Articles | Volume 13, issue 2
https://doi.org/10.5194/gi-13-225-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gi-13-225-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A tool for estimating ground-based InSAR acquisition characteristics prior to monitoring installation and survey and its differences from satellite InSAR
ISTE, University of Lausanne, 1015 Lausanne, Switzerland
Marc-Henri Derron
ISTE, University of Lausanne, 1015 Lausanne, Switzerland
Carlo Rivolta
Ellegi srl, 20123 Milan, Italy
Michel Jaboyedoff
ISTE, University of Lausanne, 1015 Lausanne, Switzerland
Related authors
No articles found.
Xiong Tang, Wei Liu, Siming He, Lei Zhu, Michel Jaboyedoff, Huanhuan Zhang, Yuqing Sun, and Zenan Huo
Geosci. Model Dev., 18, 4743–4758, https://doi.org/10.5194/gmd-18-4743-2025, https://doi.org/10.5194/gmd-18-4743-2025, 2025
Short summary
Short summary
The paper presents an explicit stabilized two-phase material point method (MPM) based on the one-point two-phase MPM scheme. The novelty of the work lies in the employment of stabilized techniques, including the strain smoothing method and the multi-field variational principle. With its effective and easy-to-implement stabilized techniques, the proposed model offers an effective and reliable approach for simulating both static and dynamic processes in solid–fluid porous media.
Clément Hibert, François Noël, David Toe, Miloud Talib, Mathilde Desrues, Emmanuel Wyser, Ombeline Brenguier, Franck Bourrier, Renaud Toussaint, Jean-Philippe Malet, and Michel Jaboyedoff
Earth Surf. Dynam., 12, 641–656, https://doi.org/10.5194/esurf-12-641-2024, https://doi.org/10.5194/esurf-12-641-2024, 2024
Short summary
Short summary
Natural disasters such as landslides and rockfalls are mostly difficult to study because of the impossibility of making in situ measurements due to their destructive nature and spontaneous occurrence. Seismology is able to record the occurrence of such events from a distance and in real time. In this study, we show that, by using a machine learning approach, the mass and velocity of rockfalls can be estimated from the seismic signal they generate.
François Noël, Michel Jaboyedoff, Andrin Caviezel, Clément Hibert, Franck Bourrier, and Jean-Philippe Malet
Earth Surf. Dynam., 10, 1141–1164, https://doi.org/10.5194/esurf-10-1141-2022, https://doi.org/10.5194/esurf-10-1141-2022, 2022
Short summary
Short summary
Rockfall simulations are often performed to make sure infrastructure is safe. For that purpose, rockfall trajectory data are needed to calibrate the simulation models. In this paper, an affordable, flexible, and efficient trajectory reconstruction method is proposed. The method is tested by reconstructing trajectories from a full-scale rockfall experiment involving 2670 kg rocks and a flexible barrier. The results highlight improvements in precision and accuracy of the proposed method.
Emmanuel Wyser, Yury Alkhimenkov, Michel Jaboyedoff, and Yury Y. Podladchikov
Geosci. Model Dev., 14, 7749–7774, https://doi.org/10.5194/gmd-14-7749-2021, https://doi.org/10.5194/gmd-14-7749-2021, 2021
Short summary
Short summary
We propose an implementation of the material point method using graphical processing units (GPUs) to solve elastoplastic problems in three-dimensional configurations, such as the granular collapse or the slumping mechanics, i.e., landslide. The computational power of GPUs promotes fast code executions, compared to a traditional implementation using central processing units (CPUs). This allows us to study complex three-dimensional problems tackling high spatial resolution.
Martin Franz, Michel Jaboyedoff, Ryan P. Mulligan, Yury Podladchikov, and W. Andy Take
Nat. Hazards Earth Syst. Sci., 21, 1229–1245, https://doi.org/10.5194/nhess-21-1229-2021, https://doi.org/10.5194/nhess-21-1229-2021, 2021
Short summary
Short summary
A landslide-generated tsunami is a complex phenomenon that involves landslide dynamics, wave dynamics and their interaction. This phenomenon threatens numerous lives and infrastructures around the world. To assess this natural hazard, we developed an efficient numerical model able to simulate the landslide, the momentum transfer and the wave all at once. The good agreement between the numerical simulations and physical experiments validates our model and its novel momentum transfer approach.
