Underwater Microscope for 

Measuring Spatial and Temporal 

Changes in Bed-Sediment Grain Size 

By David M. Rubin1, Henry Chezar2, Jodi N. Harney1,3, David J. Topping4, 

Theodore S. Melis4, Christopher R. Sherwood5

Open-File Report 2006–1360 

2006 

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

U.S. DEPARTMENT OF THE INTERIOR 

U.S. GEOLOGICAL SURVEY 

1

 U.S. Geological Survey, 400 Natural Bridges Dr., Santa Cruz, CA 95060 

2 U.S. Geological Survey, 345 Middlefield Rd., Menlo Park, CA 94025 

3 Present address: Coastal & Ocean Resources, 9865 Saanich Rd. Ste. 214, Sidney, BC 

Canada V8L 5Y8 

4

 U.S. Geological Survey, 2255 North Gemini Drive, Flagstaff, AZ 86001

5 U.S. Geological Survey, 384 Woods Hole Road, Woods Hole, MA 02543 
Abstract 

For more than a century, studies of sedimentology and sediment transport have measured 

bed-sediment grain size by collecting samples and transporting them back to the lab for grainsize analysis. This process is slow and expensive. Moreover, most sampling systems are not 

selective enough to sample only the surficial grains that interact with the flow; samples typically 

include sediment from at least a few centimeters beneath the bed surface. New hardware and 

software are available for in-situ measurement of grain size. The new technology permits rapid 

measurement of surficial bed sediment. Here we describe several systems we have deployed by 

boat, by hand, and by tripod in rivers, oceans, and on beaches. 

1. Introduction and purpose 

Grain-size analysis is fundamental to sedimentology, but despite technological advances in 

lab instruments, grain-size analysis is still time-consuming and expensive. Moreover, most 

sampling systems are poorly adapted to measuring grain size of surficial sediment—the grains 

that interact with the flow. Previous work (Rubin, 2004) has described algorithms for grain-size 

analysis from digital images. This paper describes new hardware for rapidly mapping grain size 

of surficial sediment and for tracking changes through time in grain size at a single location on 

the bed. 

2. Methods 

2.1 Traditional approach 

Most traditional approaches to grain-size analysis of sand-size sediment on the bed require 

collecting a sample using a dredge or other sampling device and then transporting the sample to 

a lab where it is analyzed using mechanical sieving (Krumbein and Pettijohn 1938), settling 

through a column of water (Gibbs 1972; Syvitski et al. 1991), or laser diffraction (Agrawal et al. 

1991). Although laser diffraction and settling can be applied in-situ to suspended sediment, insitu techniques have not been applied to bed sediment. Sampling of surficial bed sediment (the 

part interacting with the flow) is particularly difficult and typically involves sampling of 

sediment by adhesion to a greased plate and then using solvent to remove the grease prior to 

laboratory grain-size analysis.

2.2 Digital image software 

Grain size of surficial bed sediment can be determined from digital images using several 

approaches summarized by Rubin (2004). We use an autocorrelation algorithm that is based on 

the idea that the degree to which nearby pixels in an image are correlated varies with grain size; 

nearby pixels in images of coarse grains are more highly correlated than in images of finer 

grains. This approach to grain-size analysis is extremely rapid. A trained student or lab 

technician can download and process up to 100 samples per hour. Although grain size calculated 

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from images agrees relatively well with grain size measured by sieving or point counts of grains 

in the images, the main advantages of the photographic approach are high speed, low cost, and 

ability to sample surficial sediment. 

The full data flow requires the following sequence of steps: 

(1) Collect digital images of bed sediment in the field. 

(2) Make point counts of a representative subset of these images to determine known grain sizes 

(for calibration); this requires perhaps a dozen images that span the range of grain sizes 

present in the field area. 

(3) Determine spatial autocorrelation curve for the calibration images selected for step 2. 

(4) Prepare a calibration matrix relating the autocorrelation curve for each calibration image 

(from step 3) to the grain size measured from point counts (step 2). 

(5) Measure autocorrelation curve of each sample, and convert to grain size, using calibration 

matrix determined in step 4. 

Details, examples, the approach for reconstructing full grain-size distributions, and Matlab® 

code are given by Rubin (2004). 

In performing this work, it is crucial to observe several quality-control requirements: 

(1) Care must be used to prepare and test calibration curves. This step is crucial, because errors 

in calibration produce proportional errors in all subsequent grain-size analyses. 

