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Keywords:

  • large-scale particle image velocimetry;
  • image velocimetry;
  • remote measurements;
  • river hydraulics

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. LSPIV System Components
  5. 3. Measurement Outcomes and Accuracy
  6. 4. LSPIV Evolution
  7. 5. Implementation Examples
  8. 6. Lessons Learned and Future Research
  9. 7. Conclusions
  10. Acknowledgments
  11. References
  12. Supporting Information

[1] Large-scale particle image velocimetry (LSPIV) is a nonintrusive approach to measure velocities at the free surface of a water body. The raw LSPIV results are instantaneous water surface velocity fields, spanning flow areas up to hundreds of square meters. Measurements conducted in typical conditions in conjunction with appropriate selections of parameters for image processing resulted in mean velocity errors of less than 3.5%. The current article reviews the background of LSPIV and the work of three research teams spanning over a decade. Implementation examples using various LSPIV configurations are then described to illustrate the capability of the technique to characterize spatially distributed two- and three-dimensional flow kinematic features that can be related to important morphologic and hydrodynamic aspects of natural rivers. Finally, results and a critique of research methods are discussed to encourage LSPIV use and to improve its capabilities to collect field data needed to better understand complex geomorphic, hydrologic, and ecologic river processes and interactions under normal and extreme conditions.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. LSPIV System Components
  5. 3. Measurement Outcomes and Accuracy
  6. 4. LSPIV Evolution
  7. 5. Implementation Examples
  8. 6. Lessons Learned and Future Research
  9. 7. Conclusions
  10. Acknowledgments
  11. References
  12. Supporting Information

[2] Flow discharge is the most common riverine hydraulic measurement being the primary parameter for characterizing river dynamics. Until recently, discharge measurements have relied on mechanical velocimeters. These instruments, applied extensively on a global scale produced a vast amount of data that still serves as reference for more modern river flow measuring instruments. The advent of a new generation of acoustic, radar, and image-based velocity measurement methods in the 1980s, has improved hydrologic and hydraulic measurement efficiency, performance, and safety. The new instruments are fast, automated and computerized. They have no moving parts, require fewer calibrations, and are less intrusive than their predecessors. The superior efficiency of this new generation of instruments extends conventional applications beyond discharge measurements, such as documenting river hydrodynamic features, previously obtained only in laboratory conditions [Dinehart and Burau, 2005].

[3] This technological development is timely as new concerns with rivers warrant additional data. Issues of channel reconfiguration, bank stabilization, floodplain reconnection, in-stream habitat improvement and dam removal require high-resolution estimates of flow velocity, duration, timing, and rate of change of total stream discharge [Poff et al., 1997]. For example, stream restoration projects demand spatial characterization of flow distribution and regimes within the river reach subjected to retrofitting. Inferences on ecological habitats' health conditions can be made by examining velocity gradients that promote dynamically stable channel morphologies. These new data needs are expensive or impossible to obtain with conventional techniques.

[4] Currently, the new generation of instruments is replacing mechanical instruments at a considerable rate. Most notable is the steady spread of acoustic velocimeters, significantly assisted by aggressive industrial grade production and distribution [Christensen and Herrick, 1982]. Radar-based techniques have also advanced with the direct support of the USGS's Hydro 21 Committee [Costa et al., 2000]. Image-based techniques are less frequently used for field work in the hydrologic community, despite early attention received from the same USGS committee [Melcher et al., 1999]. The acoustic methods measure along verticals in the water body and the latter two techniques measure at the stream free surface. These instruments are frequently characterized as nonintrusive, though “quasi-nonintrusive” may be a more accurate description. Indeed, the instruments measure along lines or on surfaces away from their physical location. However, acoustic-based instruments require deployment of a probe under the water surface and sound wave-scattering particles suspended in the water to be measured while image and radar-based tools require distinguishable tracer or patterns on the free surface to capture the underlying water body movement. Extent of intrusiveness for all newer instruments is, however, minimal compared to conventional instruments.

[5] Historically, the image-based technique was the first and continues to serve as an important tool for flow investigation. The intricate flow patterns depicted in Leonardo da Vinci's sketches suggest that the human eye can sense important qualitative aspects of a river's flow. Transfiguring these visual impressions into quantitative river flow information, however, has only recently become possible. Developments over the last three decades in optics, lasers, electronics, and computer-related technologies have facilitated implementation of image-based techniques for flow visualization and quantitative measurements in laboratory studies. The first image-based quantitative instruments, generically labeled as particle image velocimetry (PIV), have greatly enhanced measurement techniques of instantaneous velocity vectors in a variety of laboratory flows [e.g., Adrian, 1991; Raffel et al., 1998]. Despite the increased PIV popularity in laboratories, image velocimetry was rarely applied to natural-scale flows. Some early attempts to investigate natural flows with image velocimetry were those of Leese et al. [1971] using satellite imagery to track atmospheric cloud movements, Collins and Emery [1988] sea ice, and Holland et al. [1997] for swash flow quantifications in coastal areas.

