E3S Web of Conferences 415, 02019 (2023)
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https://doi.org/10.1051/e3sconf/202341502019

Testing applicability of image analysis for measurements ofsediment concentration in laboratory experiments
Kohei Sato1 and Taro Uchida2*
1Master’s Program in Environmental Sciences, University of Tsukuba, Japan
2Faculty of Life and Environmental Sciences, University of Tsukuba, Japan

Abstract. Flume experiments are typically conducted to reveal the detailed behavior of debris flows. Direct
sampling at the lower end of a flume has been used to measure sediment concentrations in flume
experiments; however, direct sampling measurements under non-steady or non-equilibrium conditions aredifficult. In contrast, image analysis methods can acquire spatiotemporal high-resolution data in a noncontact manner. In this study, we conducted experiments in which a homogenous sediment concentration
field of 0 ~ 20 % was prepared in a water tank with a depth of 10 cm using coarse and fine sediments. We
filmed the experiments and verified the relationships between sediment concentrations and image featuresin a fixed area. As a result, the mean of pixel values for coarse sediments depends on the sedimentconcentration of up to 10 % and the number of mode pixels for fine sediments depends on the sedimentconcentration of up to 20 %. We then analyzed factors influencing image features and identified three
processes. Furthermore, we analyzed the effects of the sediment grain size on image features, and the results
show that both the brightness index and brightness uniformity index of images are affected. In conclusion,these findings indicate that image analysis can be used to clarify both sediment concentration and grain size.

1 Introduction
In previous flume experiments on debris flows and sheet
flows, sediment concentrations have been measured bydirectly sampling sediments and water at thedownstream end of a flume [1-4].
Although this method is effective under steady-stateor equilibrium condition, it is difficult to measuresediment concentration under non-steady-state or nonequilibrium conditions, such as at points of slope change.
Although image analysis has been used to determine
the flow depth and velocity distribution in previous
flume experiments [5-7], few studies have estimatedsediment concentrations in hydraulic experiments ondebris flows and sediment sheet flows using image
analysis. However, there would be numerous benefits if
sediment concentrations could be measured by image
analysis. For example, an image analysis system canobtain high- resolution spatiotemporal data, and images
are taken from outside the experimental system.
In this study, we conducted and filmed anexperiment in water tank with a uniform sedimentconcentration of 0 ~ 20 % for two different grain sizesof sediments, and then, we investigated the possibilityof measuring the sediment concentration using imagefeatures. The causes of changes in image features
depending on the sediment concentration were also
analyzed.

2 Material and methods
2.1 Outline of experimental method
An acrylic tank (width: 10 cm; height: 30 cm; depth: 10cm) was used for the experiment. The tank was covered
with blue paper on two sides so that when the sediment
was photographed, the background would be in uniform
conditions. White sediments with grain sizes of 2(coarse) and 0.2 mm (fine) were used, based on fromprevious debris flow flume experiments [1-4]. The
experiment was repeated three times for each sedimentand concentration. The experiment was recorded as avideo using a digital camera (RX100v, Sony, Japan).
Filming photographs were taken in a laboratory with thelights turned off and the light source placed in front of
the tank in a room without outside light (Fig.1).
2.2 Method of controlling concentration incoarse grain
In the experiment using coarse grains, the tank was filledwith water to a height of 20 cm. Dry sediment was stored
in a sediment supplier whose lower mouth was sealedwith a plate. Sediments were put into the tank by pullingout the plate (Fig. 1). A sieve was set between thesediment supplier and the tank for a uniform supply. The
sediment concentration was controlled from 0 to 18
volumetric % by varying the size of the sedimentsupplier and the sieve mesh size and their combinations.

Corresponding author*: uchida.taro.fw@u.tsukuba.ac.jp© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0(https://creativecommons.org/licenses/by/4.0/).

