Evaluation of the tablets’ surface flow velocities in pan coaters

AbstractIn the course of a tremendous outburst flood (jökulhlaup) following the subglacial eruption in Vatnajökull, Iceland, in October 1996, a depression in the surface of the ice cap was created as a result of ice melting from the walls of a subglacial tunnel. The surface depression was initially approximately 6 km long, 800 m wide and 100 m deep. This ˚canyon" represents a significant perturbation in the geometry of the ice cap in this area where the total ice thickness is about 200–400 m. We present results of repeated measurements of flow velocities and elevation changes in the vicinity of the canyon made over a period of about 2 years. The measurements show a reduction in the depth of the canyon and a concomitant decrease in surface flow towards it over time. By calculating the transient evolution of idealized surface depressions using both analytical zeroth- and first-order theories, as well as the shallow-ice approximation (SIA) and a finite-element model incorporating all the terms of the momentum equations we demonstrate the importance of horizontal stress gradients at the spatial scale of this canyon. The transient evolution of the canyon is calculated with a two-dimensional time-dependent finite-element model with flow parameters (the parameters A and n of Glen’s flow law) that are tuned towards an optimal agreement with measured flow velocities. Although differences between measured and calculated velocities are comparable to measurement errors, the differences are not randomly distributed. The model is therefore not verified in detail. Nevertheless the model reproduces observed changes in the geometry over a 15 month time period reasonably well The model also reproduces changes in both velocities and geometry considerably better than an alternative model based on the SIA.

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Hydromorphological monitoring of individual river reaches with UAV-data – image-based measurement of bathymetry and flow velocity

10.5194/egusphere-egu21-14276 ◽

2021 ◽

Author(s):

Anette Eltner ◽

László Bertalan ◽

Eliisa Lotsari

Keyword(s):

Flow Velocity ◽

Acoustic Doppler Current Profiler ◽

Surface Flow ◽

Ease Of Use ◽

Morphological Data ◽

Measurement Tool ◽

River Channels ◽

Spatiotemporal Resolution ◽

Morphodynamic Modelling ◽

Flow Velocities

Unmanned Aerial Vehicles (UAV) have become a commonly used measurement tool in geomorphology due to their affordable cost, flexibility, and ease of use. They are regularly used in fluvial geomorphology, among other fields, because the high spatiotemporal resolution of UAV data makes it possible to assess the continuum rather than relying on single samples.In this study, UAV data are used to hydro-morphologically describe three different river reaches of lengths between 150 and 1000&#160;m. Specifically, the surface flow velocity and bathymetry of the rivers were reconstructed. The flow velocities were calculated using the Particle Tracking Velocimetry (PTV) method applied to UAV video sequences. In addition, UAV-based imagery was acquired to perform 3D reconstruction above and below the water surface using SfM (Structure from Motion) photogrammetry, taking into account refraction effects as well as frame processing to increase the visibility of underwater features. Reference data for flow velocities were generated at selected positions using current meters as well as ADCP (Acoustic Doppler Current Profiler) readings. The image-based calculated bathymetry was compared with RTK-GNSS sampling depth measurements and also ADCP data.The developed workflow enables rapid and regular measurement of hydrological and morphological data of river channels. This ultimately enables multi-temporal assessment and significantly improves hydro-morphodynamic modelling, in particular their calibration.

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FlowVeloTool: Measuring flow velocities in terrestrial and UAV image sequences automatically with PIV and PTV

10.5194/egusphere-egu2020-17773 ◽

2020 ◽

Author(s):

Anette Eltner ◽

Jens Grundmann

Keyword(s):

Flow Direction ◽

Control Point ◽

Region Of Interest ◽

Software Tool ◽

Image Sequences ◽

Surface Flow ◽

Area Of Interest ◽

Image Velocimetry ◽

Uav Image ◽

Flow Velocities

We introduce a Python based software tool to measure surface flow velocities and to estimate discharge eventually. Minimum needed input are image sequences, some camera parameters and object space information to scale the image measurements. Reference information can be provided either indirectly via ground control point measurements or directly providing camera pose parameters. To improve the reliability and density of velocity measurements the area of interest has to be masked for image velocimetry. This can either be performed with a binary mask file or considering a 3D point cloud, for instance retrieved with Structure from Motion (SfM) photogrammetry, describing the region of interest. The tracking task can be done with particle image velocimetry (PIV) considering small interrogation regions or using particle tracking velocimetry (PTV) and thus detecting and tracking features at the water surface. To improve the robustness of the tracking results, filtering can be applied that implements statistical information about the flow direction, flow steadiness and average velocities.The FlowVeloTool has been tested with two different datasets; one at a gauging station and one at a natural river reach. Thereby, UAV and terrestrial data were acquired and processed. Velocities can be estimated with an accuracy of 0.01&#160;m/s. If information about the river topography and bathymetry are available, as in our demonstration, discharge can be estimated with an error ranging from 5 to 31&#160;% (Eltner et al. 2019). Besides these results we demonstrate further developments of the FlowVeloTool regarding filtering of tracking results, discharge estimation, and processing of time series. Furthermore, we illustrate that thermal data can be used, as well, with our tool to retrieve river surface velocities.&#160;Eltner, A., Sardemann, H., and Grundmann, J.: Flow velocity and discharge measurement in rivers using terrestrial and UAV imagery, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-289, 2019.

