FIThydrowiki - User contributions [en]

File:Lidar red green.png

2020-09-30T08:32:20Z

Kordula NTNU:

{{Information
|author= Mallet 2010
|source= Mallet C., 2010; LIDAR aéroportéstopographiques & bathymétriques; [https://www.umr-cnrm.fr/ecole_lidar/IMG/pdf/Mallet-Topo_Bathy_Veget.pdf]
|description= Comparison red vs green laser
}}

File:Lidar setup.png

2020-09-30T08:31:47Z

Kordula NTNU:

{{Information
|author= Mallet 2010
|source= Mallet C., 2010; LIDAR aéroportéstopographiques & bathymétriques; ; [https://www.umr-cnrm.fr/ecole_lidar/IMG/pdf/Mallet-Topo_Bathy_Veget.pdf]
|description= Basic set up of a Lidar measurement
}}

File:Lidar als.png

2020-09-30T08:30:50Z

Kordula NTNU:

{{Information
|author= -
|source= [(https://commons.wikimedia.org/wiki/File:Airborne_Laser_Scanning_Discrete_Echo_and_Full_Waveform_signal_comparison.svg)]
|description= Discrete Echo and waveform signal comparison for Airborne Laser Scanning
}}

File:Lidar red green.png

2020-09-30T08:28:21Z

Kordula NTNU:

{{Information
|author= Mallet 2010
|source= Mallet C., 2010; LIDAR aéroportéstopographiques & bathymétriques
|description= Comparison red vs green laser
}}

File:Lidar setup.png

2020-09-30T08:27:17Z

Kordula NTNU:

{{Information
|author= Mallet 2010
|source= Mallet C., 2010; LIDAR aéroportéstopographiques & bathymétriques
|description= Basic set up of a Lidar measurement
}}

LiDAR

2020-09-30T08:25:16Z

Kordula NTNU:

=Quick summary=
[[file:lidar_setup.png|thumb|250px|Figure 1: Basic set up of a Lidar measurement (Mallet 2010).]]
[[file:lidar_red_green.png|thumb|250px|Figure 2: Comparison red vs green laser (Mallet 2010).]]
[[file:lidar_als.png|thumb|250px|Figure 3: Discrete Echo and waveform signal comparison for Airborne Laser Scanning (https://commons.wikimedia.org/wiki/File:Airborne_Laser_Scanning_Discrete_Echo_and_Full_Waveform_signal_comparison.svg).]]

Developed by:

Date: 1998

Type: [[:Category:Devices|Device]], [[:Category:Tools|Tool]]

=Introduction=
Lidar is a measurement technique that measures the distance from a georeferenced laser sensor to a ground target by illuminating the target with pulsed light (Figure 1). The sensor detects the reflected pulses. The time shifts in laser return and wavelengths can then be used to create digital 3-D representations of the target areas, e.g. a river section. The laser pulses can have different wavelengths, most commonly red and green.

The red laser is more common and therefore cheaper. However, other than the green NeoDyn Yag laser it cannot penetrate the water surface. Therefor the green laser (wavelength 532nm) is most preferred in scientific and especially river related measurements (Figure 2). However, a certain distance from the source of the light to the eye of any person, passing accidentally the measurement, must be guaranteed due to safety reasons. This is usually not a problem, as this application is used for large scale approaches, mainly with so called Airborne Laser Scanning (ALS) where the laser is mounted to a small plane, a helicopter or even a large (more than 15 kg load) drone.

=Application=
As the laser and also the plane or helicopter are a quite expensive set of devices, the measurements are usually taken by private contractors. Most of the time the client is a municipality, the government or a hydropower operator interested in the geometry of floodplains and the bathymetry of rivers.

In Norway, most of the rivers are currently measured with a red laser (hence no underwater registrations). These are relatively easily available for research institutions through the public website hoydedata.no. Also available on this website are green laser derived bathymetry data of a selection of river reaches.

