geosciences Article
Multicamera, Multimethod Measurements for Hydromorphologic Laboratory Experiments Alessio Radice *
ID
and Barbara Zanchi
Department of Civil and Environmental Engineering, Politecnico di Milano, Milan 20133, Italy;
[email protected] * Correspondence:
[email protected]; Tel.: + 39-02-2399-6251 Received: 13 April 2018; Accepted: 2 May 2018; Published: 10 May 2018
Abstract: The realization of hydromorphologic laboratory experiments on the propagation of aggrading or degrading sediment fronts requires simultaneous measurements of the sediment feeding rate, the profile of the free surface, and the flume bed elevation. In this study, five action cameras and different image-processing techniques were employed to measure all the needed quantities automatically and with adequate temporal resolution. The measurement of the sediment feeding rate was determined by particle image velocimetry as a surrogate, correlated quantity: the surface velocity of the sediment flow along a vibrating channel was used as an upstream feeder. The profile of the free surface was measured by shooting an array of piezometers connected to the flume. Each piezometer pipe contained a buoyant black sphere that could be recognized by using tools for particle identification, thus determining the elevation of the free surface above the piezometric probe. Finally, the bed profile along the flume was measured at any instant by edge detection, locating the transition from a water layer to a sediment layer in images taken from the side of the flume. The paper describes the instrumentation and the methods, finally presenting the results obtained from a prototypal experiment. Potentialities and limitations of the proposed methods are discussed, together with some prospects on future use in systematic experimental campaigns. Keywords: image processing; action camera; lens distortion; image calibration; blob identification; particle image velocimetry; edge detection
1. Introduction Field observation of river morphodynamics reveals a number of fascinating processes (see, for example, [1–7]), but doing so is a challenge for researchers because it requires a great amount of human effort. Hydromorphologic laboratory experiments are thus often performed to reproduce reach-scale processes mimicking those typically occurring in reality. In this paper, a specific focus is given to experiments for bed aggradation/degradation in straight flumes. Such experiments can furnish insights into the mechanisms governing the observed phenomena (e.g., [8–15]) and are also instrumental for the validation of numerical models (e.g., [16–20]) that are nowadays mostly used for predictions and for interpretations of past events (as, for example, in [21–25]). In the literature, hydromorphologic experiments have been performed using a variety of instruments. Manual point gauges were used to measure bed elevation in pioneering studies (e.g., [8,9]). A bed-follower probe and an electronic point gauge were used by [11] to measure bed and water elevations, with both instruments mounted on a moving carriage. Wall-attached rulers and a crew of seven people were employed by [12]. Carriage-movable instruments (digital point gauges, echo sounders, and acoustic probes) were also used by [13,14,19] to measure bed and water surface elevations. Sediment transport rates were frequently measured by samplers (e.g., [11–14,19]). A sediment feeding rate was controlled by [19] using an Archimede’s screw with a controlled rotation velocity. Bed textures were in general Geosciences 2018, 8, 172; doi:10.3390/geosciences8050172
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observed by sampling or by photographs as done, for example, by [12,13]. In general, manual measurements may suffer from shortcomings due to the manpower needed during the experiment and user dependence on the returned values. At least for laboratory work, that can be performed in more controlled conditions than in the field. These drawbacks have motivated, for example, equipping sediment samplers or traps with scales or even alternative measurement techniques to achieve an automatic measurement of transport rates (e.g., [26,27]). Furthermore, movable instruments may reduce measuring frequency due to the time needed to move the probe from one location to another. However, fortunately, progress in experimental devices and methods is continuously enhancing the capability of detailed observation. The field of image acquisition and processing is particularly vital because these techniques enable refined studies to be performed. At the same time, increasing use is made of instruments borrowed from everyday life, which maintain affordable costs. For the particular case of bed-load sediment transport, for example, much has been achieved thanks to image-based studies of sediment mechanics (see, for example, [28–33], where different image-analysis methods have been used to investigate the temporal fluctuations of the bed-load sediment transport rate, the properties of individual grain motion by particle tracking, the statistical distributions of bed-load particle velocity, or the scaling properties of sediment diffusion). The scientific literature has also demonstrated a wide use of image-based techniques in hydromorphologic experiments at a variety of scales (e.g., [34–37], who considered, respectively: the propagation of dam-break waves on erodible beds; the creep process on planar sand beds; the coevolution of the flow and bed surfaces at a very fine scale; and the spatial distribution of the sediment transport rate in channels with a complex self-formed geometry). The realization of a hydromorphologic experiment requires the simultaneous control and/or measurement of a number of quantities: for example, the water discharge, a sediment feeding rate, the profile of the water surface, the profile of the sediment bed, the sediment transport rate, the granulometric distribution of the sediment material, the thickness of the active layer, among others. The number of quantities to be measured and the desired accuracy and precision depend, obviously, on the purpose of the study. An obvious question is: Can one design a comprehensive system to measure the relevant quantities automatically, thus reducing the manual effort needed to run and control the experiments? This paper presents an integrated measurement method that has been set up to be employed in laboratory experiments with the migration of a sediment front along a straight channel in nonequilibrium conditions (i.e., subject to an upstream sediment feeding that is different from the transport capacity of the flow). These experiments mimic the behavior of natural channels subject to an external sediment supply (for example, from the upstream part of a catchment or by a shallow landslide). Apart from the flow rate, all the measured quantities (sediment feeding rate, profiles of the water surface and sediment bed) are obtained from movies taken during the experiments with several action cameras. Data collection is thus automatized and does not require instrumentation pieces to be moved during the runs. In this way, the measurement methods presented in the paper contribute to minimizing the abovementioned shortcomings and the human effort related to performing hydromorphologic experiments in the laboratory. The image-processing procedures are first described; second, the application to a hydromorphologic experiment with downstream migration of an aggrading sediment front is documented; finally, potentialities and limitations of the experimental method are discussed. 2. Laboratory Facility and Measurement Methods The measurement methods presented here are, in principle, sufficiently general to be used in a variety of setups. However, since they were specifically developed with the intent of realizing experiments in an available flume, an essential description of the laboratory installation is provided first. It is confirmed that the described protocols can be exported to other facilities with (if any) small amendments.