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Wind effect on image-based river surface velocity measurements

10.5194/egusphere-egu2020-9943 ◽

2020 ◽

Author(s):

Salvador Peña-Haro ◽

Beat Lüthi ◽

Robert Lukes ◽

Maxence Carrel

Keyword(s):

River Flow ◽

Surface Flow ◽

Surface Velocity ◽

Low Flow ◽

Wind Effect ◽

Rhine River ◽

Average Wind Speed ◽

Flow Measurements ◽

Average Wind ◽

Flow Velocities

Image-based methods for measuring surface flow velocities in rivers have several advantages, one of them being that the sensor (camera) is not in contact with the water and its mounting position is very flexible hence there is no need of expensive structures to mount it. Additionally, it is possible to measure the whole river width. On the other hand, environmental factors, like wind, can affect the surface velocity and the have an impact on the accuracy of the measurements.Herein we present an analysis of the wind effect on the image based surface velocity at Rhine river, at the border between Switzerland and Austria. At this location the river width is of approximately 100 meters under low flow conditions, while the width of its floodplain is of about 200 m. An ATMOS 22 ultrasonic anemometer was installed at the site to measure the wind intensity as well as its direction. A time series of flow velocities and wind from May to October 2019 was analyzed. During this period, the average discharge was 320 m3/s and the average flow velocity 1.7 m/s. While the average wind velocity was of 2.3m/s which roughly follows the same direction of the river flow.A rating curve following a power law function was fitted to the image based surface flow measurements. It was found that for maximum wind speeds of 10 m/s, blowing in the opposite direction of the river flow, there was a deviation of 8%. For the average wind speed of 2.3m/s, the deviation was found to be 3%.

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A method for extracting surface flow velocities and discharge volumes from video images in laboratory

Flow Measurement and Instrumentation ◽

10.1016/j.flowmeasinst.2013.07.009 ◽

2013 ◽

Vol 33 ◽

pp. 188-196 ◽

Cited By ~ 7

Author(s):

Juan David Osorio-Cano ◽

Andrés F. Osorio ◽

Raul Medina

Keyword(s):

Surface Flow ◽

Video Images ◽

Flow Velocities

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Global mapping of surface flow velocity and re-evaluation of the volume of the world's glaciers

10.5194/egusphere-egu21-1066 ◽

2021 ◽

Author(s):

Romain Millan ◽

Jérémie Mouginot ◽

Antoine Rabatel ◽

Mathieu Morlighem

Keyword(s):

Water Resources ◽

Flow Velocity ◽

Sea Level ◽

Surface Flow ◽

Glacier Surface ◽

Ice Flow ◽

Ice Volume ◽

The World ◽

Global Mapping ◽

Flow Velocities

The effects of climate change on water resources and sea level are largely determined by the size of the ice reservoirs around the world, which still remains largely uncertain. Ice flow defines the transfer of ice within a glacier and therefore largely governs the spatial distribution of the ice volume. Although some individual regions have been mapped, there is to date no global and complete view of glacier flow. In this study, we present a global mapping of surface ice flow velocity and use it to revise the ice thickness distribution and volume of glaciers around the world. Glacier surface flow velocities were calculated using Sentinel-2/ESA, Landsat-8/USGS, Ven&#956;s/CNES-ISA, Pl&#233;iades/AirbusD&S and radar data from Sentinel-1/ESA. We designed an automated workflow that (i) downloads the data from institutional or commercial servers, (ii) prepares the images, (iii) launches the feature tracking algorithm, (iv) calibrate the glacier surface velocities, and (v) mosaics the results to obtain filtered and averaged velocity maps. For years 2017 and 2018, glacier surface flow velocities are quantified for every possible repeat cycles from the nominal cycle of the sensor (2-16 days) up to more than one year. This new database of glacier surface flow velocity is used to construct an updated global ice volume based on the well known Shallow Ice Approximation approach. We discuss the quality of our global glacier surface flow velocity product and of our new ice volume reconstruction with respect to existing state of the art estimates and quantify the impact of our results in terms of sea level rise and water resources.

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Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter

Remote Sensing ◽

10.3390/rs12081282 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1282 ◽

Cited By ~ 2

Author(s):

Carl J. Legleiter ◽

Paul J. Kinzel

Keyword(s):

Remote Sensing ◽

Field Measurements ◽

Video Camera ◽

Image Sequences ◽

Surface Flow ◽

Optical Image ◽

Frame Rate ◽

Digital Mapping ◽

Lower Frame ◽

Flow Velocities

The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement ( R 2 > 0.9 ) between remotely sensed velocities and field measurements. Similarly, analysis of the sensitivity of PIV accuracy to image sequence duration showed that dwell times as short as 16 s would be sufficient at a frame rate of 1 Hz and could be cut in half if the frame rate were doubled. The results of this investigation indicate that helicopter-based remote sensing of velocities in sediment-laden rivers could contribute to noncontact streamgaging programs and enable reach-scale mapping of flow fields.