[6] The first image velocimetry measurements in rivers were made in Japan in the mid-1990s [Fujita and Komura, 1994; Aya et al., 1995; Fujita et al., 1997]. The technique has subsequently undergone continuous development and testing in anticipation of hydraulic applications [Muste et al., 2004a]. As most of the measurements were taken over surfaces much larger than those in traditional PIV, the technique was dubbed large-scale PIV (LSPIV). The present paper introduces LSPIV to the hydrologic community by briefly reviewing the methodology, synthesizing its evolution, providing implementation examples, sharing findings, and formulating research needs for further technique optimization for a variety of riverine environment investigations.

2. LSPIV System Components

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. LSPIV System Components
  5. 3. Measurement Outcomes and Accuracy
  6. 4. LSPIV Evolution
  7. 5. Implementation Examples
  8. 6. Lessons Learned and Future Research
  9. 7. Conclusions
  10. Acknowledgments
  11. References
  12. Supporting Information

[7] Conventional PIV entails four components: flow visualization, illumination, image recording, and image processing. Given that LSPIV images cover large areas usually recorded from an oblique angle to the flow surface, an additional step is customarily involved: image orthorectification. The LSPIV measurement sequence is illustrated in Figure 1.

image

Figure 1. LSPIV measurement sequence: (a) imaging the area to be measured (white patterns indicate the natural or added tracers used for visualization of the free surface), (b) the distorted raw image, and (c) the undistorted image with the estimated velocity vectors overlaid on the image.

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2.1. Flow Visualization, Illumination, and Recording

[8] In general these technique components are strongly interrelated, such that selection of one approach for a component imposes the types of devices or approaches available for the remaining components. The selection of the components and their integrated operation in the conventional PIV is driven by established rules of thumb regarding the concentration of particles, their size with respect with the image processing parameters, and the desirable particle displacement in a series of images [Adrian, 1991]. Use of these rules is common practice for PIV measurements in the laboratory environment. Unfortunately, except possibly for sufficiently small channels and streams [Bradley et al., 2002; Jodeau et al., 2008], less than desirable laboratory recording conditions require procedural adjustments when LSPIV is implemented in field measurements. These include finding recording position(s) that mitigate two pervasive problems that impact flow visualization in the field. The first is poor or strong illumination that might occur when only natural light is used. Glare and shadows on the water surface significantly degrade image quality [Hauet et al., 2008b].

[9] A second problem is insufficient flow seeding. A favorable situation is when the free surface is visualized by naturally occurring tracers/patterns floating at the free surface (e.g., light floating debris or foam or boils created at the free surface by turbulence). These tracers are, however, not always available or in sufficient quantities in natural streams, therefore they may need to be added at the free surface. Another favorable situation is when specular reflection formed by incident light interacting with the free-surface deformations can be used as seeding surrogate. These deformations, with typical wavelengths in the 2 to 4 cm range are also used to generate the backscattering in the measurements with radar-based velocimeters. The free-surface waviness is generated by wind or large-scale turbulence structures intersecting the free surface. Using the light intensity variation associated with the free-surface deformations, field measurements have been successfully obtained using this tracer substitute [e.g., Creutin et al., 2002; Fujita and Hino, 2003]. When none of the above favorable situations occur, artificial seeding is needed. For both “naturally occurring” and artificial seeding the key requirements is that they have to accurately follow local flow movements. Tracer inertia and submergence are primary factors determining flow visualization suitability. Adverse factors for flow seeding can be strong winds at the free surface or aggregation of the seeding particles induced by particle-to-particle electrostatic forces or high-velocity gradients in the flow.

[10] The framing of the flow during the recording is decided by the availability of light and tracers at the free surface. The size of the image is commensurate with its resolution and the capability to distinguish movement of the water body in image pairs. There are situations when several images are acquired successively from various locations and subsequently assembled to cover with measurements the area of interest. Extensive LSPIV measurements acquired in a wide range of laboratory and field measurement conditions indicate a 30 Hz sampling rate of the conventional video systems is adequate for capturing velocities encountered in hydraulic and hydrologic applications. This is in contrast to the complex and sophisticated laser-based systems that are employed in conventional PIV. The use of conventional video systems for LSPIV is advantageous since the imaging devices continue to improve in spatial and temporal resolution.

2.2. Image Orthorectification

[11] River surface images are usually recorded from a bridge or river bank using an oblique angle to the free surface plane (see Figure 2a). In order to extract accurate flow data from such images, they have to be rectified by an appropriate image transformation scheme [Mikhail and Ackermann, 1976]. Generally, a conventional photogrammetric relation is applied to produce orthoimages using known coordinates of ground control points (GCPs) in the real (X, Y, and Z) and the image (x and y) coordinate systems, as shown in Figure 2. The mapping relationships between the two systems is [Fujita et al., 1998]

  • equation image

where the eleven mapping coefficients A1C3 can be determined by the least square method using the known GCPs coordinates. A minimum of 6 GCPs are needed for conducting the transformation. The control points are surveyed in the field using specialized equipment. The GCP selection is often dictated by what is accessible out in the field (e.g., trees, power line poles, building corners, etc.) rather than what is desirable. The effects of radial lens distortion throughout an image must be corrected before establishing the above relations. As a rule, the size of the non-distorted image should be nearly the same as the size of the original image.

image

Figure 2. Relationship between the camera and field coordinate systems.

