Cobble cam: grain‐size measurements of sand to boulder from digital photographs and autocorrelation analyses

Abstract

A new application of the autocorrelation grain size analysis technique for mixed to coarse sediment settings has been investigated. Photographs of sand-to boulder-sized sediment along the Elwha River delta beach were taken from approximately 1·2 m above the ground surface, and detailed grain size measurements were made from 32 of these sites for calibration and validation. Digital photographs were found to provide accurate estimates of the long and intermediate axes of the surface sediment (r 2 > 0·98), but poor estimates of the short axes (r 2 = 0·68), suggesting that these short axes were naturally oriented in the vertical dimension. The autocorrelation method was successfully applied resulting in total irreducible error of 14% over a range of mean grain sizes of 1 to 200 mm. Compared with reported edge and object-detection results, it is noted that the autocorrelation method presented here has lower error and can be applied to a much broader range of mean grain sizes without altering the physical set-up of the camera (~200-fold versus ~6-fold). The approach is considerably less sensitive to lighting conditions than object-detection methods, although autocorrelation estimates do improve when measures are taken to shade sediments from direct sunlight. The effects of wet and dry conditions are also evaluated and discussed. The technique provides an estimate of grain size sorting from the easily calculated autocorrelation standard error, which is correlated with the graphical standard deviation at an r 2 of 0·69. The technique is transferable to other sites when calibrated with linear corrections based on photo-based measurements, as shown by excellent grain-size analysis results (r 2 = 0·97, irreducible error = 16%) from samples from the mixed grain size beaches of Kachemak Bay, Alaska. Thus, a method has been developed to measure mean grain size and sorting properties of coarse sediments.

Autonomous bed‐sediment imaging‐systems for revealing temporal variability of grain size

“…error: Warrick et al 2009;Buscombe et al 2010;Buscombe and Rubin 2012;Buscombe 2013) estimates of grain size. This approach has the potential to offer unparalleled spatial or temporal resolution using relatively simple, completely automated analysis procedures.…”

“…These methods have proved to be successful in a number of sedimentary environments, with a typical error of less than 20% (Rubin et al 2007;Barnard et al 2007;Warrick et al 2009;Buscombe 2013). The method of Buscombe (2013) estimates the grain size distribution using spectral analysis on the entire image, by means of the global wavelet power spectrum that quantifies the variance contributions of grain-scale wavelengths within the image.…”

“…The 'point-counting' method of Barnard et al (2007) was employed whereby, in each digital image, a grid composed of a number of intersections was drawn and the intermediate (baxis) diameter of the grain, measured as the center-to-center distance between interstices, underneath each grid intersection measured. This procedure makes grain selection free from operator bias (Warrick et al 2009). Grain sizes as estimated using the automated techniques were within 20% (root-meansquared error) of those determined by point counts (Fig.…”

Image analysis for measuring the size stratification in sand–gravel laboratory experiments

“…In ground-based photography (Rubin, 2004;Warrick et al, 2009) and airborne photography (Carbonneau et al, 2004(Carbonneau et al, , 2005Dugdale et al, 2010), relationships were found between grain-size properties and the pixel properties. These properties, such as pixel brightness, are measured using approaches based on spatial statistics.…”

“…Images with an unknown grain-size distribution are analyzed comparing them with autocorrelation curves that are representative of a certain sediment grain size. This method used as well by Warrick et al (2009) can be applied to a large range of grain sizes. The second approach based on spatial statistics consists in measuring the local semi-variance, which is a function of the difference in brightness and the distance between these pixels (Carbonneau et al, 2004).…”