Emmanuel Wyser, Yury Alkhimenkov, Michel Jaboyedoff, and Yury Y. Podladchikov
Geosci. Model Dev., 13, 6265–6284, https://doi.org/10.5194/gmd-13-6265-2020, https://doi.org/10.5194/gmd-13-6265-2020, 2020
Short summary
Short summary
In this work, we present an efficient and fast material point method (MPM) implementation in MATLAB. We first discuss the vectorization strategies to adapt this numerical method to a MATLAB implementation. We report excellent agreement of the solver compared with classical analysis among the MPM community, such as the cantilever beam problem. The solver achieves a performance gain of 28 compared with a classical iterative implementation.
Jason Bula, Marc-Henri Derron, and Gregoire Mariethoz
Geosci. Instrum. Method. Data Syst., 9, 385–396, https://doi.org/10.5194/gi-9-385-2020, https://doi.org/10.5194/gi-9-385-2020, 2020
Short summary
Short summary
We developed a method to acquire dense point clouds with a low-cost Velodyne Puck lidar system, without using expensive Global Navigation Satellite System (GNSS) positioning or IMU. We mounted the lidar on a motor to continuously change the scan direction, leading to a significant increase in the point cloud density. The system was compared with a more expensive system based on IMU registration and a SLAM algorithm. The alignment between acquisitions with those two systems is within 2 m.
Cited articles
Abellán, A., Oppikofer, T., Jaboyedoff, M., Rosser, N. J., Lim, M., and Lato, M. J.: Terrestrial laser scanning of rock slope instabilities: State-Of-Science (Terrestrial Lidar Vs. Rock Slope Instabilities), Earth Surf. Proc. Land., 39, 80–97, https://doi.org/10.1002/esp.3493, 2014.
Addie, G.: A new true thickness formula based on the apparent dip, Econ. Geol., 63, 188–189, https://doi.org/10.2113/gsecongeo.63.2.188, 1968.
Anon: ETSI EN 300 440 v2.1.1, https://www.etsi.org/deliver/etsi_en/300400_300499/300440/02.01.01_60/en_300440v020101p.pdf (last access: 25 April 2024), 2017.
Antonello, G., Casagli, N., Farina, P., Fortuny, J., Leva, D., Nico, G., Sieber, A. J., and Tarchi, D.: A ground-based interferometer for the safety monitoring of landslides and structural deformations, in: IGARSS 2003, 2003 IEEE International Geoscience and Remote Sensing Symposium, 21–25 July 2003, Toulouse, France, 218–220, https://doi.org/10.1109/IGARSS.2003.1293729, 2003.
Berardino, P., Fornaro, G., Lanari, R., and Sansosti, E.: A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms, IEEE T. Geosci. Remote, 40, 2375–2383, https://doi.org/10.1109/TGRS.2002.803792, 2002.
Cabral-Cano, E., Dixon, T. H., Miralles-Wilhelm, F., Diaz-Molina, O., Sanchez-Zamora, O., and Carande, R. E.: Space geodetic imaging of rapid ground subsidence in Mexico City, Geol. Soc. Am. Bull., 120, 1556–1566, https://doi.org/10.1130/B26001.1, 2008.
Caduff, R., Schlunegger, F., Kos, A., and Wiesmann, A.: A review of terrestrial radar interferometry for measuring surface change in the geosciences: Terrestrial Radar Interferometry In The Geosciences, Earth Surf. Proc. Land., 40, 208–228, https://doi.org/10.1002/esp.3656, 2015.
Carlà, T., Tofani, V., Lombardi, L., Raspini, F., Bianchini, S., Bertolo, D., Thuegaz, P., and Casagli, N.: Combination of GNSS, satellite InSAR, and GBInSAR remote sensing monitoring to improve the understanding of a large landslide in high alpine environment, Geomorphology, 335, 62–75, https://doi.org/10.1016/j.geomorph.2019.03.014, 2019.
Carrea, D., Abellán, A., Guerin, A., Jaboyedoff, M., and Voumard, J.: Erosion processes in molassic cliffs: the role of the rock surface temperature and atmospheric conditions, in: EGU General Assembly 2014 , Geophysical Research Abstracts Vol. 16, EGU2014-9188-1, https://meetingorganizer.copernicus.org/EGU2014/EGU2014-9188-1.pdf (last access: 25 july 2024), 2014.