The penetration depth of the green laser through water depends highly on turbidity, flow velocity, reflections on the surface / waves and water depth. It also depends most probably on the type of suspended load. Thus, green laser measurements are in many cases supplied with echosounding data.

Problems can also be caused by any other overlap of objects such as trees above ground or submerged vegetation above river bottoms (Figure 3). This can lead to blurred areas and various z-data (height marker) for the same x/y coordinates.
The measurements generate a point cloud. Post-processing is usually done in a specific software (commercial software, but also freeware) as is the case many other applications (such as [[Structure_from_motion_(SfM)|SfM]]). However, as it is a usual output format, it is not a specific part of the Lidar system.

=Relevant mitigation measures and test cases=
{{Suitable measures for LiDAR}}

=Other information=

=Relevant literature=
*Bizzi, S., Demarchi, L., Grabowski, R.C., Weissteiner, C.J., Van de Bund, W., 2016. The use of remote sensing to characterise hydromorphological properties of European rivers. Aquatic Sciences 78, 57–70. https://doi.org/10.1007/s00027-015-0430-7
*Brock, J.C., Purkis, S.J., 2009. The Emerging Role of Lidar Remote Sensing in Coastal Research and Resource Management. Journal of Coastal Research 10053, 1–5. https://doi.org/10.2112/SI53-001.1
*Brock und Purkis - 2009 - The Emerging Role of Lidar Remote Sensing in Coast.pdf, n.d.
*Costa, B.M., Battista, T.A., Pittman, S.J., 2009. Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems. Remote Sensing of Environment 113, 1082–1100. https://doi.org/10.1016/j.rse.2009.01.015
*Costa et al. - 2009 - Comparative evaluation of airborne LiDAR and ship-.pdf, n.d.
*Gao, J., 2009. Bathymetric mapping by means of remote sensing: methods, accuracy and limitations. Progress in Physical Geography 33, 103–116. https://doi.org/10.1177/0309133309105657
*Hilldale, R.C., Raff, D., 2008. Assessing the ability of airborne LiDAR to map river bathymetry. Earth Surface Processes and Landforms 33, 773–783. https://doi.org/10.1002/esp.1575
*Hilldale und Raff - 2008 - Assessing the ability of airborne LiDAR to map riv.pdf, n.d.
*Irish, J.L., White, T.E., 1998. Coastal engineering applications of high-resolution lidar bathymetry. Coastal Engineering 35, 47–71. https://doi.org/10.1016/S0378-3839(98)00022-2
*Kinzel, P.J., Legleiter, C.J., Nelson, J.M., 2013. Mapping River Bathymetry With a Small Footprint Green LiDAR: Applications and Challenges : Mapping River Bathymetry with a Small Footprint Green LiDAR: Applications and Challenges. JAWRA Journal of the American Water Resources Association 49, 183–204. https://doi.org/10.1111/jawr.12008
*Langhammer, J., Janský, B., Kocum, J., Minařík, R., 2018a. 3-D reconstruction of an abandoned montane reservoir using UAV photogrammetry, aerial LiDAR and field survey. Applied Geography 98, 9–21. https://doi.org/10.1016/j.apgeog.2018.07.001
*Langhammer, J., Janský, B., Kocum, J., Minařík, R., 2018b. 3-D reconstruction of an abandoned montane reservoir using UAV photogrammetry, aerial LiDAR and field survey. Applied Geography 98, 9–21. https://doi.org/10.1016/j.apgeog.2018.07.001
*Lejot, J., Delacourt, C., Piégay, H., Fournier, T., Trémélo, M.-L., Allemand, P., 2007. Very high spatial resolution imagery for channel bathymetry and topography from an unmanned mapping controlled platform. Earth Surface Processes and Landforms 32, 1705–1725. https://doi.org/10.1002/esp.1595
*Lükő, G., Rüther, D.N., n.d. UAV BASED HYDROMORPHOLOGICAL MAPPING OF A RIVER REACH TO IMPROVE HYDRODYNAMIC NUMERICAL MODELS 1.
*Lükő, G., Rüther, D.N., n.d. UAV Based Hydromorphological Mapping of a River Reach to Improve Hydrodynamic Numerical Models.
*Marcus, W.A., Fonstad, M.A., 2008. Optical remote mapping of rivers at sub-meter resolutions and watershed extents. Earth Surface Processes and Landforms 33, 4–24. https://doi.org/10.1002/esp.1637
*Mallet C., 2010; LIDAR aéroportéstopographiques & bathymétriques ; https://www.umr-cnrm.fr/ecole_lidar/IMG/pdf/Mallet-Topo_Bathy_Veget.pdf
*Saylam, K., Brown, R.A., Hupp, J.R., 2017. Assessment of depth and turbidity with airborne Lidar bathymetry and multiband satellite imagery in shallow water bodies of the Alaskan North Slope. International Journal of Applied Earth Observation and Geoinformation 58, 191–200. https://doi.org/10.1016/j.jag.2017.02.012
*Wang, C.-K., Philpot, W.D., 2007. Using airborne bathymetric lidar to detect bottom type variation in shallow waters. Remote Sensing of Environment 106, 123–135. https://doi.org/10.1016/j.rse.2006.08.003
*Wang und Philpot - 2007 - Using airborne bathymetric lidar to detect bottom .pdf, n.d.
*Zhang, K., Yang, F., Zhang, H., Su, D., Li, Q., 2017. Morphological characterization of coral reefs by combining lidar and MBES data: A case study from Yuanzhi Island, South China Sea: MORPHOLOGICAL STUDY OF CORAL REEF. Journal of Geophysical Research: Oceans 122, 4779–4790. https://doi.org/10.1002/2016JC012507
*Zhang, K., Frey, H.C., 2006. Road Grade Estimation for On-Road Vehicle Emissions Modeling Using Light Detection and Ranging Data. Journal of the Air & Waste Management Association 56, 777–788. https://doi.org/10.1080/10473289.2006.10464500

