, Volume 15, Issue 3, pp 439–452 | Cite as

Mapping of slow landslides on the Palos Verdes Peninsula using the California landslide inventory and persistent scatterer interferometry

  • El Hachemi BoualiEmail author
  • Thomas Oommen
  • Rüdiger Escobar-Wolf
Original Paper


Extremely slow landslides, those with a displacement rate <16 mm/year, may be imperceptible without proper instrumentation. These landslides can cause infrastructure damage on a long-term timescale. The objective is to identify these landslides through the combination of information from the California landslide inventory (CLI) and ground displacement rates using results from persistent scatterer interferometry (PSI), an interferometric synthetic aperture radar (InSAR) stacking technique, across the Palos Verdes Peninsula in California. A total of 34 ENVISAT radar images (acquired between 2005 and 2010) and 40 COSMO-SkyMed radar images (acquired between 2012 and 2014) were processed. An InSAR landslide inventory (ILI) is created using four criteria: minimum PS count, average measured ground velocity, slope angle, and slope aspect. The ILI is divided into four categories: long-term slides (LTSs), potentially active slides (PASs), relatively stable slopes (RSSs), and unmapped extremely slow slides (UESSs). These categories are based on whether landslides were previously mapped on that slope (in the CLI), if persistent scatterers (PSs) are present, and whether PSs are unstable or stable. The final inventory includes 263 mapped landslides across the peninsula, of them 67 landslides were identified as UESS. Although UESS exhibit low velocity and are relatively small (average area of 8865 m2 per slide), their presence in a highly populated area such as the Palos Verdes Peninsula could lead to destruction of infrastructure and property over the long term.


Palos Verdes Peninsula California landslide inventory Persistent scatterer interferometry 



The study was funded through the NASA Earth and Space Science Fellowship Program (proposal: 16-EARTH16F-0086). Data were provided by many agencies and organizations. COSMO-SkyMed radar images were originally acquired by the Italian Space Agency and provided to the authors by the European Space Agency (proposal ID 31684). ENVISAT radar images were acquired and provided by the European Space Agency (proposal ID 82169). The California Landslide Inventory GIS shapefile for the Palos Verdes Peninsula was provided by the California Geological Survey, a division of the California Department of Conservation. Three digital elevation models were used: Shuttle Radar Topography Mission model is a product of the Jet Propulsion Laboratory; Advanced Spaceborne Thermal Emission and Reflection Radiometer model is a product of National Aeronautics and Space Administration (NASA) and Ministry of Economy, Trade, and Industry; a 1/3 arc-second (10-m) NED DEM from the USGS. Three-component (vertical, north, and east) displacement time-series composing the four GPS time-series were downloaded from the UNAVCO Data Archive Interface Version 2. The background image displayed in Figs. 2, 4, 5, 6, and 8 were provided by the USGS, NASA, Google, and Digital Globe. The authors would finally like to thank the Landslide editors and two reviewers for their comments and assistance.

Compliance with Ethical Standards


Maps throughout this paper were created using ArcGIS® software by Esri. ArcGIS® and ArcMap are the intellectual property of Esri and are used herein under license. Copyright © Esri. All rights reserved. For more information about Esri® software, please visit


