This page has only limited features, please log in for full access.

Unclaimed
H. Petschko
Friedrich Schiller University Jena, Department of Geography, D JENA01 Jena, Germany

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 01 January 2021 in Mitteilungen der Österreichischen Geographischen Gesellschaft
Reads 0
Downloads 0
ACS Style

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 Style

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.

Chicago/Turabian Style

Raphael 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.

Journal article
Published: 03 June 2020 in Geosciences
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (6):1.

Chicago/Turabian Style

Raphael 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.

Journal article
Published: 02 December 2019 in ISPRS International Journal of Geo-Information
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (12):551.

Chicago/Turabian Style

Raphael 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.

Conference paper
Published: 11 June 2017 in Advancing Culture of Living with Landslides
Reads 0
Downloads 0

More than 9000 sinkholes have been documented by the Geological Survey of Thuringia in different lithological units of Thuringia of which many posed a serious threat on life, personal property and infrastructure. While it is clear that they are caused by hollows which formed due to solution processes within the local bedrock material, little is known about the surface processes and dynamics of erosion of the sinkhole visible above ground. The objective of this study was to analyze sinkhole surface dynamics over time with 3D models derived from terrestrial photos by structure from motion and multi-view 3D reconstruction. The sinkhole was surveyed by terrestrial photos on two days with a two months break. During each photo session 84 and 237 photos have been taken from all around the sinkhole. The photos were processed to 3D point clouds using Agisoft PhotoScan and compared using the software CloudCompare and the M3C2 plugin. The resulting point clouds show an area with significant change that covers about 26% of the sinkhole. Toppling and a few erosion processes have successfully been detected with an observed change of up to 10 cm. Nevertheless, for future studies the study design has to be improved regarding the point cloud registration process, a longer observation duration and a quantitative evaluation of the quality of the individual point clouds is pending.

ACS Style

Helene Petschko; Jason Goetz; Max Böttner; Maximilian Firla; Sven Schmidt; Matjaz Mikos; Binod Tiwari; Yueping Yin; Kyoji Sassa. Erosion Processes and Mass Movements in Sinkholes Assessed by Terrestrial Structure from Motion Photogrammetry. Advancing Culture of Living with Landslides 2017, 227 -235.

AMA Style

Helene Petschko, Jason Goetz, Max Böttner, Maximilian Firla, Sven Schmidt, Matjaz Mikos, Binod Tiwari, Yueping Yin, Kyoji Sassa. Erosion Processes and Mass Movements in Sinkholes Assessed by Terrestrial Structure from Motion Photogrammetry. Advancing Culture of Living with Landslides. 2017; ():227-235.

Chicago/Turabian Style

Helene Petschko; Jason Goetz; Max Böttner; Maximilian Firla; Sven Schmidt; Matjaz Mikos; Binod Tiwari; Yueping Yin; Kyoji Sassa. 2017. "Erosion Processes and Mass Movements in Sinkholes Assessed by Terrestrial Structure from Motion Photogrammetry." Advancing Culture of Living with Landslides , no. : 227-235.

Journal article
Published: 16 June 2016 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reads 0
Downloads 0

Terrestrial photogrammetry nowadays offers a reasonably cheap, intuitive and effective approach to 3D-modelling. However, the important choice, which sensor and which software to use is not straight forward and needs consideration as the choice will have effects on the resulting 3D point cloud and its derivatives.

We compare five different sensors as well as four different state-of-the-art software packages for a single application, the modelling of a vegetated rock face. The five sensors represent different resolutions, sensor sizes and price segments of the cameras. The software packages used are: (1) Agisoft PhotoScan Pro (1.16), (2) Pix4D (2.0.89), (3) a combination of Visual SFM (V0.5.22) and SURE (1.2.0.286), and (4) MicMac (1.0). We took photos of a vegetated rock face from identical positions with all sensors. Then we compared the results of the different software packages regarding the ease of the workflow, visual appeal, similarity and quality of the point cloud.