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2.3 Method of controlling concentration in fine
grain

Sediments are supplied in such a way that they
spread over the entire width of the tank when thesediment concentration is 0 to 10 %, and it is assumed
that the sediments are uniformly supplied to the tanktemporally and spatially from the start to the end of thesupply. The sediment concentration C was calculatedusing equation (1):C = (100 Wc t1 ) / (σ Vc t2)

In the experiment using fine grains, sediments werespread on the bottom of the tank, water was poured to adepth of 10 cm, and a wire mesh was used to agitate anddisperse the sediments in the tank. After confirming thatthe sediments were sufficiently dispersed in water, thewire mesh was pulled up and the amount of sedimentcaught in the wire mesh was measured. The initialamount of sediment placed at the bottom of the tank wasvaried to control the sediment concentration from 0 to20 %.
It was assumed that the sediments were uniformly
dispersed in the tank immediately after the end ofagitation by the wire mesh. The water depth hf (cm) atthat time was read from the image taken immediately
after the end of agitation, and multiplied by the bottom
area of the tank, 100 cm2, to obtain the volume ofsediments dispersed. Assuming that the sediments areuniformly distributed within this volume, the sedimentconcentration C (%) was calculated using Equation (3):(1)where Wc denotes the mass of the sediment supplied by
the sediment supplier (200 or 400 g); Vc is 1000 cm3; t1
represents the time between when the top of thesediment passes the 15 cm height point from the bottomof the tank and when it passes the 5-cm height point (s);t2 denotes the time between when the top of the sedimentpasses the 15-cm height point from the bottom of thetank and when the end of the sediment passes the 15-cm
height point (s).
As it was difficult to control the sedimentconcentration across the entire width of the tank at 14 to
18 % sediment concentration, a sieve was not installed
between the sediment supplier and the tank, and thesediment was directly fed into the tank, concentratingthem on one side. The sediment concentration iscalculated under the assumption that the sedimentconcentration is uniform throughout the area where thesediments are dispersed. The area S (cm2) of dispersion
in an image, where dropped sediments were underwaterand had not reached the bottom of the tank, wascalculated by approximating it to one or more trapezoids.
Furthermore, from another angle of the image, it wasconfirmed that the sediments were supplied uniformly
from the front wall to the rear wall in the depth directionof the tank. Therefore, the volume of sediment dispersed
was S multiplied by the depth of the tank, D (10 cm).
Therefore, at C of 14 to 18 %, C (%) was calculatedusing equation (2).
C = (100 Wc) / (σ S D)

C = (Wf – Wr) / (σ hf)(3)where Wf denotes the mass of sediment placed at thebottom of the tank; Wr denotes the mass of sediment caught
in the wire mesh.
2.4 Method of image analysis
The video of the experiments was cut into RGB images
per frame, and six consecutive frames (0.05 s) wereextracted from the image when the sediments were
uniformly dispersed, at a height of 8 to 10 cm from thebottom of the tank for coarse grains and 4 to 6 cm forfine grains, at the left side of the center of the image,with a 100 p × 100 p ratio (2 cm × 2 cm). Examples ofpictures used for image analysis are depicted in Fig.2 forcoarse grains and in Fig.3 for fine grains. For eachextracted image, the mean, standard deviation, mode,and the number of mode pixels of the pixel value in each
image were obtained as image features.

(2)

Fig. 2. Examples of pictures used for image analysis (coarsegrains). The resolution of images is 0.2 mm/pixel. The images
were taken with fixed shutter speed, focal length, whitebalance, and ISO sensitivity.

Fig. 1. Location of the light, the camera, and the tank in theexperiment.

2

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Fig. 3. Examples of pictures used for image analysis (finegrains). The resolution of images is 0.2 mm/pixel. The images
were taken with fixed shutter speed, focal length, whitebalance, and ISO sensitivity.
Fig. 5. Relationship between sediment concentration and thenumber of mode pixel in R channel for coarse and fine grains.

