Use of terrestrial photosieving and airborne topographic LiDAR to assess bed grain size in large rivers: a study on the Rhine River

Abstract

Most grain size monitoring is still being conducted by manual sampling in the field, which is time consuming and has low spatial representation. Due to new remote sensing methods, some limitations have been partly overcome, but methodological progress is still needed for large rivers as well as in underwater conditions. In this paper, we tested the reliability of two methods along the Old Rhine River (France/Germany) to estimate the grain size distribution (GSD) in above-water conditions: (i) a low-cost terrestrial photosieving method based on an automatic procedure using Digital Grain Size (DGS) software and (ii) an airborne LiDAR topobathymetric survey. We also tested the ability of terrestrial photosieving to estimate the GSD in underwater conditions. Field pebble counts were performed to compare and calibrate both methods. The results showed that the automatic procedure of 1 2 terrestrial photosieving is a reliable method to estimate the GSD of sediment patches in both above-water and underwater conditions with clean substrates. Sensitivity analyses showed that environmental conditions, including solar lighting conditions and petrographic variability, significantly influence the GSD from the automatic procedure in above-water conditions. The presence of biofilm in underwater conditions significantly altered the GSD estimation using the automatic procedure, but the proposed manual procedure overcame this problem. The airborne LiDAR topographic survey is an accurate method to estimate the GSD of above-water bedforms and is able to generate grain size maps. The combination of terrestrial photosieving and airborne topographic LiDAR methods is adapted to assess the GSD along large rivers in entire sections that are several kilometers long.

Remotely sensed rivers in the Anthropocene: state of the art and prospects

“…Wavelet analysis and autocorrelation have also been demonstrated as being capable of extracting grain-size information from imagery (Rubin, 2004;Buscombe, 2008;Buscombe and Masselink, 2009;Buscombe et al, 2010). Chardon et al (2019) tested the automatic Buscombe procedure on underwater images and showed solar lighting conditions and particle petrography influence significantly the GSD. They proposed procedures to correct these effects and determine the optimal sampling area to accurately estimate the different grain size percentiles when using such a technique, which is still the only accurate approach to characterize grain size underwater.…”

“…With the advent of hyperspatial RS solutions at larger scales, grain-scale information can now cover entire river reaches of several kilometres in length. The airborne LiDAR topographic survey can also accurately generate grain-size maps when the point density is high (38-49 points m À2 , mean distance between points of 0.08-0.09 m) and the laser spot size fairly low (0.12 m at NADIR; see Chardon et al, 2019), comparative to observed grain sizes, allowing areas much larger than with drones to be covered.…”

“…Chardon et al . () tested the automatic Buscombe procedure on underwater images and showed solar lighting conditions and particle petrography influence significantly the GSD. They proposed procedures to correct these effects and determine the optimal sampling area to accurately estimate the different grain size percentiles when using such a technique, which is still the only accurate approach to characterize grain size underwater.…”

Tracking Downstream Variability in Large Grain‐Size Distributions in the South‐Central Andes

“…Other recent efforts use lidar or structure‐from‐motion point clouds and/or imagery to measure grains (e.g., Carbonneau et al., 2018; Chardon et al., 2020; Lang et al., 2020; Woodget et al., 2018). These are useful, but they still rely on texture‐based rather than segmentation‐based approaches to grain sizing (Purinton & Bookhagen, 2019a).…”

“…Other recent efforts use lidar or structure-from-motion point clouds and/or imagery to measure grains (e.g.,Carbonneau et al, 2018;Chardon et al, 2020;Lang et al, 2020;Woodget et al, 2018). These are useful,…”