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Technical Note: Flow velocity and discharge measurement in rivers using terrestrial and unmanned-aerial-vehicle imagery

Hydrology and Earth System Sciences ◽

10.5194/hess-24-1429-2020 ◽

2020 ◽

Vol 24 (3) ◽

pp. 1429-1445 ◽

Cited By ~ 6

Author(s):

Anette Eltner ◽

Hannes Sardemann ◽

Jens Grundmann

Keyword(s):

Flow Velocity ◽

Unmanned Aerial Vehicle ◽

Cross Section ◽

Flow Characteristics ◽

Technical Note ◽

Surface Flow ◽

Contactless Measurement ◽

Aerial Vehicle ◽

The Impact ◽

Flow Velocities

Abstract. An automatic workflow to measure surface flow velocities in rivers is introduced, including a Python tool. The method is based on particle-tracking velocimetry (PTV) and comprises an automatic definition of the search area for particles to track. Tracking is performed in the original images. Only the final tracks are geo-referenced, intersecting the image observations with water surface in object space. Detected particles and corresponding feature tracks are filtered considering particle and flow characteristics to mitigate the impact of sun glare and outliers. The method can be applied to different perspectives, including terrestrial and aerial (i.e. unmanned-aerial-vehicle; UAV) imagery. To account for camera movements images can be co-registered in an automatic approach. In addition to velocity estimates, discharge is calculated using the surface velocities and wetted cross section derived from surface models computed with structure-from-motion (SfM) and multi-media photogrammetry. The workflow is tested at two river reaches (paved and natural) in Germany. Reference data are provided by acoustic Doppler current profiler (ADCP) measurements. At the paved river reach, the highest deviations of flow velocity and discharge reach 4 % and 5 %, respectively. At the natural river highest deviations are larger (up to 31 %) due to the irregular cross-section shapes hindering the accurate contrasting of ADCP- and image-based results. The provided tool enables the measurement of surface flow velocities independently of the perspective from which images are acquired. With the contactless measurement, spatially distributed velocity fields can be estimated and river discharge in previously ungauged and unmeasured regions can be calculated, solely requiring some scaling information.

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Surface Flow Velocities From Space: Particle Image Velocimetry of Satellite Video of a Large, Sediment-Laden River

Frontiers in Water ◽

10.3389/frwa.2021.652213 ◽

2021 ◽

Vol 3 ◽

Author(s):

Carl J. Legleiter ◽

Paul J. Kinzel

Keyword(s):

Remote Sensing ◽

Particle Image Velocimetry ◽

River Discharge ◽

Water Surface ◽

Particle Image ◽

Field Measurements ◽

Surface Flow ◽

Cross Sectional ◽

Image Velocimetry ◽

Flow Velocities

Conventional, field-based streamflow monitoring in remote, inaccessible locations such as Alaska poses logistical challenges. Safety concerns, financial considerations, and a desire to expand water-observing networks make remote sensing an appealing alternative means of collecting hydrologic data. In an ongoing effort to develop non-contact methods for measuring river discharge, we evaluated the potential to estimate surface flow velocities from satellite video of a large, sediment-laden river in Alaska via particle image velocimetry (PIV). In this setting, naturally occurring sediment boil vortices produced distinct water surface features that could be tracked from frame to frame as they were advected by the flow, obviating the need to introduce artificial tracer particles. In this study, we refined an end-to-end workflow that involved stabilization and geo-referencing, image preprocessing, PIV analysis with an ensemble correlation algorithm, and post-processing of PIV output to filter outliers and scale and geo-reference velocity vectors. Applying these procedures to image sequences extracted from satellite video allowed us to produce high resolution surface velocity fields; field measurements of depth-averaged flow velocity were used to assess accuracy. Our results confirmed the importance of preprocessing images to enhance contrast and indicated that lower frame rates (e.g., 0.25 Hz) lead to more reliable velocity estimates because longer capture intervals allow more time for water surface features to translate several pixels between frames, given the relatively coarse spatial resolution of the satellite data. Although agreement between PIV-derived velocity estimates and field measurements was weak (R2 = 0.39) on a point-by-point basis, correspondence improved when the PIV output was aggregated to the cross-sectional scale. For example, the correspondence between cross-sectional maximum velocities inferred via remote sensing and measured in the field was much stronger (R2 = 0.76), suggesting that satellite video could play a role in measuring river discharge. Examining correlation matrices produced as an intermediate output of the PIV algorithm yielded insight on the interactions between image frame rate and sensor spatial resolution, which must be considered in tandem. Although further research and technological development are needed, measuring surface flow velocities from satellite video could become a viable tool for streamflow monitoring in certain fluvial environments.

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