While PhotoScan and Pix4D offer the user-friendliest workflows, they are also “black-box” programmes giving only little insight into their processing. Unsatisfying results may only be changed by modifying settings within a module. The combined workflow of Visual SFM, SURE and CloudCompare is just as simple but requires more user interaction. MicMac turned out to be the most challenging software as it is less user-friendly. However, MicMac offers the most possibilities to influence the processing workflow. The resulting point-clouds of PhotoScan and MicMac are the most appealing.

ACS Style

Robert Niederheiser; Martin Mokroš; Julia Lange; Helene Petschko; Guenther Prasicek; Sander Oude Elberink. DERIVING 3D POINT CLOUDS FROM TERRESTRIAL PHOTOGRAPHS - COMPARISON OF DIFFERENT SENSORS AND SOFTWARE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B5, 685 -692.

AMA Style

Robert Niederheiser, Martin Mokroš, Julia Lange, Helene Petschko, Guenther Prasicek, Sander Oude Elberink. DERIVING 3D POINT CLOUDS FROM TERRESTRIAL PHOTOGRAPHS - COMPARISON OF DIFFERENT SENSORS AND SOFTWARE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B5 ():685-692.

Chicago/Turabian Style

Robert Niederheiser; Martin Mokroš; Julia Lange; Helene Petschko; Guenther Prasicek; Sander Oude Elberink. 2016. "DERIVING 3D POINT CLOUDS FROM TERRESTRIAL PHOTOGRAPHS - COMPARISON OF DIFFERENT SENSORS AND SOFTWARE." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5, no. : 685-692.

Journal article
Published: 01 June 2016 in Geomorphology
Reads 0
Downloads 0

Empirical models are frequently applied to produce landslide susceptibility maps for large areas. Subsequent quantitative validation results are routinely used as the primary criteria to infer the validity and applicability of the final maps or to select one of several models. This study hypothesizes that such direct deductions can be misleading. The main objective was to explore discrepancies between the predictive performance of a landslide susceptibility model and the geomorphic plausibility of subsequent landslide susceptibility maps while a particular emphasis was placed on the influence of incomplete landslide inventories on modelling and validation results. The study was conducted within the Flysch Zone of Lower Austria (1354 km2) which is known to be highly susceptible to landslides of the slide-type movement. Sixteen susceptibility models were generated by applying two statistical classifiers (logistic regression and generalized additive model) and two machine learning techniques (random forest and support vector machine) separately for two landslide inventories of differing completeness and two predictor sets. The results were validated quantitatively by estimating the area under the receiver operating characteristic curve (AUROC) with single holdout and spatial cross-validation technique. The heuristic evaluation of the geomorphic plausibility of the final results was supported by findings of an exploratory data analysis, an estimation of odds ratios and an evaluation of the spatial structure of the final maps. The results showed that maps generated by different inventories, classifiers and predictors appeared differently while holdout validation revealed similar high predictive performances. Spatial cross-validation proved useful to expose spatially varying inconsistencies of the modelling results while additionally providing evidence for slightly overfitted machine learning-based models. However, the highest predictive performances were obtained for maps that explicitly expressed geomorphically implausible relationships indicating that the predictive performance of a model might be misleading in the case a predictor systematically relates to a spatially consistent bias of the inventory. Furthermore, we observed that random forest-based maps displayed spatial artefacts. The most plausible susceptibility map of the study area showed smooth prediction surfaces while the underlying model revealed a high predictive capability and was generated with an accurate landslide inventory and predictors that did not directly describe a bias. However, none of the presented models was found to be completely unbiased. This study showed that high predictive performances cannot be equated with a high plausibility and applicability of subsequent landslide susceptibility maps. We suggest that greater emphasis should be placed on identifying confounding factors and biases in landslide inventories. A joint discussion between modelers and decision makers of the spatial pattern of the final susceptibility maps in the field might increase their acceptance and applicability.

