Mass Movement

Morfeo Project: C- and X-band SAR interferometric analysis over alpine regions (Italy)

In the present work we present first results of ground deformation measurements inferred through repeat-pass Synthetic Aperture Radar (SAR) Interferometry (In-SAR) in C- and X-band over an Italian Alpine area, in Lombardia region. The activity was carried out in the framework of the MORFEO (MOnitoraggio e Rischio da Frana mediante dati EO) project, founded by the Italian Spatial Agency (ASI) and dedicated to landslide risk assessment.

External Contact Person: 

Raffaele Nutricato

Email: 

raffaele.nutricato [at] gapsrl.eu
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Bibliographic reference: 

Nutricato, R., Rana, F., Nitti, D. O., D’Aprile, C., Bovenga, F., Frattini, P., ... & Candela, L. (2009). MORFEO PROJECT: C-AND X-BAND SAR INTERFEROMETRIC ANALYSIS OVER ALPINE REGIONS (ITALY). In Fringe 2009 Workshop.

Resourcesat-1

Disaster Cycle Phase: 

  • Recovery & Reconstruction

Space Technology/Product and Application: 

  • EO/RS
  • Landslide Hazard Assessment
  • Landslide Monitoring

Field of Application: 

  • Disaster Type
  • Mass Movement
Undefined

Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods

Recognition and classification of landslides is a critical requirement in pre- and post-disaster hazard analysis. This has been primarily done through field mapping or manual image interpretation. However, image interpretation can also be done semi-automatically by creating a routine in object-based classification using the spectral, spatial and morphometric properties of landslides, and by incorporating expert knowledge.

External Contact Person: 

Email: 

Undefined

Bibliographic reference: 

Martha, T. R., Kerle, N., Jetten, V., van Westen, C. J., & Kumar, K. V. (2010). Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology,116(1), 24-36.

ERS-1,2, ENVISAT, TerraSAR-x

Disaster Cycle Phase: 

  • Recovery & Reconstruction

Space Technology/Product and Application: 

  • EO/RS
  • Landslide Hazard Assessment
  • Landslide Monitoring

Field of Application: 

  • Disaster Type
  • Mass Movement

Satellite: 

Undefined

Analysis with C- and X-band satellite SAR data of the Portalet landslide area

This paper describes the use of the Stable Point Network technique, a Persistent Scatterer Interferometry SAR technique, for the analysis of the Portalet landslide area (Central Pyrenees, Spain). For this purpose, different SAR datasets acquired by ERS-1, ERS-2, ENVISAT and TerraSAR-X satellites have been analysed. The use of different SAR images acquired by satellite radar sensors operating at different microwave lengths has allowed for a comparative assessment and illustration of the advantages and disadvantages of these satellites for landslide detection and monitoring.

External Contact Person: 

Email: 

Undefined

Bibliographic reference: 

Herrera, G., Notti, D., García-Davalillo, J. C., Mora, O., Cooksley, G., Sánchez, M., ... & Crosetto, M. (2011). Analysis with C-and X-band satellite SAR data of the Portalet landslide area. Landslides8(2), 195-206.

ERS-1,2

Disaster Cycle Phase: 

  • Mitigation

Space Technology/Product and Application: 

  • EO/RS
  • Landslide Monitoring

Field of Application: 

  • Disaster Type
  • Mass Movement

Satellite: 

Undefined

Advanced low- and full-resolution DInSAR map generation for slow-moving landslide analysis at different scales

A proper analysis of slow-moving landslides calls for several efforts aiming at their characterization and mapping. Considering the uncertainties related to the landslide inventorymaps the integration of conventional techniques with remote sensing data, such as differential SAR interferometry (DInSAR), can furnish a valuable contribution in a number of case studies. However, standardized procedures for the interpretation and the confident use of DInSAR data, according to landslide zoning developments, have not been fully investigated and validated,
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Bibliographic reference: 

Cascini, L., Fornaro, G., & Peduto, D. (2010). Advanced low-and full-resolution DInSAR map generation for slow-moving landslide analysis at different scales.Engineering Geology112(1), 29-42.

TRMM

Disaster Cycle Phase: 

  • Preparedness

Space Technology/Product and Application: 

  • EO/RS
  • Landslide Monitoring

Field of Application: 

  • Disaster Type
  • Mass Movement

Satellite: 

Undefined

A satellite-based global landslide model

Landslides are devastating phenomena that cause huge damage around the world. This paper presents a quasi-global landslide model derived using satellite precipitation data, land-use land cover maps, and 250m topography information. This suggested landslide model is based on the Support Vector Machines (SVM), a machine learning algorithm. The National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) landslide inventory data is used as observations and reference data.

External Contact Person: 

A. AghaKouchack

Email: 

amir.a [at] uci.edu
Undefined

Bibliographic reference: 

Farahmand, A., & AghaKouchak, A. (2013). A satellite-based global landslide model. Natural Hazards and Earth System Science13(5).

Multi-hazard profile of Sri Lanka (UNDRR)

Data Type: 

hazard

Costs: 

free
English

Spatial Coverage: 

Sri Lanka

Temporal Coverage: 

arch

Data accessibility: 

exportmap
statistic

Disaster Cycle Phase: 

drm

Space-based Information: 

Ground-based Information

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