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[12] In addition to the geometrical transformation applied to homologous points in the two coordinate systems, a reconstruction of the pixel intensity distribution is simultaneously made to obtain the orthorectified (nondistorted) image. Intensity reconstruction at a point in the transformed image is obtained using a cubic convolution interpolation of the intensity in 16 neighboring points of the original image [Muste et al., 1999]. The nondistorted image contains the flow image to be analyzed and possibly regions surrounding the flow which are not needed for analysis. Increased computational efficiency and processing accuracy are gained if these regions are discarded (masked) from the analysis before processing.

2.3. Image Processing

[13] The LSPIV algorithms for estimating velocities are the same with those used in conventional high-density image PIV [Adrian, 1991]. In essence, a pattern matching technique is applied to image intensity distribution in a series of images, as illustrated in Figure 3. The similarity index for patterns enclosed in a small interrogation area (IA) fixed in the first image is calculated for the same-sized window within a larger search area (SA) selected in the second image. The window pair with the maximum value for the similarity index is assumed to be the pattern's most probable displacement between two consecutive images. Once the distance between the centers of the respective small window is obtained, velocity can be calculated by dividing it with the time difference (dt) between consecutive images. This searching process is applied successively to all IAs in the image.

image

Figure 3. Conceptualization of the LSPIV image processing algorithm (the patterns in the images above are usually formed by clustering of smaller particles of the same nature, i.e., foam, leaves, or artificial seeding added to the surface for collecting the measurements).

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[14] Our image velocimetry algorithm uses the cross-correlation coefficient as a similarity index [Fujita et al., 1998]. Cross correlation is computed between an interrogation area (IA) in the first image and interrogation areas located within a search area (SA) in the second image. The pair of particles showing the maximum cross-correlation coefficient is selected as a candidate vector. In this method the cross-correlation coefficient, Rab, is defined as

  • equation image

where MX and MY are the sizes of the interrogation areas, and axy and bxy are the distributions of the gray-level intensities (ranging from 0 to 255 for an 8-bit image) in the two interrogation areas separated by the time interval dt (see Figure 3). The overbar indicates the mean value of the intensity for the interrogation area. For improving the measurement accuracy, subpixel peak detection methods using Gaussian fitting or parabolic fitting is applied to the cross-correlation distribution [Fujita et al., 1998].

[15] Our image processing algorithm is similar to the correlation imaging velocimetry of Fincham and Spedding [1997]. Both algorithms use a variance normalized correlation, in which each pixel in the IA is equally weighted, such that the background is just as important as the particle images. Consequently, the algorithm can estimate velocities from low-resolution images, such as those captured by standard video cameras. Another important feature of our algorithm is the decoupling of the interrogation area from its fixed location in the first image to any arbitrary location in the second image (see Figure 3). This process completely eliminates the velocity bias error [Adrian, 1991]. It also greatly improves the signal-to-noise ratio in the presence of large displacements, significantly extending the dynamic range of the velocity measurement. More importantly, the algorithm allows the use of relatively small sampling areas, which significantly increases the available spatial resolution and reduces the errors encountered when measuring high-vorticity flows.

3. Measurement Outcomes and Accuracy

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. LSPIV System Components
  5. 3. Measurement Outcomes and Accuracy
  6. 4. LSPIV Evolution
  7. 5. Implementation Examples
  8. 6. Lessons Learned and Future Research
  9. 7. Conclusions
  10. Acknowledgments
  11. References
  12. Supporting Information

[16] The discussion in this section uses for illustration purposes, results obtained with LSPIV in a laboratory model [Muste et al., 2004b]. The raw LSPIV measurements are instantaneous vector fields (see Figure 4b). Each IA encompassed in the original free surface image (see Figure 4a) has a vector attached. The technique is the only available that provides instantaneous velocity measurements on a plane. The LSPIV vector field so obtained makes it possible to conduct Lagrangian and Eulerian analysis for determining spatial and temporal flow features such as the mean velocity field, streamlines, and vorticity (see Figures 4c, 4d, and 4e) as well as other velocity-derived quantities (strain rates, fluxes, dispersion coefficients due to shear, etc).

image

Figure 4. LSPIV results [from Muste et al., 2004b] (with permission from ASCE): (a) video frame of the upstream reach of a 5 m × 40 m hydraulic model, (b) instantaneous vector field superposed on an undistorted video frame, (c) comparison of LSPIV velocities with ADV velocities in a cross section, (d) mean vector field, (e) streamlines established on the mean vector field, and (f) vorticity field established from the mean vector field.