ACS Style

Stefan Steger; Alexander Brenning; Rainer Bell; Helene Petschko; Thomas Glade. Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps. Geomorphology 2016, 262, 8 -23.

AMA Style

Stefan Steger, Alexander Brenning, Rainer Bell, Helene Petschko, Thomas Glade. Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps. Geomorphology. 2016; 262 ():8-23.

Chicago/Turabian Style

Stefan Steger; Alexander Brenning; Rainer Bell; Helene Petschko; Thomas Glade. 2016. "Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps." Geomorphology 262, no. : 8-23.

Journal article
Published: 20 August 2015 in Landslides
Reads 0
Downloads 0

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 km2) 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.

ACS Style

H. Petschko; R. Bell; T. Glade. Effectiveness of visually analyzing LiDAR DTM derivatives for earth and debris slide inventory mapping for statistical susceptibility modeling. Landslides 2015, 13, 857 -872.

AMA Style

H. Petschko, R. Bell, T. Glade. Effectiveness of visually analyzing LiDAR DTM derivatives for earth and debris slide inventory mapping for statistical susceptibility modeling. Landslides. 2015; 13 (5):857-872.

Chicago/Turabian Style

H. Petschko; R. Bell; T. Glade. 2015. "Effectiveness of visually analyzing LiDAR DTM derivatives for earth and debris slide inventory mapping for statistical susceptibility modeling." Landslides 13, no. 5: 857-872.

Journal article
Published: 01 August 2015 in Computers & Geosciences
Reads 0
Downloads 0

Statistical and now machine learning prediction methods have been gaining popularity in the field of landslide susceptibility modeling. Particularly, these data driven approaches show promise when tackling the challenge of mapping landslide prone areas for large regions, which may not have sufficient geotechnical data to conduct physically-based methods. Currently, there is no best method for empirical susceptibility modeling. Therefore, this study presents a comparison of traditional statistical and novel machine learning models applied for regional scale landslide susceptibility modeling. These methods were evaluated by spatial k-fold cross-validation estimation of the predictive performance, assessment of variable importance for gaining insights into model behavior and by the appearance of the prediction (i.e. susceptibility) map. The modeling techniques applied were logistic regression (GLM), generalized additive models (GAM), weights of evidence (WOE), the support vector machine (SVM), random forest classification (RF), and bootstrap aggregated classification trees (bundling) with penalized discriminant analysis (BPLDA). These modeling methods were tested for three areas in the province of Lower Austria, Austria. The areas are characterized by different geological and morphological settings.Random forest and bundling classification techniques had the overall best predictive performances. However, the performances of all modeling techniques were for the majority not significantly different from each other; depending on the areas of interest, the overall median estimated area under the receiver operating characteristic curve (AUROC) differences ranged from 2.9 to 8.9 percentage points. The overall median estimated true positive rate (TPR) measured at a 10% false positive rate (FPR) differences ranged from 11 to 15pp. The relative importance of each predictor was generally different between the modeling methods. However, slope angle, surface roughness and plan curvature were consistently highly ranked variables. The prediction methods that create splits in the predictors (RF, BPLDA and WOE) resulted in heterogeneous prediction maps full of spatial artifacts. In contrast, the GAM, GLM and SVM produced smooth prediction surfaces. Overall, it is suggested that the framework of this model evaluation approach can be applied to assist in selection of a suitable landslide susceptibility modeling technique. We modeled landslide susceptibility with statistical and machine learning techniques.We evaluate performance, predictor importance, and visual appearance of susceptibility maps.Differences in model prediction performance were for the majority non-significant.Consequently, landslide modelers may consider selecting modeling techniques based on additional practical criteria.

ACS Style

Jason Goetz; Alexander Brenning; Helene Petschko; P. Leopold. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Computers & Geosciences 2015, 81, 1 -11.

AMA Style

Jason Goetz, Alexander Brenning, Helene Petschko, P. Leopold. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Computers & Geosciences. 2015; 81 ():1-11.

