Landslides

, Volume 13, Issue 5, pp 857–872 | Cite as

Effectiveness of visually analyzing LiDAR DTM derivatives for earth and debris slide inventory mapping for statistical susceptibility modeling

Original Paper

Abstract

Landslide inventories are the most important data source for landslide process, susceptibility, hazard, and risk analyses. The objective of this study was to identify an effective method for mapping a landslide inventory for a large study area (19,186 km2) from Light Detection and Ranging (LiDAR) digital terrain model (DTM) derivatives. This inventory should in particular be optimized for statistical susceptibility modeling of earth and debris slides. We compared the mapping of a representative set of landslide bodies with polygons (earth and debris slides, earth flows, complex landslides, and areas with slides) and a substantially complete set of earth and debris slide main scarps with points by visual interpretation of LiDAR DTM derivatives. The effectiveness of the two mapping methods was estimated by evaluating the requirements on an inventory used for statistical susceptibility modeling and their fulfillment by our mapped inventories. The resulting landslide inventories improved the knowledge on landslide events in the study area and outlined the heterogeneity of the study area with respect to landslide susceptibility. The obtained effectiveness estimate demonstrated that none of our mapped inventories are perfect for statistical landslide susceptibility modeling. However, opposed to mapping polygons, mapping earth and debris slides with a point in the main scarp were most effective for statistical susceptibility modeling within large study areas. Therefore, earth and debris slides were mapped with points in the main scarp in entire Lower Austria. The advantages, drawbacks, and effectiveness of landslide mapping on the basis of LiDAR DTM derivatives compared to other imagery and techniques were discussed.

Keywords

Visual analysis Landslide inventory Mapping effectiveness LiDAR DTM Statistical susceptibility modeling 

Notes

Acknowledgments

This study was carried out within the research project “MoNOE—method development for landslide susceptibility modeling in Lower Austria” funded by the Provincial Government of Lower Austria. The authors are thankful for the provision of data by the Geological Survey of Austria and Lower Austria, the Austrian Service for Torrent and Avalanche Control, and the Provincial Government of Lower Austria. We want to thank the landslide inventory mapping team including Dr. Philip Leopold’s team at the Austrian Institute of Technology and our research assistants Mag. Christine Gassner and Ekrem Canli MSc. for their substantial work mapping landslide points in the province Lower Austria. We are grateful for the improvement of the English writing by Jason Goetz MSc. and for the thorough review and valuable comments from our anonymous reviewers, which helped in improving the quality of the manuscript.

