Räumliche Analyse von gravitativen Massenbewegungen mittels multivariater Statistik

Im August 2005 sind nach intensiven Regenfällen in der Region Gasen und Haslau in der Oststeiermark, mehr als 500 gravitative Massenbewegungen (auf 60km²) aufgetreten, die hohe ökonomische Schäden verursachten und auch 2 Todesopfer forderten. Das Ziel der vorliegenden Analyse war, eine Gefahrenhinweiskarte für die Region Gasen und Haslau zu erstellen und eine unabhängige Validierung der Ergebnisse durchzuführen. Dazu wurden die in Österreich vorhanden Geodaten in Rasterdatensätze mit 10m x 10m und 50m x 50m Rasterweite konvertiert, um auch den Effekt von unterschiedlichen Auflösungen auf die resultierende Gefahrenhinweiskarte aufzuzeigen. Für die Analyse wurde das statistische Likelihood Quotienten Modell von Chung und Fabbri (1993, 2005) angewendet. Die Ergebnisse zeigen zufriedenstellende statistische Gütemaße darunter hohe Vorhersageraten aus der unabhängige Validierung der Gefahrenhinweiskarten

Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria)

The assessment of uncertainties in landslide susceptibility modelling in a changing environment is an important, yet often neglected, task. In an Austrian case study, we investigated the uncertainty cascade in storylines of landslide susceptibility emerging from climate change and parametric landslide model uncertainty. In June 2009, extreme events of heavy thunderstorms occurred in the Styrian Basin, triggering thousands of landslides. Using a storyline approach, we discovered a generally lower landslide susceptibility for the pre-industrial climate, while for the future climate (2071–2100) a potential increase of 35 % in highly susceptible areas (storyline of much heavier rain) may be compensated for by much drier soils (−45 % areas highly susceptible to landsliding). However, the estimated uncertainties in predictions were generally high. While uncertainties related to within-event internal climate model variability were substantially lower than parametric uncertainties in the landslide susceptibility model (ratio of around 0.25), parametric uncertainties were of the same order as the climate scenario uncertainty for the higher warming levels (+3 and +4 K). We suggest that in future uncertainty assessments, an improved availability of event-based landslide inventories and high-resolution soil and precipitation data will help to reduce parametric uncertainties in landslide susceptibility models used to assess the impacts of climate change on landslide hazard and risk.</p

Terrestrial and Airborne Structure from Motion Photogrammetry Applied for Change Detection within a Sinkhole in Thuringia, Germany

Detection of geomorphological changes based on structure from motion (SfM) photogrammetry is highly dependent on the quality of the 3D reconstruction from high-quality images and the correspondingly derived point precision estimates. For long-term monitoring, it is interesting to know if the resulting 3D point clouds and derived detectable changes over the years are comparable, even though different sensors and data collection methods were applied. Analyzing this, we took images of a sinkhole terrestrially with a Nikon D3000 and aerially with a DJI drone camera in 2017, 2018, and 2019 and computed 3D point clouds and precision maps using Agisoft PhotoScan and the SfM_Georef software. Applying the &ldquo;multiscale model to model cloud comparison using precision maps&rdquo; plugin (M3C2-PM) in CloudCompare, we analyzed the differences between the point clouds arising from the different sensors and data collection methods per year. Additionally, we were interested if the patterns of detectable change over the years were comparable between the data collection methods. Overall, we found that the spatial pattern of detectable changes of the sinkhole walls were generally similar between the aerial and terrestrial surveys, which were performed using different sensors and camera locations. Although the terrestrial data collection was easier to perform, there were often challenges due to terrain and vegetation around the sinkhole to safely acquire adequate viewing angles to cover the entire sinkhole, which the aerial survey was able to overcome. The local levels of detection were also considerably lower for point clouds resulting from aerial surveys, likely due to the ability to obtain closer-range imagery within the sinkhole

Challenges and solutions of modelling landslide susceptibility in heterogeneous regions