3 Result
Fig. 4 depicts the relationship between the sedimentconcentration and the average pixel value in the R
channel for coarse and fine grains. The average pixel
values monotonically increased as the sedimentconcentration increased for coarse grains, from 0 to
10 % and for fine grains, from 0 to 1 %. For both coarse
and fine grains, the average pixel value was constantwhen the sediment concentration was greater than therespective threshold concentration.
Fig. 5 depicts the relationship between the sedimentconcentration and the number of mode pixels in the R
channel for coarse and fine grains. For coarse grains, thenumber of mode pixels did not change significantly withsediment concentration. For fine grains, the number ofmode pixels decreased monotonically for sedimentconcentrations from 0 to 2 % but increased linearly forsediment concentrations from 2 to 20 %.

4 Discussion
4.1 Effect of sediment concentration on images
In this study, the light received by the camera is most
likely either backscattered by the sediment or reflectedby the background (blue paper) and then transmitted
back into the tank. In addition, when the sedimentconcentration exceeds 2 % for coarse grains and 0.2 %for fine grains, the sediment in the image tends to appear
darker in the back and brighter in the front because thelight is extinct as it moves away from the light source.
The background becomes darker as the sedimentconcentration increases due to light extinction by the
sediment in the tank.
At low sediment concentrations, the background isthought to be affected by reflected light, whereas at
higher concentrations above a certain level, thebackground is no longer reflected, and the effect of the
background on the image is considered negligible. As
the sediment concentration increases, the proportion ofbright sediment near the camera and light source in theimage is expected to increase. The above process can be
represented schematically as shown in Fig. 6. Based on
this assumption, in this study, there are three possible
processes by which the image changes with the sedimentconcentration: shielding of the background by sediments,
darkening of the background, and changing thebrightness of the sediments.
These hypotheses were verified using images ofcoarse grains. For verification, it is necessary to classify
pixels into sediment and background pixels. In thisstudy, pixels with pixel values greater than 100 in the R
channel are distinguished as sediment pixels, and pixels
with pixel values of 100 and less than 100 are
distinguished as background pixels. As a result, it isverified that the shielding of the background bysediments and the darkening of the background are
effective for 0 ~ 4 % sediment concentrations andchanging the brightness of sediments is effective for 4 ~20 % sediment concentrations.

Fig. 4. Relationship between sediment concentration and theaverage pixel value in R channel for coarse and fine grains.

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5 Conclusion
In this study, experiments were conducted to create a
uniform concentration field with sediment concentration
ranging from 0 to 20 % using white sediments with grain
sizes of 2 and 0.2 mm. The experiments were captured
with a digital camera, and the relationship between
sediment concentration and image features wasanalyzed.
The results suggest the possibility of using image
analysis to measure the sediment concentration in therange of 0 ~ 10 % using the average pixel value forcoarse grains and 0 ~ 20 % using the number of mode
pixels for fine grains.
The effects of sediment concentration on the image
were threefold, namely, shielding of the background bysediments, darkening of the background, and changes inthe brightness of the sediment, which were verified
using coarse-grained images. The sediment diameter
affects the average pixel value and the number of mode
pixels.

Fig. 6. Difference in the projection plane according tosediment concentration seen from above. (The red lines are theprojection plane of the image.)

4.2 Effect of sediment diameter on images
Fig. 4 shows that the threshold, at which the average
pixel value does not change with increasing sedimentconcentration, is greater for coarse grains than for finegrains. This is thought to be because light extinction
becomes more pronounced as the product of the particle
cross-section and the number concentration of grains
and is inversely proportional to the particle size for thesame volumetric concentration. Fig. 7 depicts therelationship between C (D/d), the value obtained by
dividing the grain size d by the depth D (10 cm) of thetank, and the sediment concentration C, and the average
pixel value of coarse and fine grains. In other words, theassumption in Fig. 6 holds true for fine grains as well,and this effect on the images can be expressed by C(D/d).
Fig. 5 depicts that the number of mode pixels isalways larger for fine grains than for coarse grains, andthe relationship between the sediment concentration andthe number of mode pixels differs significantly forcoarse and fine grains. This is thought to be because
differences in the ratio of grain size to pixel size affectthe degree of uniformity of pixel values.

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Fig. 7. Comparison of the relationship between sediment C(D/d) and the average pixel value for coarse and fine grain in
the R channel.

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