Analyzing glacier retreat and mass balances using aerial and UAV photogrammetry in the Ötztal Alps, Austria

Geissler, Joschka; Mayer, Christoph; Jubanski, Juilson; Münzer, Ulrich; Siegert, Florian

doi:10.5194/tc-15-3699-2021

Cited by 17 publications

(4 citation statements)

References 52 publications

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“…Considering the inhomogeneity of the aerial imagery, the quantitative data described above are comparable to other published results obtained by digital photogrammetry (e.g. (Kaufmann and Ladstädter, 2003;Schiefer and Gilbert, 2007;Marzolff and Poesen, 2009;Fabris and Pesci, 2009;Micheletti et al, 2015;Hilger and Beylich, 420 2018;Geissler et al, 2021). The authors acknowledge the limitations of aerial imagery to depict vertical surfaces and in particular negative vertical surfaces.…”

Section: Validity Of Measurementssupporting

confidence: 85%

Massive sediment pulses triggered by a multi-stage alpine cliff fall (Hochvogel, DE/AT)

Barbosa

Leinauer

Jubanski

et al. 2023

Preprint

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Abstract. Massive sediment pulses in catchments are a key alpine multi-risk component. Substantial sediment redistribution in alpine catchments frequently causes flooding, river erosion, and landsliding, and affects infrastructure such as dam reservoirs as well as aquatic ecosystems and water quality. While systematic rock slope failure inventories have been collected in several countries, the subsequent cascading sediment redistribution is virtually unaccessed. This contribution reports for the first time the massive sediment redistribution triggered by the multi-stage failure of more than 150,000 m3 from the Hochvogel dolomite peak during the summer of 2016. We applied change detection techniques on seven 3D-coregistered high-resolution true-orthophotos and digital surface models (DSM) obtained through digital aerial photogrammetry later optimized for precise volume calculation in steep terrain. The analysis of seismic information from surrounding stations revealed the temporal evolution of the cliff fall. We identified the proportional contribution of > 600 rockfall events (>1 m3) from 4 rock slope catchments with different aspects and their volume estimates. In a sediment cascade approach, we evaluated erosion, transport, and deposition from the rockface to the upper channelized erosive debris flow channel, then to the widened dispersive debris flow channel, and finally to the outlet into the braided sediment-supercharged Jochbach river. We observe the decadal flux of more than 400,000 m3 of sediment with massive sediment pulses that (i) respond with reaction times of 0–4 years and relaxation times beyond 10 years, (ii) with faster response times of 0–2 years in the upper catchment and more than 2 years response times in the lower catchments, (iii) the inversion of sedimentary (102–103 mm/a) to massive erosive regimes (102 mm/a) within single years and the (iv) dependency of redistribution to rainfall frequency and intensity. This study provides generic information on spatial and temporal patterns of massive sediment pulses in highly-charged alpine catchments.

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Section: Validity Of Measurementssupporting

confidence: 85%

Massive sediment pulses triggered by a multi-stage alpine cliff fall (Hochvogel, DE/AT)

Barbosa

Leinauer

Jubanski

et al. 2023

Preprint

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“…The drawback of UAVs is the short flight duration and thus low spatial coverage, making airborne systems still important for larger-scale applications (Kostadinov et al, 2019). A compromise between the high spatial resolution and increased flexibility of UAVs and, on the other side, larger spatial coverages can be achieved using fixed-wing UAVs (Geissler et al, 2021). Fixed-wing UAVs, in contrast to multi-rotor UAVs, are equipped with wings and rely on forward (instead of downward) thrust.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Snow Variability in a Sub-Alpine Forest predicted by Machine Learning and UAV-based LiDAR Snow Depth Maps

Geissler

Rathmann

Weiler

2023

Preprint

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Snow interacts with its environment in many ways, is constantly changing with time, and thus has a highly heterogeneous spatial and temporal variability. Therefore, modeling snow variability is difficult, especially when additional components such as vegetation add complexity. To increase our understanding of the spatio-temporal variability of snow and to validate snow models, we need reliable observation data at similar spatial and temporal scales. For these purposes, airborne LiDAR surveys or time series derived from snow sensors on the point scale are commonly used. However, these are limited either to one point in space or in time. We present a new, extensive dataset of snow variability in a sub-alpine forest in the Alptal, Switzerland. The core dataset consists of a dense sensor network, repeated high-resolution LiDAR data acquired using a fixed-wing UAV, and manual snow depth and snow density measurements. Using machine learning algorithms, we determine four distinct spatial clusters of similar snow depth dynamics. These clusters are characterized and further used to derive daily snow depth and snow water equivalent (SWE) maps. The results underline the complex relation of topography and canopy cover towards snow accumulation and ablation. The derived products are the first to our knowledge that provide daily, high-resolution snow depth and SWE based almost exclusively on field data. They are therefore ideally suited for the validation of distributed snow models. Our approach can be applied to other project areas and improve our understanding of the spatio-temporal variability of snow in forested environments.

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“…In contrast to multi‐rotor UAVs, fixed‐wing UAVs are equipped with wings and rely on forward (instead of downward) thrust. This reduces the in‐flight power consumption and thus increases flight times and spatial coverage (Geissler et al., 2021). Due to limited payload capacities of fixed‐wing UAVs, they have so far only been used in combination with photogrammetric sensors to map snow distribution (Michele et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

Combining Daily Sensor Observations and Spatial LiDAR Data for Mapping Snow Water Equivalent in a Sub‐Alpine Forest

Geissler,

Rathmann,

Weiler

2023

Water Resources Research

Self Cite

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Snow interacts with its environment in many ways and thus has a highly heterogeneous spatial and temporal variability. Therefore, modeling snow variability is difficult, especially in forested environments. To increase the understanding of the spatio‐temporal variability of snow and to validate snow models, reliable observation data at similar spatial and temporal scales is needed. For these purposes, airborne LiDAR surveys or time series derived from ground‐based snow sensors are commonly used. However, these are limited either to one point in space or in time. A new, extensive dataset of daily snow variability in a sub‐alpine forest in the Alptal valley, Switzerland is presented. The core dataset consists of a dense sensor network, repeated high‐resolution LiDAR data acquired using a fixed‐wing UAV, and manual snow depth and snow density measurements. Using machine learning algorithms, four distinct spatial clusters of similar snow depth dynamics are determined. By combining these spatial clusters with the observed snow depth time series, daily high‐resolution maps of snow depth and snow water equivalent (SWE) are derived. These products are the first to our knowledge that provide spatio‐temporally continuous snow depth and SWE based almost exclusively on field data. The presented workflow is transferrable to different regions, climates and scales. Moreover, the results allow future field campaigns to find representative sensor locations and target their LiDAR surveys to derive similar continuous products with less involved effort.

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Analyzing glacier retreat and mass balances using aerial and UAV photogrammetry in the Ötztal Alps, Austria

Cited by 17 publications

References 52 publications

Massive sediment pulses triggered by a multi-stage alpine cliff fall (Hochvogel, DE/AT)

Massive sediment pulses triggered by a multi-stage alpine cliff fall (Hochvogel, DE/AT)

Spatio-temporal Snow Variability in a Sub-Alpine Forest predicted by Machine Learning and UAV-based LiDAR Snow Depth Maps

Combining Daily Sensor Observations and Spatial LiDAR Data for Mapping Snow Water Equivalent in a Sub‐Alpine Forest

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