Innovative Techniques for the Detection and Characterization of the Kinematics of Slow-Moving Landslides

  • Ping Lu
  • Alexander Daehne
  • Julien Travelletti
  • Nicola Casagli
  • Alessandro Corsini
  • Jean-Philippe Malet
Chapter
Part of the Advances in Natural and Technological Hazards Research book series (NTHR, volume 34)

Abstract

Remote sensing has been proven useful for landslide studies. However, conventional remote sensing techniques based on aerial photographs and optical imageries seem to be more suitable for detecting and characterizing rapid-moving landslides. This section introduces several innovative remote sensing techniques aiming at the characterization of the kinematics (e.g. displacement pattern, deformation, strain) of slow- to moderate-moving landslides. These methods include Persistent Scatterers Interferometry (PSI), automatic surveying using total station integrated with GPS, Ground-Based Synthetic Aperture Radar Interferometry (GB-InSAR), image correlation of catalogues of optical photographs (TOP) and Terrestrial Laser Scanner (TLS) point clouds. Three case studies, including the Arno river basin (Italy), the Valoria landslide (Italy) and the Super-Sauze landslide (France) are presented in order to highlight the usefulness of these techniques.

Keywords

Point Cloud Total Station Terrestrial Laser Scanner Correlation Window Persistent Scatterer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

ALS

Airborne Laser Scanner

CPT

Coherent Pixels Technique

DInSAR

Differential InSAR

EW

Early Warning

GB-InSAR

Ground-based Synthetic Aperture Radar Interferometry

IPTA

Interferometric Point Target Analysis

LOS

Line-of-Sight

PS

Persistent Scatterers

PSI

Persistent Scatterers Interferometry

InSAR

SAR Interferometry

SBAS

Small Baseline Subset

SPN

Stable Point Network

StaMPS

Stanford Method for Persistent Scatterers

TLS

Terrestrial Laser Scanner

TOP

Terrestrial Optical Photogrammetry

DEM

Digital Elevation Model

GPS

Global Positioning System

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Ping Lu
    • 1
  • Alexander Daehne
    • 2
    • 3
  • Julien Travelletti
    • 4
    • 5
  • Nicola Casagli
    • 1
  • Alessandro Corsini
    • 2
    • 3
  • Jean-Philippe Malet
    • 4
    • 5
  1. 1.Department of Earth SciencesUniversity of FirenzeFlorenceItaly
  2. 2.Department of Earth SciencesUniversity of Modena and Reggio Emilia UniversityModenaItaly
  3. 3.Department of GeosciencesUniversity of Missouri – Kansas CityKansas CityUSA
  4. 4.Institut de Physique du Globe de Strasbourg, CNRS UMR 7516Université de Strasbourg/EOSTStrasbourg CedexFrance
  5. 5.BEG, Bureau d’Etudes Géologiques SAAprozSwitzerland

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