| Zusammenfassung |
Large areas in southern Kyrgyzstan are subjected to high and ongoing landslide activity;
however, an objective and systematic assessment of landslide susceptibility at a regional level has not
yet been conducted. In this paper, we investigate the contribution that remote sensing can provide to
facilitate a quantitative landslide hazard assessment at a regional scale under the condition of data
scarcity. We performed a landslide susceptibility and hazard assessment based on a multi-temporal
landslide inventory that was derived from a 30-year time series of satellite remote sensing data
using an automated identification approach. To evaluate the effect of the resulting inventory on the
landslide susceptibility assessment, we calculated an alternative susceptibility model using a historical
inventory that was derived by an expert through combining visual interpretation of remote sensing
data with already existing knowledge on landslide activity in this region. For both susceptibility
models, the same predisposing factors were used: geology, stream power index, absolute height,
aspect and slope. A comparison of the two models revealed that using the multi-temporal landslide
inventory covering the 30-year period results in model coefficients and susceptibility values that
more strongly reflect the properties of the most recent landslide activity. Overall, both susceptibility
maps present the highest susceptibility values for similar regions and are characterized by acceptable
to high predictive performances. We conclude that the results of the automated landslide detection
provide a suitable landslide inventory for a reliable large-area landslide susceptibility assessment.
We also used the temporal information of the automatically detected multi-temporal landslide
inventory to assess the temporal component of landslide hazard in the form of exceedance probability.
The results show the great potential of satellite remote sensing for deriving detailed and systematic
spatio-temporal information on landslide occurrences, which can significantly improve landslide
susceptibility andhazard assessment at a regional scale, particularly in data-scarce regions such
as Kyrgyzstan. |