References

  1. Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44CrossRefGoogle Scholar
  2. Amt der NÖ Landesregierung (2013) Niederösterreich Atlas. http://atlas.noe.gv.at. Accessed 28 Oct 2013
  3. Anders NS, Seijmonsbergen H (2008) Laser altimetry and terrain analysis—a revolution in geomorphology. GIM Int 36–39Google Scholar
  4. Antonini G, Ardizzone F, Cardinali M, et al. (2002) Surface deposits and landslide inventory map of the area affected by the 1997 Umbria-Marche earthquakes. Boll Soc Geol It Volume speciale n.1:843–853sGoogle Scholar
  5. Ardizzone F, Cardinali M, Carrara A et al (2002) Impact of mapping errors on the reliability of landslide hazard maps. Nat Hazards Earth Syst Sci 2:3–14CrossRefGoogle Scholar
  6. Ardizzone F, Cardinali M, Galli M, et al (2007) Identification and mapping of recent rainfall-induced landslides using elevation data collected by airborne Lidar. Nat Hazards Earth Syst Sci 7:637–650Google Scholar
  7. Ardizzone F, Fiorucci F, Santangelo M, et al (2013) Very-high resolution stereoscopic satellite images for landslide mapping. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice. Springer Berlin Heidelberg, pp 95–101Google Scholar
  8. Atkinson P, Jiskoot H, Massari R, Murray T (1998) Generalized linear modelling in geomorphology. Earth Surf Process Landf 23:1185–1195CrossRefGoogle Scholar
  9. Barlow J, Franklin S, Martin Y (2006) High spatial resolution satellite imagery, DEM derivatives, and image segmentation for the detection of mass wasting processes. Photogramm Eng Remote Sens 72:687–692CrossRefGoogle Scholar
  10. Beguería S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37:315–329CrossRefGoogle Scholar
  11. Bell R (2007) Lokale und regionale Gefahren-und Risikoanalyse gravitativer Massenbewegungen an der Schwäbischen Alb. Rheinische Friedrich-Wilhelms-Universität Bonn (available at http://hss.ulb.uni-bonn.de/2007/1107/1107.htm, 29 March 2014)
  12. Bell R, Petschko H, Röhrs M, Dix A (2012) Assessment of landslide age, landslide persistence and human impact using airborne laser scanning digital terrain models. Geogr Ann Ser Phys Geogr 94:135–156CrossRefGoogle Scholar
  13. Booth AM, Roering JJ, Perron JT (2009) Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon. Geomorphology 109:132–147CrossRefGoogle Scholar
  14. Calvello M, Cascini L, Mastroianni S (2013) Landslide zoning over large areas from a sample inventory by means of scale-dependent terrain units. Geomorphology 182:33–48CrossRefGoogle Scholar
  15. Cardinali M, Guzzetti F, Brabb EE (1990) Preliminary map showing landslide deposits and related features in New Mexico. U.S. Geological Survey Open File Report 90/293, 4 sheets, scale 1:500,000Google Scholar
  16. Cardinali M, Ardizzone F, Galli M, et al. (2000) Landslides triggered by rapid snow melting: the December 1996–January 1997 event in Central Italy. In: Claps P, Siccardi F (eds) Proc. 1st Plinius Conf. Bios Publisher, Cosenza, Maratea, pp 439–448Google Scholar
  17. Carrara A, Merenda L (1976) Landslide inventory in northern Calabria, southern Italy. Geol Soc Amer Bull 87:1153–1162CrossRefGoogle Scholar
  18. Cascini L (2008) Applicability of landslide susceptibility and hazard zoning at different scales. Eng Geol 102:164–177CrossRefGoogle Scholar
  19. Chigira M, Duan F, Yagi H, Furuya T (2004) Using an airborne laser scanner for the identification of shallow landslides and susceptibility assessment in an area of ignimbrite overlain by permeable pyroclastics. Landslides 1:203–209CrossRefGoogle Scholar
  20. Christman MC (2000) A review of quadrat-based sampling of rare, geographically clustered populations. J Agric Biol Environ Stat 5:168–201CrossRefGoogle Scholar
  21. Chung CJ, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472CrossRefGoogle Scholar
  22. Cigna F, Bianchini S, Casagli N (2012) How to assess landslide activity and intensity with persistent scatterer interferometry (PSI): the PSI-based matrix approach. Landslides 10:267–283CrossRefGoogle Scholar