(2) Hardware must be assembled in the same way for all data collection. This includes, using 

the same camera settings, particularly zoom, focus, f-stop, and image-file format. Viewing 

windows must be replaced when they become scratched. Illumination angle must remain 

constant, although light intensity is not critical. 

(3) It is useful to prepare a sample of uniform-size sand for quality-control tests (to verify that 

the hardware is configured in the same manner). Similar quality control can be 

accomplished by performing point counts on a small subset of processed images. 

2.3 Underwater microscope hardware 

Digital images of bed sediment can be collected by a digital camera or video camera placed 

on the bed by hand, lowered by winch or by hand from a boat, or using self-contained tripodmounted systems that lower a camera to the bed at pre-set time intervals. In all cases, hardware 

can be simplified and image quality can be improved by following the following design 

guidelines: 

(1) The housing is designed with a window that rests on the bed. This allows photography 

even in water as turbid as the Colorado River at flood stage, because the optical path is 

through air within the housing rather than through water (Figure 1). 

(2) The camera is pre-focused to the outside surface of the viewing window of the housing. 

This ensures sharp focus and maintains a constant scale from image to image. 

(3) A light ring of lights is contained within the housing to insure uniform illumination. 

We have constructed and employed eight systems based on these specifications (US Geological 

Survey, 2001; Chezar and Rubin, 2004), several of which are described below. The first was 

fabricated by inserting a plumbing-inspection video camera inside a 50-kg wrecking ball (Fig. 

2a). We constructed this system to map spatial and temporal variability of grain size of sand on 

the bed of the Colorado River in the Grand Canyon; previous work had shown that changes in 

grain size on the bed are as important as changes in flow in regulating transport down the river 

(Rubin and Topping, 2001). Analog images from the underwater video are recorded digitally on 

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deck, and selected images (such as Fig. 2b) are used for grain-size analysis in the office. This 

hardware is capable of collecting hundreds of images per day. Although the images have 

relatively low resolution (video resolution of less than one megapixel), they are nevertheless 

adequate to determine mean grain size of sediment coarser than very fine sand in a 1-cm square 

area of the bed surface. 

The wrecking-ball hardware was designed to sink to the bed of the Colorado River at flood 

stage and is much too heavy for deployment by hand (for mapping grain size on beaches). For 

this latter purpose, we placed a 5-megapixel digital camera with macro lens into an underwater 

housing that was modified to accommodate a light ring and that was lengthened to the predetermined focal distance (Fig. 3a). The resulting system is light, compact, waterproof, and easy 

to operate (Fig. 3b). Depending on the camera and model of macro lens, the area being 

photographed is typically ~2 cm across, and pixel size is on the order of 10 microns. 

Figure 1. Diagram illustrating the important design requirements of underwater microscope: 

camera housing rests on the bed; camera is pre-focused to the distance to the bed (outside 

surface of the window of the housing); housing contains lights that insure uniform illumination. 

We developed another kind of grain-size microscope for marine use. It includes both a lowresolution video camera and 5-megapixel digital still camera. The video camera gives a standard 

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underwater-video view, and the still camera gives a microscopic view of grains on the bed. The 

optical devices and electronics are housed in an underwater case that functions as one of the 

three legs of a tripod (Fig. 4), to insure that the camera is oriented perpendicular to the bed. An 

ideal system would include a computer or video monitor on deck so that the user can watch 

video images in real time as the device is lowered to the bed and also can view microscopic 

images seen by the digital camera after it reaches the bed; this microscopic image can either be a 

low-resolution video output that many digital cameras can output or a high-resolution image 

visible over USB cable and electronic components to extend the range of the USB signal. When 

the user is satisfied with the area of the bed being viewed, they would trigger the camera to store 

a high-resolution microscopic image either in camera memory or on a shipboard computer. 

Because digital-camera technology and computer interfaces are changing so rapidly, any 

components we specify now will likely be outdated by the time this report is published. 

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Figure 2. Underwater microscope (“flying eyeball”) consisting of a plumbing-inspection video 

camera inside a wrecking ball. (a) View of boat showing frame, winch, and wrecking ball; the 

low-resolution analog images are recorded continuously on a digital video recorder. (b) Sample 

image from the system in (a), cropped to show the region of the image used for grain-size 

analysis; image is approximately 1 cm across. 

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Figure 3. Lightweight underwater microscope for hand-held use on beaches or by divers in 

shallow water. (a) Closeup of camera and housing; design follows requirements specified in Fig. 

1. (b) Camera in use. 

