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

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

doi:https://doi.org/10.5194/esurf-2022-19

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https://doi.org/10.5194/esurf-2022-19

Preprints

23 May 2022

Status: this preprint is currently under review for the journal ESurf.

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.

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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

Received: 05 Apr 2022 – Discussion started: 23 May 2022

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).

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David Mair et al.

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RC1: 'Comment on esurf-2022-19', Patrice Carbonneau, 29 Jun 2022 reply

This is a welcome addition to the UAV literature and should be accepted with very minor changes. The method is sound and very robust. Outcomes are clear as are some recommendations. There is a good acknowledgement of previous litrature. I've uploaded an annotated manuscript with a small number of small issues.

The main comment I would like the authors to consider relates to the practical implications of their recommendations. 1 element that is missing from table 1 and survey description is some indication of survey duration or drone velocity. The authors are proposing a method where the drone flies at 5-7m and still acquires images at a high overlap as per SfM guidelines. To make things more difficult, they are recommending that imagery be acquired in raw format, ie much bigger files. Most readers might not realise this, but flying at such low altitudes and still getting photogrammetric coverage, compounded by the need to give the CPU bus on the drone time to write large files to the card, leads to very low forward velocities, ie, it takes a lot of time to acquire the data. This is not made clear. I'd like some explicit detail about the time and battery cost of the recommendations. This will help the reader get a better sense of the true cost of these methods should they wish to adopt them.

My underlying concern is that these recommendations are limiting the scale of application of UAVs. For example, in Carbonneau et al 2020 ESPL (Using UAVs to train fuzzy classification algorithms for Sentinel 2 imagery) and recently in Marchetti et al 2022 ESPL (Using UAVs to calibrate grain size estimation from Sentinel 2) we were using 5 sites, some with very large river bars that apporached 1Km2. We used the Robotic Photosiving method which was explicitely designed to avoid image overlap at low altitudes, speed up acquisitions and allow for work on larger scales. To my knowledge this dataset remains the largest area coverd by UAV work (about 5km2, with at least 2 repeat acquisitions per site). It would not have been possible to follow the recommendations here and work at this scale. There is nothing implicitely wrong with work at small scales, but my sense is that the direction of travel for airborne fluvial remote sensing should be upscaling, not downscaling. I would therefore like the authors to reflect and comment a bit more thoroughly on the practical implications of their findings and their meaning for the wider field of UAV applications in geomorphology.

Patrice Carbonneau

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Citation: https://doi.org/10.5194/esurf-2022-19-RC1

David Mair et al.

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https://doi.org/10.5194/esurf-2022-19-supplement

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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.


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