  1. Antronico L, Borrelli L, Peduto D, Fornaro G, Gullà G, Paglia L, Zeni G (2013) Conventional and innovative techniques for the monitoring of displacements in landslide affected area. In Landslide Science and Practice Proceedings of The Second World Landslide Forum, 3–9 October 2011, Rome Vol. 2, 125–131. DOI–3–642-31445-2_16, Springer-Verlag Berlin Heidelberg 2013, ISBN 978-3-642-31444-5
  2. Baran I, Stewart MP, Kampes BM, Perski Z, Lilly P (2003) A modification to the Goldstein radar interferogram filter. IEEE Trans Geosci Remote Sens 41(9):2114–2118CrossRefGoogle Scholar
  3. Berardino P, Fornaro G, Lanari R, Sansosti E (2002) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans Geosci Remote Sens 40(11):2375–2383CrossRefGoogle Scholar
  4. Bianchini S, Herrera G, Mateos RM, Notti D, Garcia I, Mora O, Moretti S (2013) Landslide activity maps generation by means of persistent scatterer interferometry. Remote Sens 5:6198–6222CrossRefGoogle Scholar
  5. Bouali EH, Oommen T, Escobar-Wolf R (2016a) Interferometric stacking toward geohazard identification and geotechnical asset monitoring. J Infrastruct Syst 22(2):12CrossRefGoogle Scholar
  6. Bouali EH, Oommen T, Vitton S, Escobar-Wolf R, Brooks C (2016b) Rockfall hazard rating system: benefits of utilizing remote sensing. Env Eng Geosci:1078–7275. doi:
  7. Bouali EH, Oommen T, Escobar-Wolf R (2017) Structure mapping through spatial and temporal deformation monitoring using persistent scatterer interferometry and geographic information systems. Geotech Front 278:509–519Google Scholar
  8. Calabro MD, Schmidt DA, Roering JJ (2010) An examination of seasonal deformation at the Portuguese Bend landslide, Southern California, using radar interferometry. J Geophys Res 115(F2):10CrossRefGoogle Scholar
  9. California Department of Water Resources (CDWR) (2014) Summary of recent, historical, and estimated potential for future land subsidence in California. State of California Department of Water Resources. Accessed 6 January 2017
  10. California Geological Survey (CGS) (2017) California landslide inventory [web-based GIS]. California Department of Conservation, Accessed 13 August 2016
  11. Calvello M, Peduto D, Arena A (2017) Combined use of statistical and DInSAR data analyses to define the state of activity of slow-moving landslides. Landslides 14(2):473–489. doi: CrossRefGoogle Scholar
  12. Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Process Landf 16:427–445CrossRefGoogle Scholar
  13. Casagli N, Frodella W, Morelli S, Tofani V, Ciampalini A, Intrieri E, Raspini F, Rossi G, Tanteri L, Lu P (2017) Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning. Geo Environ Disasters 4.1(9):1–23Google Scholar
  14. Cascini L, Peduto D, Pisciotta G, Arena L, Ferlisi S, Fornaro G (2013) The combination of DInSAR and facility damage data for the updating of slow-moving landslide inventory maps at medium scale. Nat Hazards Earth Syst Sci 13:1527–1549CrossRefGoogle Scholar
  15. Catani F, Casagli N, Ermini L, Righini G, Menduni G (2005) Landslide hazard and risk mapping at catchment scale in the Arno River basin. Landslides 2:329–342CrossRefGoogle Scholar
  16. Cigna F, Bianchini S, Casagli N (2013) How to assess landslide activity and intensity with persistent scatterer interferometry (PSI): the PSI-based matrix approach. Landslides 10:267–283CrossRefGoogle Scholar
  17. City of Los Angeles (CLA) (2016) White Point landslide: project summary Accessed 18 May 2017
  18. City of Rancho Palos Verdes (CRPV) (2012) Landslide Workshop Accessed 22 November 2016
  19. Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sens 65(2):1389–1399Google Scholar
  20. Colesanti C, Wasowski J (2006) Investigating landslides with space-borne synthetic aperture radar (SAR) interferometry. Eng Geol 88:173–199CrossRefGoogle Scholar
  21. Constantini M (1998) A novel phase unwrapping method based on network programming. IEEE Trans Geosci Remote Sens 36(3):813–821CrossRefGoogle Scholar
  22. Constantini M, Falco S, Malvarosa F, Minati F (2008) A new method for identification and analysis of persistent scatterers in series of SAR images. IEEE International Geoscience and Remote Sensing Symposium 2:449–452Google Scholar
  23. Crosetto M, Biescas E, Duro J (2008) Generation of advanced ERS and Envisat interferometric SAR products using the stable point network technique. Photogramm Eng Remote Sens 4:443–450CrossRefGoogle Scholar
