, 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

  • H. PetschkoEmail author
  • R. Bell
  • T. Glade
Original Paper


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.


Visual analysis Landslide inventory Mapping effectiveness LiDAR DTM Statistical susceptibility modeling 



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.


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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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