GSA Annual Meeting in Seattle, Washington, USA - 2017

Paper No. 59-4
Presentation Time: 2:30 PM

SLOW LANDSLIDE IDENTIFICATION USING INSAR TO UPDATE THE CALIFORNIA LANDSLIDE INVENTORY ON THE PALOS VERDES PENINSULA


BOUALI, El Hachemi Y., Geological and Mining Engineering and Sciences, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, OOMMEN, Thomas, Geological and Mining Engineering and Sciences, Michigan Technological University, 1400, Houghton, MI 49931 and ESCOBAR WOLF, RĂ¼diger, Geological Engineering & Sciences, Michigan Technological University, Houghton, MI 49931, eybouali@mtu.edu

Sudden, quick-moving landslides are well-documented natural hazard events that can cause extreme damage and loss of life. Slow landslides, those with displacement rates less than 16 mm/year, may be imperceptible without proper instrumentation, but can also damage infrastructure and require expensive reconstruction efforts, typically on a long-term timescale. The objective of this presentation is to update the California Landslide Inventory (CLI) with slow landslides information from the Palos Verdes Peninsula using the Interferometric Synthetic Aperture Radar (InSAR) technique known as Persistent Scatterer Interferometry (PSI). 34 ENVISAT (2005-2010) and 40 COSMO-SkyMed (2012-2014) radar images were processed. Slow landslides detected using InSAR comprise the InSAR Landslide Inventory (ILI), which was created using four criteria: a minimum PS count, average ground velocity, slope angle, and slope aspect. Landslides in the ILI are further divided into four categories: (1) long-term slides, (2) potentially active slides, (3) relatively stable slopes, and (4) unmapped extremely slow slides. The four categories were based on whether landslides were previously mapped on that slope (in the CLI), if persistent scatterers (PS) were present, and whether PS are stable or unstable. The final inventory included 263 mapped landslides across the Palos Verdes Peninsula, of them 67 landslides were identified as unmapped extremely slow slides.
Handouts
  • Bouali et al GSA2017.pptx (46.1 MB)