Chicago/Turabian Style

Jason Goetz; Alexander Brenning; Helene Petschko; P. Leopold. 2015. "Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling." Computers & Geosciences 81, no. : 1-11.

Book chapter
Published: 04 January 2015 in Engineering Geology for Society and Territory - Volume 2
Reads 0
Downloads 0

Landslide susceptibility maps can be elaborated using a variety of methodological approaches. This study investigates quantitative and qualitative differences between two statistical modelling methods, taking into account the impact of two different response variables (landslide inventories) for the Rhenodanubian Flysch zone of Lower Austria. Quantitative validation of the four generated susceptibility maps is conducted by calculating conventional accuracy statistics for an independent random landslide subsample. Qualitative geomorphic plausibility is estimated by comparing the final susceptibility maps with hillshades of a high resolution Airborne Laser Scan Digital Terrain Model (ALS-DTM). Spatial variations between the final susceptibility maps are displayed by difference maps and their densities. Although statistical quality criterions reveal similar qualities for all maps, difference maps and geomorphic plausibility expose considerable differences between the maps. Given that, this conclusion could only be drawn by evaluating additionally the geomorphic plausibility and difference maps. Therefore, we indicate that conventional statistical quality assessment should be combined with qualitative validation of the maps.

ACS Style

Stefan Steger; Rainer Bell; Helene Petschko; Thomas Glade. Evaluating the Effect of Modelling Methods and Landslide Inventories Used for Statistical Susceptibility Modelling. Engineering Geology for Society and Territory - Volume 2 2015, 201 -204.

AMA Style

Stefan Steger, Rainer Bell, Helene Petschko, Thomas Glade. Evaluating the Effect of Modelling Methods and Landslide Inventories Used for Statistical Susceptibility Modelling. Engineering Geology for Society and Territory - Volume 2. 2015; ():201-204.

Chicago/Turabian Style

Stefan Steger; Rainer Bell; Helene Petschko; Thomas Glade. 2015. "Evaluating the Effect of Modelling Methods and Landslide Inventories Used for Statistical Susceptibility Modelling." Engineering Geology for Society and Territory - Volume 2 , no. : 201-204.

Book chapter
Published: 29 April 2014 in Landslide Science for a Safer Geoenvironment
Reads 0
Downloads 0

In statistical landslide susceptibility modelling the identification of appropriate explanatory variables describing the predisposing and preparatory factors for the landslides of a given inventory is important. In this context information on the age and the respective land cover at the time of occurrence is beneficiary. The potential of mapping very old (or prehistoric) landslides using LiDAR derivatives has not been analysed yet. Additionally, performing a visual interpretation of derivatives of a single LiDAR DTM it is not possible to assign the accurate age or date of the occurrence of the event to each mapped landslide. Therefore, commonly no information on the land cover at the time of landslide occurrence for these very old landslides (but also for younger ones) is available. The objective of this study is, to estimate the relative age of landslides during the mapping and to explore differences of the recent land cover distribution in the relative ages of the landslides. This is performed to evaluate the sustainability of including recent land cover data into susceptibility modelling. The relative age of the landslides is estimated for each landslide according to its morphological footprint on the LiDAR DTM derivatives and to its appearance on the orthophoto. The different relative ages assigned are “very old”, “old”, “young” and “very young”. The study area is located in three districts of Lower Austria, namely Amstetten, Baden and Waidhofen/Ybbs. The resulting inventory includes 1834 landslides and shows that the “very old” and “old” landslides (60 % of all mapped landslides) are mainly covered by forest (~60 % of all land cover types). We conclude that using this inventory including recent land cover data in the susceptibility model is not appropriate for Lower Austria. There is a potential of mapping “old” or “very old” landslides on the LiDAR derivatives. The absolute age remains unknown.

ACS Style

Helene Petschko; Rainer Bell; Thomas Glade. Relative Age Estimation at Landslide Mapping on LiDAR Derivatives: Revealing the Applicability of Land Cover Data in Statistical Susceptibility Modelling. Landslide Science for a Safer Geoenvironment 2014, 337 -343.