Carrea, D., Abellan, A., Derron, M.-H., and Jaboyedoff, M.: Automatic Rockfalls Volume Estimation Based on Terrestrial Laser Scanning Data, in: Engineering Geology for Society and Territory – Volume 2, edited by: Lollino, G., Giordan, D., Crosta, G. B., Corominas, J., Azzam, R., Wasowski, J., and Sciarra, N., Springer International Publishing, Cham, 425–428, https://doi.org/10.1007/978-3-319-09057-3_68, 2015.
Casagli, N., Farina, P., Leva, D., Nico, G., and Tarchi, D.: Ground-based SAR interferometry as a tool for landslide monitoring during emergencies, in: IGARSS 2003, 2003 IEEE International Geoscience and Remote Sensing Symposium, 21–25 July 2003, Toulouse, France, 2924–2926, https://doi.org/10.1109/IGARSS.2003.1294633, 2003.
Catani, F., Canuti, P., and Casagli, N.: The Use of Radar Interferometry in Landslide Monitoring, in: Landslides in Cold Regions in the Context of Climate Change, edited by: Shan, W., Guo, Y., Wang, F., Marui, H., and Strom, A., Springer International Publishing, Cham, 177–190, https://doi.org/10.1007/978-3-319-00867-7_13, 2014.
Cazzanil, L., Colesanti, C., Leva, D., Nesti, G., Prati', C., Roccal, F., Tarchi', D., and di Milano, P.: A ground based parasitic SAR experiment, Geoscience and Remote Sensing, IEEE Trans., 38, 2132–2141, 2000.
Colesanti, C. and Wasowski, J.: Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry, Eng. Geol., 88, 173–199, https://doi.org/10.1016/j.enggeo.2006.09.013, 2006.
Dai, K., Deng, J., Xu, Q., Li, Z., Shi, X., Hancock, C., Wen, N., Zhang, L., and Zhuo, G.: Interpretation and sensitivity analysis of the InSAR line of sight displacements in landslide measurements, GISci. Remote Sens., 59, 1226–1242, https://doi.org/10.1080/15481603.2022.2100054, 2022.
Dehls, J., Giudici, D., Farina, P., Martin, D., and Froese, D.: Monitoring Turtle Mountain using ground-basedsynthetic aperture radar (GB-InSAR), GeoCalgary, Calgary, AB, Canada, 1635–1640, https://members.cgs.ca/documents/conference2010/GEO2010/pdfs/GEO2010_218.pdf (last access: 25 July 2024), 2010.
Eltner, A. and Sofia, G.: Structure from motion photogrammetric technique, in: Developments in Earth Surface Processes, vol. 23, Elsevier, 1–24, https://doi.org/10.1016/B978-0-444-64177-9.00001-1, 2020.
Fei, L., Choanji, T., Derron, M.-H., Jaboyedoff, M., Sun, C., and Wolff, C.: Retreat analysis of a sandstone marl interbedded cliff based on a three-year remote sensing survey: A case study at La Cornalle, Switzerland, in: EGU General Assembly 2023, Vienna, Austria, 23–28 April 2023, EGU23-7617, https://doi.org/10.5194/egusphere-egu23-7617, 2023.
Ferretti, A., Monti-Guarnieri, A., Prati, C., Rocca, F., and Massonnet, D.: SAR images of the Earth's surface, in: InSAR Principles: Guidelines for SAR Interferometry Processing and Interpretation, the Netherlands, 11–38, https://www.esa.int/About_Us/ESA_Publications/InSAR_Principles_Guidelines_for_SAR_Interferometry_Processing_and_Interpretation_br_ESA_TM-19 (last access: 16 December 2022), 2007.
Ferretti, A., Rucci, A., Tamburini, A., Del Conte, S., and Cespa, S.: Advanced InSAR for Reservoir Geomechanical Analysis, in: EAGE Workshop on Geomechanics in the Oil and Gas Industry, May 2014, Dubai, United Arab Emirates, https://doi.org/10.3997/2214-4609.20140459, 2014.
Frattini, P., Crosta, G. B., Rossini, M., and Allievi, J.: Activity and kinematic behaviour of deep-seated landslides from PS-InSAR displacement rate measurements, Landslides, 15, 1053–1070, https://doi.org/10.1007/s10346-017-0940-6, 2018.
Gabriel, A. K., Goldstein, R. M., and Zebker, H. A.: Mapping small elevation changes over large areas: Differential radar interferometry, J. Geophys. Res., 94, 9183, https://doi.org/10.1029/JB094iB07p09183, 1989.