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[17] The LSPIV surface velocity in conjunction with bathymetry can provide flow rates in streams. The method used for estimation of the discharge is the velocity area method (VAM), as illustrated in Figure 5. The channel bathymetry can be obtained from direct surveys using specialized instruments (e.g., sonars or acoustic Doppler current profilers). The channel bathymetry can be surveyed at the time of the LSPIV measurements or prior to them under the assumption that bathymetry is not changing in the time interval between the bed and free-surface measurements. Surface velocities at several points along the surveyed cross section (Vi in Figure 5) are computed by linear interpolation from neighboring grid points of the PIV-estimated surface velocity vector field (Vs). Assuming that the shape of the vertical velocity profile is the same at each point i, (see Figure 5) the depth-averaged velocity at each i vertical is related to the free-surface velocity by a velocity index. The discharge for each river subsection (i, i + 1) is computed following the classical VAM procedure [Rantz, 1982].

image

Figure 5. LSPIV-based discharge measurement procedure.

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[18] The index velocity value was, and continues to be, a subject of research [Polatel, 2005]. The index is dependent on the shape of the vertical velocity profile, which is affected by the flow aspect ratio, Froude and Reynolds number, micro and macro bed roughness, and relative submergence of the large-scale roughness elements. An attempt to articulate this intricate dependence was made by Polatel [2005] in a series of laboratory experiments with varying velocity flows over smooth bed and bed roughened with dunes and ribs. For these experimental conditions, the velocity index varied between 0.789 and 0.928. The results showed that the velocity indices are higher for smooth bed and larger flow depths. Considering the substantial changes in the roughness conditions, it was, however, concluded that the range of variation of velocity indices was fairly small. It is obvious that more research is needed to further explore the variation of the indices for other ranges of conditions and assemble a matrix of indices covering the range of natural flow situations. For the LSPIV results presented herein, a value of k = 0.85 for the index velocity is used. This value is generally accepted for river flows by the hydraulic community and used in conjunction with other measurement techniques [Costa et al., 2000].

[19] The spatial nature of the LSPIV measurement complicates the LSPIV uncertainty analysis. For instance, because image perspective distortion is always affecting the field recorded images, transformed image quality is not uniform; objects in the near field are better resolved than those in the far field. Further, nonuniform seeding densities over the area to be measured will result in inadequate flow visualization. Consequently the accuracy of the velocity obtained with LSPIV varies spatially, depending on the obliqueness of the image distortion, seeding density and distribution, local illumination and other factors. A total of 27 elemental error sources have been identified that affect the LSPIV measurements [Kim, 2006]. The errors are generated in all stages of the LSPIV measurement process, i.e., illumination, seeding, recording, transformation, and processing. The sensitivity analysis for the LSPIV velocity uncertainty, conducted by Kim [2006], indicates that the relative contribution of the elemental errors to the final results is mostly affected by (listed in order): seeding density, identification of the GCPs, accuracy of flow tracing by the seeding particles, and sampling time.

[20] The present authors attempted to estimate the LSPIV measurement accuracy using both standardized uncertainty analysis methodology [American Institute for Aeronautics and Astronautics, 1995] as well as by comparing LSPIV with alternative instrument measurements. Most of the twenty seven elemental error sources needed for conducting the standardized uncertainty analysis have yet to be estimated because of the prohibitive degree of processing and expense required for assessment. Use of the American Institute for Aeronautics and Astronautics uncertainty analysis in conjunction with the best available information on elemental error sources to a LSPIV measurement situation in adverse field conditions (low visibility) led to an average total error in velocity of 10% and maximum error of 35% [Kim, 2006]. Several assessments of the LSPIV velocity accuracy were made through direct instrument comparisons. Comparison of LSPIV velocity was obtained in the laboratory by moving a cart over a fixed surface containing graphical patterns with the cart velocity resulted in average difference of 3.5% [Muste et al., 1999]. Comparisons of velocities obtained with LSPIV in field conditions and acoustic Doppler velocimeters at the same measurement location displayed differences up to 10% [Muste et al., 2004b]. The same comparison against mechanical current meters showed a 16% difference [Bradley et al., 2002].

[21] The accuracy of the discharge measurements using LSPIV is slightly better compared to the velocity because of the inherent spatial averaging involved in the estimation of discharges with the velocity area method [Muste et al., 2004a]. For example, the LSPIV estimated discharges for a relatively small creek (width of about 12m) were 2% higher than those conducted simultaneously with an acoustic Stream PRO velocity profiler (TeledyneRDI Inc). The USGS rating curve located at the measurement site indicated 3.5% higher discharge than the LSPIV estimate. Concurrent measurements in a larger river (width of about 70 m), displayed LSPIV discharges 5.6% lower than the reading of the USGS gauging station located at the measurement site and 1.4% higher than the discharge provided by an ADCP (TeledyneRDI Inc).

