Analysis and Reconstructed Modelling of the Debris Flow Event of the 21st of July 2012 of St. Lorenzen (Styria, Austria)

Janu, Stefan; Mehlhorn, Susanne; Moser, Markus

doi:10.1007/978-3-319-09057-3_86

Cited by 1 publication

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“…In comparison, a volume of about 123,000 m 3 of sediments were mobilized in the catchment area Lorenzerbach in July 2012 due to WLV estimates (Janu & Mehlhorn, 2013 ) and about 300 m 3 in the Rettenbach area in May 2013 (no uncertainty range provided for both areas). These amounts are relatively close to the results of our change detection analysis (Lorenzerbach 90,000 m 3 ± 35,000 m 3 ; Rettenbach 800 m 3 ± 450 m 3 ; based on the QCD‐tool described in Krenn et al, in review ).…”

Section: Resultsmentioning

confidence: 99%

“…For example, within the Schöttlbach catchment, class 1 means a maximum height error of 0.1 m and a volume-uncertainty of 0.025 m 3 /cell, while class 4 means a maximum height error of 0.4 m and a volume uncertainty of 0.1 m 3 /cell.According to the WLV, a sediment volume of 100,000 m 3 (no uncertainty range provided) was transported from the catchment area Schöttlbach in August 2017. We calculated a volume of 131,000 m 3 (± 61,000 m 3 ) of transported sediments using the QCDtool (detailed information are available inKrenn et al, in review).In comparison, a volume of about 123,000 m 3 of sediments were mobilized in the catchment area Lorenzerbach in July 2012 due to WLV estimates(Janu & Mehlhorn, 2013) and about 300 m 3 in the Rettenbach area in May 2013 (no uncertainty range provided for both areas). These amounts are relatively close to the results of our change detection analysis (Lorenzerbach 90,000 m 3 ± 35,000 m 3 ; Rettenbach 800 m 3 ± 450 m 3 ; based on the QCD-tool described inKrenn et al, in review).…”

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Comparability of multi‐temporal DTMs derived from different LiDAR platforms: Error sources and uncertainties in the application of geomorphic impact studies

Kamp

Krenn

Avian

et al. 2023

Earth Surf Processes Landf

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Multi‐temporal digital terrain models (DTMs) derived from airborne or uncrewed aerial vehicle (UAV)‐borne light detection and ranging (LiDAR) platforms are frequently used tools in geomorphic impact studies. Accurate estimation of mobilized sediments from multi‐temporal DTMs is indispensable for hazard assessment. To study volumetric changes in alpine environments it is crucial to identify and discuss different kind of error sources in multi‐temporal data. We subdivided errors into those caused by data acquisition, data processing, and spatial properties of the terrain. In terms of the quantification of surface changes, the propagation of errors can lead to high uncertainties. Three alpine catchments with different LiDAR point clouds of different origins (airborne laser scanning [ALS], UAV‐borne laser scanning [ULS]), varying point densities, accuracies and qualities were analysed, and used as basis for interpolating DTMs. The workflow was developed in the Schöttlbach area in Styria and later applied to further catchments in Austria. The main aim of the presented work is a comprehensive DTM uncertainty analysis specially designed for geomorphic impact studies, with a resulting uncertainty analysis serving as input for a change detection tool. Our findings reveal that geomorphic impact studies need the careful distinction between actual surface changes and different data uncertainties. ULS combines the benefits of terrestrial laser scanning with all the benefits of ALS. However, the use of ULS data does not necessarily improve the results of the analysis since the high level of detail is not always helpful in geomorphic impact studies. In order to make the different point clouds and DTMs comparable the quality of the ULS point cloud had to be reduced to fit the accuracy of the reference data (older ALS point clouds). Using a point cloud with a high point density with a regular planimetric point spacing and less data gaps, in the best case collected during leaf‐off conditions (e.g., cross‐flight strategy) turned out to be sufficient for our geomorphic research purposes.

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confidence: 99%