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

2019 ◽  
Vol 7 (3) ◽  
pp. 859-877 ◽  
Author(s):  
Benjamin Purinton ◽  
Bodo Bookhagen
Keyword(s):  
Grain Size ◽  
High Energy ◽  
High Mountain ◽  
Size Distributions ◽  
Mountain Rivers ◽  
Gravel Bed ◽  
Automated Method ◽  
Grain Size Distributions ◽  
Manual Selection ◽  
Gravel Bed Rivers

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.

scholarly journals Introducing PebbleCounts: A grain-sizing tool for photo surveys of dynamic gravel-bed rivers

2019 ◽  
Author(s):  
Benjamin Purinton ◽  
Bodo Bookhagen
Keyword(s):  
Grain Size ◽  
High Energy ◽  
Size Distributions ◽  
Mountain Rivers ◽  
Gravel Bed ◽  
Automated Method ◽  
Grain Size Distributions ◽  
Manual Selection ◽  
Pixel Image ◽  
Gravel Bed Rivers

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 unmanned aerial vehicles and increasingly high-resolution cameras, we can now generate orthoimagery over hectares at sub-cm 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 to automatically segment entire images, our open-source algorithm, PebbleCounts, relies on k-means clustering in the spatial and spectral domain and rapid manual selection of well-delineated grains. The result is improved grain-size estimates for complex river-bed imagery, without any post processing. In a second step, we 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 images, and 0.07 and 0.05 ψ for one 0.32 mm/pixel image. The automatic approach has higher bias and precision of 0.13 and 0.15 ψ, respectively, for ~ 1.16 mm/pixel images, but similar values of −0.06 and 0.05 ψ for one 0.32 mm/pixel 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 scale, where the algorithm can be rapidly applied in ~ 5 minutes in many small areas to acquire accurate grain-size data over 10–100 m2 areas. These data can be used to validate PebbleCountsAuto 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.

scholarly journals Grain-size distributions on high-energy sandy beaches and their relation to wave dissipation

Sedimentology ◽  
2017 ◽  
Vol 64 (5) ◽  
pp. 1289-1302 ◽  
Author(s):  
Sam Prodger ◽  
Paul Russell ◽  
Mark Davidson
Keyword(s):  
Grain Size ◽  
High Energy ◽  
Sandy Beaches ◽  
Size Distributions ◽  
Wave Dissipation ◽  
Grain Size Distributions

scholarly journals Uniform grain-size distribution in the active layer of a shallow, gravel-bed, braided river (the Urumqi River, China) and implications for paleo-hydrology

2018 ◽  
Author(s):  
Laure Guerit ◽  
Laurie Barrier ◽  
Youcun Liu ◽  
Clément Narteau ◽  
Eric Lajeunesse ◽  
...  
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Active Layer ◽  
Size Distributions ◽  
Gravel Bed ◽  
Surface Samples ◽  
River Bed ◽  
Braided Stream ◽  
Grain Size Distributions

Abstract. The grain-size distribution of ancient alluvial systems is commonly determined from surface samples of vertically exposed sections of gravel deposits. This method relies on the hypothesis that the grain-size distribution obtained from a vertical cross-section is equivalent to that of the river bed. We report a field test of this hypothesis on samples collected on an active, gravel-bed, braided stream: the Urumqi River in China. We compare data from volumetric samples of a trench excavated in an active thread and surface counts performed on the trench vertical faces. We show that the grain-size distributions obtained from all samples are similar and that the deposit is uniform at the scale of the river active layer, a layer extending from the surface to a depth of approximately ten times the size of the largest clasts.

scholarly journals The Grain-size Patchiness of Braided Gravel-Bed Streams – example of the Urumqi River (northeast Tian Shan, China)

2014 ◽  
Vol 37 ◽  
pp. 27-39 ◽  
Author(s):  
L. Guerit ◽  
L. Barrier ◽  
C. Narteau ◽  
F. Métivier ◽  
Y. Liu ◽  
...  
Keyword(s):  
Grain Size ◽  
Coarse Grained ◽  
Gravel Bed ◽  
Transport Dynamics ◽  
Grain Sizes ◽  
River Bed ◽  
Braided Streams ◽  
Grain Size Distributions ◽  
Gravel Bed Rivers ◽  
Gravel Bed Streams

Abstract. In gravel-bed rivers, sediments are often sorted into patches of different grain-sizes, but in braided streams, the link between this sorting and the channel morpho-sedimentary elements is still unclear. In this study, the size of the bed sediment in the shallow braided gravel-bed Urumqi River is characterized by surface-count and volumetric sampling methods. Three morpho-sedimentary elements are identified in the active threads of the river: chutes at flow constrictions, which pass downstream to anabranches and bars at flow expansions. The surface and surface-layer grain-size distributions of these three elements show that they correspond to only two kinds of grain-size patches: (1) coarse-grained chutes, coarser than the bulk river bed, and (2) finer-grained anabranches and bars, consistent with the bulk river bed. In cross-section, the chute patches are composed of one coarse-grained top layer, which can be interpreted as a local armour layer overlying finer deposits. In contrast, the grain size of the bar-anabranch patches is finer and much more homogeneous in depth than the chute patches. Those patches, which are features of lateral and vertical sorting associated to the transport dynamics that build braided patterns, may be typical of active threads in shallow gravel-bed rivers and should be considered in future works on sorting processes and their geomorphologic and stratigraphic results.