4. LSPIV Evolution

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. LSPIV System Components
  5. 3. Measurement Outcomes and Accuracy
  6. 4. LSPIV Evolution
  7. 5. Implementation Examples
  8. 6. Lessons Learned and Future Research
  9. 7. Conclusions
  10. Acknowledgments
  11. References
  12. Supporting Information

[22] The second author placed the foundation of PIV for hydraulic applications in the late 1980s [Fujita and Komura, 1988]. Since 1994, three research institutions, Kobe University, The University of Iowa's IIHR–Hydroscience and Engineering (IIHR), and the Institute National Polytechnique Grenoble (INPG), have been actively collaborating on LSPIV developments. Efforts at IIHR and Kobe University were initiated by the first two authors, respectively. Developments at INPG were initiated by Creutin [2001] and by the third author, an alumna of INPG. For more than one decade, the authors have collaboratively addressed the multifaceted aspects of LSPIV. The first decade of LSPIV development and implementation was dominated by the adjustment of the conventional PIV techniques and algorithms to measure large-scale flows specific to hydraulic and hydrologic applications and the transfer of the laboratory experience to field conditions. Over the years, areas from 100 to 5000 m2 have been mapped nonintrusively with LSPIV to provide instantaneous surface velocity vector fields, document flow patterns, and measure river discharges [Fujita et al., 1998; Fujita and Aya, 2000; Muste et al., 2000; Bradley et al., 2002; Muto et al., 2002: Creutin et al., 2003; Muste et al., 2004b; Hauet et al., 2006; Hauet, 2006]. The initial success of this research has attracted the interest of other researchers to LSPIV [e.g., Muller et al., 2002; Admiraal et al., 2004, Harpold and Mostaghimi, 2004, Weitbrecht et al., 2007; Chen et al., 2007].

[23] Subsequently, the LSPIV flow visualization capability and reliability was compared to other measurement instruments through laboratory investigations [Muste et al., 2000, Kim et al., 2007, Hauet et al., 2008a]. Error analysis due to imaging from oblique angles was estimated in a controlled laboratory environment [Muste et al., 1999, Kim, 2006]. The coupling of image velocimetry and numerical simulation for inference of flow information outside the measured area has been investigated through several approaches [Muste et al., 2000; Bradley et al., 2002; Jodeau et al., 2008]. More recently, LSPIV was fitted with new image enhancement and processing algorithms developed by Fujita et al. [2007a] and Hauet et al. [2008b]. Concurrent, ongoing hardware improvements are improving LSPIV configurations and operational capabilities. Most notable is the advancements of commercial computers, digital cameras, and surveying equipment. These upgrades have been incorporated in new LSPIV configurations as soon as they became available. A summary of recent LSPIV configurations developed by the present authors is presented in Figure 6. Each of these LSPIV alternatives was developed to address a specific purpose (see Figure 6 for more details).

image

Figure 6. Configurations developed for the improvement of LSPIV performance and capabilities.

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image

Figure 6. (continued)

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[24] 1. Space-time image velocimetry measures without seeding.

[25] 2. Large-scale adaptive PIV is used for verification of image processing and transformation robustness.

[26] 3. LSPIV simulator is used for assessment of the measurement accuracy.

[27] 4. Real-time LSPIV provides continuous measurements.

[28] 5. Mobile LSPIV uses the technique at ungauged sites or during floods.

[29] 6. Controlled surface wave image velocimetry provides measurements without seeding.

[30] 7. River digital mapping provides quantitative information about the stream velocity and waterway surrounding (bank, floodplain) using the same instrument.

5. Implementation Examples

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. LSPIV System Components
  5. 3. Measurement Outcomes and Accuracy
  6. 4. LSPIV Evolution
  7. 5. Implementation Examples
  8. 6. Lessons Learned and Future Research
  9. 7. Conclusions
  10. Acknowledgments
  11. References
  12. Supporting Information

[31] The following are sample LSPIV measurements that aim at illustrating the capabilities of the technique to quickly and remotely measure whole-velocity fields over large flow areas. Two types of measurement are portrayed in this section: measurement and mapping of the flow distribution (1) during floods and (2) in the vicinity of hydraulic structures. While the examples are not exhaustive, they are intended to illustrate that LSPIV can quickly and safely take measurements in natural-scale streams for providing comprehensive, quantitative flow information over a wide range of flow types (uniform, nonuniform) and measurement conditions (e.g., floods, low, shallow flows) with minimum or no site preparation.

5.1. Floods

[32] In most cases, flow velocity measurements during floods cannot be conducted because of the danger posed by high water velocities on equipment deployment and operation. A safer alternative is to record images of the flow free surface from shore or from the air (see Figure 7). Video recordings of the flooded areas was often the only raw information needed for measuring flow, as the seeding and GCPs were readily available in those images [Fujita and Komura, 1994; Fujita et al., 2007b]. Seeding of various types can be generated and enhanced by the large velocities and turbulence occurring during flooding. First, there is reasonable probability that vigorous kolks, boils, and ripples generated by the large-scale turbulent eddies moving over the bed forms and roughness will be observable at the free surface. These free surface perturbations act as reflectors of the sun or other dominant ambient light playing the role of natural seeding for the LSPIV measurements [Fujita et al., 2007b, Figure 6a]. A second type of tracer that can occur naturally during floods is created by large-scale turbulent eddies entraining sediment throughout the flow depth [Fujita and Hino, 2003, Figure 6b]. The entrainment results are the sediment “clouds,” patterns that can be distinguished by their color gradients. The third tracer type, ubiquitous during floods, is the floating debris conveyed by the stream. The latter is easily observable during daylight irrespective of the illumination situation [Fujita and Komura, 1994].

image

Figure 7. Mean flow distribution during floods measured from helicopter (Japan): (a) cross section in the Katsura River (river width 90 m) and (b) flow distribution measured during a levee breach on the Shinkawa River (river width 80 m).