Garthwaite, M. C., Miller, V. L., Saunders, S., Parks, M. M., Hu, G., and Parker, A. L.: A Simplified Approach to Operational InSAR Monitoring of Volcano Deformation in Low- and Middle-Income Countries: Case Study of Rabaul Caldera, Papua New Guinea, Front. Earth Sci., 6, 240, https://doi.org/10.3389/feart.2018.00240, 2019.
Goldstein, R. M., Zebker, H. A., and Werner, C. L.: Satellite radar interferometry: Two-dimensional phase unwrapping, Radio Sci., 23, 713–720, https://doi.org/10.1029/RS023i004p00713, 1988.
Griffiths, H.: Interferometric synthetic aperture radar, Electron. Commun. Eng. J., 7, 247–256, https://doi.org/10.1049/ecej:19950605, 1995.
Hein, A.: Processing of SAR Data: Fundamentals, in: Signal Processing, Interferometry, Springer, https://doi.org/10.1007/978-3-662-09457-0, 2004.
Henderson, F. M. and Lewis, A. J.: Radar Fundamentals: The Geoscience Perspective, in: Principles and Application of Imaging Radar: Manual of Remote Sensing, John Wiley, New York, 131–181, ISBN 9780471294061, 1998.
Hilley, G. E., Bürgmann, R., Ferretti, A., Novali, F., and Rocca, F.: Dynamics of Slow-Moving Landslides from Permanent Scatterer Analysis, Science, 304, 1952–1955, https://doi.org/10.1126/science.1098821, 2004.
Jaboyedoff, M., Blikra, L., Crosta, G. B., Froese, C., Hermanns, R., Oppikofer, T., Böhme, M., and Stead, D.: Fast assessment of susceptibility of massive rock instabilities, in: Landslides and Engineered Slopes: Protecting Society through Improved Understanding, Taylor & Francis Group, London, UK, 459–465, ISBN 978-0-415-62123-6, 2012.
Jensen, J. R.: chap. 9. Active and Passive Microwave Remote Sensing, in: Remote Sensing of the Environment: An Earth Resource Perspective, 2nd Edn., Artech House, ISBN 13:978-0890061923, 2006.
Klauder, J. R., Price, A. C., Darlington, S., and Albersheim, W. J.: The Theory and Design of Chirp Radars, Bell Syst. Tech. J., 39, 745–808, https://doi.org/10.1002/j.1538-7305.1960.tb03942.x, 1960.
Kropatsch, W. G. and Strobl, D.: The generation of SAR layover and shadow maps from digital elevation models, IEEE T. Geosci. Remote, 28, 98–107, https://doi.org/10.1109/36.45752, 1990.
Leva, D., Nico, G., Tarchi, D., Fortuny-Guasch, J., and Sieber, A. J.: Temporal analysis of a landslide by means of a ground-based SAR interferometer, IEEE T. Geosci. Remote, 41, 745–752, https://doi.org/10.1109/TGRS.2003.808902, 2003.
Lin, Y.-C. and Fuh, C.-S.: Distortion correction for digital cameras, in: Proceedings SIBGRAPI'98, International Symposium on Computer Graphics, Image Processing, and Vision, 20–23 October 1998, Rio de Janeiro, Brazil, 396–401, https://doi.org/10.1109/SIBGRA.1998.722778, 1998.
Lingua, A., Piatti, D., and Rinaudo, F.: Remote Monitoring Of A Landslide Using An Integration Of GB-INSAR And Lidar Techniques, in: Technical Commission I, XXIst ISPRS Congress, Beijing, China, 361–366, https://www.isprs.org/proceedings/XXXVII/congress/1_pdf/60.pdf (last access: 25 July 2024), 2008.
Lipson, S. G., Lipson, H., and Tannhauser, D. S.: Waves, in: Optical Physics, The press syndicate of the university of Cambridge, Cambridge, 15–35, ISBN 978-0-521-43047-0, 1995.
Mahafza, B. R.: Radar systems analysis and design using Matlab, Chapman & Hall/CRC, Boca Raton, 529 pp., ISBN 978-1-58488-182-7, 2000.
Mancini, F., Grassi, F., and Cenni, N.: A Workflow Based on SNAP–StaMPS Open-Source Tools and GNSS Data for PSI-Based Ground Deformation Using Dual-Orbit Sentinel-1 Data: Accuracy Assessment with Error Propagation Analysis, Remote Sens., 13, 753, https://doi.org/10.3390/rs13040753, 2021.
