Understanding and predicting the temporal variability of sediment grain size characteristics on high-energy beaches

Prodger, Sam; Russell, Paul; Davidson, Mark; Miles, Jon; Scott, Tim

doi:10.1016/j.margeo.2016.04.003

Cited by 50 publications

(42 citation statements)

References 32 publications

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“…Prodger et al . () found, when describing temporal grain‐size patterns at Perranporth over a number of years, that an optimum fit to the antecedent wave conditions were obtained with a weighting function decaying at a rate that reached 10% ( ϕ ) of the present value at 83 days and 1% (2 ϕ ) at 166 days. Despite the inclusion of this antecedent term, the most recent wave conditions are still the most important, with rapid change possible if the current conditions are sufficiently different from the antecedent conditions.…”

Section: The Relationship Between the Spatial Grain‐size Patterns Andmentioning

confidence: 99%

Grain‐size distributions on high‐energy sandy beaches and their relation to wave dissipation

2017

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Grain size and sorting represent two key parameters when characterizing sediments or modelling beach morphology and sediment transport. Traditionally, an average value for grain size or sorting is often assumed for the entire area, determined from only a few sediment samples, ignoring any spatial (or temporal) variability in sediment characteristics. This contribution uses a data set of physical surface sediment samples from 53 beach locations around the south‐west peninsula of the United Kingdom, in addition to bi‐monthly, high spatial resolution (mean 240 samples) digital grain‐size surveys from a high‐energy, oceanic, sandy beach (Perranporth, North Cornwall). Systematic spatial variations in grain size and sorting were consistently observed in the seaward direction across the intertidal zone of sandy beaches, with grain‐sizes coarsening and sorting improving by up to 51·7% and 64·3%, respectively. This variability was deterministically related to the time‐averaged, antecedent‐adjusted energy dissipated by breaking waves, with the observed maximum grain‐size and sorting values correlating with the location of peak wave energy dissipation (r2 = 0·998, P < 0·01).

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Section: The Relationship Between the Spatial Grain‐size Patterns Andmentioning

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Grain‐size distributions on high‐energy sandy beaches and their relation to wave dissipation

2017

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“…Sin embargo, este valor responde a la toma de muestras realizada en un único punto y momento, con la consiguiente limitación de su representatividad. Diferentes estudios apuntan a la existencia de una gran variabilidad espacial y temporal en la textura del sedimento (Prodger et al 2016;Gujar et al 2011;Gallagher et al 2011), que lleva a una redistribución de los diferentes tamaños de grano a lo largo de un perfil de playa (Medina et al 1994).…”

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“…Las playas son espacios naturales que proporcionan protección frente al oleaje y temporales, representan un importante recurso socioeconómico (Prodger et al 2016), y actúan como soporte físico para los organismos que allí habitan.…”

Section: Introductionunclassified

“…Sin embargo, las características sedimentarias, particularmente las texturales, pueden ser muy variables, tanto espacial como temporalmente (Guillén 2000;Prodger et al 2016). Obtener datos actualizados de la textura de los sedimentos de las playas resulta costoso al requerir trabajo de campo y análisis, imposibilitando la monitorización a gran escala.…”

Section: Introductionunclassified

“…La textura del sedimento y la anchura de las playas son factores clave para permitir el mantenimiento de sus funciones. Por tanto, resulta de gran interés disponer de información actualizada de ambos elementos para conocer el estado de la playa y plantear una gestión adecuada.Sin embargo, las características sedimentarias, particularmente las texturales, pueden ser muy variables, tanto espacial como temporalmente (Guillén 2000;Prodger et al 2016). Obtener datos actualizados de la textura de los sedimentos de las playas resulta costoso al requerir trabajo de campo y análisis, imposibilitando la monitorización a gran escala.…”

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Variabilidad De La Línea De Costa a Partir De Imágenes De Satélite Y Su Relación Con La Textura Del Sedimento