AMA Style

Helene Petschko, Rainer Bell, Thomas Glade. Relative Age Estimation at Landslide Mapping on LiDAR Derivatives: Revealing the Applicability of Land Cover Data in Statistical Susceptibility Modelling. Landslide Science for a Safer Geoenvironment. 2014; ():337-343.

Chicago/Turabian Style

Helene Petschko; Rainer Bell; Thomas Glade. 2014. "Relative Age Estimation at Landslide Mapping on LiDAR Derivatives: Revealing the Applicability of Land Cover Data in Statistical Susceptibility Modelling." Landslide Science for a Safer Geoenvironment , no. : 337-343.

Journal article
Published: 16 January 2014 in Natural Hazards and Earth System Sciences
Reads 0
Downloads 0

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 km2) 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.

ACS Style

H. Petschko; A. Brenning; R. Bell; J. Goetz; T. Glade. Assessing the quality of landslide susceptibility maps – case study Lower Austria. Natural Hazards and Earth System Sciences 2014, 14, 95 -118.

AMA Style

H. Petschko, A. Brenning, R. Bell, J. Goetz, T. Glade. Assessing the quality of landslide susceptibility maps – case study Lower Austria. Natural Hazards and Earth System Sciences. 2014; 14 (1):95-118.

Chicago/Turabian Style

H. Petschko; A. Brenning; R. Bell; J. Goetz; T. Glade. 2014. "Assessing the quality of landslide susceptibility maps – case study Lower Austria." Natural Hazards and Earth System Sciences 14, no. 1: 95-118.

Book chapter
Published: 02 February 2013 in Landslide Science and Practice
Reads 0
Downloads 0

Landslide inventories, their accuracy and the stored information are of major importance for landslide susceptibility modelling. Working on the scale of a province (Lower Austria with about 10,000 km2) challenges arise due to data availability and its spatial representation. Furthermore, previous studies on existing landslide inventories showed that only few inventories can be used for statistical susceptibility modelling. In this study two landslide inventories and their resulting susceptibility maps are compared: the Building Ground Register (BGR) of the Geological Survey of Lower Austria and an inventory that was mapped on the basis of a high resolution LiDAR DTM. This analysis was performed to estimate minimum requirements on landslide inventories to allow for deriving reliable susceptibility maps while minimizing mapping efforts. Therefore a consistent landslide inventory once from the BGR and once from the mapping was compiled. Furthermore, a logistic regression model was fitted with randomly selected points of each landslide inventory to compare the resulting maps and validation rates. The resulting landslide susceptibility maps show significant differences regarding their visual and statistical quality. We conclude that the application of randomly selected points in the main scarp of the mapped landslides gives satisfactory results.

ACS Style

Helene Petschko; Rainer Bell; Philip Leopold; Gerhard Heiss; Thomas Glade. Landslide Inventories for Reliable Susceptibility Maps in Lower Austria. Landslide Science and Practice 2013, 281 -286.

AMA Style

Helene Petschko, Rainer Bell, Philip Leopold, Gerhard Heiss, Thomas Glade. Landslide Inventories for Reliable Susceptibility Maps in Lower Austria. Landslide Science and Practice. 2013; ():281-286.

Chicago/Turabian Style

Helene Petschko; Rainer Bell; Philip Leopold; Gerhard Heiss; Thomas Glade. 2013. "Landslide Inventories for Reliable Susceptibility Maps in Lower Austria." Landslide Science and Practice , no. : 281-286.

Book chapter
Published: 02 February 2013 in Landslide Science and Practice
Reads 0
Downloads 0

Landslides threaten most parts of the provincial state of Lower Austria and cause damage to agricultural land, forests, infrastructure, settlements and people. Thus, the project “MoNOE” (Method development for landslide susceptibility modelling in Lower Austria) was initiated by the provincial government to tackle these problems and to reduce further damage by landslides. The main aim is to prepare landslide susceptibility maps for slides and rock falls and to implement these maps into the spatial planning strategies of the provincial state.