  24. Crosetto M, Monserrat O, Cuevas-González M, Devanthéry N, Crippa B (2016) Persistent scatterer interferometry: a review. ISPRS J Photogramm Remote Sens 115:78–89CrossRefGoogle Scholar
  25. Cruden DM (1991) A simple definition of a landslide. Bull Int Assoc Eng Geol 43:27–29CrossRefGoogle Scholar
  26. Cruden DM, Varnes DJ (1996) Landslide types and processes. In: Turner KA, Schuster RL (eds) Landslides: Investigation and Mitigation (Chapter 3), Transportation Research Board Special Report 247:36–75Google Scholar
  27. Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: an overview. Eng Geol 64:65–87CrossRefGoogle Scholar
  28. Ehlig PL (1982) The Palos Verdes Peninsula: its physiography, land use and geologic setting. In: Cooper JD (ed) Volume and Guidebook: Landslides and Landslide Abatement, Geological Society of America, Palos Verdes Peninsula, Southern California, Cordilleran Section, 78th Annual Meeting, pp 3–6Google Scholar
  29. Ehlig PL, Bean RT (1982) Dewatering of the Abalone Cove landslide, Rancho Palos Verdes, Los Angeles County, CA. In: Cooper JD (comp) Volume and Guidebook: Landslides and Landslide Abatement, Geological Society of America, Palos Verdes Peninsula, Southern California, Cordilleran Section, 78th Annual Meeting, 67–79Google Scholar
  30. Fell R (1994) Landslide risk assessment and acceptable risk. Can Geotech J 31:261–272CrossRefGoogle Scholar
  31. Fell R, Hartford D (1997) Landslide risk management. In: Cruden D, Fell R (eds) Landslide Risk Assessment. Balkema, Rotterdam, pp 51–109Google Scholar
  32. Ferretti A, Prati C, Rocca F (2000) Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Trans Geosci Remote Sens 38(5):2202–2212CrossRefGoogle Scholar
  33. Ferretti A, Prati C, Rocca F (2001) Permanent scatterers in SAR interferometry. IEEE Trans Geosci Remote Sens 39(1):8–20CrossRefGoogle Scholar
  34. Finlay PJ (1996) The risk assessment of slopes. University of New South Wales School of Civil Engineering, DissertationGoogle Scholar
  35. Fisher MA, Normark WR, Langenheim VE, Calvert AJ, Sliter R (2004) The offshore Palos Verdes Fault Zone near San Pedro, Southern California. Bull Seismol Soc Am 94(2):506–530CrossRefGoogle Scholar
  36. Gatelli F, Guamieri AM, Parizzi F, Pasquali P, Prati C, Rocca F (1994) The wave number shift in SAR interferometry. IEEE Trans Geosci Remote Sens 32(4):855–865CrossRefGoogle Scholar
  37. Ghulam A, Amer R, Ripperdan R (2010) A filtering approach to improve deformation accuracy using large baseline, low coherence DInSAR phase images. Geoscience and Remote Sensing Symposium (GARSS), 2010 I.E. International, Honolulu, p 3494–3497Google Scholar
  38. Göblirsch W, Pasquali P (1996) Algorithms for calculation of digital surface models from the unwrapped interferometric phase. Remote sensing—a scientific vision for sustainable development 1997. IEEE Int 1:656–658Google Scholar
  39. Goldstein RM, Werner CL (1998) Radar interferogram filtering for geophysical applications. Geophys Rese Lett 25(21):4035–4038CrossRefGoogle Scholar
  40. Guarnieri AM, Guccione P, Pasquali P, Desnos YL (2003) Multi-mode ENVISAT ASAR interferometry: techniques and preliminary results. IEE Proc-Radar, Sonar Navigation 150(3):193–200CrossRefGoogle Scholar
  41. Gullà G, Peduto L, Antronico L, Fornaro G (2017) Geometric and kinematic characterization of landslides affecting urban areas: the Lungro case study (Calabria, southern Italy). Landslides 14:171–188CrossRefGoogle Scholar
  42. Herrera G, Gutierrez F, Garcia-Davalillo JC, Guerrero J, Notti D, Galve JP, Fernandez-Merodo JA, Cooksley G (2013) Multi-sensor advanced DInSAR monitoring of very slow landslides: the Tena Valley case study (central Spanish Pyrenees). Remote Sens Environ 128:31–43CrossRefGoogle Scholar
  43. Holecz F, Moreira J, Pasquali P, Voight S, Meier E, Nuesch D (1997) Height model generation, automatic geocoding and a mosaicking using airborne AeS-1 InSAR data. Remote Sensing—A Scientific Vision for Sustainable Development, 1997 I.E. International 4:1929–1931Google Scholar
  44. Hooper A, Zebker P, Segall P, Kampes B (2004) A new method for measuring deformation on volcanoes and other non-urban areas using InSAR persistent scatterers. Geophys Res Lett 31(23):1–5CrossRefGoogle Scholar