Cabezas-Rabadán

Almonacid-Caballer

Pardo-Pascual

et al. 2017

Proceedings - 1st Congress in Geomatics Engineering - CIGeo

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Abstract:Beaches are natural environments of great interest for our society. They go through remarkable changes run by key factors that are interconnected according to the literature. A better understanding of these parameters, such as sediment texture and shoreline variability, would be of a great interest for coastal monitoring and planning.Shorelines of all Landsat 8 (OLI) images available over the course of one year have been obtained for determining the variability that has occurred in different Valencian beaches. Likewise, the relation between shoreline variability and sediment texture has been evaluated, showing that beaches with higher variability over the year have smaller sediment texture, which is also related with gentle slopes, and vice versa.The methodology allows obtaining the shoreline variability, a key parameter of beach morphodynamics, in a semiautomatic way. The variability allows developing a gross estimate of beach texture.Key words: Shoreline Detection, Coastline Variability, Beach Sediment, Sediment Texture, Landsat 8, Remote Sensing Resumen:Las playas son entornos naturales de enorme interés para nuestra sociedad. Estos espacios están sometidos a grandes cambios regidos por factores clave fuertemente interrelacionados según la literatura. Un mayor conocimiento de estos parámetros, como la textura del sedimento o la variabilidad de la línea de costa de las playas resultaría de gran interés para la monitorización y gestión de la costa.Se han obtenido las líneas de costa de todas las escenas Landsat 8 (OLI) disponibles a lo largo de un año para, a partir de ellas, determinar la variabilidad de diferentes playas valencianas. Asimismo, se ha evaluado la relación existente entre dicha variabilidad y la textura del sedimento de las playas, mostrando que las playas con mayores cambios en la línea de costa a lo largo del año son aquellas con un tamaño de grano menor, asociado a pendientes más suaves y viceversa.La metodología seguida incluye la obtención de forma semiautomática de la variabilidad de la línea de costa, un parámetro clave de la morfodinámica costera, y a partir de ella la estimación a grandes rasgos de la textura del sedimento de las playas.Palabras clave: Detección de la línea de costa, Variabilidad de la línea de costa, Sedimento de playa, Textura del sedimento, Landsat 8, Teledetección IntroducciónLas playas son espacios naturales que proporcionan protección frente al oleaje y temporales, representan un importante recurso socioeconómico (Prodger et al. 2016), y actúan como soporte físico para los organismos que allí habitan.Las actuaciones antrópicas en la zona costera generan cambios en el sistema, modifican la distribución y las características del sedimento y en consecuencia alteran la morfología de las playas. La textura del sedimento y la anchura de las playas son factores clave para permitir el mantenimiento de sus funciones. Por tanto, resulta de gran interés disponer de información actualizada de ambos elementos para conocer el estado de la playa y plantear una gestión a...

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SediNet: a configurable deep learning model for mixed qualitative and quantitative optical granulometry

Buscombe

2019

Earth Surf Processes Landf

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I describe a configurable machine‐learning framework to estimate a suite of continuous and categorical sedimentological properties from photographic imagery of sediment, and to exemplify how machine learning can be a powerful and flexible tool for automated quantitative and qualitative measurements from remotely sensed imagery. The model is tested on a dataset consisting of 409 images and associated detailed label data. The data are from a much wider sedimentological spectrum than previous optical granulometry studies, consisting of both well‐ and poorly sorted sediment, terrigenous, carbonate, and volcaniclastic sands and gravels and their mixtures, and grain sizes spanning over two orders of magnitude. I demonstrate the model framework by configuring it in several ways, to estimate two categories (describing grain shape and population, respectively) and nine numeric grain size percentiles in pixels from a single input image. Grain size is then recovered using the physical size of a pixel. Finally, I demonstrate that the model can be configured and trained to estimate equivalent sieve diameters directly from image features, without the need for area‐to‐mass conversion formulas and without even knowing the scale of one pixel. Thus it is the only optical granulometry method proposed to date that does not necessarily require image scaling. The flexibility of the model framework should facilitate numerous application in the spatiotemporal monitoring of the grain size distribution, shape, mineralogy and other quantities of interest of sedimentary deposits as they evolve, as well as other texture‐based proxies extracted from remotely sensed imagery. © 2019 John Wiley & Sons, Ltd.

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Understanding and predicting the temporal variability of sediment grain size characteristics on high-energy beaches

Abstract: Understanding and predicting the temporal variability of sediment grain size characteristics on high-energy beaches.

Cited by 50 publications

References 32 publications

Grain‐size distributions on high‐energy sandy beaches and their relation to wave dissipation

Grain‐size distributions on high‐energy sandy beaches and their relation to wave dissipation

Variabilidad De La Línea De Costa a Partir De Imágenes De Satélite Y Su Relación Con La Textura Del Sedimento

SediNet: a configurable deep learning model for mixed qualitative and quantitative optical granulometry

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