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Abstract. Landslide susceptibility maps are helpful tools to identify areas potentially prone to future landslide occurrence. As more and more national and provincial authorities demand for these maps to be computed and implemented in spatial planning strategies, several aspects of the quality of the landslide susceptibility model and the resulting classified map are of high interest. In this study of landslides in Lower Austria, we focus on the model form uncertainty to assess the quality of a flexible statistical modelling technique, the generalized additive model (GAM). The study area (15 850 km 2 ) is divided into 16 modelling domains based on lithology classes. A model representing the entire study area is constructed by combining these models. The performances of the models are assessed using repeated k-fold cross-validation with spatial and random subsampling. This reflects the variability of performance estimates arising from sampling variation. Measures of spatial transferability and thematic consistency are applied to empirically assess model quality. We also analyse and visualize the implications of spatially varying prediction uncertainties regarding the susceptibility map classes by taking into account the confidence intervals of model predictions. The 95 % confidence limits fall within the same susceptibility class in 85 % of the study area. Overall, this study contributes to advancing open communication and assessment of model quality related to statistical landslide susceptibility models.

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Bell, R., Petschko, H., Röhrs, M. and Dix, A. Assessment of landslide age, landslide persistence and human impact using airborne laser scanning digital terrain models. Geografiska Annaler: Series A, Physical Geography, 94, 135–156. doi:10.1111/j.1468‐0459.2012.00454.x ABSTRACT Landslides occur worldwide and contribute significantly to sediment budgets as well as to landform evolution. Furthermore, they pose hazards and risks to people and their goods. To assess the role of landslides, information on their age or persistence (i.e. the length of time the morphological characteristics of a landslide remain recognizable in the terrain) is essential. In this study, the potential of airborne laser scanning digital terrain models (ALS DTMs) is analysed for estimating landslide age, landslide persistence and human impact. Therefore, landslides in two study areas, Swabian Alb in Germany and Lower Austria in Austria, are mapped from hillshades of ALS DTMs and combined with historical information on landslide occurrence. It is tested whether the modification of the geomorphological features of landslides can be used to assess landslide age. In the Swabian Alb older landslides might show fresher features than younger ones because of different degrees of human impact, natural erosion and different histories of landslide reactivation. Estimated persistence times range between 27 and 320 years but are minimum values only. In Lower Austria four landslides show estimated minimum persistence times between 4 and 28 years. In Lower Austria 27 landslides disappeared in less than 7 years after occurrence mainly because of planation by farmers. The results show no clear trend in landslide persistence, neither regarding landslide magnitude, nor regarding land use. However, it is evident that human impact plays a major role in landslide persistence.

Landslide inventories are the most important data source for landslide process, susceptibility, hazard, and risk analyses. The objective of this study was to identify an effective method for mapping a landslide inventory for a large study area (19,186 km 2 ) from Light Detection and Ranging (LiDAR) digital terrain model (DTM) derivatives. This inventory should in particular be optimized for statistical susceptibility modeling of earth and debris slides. We compared the mapping of a representative set of landslide bodies with polygons (earth and debris slides, earth flows, complex landslides, and areas with slides) and a substantially complete set of earth and debris slide main scarps with points by visual interpretation of LiDAR DTM derivatives. The effectiveness of the two mapping methods was estimated by evaluating the requirements on an inventory used for statistical susceptibility modeling and their fulfillment by our mapped inventories. The resulting landslide inventories improved the knowledge on landslide events in the study area and outlined the heterogeneity of the study area with respect to landslide susceptibility. The obtained effectiveness estimate demonstrated that none of our mapped inventories are perfect for statistical landslide susceptibility modeling. However, opposed to mapping polygons, mapping earth and debris slides with a point in the main scarp were most effective for statistical susceptibility modeling within large study areas. Therefore, earth and debris slides were mapped with points in the main scarp in entire Lower Austria. The advantages, drawbacks, and effectiveness of landslide mapping on the basis of LiDAR DTM derivatives compared to other imagery and techniques were discussed.