Open image in new windowMulti-sensor a Priori PSI Visibility Map for Nationwide Landslide Detection in Austria

Conference paper


This paper proposes a multi-sensor a priori PSI visibility map for Austria in order to evaluate the feasibility of Differential SAR Interferometric (DInSAR) applications for landslide-affected slopes. For this purpose, the range index RI, introduced for the determination of areas in layover and foreshortening on both ascending and descending acquisition geometries, is computed and applied to the most diffuse X-C-L band SAR sensors. A new method is introduced to improve the accuracy of those products by fusing CORINE data with sharper European JRC forest map and Imperviousness Copernicus map. The results are tested with six different available PSI datasets over Austria. Then, a priori visibility map and a PSI density map are also derived for seven different satellites by combining the RI index and an enhanced CORINE land cover map. Finally, PSI velocity values, along the Line of Sight (VLos) and projected along the steepest slope direction (VSlope), are used in order to produce a landslide velocity map for the Austrian region of Vorarlberg.


Landslides A priori PSI visibility map Copernicus services Natural hazard PSI 



This research has been partly supported by the Austrian Research Promotion Agency FFG in the Austrian Space Applications Program (ASAP 11) through the project “Land@Slide” (contract no: 847970)”.


  1. Bardi F, Frodella W, Ciampalini A, Bianchini S, Del Ventisette C, Gigli G, Fanti R, Moretti S, Basile G, Casagli N (2014) Integration between ground based and satellite SAR data in landslide mapping: The San Fratello case study. Geomorphology 223(2014):45–60CrossRefGoogle Scholar
  2. Bianchini S, Cigna F, Righini G, Proietti C, Casagli N (2012) Landslide hotspot mapping by means of persistent scatterer interferometry. Environ Earth Sci 67:1155–1172CrossRefGoogle Scholar
  3. 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 5:6198–6222. doi: 10.3390/rs5126198
  4. Calvello M, Peduto D, Arena L (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: 10.1007/s10346-016-0722-6
  5. Caro Cuenca M, Hanssen R, Hooper A, aArikan M (2011) Surface deformation of the whole Netherlands after PSI analysis. In: Proceedings ‘fringe 2011 workshop’, Frascati, Italy, 19–23 Sept 2011 (ESA SP-697, Jan 2012)Google Scholar
  6. Cascini L, Fornaro G, Peduto D (2009) Analysis at medium scale of low-resolution DInSAR data in slow-moving landslide-affected areas. ISPRS J Photogram Remote Sens 64(6):598–611. doi: 10.1016/j.isprsjprs.2009.05.003
  7. Cascini L, Fornaro G, Peduto D (2010) Advanced low- and full-resolution DInSAR map generation for slow-moving landslide analysis at different scales. Eng Geol 112(1–4):29–42. doi: 10.1016/j.enggeo.2010.01.003 CrossRefGoogle Scholar
  8. 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 13:1527–1549. doi: 10.5194/nhess-13-1527-2013 CrossRefGoogle Scholar
  9. Cigna F, Del Ventisette C, Liguori V, Casagli N (2011) Advanced radar-interpretation of InSAR time series for mapping and characterization of geological processes. Nat Haz Earth Syst Sci 11:865–881CrossRefGoogle Scholar
  10. Cigna F, Bianchini S, Casagli N (2013) How to assess landslide activity and intensity with persistent scatterer interferometry (PSI): the PSI-based matrix approach. June 2013 10(3):267–283. doi: 10.1007/s10346-012-0335-7
  11. Cigna F, Bateson BL, Jordan CJ, Dashwood C (2014) Simulating SAR geometric distortions and predicting persistent scatterer densities for ERS-1/2 and ENVISAT C-band SAR and InSAR applications: nationwide feasibility assessment to monitor the landmass of Great Britain with SAR imagery. Remote Sens Environ 152:441–466Google Scholar
  12. Colesanti C, Wasowski J (2006) Investigating landslides with space-borne synthetic aperture radar (SAR) interferometry. Eng Geol 88(2006):173–199CrossRefGoogle Scholar
  13. Copernicus Land Monitoring Services (2016) Last accessed 07 Sept 2016
