A Simple Autocorrelation Algorithm for Determining Grain Size from Digital Images of Sediment

Rubin, David M.

doi:10.1306/052203740160

Cited by 201 publications

(214 citation statements)

References 10 publications

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“…We use a tripod-mounted camera to take digital photographs of beach sediments along topographic survey lines. We then analyze images for grain-size using an autocorrelation technique (Rubin, 2004) that we calibrated specifically for Elwha beach sediment. Error analyses suggest that the 95 percent confidence interval for the technique is 0.5-phi.…”

Section: Beach Characterizationmentioning

confidence: 99%

See 1 more Smart Citation

Extended abstracts from the Coastal Habitats in Puget Sound (CHIPS) 2006 Workshop

Gelfenbaum¹,

Fuentes²,

Duda³

et al. 2010

Open-File Report

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For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment, visit http://www.usgs.gov or call 1-888-ASK-USGS.For an overview of USGS information products, including maps, imagery, and publications, visit http: //www.usgs.gov/pubprod To order this and other USGS information products, visit http://store.usgs.gov Suggested citation: Gelfenbaum, G., Fuentes, T.L., Duda, J.J., Grossman, E.E., and Takesue, R.K., eds. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this report is in the public domain, permission must be secured from the individual copyright owners to reproduce any copyrighted material contained within this report.iii Puget Sound is the second largest estuary in the United States. Its unique geology, climate, and nutrient-rich waters produce and sustain biologically productive coastal habitats. These same natural characteristics also contribute to a high quality of life that has led to a significant growth in human population and associated development. This population growth, and the accompanying rural and urban development, has played a role in degrading Puget Sound ecosystems, including declines in fish and wildlife populations, water-quality issues, and loss and degradation of coastal habitats.In response to these ecosystem declines and the potential for strategic large-scale preservation and restoration, a coalition of local, State, and Federal agencies, including the private sector, Tribes, and local universities, initiated the Puget Sound Nearshore Ecosystem Restoration Project (PSNERP). The Nearshore Science Team (NST) of PSNERP, along with the U.S. Geological Survey, developed a Science Strategy and Research Plan (Gelfenbaum and others, 2006) (1) restoration of large river deltas; (2) recovery of the nearshore ecosystem of the Elwha River; and (3) effects of urbanization on nearshore ecosystems. The more than 35 presentations covered a wide range of ongoing inter-disciplinary research, including studies of sediment geochemistry of aquatic environments, sediment budgets, tracking fish pathways, expansion of invasive forams, beach and nearshore sedimentary environments, using influence diagrams as a decision support tool, forage fish, submarine groundwater, and much, much more.The primary focus within these themes was on developing information on the physical, chemical, and biological processes, as well as the human dimensions, associated with the restoration or rehabilitation of the nearshore environment. The workshop was an excellent opportunity for USGS scientists and collaborators who are working on Puget Sound coastal habitats to present their preliminary findings, discuss upcoming research, and to identify opportunities for interdisciplinary collaboration.A compilation of extended abstracts from workshop participants, this proceedings volume serves as a useful reference for attendees of the workshop and f...

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Section: Beach Characterizationmentioning

confidence: 99%

“…When sediments are coarser, we use a modified version of the Rubin (2004) autocorrelation algorithm to obtain mean grain size from digital photographs. This method involves photographing sediment from an angle orthogonal to the bed with a digital camera ('CobbleCam') mounted on a tripod.…”

Section: Characterizing Sediment Grain Sizementioning

confidence: 99%

Extended abstracts from the Coastal Habitats in Puget Sound (CHIPS) 2006 Workshop

Gelfenbaum¹,

Fuentes²,

Duda³

et al. 2010

Open-File Report

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…These properties, such as pixel brightness, are measured using approaches based on spatial statistics. Rubin (2004) presented a spatial autocorrelation method to measure the correlation between the pixel intensity of two regions of the image. The method was applied to measure subaqueously the grain-size distribution of the bed surface and vertical profiles.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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Abstract. Measurements of spatial and temporal changes in the grain-size distribution of the bed surface and substrate are crucial to improving the modelling of sediment transport and associated grain-size selective processes. We present three complementary techniques to determine such variations in the grain-size distribution of the bed surface in sand-gravel laboratory experiments, as well as the resulting size stratification: (1) particle colouring, (2) removal of sediment layers, and (3) image analysis. The resulting stratification measurement method was evaluated in two sets of experiments. In both sets three grain-size fractions within the range of coarse sand to fine gravel were painted in different colours. Sediment layers are removed using a wet vacuum cleaner. Subsequently areal images are taken of the surface of each layer. The areal fraction content, that is, the relative presence of each size fraction over the bed surface, is determined using a colour segmentation algorithm which provides the areal fraction content of a specific colour (i.e. grain size) covering the bed surface. Particle colouring is not only beneficial to this type of image analysis but also to the observation and understanding of grain-size selective processes. The size stratification based on areal fractions is measured with sufficient accuracy. Other advantages of the proposed size stratification measurement method are (a) rapid collection and processing of a large amount of data, (b) a very high spatial density of information on the grain-size distribution, (c) the lack of disturbances to the bed surface, (d) only minor disturbances to the substrate due to the removal of sediment layers, and (e) the possibility to return a sediment layer to its original elevation and continue the flume experiment. The areal fractions are converted into volumetric fractions using an existing conversion model.

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“…The intended method of tracer monitoring for this experiment was an innovative two-camera setup that utilizes rapid on-site digital image analysis (algorithm for UV grain counts modified after Rubin, 2004) to capture a three-dimensional view of the tracer distribution at a grid of sampled locations on the shoreface. The general setup of the instrumentation is similar to the all-terrain vehicle (ATV)-mounted SADAM system used by Pinto and others (1994) for automated surface grain counts.…”

mentioning

confidence: 99%

The fluorescent tracer experiment on Holiday Beach near Mugu Canyon, Southern California

Kinsman¹,

Xu²

2012

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For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment-visit http://www.usgs.gov or call 1-888-ASK-USGS For an overview of USGS information products, including maps, imagery, and publications, visit http://www.usgs.gov/pubprodTo order this and other USGS information products, visit http://store.usgs.gov Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this report is in the public domain, permission must be secured from the individual copyright owners to reproduce any copyrighted material contained within this report. AbstractAfter revisiting sand tracer techniques originally developed in the 1960s, a range of fluorescent coating formulations were tested in the laboratory. Explicit steps are presented for the preparation of the formulation evaluated to have superior attributes, a thermoplastic pigment/dye in a colloidal mixture with a vinyl chloride/vinyl acetate copolymer. In September 2010, 0.59 cubic meters of fluorescent tracer material was injected into the littoral zone about 4 kilometers upcoast of Mugu submarine canyon in California. The movement of tracer was monitored in three dimensions over the course of 4 days using manual and automated techniques. Detailed observations of the tracer's behavior in the coastal zone indicate that this tracer successfully mimicked the native beach sand and similar methods could be used to validate models of tracer movement in this type of environment. Recommendations including how to time successful tracer studies and how to scale the field of view of automated camera systems are presented along with the advantages and disadvantages of the described tracer methodology.

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A Simple Autocorrelation Algorithm for Determining Grain Size from Digital Images of Sediment

Cited by 201 publications

References 10 publications

Extended abstracts from the Coastal Habitats in Puget Sound (CHIPS) 2006 Workshop

Extended abstracts from the Coastal Habitats in Puget Sound (CHIPS) 2006 Workshop

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

The fluorescent tracer experiment on Holiday Beach near Mugu Canyon, Southern California

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