  23. Colesanti C, Wasowski J (2006) Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry. Eng Geol 88:173–199CrossRefGoogle Scholar
  24. Cruden DM, Varnes DJ (1996) Landslide types and processes. In: Turner AK, Schuster RL (eds) Landslides, investigation and mitigation. Transportation Research Board Special Report 247, Washington D.C., pp 36–75Google Scholar
  25. Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228CrossRefGoogle Scholar
  26. Dalyot S, Keinan E, Doytsher Y (2008) Landslide morphology analysis model based on LiDAR and topographic dataset comparison. Surv Land Inf Sci 68:155–170Google Scholar
  27. Eisinger U, Gutdeutsch R, Hammerl C (1992) Beiträge zur Erdbebengeschichte von Niederösterreich. Amt der NÖ Landesregierung, Landesamtsdirektion, Wien, pp 154Google Scholar
  28. Felicísimo ÁM, Cuartero A, Remondo J, Quirós E (2012) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10:175–189CrossRefGoogle Scholar
  29. Fell R, Corominas J, Bonnard C et al (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol 102:85–98CrossRefGoogle Scholar
  30. Fernández T, Irigaray C, El Hamdouni R, Chacón J (2003) Methodology for landslide susceptibility mapping by means of a GIS. Application to the Contraviesa area (Granada, Spain). Nat Hazards 30:297–308CrossRefGoogle Scholar
  31. Fiorucci F, Cardinali M, Carlà R et al (2011) Seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images. Geomorphology 129:59–70CrossRefGoogle Scholar
  32. Galli M, Ardizzone F, Cardinali M et al (2008) Comparing landslide inventory maps. Geomorphology 94:268–289CrossRefGoogle Scholar
  33. Ghosh S, van Westen CJ, Carranza EJM et al (2012) Generating event-based landslide maps in a data-scarce Himalayan environment for estimating temporal and magnitude probabilities. Eng Geol 128:49–62CrossRefGoogle Scholar
  34. Glade T, Anderson MG, Crozier MJ (2005) Landslide hazard and risk. John Wiley & Sons, Ltd, ChichesterCrossRefGoogle Scholar
  35. Glenn NF, Streutker DR, Chadwick DJ et al (2006) Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity. Geomorphology 73:131–148CrossRefGoogle Scholar
  36. Goetz JN, Bell R, Brenning A (2014) Could surface roughness be a poor proxy for landslide age? Results from the Swabian Alb, Germany. Earth Surf Process Landf 39:1697–1704Google Scholar
  37. Guzzetti F, Cardinali M (1989) Carta inventario dei fenomeni franosi della Regione dell'Umbria ed aree limitrofe. CNR, Gruppo Nazionale per la Difesa dalle Catastrofi Idrogeologiche, Publication n. 204, 2 sheets, scale 1:100,000, (in Italian)Google Scholar
  38. Guzzetti F (2005) Landslide hazard and risk assessment. Dissertation, Rheinischen Friedrich-Wilhelms-Universität Bonn. (Available at http://hss.ulb.uni-bonn.de/2006/0817/0817.htm, 29 March 2014)
  39. Guzzetti F, Cardinali M, Reichenbach P, et al (2004) Landslides triggered by the 23 November 2000 rainfall event in the Imperia Province, Western Liguria, Italy. Eng Geol 73:229–245Google Scholar
  40. Guzzetti F, Mondini A, Cardinali M et al (2012) Landslide inventory maps: new tools for an old problem. Earth-Sci Rev 112:42–66CrossRefGoogle Scholar
  41. Guzzetti F, Reichenbach P, Ardizzone F et al (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81:166–184CrossRefGoogle Scholar
  42. Haneberg WC, Creighton AL, Medley EW, Jonas DA (2005) Use of LiDAR to assess slope hazards at the Lihir gold mine, Papua New Guinea. In: Hungr O, Fell R, Couture R, Eberhardt E (eds) Landslide risk management. Proceedings of International Conference on Landslide Risk Management. Vancouver, CanadaGoogle Scholar
  43. Harp EL, Keefer DK, Sato HP, Yagi H (2011) Landslide inventories: the essential part of seismic landslide hazard analyses. Eng Geol 122:9–21CrossRefGoogle Scholar
  44. Herrera G, Notti D, García-Davalillo JC et al (2010) Analysis with C- and X-band satellite SAR data of the Portalet landslide area. Landslides 8:195–206CrossRefGoogle Scholar