MATLAB: gradientm, https://ch.mathworks.com/help/map/ref/gradientm.html?searchHighlight=gradientm&s_tid=srchtitle_support_results_1_gradientm (last access: 2 November 2023), 2023.
Massonnet, D., Rossi, M., Carmona, C., Adragna, F., Peltzer, G., Feigl, K., and Rabaute, T.: The displacement field of the Landers earthquake mapped by radar interferometry, Nature, 364, 138–142, https://doi.org/10.1038/364138a0, 1993.
McCandless, S. and Jackson, C.: Chapter 1. Principles of Synthetic Aperture Radar, in: Synthetic Aperture Radar Marine User's Manual, NOAA, https://sarusersmanual.com/ (last access: 16 December 2022), 2004.
Miron, D.: Chapter 2 – Antenna Fundamentals I, in: Small Antenna Design, Small Antenna Design, 2006, 9–41, https://doi.org/10.1016/B978-075067861-2/50004-0, 2006.
Nadav, L.: Radar, in: Encyclopedia of Physical Science and Technology, 3rd Edn., Academic Press, 497–510, ISBN 978-0-12-227410-7, 2003.
Noferini, L., Pieraccini, M., Mecatti, D., Luzi, G., Atzeni, C., Tamburini, A., and Broccolato, M.: Permanent scatterers analysis for atmospheric correction in ground-based SAR interferometry, IEEE T. Geosci. Remote, 43, 1459–1471, https://doi.org/10.1109/TGRS.2005.848707, 2005.
Pedrazzini, A., Jaboyedoff, M., Derron, M.-H., Abellán, A., and Orozco, C. V.: Reinterpretation of displacements and failure mechanisms of the upper portion of Randa rock slide, GeoCalgary 2010, Calgary, AB, Canada, https://members.cgs.ca/documents/conference2010/GEO2010/pdfs/GEO2010_118.pdf (last access: 14 July 2024), 2010.
Pieraccini, M. and Miccinesi, L.: Ground-Based Radar Interferometry: A Bibliographic Review, Remote Sens., 11, 1029, https://doi.org/10.3390/rs11091029, 2019.
Pipia, L., Fabregas, X., Aguasca, A., and Lopez-Martinez, C.: Atmospheric Artifact Compensation in Ground-Based DInSAR Applications, IEEE Geosci. Remote Sens. Lett., 5, 88–92, https://doi.org/10.1109/LGRS.2007.908364, 2008.
Rees, W. G.: Technical note: Simple masks for shadowing and highlighting in SAR images, Int. J. Remote Sens., 21, 2145–2152, https://doi.org/10.1080/01431160050029477, 2000.
Rouyet, L., Kristensen, L., Derron, M.-H., Michoud, C., Blikra, L. H., Jaboyedoff, M., and Lauknes, T. R.: Evidence of rock slope breathing using ground-based InSAR, Geomorphology, 289, 152–169, https://doi.org/10.1016/j.geomorph.2016.07.005, 2017.
Rudolf, H., Leva, D., Tarchi, D., and Sieber, A. J.: A mobile and versatile SAR system, in: IEEE 1999 International Geoscience and Remote Sensing Symposium, IGARSS'99, 28 June–2 July 1999, Hamburg, Germany, 592–594, https://doi.org/10.1109/IGARSS.1999.773575, 1999.
Sabins, F. F.: Remote Sensing: Principles and Interpretation, in: 3rd Edn., W. H. Freeman, 494 pp., ISBN 0716724421, 1997.
Stimson, G. W.: chap. 30. Meeting High Resolution Ground Mapping Requirements, in: Introduction to Airborne Radar, vol. PM56, SPIE Press, 393–424, ISBN 1-891121-01-4, 1998.
Strozzi, T., Antonova, S., Günther, F., Mätzler, E., Vieira, G., Wegmüller, U., Westermann, S., and Bartsch, A.: Sentinel-1 SAR Interferometry for Surface Deformation Monitoring in Low-Land Permafrost Areas, Remote Sens., 10, 1360, https://doi.org/10.3390/rs10091360, 2018.
Sturzenegger, M., Yan, M., Stead, D., and Elmo, D.: Application and limitations of ground-based laser scanning in rock slope characterization, in: Rock Mechanics: Meeting Society's Challenges and Demands, edited by: Eberhardt, E., Stead, D., and Morrison, T., Taylor & Francis, 29–36, ISBN 978-0-415-44401-9, ISBN 978-1-4398-5657-4, 2007.
