ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 719–726, 2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 719–726, 2020

  03 Aug 2020

03 Aug 2020


I. Gutierrez1, E. Før Gjermundsen2, W. D. Harcourt3, M. Kuschnerus4, F. Tonion5, and T. Zieher6 I. Gutierrez et al.
  • 1Department of Energy Resources, University of Stavanger, Kristine Bonnevies Vei 22, 4021 Stavanger, Norway
  • 2GIS-group, School of Business, University of South-Eastern Norway, Lærerskoleveien 40, 3679 Notodden, Norway
  • 3School of Physics and Astronomy, University of St Andrews, St Andrews, KY16 9SS, UK
  • 4Department of Geoscience and Remote Sensing, Delft University of Technology, Stevinweg 1, 2628CN Delft, The Netherlands
  • 5Department TESAF, University of Padua, Viale dell’Universita’, Legnaro (PD), Italy
  • 6Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, Technikerstr. 21a, 6020 Innsbruck, Austria

Keywords: LiDAR, Change detection, Landslide, Terrain extraction, ISPRS Innsbruck Summer School of Alpine Research

Abstract. Landslides endanger settlements and infrastructure in mountain areas across the world. Monitoring of landslides is therefore essential in order to understand and possibly predict their behavior and potential danger. Terrestrial laser scanning has proven to be a successful tool in the assessment of changes on landslide surfaces due to its high resolution and accuracy. However, it is necessary to classify the 3D point clouds into vegetation and bare-earth points using filtering algorithms so that changes caused by landslide activity can be quantified. For this study, three classification algorithms are compared on an exemplary landslide study site in the Oetz valley in Tyrol, Austria. An optimal set of parameters is derived for each algorithm and their performances are evaluated using different metrics. The volume changes on the study site between the years 2017 and 2019 are compared after the application of each algorithm. The results show that (i) the tested filter techniques perform differently, (ii) their performance depends on their parameterization and (iii) the best-performing parameterization found over the vegetated test area will yield misclassifications on non-vegetated rough terrain. In particular, if only small changes have occurred the choice of the filtering technique and its parameterization play an important role in estimating volume changes.