Preprints
https://doi.org/10.5194/esurf-2022-19
https://doi.org/10.5194/esurf-2022-19
 
23 May 2022
23 May 2022
Status: a revised version of this preprint was accepted for the journal ESurf and is expected to appear here in due course.

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

David Mair1, Ariel Henrique Do Prado1, Philippos Garefalakis1, Alessandro Lechmann1, Alexander Whittaker2, and Fritz Schlunegger1 David Mair et al.
  • 1Institute of Geological Sciences, University of Bern, Baltzerstrasse 1+3, 3012 Bern, Switzerland
  • 2Imperial College, Department of Earth Science and Engineering, South Kensington Campus, London SW7 2AZ, United Kingdom

Abstract. Data on grain sizes of pebbles in gravel-bed rivers are of key importance for the understanding of river systems. To gather these data efficiently, low-cost UAV (unmanned aerial vehicle) platforms have been used to collect images along rivers. Several methods to extract pebble size data from such UAV imagery have been proposed. Yet, despite the availability of information on the precision and accuracy of UAV surveys, a systematic analysis of the uncertainties that might be introduced into the resulting grain size distributions is still missing. Here we present the results of three close-range UAV surveys conducted along Swiss gravel-bed rivers with a consumer-grade UAV. We measure grain sizes on these images by segmenting grains, and we assess the dependency of the results and their uncertainties on the photogrammetric models. We employ a combined bootstrapping and Monte Carlo (MC) modelling approach to model percentile uncertainties while including uncertainty quantities from the photogrammetric model.

Our results show that uncertainty in the grain size dataset is controlled by counting statistics, the selected orthoimage format, and the way the images are segmented. Therefore, our results highlight that grain size data are more precise and accurate, and largely independent on the quality of the photogrammetric model, if the data is extracted from single, undistorted orthoimages. In addition, they reveal that environmental conditions (e.g., exposure to light), which control the quality of the photogrammetric model, also influence the detection of grains during image segmentation, which can lead to a higher uncertainty in the grain size dataset. Generally, these results indicate that even relative imprecise and not accurate UAV imagery can yield acceptable grain size data, under the conditions that the photogrammetric alignment was successful and that suitable image formats were selected (preferentially single orthoimages).

David Mair et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esurf-2022-19', Patrice Carbonneau, 29 Jun 2022
    • AC1: 'Reply on RC1', David Mair, 08 Aug 2022
  • RC2: 'Comment on esurf-2022-19', Anonymous Referee #2, 12 Jul 2022
    • AC2: 'Reply on RC2', David Mair, 08 Aug 2022
  • AC3: 'General response to Reviews', David Mair, 08 Aug 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esurf-2022-19', Patrice Carbonneau, 29 Jun 2022
    • AC1: 'Reply on RC1', David Mair, 08 Aug 2022
  • RC2: 'Comment on esurf-2022-19', Anonymous Referee #2, 12 Jul 2022
    • AC2: 'Reply on RC2', David Mair, 08 Aug 2022
  • AC3: 'General response to Reviews', David Mair, 08 Aug 2022

David Mair et al.

Data sets

Data and code for: Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data David Mair https://doi.org/10.5281/zenodo.6415047

David Mair et al.

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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 aircrafts. However, uncertainty in grain size data from such image-based products is poorly studied. Here we present a new way of uncertainty estimation that includes fully propagated errors. We use this technique to assess the effect of several image acquisition aspects on grain size uncertainty.