2014 GSA Annual Meeting in Vancouver, British Columbia (19–22 October 2014)

Paper No. 34-9
Presentation Time: 11:00 AM


EISANK, Clemens, HÖLBLING, Daniel and FRIEDL, Barbara, Department of Geoinfomatics -Z_GIS, University of Salzburg, Schillerstrasse 30, Salzburg, 5020, Austria

In automated landslide mapping, digital elevation models (DEMs) and derived terrain objects (e.g. landforms) and terrain variables (e.g. slope, curvatures) are commonly integrated with Earth observation (EO) data, mostly optical satellite images, to map landslides and classify landslide types. Ideally, the EO data and the DEM should document the same state of the environment, i.e. data should be acquired at a similar point in time. However, this is rarely the case, since EO data is being produced at higher temporal frequencies than DEMs. Consequently, the DEMs used for automated landslide mapping are often outdated, i.e. they are significantly older than the EO data. This leads to the problem that the DEM does not represent all the landslides that are present in the EO data.

The aim of this study is to analyze how well terrain objects that are derived from a pre-event (outdated) DEM spatially correspond to post-event landslides. The multiresolution segmentation as implemented in eCognition was employed to partition terrain variables into homogeneous terrain objects. Next to curvatures and slope, more complex variables such as topographic openness and sky-view factor were segmented, individually and in combination. By changing the scale parameter of the algorithm different scales of terrain objects – ranging from hillslope to sub-landslide – were generated for the same variable(s). Multiresolution segmentation was statistically optimized through multi-scale analysis of the local variance of terrain objects. The optimized terrain object scales were finally intersected with manually derived landslide reference polygons to determine the overlapping areas. Hence, the total spatial coincidence between terrain objects and reference polygons was analyzed in relation to the input variable(s) and scale factors.

Results show that the overlaps are generally small indicating a relatively low landslide predictive capacity of terrain objects that are based on pre-event DEMs. However, some terrain variables generated significantly higher overlaps with the post-event landslides than others. These variables should be preferred for automated EO-based landslide mapping in cases where no post-event DEM is at hand.

  • Eisank_et_al_GSA_Poster.pdf (4.0 MB)