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

Abstract

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 (uncrewed 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 as well as knowledge of errors from image-based grain size measurements, open questions on how uncertainties influence the resulting grain size distributions still persist. 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) modeling 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 processed image format, and the way the images are segmented. Therefore, our results highlight that grain size data are more precise and accurate, and largely independent of the quality of the photogrammetric model, if the data are extracted from single, undistorted nadir images in opposition to orthophoto mosaics. 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 relatively imprecise and inaccurate 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, undistorted nadir images).

Sediment controls on the transition from debris flow to fluvial channels in steep mountain ranges

“…We focused on the (median) and grain size fractions, which are commonly used to parameterize sediment transport and flow hydraulics (Equations –). We relied on the large number of individual surveys to define downstream trends, which can be obscured if small sample sizes are used due to spatial and temporal variation in local hillslope sediment delivery (Marc et al, 2021; Roda‐Boluda et al, 2018) or varying accuracy of sediment size measurements on both scaled imagery and orthoimagery (Mair et al, 2022). We calculated the mean and for 10 logarithmically‐spaced bins of drainage area from 0.001 km 2 to 30 km 2 .…”

On the use of packing models for the prediction of fluvial sediment porosity

“…If the sediment density is known to vary for the size classes, e.g., as a result of different lithologies in the deposit, these variations should be accounted for when converting the mass fractions to volume fractions. Especially for field studies, photogrammetric approaches are a promising alternative to sieving but are currently still limited to rather large grain sizes (Mair et al, 2022).…”

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

“…Most of such processed photos are undistorted nadir images, except one image from the Guerbe River, for which we used an orthophoto mosaic. For some photos, the UAV image acquisition was accomplished in the raw (DNG) format, whereas others, for example, all S1 images, were acquired in a pre-processed JPEG format (for details, see Mair et al, 2022a). However, after the photogrammetric alignment, all images were converted to the JPEG format.…”

“…Second, image data need to be scaled and pre-processed accordingly, which might include a rectification and a photogrammetric alignment through SfM/MVS methods (e.g., James et al, 2019James et al, , 2020. Especially for data acquired with UAVs, image distortion and systematic errors stemming from the photogrammetric alignment can have a significant impact on the results' quality (e.g., Carbonneau & Dietrich, 2017;Woodget et al, 2018;Mair et al, 2022a). Third, our approach and other models (e.g., Weigert et al, 2020), which are based on microscopy images, are not well suited for a 3D segmentation of sedimentary particles, despite a dedicated 3D segmentation functionality.…”

“…Although these methods allowed for a faster and remote measurement of a larger number of grains, they still need a calibration in the field for texturebased approaches, or they require a manual correction of individual grains for segmentation-based methods. In addition, both tend to systematically over-/underestimate the sizes of grains (e.g., Chardon et al, 2020;Chardon, Piasny, & Schmitt, 2022;Mair et al, 2022a).…”