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2.1. Facility The sample hydromorphologic experiment presented in this work was performed in a tilting flume with a rectangular cross section, located at the Mountain Hydraulics Lab of the Politecnico di Milano. Geosciences 2018, 8, x FOR PEER REVIEW 3 of 17 The flume was 5.2 m long, 0.3 m wide, and 0.45 m high. A 15-cm layer of loose sediment was placed onThe thesample channel bottom. The sediment made in of this Polyvinyl Chloride (PVC) hydromorphologic experimentwas presented work was performed in a cylindrical tilting particles with size = 3.8 mm, geometric standard deviation of the grain size distribution = 1.04, flume with a rectangular cross section, located at the Mountain Hydraulics Lab of the Politecnico di 3 and density = 1443 kg/m . m Water bymahigh. submerged pump flow rate Milano. The flume was 5.2 long, was 0.3 mdischarged wide, and 0.45 A 15-cm layer of and loosethe sediment was was placedby onanthe channel bottom.flowmeter. The sediment made Polyvinylwith Chloride (PVC)regulation cylindricalto set measured electromagnetic Thewas flume wasofequipped a tailgate particles with size flow = 3.8 mm, geometric standard deviation of the grain size distribution 1.04, a desired downstream depth. Nine piezometric probes were present along the bottom= of theand channel density = 1443 kg/m³. Water was discharged by a submerged pump and the flow rate was measured at mid-width and were connected with piezometers attached to the wall by means of an appropriate by an electromagnetic flowmeter. The flume was equipped with a tailgate regulation to set a desired panel. Finally, at the flume inlet, a sediment feeder was present, which was a compound of a vibrating downstream flow depth. Nine piezometric probes were present along the bottom of the channel at channel with a hopper above. More details about the experimental facility are given by [15]. mid-width and were connected with piezometers attached to the wall by means of an appropriate Experimental wereinlet, run aby: (i) setting a slope; (ii) carefully scraping the sediment layer to panel. Finally, attests the flume sediment feeder was present, which was a compound of a vibrating obtain an even bed; (iii) spraying the bed with water to avoid particle buoyancy due to surface tension channel with a hopper above. More details about the experimental facility are given by [15]. at water arrival; (iv) filling the flume watera at a very low rate scraping and with tailgatelayer regulation Experimental tests were run by:with (i) setting slope; (ii) carefully thethe sediment to obtainaan evenbackwater; bed; (iii) spraying the bed with to avoid particle buoyancy duepipes; to surface inducing high (v) removing airwater bubbles from the piezometer (vi)tension achieving at water arrival; (iv) filling the flume discharge with water and at a very low(vii) rate switching and with the regulation a quasi-uniform flow with prescribed depth; thetailgate sediment feeder on; inducing a high backwater; (v) removing air bubbles from the piezometer pipes; (vi) achieving a or (viii) letting a channel morphology develop, with the downstream propagation of an aggrading quasi-uniform flow with prescribed discharge and depth; (vii) switching the sediment feeder on; (viii) degrading sediment front (only one proof-of-concept experiment with bed aggradation is described in letting a channel morphology develop, with the downstream propagation of an aggrading or the paper); (ix) periodically refilling the sediment hopper; and (x) after some time, inverting the order degrading sediment front (only one proof-of-concept experiment with bed aggradation is described of the operations (reduce discharge, induce backwater, switch the vibrating channel off) to stop the in the paper); (ix) periodically refilling the sediment hopper; and (x) after some time, inverting the experiment. brief outline diddischarge, not include the image-based measurements, order of This the operations (reduce induce backwater, switch the vibrating which channelare off)described to stop in detailthe in the following sections. experiment. This brief outline did not include the image-based measurements, which are described in detail in the following sections.
2.2. Measurement of the Profile of the Free Surface 2.2. Measurement of the Profile of the Free Surface
The panel with the piezometers was filmed for the entire duration of the experiment with an The panel with the piezometers was filmed forHD the entire of MediaCom, the experiment with an UK), action camera (MEDIACOM SportCam XPRO 215 Wi-Fi,duration made by London, action camera (MEDIACOM SportCam HDframes Wi-Fi, made by MediaCom, London,placement UK), at at a resolution of 1920 × 1080 pixels and XPRO a rate215 of 30 per second. The camera is a resolution of 1920 × 1080 pixels and a rate of 30 frames per second. The camera placement is shown shown in Figure 1. This was similar to what was done in [15], where water profiles had to be extracted in Figure 1. This was similar to what was done in [15], where water profiles had to be extracted manually (and with high time consumption) by a user watching the movies after each experiment. manually (and with high time consumption) by a user watching the movies after each experiment. In In the present case, instead, black plastic spheres were present in the piezometer pipes, as is visible in the present case, instead, black plastic spheres were present in the piezometer pipes, as is visible in Figure 1. These spheres enabled ananautomatic performed of the profile offree the free Figure 1. These spheres enabled automatic measurement measurement totobebe performed of the profile of the surface by a suitable image-processing code. surface by a suitable image-processing code.
Figure 1. Camera shooting the piezometer panel.
Figure 1. Camera shooting the piezometer panel.
2.2.1. Removal of Lens Distortion, Region Choice, and Image Calibration
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2.2.1. Removal of Lens Distortion, Region Choice, and Image Calibration The acquired images were highly distorted by a short lens mounted on the camera (Figure 2a). The acquired images were highly distorted by a short lens mounted on the camera (Figure 2a). Therefore, image distortion had to be removed by applying a radial transformation to the coordinates Therefore, image distortion had to be removed by applying a radial transformation to the coordinates of of the pixels in the image, as described by [38]. Pixel radial coordinates r were transformed into new the pixels in the image, as described by [38]. Pixel radial coordinates r were transformed into new ones ones rt as follows: rt = r/(1 + k × r), where k is a scalar parameter that must be determined by the user rt as follows: rt = r/(1 + k × r), where k is a scalar parameter that must be determined by the user on on the basis of a sample image imposing that curved lines become rectilinear (for a visual the basis of a sample image imposing that curved lines become rectilinear (for a visual demonstration, demonstration, see a recent video article by [39]). It is better to repeat this operation after each see a recent video article by [39]). It is better to repeat this operation after each experiment, even though, experiment, even though, if the camera is always placed in the same position, the required image if the camera is always placed in the same position, the required image transformation will be always transformation will be always the same. Figure 2b presents the result of the image transformation the same. Figure 2b presents the result of the image transformation (where the image was also (where the image was also rotated by 0.9° in the clockwise direction and a value of k = 0.22 was used). rotated by 0.9◦ in the clockwise direction and a value of k = 0.22 was used). The removal of distortion The removal of distortion was successful in the center of the image, while some residual distortion was successful in the center of the image, while some residual distortion may have remained in the may have remained in the peripheral parts. However, a working region was chosen to be used for peripheral parts. However, a working region was chosen to be used for the following operations as the following operations as demonstrated in Figure 2c. In this way, most of the image was not used. demonstrated in Figure 2c. In this way, most of the image was not used. A closer position of the A closer position of the camera would reduce the image portion that must be discarded, but also camera would reduce the image portion that must be discarded, but also dramatically increase the dramatically increase the image distortion due to the lens. Therefore, the distance of the camera from image distortion due to the lens. Therefore, the distance of the camera from the target must be chosen the target must be chosen seeking a compromise between a good resolution and a manageable image seeking a compromise between a good resolution and a manageable image distortion. For the example in distortion. For the example in Figure 2, the distance of the camera from the target was approximately Figure 2, the distance of the camera from the target was approximately 40 cm. 40 cm.
(a)
(b) Figure 2. Cont.
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(c) Figure 2. (a) An acquired image of the piezometer pipes; (b) The image after distortion removal; (c) Figure 2. (a) An acquired image of the piezometer pipes; (b) The image after distortion removal; A working region within thethe image with thethe points (c)selected A selected working region within image with points(indicated (indicatedbybyhollow hollowcircles) circles)used usedto to determine the image calibration. determine the image calibration.