ACS Style

Rainer Bell; Thomas Glade; Klaus Granica; Gerhard Heiss; Philip Leopold; Helene Petschko; Gilbert Pomaroli; Herwig Proske; Joachim Schweigl. Landslide Susceptibility Maps for Spatial Planning in Lower Austria. Landslide Science and Practice 2013, 467 -472.

AMA Style

Rainer Bell, Thomas Glade, Klaus Granica, Gerhard Heiss, Philip Leopold, Helene Petschko, Gilbert Pomaroli, Herwig Proske, Joachim Schweigl. Landslide Susceptibility Maps for Spatial Planning in Lower Austria. Landslide Science and Practice. 2013; ():467-472.

Chicago/Turabian Style

Rainer Bell; Thomas Glade; Klaus Granica; Gerhard Heiss; Philip Leopold; Helene Petschko; Gilbert Pomaroli; Herwig Proske; Joachim Schweigl. 2013. "Landslide Susceptibility Maps for Spatial Planning in Lower Austria." Landslide Science and Practice , no. : 467-472.

Book chapter
Published: 02 February 2013 in Landslide Science and Practice
Reads 0
Downloads 0

This study focuses on the comparison of different approaches for landslide susceptibility modelling and is part of the research project “MoNOE” (Method development for landslide susceptibility modelling in Lower Austria). The main objective of the project is to design a method for landslide susceptibility modelling for a large study area. For other objectives of the project we refer to Bell et al. (Proceedings of the 2nd world landslide forum, Rome, 3–7 Oct 2011, this volume). To reach the main objective, the two different statistical models “Weights of Evidence” and “Logistic Regression” are applied and compared. By using nearly the same input data in test areas it is possible to compare the capabilities of both methods. First results of the comparison indicate that in valleys and on south facing slopes the results are quite similar. In contrast, the analysis on north facing slopes shows differences. In the ongoing work the reasons for these differences will be analysed. Furthermore, attention will be paid to finding adequate validation methods for the two modelling approaches.

ACS Style

Philip Leopold; Gerhard Heiss; Helene Petschko; Rainer Bell; Thomas Glade. Susceptibility Maps for Landslides Using Different Modelling Approaches. Landslide Science and Practice 2013, 353 -356.

AMA Style

Philip Leopold, Gerhard Heiss, Helene Petschko, Rainer Bell, Thomas Glade. Susceptibility Maps for Landslides Using Different Modelling Approaches. Landslide Science and Practice. 2013; ():353-356.

Chicago/Turabian Style

Philip Leopold; Gerhard Heiss; Helene Petschko; Rainer Bell; Thomas Glade. 2013. "Susceptibility Maps for Landslides Using Different Modelling Approaches." Landslide Science and Practice , no. : 353-356.

Journal article
Published: 01 March 2012 in Geografiska Annaler: Series A, Physical Geography
Reads 0
Downloads 0
ACS Style

Rainer Bell; Helene Petschko; Matthias Röhrs; Andreas Dix. Assessment of landslide age, landslide persistence and human impact using airborne laser scanning digital terrain models. Geografiska Annaler: Series A, Physical Geography 2012, 94, 135 -156.

AMA Style

Rainer Bell, Helene Petschko, Matthias Röhrs, Andreas Dix. Assessment of landslide age, landslide persistence and human impact using airborne laser scanning digital terrain models. Geografiska Annaler: Series A, Physical Geography. 2012; 94 (1):135-156.

Chicago/Turabian Style

Rainer Bell; Helene Petschko; Matthias Röhrs; Andreas Dix. 2012. "Assessment of landslide age, landslide persistence and human impact using airborne laser scanning digital terrain models." Geografiska Annaler: Series A, Physical Geography 94, no. 1: 135-156.