  45. Hu J, Li ZW, Ding XL, Zhu JJ, Zhang L, Sun Q (2014) Resolving three-dimensional surface displacements from InSAR measurements: a review. Earth-Sci Rev 133:1–17CrossRefGoogle Scholar
  46. Hungr O (2007) Dynamics of rapid landslides. In: Fukuoka H (ed) Progress of landslide science. Springer, Berlin Heidelberg, pp 47–57CrossRefGoogle Scholar
  47. Jennings CW, Strand RG, Rogers TH (1977) Geologic map of California, California Division of Mines and GeologyGoogle Scholar
  48. Jones CE, An K, Blom RG, Kent JD, Ivins ER, Bekaert D (2016) Anthropogenic and geologic influences on subsidence in the vicinity of New Orleans, Louisiana. J Geophys Res Solid Earth 121(5):3867–3887CrossRefGoogle Scholar
  49. Kayen RE, Lee HJ, Hein JR (2002) Influence of the Portuguese Bend landslide on the character of the effluent-affected sediment deposit, Palos Verdes margin, Southern California. Cont Shelf Res 22:911–922CrossRefGoogle Scholar
  50. Kousteni A, Hill R, Dixon N, Kavanagh J (1999) Acoustic emission technique for monitoring soil and rock slope instability. In: Yagi N, Yamagami T, Jiang JC (eds) Slope stability engineering. Balkema, Rotterdam, pp 150–156Google Scholar
  51. Leroueil S, Locat J, Vaunat J, Picarelli L, Lee H, Faure R (1996) Geotechnical characterization of slope movements. In: Senneset K (ed) Landslides. Balkema, Rotterdam, pp 53–74Google Scholar
  52. Lu P, Stumpf A, Norman K, Casagli N (2011) Object-oriented change detection for landslide rapid mapping. IEEE Geosci Remote Sens Lett 8(4):701–705CrossRefGoogle Scholar
  53. Lu P, Catani F, Tofani V, Casagli N (2014) Quantitative hazard and risk assessment for slow-moving landslides from persistent scatterer interferometry. Landslides 11(4):685–696CrossRefGoogle Scholar
  54. Mayuga MN, Allen DR (1970) Subsidence in the Wilmington oil field, Long Beach, California, USA. Land Subsid 1:66–79Google Scholar
  55. Mazzanti P, Rocca A, Bozzano F, Cossu R, Floris M (2012) Landslide forecasting analysis by displacement time series derived from satellite InSAR data: preliminary results. ESA SP-697:1–8Google Scholar
  56. McNulty B (2012) Geology of the Palos Verdes Peninsula, Los Angeles, CA: A Field Guide for the Non-Geologist. Department of Earth Science, California State University Dominguez Hills and CSUDH Presidential Creative Initiative Fund. Accessed September 20 2016
  57. Merriam R (1960) Portuguese Bend landslide, Palos Verdes Hills, California. J Geol 68(2):140–153Google Scholar
  58. Morgenstern NR (1997) Toward landslide risk assessment in practice. In: Cruden D, Fell R (eds) Landslide risk assessment. Balkema, Rotterdam, pp 15–23Google Scholar
  59. Nascetti A, Capaldo P, Porfini M, Pieralice F, Fratarcangeli F, Benenati L, Crespi M (2015) Fast terrain modelling for hydrogeological risk mapping and emergency management: the contribution of high-resolution satellite SAR imagery. Geomat, Nat Hazards Risk 6(5–7):554–582CrossRefGoogle Scholar
  60. Notti D, Davalillo JC, Herrera G, More O (2010) Assessment of the performance of X-band satellite radar data for landslide mapping and monitoring: Upper Tena Valley case study. Nat Hazards Earth Syst Sci 10:1865–1875CrossRefGoogle Scholar
  61. Novellino A, Cigna F, Sowter A, Ramondini M, Calcaterra D (2017) Exploitation of the intermittent SBAS (ISBAS) algorithm with COSMO-SkyMed data for landslide mapping in north-western Sicily, Italy. Geomorphology 280:153–166CrossRefGoogle Scholar
  62. Parise M (2001) Landslide mapping techniques and their use in the assessment of the landslide hazard. Phys Chem Earth C 26(9):679–703Google Scholar
  63. Parise M (2003) Observation of surface features on an active landslide, and implications for understanding its history of movement. Nat Hazards Earth Syst Sci 3:569–580CrossRefGoogle Scholar
  64. Peduto D, Ferlisi S, Nicodemo G, Reale D, Pisciotta G, Gullà G (2017) Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales. Landslides (in press) DOI:
  65. Ramsey E III, Werle D, Lu Z, Rangoonwala A, Suzuoki Y (2009) A case of timely satellite image acquisitions in support of coastal emergency environmental response management. J Coast Res:1168–1172Google Scholar
  66. Reigber A, Moreira J (1997) Phase unwrapping by fusion of local and global methods. Remote sensing—a scientific vision for sustainable development, 1997. IEEE Int 2:869–871Google Scholar