Talich, M.: The Deformation Monitoring of Dams by the Ground-Based InSAR Technique – Case Study of Concrete Hydropower Dam Orlík, Int. J. Adv. Agricult. Environ. Eng., 3, 192–197,https://doi.org/10.15242/IJAAEE.A0416051, 2016.
Tapete, D., Casagli, N., Luzi, G., Fanti, R., Gigli, G., and Leva, D.: Integrating radar and laser-based remote sensing techniques for monitoring structural deformation of archaeological monuments, J. Archaeolog. Sci., 40, 176–189, https://doi.org/10.1016/j.jas.2012.07.024, 2013.
Tarchi, D.: Monitoring landslide displacements by using ground-based synthetic aperture radar interferometry: Application to the Ruinon landslide in the Italian Alps, J. Geophys. Res., 108, 2387, https://doi.org/10.1029/2002JB002204, 2003.
Tarchi, D., Ohlmer, E., and Sieber, A.: Monitoring of Structural Changes by Radar Interferometry, Res. Nondestruct. Eval., 9, 213–225, https://doi.org/10.1080/09349849709414475, 1997.
Toomay, J. C. and Hannen, P. J.: Radar Principles for the Non-specialist, in: 3rd Edn., Scitech Publishing, ISBN 978-1-891121-28-9, 2004.
Turner, I. L., Harley, M. D., Almar, R., and Bergsma, E. W. J.: Satellite optical imagery in Coastal Engineering, Coast. Eng., 167, 103919, https://doi.org/10.1016/j.coastaleng.2021.103919, 2021.
Usai, S. and Hanssen, R.: Long time scale INSAR by means of high coherence features, in: Proc. Ers. Symposium, 414 pp., https://repository.tudelft.nl/record/uuid:d1df5565-3451-4d9e-a219-7c15549cba43 (last access: 25 July 2024), 1997.
Werner, C., Strozzi, T., Wiesmann, A., and Wegmuller, U.: A real aperture radar for ground-based differential interferometry, in: Proc. IGARSS, 7–11 July 2008, Boston, MA, 1–2, https://doi.org/10.1109/IGARSS.2008.4779320, 2008.
Wicks, C., Thatcher, W., and Dzurisin, D.: Migration of Fluids Beneath Yellowstone Caldera Inferred from Satellite Radar Interferometry, Science, 282, 458–462, https://doi.org/10.1126/science.282.5388.458, 1998.
Wolberg, J.: Chapter 2: The method of Least Squares, in: Data Analysis Using the Method of Least Squares: Extracting the Most Information from Experiments, Germany, Springer, Berlin, Heidelberg, 31–49, ISBN 3-540-25674-1, 2006.
Wolff, C.: Frequency-Modulated Continuous-Wave Radar (FMCW Radar): https://www.radartutorial.eu/ (last access: 25 April 20230, 1998.
Wolff, C.: charlottewolff/GB-PAR: first (first), Zenodo [code], https://doi.org/10.5281/zenodo.12820384, 2024.
Wolff, C., Jaboyedoff, M., Fei, L., Pedrazzini, A., Derron, M.-H., Rivolta, C., and Merrien-Soukatchoff, V.: Assessing the Hazard of Deep-Seated Rock Slope Instability through the Description of Potential Failure Scenarios, Cross-Validated Using Several Remote Sensing and Monitoring Techniques, Remote Sens., 15, nb:5396, https://doi.org/10.3390/rs15225396, 2023.
Woodhouse, I. H.: chap. 10. Imaging Radar, in: Introduction to Microwave Remote Sensing, CRC Press, 45 pp., ISBN 978-1-315-27257-3, 2006.
Zebker, H. A. and Villasenor, J.: Decorrelation in interferometric radar echoes, IEEE T. Geosci. Remote, 30, 950–959, https://doi.org/10.1109/36.175330, 1992.
Short summary
The remote-sensing InSAR technique is vital for monitoring slope instabilities but requires understanding. This paper delves into differences between satellite and GB-InSAR. It offers a tool to determine the optimal GB-InSAR installation site, considering various technical, meteorological, and topographical factors. By generating detailed maps and simulating radar image characteristics, the tool eases the setup of monitoring campaigns for effective and accurate ground movement tracking.
The remote-sensing InSAR technique is vital for monitoring slope instabilities but requires...