Journal metrics

Journal metrics

  • IF value: 3.176 IF 3.176
  • IF 5-year value: 3.108 IF 5-year 3.108
  • CiteScore value: 3.06 CiteScore 3.06
  • SNIP value: 0.978 SNIP 0.978
  • SJR value: 1.421 SJR 1.421
  • IPP value: 2.88 IPP 2.88
  • h5-index value: 13 h5-index 13
  • Scimago H index value: 13 Scimago H index 13
Volume 6, issue 2 | Copyright

Special issue: 4-D reconstruction of earth surface processes: multi-temporal...

Earth Surf. Dynam., 6, 303-317, 2018
https://doi.org/10.5194/esurf-6-303-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 16 Apr 2018

Research article | 16 Apr 2018

Identification of stable areas in unreferenced laser scans for automated geomorphometric monitoring

Daniel Wujanz1, Michael Avian2, Daniel Krueger3, and Frank Neitzel1 Daniel Wujanz et al.
  • 1Institute of Geodesy and Geoinformation Science, Technische Universität Berlin, Berlin, Germany
  • 2Austrian Institute of Technology GmbH, Tulln, Austria
  • 3GFaI – Society for the Advancement of Applied Computer Science, Berlin, Germany

Abstract. Current research questions in the field of geomorphology focus on the impact of climate change on several processes subsequently causing natural hazards. Geodetic deformation measurements are a suitable tool to document such geomorphic mechanisms, e.g. by capturing a region of interest with terrestrial laser scanners which results in a so-called 3-D point cloud. The main problem in deformation monitoring is the transformation of 3-D point clouds captured at different points in time (epochs) into a stable reference coordinate system. In this contribution, a surface-based registration methodology is applied, termed the iterative closest proximity algorithm (ICProx), that solely uses point cloud data as input, similar to the iterative closest point algorithm (ICP). The aim of this study is to automatically classify deformations that occurred at a rock glacier and an ice glacier, as well as in a rockfall area. For every case study, two epochs were processed, while the datasets notably differ in terms of geometric characteristics, distribution and magnitude of deformation. In summary, the ICProx algorithm's classification accuracy is 70% on average in comparison to reference data.

Download & links
Publications Copernicus
Special issue
Download
Short summary
The importance of increasing the degree of automation in the context of monitoring natural hazards or geological phenomena is apparent. A vital step in the processing chain of monitoring deformations is the transformation of captured epochs into a common reference systems. This led to the motivation to develop an algorithm that realistically carries out this task. The algorithm was tested on three different geomorphic events while the results were quite satisfactory.
The importance of increasing the degree of automation in the context of monitoring natural...
Citation
Share