=Contact information=

[[Category:Devices]][[Category:Tools]]

LiDAR

2020-09-30T08:23:40Z

Kordula NTNU:

=Quick summary=
[[file:lidar_setup.png|thumb|250px|Figure 1: Basic set up of a Lidar measurement (Mallet 2010).]]
[[file:lidar_red_green.png|thumb|250px|Figure 2: Comparison red vs green laser (Mallet 2010).]]
[[file:lidar_als.png|thumb|250px|Figure 3: Discrete Echo and waveform signal comparison for Airborne Laser Scanning (https://commons.wikimedia.org/wiki/File:Airborne_Laser_Scanning_Discrete_Echo_and_Full_Waveform_signal_comparison.svg).]]

Developed by:

Date: 1998

Type: [[:Category:Devices|Device]], [[:Category:Tools|Tool]]

=Introduction=
Lidar is a measurement technique that measures the distance from a georeferenced laser sensor to a ground target by illuminating the target with pulsed light (Figure 1). The sensor detects the reflected pulses. The time shifts in laser return and wavelengths can then be used to create digital 3-D representations of the target areas, e.g. a river section. The laser pulses can have different wavelengths, most commonly red and green.

The red laser is more common and therefore cheaper. However, other than the green NeoDyn Yag laser it cannot penetrate the water surface. Therefor the green laser (wavelength 532nm) is most preferred in scientific and especially river related measurements (Figure 2). However, a certain distance from the source of the light to the eye of any person, passing accidentally the measurement, must be guaranteed due to safety reasons. This is usually not a problem, as this application is used for large scale approaches, mainly with so called Airborne Laser Scanning (ALS) where the laser is mounted to a small plane, a helicopter or even a large (more than 15 kg load) drone.

=Application=
As the laser and also the plane or helicopter are a quite expensive set of devices, the measurements are usually taken by private contractors. Most of the time the client is a municipality, the government or a hydropower operator interested in the geometry of floodplains and the bathymetry of rivers.