An image calibration must be determined in terms of a conversion factor between the pixel An image calibration must be determined in terms of a conversion factor between the pixel coordinates in the image and corresponding real elevations. To this purpose, the piezometers were coordinates in the image and corresponding real elevations. To this purpose, the piezometers were placed placed on a sheet of graph paper and a vertical ruler was also attached to the panel (visible in Figures on a sheet of graph paper and a vertical ruler was also attached to the panel (visible in Figures 1 and 2). 1 and 2). Pixel coordinates and corresponding graph paper values must be known for at least two Pixel coordinates and corresponding graph paper values must be known for at least two points in the points in the image. Based on that, real coordinates may be obtained from pixel ones for any point in image. Based on that, real coordinates may be obtained from pixel ones for any point in the image. the image. The above procedures were also required for all the measurements described in Sections 2.3 and 2.4, The above procedures were also required for all the measurements described in Sections 2.3 and but a similar description is not repeated as the methods are always the same (key changes in the 2.4, but a similar description is not repeated as the methods are always the same (key changes in the reference for only be mentioned). Furthermore, additional operations may be may required reference for calibration calibrationwill will only be mentioned). Furthermore, additional operations be for specific measurements. For the profile of the free surface, a correspondence between the values on required for specific measurements. For the profile of the free surface, a correspondence between the the graph and the actualand water be depths determined ad hoc preliminary tests. values on paper the graph paper thedepths actualmust water mustduring be determined during ad hoc preliminary tests. 2.2.2. Measurement of the Water Elevation in the Piezometer Pipes measurement based on identifying the position of a black sphere. The operations 2.2.2. The Measurement of thewas Water Elevation in the Piezometer Pipes were similar to those performed for the identification of a blob centroid in particle tracking. First, The measurement was based on identifying the position of a black sphere. The operations were an image like that in Figure 2c was divided in vertical slices, each of which corresponded to one similar to those performed for the identification of a blob centroid in particle tracking. First, an image piezometer (Figure 3a). The image was then converted into its corresponding negative one (Figure 3b), like that in Figure 2c was divided in vertical slices, each of which corresponded to one piezometer where the plastic sphere is white. Finally, the negative image was converted into a binary one based (Figure 3a). The image was then converted into its corresponding negative one (Figure 3b), where the on an intensity threshold. The binary image (Figure 3c) is almost completely black, with a white spot plastic sphere is white. Finally, the negative image was converted into a binary one based on an corresponding to the sphere. The vertical pixel coordinate of the centroid was determined as the intensity threshold. The binary image (Figure 3c) is almost completely black, with a white spot mean value of all the vertical coordinates for white pixels. This coordinate was finally converted into corresponding to the sphere. The vertical pixel coordinate of the centroid was determined as the mean an elevation value on the graph paper thanks to the image calibration procedure of Section 2.2.1. value of all the vertical coordinates for white pixels. This coordinate was finally converted into an The elevation of the center of a plastic sphere does not correspond to the water elevation that elevation value on the graph paper thanks to the image calibration procedure of Section 2.2.1. one would read looking at a water surface within the piezometer pipe. Therefore, the difference between these two elevations must be determined by ad hoc preliminary tests, to be later applied to the automatic measurements. In this work, this difference was found to be around 3 mm (consider that the inner diameter of the piezometer pipe was 8 mm and the diameter of a sphere was 6 mm). The uncertainty of the measurement can be quantified as large as ±2 mm. The measurement depends on only one user-defined parameter, that is, the intensity threshold required to convert a negative image into a binary one. Low values of the threshold do not enable a clear identification of the sphere (Figure 4); on the other hand, high values may split the sphere into several white spots, negatively affecting the determination of the blob centroid or even making the sphere no longer visible. For the piezometer pipe of Figure 3, a threshold of 0.77 times the intensity of white (equal to 255) was used (third-last panel of Figure 4 that is the same picture as in Figure 3c), that was an appropriate intermediate value returning a good shape of white blobs in the binary images. In general, this parameter must be tuned by the user and may change, depending mostly on the ambient light conditions.
plastic sphere is white. Finally, the negative image was converted into a binary one based on an intensity threshold. The binary image (Figure 3c) is almost completely black, with a white spot Geosciences 2018,to 8, the x FOR PEER REVIEW of 17 corresponding sphere. The vertical pixel coordinate of the centroid was determined as the6mean value of all the vertical coordinates for white pixels. This coordinate was finally converted into an Geosciences 8, 172 6 of 17 Figure 3.2018, (a) slice depicting oneto ofthe the image piezometers; (b) The procedure corresponding elevation value onAn theimage graph paper thanks calibration of negative Section image; 2.2.1. (c) The binary image with a white blob corresponding to the plastic sphere.
The elevation of the center of a plastic sphere does not correspond to the water elevation that one would read looking at a water surface within the piezometer pipe. Therefore, the difference between these two elevations must be determined by ad hoc preliminary tests, to be later applied to the automatic measurements. In this work, this difference was found to be around 3 mm (consider that the inner diameter of the piezometer pipe was 8 mm and the diameter of a sphere was 6 mm). The uncertainty of the measurement can be quantified as large as ±2 mm. The measurement depends on only one user-defined parameter, that is, the intensity threshold required to convert a negative image into a binary one. Low values of the threshold do not enable a clear identification of the sphere (Figure 4); on the other hand, high values may split the sphere into several white spots, negatively affecting the determination of the blob centroid or even making the sphere no longer visible. For the piezometer pipe of Figure 3, a threshold of 0.77 times the intensity of white (equal to 255) was used (third-last panel of Figure 4 that is the same picture as in Figure 3c), that was an appropriate intermediate value returning a good shape of white blobs in the binary images. In general, this parameter must be tuned by the user and may change, depending mostly on Figure 3. (a) An image slice depicting one of the piezometers; (b) The corresponding negative image; the ambient light conditions. (c) The binary image with a white blob corresponding to the plastic sphere.
Figure 4. The sphere identification by changing values of the intensity threshold used to convert
Figure 4. The sphere identification by changing values of the intensity threshold used to convert a a negative image into a binary one. Threshold values in this picture vary (left to right) every 0.1 from negative image into a binary one. Threshold values in this picture vary (left to right) every 0.1 from 0.47 to 0.97 times the white level of 255. 0.47 to 0.97 times the white level of 255.
2.3. Measurement of the Sediment Feeding Rate
2.3. Measurement of the Sediment Feeding Rate
The sediment feeding rate can be controlled by setting the opening of a sluice gate between
Thehopper sediment feeding rate channel, can be controlled by intensity setting the opening of a sluice gate between the the and the vibrating as well as the of vibration. Preliminary experiments hopper and the vibrating channel, between as well as intensity vibration. Preliminary experiments of [15] returned a correspondence thethe above settingsof and the obtained sediment feeding rate. of is also useful to continuously the feeding the sediment hydromorphologic [15] However, returned ait correspondence between themeasure above settings andrate theduring obtained feeding rate. experiments to ensure a correct valuemeasure is accounted in therate following To this However, it is also usefulthat to continuously the for feeding duringdata theanalysis. hydromorphologic aim, the vibrating channel was filmed from the top (Figure 5). An action camera (GoPro Hero 4 experiments to ensure that a correct value is accounted for in the following data analysis. ToBlack this aim, Edition, made by GoPro, San Mateo County, CA, USA) was used for the purpose, with a resolution the vibrating channel was filmed from the top (Figure 5). An action camera (GoPro Hero 4ofBlack 1920 × 1080 pixels and operating at 30 frames per second. Edition, made by GoPro, San Mateo County, CA, USA) was used for the purpose, with a resolution of 1920 × 1080 pixels and operating at 30 frames per second.
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Figure 5. 5. Camera the top. top.Another Anothercamera cameraisis depicted that shoots Figure Camerashooting shootingthe thevibrating vibrating channel channel from the depicted that shoots a sediment slide; this second camera was used in additional method that was later abandoned and aFigure sediment slide; this second camera was used an additional method that was later abandoned and 5. Camera shooting the vibrating channel from the top. Another camera is depicted that shoots is not described in this paper. is not described in this paper. a sediment slide; this second camera was used in an additional method that was later abandoned and is not described in this paper.
