Abstract
Many examples of regional scale statistical landslide susceptibility assessments can be found in scientific literature. A real-life application of these maps for spatial planning decisions is less common. As result of the MoNOE research project (Method development for landslide susceptibility modelling in Lower Austria), a landslide susceptibility map has been created. Since 2014, this map is constantly used by provincial spatial planners and geologists to guide strategic settlement development in Lower Austria (approx. 19200 km²). Resulting from a multi-temporal inventory of 12,889 slides, a generalized additive model (GAM) was applied to predict the landslide susceptibility using a variety of meaningful morphological and geo-environmental predictors. These easily-applicable, local-scale (1:25,000) landslide susceptibility maps consist of three susceptibility classes. The three classes correspond to low landslide susceptibility (covering 78% of all pixels within the study area), moderate (16% of all pixels) and high (6% of all pixels). Although well accepted by the stakeholders, a few important questions recently arise: a) Is this map able to correctly predict new landslide events that occurred after the implementation of this map? b) With the inclusion of these new samples, is the terrain susceptibility still the same? c) If the terrain susceptibility has changed with the inclusion of the unused (partly recently mapped) samples, why and to what extent?
By aiming to answer these questions, a review project named MoNEW is currently in place, which has the overall objective to quantify the accuracy of the MoNOE spatial predictions. The new landslides were obtained from two main different sources: 1) recently occurred damage related landslides from a cadaster of landslide events (in German: “Baugrundkataster"), and 2) landslides mapped from hillshades of a high-resolution LiDAR DTM. Based on these new landslides, the final quality of MoNOE will be explored and the landslide susceptibility recalculated to identify potential differences. Therefore, the identical MoNOE methodological design will be applied to ensure comparability and quality control. Changes in the spatial prediction will be quantified and deeply explored.
First exploratory analysis has demonstrated that most of the new landslides occurred within the highest landslide susceptibility class, indicating an apparent good ability of the past MoNOE susceptibility model to predict these landslides. Depending on the inventory source, 34 to 64% of the landslides occurred within the higher susceptibility class (this percentage was 70% by design in the original MoNOE project). This variation might be explained by the positional accuracy and mapping methodologies of the new landslides. Additionally, it was observed that most of the new landslides occurring in other less susceptible classes (i.e., “low” and “moderate”) were actually located in close proximity to the highest susceptibility class. Given the applicability scale of the MoNOE landslide susceptibility map (1:25,000), these (mostly very low) quantified distances between the landslide locations and the high susceptibility pixels might be inside of the new landslide mapping accuracy. However, how much the landslide susceptibility of the terrain might change with the addition of these new samples is currently under analysis.