In Norway, most of the rivers are currently measured with a red laser (hence no underwater registrations). These are relatively easily available for research institutions through the public website hoydedata.no. Also available on this website are green laser derived bathymetry data of a selection of river reaches.

The penetration depth of the green laser through water depends highly on turbidity, flow velocity, reflections on the surface / waves and water depth. It also depends most probably on the type of suspended load. Thus, green laser measurements are in many cases supplied with echosounding data.

Problems can also be caused by any other overlap of objects such as trees above ground or submerged vegetation above river bottoms (Figure 3). This can lead to blurred areas and various z-data (height marker) for the same x/y coordinates.
The measurements generate a point cloud. Post-processing is usually done in a specific software (commercial software, but also freeware) as is the case many other applications (such as [[Structure for Motion|SfM]]). However, as it is a usual output format, it is not a specific part of the Lidar system.

=Relevant mitigation measures and test cases=
{{Suitable measures for LiDAR}}

=Other information=

=Relevant literature=
*Bizzi, S., Demarchi, L., Grabowski, R.C., Weissteiner, C.J., Van de Bund, W., 2016. The use of remote sensing to characterise hydromorphological properties of European rivers. Aquatic Sciences 78, 57–70. https://doi.org/10.1007/s00027-015-0430-7
*Brock, J.C., Purkis, S.J., 2009. The Emerging Role of Lidar Remote Sensing in Coastal Research and Resource Management. Journal of Coastal Research 10053, 1–5. https://doi.org/10.2112/SI53-001.1
*Brock und Purkis - 2009 - The Emerging Role of Lidar Remote Sensing in Coast.pdf, n.d.
*Costa, B.M., Battista, T.A., Pittman, S.J., 2009. Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems. Remote Sensing of Environment 113, 1082–1100. https://doi.org/10.1016/j.rse.2009.01.015
*Costa et al. - 2009 - Comparative evaluation of airborne LiDAR and ship-.pdf, n.d.
*Gao, J., 2009. Bathymetric mapping by means of remote sensing: methods, accuracy and limitations. Progress in Physical Geography 33, 103–116. https://doi.org/10.1177/0309133309105657
*Hilldale, R.C., Raff, D., 2008. Assessing the ability of airborne LiDAR to map river bathymetry. Earth Surface Processes and Landforms 33, 773–783. https://doi.org/10.1002/esp.1575
*Hilldale und Raff - 2008 - Assessing the ability of airborne LiDAR to map riv.pdf, n.d.
*Irish, J.L., White, T.E., 1998. Coastal engineering applications of high-resolution lidar bathymetry. Coastal Engineering 35, 47–71. https://doi.org/10.1016/S0378-3839(98)00022-2
*Kinzel, P.J., Legleiter, C.J., Nelson, J.M., 2013. Mapping River Bathymetry With a Small Footprint Green LiDAR: Applications and Challenges : Mapping River Bathymetry with a Small Footprint Green LiDAR: Applications and Challenges. JAWRA Journal of the American Water Resources Association 49, 183–204. https://doi.org/10.1111/jawr.12008
*Langhammer, J., Janský, B., Kocum, J., Minařík, R., 2018a. 3-D reconstruction of an abandoned montane reservoir using UAV photogrammetry, aerial LiDAR and field survey. Applied Geography 98, 9–21. https://doi.org/10.1016/j.apgeog.2018.07.001
*Langhammer, J., Janský, B., Kocum, J., Minařík, R., 2018b. 3-D reconstruction of an abandoned montane reservoir using UAV photogrammetry, aerial LiDAR and field survey. Applied Geography 98, 9–21. https://doi.org/10.1016/j.apgeog.2018.07.001
*Lejot, J., Delacourt, C., Piégay, H., Fournier, T., Trémélo, M.-L., Allemand, P., 2007. Very high spatial resolution imagery for channel bathymetry and topography from an unmanned mapping controlled platform. Earth Surface Processes and Landforms 32, 1705–1725. https://doi.org/10.1002/esp.1595
*Lükő, G., Rüther, D.N., n.d. UAV BASED HYDROMORPHOLOGICAL MAPPING OF A RIVER REACH TO IMPROVE HYDRODYNAMIC NUMERICAL MODELS 1.
*Lükő, G., Rüther, D.N., n.d. UAV Based Hydromorphological Mapping of a River Reach to Improve Hydrodynamic Numerical Models.
*Marcus, W.A., Fonstad, M.A., 2008. Optical remote mapping of rivers at sub-meter resolutions and watershed extents. Earth Surface Processes and Landforms 33, 4–24. https://doi.org/10.1002/esp.1637
*Mallet C., 2010; LIDAR aéroportéstopographiques & bathymétriques ; https://www.umr-cnrm.fr/ecole_lidar/IMG/pdf/Mallet-Topo_Bathy_Veget.pdf
*Saylam, K., Brown, R.A., Hupp, J.R., 2017. Assessment of depth and turbidity with airborne Lidar bathymetry and multiband satellite imagery in shallow water bodies of the Alaskan North Slope. International Journal of Applied Earth Observation and Geoinformation 58, 191–200. https://doi.org/10.1016/j.jag.2017.02.012
*Wang, C.-K., Philpot, W.D., 2007. Using airborne bathymetric lidar to detect bottom type variation in shallow waters. Remote Sensing of Environment 106, 123–135. https://doi.org/10.1016/j.rse.2006.08.003
*Wang und Philpot - 2007 - Using airborne bathymetric lidar to detect bottom .pdf, n.d.
*Zhang, K., Yang, F., Zhang, H., Su, D., Li, Q., 2017. Morphological characterization of coral reefs by combining lidar and MBES data: A case study from Yuanzhi Island, South China Sea: MORPHOLOGICAL STUDY OF CORAL REEF. Journal of Geophysical Research: Oceans 122, 4779–4790. https://doi.org/10.1002/2016JC012507
*Zhang, K., Frey, H.C., 2006. Road Grade Estimation for On-Road Vehicle Emissions Modeling Using Light Detection and Ranging Data. Journal of the Air & Waste Management Association 56, 777–788. https://doi.org/10.1080/10473289.2006.10464500