When the sediment was fed into the flume, a layer of particles moved along the vibrating channel When the sediment was fed into the flume, a layer of particles moved along the vibrating channel of theWhen feeder. The motionwas of these particles was visibly regular, thus suggesting use of thechannel surface the fed into the flume, a layer of particles moved along the the vibrating of the feeder. Thesediment motion of these particles was visibly regular, thus suggesting the use of the surface velocity of the sediment flow as an indicator of the feeding rate. The surface velocity was measured of the feeder. The motion of these was regular, thus suggesting the use of themeasured surface velocity of the sediment flow as anparticles indicator of visibly the feeding rate. The surface velocity was from theof movies collected by the upper camera in Figure 5. rate. Typical movie frames were like the one velocity the sediment flow as an indicator of the feeding The surface velocity was measured from the movies collected by converted the upper camera in Figure 5. Typical movie frames wereframes like thefor one shown inmovies Figure 6collected (that was grayscale). One 5. should remember that original from the by the uppertocamera in Figure Typical movie frames were like the one shown in Figure 6 (that was converted to grayscale). One should remember that original frames the camera must6then a rotation, removal of lens distortion, calibration, and choice a shown in Figure (thatundergo was converted to grayscale). One should remember that original framesoffor forworking the camera must then undergo a rotation, removal of lens distortion, calibration, and choice of region. to the description of Section 2.2, distortion, for the present images,and the choice calibration the camera mustCompared then undergo a rotation, removal of lens calibration, of a a working region. Compared to the description of Section 2.2, for the present images, the calibration was performed on theto known width of the channel ideally worked the graph working region.based Compared the description of vibrating Section 2.2, for thethat present images, theascalibration was performed based on the known width of the vibrating channel that ideally worked as the graph paper above. was performed based on the known width of the vibrating channel that ideally worked as the graph paper above. paper above.
Figure 6. An acquired image for the sediment vibrating channel. Sediment motion is from the bottom to the top of the image.image for the sediment vibrating channel. Sediment motion is from the bottom Figure 6. An acquired Figure 6. An acquired image for the sediment vibrating channel. Sediment motion is from the bottom to the top of the image. to the top of the image.
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The sediment velocity was measured using a particle image velocimetry (PIV) tool built upon the one originally proposed by [40]. The original method was developed to measure the velocity of grains in weak bed-load transport. In those conditions, only some of the sediment particles moved and needed to be highlighted for their velocity to be measured. Therefore, in [40], the PIV algorithm was applied to images obtained as differences between couples of the movie frames Geosciences 2018, 8, x FOR PEER REVIEW 8 of(it 17 was also discussed, among others, by [41] that still particles are filtered out by image subtraction while only the The sediment velocity was measured using a particle image velocimetry (PIV) tool built upon moving ones are visible in the difference images). The algorithm was extensively used for analysis the one originally proposed by [40]. The original method was developed to measure the velocity of of weak bed-load transport and sediment motion in local (e.g., [42–45]) and also for grains in weak bed-load transport. In those conditions, onlyscour some processes of the sediment particles moved and needed to be highlighted theirby velocity to be measured. Therefore, in [40], the PIV algorithm discharge measurements in a small for creek large-scale PIV [46]. In the present case, the sediment flow was appliedchannel to imagesinvolved obtained as between couples of the the algorithm movie frameswas (it was also directly along the vibrating alldifferences the surface grains, thus applied discussed, among others, by [41] that still particles are filtered out by image subtraction while only to the movie frames without computing any difference. The PIV tool requires an Eulerian grid of the moving ones are visible in the difference images). The algorithm was extensively used for analysis measuringofcells one transport velocityand vector to bemotion determined for each cell), and the maximum weak(for bed-load sediment in local scour processes (e.g., [42–45]) and also for values of discharge measurements in a small creek large-scale [46]. In the present the sediment the velocity components to be measured. Forbythe presentPIV situation, only one case, measuring cell was used flow along the vibrating channel involved all the surface grains, thus the algorithm was applied that corresponds to almost the entire surface area of the sediment flow. Additional parameters to be directly to the movie frames without computing any difference. The PIV tool requires an Eulerian provided are the “step” (the distance between two frames for a velocity measurement, that are frame j grid of measuring cells (for one velocity vector to be determined for each cell), and the maximum and frame values j + step) and the “jump” (if atovelocity measurement is performed using images jcell and j + step, of the velocity components be measured. For the present situation, only one measuring wasisused that corresponds to almost entire surface area +ofjump). the sediment flow. Additional the next one performed with images j +the jump and j + step For the present experiments, parameters be provided are the “step” (the distance betweenmeasurements two frames for and a velocity the jump was alwaystoequal to 1 to maximize the number of velocity thus the size of measurement, that are frame j and frame j + step) and the “jump” (if a velocity measurement is the samples, while the step was varied. In fact, for an intense feeding rate, the step can be maintained performed using images j and j + step, the next one is performed with images j + jump and j + step + at a value jump). of 1 because the sediment velocity is appreciable andtothe motion between two For the present experiments, the jump was always equal 1 tosediment maximize the number of velocity measurements and thus the size of the samples, while the step was varied. In fact, for an consecutive images is large enough for a reliable velocity measurement. By contrast, for a low sediment intense feeding rate,velocity the step is can be maintained at negligible a value of 1difference because thebetween sediment consecutive velocity is feeding rate, the sediment small, implying movie appreciable and the sediment motion between two consecutive images is large enough for a reliable frames. Invelocity these measurement. conditions,By the performance of the PIV tool may be poor and larger steps have contrast, for a low sediment feeding rate, the sediment velocity is small, to be usedimplying to increase the goodness the velocity measurement. goodconditions, practice, the the velocity negligible difference of between consecutive movie frames.As In athese performance of the PIVseveral tool maytimes be poor andvariable larger steps haveto toassess be usedthe to increase the goodness measurement was performed with steps sensitivity of the time-mean of the velocity measurement. Asthat a good practice, velocity measurement was performed several sediment velocity to the step value was used,the while trying to achieve an independency of the times with variable steps to assess the sensitivity of the time-mean sediment velocity to the step value time-meanthat surface velocity on the step. Figure 7 presents an example of velocity values obtained by was used, while trying to achieve an independency of the time-mean surface velocity on the step. changing the step. Uncertainty barsoffor the mean surface by velocity were computed as follows. Figure 7 presents an example velocity values obtained changing the step. Uncertainty bars for Given a the mean surface velocity were computed as follows. Given a succession of measured velocity values succession of measured velocity values (see Figure 8a), the corresponding autocorrelation function was 2 (see Figure 8a), the corresponding autocorrelation function was computed as A(τ) = [V’(i) × V’(I + of values, computed as A(τ) = [V’(i) × V’(I + τ)]av /σ , where τ is a lag expressed in terms of a number τ)]av/σ2, where τ is a lag expressed in terms of a number of values, V′ is a deviation of an instantaneous V0 is a deviation of an instantaneous velocity from the mean one, i is a value counter, subscript ‘av’ velocity from the mean one, i is a value counter, subscript ‘av’ denotes averaging, and σ is the denotes averaging, and σ is standard of the (see Figure The 95% confidence standard deviation of the the sample (see deviation Figure 8b). The 95%sample confidence limit for the8b). mean value was 0.5 , where determined as equal 2σ/(N/λ)0.5, where N is the of measured velocity and λ is the limit for the mean value wastodetermined as equal tonumber 2σ/(N/λ) N isvalues the number of measured integral scale (corresponding to a lag of 85(corresponding in Figure 8b). velocity values and λofiscorrelation the integral scale of correlation to a lag of 85 in Figure 8b).