  45. Hydrographischer Dienst des Landes Niederösterreich (Hydrographic Service of Lower Austria) (2011) Wasserstandsnachrichten und Hochwasserprognosen Niederösterreich. http://www.noel.gv.at/Externeseiten/wasserstand/wiskiwebpublic/maps_N_0.htm?entryparakey=N. Accessed 2 Mar 2011
  46. Jaboyedoff M, Oppikofer T, Abellán A et al (2010) Use of LIDAR in landslide investigations: a review. Nat Hazards 61:5–28CrossRefGoogle Scholar
  47. Krebs CJ (1999) Ecological methodology, 2nd edition. Addison-Wesley Educational Publishers Inc., Benjamin Cummings, Menlo Park, CAGoogle Scholar
  48. Leica Geosystems (2003) ALS50 Airborne Laser Scanner, product description. Atlanta, USA, pp 10Google Scholar
  49. Malamud BD, Turcotte DL, Guzzetti F, Reichenbach P (2004) Landslide inventories and their statistical properties. Earth Surf Process Landf 29:687–711CrossRefGoogle Scholar
  50. Martha TR, Kerle N, Jetten V et al (2010) Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology 116:24–36CrossRefGoogle Scholar
  51. Martha TR, Kerle N, van Westen CJ et al (2012) Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories. ISPRS J Photogramm Remote Sens 67:105–119CrossRefGoogle Scholar
  52. McCalpin J (1984) Preliminary age classification of landslides for inventory mapping. In: Proceedings 21st annual Engineering Geology and Soils Engineering Symposium, 5-6 April, University of Idaho, Moscow, Idaho, pp 99–111Google Scholar
  53. McKenna JP, Lidke DJ, Coe JA (2008) Landslides mapped from LIDAR imagery, Kitsap County, Washington. US Geol Surv Open-File Rep 1292:81Google Scholar
  54. Mondini AC, Guzzetti F, Reichenbach P et al (2011) Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sens Environ 115:1743–1757CrossRefGoogle Scholar
  55. Optech (2008a) ALTM 3100. Optech, Canada, pp 2Google Scholar
  56. Optech (2008b) ALTM Gemini. Optech, Canada, pp 2Google Scholar
  57. Petschko H, Glade T, Bell R, et al. (2010) Landslide inventories for regional early warning systems. In: Malet J P, Glade T, Casagli N (eds) Proceedings of the International Conference Mountain Risks: Bringing Science to Society’, Firenze, 24–26 November 2010, CERG Editions, Strasbourg, pp 277–282Google Scholar
  58. Petschko H, Bell, R., Brenning A, Glade T (2012) Landslide susceptibility modeling with generalized additive models—facing the heterogeneity of large regions. In: Eberhardt E, Froese C, Turner AK, Leroueil S (eds)Taylor & Francis, Banff, Alberta, Canada, pp 769–777Google Scholar
  59. Petschko H, Bell R, Leopold P, et al. (2013) Landslide inventories for reliable susceptibility maps in Lower Austria. In: Margottini C, Canuti P, Sassa K (eds) Landslide Science and Practice, Springer, pp 281–286Google Scholar
  60. Petschko H, Bell R, Glade T (2014a) Relative age estimation at landslide mapping on LiDAR derivatives: revealing the applicability of land cover data in statistical susceptibility modelling. In: Sassa K, Canuti P, Yin Y (eds) Landslide science for a safer geoenvironment. Springer International Publishing, pp 337-343Google Scholar
  61. Petschko H, Brenning A, Bell R et al (2014b) Assessing the quality of landslide susceptibility maps—case study Lower Austria. Nat Hazards Earth Syst Sci 14:95–118CrossRefGoogle Scholar
  62. Remondo J, González A, De Terán JRD et al (2003) Validation of landslide susceptibility maps; examples and applications from a case study in Northern Spain. Nat Hazards 30:437–449CrossRefGoogle Scholar
  63. Rib HT, Liang T (1978) Recognition and identification. In: Schuster RL, Krizek RJ (eds) Landslide analysis and control. National Academy of Sciences, Washington, pp 34–80Google Scholar
  64. Riegl (2010) LMS-Q560 airborne laser scanner for full waveform analysis. Austria, Japan, USAGoogle Scholar
  65. Santacana N, Baeza B, Corominas J et al (2003) A GIS-based multivariate statistical analysis for shallow landslide susceptibility mapping in La Pobla de Lillet area (Eastern Pyrenees, Spain). Nat Hazards 30:281–295CrossRefGoogle Scholar