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5.2. Hydraulic Structures

[33] An increasing number of ecohydrological applications (such as studies of stream restoration, the design of fish-friendly structures, and creation of suitable riverine ecohabitat) requires documentation of the flow field along the river banks, around and through river structures, or over the entire river reach. LSPIV can provide these flow fields as it is the only measurement techniques capable of simultaneously measuring two-component velocities on a surface, rather than at isolated points or along lines. Illustrative sample measurements are provided in Figure 8, where velocity fields within and over groins in Uji River (Japan) are measured for a range of flow conditions [Fujita et al., 2003].

image

Figure 8. Mean flow distribution documented with LSPIV: (a) raw image of the river flow near groins, (b) velocity distribution within a nonsubmersed groin pair, (c) velocity distribution over a submersed groin pair, (d) orthorectified image of the flow area encompassed by four groin pairs, (e) velocity distribution within three nonsubmersed groin pairs, and (f) velocity distribution over three submersed groin pairs.

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[34] The area encompassed between two consecutive groins is approximate 40 m by 20 m. To capture details of the flow structure within the groins required additional seeding besides the one provided by the naturally occurring ripples at the free surface. For this purpose, biodegradable packaging particles were strategically released at few points in the vicinity of the groins. Recordings of the seeded flow areas were taken for about five minutes. The measurements clearly document changes that occur in the flow distribution within a groin pair when the river flow depth changes (see Figures 8b and 8c). Assembling multiple recordings of the flow over consecutive groin pairs allows documenting the flow distribution over a set of groins, as illustrated in Figure 8d. The vectors fields plotted in Figures 8e and 8f clearly illustrate the change in the free surface flow distribution with the change in the river stage.

6. Lessons Learned and Future Research

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. LSPIV System Components
  5. 3. Measurement Outcomes and Accuracy
  6. 4. LSPIV Evolution
  7. 5. Implementation Examples
  8. 6. Lessons Learned and Future Research
  9. 7. Conclusions
  10. Acknowledgments
  11. References
  12. Supporting Information

[35] This section will first review key findings regarding techniques and later offer research recommendations. Research and testing conducted by the present research team strongly suggests that LSPIV is a promising technique for documenting the hydrodynamics of riverine environments. Our cumulative experience with digital imagery facilitated an appreciation of both the capabilities and limitations of LSPIV as well as increased awareness of research challenges regarding further applications in field conditions.

[36] Although the new acoustic and radar-based technologies can nonintrusively provide velocities at a point on or along a line, respectively, the key advantage of image velocimetry is that it instantaneously measures velocities in a flow plane. The use of images instead of transducer output such as signals, makes image velocimetry more user friendly. The technique does not require calibration and allows reprocessing of the raw information with variable spatial and temporal resolutions to obtain flow data. The mean vector field, turbulence characteristics, flow patterns (streamlines, pathlines), vorticity, and discharges can all be readily obtained from the raw image-based measured velocities. The grid-attached nature of the measurements complements efficiently the requirements for the calibrations/validations of the numerical simulations. The above capabilities have rapidly established image velocimetry as a preferred choice for documenting detailed turbulence of two- and three-dimensional laboratory flows.

[37] Selected characteristics of LSPIV compared with other contemporary river measurement techniques are summarized in Table 1. Regarding practical LSPIV implementation, the present authors conclude that if the human eye can detect the movement of a water body, LSPIV can capture and quantify it. The relatively low flow velocities in hydrologic applications allow for the use of simple image registration devices. Specifically, standard video recording equipment and natural illumination suffice for acquiring images. These features contribute to an efficient and inexpensive technique compared to existing point or line velocity instruments. The digital domain of the components considerably facilitates data management and makes LSPIV feasible for real-time system configurations.

Table 1. Comparison of Selected Characteristics of the Acoustic Doppler, Radar-, and Image-Based Velocimeters for Riverine Environments
Technique or CharacteristicsAcousticRadarImage
Measurement typeProfile: along the acoustic beam path (verticals); three-component velocity.Point: at the intersection of the beam with the free surface; one-component velocity.Surface: instantaneous vector field at the free surface; two-component velocity.
Flow tracersSmall particles usually naturally suspended in the water column.Small surface waves created by wind or by flow turbulence at the free surface.Foams, debris, ice floes, and specular reflections on the free surface deformations (waves, boils, kolks). Added seeding.
Operating constraintsInstrument probe in contact with the flow. The flow assumed horizontally homogeneous.The ratio between the Incident electromagnetic and water wave wavelengths within specified range. Instrument aligned with the dominant velocity.Survey of minimum six points within the imaged area. Occasionally, additional seeding and illumination.
Output qualityGood spatial and temporal resolution. Inaccurate for very slow flows.Limited spatial and temporal resolution. No reverse flows. Inaccurate for very slow flows.Good spatial and temporal resolution.

