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Landslide Inventories for Reliable Susceptibility Maps in Lower Austria

  • Helene PetschkoEmail author
  • Rainer Bell
  • Philip Leopold
  • Gerhard Heiss
  • Thomas Glade
Chapter

Abstract

Landslide inventories, their accuracy and the stored information are of major importance for landslide susceptibility modelling. Working on the scale of a province (Lower Austria with about 10,000 km2) challenges arise due to data availability and its spatial representation. Furthermore, previous studies on existing landslide inventories showed that only few inventories can be used for statistical susceptibility modelling. In this study two landslide inventories and their resulting susceptibility maps are compared: the Building Ground Register (BGR) of the Geological Survey of Lower Austria and an inventory that was mapped on the basis of a high resolution LiDAR DTM. This analysis was performed to estimate minimum requirements on landslide inventories to allow for deriving reliable susceptibility maps while minimizing mapping efforts. Therefore a consistent landslide inventory once from the BGR and once from the mapping was compiled. Furthermore, a logistic regression model was fitted with randomly selected points of each landslide inventory to compare the resulting maps and validation rates. The resulting landslide susceptibility maps show significant differences regarding their visual and statistical quality. We conclude that the application of randomly selected points in the main scarp of the mapped landslides gives satisfactory results.

Keywords

Landslide inventory mapping Minimum requirements Archive data LiDAR DTM Lower Austria 

Notes

Acknowledgments

The authors thank several institutions and individuals for their support: the colleagues from the Geological Survey of Austria and from the Austrian Service for Torrent and Avalanche Control for providing inventory data; the Provincial Government of Lower Austria in particular the colleagues from the Geological Survey and the Department of Spatial Planning and Regional Policy for inventory data. Thanks to them also for supporting the MoNOE-project and for numerous fruitful discussions. Thanks also to the Department for Hydrology and Geoinformation for providing LiDAR and orthophoto data.

Finally, the authors are grateful for financial support of the Provincial Government of Lower Austria.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Helene Petschko
    • 1
    Email author
  • Rainer Bell
    • 1
  • Philip Leopold
    • 2
  • Gerhard Heiss
    • 2
  • Thomas Glade
    • 1
  1. 1.Department of Geography and Regional ResearchUniversity of ViennaWien, ViennaAustria
  2. 2.Health and Environment Department, Environmental Resources and TechnologiesAIT – Austrian Institute of Technology GmbHTullnAustria

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