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Figure 4. Integrated camera and video system for marine grain-size mapping. Video camera, 

digital still camera with macro lens, and electronics are contained within a single underwater 

housing that also functions as one of the three legs of the tripod. 

All of the systems described above were designed to map grain size spatially, but grain size 

also varies through time at a point. Although even the crudest sediment-transport models 

incorporate the effects of bed-sediment grain size on transport, no instruments have been 

developed previously to monitor temporal changes in bed sediment. Marine tripods routinely 

contain 

sophisticated instruments (worth a small fortune) that are capable of measuring flow velocity and 

suspended sediment at frequencies up to 100 Hz. In contrast, grain size of bed sediment might 

be measured only at the beginning and end of a deployment (by a grab sample or two). To 

address this deficiency, we have assembled and tested several autonomous bed-sediment 

microscopes. The best design we have tested consists of a bed-sediment camera, electronic 

controller, hydraulic-piston drive system, and bottom-sensing switch (Fig. 5a). An image from 

the previous autonomous system illustrates the exceptional resolution of a 5-megapixel image of 

sand grains, shell fragments, and biological pellets on the bed (Fig. 5b). 

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Figure 5. Autonomous bed-sediment microscope for tripod deployments. (a) Photo of 

instrument mounted on tripod. 

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Figure 5. Autonomous bed-sediment microscope for tripod deployments. (b) Sample image from 

10-m depth in the Adriatic Sea taken with underwater microscope in Fig. 5a. 

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3. Examples 

3.1 Colorado River bar sediment 

In preparation for the 2004 experimental restoration flood in Grand Canyon (Topping et 

al., 2006), we used the camera illustrated in Figure 3 to collect several thousand images of 

sediment on the surface and 1 cm below the surface on emergent sand bars along the Colorado 

River. At several dozen locations, we retained for sieve analysis the 1-centimeter thickness of 

sand located vertically between the two images. The results are instructive for testing the 

accuracy of the digital grain-size analysis as well as understanding processes that are active on 

the bar surfaces. 

Grain sizes determined by analysis of the images collected 1 cm beneath the bar surface 

are in good agreement with results obtained by sieving (Fig. 6a). The correlation between results 

determined by the two methods is 0.88, but more importantly, the regression line is very close to 

the line of perfect agreement. This latter result is a more important test, because we would 

expect to find differences between the grains photographed in a single horizontal section and the 

grains sampled from the overlying centimeter of sand. 

Unlike results for the subsurface sediment images, images of surface sediment do not 

agree with the sieve results for the top centimeter of sediment (Fig. 6b); the surface sediment is 

systematically coarser by 30-50% (~0.04 to 0.10 mm). We interpret this difference to arise from 

real trends in grain size with depth, rather than differences in technology used for grain-size 

analysis. Three observations support this interpretation: (1) image analysis worked accurately at 

a depth of 1 cm in the same sediment, (2) results of the image analysis indicate that the sediment 

on the surface is coarser than in the subsurface, and (3) all surface samples were from areas that 

had not been inundated for at least 2.5 months, during which time the surface sediment was 

subject to winnowing by wind. These results (grain size of the uppermost 1 cm of sediment finer 

than the bed surface and not detectably coarser than the grain size at a depth of 1 cm) indicate 

that the depth of winnowing by wind is probably no thicker than a millimeter or two. 

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Figure 6. Comparison of sediment grain size determined by image analysis and by sieving for 

samples from bars along the Colorado River in Grand Canyon. Pairs of digital images were 

collected at the bed surface and at a depth of 1 cm below the surface using the camera shown in 

Figure 3. Sediment between each pair of images was collected in the field and measured by 

sieve analysis in the lab. (a) Comparison of surface sediment analyzed from digital imagery 

with the underlying centimeter of sediment analyzed by sieving; grain sizes determined using the 

two techniques are in close agreement. (b) Comparison of sediment 1 centimeter beneath the bar 

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surface analyzed from digital imagery with the overlying sediment analyzed by sieving. 

Surficial sediment is systematically coarser, as a result of winnowing by wind. 

Figure 7. Vertical profile of grain size through laminae deposited by wind ripples. Each wind 

ripple deposited a layer that coarsens upward (Hunter, 1977), followed by abrupt fining. 