[38] A unique LSPIV feature among the new generation of river instruments is swift and convenient estimation of whole-field velocity for stream segments during extreme flows (i.e., droughts or floods) without flow contact. During droughts, rivers are shallow and velocities are low, such that there are few (if any) alternative measurement instruments to use [Weitbrecht et al., 2007]. For such situations, acoustic instruments cannot be deployed because of the small flow depths while radar will not operate in the absence of the free-surface waviness. LSPIV is the desirable alternative in high flow measurement situations because the fast velocities pose risks to equipment and staff operating the acoustic instruments on one hand and the radar measurements are compromised by nonuniformity of waviness on the other.

[39] The visualization of the free surface for the presented LSPIV measurements was accomplished with a variety of tracers. A favorable measurement situation is when naturally occurring seeding or patterns are floating at the free surface. They can consist of foam, light debris or small free-surface deformations created by turbulent eddies intersecting the free surface. In the absence of “natural seeding” adequate measurement conditions must be created. Synthetic material can be chosen to visually contrast against the background water body's color, accurately trace the flow, and be large enough to be detected at the individual particle level or in particle clumps. Convenient, natural seeding alternatives are dry, light vegetation, wood debris or other ecologically harmless materials such as the biodegradable ecofoam peanuts (an off-the-shelf granular packaging material containing 99% corn syrup). For accurate data collection, the particles must closely trace the flow features of interest, be partially submersed to avoid possible complications due to wind interference, and be uniformly dispersed onto the free surface within the measured area.

[40] Properly aiming the camera at the flow area of study is critical in several respects. Whenever possible, the camera should be placed at a comparatively high vantage point with the optical axis perpendicular to the flow direction. Imaging from a small tilting angle will compromise image integrity via distortions that are subsequently difficult to correct with the image transformation algorithms. A tilting angle of 10 degrees was found by Kim et al. [2007] as the acceptable limit. The presence of glares or shadows on the free surface will hamper the image processing resulting in erroneous or absent vectors. Diffuse light or midday recordings are recommended for minimizing these detrimental illumination effects. Measures must be taken to minimize transmissions of extraneous physical vibrations to the camera during exposures (i.e., gusts of wind, nearby heavy construction, vehicle traffic, or inadvertent human camera jostling). The image orthorectification is directly related to the accuracy of the surveying equipment used for the GCPs. While a totally stationary survey is always recommended where measurement accuracy is crucial, fast methods such as those based on laser and radar ranging or handheld Global Positioning Systems are working alternatives albeit under continuous scrutiny with respect to their accuracy. An alternative image orthorectification algorithm that does not require a survey of GCPs is currently being studied by the authors. The alternative approach uses a three-dimensional conformal coordinate transformation [Wolf, 1983], whereby the input data is the camera orientation, its distance from the water surface, and the camera optical parameters.

[41] The present LSPIV applications were recorded with digital video (DV) cameras, digital cameras, and high-definition (HD) video cameras. Data suggests the quality of recordings to be directly related to the size of the imaging device sensor. HD cameras facilitate detailed measurements of large-scale turbulence, but require large computer memory for storage. For most riverine applications, cameras with approximately 1000 × 1000 pixels are of sufficient resolution. The continuous improvements in temporal and spatial resolutions of the standard image recording equipment hold, however, great promise for enhancements of LSPIV performance and expanding its applicability at no cost to the hydrologic and hydraulic communities.

[42] The current LSPIV developmental efforts continue to be focused on overcoming challenges posed by measurements in natural rivers conditions and making the technique robust and reliable for a wide range of measurement environments and conditions. At the time of this writing, the authors are investigating new visualization means (e.g., particles, specular reflection) and their respective flow tracing accuracy. Recognizing that the ability to detect motion from a series of digital images is the most critical issue for LSPIV field applications, this research team is developing algorithms that will improve image quality for poor visibility conditions and processing algorithms to more robustly match image patterns in recordings with low particle densities. The index velocity used to determine depth-averaged velocities for various flow conditions and bed roughness is another subject of research. Additional efforts are underway to turn the detrimental wind effect into an advantage: preliminary experiments suggest that a constant wind on the free surface can be successfully used as tracer for an “unseeded” flow over a wide range of dynamic conditions [Muste et al., 2005]. With the wind effects properly identified, the LSPIV wind-affected measurement can be compensated to provide reliable estimates of underlying water body movements. Finally, developing LSPIV systems capable of continuous day or night operation are planned.