(a)
(b)
Figure 7. Sensitivity of the time-mean surface sediment velocity to the step value for (a) low and (b)
Figure 7. Sensitivity the time-mean surface bars sediment velocity the step value forsize (a) low and high sedimentof feeding rate. In (a), uncertainty are displayed only to if larger than the marker (b) high sediment feeding rate. In (a), uncertainty bars are displayed only if larger than the marker size (threshold value for display = 0.002 cm/s). The arrow indicates a measurement that is further explored in Figure 8.
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(threshold value for display = 0.002 cm/s). The arrow indicates a measurement that is further explored Geosciences 8, x FOR PEER REVIEW Geosciences 2018,2018, 8, 172 in Figure 8.
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Figure leading toto thethe mean velocity marked with an Figure 8. 8. (a) (a) The Thesuccession successionofofmeasured measuredvelocity velocityvalues values leading mean velocity marked with Figure 8. (a) The succession of measured velocity values leading to the mean velocity marked with an arrow in Figure 9; (b) thethe corresponding autocorrelation an arrow in Figure 9; (b) corresponding autocorrelationfunction function(lag (lagexpressed expressedin interms terms of of number number arrow in Figure 9; (b) the corresponding autocorrelation function (lag expressed in terms of number of values). of values). of values).
The correspondence between the time-mean surface sediment velocity and sediment feeding correspondence between time-mean surfacesediment sedimentvelocity velocity and and sediment sediment feeding The The correspondence between thethe time-mean surface feeding rate rate rate (that waswas determined, also ininthis case, during preliminary preliminaryexperiments experiments by weighing determined, also this case, during by weighing the the (that was (that determined, also in this case, during preliminary experiments by weighing the sediment fed sediment fed fed in ainprescribed time) in Figure Figure9.9.The Theuncertainty uncertainty for mean sediment sediment a prescribed time)isisdepicted depicted in barsbars for mean sediment in a prescribed time) is depicted in Figure 9. The uncertainty bars for mean sediment velocity values velocity values were determined Uncertainty bounds sediment feeding velocity values were determinedasasdescribed described above. above. Uncertainty bounds for for the the sediment feeding were determined as described above. Uncertainty bounds for the sediment feeding rate depended on rate depended on on thethe precision stopwatchused used preliminary but were rate depended precisionofofthe thescale scale and and stopwatch in in thethe preliminary tests,tests, but were the precision of the scale andAstopwatch used inwas the preliminarybased tests, but were always negligibly small. always negligibly small. transferfunction function onon which the the mean sediment always negligibly small. A transfer was determined, determined, based which mean sediment 3 /s) is equal A transfer function was determined, based on which the mean sediment feeding rate (in m 3 −7 2 −6 feeding (in3/s) m /s) is equal 7.50××10 10−7 ×× V V2 + + 7.00 V is mean sediment surface feeding raterate (in m is equal toto7.50 7.00××10 10−6× ×V,V,where where Vthe is the mean sediment surface 7 × V 2 + 7.00 × 10−6 × V, where V is the mean sediment surface velocity. The uncertainty to 7.50 × 10−The velocity. uncertainty in the estimation of the sediment feeding rate from the surface velocity was velocity. The uncertainty in the estimation of the sediment feeding rate from the surface velocity was in thequantified estimation of the sediment feeding rate from theline surface velocity was quantified the rms based rms deviation of the trend 9 from the measuredbased values,onand quantified based on on thethe rms deviation of the trend lineininFigure Figure 9 from the measured values, and 3/s. was equal ±4.32 line × −710−7in3mFigure deviation of thetotrend 9 from the measured values, and was equal to ±4.32 × 10−7 m3 /s. was equal to ±4.32 × 10 m /s. 8 8.E-05 × 10−5
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Figure 9. Relationship betweenthe thetime-mean time-mean values of sediment velocity and and Figure 9. Relationship between valuesvelocity ofthe thesediment sediment surface sediment velocity Surface (cm/s) surface feeding rate. Uncertainty bars for sediment velocity are displayed only if larger than the marker size feeding rate. Uncertainty bars for sediment velocity are displayed only if larger than the marker size (threshold value display = 0.04 cm/s). Uncertainty for sediment feeding rate are never visible. (threshold value for for display = 0.04 Uncertainty bars for sediment feeding rate are never visible. Figure 9. Relationship between thecm/s). time-mean valuesbars of the sediment surface sediment velocity and
feeding rate. Uncertainty bars for sediment velocity are displayed only if larger than the marker size 2.4. Measurement of the Bed Profile 2.4. Measurement of the Profile (threshold value for Bed display = 0.04 cm/s). Uncertainty bars for sediment feeding rate are never visible. For this measurement, three MEDIACOM SportCam XPRO 215 HD Wi-Fi action cameras were For this measurement, three MEDIACOM 215 HD Wi-Fi action placed besides shooting the lateral wallSportCam (Figure 10).XPRO Each camera was mounted on acameras support were 2.4. Measurement ofthe theflume Bed Profile placed besides theaflume shooting wall of (Figure 10). Each camerathe was mounted on a support and captured 1.3-m portion of the the lateral total length the channel. Therefore, instantaneous profile
For this measurement, three MEDIACOM XPROTherefore, 215 HD Wi-Fi action camerasprofile were and captured a 1.3-m portion of the total lengthSportCam of the channel. the instantaneous placed besides the flume shooting the lateral wall (Figure 10). Each camera was mounted on a support of the near-wall bed elevation was measured along 3.9 m of the flume. A sample image is given in and captured a 1.3-m portion of the total length of above the channel. Therefore, Figure 11a. This picture was obtained as described for other images, the the instantaneous only differenceprofile being again in the image calibration. The latter was determined based on vertical rulers attached to the
of the near-wall bed elevation was measured along 3.9 m of the flume. A sample image is given in Figure 11a. This picture was obtained as described above for other images, the only difference being Geosciences 2018, 8, x FOR PEER REVIEW 10 of 17 again in the image calibration. The latter was determined based on vertical rulers attached to the wall and the known length m) ofwas each flangedalong flume Since theAsediment bed isseemed easily of the near-wall bed (1.3 elevation measured 3.9reach. m of the flume. sample image given in Geosciences 2018, 8, 172 10 of 17 Figure 11a. This picture was obtained as described above for other images, the only difference being distinguishable from the water layer in the obtained images, an edge-detection method was used to againthe in the imageofcalibration. The latter determined based on vertical rulers attachedin to the wall identify profile the sediment bed.was Similar techniques have been employed a variety of and the the known length m) flume reach. Since the the sediment bedwas seemed easilyto wall and known length(1.3 (1.3 m)ofofeach eachflanged flanged flume reach. Since sediment bed seemed easily applications (e.g., [47,48]). A Sobel edge-detection routine (built-in in MatLab) applied the distinguishable from waterlayer layer in in the images, an edge-detection method was used to distinguishable from thethe water the obtained obtained images, edge-detection method was frames, obtaining binary images that were white or black an where an edge was found orused not, identify the profile of the sediment bed. Similar techniques have been employed in a variety of to identify thebased profile the sediment bed. gradients. Similar techniques have been in a variety of respectively, onof the image intensity The expectation wasemployed that the water–sediment applications (e.g., [47,48]). A Sobel edge-detection routine (built-in in MatLab) was applied to the applications (e.g., [47,48]). A Sobeland edge-detection routine (built-in in MatLab) waswas applied to the interface presents a large contrast be white unambiguously identified [34]. This indeed frames, obtaining binary images thatcan were or black where an edge was found or not, frames, obtaining binary images that were white or black where an edge was found or not, respectively, case, respectively, but unfortunately, the contours of the individual sediment particles as evident as the edges based on the image intensity gradients. The expectation was were that the water–sediment based on the image intensity gradients. The expectation was that the water–sediment interface identifying the sediment bed, as shown by Figure 11b. Therefore, additional image analysis interface presents a large contrast and can be unambiguously identified [34]. This was indeed presents the was aperformed large contrast and can be unambiguously identified [34]. This was indeed the case, but unfortunately, asunfortunately, follows. case, but the contours of the individual sediment particles were as evident as the edges the contours of the sediment particles were11b. as evident as the edges identifying the sediment identifying the individual sediment bed, as shown by Figure Therefore, additional image analysis was performed as follows. bed, as shown by Figure 11b. Therefore, additional image analysis was performed as follows.
