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

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Mountain Risks: From Prediction to Management and Governance

Part of the book series: Advances in Natural and Technological Hazards Research ((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.

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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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Author information

Authors and Affiliations

  1. Department of Earth Sciences, University of Firenze, Via G. La Pira 4, IT-50121, Florence, Italy

    Ping Lu & Nicola Casagli

  2. Department of Earth Sciences, University of Modena and Reggio Emilia University, Largo Sant’ Eufemia 19, IT-41100, Modena, Italy

    Alexander Daehne & Alessandro Corsini

  3. Department of Geosciences, University of Missouri – Kansas City, 5110 Rockhill Road, Kansas City, MO, 64110, USA

    Alexander Daehne & Alessandro Corsini

  4. Institut de Physique du Globe de Strasbourg, CNRS UMR 7516, Université de Strasbourg/EOST, 5 rue René Descartes, F-67084, Strasbourg Cedex, France

    Julien Travelletti & Jean-Philippe Malet

  5. BEG, Bureau d’Etudes Géologiques SA, Rue de la Printse 4, CH-1994, Aproz, Switzerland

    Julien Travelletti & Jean-Philippe Malet

Authors
  1. Ping Lu
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  2. Alexander Daehne
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  3. Julien Travelletti
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  4. Nicola Casagli
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  5. Alessandro Corsini
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  6. Jean-Philippe Malet
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Corresponding author

Correspondence to Ping Lu .

Editor information

Editors and Affiliations

  1. Department of Physical Geography, Utrecht University, Utrecht, The Netherlands

    Theo Van Asch

  2. Department of Geotechnical Engineering, Technical University of Catalonia, Barcelona, Spain

    Jordi Corominas

  3. Institute of Spatial Planning, Technical University of Dortmund, Dortmund, Germany

    Stefan Greiving

  4. CNRS, Institut de Physique du Globe du Strasbourg, University of Strasbourg, Strasbourg, France

    Jean-Philippe Malet

  5. Institute for the Dynamic of Environmental Processes, National Research Council of Italy, Milan, Italy

    Simone Sterlacchini

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Lu, P., Daehne, A., Travelletti, J., Casagli, N., Corsini, A., Malet, JP. (2014). Innovative Techniques for the Detection and Characterization of the Kinematics of Slow-Moving Landslides. In: Van Asch, T., Corominas, J., Greiving, S., Malet, JP., Sterlacchini, S. (eds) Mountain Risks: From Prediction to Management and Governance. Advances in Natural and Technological Hazards Research, vol 34. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6769-0_2

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