Die Berücksichtigung gravitativer Massenbewegungen, insbesondere Rutschungen, stellt eine besondere Herausforderung in der Raumordnung dar. In diesem Kontext wurden Gefahrenhinweiskarten für gravitative Massenbewegungen als hilfreiche Grundlage identifiziert, welche den Planungsprozess in der örtlichen Raumplanung unterstützen kann. Diese Gefahrenhinweiskarten weisen flächendeckend drei unterschiedliche Klassen aus. Jede Klasse entspricht einer Empfehlung zu Maßnahmen, welche im Fall einer Widmung, bzw. vor einem Bauvorhaben seitens der Gemeinde getätigt werden können. Diese Maßnahmen sollen dazu beitragen unerwünschte Entwicklungsarten (Widmungsarten) zu vermeiden, bzw. möglichen Schäden durch gravitative Massenbewegungen vorzubeugen. Vor allem in Fällen wo Gefahrenhinweiskarten in der örtlichen Raumplanung zur Anwendung kommen, ist eine Aussage über ihre Qualität und deren zulässigen Interpretation unabkömmlich. Die Qualität einer Gefahrenhinweiskarte wird maßgeblich durch die Eingangsdaten, allen voran dem Inventar zu gravitativen Massenbewegungen, beeinflusst. In der vorliegenden Dissertation wurden verschiedene Aspekte der Qualität (Modellgüte, thematische Übereinstimmung verschiedener Modellläufe, Übertragbarkeit des Modells auf andere Gebiete) einer Gefahrenhinweiskarte vor allem mit quantitativen Methoden, wie der wiederholten mehrfachen Kreuzvalidierung, untersucht. Speziell bei der Bearbeitung von sehr großen und heterogenen Gebieten entstehen Herausforderungen bezüglich Datenverfügbarkeit, beschränkter Ressourcen zur Kartierung und der Vergleichbarkeit der Gefährdung in allen Bereichen des Untersuchungsgebietes. Diesen Herausforderungen wurde mit Überlegungen zur größtmöglichen Effektivität bei der Kartierung von Rutschungen und einem neuen Forschungsdesign zur statistischen Modellierung der Rutschungsanfälligkeit eines Gebietes begegnet. Die Kartierung erfolgte auf Basis von Schummerungen eines hochauflösenden digitalen Geländemodells. Die Modellierung der Gefährdung wurde innerhalb geotechnisch und topographisch homogener Teilgebiete durchgeführt. Die Vollständigkeit des Inventars und der menschliche Einfluss auf die Auslösung von Rutschungen wurden mittels einer Persistenzanalyse der Morphologie der Rutschung und der Abschätzung des Einflusses von alten Weganalagen auf das Einzugsgebiet einer Rutschung beurteilt. Zusätzlich wurden Unsicherheiten in der „Vorhersage“ der Rutschungsgefährdung durch die Analyse ihrer Konfidenzintervalle bestimmt. Die Überlappung von Gefährdungsklassen der Gefahrenhinweiskarten der vorhergesagten Auftretens-Wahrscheinlichkeit von Rutschungen und der Konfidenzintervalle der Vorhersage wurde ermittelt. Diese Überlappungen entsprechen den räumlichen Unsicherheiten, welche auf einer Karte visualisiert wurden. Diese Visualisierung soll die Kommunikation über Unsicherheiten in der Gefährdungsmodellierung mit den Anwendern der Gefahrenhinweiskarte erleichtern bzw. eine Diskussion zum zulässigen Ausmaß von Unsicherheiten ankurbeln. Im Rahmen dieser Forschungsarbeit konnte eine effektive Methode zur Kartierung von Rutschungen auf Basis des hochaufgelösten digitalen Geländemodells erarbeitet werden. Das resultierende Inventar ist besonders auf die Anforderungen der statistischen Gefährdungsmodellierung von heterogenen Gebieten abgestimmt. Die Ergebnisse bestätigen die Entscheidung für das neue Forschungsdesign, da in jedem homogenen Teilgebiet eine unterschiedliche Variablenauswahl zur besten Charakterisierung der Rutschungsanfälligkeit geführt hat. Des Weiteren konnten Unterschiede bezüglich der Modellgüte, je nach Größe der verwendeten Stichprobe zum Modellieren festgestellt werden. Teilgebiete mit sehr großen Stichproben zeigten eine geringere Spannweite der Validierungsmaße auf als Teilgebiete mit kleinen Stichprobengrößen. Die Darstellung der Überlappung von verschiedenen Gefährdungsklassen, welche durch Berechnung der Konfidenzintervalle der Vorhersage ermittelt wurden, mit der ursprünglichen Gefahrenhinweiskarte zeigen deutlich Bereiche mit sehr großen Unsicherheiten aber auch mit sehr kleinen Unsicherheiten auf. Die Bearbeitung der Fortpflanzung von Unsicherheiten der Eingangsdaten zu Unsicherheiten der Modellierung ist eine der Perspektiven dieser Forschungsarbeit.Landslide susceptibility maps have been identified as powerful tool for the identification of areas susceptible to landslides. As they provide spatial information on the level of susceptibility these maps are very valuable for spatial planning strategies. The scope of this thesis was the statistical modelling of landslide susceptibility of a large and heterogeneous area, including the preparation of an inventory with high positional accuracy and the evaluation of different aspects of quality of the landslide susceptibility map. Landslides of the type earth and debris slides were visually mapped on the basis of high resolution LiDAR DTM derivatives. Assessing the effectiveness of mapping landslides for statistical susceptibility modelling the mapping of polygons and points was compared according to resources needed for the mapping and the potential output. Furthermore, the persistence and relative age of the landslide forms was assessed to estimate the incompleteness of the inventory mapped solely using LiDAR DTM derivatives and the sustainability of using land cover as explanatory variables. The landslide susceptibility modelling was performed using generalized additive models. Information on their quality is of high importance, as the final landslide susceptibility maps may be implemented in spatial planning practices. The model performance was assessed applying a repeated k-fold cross-validation, once with random subsampling and once with spatial subsampling. This was done to estimate the range of performance measures using different samples for the modelling and to assess the transferability of the model. Furthermore, a good susceptibility model can be described by a high thematic consistency, which selects similar sets of input variables within each model run. As the study area was very large and heterogeneous, as defined by a large range of lithological units and geotechnical characteristics present in the area, a new study design had to be developed. This was done to characterize the landslide susceptibility of the study area in its best possible way and to allow for the comparison of the susceptibility throughout the study area. The new study design included modelling within homogeneous modelling domains. The uncertainty of the modelling result was visualized using the 95% confidence intervals of the predicted probability of landslide susceptibility. These maps were compared with the map of the predicted probability and overlaps of different susceptibility classes were derived. The visualization of these class overlaps might aid stakeholders in the interpretation of the uncertainties of a modelling result and start the discussion about acceptable uncertainty levels. With the applied methods it was possible to identify an effective landslide inventory mapping method optimized for statistical landslide susceptibility modelling mapping points in the main scarp. Furthermore, the quality of the landslide susceptibility map was assessed quantitatively showing a different transferability and thematic consistency depending on the modelling domain size and the sample size used within the analysis. The visualization of the spatially varying prediction uncertainties was successful showing areas of high uncertainty and of low uncertainty. This allows the identification of hot spots for future slope stability analysis with more detailed, maybe physically based models