=Contact information=

[[Category:Devices]][[Category:Tools]]

File:Piv airborne.png

2020-09-30T07:50:45Z

Kordula NTNU:

{{Information
|author= Detert et al. 2019
|source= Detert, M., Cao, L. & Albayrak, I. (2019). Airborne image velocimetry measurements at the hydropower plant Schiffmühle on Limmat river, Switzerland. HydroSenSoft, International Symposium and *Exhibition on Hydro-Environment Sensors and Software, Madrid, Spain, ISBN: 978-90-824846-4-9
|description= Swiss grid georeferenced surface velocity field and streamlines measured by AIV, including areas of noisy velocity data. (Note that according to Le Coz et al. (2010), depth averaged velocities can be estimated by multiplying the surface velocity by factors 0.79-0.89, with a central value close to 0.85)
}}

File:Piv large scale.png

2020-09-30T07:48:55Z

Kordula NTNU:

{{Information
|author= Muste et al. 2008
|source= Muste, M., Fujita, I., & Hauet, A. (2008). Large-scale particle image velocimetry for measurements in riverine environments. Special Issue on Hydrologic Measurements, Water Resources Research, 44
|description= 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 velocity vectors overlaid on the image
}}

File:Piv 2d system.png

2020-09-30T07:47:53Z

Kordula NTNU:

{{Information
|author= Raffel et al 2007
|source= Raffel, M., Willert, C., Wereley, S.T., Kompenhans, J. (2007). Particle Image Velocimetry: A Practical Guide.
|description= Set-up of a standard 2D PIV system
}}

File:Shelter measurements shelter tube.png

2020-09-30T07:45:59Z

Kordula NTNU:

{{Information
|author= Forseth et al., 2014
|source= Forseth, T., Harby, A., Ugedal, O., Pulg, U., Fjeldstad, H.-P., Robertsen, G., Barlaup, B.T., Alfredsen, K., Sundt, H., Salveit, S.J., Skoglund, H., Kvingedal, E., Sundt-Hansen, L.E.B., Finstad, A., Einum, S., Arnekleiv, J.V., 2014. Handbook for environmental design in regulated salmon rivers (NINA temahefte No. 53). Norsk institutt for naturforskning, Trondheim, Norway.
|description= Example showing hiding depth of 2cm (first marker covered) on a tube
}}

File:Shelter measurements salmon.png

2020-09-30T07:44:38Z

Kordula NTNU:

{{Information
|author= Forseth et al., 2014
|source= Forseth, T., Harby, A., Ugedal, O., Pulg, U., Fjeldstad, H.-P., Robertsen, G., Barlaup, B.T., Alfredsen, K., Sundt, H., Salveit, S.J., Skoglund, H., Kvingedal, E., Sundt-Hansen, L.E.B., Finstad, A., Einum, S., Arnekleiv, J.V., 2014. Handbook for environmental design in regulated salmon rivers (NINA temahefte No. 53). Norsk institutt for naturforskning, Trondheim, Norway
|description= Juvenile salmon in the upper layers of river bed sediment
}}

File:Sf mesh.png

2020-09-30T07:40:54Z

Kordula NTNU:

{{Information
|author= Kordula Schwarzwälder, NTNU
|source= Kordula Schwarzwälder, NTNU
|description= Mesh generated from SfM from a section of the residual flow reach of Anundsjø
}}

File:Sfm workflow.png

2020-09-30T07:39:58Z

Kordula NTNU:

{{Information
|author= Kordula Schwarzwälder, NTNU
|source= Kordula Schwarzwälder, NTNU
|description= Screenshot of the Argisoft Photoscan Workflow
}}

File:Sfm evaluation.png

2020-09-30T07:38:27Z

Kordula NTNU:

{{Information
|author= Kordula Schwarzwälder, NTNU
|source= Kordula Schwarzwälder, NTNU
|description= First run of a structure from motion evaluation in the lab of NTNU. The blue field indicates the camera position
}}

File:Sfm drone picture.png

2020-09-30T07:37:12Z

Kordula NTNU:

{{Information
|author= Kordula Schwarzwälder, NTNU
|source= Kordula Schwarzwälder, NTNU
|description= Drone picture taken in a height of 50 m over ground showing the residual flow area in Anundsjø HPP, ice cover and the drone controller
}}

Structure from motion (SfM)

2020-09-30T07:36:30Z

Kordula NTNU:

=Quick summary=
[[file:sfm_drone_picture.png|thumb|250px|Figure 1: Drone picture taken in a height of 50 m over ground showing the residual flow area in Anundsjø HPP, ice cover and the drone controller (Kordula Schwarzwälder, NTNU).]]
[[file:sfm_evaluation.png|thumb|250px|Figure 2: First run of a structure from motion evaluation in the lab of NTNU. The blue field indicates the camera position (Kordula Schwarzwälder, NTNU).]]
[[file:sfm_workflow.png|thumb|250px|Figure 3: Screenshot of the Argisoft Photoscan Workflow.]]
[[file:sf_mesh.png|thumb|250px|Figure 4: Mesh generated from SfM from a section of the residual flow reach of Anundsjø (Kordula Schwarzwälder, NTNU).]]

Developed by: Various Companies

Date: -

Type: [[:Category:Devices|Device]], [[:Category:Tools|Tool]]

=Introduction=
The technique of structure from motion was developed for the video-game industry to allow fast and easy 3D detection and evaluation of bodies. With this technique it is possible, based on pictures of an object taken with a camera, to combine these pictures. There are several approaches to generate a 3D model from SfM. In incremental SFM (Schönberger & Frahm 2016) camera poses are solved and added one by one. In global SFM (Govindu 2001) the poses of all cameras are solved at the same time.

