Articles | Volume 10, issue 5
https://doi.org/10.5194/esurf-10-953-2022
https://doi.org/10.5194/esurf-10-953-2022
Research article
 | 
07 Oct 2022
Research article |  | 07 Oct 2022

Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data

David Mair, Ariel Henrique Do Prado, Philippos Garefalakis, Alessandro Lechmann, Alexander Whittaker, and Fritz Schlunegger

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Cited articles

Attal, M., Mudd, S. M., Hurst, M. D., Weinman, B., Yoo, K., and Naylor, M.: Impact of change in erosion rate and landscape steepness on hillslope and fluvial sediments grain size in the Feather River basin (Sierra Nevada, California), Earth Surf. Dynam., 3, 201–222, https://doi.org/10.5194/esurf-3-201-2015, 2015. 
Bekaddour, T., Schlunegger, F., Attal, M., and Norton, K. P.: Lateral sediment sources and knickzones as controls on spatio-temporal variations of sediment transport in an Alpine river, Sedimentology, 60, 342–357, https://doi.org/10.1111/sed.12009, 2013. 
Brasington, J., Vericat, D., and Rychkov, I.: Modeling river bed morphology, roughness, and surface sedimentology using high resolution terrestrial laser scanning, Water Resour. Res., 48, 1–18, https://doi.org/10.1029/2012WR012223, 2012. 
Bunte, K. and Abt, S. R.: Sampling Surface and Subsurface Particle-Size Distributions in Wadable Gravel- and Cobble-Bed Streams for Analyses in Sediment Transport, Hydraulics, and Streambed Monitoring, 428 pp. https://doi.org/10.2737/RMRS-GTR-74, 2001. 
Buscombe, D.: Estimation of grain-size distributions and associated parameters from digital images of sediment, Sediment. Geol., 210, 1–10, https://doi.org/10.1016/j.sedgeo.2008.06.007, 2008. 
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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.
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