[43] In addition to the efforts dedicated to improve the overall performance of the technique, the ongoing research will also target expansion of the LSPIV measurement capabilities. Preliminary laboratory tests suggest that for flow situations where the shallow water theory framework is valid, LSPIV can be used to determine changes in the channel bed geometry on the basis of the divergence of the velocity vector field measured at the free surface [Muste et al., 2004b; Hauet et al., 2007]. In general, since the variations of flow velocities within a river reach are related to variations in channel bed bathymetry, additional flow kinematic analyses will be elaborated to diagnose more complex changes in bed geometry.

[44] Another series of preliminary research have identified correlations between the free surface texture and the state of the channel. The correlations are determined by the combined effect of channel roughness, relative roughness submergence and flow velocity [Polatel, 2006; Polatel et al., 2006]. Investigations of the relationship between free-surface appearance and the velocity distribution in the vertical will continue as this subject is not only critical for LSPIV discharge measurements, but it is important for the understanding of gas and heat transfer and sediment transport processes.

7. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. LSPIV System Components
  5. 3. Measurement Outcomes and Accuracy
  6. 4. LSPIV Evolution
  7. 5. Implementation Examples
  8. 6. Lessons Learned and Future Research
  9. 7. Conclusions
  10. Acknowledgments
  11. References
  12. Supporting Information

[45] The application of LSPIV in a measurement situation requires a full understanding of the instrument underlying principles and of various parameters involved in image recording and processing, as well as of the flow subjected to measurements and its interaction with the surrounding. Under laboratory conditions the LSPIV approach yields reliable and accurate measurements. Field measurements may be confounded by poor free-surface illumination, scarce seeding, or adverse conditions acting on the free surface (such as strong winds). These factors can drastically reduce measurement accuracy or prevent measurements entirely. In typical flow situations, where almost all LSPIV requirements are met, the technique provides accurate velocity measurements compared to point-based and profiling instruments that require considerable efforts to obtain comparable data. In some situations, such as measurements during extreme flow events (floods, hurricanes) or very slow and shallow flows (wetlands, small streams), LSPIV may be the only measurement alternative. LSPIV should not be construed as the magic instrument. It is most appropriate when considered as a complementary alternative of an overall measurement strategy. In this context, it robustly supports a variety of measurement purposes.

[46] Since its inception more than a decade ago, LSPIV has continuously capitalized on the symbiosis of imaging technology, engineering, and computer science to produce a promising measurement tool for hydraulics and hydrology. The mobility, autonomy, and the expedient measurement procedures make the LSPIV ideal for intensive measurement at sites deemed otherwise difficult to access during normal and extreme hydrological events. In addition to conventional one-dimensional river measurements (i.e., discharges), this new technology brings in new measurement capabilities at reduced costs and increased accuracy (particularly in extreme conditions). The LSPIV capacity to provide 2-D and 3-D river information may shed light on critically important processes, such as interaction between main channel and overbank (floodplain), floods, the impact of floodplain flows on riparian vegetation and habitat, evolution of meandering streams, and the effect of river structures on the river ecosystem. The higher-dimensional flow component estimates provided by LSPIV may also lead to advances in river monitoring of channel stabilization, bathymetry change due to dam removal, bank erosion, stream and wetland ecology, stream corridor restoration, and environmental impact.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. LSPIV System Components
  5. 3. Measurement Outcomes and Accuracy
  6. 4. LSPIV Evolution
  7. 5. Implementation Examples
  8. 6. Lessons Learned and Future Research
  9. 7. Conclusions
  10. Acknowledgments
  11. References
  12. Supporting Information

[47] The developments described in this paper were made possible through important contributions by many researchers, technicians, and shop personnel. LSPIV supporters and developers at university institutions include A. Kruger, A. Bradley, W. Krajewski, and Larry Weber (IIHR): S. Komura (Gifu University); S. Aya (Osaka Institute of Technology); R. Tsubaki (Nagoya University); Y. Muto (Kyoto University); T. Okabe (Tokushima University); and D. Creutin (INPG France). Several Ph.D. and master's students have also contributed to developments (Y. S. Kim, K. Yu, J. Schöne, Z. Xiong, and Z. Li). Funding supporting LSPIV developments and implementation has been provided by IIIHR; Iowa Department of Transportation; University of Kobe's Grant-in-Aid for Scientific Research, River Environment Fund, Tokushima University; INP Grenoble; Cemagref Lyon; and OHM-CV Observatory. The authors gratefully acknowledge their contributions.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. LSPIV System Components
  5. 3. Measurement Outcomes and Accuracy
  6. 4. LSPIV Evolution
  7. 5. Implementation Examples
  8. 6. Lessons Learned and Future Research
  9. 7. Conclusions
  10. Acknowledgments
  11. References
  12. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. LSPIV System Components
  5. 3. Measurement Outcomes and Accuracy
  6. 4. LSPIV Evolution
  7. 5. Implementation Examples
  8. 6. Lessons Learned and Future Research
  9. 7. Conclusions
  10. Acknowledgments
  11. References
  12. Supporting Information
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wrcr11779-sup-0001-t01.txtplain text document1KTab-delimited Table 1.

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