Sunken Roads and Palaeosols in Loess Areas in Lower Austria: Landform Development and Cultural Importance

Loess, a light-yellowish sediment composed of silt-sized material, takes up a large proportion of the northeast of Lower Austria and is full of surprises of cultural and scientific importance. On the one hand, the presence of loess results in a particular set of landforms such as loess dells, gullies, sunken roads and sinkholes of both natural and anthropogenic origin. On the other hand, the many outcrops of loess with several metres of height still reveal unprecedented insights in the climatic and settlement history of Lower Austria. Both, sunken roads and palaeosols may be seen as silent witnesses of past landscape changes but on a very different time scale. Originating from a natural gully erosion process, sunken roads evolved from gullies by their transformation in access paths leading to agricultural fields. Many driving forces influenced the vertical and lateral erosion of sunken roads, which showed increasing erosion rates of up to 15–30 cm per year until the mid-twentieth century. Nowadays, many sunken roads have disappeared and many remaining ones are paved or protected from further erosion and are transformed to cellar lanes (sunken roads with wine cellars dug next to each other into the loess wall). Results from analysing the ages of palaeosols found in Lower Austrian loess profiles are internationally important. Still to the day, with upcoming new dating methods and the chance for more detailed analysis, new insights into the stratigraphy, the related ages, palaeorelief and climate oscillations are found in close collaborations of geographers and archaeologists. Many internationally important archaeological artefacts and presumably the oldest loess of Europe were found in Lower Austria

Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software

With the increased availability of high-resolution digital terrain models (HRDTM) generated using airborne light detection and ranging (LiDAR), new opportunities for improved mapping of geohazards such as landslides arise. While the visual interpretation of LiDAR, HRDTM hillshades is a widely used approach, the automatic detection of landslides is promising to significantly speed up the compilation of inventories. Previous studies on automatic landslide detection often used a combination of optical imagery and geomorphometric data, and were implemented in commercial software. The objective of this study was to investigate the potential of open source software for automated landslide detection solely based on HRDTM-derived data in a study area in Burgenland, Austria. We implemented a geographic object-based image analysis (GEOBIA) consisting of (1) the calculation of land-surface variables, textural features and shape metrics, (2) the automated optimization of segmentation scale parameters, (3) region-growing segmentation of the landscape, (4) the supervised classification of landslide parts (scarp and body) using support vector machines (SVM), and (5) an assessment of the overall classification performance using a landslide inventory. We used the free and open source data-analysis environment R and its coupled geographic information system (GIS) software for the analysis; our code is included in the Supplementary Materials. The developed approach achieved a good performance (&kappa; = 0.42) in the identification of landslides

Event-Based Landslide Modeling in the Styrian Basin, Austria: Accounting for Time-Varying Rainfall and Land Cover

In June 2009 and September 2014, the Styrian Basin in Austria was affected by extreme events of heavy thunderstorms, triggering thousands of landslides. Since the relationship between intense rainfall, land cover/land use (LULC), and landslide occurrences is still not fully understood, our objective was to develop a model design that allows to assess landslide susceptibility specifically for past triggering events. We used generalized additive models (GAM) to link land surface, geology, meteorological, and LULC variables to observed slope failures. Accounting for the temporal variation in landslide triggering, we implemented an innovative spatio-temporal approach for landslide absence sampling. We assessed model performance using k-fold cross-validation in space and time to estimate the area under the receiver operating characteristic curve (AUROC). Furthermore, we analyzed the variable importance and its relationship to landslide occurrence. Our results showed that the models had on average acceptable to outstanding landslide discrimination capabilities (0.81&ndash;0.94 mAUROC in space and 0.72&ndash;0.95 mAUROC in time). Furthermore, meteorological and LULC variables were of great importance in explaining the landslide events (e.g., five-day rainfall 13.6&ndash;17.8% mean decrease in deviance explained), confirming their usefulness in landslide event analysis. Based on the present findings, future studies may assess the potential of this approach for developing future storylines of slope instability based on climate and LULC scenarios