The best result can be achieved when taking pictures from as many positions as possible 360 degree around the object of interest. However, this is not possible for rivers. Depending on the riverbank vegetation and the specific location of each river (such as located in a canyon etc.) only a flight directly above, but with no relevant angle to the sides, might be possible (Figure 1).
The same problem might appear when using the technique in the lab (Figure 2).

For a sufficient positioning accuracy of the results, at least a camera with a high-quality GPS sensor needs to be used. The use of targets or even coded targets to allow a more accurate positioning would be more favourable, however. Without such an accurate positioning of the pictures in the space, the result would not be correct as the cameras could not be located correctly in dependency to each other. Targets on the ground, especially for field measurements, are highly recommended. The position of the targets can be measured with a GPS and the targets can be redetected later in the pictures.

Depending on the software used this process of “re-finding” can be done automatically or needs to be done manually.
In general are there different types of software available, commercial and non-commercial ones. As there is a very fast development and most non-commercial tools need a lot of experience with picture modifications etc. it is recommended, despite the costs, to use a commercial one such as Agisoft Photoscan for instance. In this software, the user can follow relatively easy the workflow, provided by the program (Figure 3), to produce a DEM. It further offers a quite comprehensive manual.

=Application=
As for other picture-based evaluation methods, the data processing is the most demanding part of the measurement. When starting with the measurements in the field, it is highly recommended to use targets. The rough workflow is then as follows:

* Putting targets on the ground with sufficient visibility (from an aerial view). The quality of the results increases in case there are two to three targets visible in every picture. Hence the distribution of the targets depends on the flight height of the camera.
* The camera is usually mounted on a drone, however other constructions as a crane etc. are possible.
* Speed and height over ground depend on the area to be evaluated.
* In case large areas need to be covered an automated flight route programmed for the drone would be useful.
* Starting and landing the drone can be a crucial point, especially in the case this is done automatic and the connection is nut sufficient.
* Further problems can be caused by wind (for the flying performance) and by reflections (for the resulting pictures).
* Always be aware of any kind of regulation, law etc. which might restrict or limit the use and handling of a drone!

Once the measurements are done the evaluation is the most crucial point. Depending on the number of pictures evaluated the result quality de- or increases and in a reverse way the computer performance and power needed in- or decreases (Figure 4).

=Relevant mitigation measures and test cases=
{{Suitable measures for 3D fish tracking system}}

=Other information=
The highest cost is the software license in case a commercial product is used.