Figure 10. The cameras themeasurement measurement of of the (flume view from upstream). Figure cameras forfor the from upstream). 10. The the bed bedelevation elevation (flume view from upstream).
(a)
(a)
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(b) Figure 11. (a) A movie frame; (b) The image obtained from the Sobel edge-detection method. Figure 11. (a) A movie frame; (b) The image obtained from the Sobel edge-detection method. Figure 11. (a) A movie frame; (b)interface The image obtained fromand the Sobel edge-detection method. The measurement relates the between water sediment to a transition from a negligible to a significant percentage of white pixels in an interrogation area of binary images like the The measurement relates the interface between water and sediment to a transition from a one measurement in Figure 11b. relates If one the usesinterface an interrogation area like the white rectangle in thefrom image, the The between water and sediment to a transition a negligible negligible to a significant percentage of white pixels interrogation of binary images like the percentage of white pixels in the area will be zeroin oran negligible. Now, area shifting progressively the to a significant percentage of white in an interrogation area of binary images like image, the onethe in one in Figuredownwards, 11b. If onethe uses an pixels interrogation areawill like the white in the rectangle percentage of white pixels initially remainrectangle low, then increase when Figure 11b. If one uses an interrogation area like the white rectangle in the image, the percentage percentage of white pixels in the area will be zerosome or negligible. Now,ashifting the sediment bed is encountered, and finally reach plateau assuming uniform progressively concentration the of white pixels in the area will be zero or negligible. Now, shifting progressively the rectangle of white pixels in thethe sediment layer.of A white samplepixels vertical profile of theremain percentage white pixels iswhen rectangle downwards, percentage will initially low,ofthen increase downwards, percentage white pixels will initially remain low, then increase when the sediment shown inthe Figure 12. Two of criteria have been tested to recognize theassuming bed elevation based on vertical the sediment bed is encountered, and finally reach some plateau a uniform concentration
bed is encountered, and finally reach plateau assuming a uniform concentration white pixels of white pixels in the sediment layer.some A sample vertical profile of the percentage of of white pixels is in the sediment layer. A sample vertical profile of the percentage of white pixels is shown in Figure 12. shown in Figure 12. Two criteria have been tested to recognize the bed elevation based on vertical Two criteria have been tested to recognize the bed elevation based on vertical profiles like the presented one. The first criterion was related with finding the first big change in the percentage as the vertical profile travelled from the top to the bottom of the image. The second one was instead related with splitting the percentage profile into different reaches with similar slope and locating the slope change
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Pixel row from image top Pixel row from image top
profiles like the presented one. The first criterion was related with finding the first big change in the profiles like the presented one. The first criterion was related with finding the first big change in the percentage as the vertical profile travelled from the top to the bottom of the image. The second one Geosciences 2018, 172 vertical profile travelled from the top to the bottom of the image. The second 11 of 17 percentage as8,the one was instead related with splitting the percentage profile into different reaches with similar slope and was instead related with splitting the percentage profile into different reaches with similar slope and locating the slope change towards a much lower slope value. The division of the profile into reaches locating the slope change towards a much lower slope value. The division of the profile into reaches with similar slope can be performed using the MatLab built-ininto command The second towards a much lower slope value. The division of the profile reaches findchangepts. with similar slope can be with similar slope can be performed using the MatLab built-in command findchangepts. The second method worked and was thuscommand preferred.findchangepts. An example The of profile also depicted in performed using better the MatLab built-in secondsplitting method is worked better and method worked better and was thus preferred. An example of profile splitting is also depicted in Figure 13.preferred. The bed elevation wasofidentified with theischange of slopein(at a vertical coordinate of 57 was thus An example profile splitting also depicted Figure 13. The bed elevation Figure 13. The bed elevation was identified with the change of slope (at a vertical coordinate of 57 pixels from the topthe of change the image) minus of the height of searchfrom area. the was was identified with of slope (at ahalf vertical coordinate of the 57 pixels theOnce top of thejob image) pixels from the top of the image) minus half of the height of the search area. Once the job was completed oneheight vertical the area. interrogation area backfor to one the top and profile, shifted minus half for of the ofprofile, the search Once the jobwas wasmoved completed vertical completed for one vertical profile, the interrogation area was moved back to the top and shifted horizontally for a area new vertical profile of percentage to be determined. The process wasvertical stoppedprofile when the interrogation was moved back to the top and shifted horizontally for a new horizontally for a new vertical profile of percentage to be determined. The process was stopped when thepercentage entire image been explored. bed profile obtained fromthe theentire picture in Figure 11 is explored. depicted of to had be determined. TheThe process was stopped when image had been the entire image had been explored. The bed profile obtained from the picture in Figure 11 is depicted in Figure 13. After the bed profile had been obtained for each camera, the13. three pieces of information The bed profile obtained from the picture in Figure 11 is depicted in Figure After the bed profile had in Figure 13. After the bed profile had been obtained for each camera, the three pieces of information were obtained linked tofor each other to determine profile along 3.9 m were of channel. been each camera, the threethe pieces of information linked to each other to determine were linked to each other to determine the profile along 3.9 m of channel. the profile along 3.9 m of channel. 0 0
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Figure 12. 12. A sample vertical profile of percentage of white pixels in a search area (white circles) and Figure Figure 12. A sample vertical profile of percentage of white pixels in a search area (white circles) and the profile reaches reaches based based on on slope slope values values (gray (gray dashed dashed line). line). the profile reaches based on slope values (gray dashed line).
Figure 13. Detected Detected bed bed profile, with the arrow indicating the bed elevation determined from the Figure 13. Detected bed profile, with the arrow indicating the bed elevation determined from the vertical profile of Figure Figure 13. 13. vertical profile of Figure 13.