Analysis of controlling factors on boulder production on the hillslopes of the upper Bhote Koshi catchment, Nepal

2021 ◽  
Author(s):  
Benedetta Dini ◽  
Georgina L. Bennett
Keyword(s):  
Grain Size ◽  
High Energy ◽  
River Network ◽  
Population Pressure ◽  
Hazard Mapping ◽  
Size Distributions ◽  
Large Dataset ◽  
Environmental Controls ◽  
Grain Size Distributions ◽  
Mode Of Transport

&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Landslides from mountainous bedrock hillslopes often contain boulders, the presence of which has been shown to influence landscape evolution by altering hillslope geomorphic processes and river erosion. Furthermore, the presence in various proportions of large grain sizes on hillslopes can amplify both landslide and flood hazards in largely unquantified ways. Boulders can have an immediate destructive potential on properties and infrastructure and can hinder response and recovery by blocking access routes, posing a challenge for removal. On entering the river network, they might have far reaching effects if remobilised in high flows, damaging or destroying key infrastructure such as hydropower plants and inducing significant knock-on effects on local economies. A fundamental step towards quantification of increased hazard potential is the understanding environmental controls on boulder production. Despite their potential to enhance hazard, the probability of large boulders being produced within different landslide types has not been directly accounted for in landslide hazard mapping.&lt;/p&gt;&lt;p&gt;Our study focuses on the upper Bhote Koshi catchment, northeast of Kathmandu (Nepal), characterised by extreme topographic gradients, seismicity and monsoonal climate, and subjected to frequent landslides and floods. This, coupled with increased population pressure and infrastructure growth, makes the area prone to natural disasters.&lt;/p&gt;&lt;p&gt;We used high resolution optical imagery to map more than 11300 boulders and analysed this large dataset in combination with lithology and topography, and well as structural and landslide data, to investigate controls on boulder production and grain size distributions in different lithological, structural and geomorphic settings of the landscape.&lt;/p&gt;&lt;p&gt;Lithology appears to exert a significant influence on boulder sizes, with statistically significantly larger boulders observed in crystalline rocks of the Higher Himalaya Sequence than in metasedimentary rocks of the Lesser Himalaya Sequence. We also observe that the spacing of the most pervasive fracture set, parallel to foliation, influences boulder size distributions in some lithologies, whilst other dominant regional fracture sets appear not to strongly correlate with mapped boulder sizes.&lt;/p&gt;&lt;p&gt;Although recent studies have shown the importance of structural control on boulder sizes, our large dataset reveals that for complex, high-relief landscapes, with high erosion rates, fracture characteristics do not fully explain grain size distribution.&lt;/p&gt;&lt;p&gt;The type of processes involved in boulder production and transport on slopes, before reaching the river network, also appears to exert a control over grain size distributions and boulder density, with rockfall processes appearing to be responsible for producing boulders with largest sizes as opposed to rockslides, where the high energy and mode of transport is likely associated with increased fragmentation. &amp;#160;&lt;/p&gt;&lt;p&gt;Analysing lithological and structural characteristics alone may not be sufficient to explain the observed distribution and would thus only give a limited insight in the enhanced hazard levels posed by boulders across different sectors of a landscape and other factors, such as distance from source and mode of transport at shorter temporal scales, must be taken into account. &amp;#160;&lt;/p&gt;

scholarly journals Effect of Strain-Induced Precipitation on the Recrystallization Kinetics in a Model Alloy

2021 ◽  
Author(s):  
Mo Ji ◽  
Martin Strangwood ◽  
Claire Davis
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Avrami Exponent ◽  
Model Alloy ◽  
Size Distributions ◽  
Recrystallization Kinetics ◽  
Grain Size Distributions ◽  
Recrystallized Grain Size ◽  
Nb Addition

AbstractThe effects of Nb addition on the recrystallization kinetics and the recrystallized grain size distribution after cold deformation were investigated by using Fe-30Ni and Fe-30Ni-0.044 wt pct Nb steel with comparable starting grain size distributions. The samples were deformed to 0.3 strain at room temperature followed by annealing at 950 °C to 850 °C for various times; the microstructural evolution and the grain size distribution of non- and fully recrystallized samples were characterized, along with the strain-induced precipitates (SIPs) and their size and volume fraction evolution. It was found that Nb addition has little effect on recrystallized grain size distribution, whereas Nb precipitation kinetics (SIP size and number density) affects the recrystallization Avrami exponent depending on the annealing temperature. Faster precipitation coarsening rates at high temperature (950 °C to 900 °C) led to slower recrystallization kinetics but no change on Avrami exponent, despite precipitation occurring before recrystallization. Whereas a slower precipitation coarsening rate at 850 °C gave fine-sized strain-induced precipitates that were effective in reducing the recrystallization Avrami exponent after 50 pct of recrystallization. Both solute drag and precipitation pinning effects have been added onto the JMAK model to account the effect of Nb content on recrystallization Avrami exponent for samples with large grain size distributions.

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