Articles | Volume 10, issue 5
https://doi.org/10.5194/esurf-10-953-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-10-953-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Oct 2022
Research article |
| 07 Oct 2022
| 07 Oct 2022
Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data
Institute of Geological Sciences, University of Bern,
Baltzerstrasse 1 + 3, 3012 Bern, Switzerland
Ariel Henrique Do Prado
Institute of Geological Sciences, University of Bern,
Baltzerstrasse 1 + 3, 3012 Bern, Switzerland
Philippos Garefalakis
Institute of Geological Sciences, University of Bern,
Baltzerstrasse 1 + 3, 3012 Bern, Switzerland
Alessandro Lechmann
Institute of Geological Sciences, University of Bern,
Baltzerstrasse 1 + 3, 3012 Bern, Switzerland
Alexander Whittaker
Department of Earth Science and Engineering, Imperial College, South Kensington Campus, London, SW7 2AZ, United Kingdom
Fritz Schlunegger
Institute of Geological Sciences, University of Bern,
Baltzerstrasse 1 + 3, 3012 Bern, Switzerland
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Cited
13 citations as recorded by crossref.
- On the use of packing models for the prediction of fluvial sediment porosity C. Rettinger et al. 10.5194/esurf-11-1097-2023
- Robust estimations of areal grain size distribution from geometric surface roughness in a proglacial outwash area C. Hiller et al. 10.1016/j.geomorph.2023.108857
- Assessing the impact of sediment characteristics on vegetation recovery in debris flow fans: A case study of the Ohya Region, Japan S. Yousefi & F. Imaizumi 10.1016/j.ecoleng.2024.107408
- Check dam impact on sediment loads: example of the Guerbe River in the Swiss Alps – a catchment scale experiment A. do Prado et al. 10.5194/hess-28-1173-2024
- Comparison of three grain size measuring methods applied to coarse-grained gravel deposits P. Garefalakis et al. 10.1016/j.sedgeo.2023.106340
- Sediment controls on the transition from debris flow to fluvial channels in steep mountain ranges A. Neely & R. DiBiase 10.1002/esp.5553
- Automated grain sizing from uncrewed aerial vehicles imagery of a gravel‐bed river: Benchmarking of three object‐based methods R. Miazza et al. 10.1002/esp.5782
- Deep Learning and Histogram-Based Grain Size Analysis of Images W. Wei et al. 10.3390/s24154923
- Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning D. Mair et al. 10.1002/esp.5755
- The grain size of sediments delivered to steep debris‐flow prone channels prior to and following wildfire A. Neely et al. 10.1002/esp.5819
- Drone-based photogrammetry for riverbed characteristics extraction and flood discharge modeling in taiwan’s mountainous rivers L. Liu 10.1016/j.measurement.2023.113386
- Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning D. Mair et al. 10.1002/esp.5755
- Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data D. Mair et al. 10.5194/esurf-10-953-2022
11 citations as recorded by crossref.
- On the use of packing models for the prediction of fluvial sediment porosity C. Rettinger et al. 10.5194/esurf-11-1097-2023
- Robust estimations of areal grain size distribution from geometric surface roughness in a proglacial outwash area C. Hiller et al. 10.1016/j.geomorph.2023.108857
- Assessing the impact of sediment characteristics on vegetation recovery in debris flow fans: A case study of the Ohya Region, Japan S. Yousefi & F. Imaizumi 10.1016/j.ecoleng.2024.107408
- Check dam impact on sediment loads: example of the Guerbe River in the Swiss Alps – a catchment scale experiment A. do Prado et al. 10.5194/hess-28-1173-2024
- Comparison of three grain size measuring methods applied to coarse-grained gravel deposits P. Garefalakis et al. 10.1016/j.sedgeo.2023.106340
- Sediment controls on the transition from debris flow to fluvial channels in steep mountain ranges A. Neely & R. DiBiase 10.1002/esp.5553
- Automated grain sizing from uncrewed aerial vehicles imagery of a gravel‐bed river: Benchmarking of three object‐based methods R. Miazza et al. 10.1002/esp.5782
- Deep Learning and Histogram-Based Grain Size Analysis of Images W. Wei et al. 10.3390/s24154923
- Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning D. Mair et al. 10.1002/esp.5755
- The grain size of sediments delivered to steep debris‐flow prone channels prior to and following wildfire A. Neely et al. 10.1002/esp.5819
- Drone-based photogrammetry for riverbed characteristics extraction and flood discharge modeling in taiwan’s mountainous rivers L. Liu 10.1016/j.measurement.2023.113386
2 citations as recorded by crossref.
- Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning D. Mair et al. 10.1002/esp.5755
- Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data D. Mair et al. 10.5194/esurf-10-953-2022
Latest update: 23 Nov 2024
Short summary
Grain size data are important for studying and managing rivers, but they are difficult to obtain in the field. Therefore, methods have been developed that use images from small and remotely piloted aircraft. However, uncertainty in grain size data from such image-based products is understudied. Here we present a new way of uncertainty estimation that includes fully modeled errors. We use this technique to assess the effect of several image acquisition aspects on grain size uncertainty.
Grain size data are important for studying and managing rivers, but they are difficult to obtain...