The parameters parameters for this this method were were the threshold threshold for application application of the the Sobel edge-detection edge-detection The The parameters for for this method method were the the threshold for for application of of the Sobel Sobel edge-detection method, the size of interrogation areas like the one in Figure 11b, the vertical and horizontal shifts of method, the size size of of interrogation interrogationareas areaslike likethe theone oneininFigure Figure11b, 11b,the the vertical and horizontal shifts method, the vertical and horizontal shifts of thethe search rectangle, andand the threshold for application of findchangepts to splittothe percentage profile of search rectangle, the threshold for application of findchangepts split the percentage the search rectangle, and the threshold for application of findchangepts to split the percentage profile into reaches. Used values the example in Figures were: 0.09 for the threshold of the Sobel profile into reaches. Usedfor values for the example in 11–13 Figures 11–13 were: 0.09 for the threshold of into reaches. Used values for the example in Figures 11–13 were: 0.09 for the threshold of the Sobel edge-detection method; 30 × 20 pixels for the size of the interrogation area; 1 and 5 pixels for the the Sobel edge-detection method; 30 × 20 pixels for the size of the interrogation area; 1 and 5 pixels edge-detection method; 30 × 20 pixels for the size of the interrogation area; 1 and 5 pixels for the vertical and horizontal shifts of the of search rectangle, respectively; and 0.008 for the for for the vertical and horizontal shifts the search rectangle, respectively; for threshold the threshold vertical and horizontal shifts of the search rectangle, respectively; and and 0.0080.008 for the threshold for application of the findchangepts function. The measurement uncertainty was quantified as equal to ±5 for application of the findchangepts function. measurement uncertainty was quantified as equal application of the findchangepts function. The The measurement uncertainty was quantified as equal to ±5 mm. to ±5 mm. mm. 3. Application 3. Application to to an an Experiment Experiment 3. Application to an Experiment results for an experiment with a channel slope of 0.004, flow rate This section briefly presents the results This33section briefly presents the results for an experiment with a channel slope of 0.004, flow rate of 0.01 0.01 m m /s, /s, and water depth of 0.065 m.AAcorresponding correspondingequilibrium equilibriumsediment sediment transport capacity and of 0.065 m. transport capacity of 3/s, and water depth of 0.065 m. A corresponding equilibrium sediment transport capacity of 0.01 m −5 3 − 5 3 of 2.9 × 10 m m /s /s was determined after the experiment by collecting the sediment discharged through 2.9 × 10 was determined after the experiment by collecting sediment of 2.9 × 10−5 m3/s was determined after the experiment by collecting the sediment discharged through storage tank. tank. the channel outlet into a downstream storage the channel outlet into a downstream storage tank. Figure 14 depicts the temporal evolution of the sediment surface velocity measured as described in Section 2.3. The mean velocity value was 4.1 cm/s, that could be converted into a sediment feeding
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in Section 2.3. The mean velocity value was 4.1 cm/s, that could be converted into a sediment feeding Figure 14−5depicts the temporal evolution of the sediment surface velocity measured as described Geosciences 172 12 of 17 3/s thanks to the transfer function found from Figure 9. The sediment feeding rate of 4.1 ×2018, 10 8,m rate in Section 2.3. The mean velocity value was 4.1 cm/s, that could be converted into a sediment feeding was thus 40% larger than the sediment transport capacity of the flow and able to induce a mild 3/s thanks to the transfer function found from Figure 9. The sediment feeding rate rate of 4.1 × 10−5 m aggradation the−5bed. rate of 4.1 of × 10 m3 /s thanks to the transfer function found from Figure 9. The sediment feeding was thus 40% larger than the sediment transport capacity of the flow and able to induce a mild rate was thus 40% larger than the sediment transport capacity of the flow and able to induce a mild aggradation of the bed. aggradation of5the bed. Surface velocity Surface velocity (cm/s)(cm/s)
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Time (s) Figure 14. Temporal evolution of the sediment velocity along the vibrating channel for the sample Figure 14.presented. Temporal evolution of the sediment velocity along the vibrating channel for the sample experiment Figure 14. Temporal evolution of the sediment velocity along the vibrating channel for the sample experiment presented. experiment presented. The morphologic evolution of the channel was surveyed for 17 min, after which the experiment
was stopped due to limited availability sediment be fed into the flume. channel bed profile The morphologic evolution of the of channel was to surveyed for 17 min, afterThe which the experiment The morphologic evolution of the channel was surveyed for 17 min, after which the experiment stopped tothe limited availability of sediment to beelevation fed into the The channel profile in at was several timesdue and temporal evolution of the bed at flume. some locations arebed depicted was stoppedtimes due to limited availability of sediment to be fed into at thesome flume. The channel bed profile at several the temporal of theelevation bed elevation locations depicted in Figures 15 and 16, and respectively. The evolution water surface is also presented in the are plots. The initial at Figures several 15 times and the temporal evolution of the bed elevation at some locations areThe depicted in and 16, respectively. The water surface elevation is also presented in the plots. initial bump in the time evolution of the water surface elevation in Figure 16 corresponds to the initial Figures 15 andtime 16, evolution respectively. water surface elevation is also presented intothe The initial bump regulation in the of the theThe water surface Figure 16 corresponds theplots. initial channel channel (increasing flow rate andelevation loweringinthe tailwater condition). Some irregularities bump in the time evolution of the water surface elevation in Figure 16 corresponds to the initial (increasing the flow rate and the due tailwater condition). Someto irregularities are still areregulation still present in the bed profiles that lowering are mostly to stickers attached the flume wall (see channel regulation (increasing the flow ratedue andto lowering the tailwater condition). Some irregularities present in the bed profiles that are mostly stickers attached to the flume wall (see Figure 13). Figure 13). These mistakes can be manually removed for a subset of profiles used in following areThese still mistakes present incan the bed profiles that arefor mostly due stickers attached to theanalysis flume wall (see manually removed a subset ofto profiles following of the analysis of the results.beHowever, the measurements enabled theused key in features of the morphologic Figure 13).However, These mistakes can be manually for a subset of profiles used in following results. the measurements enabled removed the key features of the morphologic evolution to be evolution to be observed, with the simultaneous downstream propagation of an aggrading sediment analysis of the results. However, the measurements enabled the key features of the morphologic observed, with the simultaneous downstream propagation of an aggrading sediment front (at 1800 and front (at 1800 and 2340 mm for times of 80 and 90 s, respectively) and dunes. Arguments on how the 2340 mmtofor of 80 and s, respectively) and dunes. Arguments on howofthe morphological evolution betimes observed, with90the simultaneous downstream propagation an two aggrading sediment two morphological features can/should be separated were given, for example, by [11,15], but are features can/should separated were by [11,15], are beyond the scope of thethe front (at 1800 and 2340bemm for times of given, 80 andfor 90example, s, respectively) andbut dunes. Arguments on how beyond the scope of the present paper. present paper. two morphological features can/should be separated were given, for example, by [11,15], but are beyond the scope of the present paper. 150
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3900 2600 1300 3900 2600 1300 Distance along the flume (mm) Distance along the flume (mm) (c) (c) Figure 15. Longitudinal profiles of the water surface (dashed) and bed elevation (continuous) at (a) Figure 15. Longitudinal profiles of the water surface (dashed) and elevation (continuous)at at(a) (a) 70 s, Figure of the water surface (dashed) andbed bed elevation 70 s, (b)15. 80 Longitudinal s, and (c) 90 profiles s from the beginning of sediment feeding for the sample(continuous) experiment. The (b) 80 s, and (c) 90 s from the beginning of sediment feeding for the sample experiment. The original 70 s, (b)bed 80 s,elevation and (c) 90 s from the beginning of line. sediment theright sample experiment. The indicated by the gray Note feeding the flowfor from to left, consistently original z0 is bed with elevation z10, is11indicated by the gray line. fromfrom right to to left, 0elevation by the grayNote line. the Noteflow the flow right left,consistently consistently with original bed z0 is indicated Figure and 13. Figures 11 and with10, Figure 10, 13. 11 and 13.
