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Raphael Knevels; Alexander Brenning; Simone Gingrich; Elisabeth Gruber; Theresia Lechner; Philip Leopold; Helene Petschko; Christoph Plutzar. Kulturlandschaft im Wandel: Ein indikatorenbasierter Rückblick bis in das 19. Jahrhundert. Fallstudie anhand der Gemeinden Waidhofen/Ybbs und Paldau. Mitteilungen der Österreichischen Geographischen Gesellschaft 2021, 1, 255 -285.
AMA StyleRaphael Knevels, Alexander Brenning, Simone Gingrich, Elisabeth Gruber, Theresia Lechner, Philip Leopold, Helene Petschko, Christoph Plutzar. Kulturlandschaft im Wandel: Ein indikatorenbasierter Rückblick bis in das 19. Jahrhundert. Fallstudie anhand der Gemeinden Waidhofen/Ybbs und Paldau. Mitteilungen der Österreichischen Geographischen Gesellschaft. 2021; 1 ():255-285.
Chicago/Turabian StyleRaphael Knevels; Alexander Brenning; Simone Gingrich; Elisabeth Gruber; Theresia Lechner; Philip Leopold; Helene Petschko; Christoph Plutzar. 2021. "Kulturlandschaft im Wandel: Ein indikatorenbasierter Rückblick bis in das 19. Jahrhundert. Fallstudie anhand der Gemeinden Waidhofen/Ybbs und Paldau." Mitteilungen der Österreichischen Geographischen Gesellschaft 1, no. : 255-285.
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 (0.81–0.94 mAUROC in space and 0.72–0.95 mAUROC in time). Furthermore, meteorological and LULC variables were of great importance in explaining the landslide events (e.g., five-day rainfall 13.6–17.8% 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.
Raphael Knevels; Helene Petschko; Herwig Proske; Philip Leopold; Douglas Maraun; Alexander Brenning. Event-Based Landslide Modeling in the Styrian Basin, Austria: Accounting for Time-Varying Rainfall and Land Cover. Geosciences 2020, 10, 1 .
AMA StyleRaphael Knevels, Helene Petschko, Herwig Proske, Philip Leopold, Douglas Maraun, Alexander Brenning. Event-Based Landslide Modeling in the Styrian Basin, Austria: Accounting for Time-Varying Rainfall and Land Cover. Geosciences. 2020; 10 (6):1.
Chicago/Turabian StyleRaphael Knevels; Helene Petschko; Herwig Proske; Philip Leopold; Douglas Maraun; Alexander Brenning. 2020. "Event-Based Landslide Modeling in the Styrian Basin, Austria: Accounting for Time-Varying Rainfall and Land Cover." Geosciences 10, no. 6: 1.
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 (κ = 0.42) in the identification of landslides.
Raphael Knevels; Helene Petschko; Philip Leopold; Alexander Brenning. Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software. ISPRS International Journal of Geo-Information 2019, 8, 551 .
AMA StyleRaphael Knevels, Helene Petschko, Philip Leopold, Alexander Brenning. Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software. ISPRS International Journal of Geo-Information. 2019; 8 (12):551.
Chicago/Turabian StyleRaphael Knevels; Helene Petschko; Philip Leopold; Alexander Brenning. 2019. "Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software." ISPRS International Journal of Geo-Information 8, no. 12: 551.