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

Purinton, Benjamin; Bookhagen, Bodo

doi:10.1029/2021jf006260

Cited by 9 publications

(16 citation statements)

References 90 publications

(219 reference statements)

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“…Methodological differences in sample size may also affect the measured GSDs and explain the differences in manual photo count and Wolman pebble count GSDs (Church et al, 1987; Purinton & Bookhagen, 2021; Storz‐Peretz & Laronne, 2013). Primarily, the issue of sample size relates to the ability to accurately capture the coarse end of the distribution (Church et al, 1987).…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, we have also given the minimum and maximum grain-size bin for each percentile in brackets. b These percentiles were generated using the QuantBD function developed by Eaton et al (2019) and translated into Python by Purinton and Bookhagen (2021). The percentile uncertainty is quantified using binomial theory for each percentile based on the number of measurements.…”

Section: Sampling Methods Uncertaintymentioning

confidence: 99%

“…The PebbleCounts algorithm can detect grains ≥20 pixels (Purinton & Bookhagen, 2019). Purinton and Bookhagen (2021) use automated PebbleCounts with minimum grain size of 2.5 cm.…”

Section: Introductionmentioning

confidence: 99%

“…Automated image segmentation methods can use cameras on tripods or UAVs to survey large areas. For example, Purinton and Bookhagen (2021) surveyed areas between 944 and 3470 m 2 for sand‐ and gravel‐bed rivers in the South‐Central Andes.…”

Section: Introductionmentioning

confidence: 99%

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Measuring the grain‐size distributions of mass movement deposits

Harvey

Hales

Hobley

et al. 2022

Earth Surf Processes Landf

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Mass movement deposit grain-size distributions (GSDs) record initiation, transport and deposition mechanisms, and contribute to the rate at which sediment is exported from hillslopes to channels. Defining the GSD of a mass movement deposit is a significant challenge because they are often difficult to access, are heterogeneous in planform and with depth, contain grain sizes from clay (<63 μm) to boulders (>1 m), and require considerable time to calculate accurately. There are numerous methods used to measure mass movement GSDs, but no single method alone can measure the entire range of grain sizes. This paper compares five common methods for determining mass movement deposit GSDs to assess how their accuracy may affect their applicability to different research areas. We applied an automated wavelet analysis (pyDGS), Wolman pebble counts, survey tape counts, manual photo counts and sieving to three different mass movement deposits (two debris flows, one rockslide) in Tredegar, Wales and the Longmen Shan, China. We found that pyDGS and survey tape counts produced comparable GSDs to sieving over a single order of magnitude.PyDGS required calibration to achieve accurate results, limiting its use for many applications. In Tredegar, Wolman pebble counts over-estimated grain sizes in the lower 80% of the distribution relative to the other four methods used. We demonstrate that method choice can introduce significant uncertainties, particularly at the edges of the distributions, such that D 16 values differ by up to a factor of five. These methodological uncertainties limit GSD comparisons across studies, particularly where these are used to infer processes within deposits. To minimize these challenges, the methods chosen should be both carefully reported and consistent with the research question.

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Section: Discussionmentioning

confidence: 99%

Section: Sampling Methods Uncertaintymentioning

confidence: 99%

“…The PebbleCounts algorithm can detect grains ≥20 pixels (Purinton & Bookhagen, 2019). Purinton and Bookhagen (2021) use automated PebbleCounts with minimum grain size of 2.5 cm.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measuring the grain‐size distributions of mass movement deposits

Harvey

Hales

Hobley

et al. 2022

Earth Surf Processes Landf

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“…This software was developed in Python language and used initially to estimate the apparent GSD of above-water bar deposits using aerial imagery from a drone (Purinton & Bookhagen, 2019). This software was recently used to estimate the longitudinal bed grain size variations along 100 km of river length of the Toro watershed in Argentina (Purinton & Bookhagen, 2021). In this study, we used the automatic procedure called PebbleCountsAuto (AIF), which used edge detection and filter automatically suspect grains (Purinton & Bookhagen, 2019).…”

Section: Pebblecounts Softwarementioning

confidence: 99%

Comparison of software accuracy to estimate the bed grain size distribution from digital images: A test performed along the Rhine River

Chardon

Piasny

Schmitt

2021

River Research & Apps

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The quantification of the bed grain size distribution (GSD) of river surfaces is primarily conducted through manual approaches in the field. These methods are time consuming and not able to accurately represent the spatial diversity of the grain size distribution of rivers. Recently, several software programs and procedures have been developed using semi-automatic and automatic methods to estimate bed GSD from digital imagery. The purpose of this study is to compare softwares accuracy between reference GSDs and estimated GSDs using geometric approaches (Basegrain software and a procedure developed on ImageJ), statistical approaches (digital grain size[DGS] and PebbleCounts softwares), and a machine learning framework (SediNet).This study evaluates ten digital images recorded along the Rhine River downstream of the city of Basel. The results showed that all software programs considerably underestimated the manually measured GSDs. Nevertheless, it is possible to significantly improve the estimation of bed GSD by applying calibration laws. Both DGS and Basegrain softwares are reliable to estimate the GSD, while the three others softwares are accurate for percentiles equal and higher than the D 50 . After linear regression correction, the mean normalized root mean square error of percentile errors did not exceed 13% for DGS and Basegrain software, while the others did not exceed 22% for percentiles coarser than the D 50 .

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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

Mair,

Witz,

Do Prado

et al. 2023

Earth Surf Processes Landf

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The size of sedimentary particles in rivers bears information on the sediment entrainment or deposition mechanisms and the hydraulic conditions controlling them. However, collecting such data from coarse‐grained sediments is work intensive, both in the field and remotely. Therefore, attention has turned to machine learning models to improve the data acquisition. Despite their success, current methods need large quantities of data and yield results limited to a few percentile values of grain size datasets, often additionally affected by a systematic bias. In most cases, the root of these limitations is the challenge of accurately segmenting grains. Here, we present a new approach to improve the segmentation of individual grains based on the capacity of transfer learning in convolutional neural networks. Specifically, we re‐train a state‐of‐the‐art model for cell segmentation in biomedical images to find and segment coarse‐grained particles in images of fluvial sediments. Our results show that the performance in the segmentation tasks can be directly transferred to images of fluvial sediments and that our re‐trained models outperform existing methods. We document that our results are achievable with only 10%–20% of the data needed for training other deep learning models designed to measure the size of fluvial sediments. Moreover, we find that traits in our data control the segmentation performance. This enables data‐driven approaches to create specialist segmentation models. Additionally, comparing our automatically obtained datasets with the results retrieved from image and field‐based surveys confirms that improvements in segmentation are directly leading to more precise and more accurate grain size data even if data collection occurs in images taken at different conditions. Finally, we release a software package, the trained models and our data. The goal is to offer a tool to efficiently segment and measure grains in sediment images in an automated way, which can be adapted to different settings.

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

Abstract: It has long been recognized that the grain-size distribution in mixed sand-and gravel-bed channels-and the deposits they form-encode end transport conditions (e.g.,

Cited by 9 publications

References 90 publications

Measuring the grain‐size distributions of mass movement deposits

Measuring the grain‐size distributions of mass movement deposits

Comparison of software accuracy to estimate the bed grain size distribution from digital images: A test performed along the Rhine River

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

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