Uncertainty of grain sizes from close-range UAV imagery in gravel bars

Data on grain sizes of pebbles in gravel-bed rivers are a well-known proxy for sedimentation and transport conditions, and thus a key quantity for the understanding of a river system. Therefore, methods have been developed to quantify the size of gravels in rivers already decades ago. These methods involve time-intensive fieldwork and bear the risk of introducing sampling biases. More recently, low-cost UAV (unmanned aerial vehicle) platforms have been employed for the collection of referenced images along rivers with the aim to determine the size of grains. To this end, 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 uncertainty that is introduced into the resulting grain size distribution 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 use these surveys to assess the dependency of grain size measurements and associated uncertainties from photogrammetric models, in turn generated from segmented UAV imagery. In particular, we assess the effect of (i) different image acquisition formats, (ii) specific survey designs, and (iii) the orthoimage format used for grain size estimates. To do so, we use uncertainty quantities from the photogrammetric model and the statistical uncertainty of the collected grain size data, calculated through a combined bootstrapping and Monte Carlo (MC) modelling approach.

First, our preliminary results suggest some influence of the image acquisition format on the photogrammetric model quality. However, different choices for UAV surveys, e.g., the inclusion of oblique camera angles, referencing strategy and survey geometry, and environmental factors, e.g., light conditions or the occurrence of vegetation and water, exert a much larger control on the model quality. Second, MC modelling of full grain size distributions with propagated UAV uncertainties shows that measured size uncertainty is at the first order controlled by counting statistics, the selected orthoimage format, and limitations of the grain size determination itself, i.e., the segmentation in images. Therefore, our results highlight that grain size data are consistent and mostly insensitive to photogrammetric model quality when the data is extracted from single, undistorted orthoimages. This is not the case for grain size data, which are extracted from orthophoto mosaics. Third, upon looking at the results in detail, they reveal that environmental factors and specific survey strategies, which contribute to the decrease of the photogrammetric model quality, also decrease the detection of grains during image segmentation. Thereby, survey conditions that result in a lower quality of the photogrammetric model also lead to a higher uncertainty in grain size data.

Generally, these results indicate that even relative imprecise and not accurate UAV imagery can yield acceptable grain size data for some applications, under the conditions of correct photogrammetric alignment and a suitable image format. Furthermore, the use of a MC modelling strategy can be employed to estimate the grain size uncertainty for any image-based method in which individual grains are measured.