Introducing <i>PebbleCounts</i>: a grain-sizing tool for photo surveys of dynamic gravel-bed rivers

Abstract

Abstract. Grain-size distributions are a key geomorphic metric of gravel-bed rivers. Traditional measurement methods include manual counting or photo sieving, but these are achievable only at the 1–10 m2 scale. With the advent of drones and increasingly high-resolution cameras, we can now generate orthoimagery over hectares at millimeter to centimeter resolution. These scales, along with the complexity of high-mountain rivers, necessitate different approaches for photo sieving. As opposed to other image segmentation methods that use a watershed approach, our open-source algorithm, PebbleCounts, relies on k-means clustering in the spatial and spectral domain and rapid manual selection of well-delineated grains. This improves grain-size estimates for complex riverbed imagery, without post-processing. We also develop a fully automated method, PebbleCountsAuto, that relies on edge detection and filtering suspect grains, without the k-means clustering or manual selection steps. The algorithms are tested in controlled indoor conditions on three arrays of pebbles and then applied to 12 × 1 m2 orthomosaic clips of high-energy mountain rivers collected with a camera-on-mast setup (akin to a low-flying drone). A 20-pixel b-axis length lower truncation is necessary for attaining accurate grain-size distributions. For the k-means PebbleCounts approach, average percentile bias and precision are 0.03 and 0.09 ψ, respectively, for ∼1.16 mm pixel−1 images, and 0.07 and 0.05 ψ for one 0.32 mm pixel−1 image. The automatic approach has higher bias and precision of 0.13 and 0.15 ψ, respectively, for ∼1.16 mm pixel−1 images, but similar values of −0.06 and 0.05 ψ for one 0.32 mm pixel−1 image. For the automatic approach, only at best 70 % of the grains are correct identifications, and typically around 50 %. PebbleCounts operates most effectively at the 1 m2 patch scale, where it can be applied in ∼5–10 min on many patches to acquire accurate grain-size data over 10–100 m2 areas. These data can be used to validate PebbleCountsAuto, which may be applied at the scale of entire survey sites (102–104 m2). We synthesize results and recommend best practices for image collection, orthomosaic generation, and grain-size measurement using both algorithms.

A Deep Learning-Based Method for Quantifying and Mapping the Grain Size on Pebble Beaches

“…Most undetected objects have a long axis of less than 2 mm, i.e., 4 px, and are among the smallest sediment particles that were measured. Other methods relying on the segmentation of photographs of quadra structures show a minimal detection threshold ranging between 8 and 23 mm [19,[26][27][28]37]. On rare occasions, Mask R-CNN misclassifies partial clasts as complete ones.…”

“…Initially, these methods required the intervention of an operator to manually digitize the clasts; this procedure could take more than one hour per image [19], which is approximately the time required to analyze a physical sample. These methods have been improved and automated through numerous studies, and now there can be distinguished two categorical approaches [20]: analysis of image texture properties [21][22][23][24][25], and characterization of individual clasts [15,[26][27][28][29].…”

“…To overcome this issue, Detert and Weitbrecht [30] proposed the use of an additional watershed segmentation step whose purpose is to identify the relevant particles. However the algorithm still suffers from misidentifications and its complexity requires a high expertise while remaining limited to areas smaller than 10 m 2 for computational reasons [28]. More recently, Purinton and Bookhagen [28] have developed a semi-automatic method based on the use of a k-mean clustering.…”

“…However the algorithm still suffers from misidentifications and its complexity requires a high expertise while remaining limited to areas smaller than 10 m 2 for computational reasons [28]. More recently, Purinton and Bookhagen [28] have developed a semi-automatic method based on the use of a k-mean clustering. Although they greatly improve data processing time, these methods are still dependent on user expertise to determine image processing parameters [28,30], and the processing time required ranges from one to several tens of minutes per image.…”

“…More recently, Purinton and Bookhagen [28] have developed a semi-automatic method based on the use of a k-mean clustering. Although they greatly improve data processing time, these methods are still dependent on user expertise to determine image processing parameters [28,30], and the processing time required ranges from one to several tens of minutes per image.…”