, C.; Bianchi, M. Geological interpretation of PSInSAR Data at regional scale. Sensors
**2008**, 8, 7469–7492. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Manzo, M.; Ricciardi, G.P.P.; Casu, F.; Ventura, G.; Zeni, G.; Borgström, S.; Berardino, P.; Del Gaudio, C.; Lanari, R. Surface deformation analysis in the Ischia Island (Italy) based on spaceborne radar interferometry. J. Volcanol. Geotherm. Res.
**2006**, 151, 399–416. [Google Scholar] [CrossRef] - Dalla Via, G.; Crosetto, M.; Crippa, B. Resolving vertical and east-west horizontal motion from differential interferometric synthetic aperture radar: The L’Aquila earthquake. J. Geophys. Res. Solid Earth
**2012**, 117. [Google Scholar] [CrossRef] [Green Version] - Ferretti, A. Satellite InSAR Data: Reservoir Monitoring from Space; EAGE Publications: Houten, The Netherlands, 2014; ISBN 978-90-73834-71-2. [Google Scholar]
- Baulig, H. Sur une méthode altimétrique d’analyse morphologique appliquée à la Bretagne péninsulaire. Bull. Assoc. Geogr. Fr.
**1926**, 3, 7–9. [Google Scholar] [CrossRef] - Grohmann, C.H. Morphometric analysis in geographic information systems: Applications of free software GRASS and R. Comput. Geosci.
**2004**, 30, 1055–1067. [Google Scholar] [CrossRef] [Green Version] - Sibson, R. A Brief Description of Natural Neighbor Interpolation chapter 2. In Interpolating Multivariate Data; John Wiley & Sons: New York, NY, USA, 1981; pp. 21–36. [Google Scholar]
- Hammah, R.E.; Yacoub, T.; Corkum, B.; Curran, J.H. The practical modelling of discontinuous rock masses with finite element analysis. In Proceedings of the The 42nd US Rock Mechanics Symposium (USRMS), San Francisc, CA, USA, 29 June–2 July 2008. [Google Scholar]
- Riahi, A.; Hammah, E.R.; Curran, J.H. Limits of applicability of the finite element explicit joint model in the analysis of jointed rock problems. In Proceedings of the 44th US Rock Mechanics Symposium and 5th US-Canada Rock Mechanics Symposium, Salt Lake City, UT, USA, 27–30 June 2010. [Google Scholar]
- Dawson, E.M.; Roth, W.H.; Drescher, A. Slope stability analysis by strength reduction. Geotechnique
**1999**, 49, 835–840. [Google Scholar] [CrossRef] - Agliardi, F.; Scuderi, M.M.; Fusi, N.; Collettini, C. Slow-to-fast transition of giant creeping rockslides modulated by undrained loading in basal shear zones. Nat. Commun.
**2020**, 11, 1–11. [Google Scholar] [CrossRef] [PubMed] - Peduto, D.; Ferlisi, S.; Nicodemo, G.; Reale, D.; Pisciotta, G.; Gullà, G. Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales. Landslides
**2017**, 14, 1993–2007. [Google Scholar] [CrossRef] - Uzielli, M.; Catani, F.; Tofani, V.; Casagli, N. Risk analysis for the Ancona landslide—I: Characterization of landslide kinematics. Landslides
**2015**, 12, 69–82. [Google Scholar] [CrossRef] [Green Version] - Intrieri, E.; Frodella, W.; Raspini, F.; Bardi, F.; Tofani, V. Using satellite interferometry to infer landslide sliding surface depth and geometry. Remote Sens.
**2020**, 12, 1462. [Google Scholar] [CrossRef] - Eriksen, H.Ø. Combining Satellite and Terrestrial Interferometric Radar Data to Investigate Surface Displacement in the Storfjord and Kåfjord Area, Northern Norway. Ph.D. Thesis, The Arctic University of Norway, Tromsø, Norway, 2017. [Google Scholar]

**Figure 1.**Schematic sketches depicting the surface morphostructural features commonly associated, to different extents, with slow rock-slope deformations with (

**a**) translational, (

**b**) rotational, and (

**c**) compound kinematics. Labels refer to (1) head scarp, (2) scarps, (3) trenches, (4) basal shear zones, (5) counterscarp, (6) toe, and (7) nested landslide.

**Figure 2.**Case studies considered in the analysis; (

**a**) major tectonic units in Lombardy region (Schmid et al., 2004) and location of the selected case studies: (

**b**) Mt. Solena; (

**c**) Corna Rossa; (

**d**) Mt. Farinaccio.

**Figure 4.**Representation of the acquisition geometries and corresponding datasets. Sketch of (

**a**) ascending and (

**b**) descending satellite geometry and corresponding LOS direction; (

**c**) configuration of combined ascending and descending orbit to retrieve 2D displacement components: vertical displacement (V

_{v}), horizontal displacement (V

_{e}), total vector displacement (T); PS of the (

**d**) ascending and (

**e**) descending Sentinel dataset and (

**f**) pseudo-PS retrieved from the combination of the two geometries using a cell size of 25 × 25 m.

**Figure 5.**Different cell dimension for the extraction of Pseudo PS over a complex landslide area with multiple nested sectors: (

**a**) 10 × 10 m cell size results below the spatial resolution of the sensor; (

**b**) 25 × 25 m; (

**c**) 50 × 50 m; (

**d**) 100 × 100 m.

**Figure 6.**2DInSAR vector decomposition. (

**a**) combination of ascending and descending geometries to extract horizontal (V

_{e}), vertical (V

_{v}) and total (T) 2D displacement components; (

**b**) Simplified view of common Δ trend along slope; (

**c**) example of translational landslides with T vectors along slope; (

**d**) example of rotational landslide with T vector dipping in the slope.

**Figure 7.**Corna Rossa slow rock slope deformation used as test site on which run a spatial analysis of PSI data; (

**a**) swath analysis on V

_{LOS}; (

**b**) swath analysis on τ; (

**c**) neighborhood statistics on V

_{LOS}; (

**d**) neighborhood statistics on τ (

**e**) natural neighbor interpolation on V

_{LOS}; (

**f**) natural neighbor interpolation on τ. Point statistics is calculated using a circular buffer of 5cell of radius, Natural Neighbor interpolation is performed on a 20 × 20 m grid.

