A universal approximation of grain size from images of noncohesive sediment

Abstract

[1] The two-dimensional spectral decomposition of an image of sediment provides a direct statistical estimate, grid-by-number style, of the mean of all intermediate axes of all single particles within the image. We develop and test this new method which, unlike existing techniques, requires neither image processing algorithms for detection and measurement of individual grains, nor calibration. The only information required of the operator is the spatial resolution of the image. The method is tested with images of bed sediment from nine different sedimentary environments (five beaches, three rivers, and one continental shelf), across the range 0.1 mm to 150 mm, taken in air and underwater. Each population was photographed using a different camera and lighting conditions. We term it a "universal approximation" because it has produced accurate estimates for all populations we have tested it with, without calibration. We use three approaches (theory, computational experiments, and physical experiments) to both understand and explore the sensitivities and limits of this new method. Based on 443 samples, the root-mean-squared (RMS) error between size estimates from the new method and known mean grain size (obtained from point counts on the image) was found to be ±≈16%, with a 95% probability of estimates within ±31% of the true mean grain size (measured in a linear scale). The RMS error reduces to ≈11%, with a 95% probability of estimates within ±20% of the true mean grain size if point counts from a few images are used to correct bias for a specific population of sediment images. It thus appears it is transferable between sedimentary populations with different grain size, but factors such as particle shape and packing may introduce bias which may need to be calibrated for. For the first time, an attempt has been made to mathematically relate the spatial distribution of pixel intensity within the image of sediment to the grain size.

Tributary connectivity, confluence aggradation and network biodiversity

“…Fonstad and Marcus, 2010). Early progress toward automated estimation of bed material grain size at bar-scales (Chandler et al, 2004;Verdu et al, 2005) and along entire rivers (Carbonneau et al, 2004(Carbonneau et al, , 2005 has been extended by using imagery to accomplish the necessary grain-size calibration (Dugdale et al, 2010), by avoiding the need for calibration at all (Buscombe and Masselink, 2009;Buscombe et al, 2010) and by using hyperspatial data (<100mm resolution) to map sub-pixel grain sizes (Black et al, 2014). These advances mean that it is feasible to aquire continuous bed material grain size information and identify tributary-driven confluence aggradation and sedimentary links at network scales (Fonstad and Marcus, 2010).…”

Taking the river inside: Fundamental advances from laboratory experiments in measuring and understanding bedload transport processes

“…With photogrammetry, the surface grain size is estimated from the two-dimensional projection of a three-dimensional structure (Buscombe et al, 2010). A variety of techniques such as autocorrelation (Rubin, 2004;Barnard et al, 2007;Warrick et al, 2009), semivariograms of image texture (Verdú et al, 2005), fractal dimension (Buscombe and Masselink, 2009), or spectral decomposition of image intensity (Buscombe et al, 2010) are used to estimate individual grain sizes.…”

Geosalar: Innovative Remote Sensing Methods for Spatially Continuous Mapping of Fluvial Habitat at Riverscape Scale

“…Furthermore, significant progress has been made by other authors working in coastal environments. Buscombe and Masselink (2009) and Buscombe et al (2010) have developed a method based on Fourier analysis which can derive particle sizes without any calibration from field or on-screen data. This method has been successfully applied to coastal environments and shows much promise for river environments.…”