  67. Salvi S, Tolomei C, Boncori JPM, Pezzo G, Atzori S, Antonioli A, Trasatti E, Giuliani R, Zoffoli S, Coletta A (2012) Activation of the SIGRIS monitoring system for ground deformation mapping during the Emilia 2012 seismic sequence, using COSMO-SkyMed InSAR data. Ann Geophys 55(4):797–802Google Scholar
  68. Sarmap (2009) Synthetic aperture radar and SARscape guidebook. 274 pGoogle Scholar
  69. Schaefer LN, Lu Z, Oommen T (2015) Dramatic volcanic instability revealed by InSAR. Geol 43(8):743–746CrossRefGoogle Scholar
  70. Schaefer LN, Lu Z, Oommen T (2016) Post-eruption deformation processes measured using ALOS-1 and UAVSAR InSAR at Pacaya Volcano, Guatemala. Remote Sens 8(1):15 pGoogle Scholar
  71. Schaefer LN, Wang T, Escobar-Wolf R, Oommen T, Lu Z, Kim J, Lundgren PR, Waite GP (2017) Three-dimensional displacements of a large volcano flank movement during the May 2010 eruptions at Pacaya Volcano, Guatemala. Geophys Res Let 44(1):135–142CrossRefGoogle Scholar
  72. Scheidegger A (1973) On the prediction of the reach and velocity of catastrophic landslides. Rock Mech 5:231–236CrossRefGoogle Scholar
  73. Schulz WH, Coe JA, Ricci PP, Smoczyk GM, Shurtleff BL, Panosky J (2017) Landslide kinematics and their potential controls from hourly to decadal timescales: insights from integrating ground-based InSAR measurements with structural maps and long-term monitoring data. Geomorphology 285:121–136CrossRefGoogle Scholar
  74. Simons M, Rosen PA (2007) 3.12—interferometric synthetic aperture radar geodesy. Treatise Geophys 3:391–446CrossRefGoogle Scholar
  75. Stephenson WJ, Rockwell TK, Odum JK, Shedlock KM, Okaya DA (1995) Seismic reflection and geomorphic characterization of the onshore Palos Verdes Fault Zone, Los Angeles, California. Bull Seismol Soc Am 85(3):943–950Google Scholar
  76. Terzaghi K (1950) Mechanisms of landslides. In: Paige S (ed) Application of geology to engineering practice. Geological Society of America, New York, pp 83–123Google Scholar
  77. Tofani V, Raspini F, Catani F, Casagli N (2013) Persistent scatterer interferometry (PSI) technique for landslide characterization and monitoring. Remote Sens 5:1045–1065CrossRefGoogle Scholar
  78. United States Geological Survey (USGS) (2005) Landslide hazards—a national threat. USGS Fact Sheet 2005–3156, Dec. 2005, 2 pGoogle Scholar
  79. United States Geological Survey (USGS) (2017) National Elevation Dataset (NED) 1/3 arc-second (10-m) DEM Accessed 16 May 2017
  80. van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomena through GIS-based hazard zonation. Geol Rundsch 86:404–414CrossRefGoogle Scholar
  81. Vonder Linden K (1989) The Portuguese Bend landslide. Eng Geol 27:301–373CrossRefGoogle Scholar
  82. Vonder Linden K, Lindvall CE (1982) The Portuguese Bend landslide. In: Cooper JD (ed) Volume and Guidebook: Landslides and Landslide Abatement, Geological Society of America, Palos Verdes Peninsula, Southern California, Cordilleran Section, 78th Annual Meeting, pp 49–56Google Scholar
  83. Wang Y, Zhu XX, Zeisl B, Pollefeys M (2017) Fusing meter-resolution 4-D InSAR point clouds and optical images for semantic urban infrastructure monitoring. IEEE Trans Geosci Remote Sens 55(1):14–26CrossRefGoogle Scholar
  84. Wei M, Sandwell DT (2010) Decorrelation of L-band and C-band interferometry over vegetated areas in California. IEEE Trans Geosci Remote Sens 48(7):2942–2952CrossRefGoogle Scholar
  85. Woodring WP, Bramlette MN, Kew WSW (1946) Geology and paleontology of the Palos Verdes Hills, California. U.S. Geological Survey Professor Papers 207, 145 p Google Scholar
  86. Wright TL (1991) Structural geology and tectonic evolution of the Los Angeles basin, California. In: Biddle KT (ed) Active margin basins, American Association of Petroleum Geologists memoir, vol 52, pp 35–134Google Scholar
  87. Zhao C, Zhang Q, He Y, Peng J, Yang C, Kang Y (2016) Small-scale loess landslide monitoring with small baseline subsets interferometric synthetic aperture radar technique—case study of Xingyuan landslide, Shaanxi, China. J Appl Remote Sens 10(2):1–14CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Michigan Technological UniversityHoughtonUSA

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