  14. CORINE—Copernicus Land Monitoring Services (2016) Last accessed 07 Sept 2016
  15. Farina P, Colombo D, Fumagalli A, Marks F, Moretti S (2006) Permanent scatterers for landslide investigations: outcomes from the ESA-SLAM project. Eng Geol 88:200–217CrossRefGoogle Scholar
  16. Ferretti A, Monti-Guarnieri A, Prati C, Rocca F (2007) InSAR principles: guidelines for SAR interferometry processing and interpretation. ESTEC, Noordwijk, Netherlands: ESA Publications, TM-19Google Scholar
  17. Gullà G, Peduto D, Borrelli L, Antronico L, Fornaro G (2017) Geometric and kinematic characterization of landslides affecting urban areas: the Lungro case study (Calabria, Southern Italy). Landslides, 14(1):171–188, doi: 10.1007/s10346-015-0676-0
  18. Herrera G, Notti D, García-Davalillo JC, Mora O, Cooksley G, Sanchez M, Arnaud A, Crosetto M (2010) Analysis with C- and X-band satellite SAR data of the Portalet landslide area. LandslidesGoogle Scholar
  19. Herrera G, Gutierrez F, Garcıa-Davalillo J, Guerrero J, Notti D, Galve J, Fernandez-Merodo J, 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
  20. Joint Research Centre (2015) Last accessed 07 Sept 2016
  21. MATIST—Monitoring Alpine Transportation Infrastructures using Space Techniques (2014) Last accessed 07 Sept 2016
  22. MATTM: Piano Straordinario di Telerilevamento Ambientale (PSTA) (2010) Linee guida per l’analisi dei dati intereferometrici satellitari in aree soggette a dissesti idrogeologici, Italian Ministry of the Environment and Protection of Land and Sea (MATTM), p 108Google Scholar
  23. Meisina C, Zucca F, Notti D, Colombo A, Cucchi A, Savio G, Giannico C, Bianchi M (2008) Geological interpretation of PSInSAR data at regional scale. Sensors 8:7469–7492CrossRefGoogle Scholar
  24. Notti D, Davalillo J, Herrera G, Mora O (2010) Assessment of the performance of X-band satellite radar data for landslide mapping and monitoring: upper Tena Valley case study. Nat Haz Earth Syst Sci 10:1865–1875CrossRefGoogle Scholar
  25. Notti D, Herrera G, Bianchini S, Meisina C, García-Davalillo JC, Zucca F (2014) A methodology for improving landslide PSI data analysis. Int J Remote Sens 35(6)Google Scholar
  26. OpenSteetMap data download (2016) Last accessed 07 Sept 2016
  27. PanGeo Project (2014) Last accessed 07 Sept 2016
  28. Plank S, Singer J, Minet C, Thuro K (2010) GIS based suitability evaluation of the differential radar interferometry method (DInSAR) for detection and deformation monitoring of landslides. In: Proceedings of fringe 2009 workshop, 30 November–4 December 2009, ESRIN, Frascati, Italy (ESA SP-677, March 2010), p 8, ISBN: 978-92-9221-241-4Google Scholar
  29. Plank S, Singer J, Thuro K (2013) Assessment of number and distribution of persistent scatterers prior to radar acquisition using open access land cover and topographical data. ISPRS J Photogram Remote Sens 85(2013):132–147CrossRefGoogle Scholar
  30. Righini G, Pancioli V, Casagli N (2011) Updating landslide inventory maps using persistent scatterer interferometry (PSI). Int J Remote Sens 33(7), 2012Google Scholar
  31. Strozzi T, Ambrosi C, Raetzo H (2013) Interpretation of aerial photographs and satellite SAR interferometry for the inventory of landslides. Remote Sens 5(5):2554–2570. doi: 10.3390/rs5052554 CrossRefGoogle Scholar
  32. Tilch N, Kociu A, Haberler A, Melzner S, Schwarz L, Lotter M (2011) The data management system GEORIOS of the geological survey of Austria (GBA). Interdisciplinary Rockfall Workshop, InnsbruckGoogle Scholar
  33. Wasowski J, Bovenga F (2014) Investigating landslides and unstable slopes with satellite multi temporal interferometry: current issues and future perspectives. Eng Geol 174:103–138CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Filippo Vecchiotti
    • 1
  • Dario Peduto
    • 2
  • Tazio Strozzi
    • 3
  1. 1.Department of Geology EngineeringGeological Survey of AustriaViennaAustria
  2. 2.Department of Civil EngineeringUniversity of SalernoFiscianoItaly
  3. 3.GAMMA Remote SensingGümligenSwitzerland

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