  66. Santangelo M, Cardinali M, Rossi M et al (2010) Remote landslide mapping using a laser rangefinder binocular and GPS. Nat Hazards Earth Syst Sci 10:2539–2546CrossRefGoogle Scholar
  67. Schnabel W (2002) Geologische Karte von Niederösterreich 1:200,000. Geological Survey of Austria, ViennaGoogle Scholar
  68. Schulz WH (2004) Landslides mapped using LIDAR imagery, Seattle, Washington. US Geol. Surv. Open-File Rep. 1396:11Google Scholar
  69. Schweigl J, Hervás J (2009) Landslide mapping in Austria. European Commission Joint Research Centre, Institute for Environment and Sustainability, Italy, pp 65Google Scholar
  70. Schwenk H (1992) Massenbewegungen in Niederösterreich 1953–1990. Jahrb. Geol. Bundesanst. Geologische Bundesanstalt, Wien, pp 597–660Google Scholar
  71. Soeters R, Van Westen CJ (1996) Slope instability recognition, analysis and zonation. In: Turner AK, Schuster RL (eds) Landslides, investigation and mitigation. National Academy Press, Washington, USA, p 129-177Google Scholar
  72. Stumpf A, Malet J-P, Kerle N et al (2013) Image-based mapping of surface fissures for the investigation of landslide dynamics. Geomorphology 186:12–27CrossRefGoogle Scholar
  73. Tarolli P, Sofia G, Dalla Fontana G (2012) Geomorphic features extraction from high-resolution topography: landslide crowns and bank erosion. Nat Hazards 61:65–83CrossRefGoogle Scholar
  74. Thompson SK (2012) Sampling, 3rd edn. John Wiley & Sons Inc., Hoboken, p 472CrossRefGoogle Scholar
  75. Tobler D, Riner R, Pfeifer R (2013) Runout modelling of shallow landslides over large areas with SliDepot. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice. Springer, Heidelberg, pp 239–245Google Scholar
  76. Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. United Nations Educational, Scientific and Cultural Organization, Paris, FranceGoogle Scholar
  77. Van Asselen S, Seijmonsbergen AC (2006) Expert-driven semi-automated geomorphological mapping for a mountainous area using a laser DTM. Geomorphology 78:309–320. doi:10.1016/j.geomorph.2006.01.037 CrossRefGoogle Scholar
  78. Van Den Eeckhaut M, Kerle N, Poesen J, Hervás J (2012) Object-oriented identification of forested landslides with derivatives of single pulse LiDAR data. Geomorphology 173–174:30–42CrossRefGoogle Scholar
  79. Van Den Eeckhaut M, Poesen J, Verstraeten G et al (2007) Use of LIDAR-derived images for mapping old landslides under forest. Earth Surf Process Landf 32:754–769CrossRefGoogle Scholar
  80. Van Den Eeckhaut M, Vanwalleghem T, Poesen J et al (2006) Prediction of landslide susceptibility using rare events logistic regression: a case-study in the Flemish Ardennes (Belgium). Geomorphology 76:392–410CrossRefGoogle Scholar
  81. Van Westen CJ, Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation—why is it still so difficult? Bull Eng Geol Environ 65:167–184CrossRefGoogle Scholar
  82. Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131CrossRefGoogle Scholar
  83. Van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomena through GIS-based hazard zonation. Geol Rundsch 86:404–414CrossRefGoogle Scholar
  84. Ver Hoef J (2002) Sampling and geostatistics for spatial data. Ecoscience 9:152–161Google Scholar
  85. Wessely G (2006) Geologie der österreichischen Bundesländer-Niederösterreich. Geological Survey Austria, ViennaGoogle Scholar
  86. Whitworth MCZ, Giles DP, Murphy W (2005) Airborne remote sensing for landslide hazard assessment: a case study on the Jurassic escarpment slopes of Worcestershire, UK. Q J Eng Geol Hydrogeol 38:285–300CrossRefGoogle Scholar
  87. Wieczorek G (1984) Preparing a detailed landslide-inventory map for hazard evaluation and reduction. G Bull Assoc Engng Geol 21:337–342Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of GeographyFriedrich Schiller University JenaJenaGermany
  2. 2.Department of Geography and Regional ResearchUniversity of ViennaViennaAustria

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