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(c) Figure 16. Temporal evolution of the water surface (dashed) and bed elevation (continuous) at (a) 1.5
Figure 16. Temporal evolution of the water surface (dashed) and bed elevation (continuous) at (a) 1.5 m; m; (b) 1.75 m and (c) 2 m from the flume inlet for the sample experiment. The original bed elevation (b) 1.75 m and (c) 2 m from the flume inlet for the sample experiment. The original bed elevation z0 is z0 is indicated by the gray line. indicated by the gray line.
4. Discussion and Conclusions
4. Discussion and Conclusions
A novel system was designed and setup for the simultaneous measurement of several key quantities in hydromorphologic laboratory experiments. The measuredmeasurement quantities involved both key A novel system was designed and setup for the simultaneous of several experimental controls (with a continuous measurement of the sediment feeding rate and the water quantities in hydromorphologic laboratory experiments. The measured quantities involved both depth at the downstream end of the flume) and the morphologic evolution of the flume subject to experimental controls (with a continuous measurement of the sediment feeding rate and the water nonequilibrium sediment feeding (continuous measurement of the longitudinal profiles of the depth at the downstream end of the flume) and the morphologic evolution of the flume subject to sediment bed and of the coevolving free surface). The system is based on using five action cameras nonequilibrium sediment feeding (continuous measurement thefor longitudinal profiles ofused the sediment shooting different components of the experimental facility: of one the vibrating channel for bed and of the coevolving surface). The system is based onthree using cameras shooting sediment feeding, one forfree a wall-attached piezometer panel, and forfive threeaction consecutive reaches different thecameras experimental facility: for the vibrating channel for sediment of thecomponents flume. Onceof the are triggered, theone researcher can concentrate onlyused on carefully feeding, one for piezometer panel, and three foracquired three consecutive reaches of the operating thea wall-attached run, while all the information is continuously and the measurements areflume. performed in the aftermath of the experiment. In this sense, theon measuring meetsthe a run, Onceactually the cameras are triggered, the researcher can concentrate only carefullysystem operating of reducing the human effort related with performing the experiments.are actually performed in the whilegoal all the information is continuously acquired and the measurements image-analysis routines employed in this work are,meets in general, from similar aftermathWhile of thethe experiment. In this sense, the measuring system a goalderived of reducing the human ones developed in earlier works, the added value of the present study is an efficient integration of effort related with performing the experiments. existing processing tools into a comprehensive measuring system. Notably, the latter is relatively low While the image-analysis routines employed in this work are, in general, derived from similar cost in spite of several cameras used. Industrial cameras are not involved and, in the field of cameras onesfor developed earlier works, the and added of the present is anmeasurements efficient integration of daily use, in a combination of more less value expensive ones is set instudy place. The of the existing processing tools into a comprehensive measuring system. Notably, the latter is relatively profile of the free surface and of the sediment bed elevation can be performed with cheaper tools, low cost spite of several cameras used. Industrial not involvedcamera. and, inThe thekey field of whileinthe measurements of the sediment feeding ratecameras needs a are more expensive cameras for daily use, athe combination ofmeasurements more and lessisexpensive ones is set in place. Theframe measurements difference between two kinds of that the former requires only one to be performed, the latter applies velocimetry to two frames are, in some cases, of the profile ofwhile the free surface andparticle of the image sediment bed elevation can bethat performed with cheaper separated by a very short time interval. Therefore, for the measurement of the sediment feeding rate, tools, while the measurements of the sediment feeding rate needs a more expensive camera. The key it is important the kinds cameras acquisition cards respect the imposed sampling difference between that the two of and measurements is thatreliably the former requires only one frame to be frequency (in the present case, 30 fps). performed, while the latter applies particle image velocimetry to two frames that are, in some cases, Automatic measurements dramatically reduce the time needed to collect data. Moreover, separated by a very short time interval. Therefore, for the measurement of the sediment feeding rate, avoiding manual readings also virtually eliminates the dependence of the measured values on who it is important therespect, cameras and acquisition cardsthat reliably respectofthe imposed sampling frequency takes them.that In this it must be acknowledged application image processing to physical (in the present case, fps). measurements is 30 never exempt from some intervention by the user. In this work, several parameters Automatic measurements dramatically reduce the timeby needed to collect Moreover, avoiding used to process the images need some expert-based setting the user. All thedata. involved parameters manual also virtually eliminates the dependence ofinthe values on who havereadings been mentioned in the paper, providing the values used themeasured present work. However, thetakes most them. suitable parameter values may change that for different conditions (principally, theto lighting conditions in In this respect, it must be acknowledged application of image processing physical measurements the laboratory where the experiments are performed and the sediment particles used for the sediment is never exempt from some intervention by the user. In this work, several parameters used to process layer). On the other once set, the image-processing toolAll works in the same way for the been the images need somehand, expert-based setting by the user. theexactly involved parameters have entire duration of the experiment and the measurements are fully repeatable, a condition that is not mentioned in the paper, providing the values used in the present work. However, the most suitable met by a human.
parameter values may change for different conditions (principally, the lighting conditions in the laboratory where the experiments are performed and the sediment particles used for the sediment layer). On the other hand, once set, the image-processing tool works exactly in the same way for the
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entire duration of the experiment and the measurements are fully repeatable, a condition that is not met by a human. Even though the system used five cameras at the same time, the issue of camera synchronization was not addressed in this paper. The importance of this aspect depends on the time scales of the processes under investigation. In some earlier works related to sediment transport mechanics and interaction with a turbulent flow [39,49], the cameras were synchronized (possibly, also with other instruments) using suitable methods such as correlating spikes in temporal signals or by switching the room lights on and off. In the present study, instead, the time scales of the sediment front migration are relatively long and, therefore, the cameras could be synchronized up to a precision of 1 s using a stopwatch and marking on a paper the time at which the shooting was triggered for each camera. As a future prospect, after having the measurement system set, it will be extensively used for hydromorphologic experiments with downstream migration of aggrading and degrading sediment fronts. Two main issues that will be considered analyzing the data are the following ones. First, the identification of a sediment front may be easier or more difficult for translating or dispersing fronts, respectively. The scientific community has not yet achieved a consensus on which dynamics should prevail in different yield and transport conditions (see, for example, [8,11,13,50]). Another issue that will be considered in follow-up development of the measurement technique is the effect of three-dimensional bed forms. Since in the present case the sediment bed elevation is surveyed from the side, an assumption that the sediment front can be represented well by these measurements shall be appropriately verified. Author Contributions: A.R. conceived and designed the experimental configuration, and developed the measurement methods for sediment feeding rate and water surface; A.R. and B.Z. developed the measurement method for the bed elevation and performed the sample experiment; B.Z. produced the results for the experiment; A.R. wrote the paper. Acknowledgments: The authors are grateful to Michael Nones for inviting the present paper as a feature paper in Geosciences. This research was partially supported by Fondazione Cariplo (Italy) through the project entitled Sustainable MAnagement of sediment transpoRT in responSE to climate change conditions (SMART-SED). Matteo Zucchi is gratefully acknowledged for contributing to the experiment through his thesis in Civil Engineering for Risk Mitigation at the Politecnico di Milano. Thought-provoking comments and suggestions by three anonymous Reviewers enabled the paper to be considerably improved. Conflicts of Interest: The authors declare no conflict of interest.
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