**Figure 8.**Examples of along slope profiles. (

**a**,

**b**) comparison between profiles extracted using different swath width on Corna Rossa Wider swaths have smoother trend and a smaller velocity ranges; (

**c**,

**d**) comparison of τ profiles and LOS profiles extracted using point statistics, swath, and natural neighbor approach; see text for discussion.

**Figure 9.**Displacement curves obtained from simplified 2DFEM of rotational, translational and compound mechanisms. Vertical (V

_{v}) and horizontal (V

_{e}) components are normalized to be better compared. τ represents the inclination of the 2D displacement vector along slope.

**Figure 10.**Example of LOS and 2DInSAR profiles extracted along a translational (Mt. Solena) and a rotational (Mt. Farinaccio) slow rock slope deformation using natural neighbor approach. (

**a**) LOS values distribution on Mt Solena; (

**b**) τ distribution on Mt Solena; (

**c**) V

_{v}distribution on Mt Solena; (

**d**) LOS values distribution on Mt. Farinaccio; (

**e**) τ distribution on Mt. Farinaccio; (

**f**) V

_{v}distribution on Mt. Farinaccio; (

**g**) velocity and 2DInSAR profiles for Mt. Solena; (

**h**) velocity and 2DInSAR profiles for Mt. Farinaccio.

**Figure 11.**Integration between morphostructural mapping and Δ profiles: (

**a**) Mt. Solena morphostructural mapping and relative (

**b**) Δ profile; (

**c**) 2DFEM Δ reference profile for a translational kinematic; (

**d**) Mt. Farinaccio morphostructural mapping and relative (

**e**) Δ profile; (

**f**) 2DFEM Δ reference profile for a rotational kinematics; (

**g**) Corna Rossa and relative morphostructural mapping (

**h**) Δ profile; (

**i**) 2DFEM Δ reference profile for a compound kinematics. 2DFEM Δ profiles are extracted assuming a constant slope of 30°.

**Figure 12.**Profiles extracted using natural neighbor interpolation from 3 different sectors of Corna Rossa slow RSD. (

**a**) LOS velocity, (

**b**) τ values, (

**c**) vertical component values interpolated over the landslide. (

**d**) V

_{v}, V

_{LOS}, τ values and (

**e**) Δ values along profile1; (

**f**) V

_{v}, V

_{LOS}, τ values and (

**g**) Δ values along profile 2; (

**h**) V

_{v}, V

_{LOS}, τ values and (

**i**) Δ values along profile3. Their trends suggest a strong internal partitioning that causes changes in kinematics along the slope, as reflected by the presence of different mapped morphostructures.

Satellite | PSI Technique | Mode | Θ (°) | Δ (°) | Revisit Time (Days) | Time Interval (Years) |
---|---|---|---|---|---|---|

Sentinel 1A/B | SqueeSAR™ | Ascending | 41.99 | 10.23 | 12 (6 after 2016) | 2015–2017 |

Sentinel 1A/B | SqueeSAR™ | Descending | 41.78 | 8.89 | 2015–2017 |

Slope Material below Unstable Mass | Very Stiff | Weak | Schist | Gneiss | Granitoid | |
---|---|---|---|---|---|---|

Unit Weight (MN/m^{3}) | 0.027 | 0.027 | 0.027 | 0.027 | 0.027 | 0.027 |

Stiffness | isotropic | isotropic | isotropic | isotropic | isotropic | isotropic |

Poisson Ratio | 0.3 | 0.3 | 0.3 | 0.3 | 0.3 | 0.3 |

Young Modulus | 100,000 | 100,000 | 10,000 | 19,000 | 31,000 | 41,000 |

Material Type | elastic | plastic | plastic | plastic | plastic | plastic |

Peak Tensile Strength (MPa) | 5 | 0.4 | 0.4 | 0.1 | 0.15 | 0.2 |

Peak Cohesion (MPa) | 5 | 0.75 | 0.3 | 0.4 | 0.5 | 0.7 |

Peak Friction Angle (°) | 35 | 35 | 30 | 36 | 47 | 50 |

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Crippa, C.; Agliardi, F.
Practical Estimation of Landslide Kinematics Using PSI Data. *Geosciences* **2021**, *11*, 214.
https://doi.org/10.3390/geosciences11050214

**AMA Style**

Crippa C, Agliardi F.
Practical Estimation of Landslide Kinematics Using PSI Data. *Geosciences*. 2021; 11(5):214.
https://doi.org/10.3390/geosciences11050214

**Chicago/Turabian Style**

Crippa, Chiara, and Federico Agliardi.
2021. "Practical Estimation of Landslide Kinematics Using PSI Data" *Geosciences* 11, no. 5: 214.
https://doi.org/10.3390/geosciences11050214