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These results illustrate two properties resulting from the high spatial resolution of digital 

grain-size analysis. Because the grain size calculated for an image applies only to that image (or 

to a part of an image, as illustrated in the following section), extremely high spatial resolution is 

possible. The technology has the capability of documenting spatial variability in grain size on 

the scale of a millimeter. The disadvantage of such high resolution is the limited spatial scale of 

each sample. Because grain size varies spatially, nearby samples may have differing results, and 

multiple samples are required to characterize the grain size at a particular region of a bed or 

stratigraphic horizon. 

3.2 Vertical profiles through inversely graded wind-ripple deposits 

Digital photography also can be used to make vertical profiles in grain size though a 

sediment deposit—in the sidewall of a trench or in a core (Rubin 2004). The hardware required 

for this purpose is much simpler than for underwater use; all that is required is a digital camera. 

Grain size can be measured on a smaller scale than can be sampled physically (a thousand 

measurements over a few cm), and calculations are extremely rapid (less than 10 seconds to 

compute the vertical grain-size profile through an image). An example of inverse grading in 

wind-ripple deposits is shown in Figure 6. 

4. Conclusions 

Digital analysis of microscopic images can be used to measure grain size of bed 

sediment. Here we describe the hardware used for this work and some of the considerations for 

applying this approach. The technology is ideally suited for collecting large numbers of grainsize measurements for mapping surficial grain size of sand. Because a few days are required to 

learn how to use this technology, to make calibration curves, and to test the hardware and 

software are working properly, the technology is advantageous only for large numbers of 

samples (perhaps 100 samples or more). Once the calibration curves are prepared, a trained 

worker can process 500 samples per day. In Grand Canyon, we routinely collect several 

thousand images per survey. In addition to rapid data collection and processing, this approach 

offers high spatial resolution and convenient analysis of the uppermost layer of grains on the 

bed—the grains that interact with the flow. 

Acknowledgements 

This work was supported by the Grand Canyon Monitoring and Research Center of the 

USGS; adaptation of instruments for autonomous tripod deployment was supported by the Office 

of Naval Research. Alexander Rubin collected the grain-size images from Grand Canyon, and 

Carissa Carter and Tristan Rhodes performed grain-size analysis using those images. Eric 

Grossman and Curt Storlazzi (USGS) reviewed an earlier draft of this manuscript. Use of trade 

names is for descriptive purposes and does not imply endorsement by the USGS. 

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References 

Agrawal, Y.C., McCave, I.N., and Riley, J.B., 1991, Laser diffraction size analysis, in Syvitski, 

J.M.P., ed., Principles, Methods and Application of Particle Size Analysis: Cambridge, UK, 

Cambridge University Press, pp. 119–128. 

Chezar, H., 2001, Underwater microscope system: USGS Fact Sheet 135-01, Washington, D.C., 

U.S. Government Printing Office, 2 p. 

Chezar, H., and Rubin, D., 2004, Underwater microscope system: U.S. Patent and Trademark 

Office, patent number 6,680,795, January 20, 2004, 9 pp. 

Gibbs, R.J., 1972, The accuracy of particle-size analyses utilizing settling tubes: Journal of 

Sedimentary Petrology, 42, 141-145. 

Hunter, R. E., 1977. Basic types of stratification in small eolian dunes: Sedimentology, 24, 361-

387. 

Krumbein, W.C. and Pettijohn, F.J., 1938, Manual of Sediment Petrography: New York, 

Appleton-Century Crofts, Inc., 549 p. 

Rubin, D.M., 2004, A simple autocorrelation algorithm for determining grain size from digital 

images of sediment: Journal of Sedimentary Research, 74, 160-105. 

Rubin, D.M. and Topping, D.J., 2001, Quantifying the relative importance of flow regulation 

and grain-size regulation of suspended-sediment transport (•) and tracking changes in grain 

size on the bed (•): Water Resources Research, 37, 133-146. 

Syvitski, J.P.M., Asprey, K.W., and Clattenburg, D.A., 1991, Principles, design and calibration 

of settling tubes, in Syvitski, J.M.P., ed., Principles, Methods and Application of Particle 

Size Analysis: Cambridge, UK, Cambridge University Press, pp. 45-63. 

Topping, D.J., Rubin, D.M., Schmidt, J.C., Hazel, J.E., Jr., Melis, T.S., Wright, S.A., Kaplinski, 

M., Draut, A.E., and Breedlove, M.J., 2006, Comparison of sediment-transport and barresponse results from the 1996 and 2004 controlled-flood experiments on the Colorado River 

in Grand Canyon: Proceedings of the 8th Inter-Agency Sedimentation Conference, April 2-6, 

2006, Reno, Nevada, United States. 

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