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Grain-size information from the statistical properties of digital images of sediment |
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| Authors: | DANIEL BUSCOMBE GERHARD MASSELINK |
| Institution: | School of Geography, University of Plymouth, Plymouth PL4 8AA, UK (E-mail:;) Associate Editor: Susan Marriott |
| Abstract: | The autocorrelation technique for estimating grain-size from digital images of sand beds has been extended and validated for use on coarse sand (0·7 mm) and gravel (up to ～20 mm). A number of aspects of the technique have been explored and some potential improvements suggested. Autocorrelation is just one suitable statistical method sensitive to the grain-size of sediment in digital images; four additional techniques are presented and their relative merits discussed. A collective suite of techniques applicable to the general problem of grain-size estimation from digital images of sediment might broaden the applicability to more sedimentary environments, as well as improve its accuracy. These techniques are compared using a large data set from a gravel barrier beach in southern England. Based on over 180 samples, mean grain-size of sieved and imaged sediments correspond to within between 8% and 16%. Some theoretical aspects of the spatial arrangement of image intensity in digital images of natural sediments are addressed, including the fractal nature of sediments in images, which has potential implications for derivation of grain-size distributions from images of sand-sized material through segmentation and thresholding. The methods outlined in this contribution may also find application in further uncovering the geometric structure of sediment beds, as well as in the simulation of sedimentation processes. |
| Keywords: | Grain size analysis digital images statistical models gravel beach |