=Relevant literature=
*Alfredsen, K., Haas, C., Tuthan, J., Zinke, P., (2018). Brief Communication: Mapping river ice using drones and structure from motion.
*Bouhoubeiny, E., Germain, G., Druault, P., (2011). Time-Resolved PIV investigations of the flow field around cod-end net structures. Fisheries Research 108, 344–355. https://doi.org/10.1016/j.fishres.2011.01.010
*Buscombe, D., (2016a). Spatially explicit spectral analysis of point clouds and geospatial data. Computers & Geosciences 86, 92–108. https://doi.org/10.1016/j.cageo.2015.10.004
*Buscombe, D., (2016b). Spatially explicit spectral analysis of point clouds and geospatial data. Computers & Geosciences 86, 92–108. https://doi.org/10.1016/j.cageo.2015.10.004
*Cunliffe, A.M., Brazier, R.E., Anderson, K., (2016). Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sensing of Environment 183, 129–143. https://doi.org/10.1016/j.rse.2016.05.019
*Flammang, B.E., Lauder, G.V., Troolin, D.R., Strand, T.E., (2011). Volumetric imaging of fish locomotion. Biol. Lett. 7, 695–698. https://doi.org/10.1098/rsbl.2011.0282
*Govindu, V.M. (2001). Combining two-view constraints for motion estimation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
*Koci, J., Jarihani, B., Leon, J.X., Sidle, R., Wilkinson, S., Bartley, R., (2017). Assessment of UAV and Ground-Based Structure from Motion with Multi-View Stereo Photogrammetry in a Gullied Savanna Catchment. ISPRS International Journal of Geo-Information 6, 328. https://doi.org/10.3390/ijgi6110328
*Kothnur, P.S., Tsurikov, M.S., Clemens, N.T., Donbar, J.M., Carter, C.D., (2002). Planar imaging of CH, OH, and velocity in turbulent non-premixed jet flames. Proceedings of the Combustion Institute 29, 1921–1927. https://doi.org/10.1016/S1540-7489(02)80233-4
*Langhammer, J., Lendzioch, T., Miřijovský, J., Hartvich, F., (2017). UAV-Based Optical Granulometry as Tool for Detecting Changes in Structure of Flood Depositions. Remote Sensing 9, 240. https://doi.org/10.3390/rs9030240
*Li, S., Cheng, W., Wang, M., Chen, C., (2011). The flow patterns of bubble plume in an MBBR. Journal of Hydrodynamics, Ser. B 23, 510–515. https://doi.org/10.1016/S1001-6058(10)60143-6
*Lükő, G., Baranya, S., Rüther, D.N., (2017). UAV Based Hydromorphological Mapping of a River Reach to Improve Hydrodynamic Numerical Models.19th EGU General Assembly, EGU2017, proceedings from the conference held 23-28 April, 2017 in Vienna, Austria., p.13850
*Morgan, J.A., Brogan, D.J., Nelson, P.A., (2017). Application of Structure-from-Motion photogrammetry in laboratory flumes. Geomorphology 276, 125–143. https://doi.org/10.1016/j.geomorph.2016.10.021
*Paterson, D.M., Black, K.S., (1999). Water Flow, Sediment Dynamics and Benthic Biology, in: D.B. Nedwell and D.G. Raffaelli (Ed.), Advances in Ecological Research. Academic Press, pp. 155–193.
*Schönberger, J.L & Frahm, J.M. (2016). Structure-from-Motion Revisited. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
*Tytell, E.D., (2011). Buoyancy, locomotion and movement fishes | Experimental Hydrodynamics, in: Anthony P. Farrell (Ed.), Encyclopedia of Fish Physiology. Academic Press, San Diego, pp. 535–546.
*Vázquez-Tarrío, D., Borgniet, L., Liébault, F., Recking, A., (2017). Using UAS optical imagery and SfM photogrammetry to characterize the surface grain size of gravel bars in a braided river (Vénéon River, French Alps). Geomorphology 285, 94–105. https://doi.org/10.1016/j.geomorph.2017.01.039
*Westoby, M.J., Brasington, J., Glasser, N.F., Hambrey, M.J., Reynolds, J.M., (2012). ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology 179, 300–314. https://doi.org/10.1016/j.geomorph.2012.08.021

=Contact information=

[[Category:Devices]][[Category:Methods]]

File:Sfm drone picture.png

2020-09-30T07:34:14Z

Kordula NTNU:

{{Information
|author= Kordula Schwarzwälder, NTNU
|source= Kordula Schwarzwälder, NTNU
|description=
}}

Structure from motion (SfM)

2020-09-30T07:25:59Z

Kordula NTNU:

=Quick summary=
[[file:sfm_drone_picture.png|thumb|250px|Figure 1: Drone picture taken in a height of 50 m over ground showing the residual flow area in Anundsjø HPP, ice cover and the drone controller (NTNU).]]
[[file:sfm_evaluation.png|thumb|250px|Figure 2: First run of a structure from motion evaluation in the lab of NTNU. The blue field indicates the camera position.]]
[[file:sfm_workflow.png|thumb|250px|Figure 3: Screenshot of the Argisoft Photoscan Workflow.]]
[[file:sf_mesh.png|thumb|250px|Figure 4: Mesh generated from SfM from a section of the residual flow reach of Anundsjø (NTNU).]]

Developed by: Various Companies

Date: -

Type: [[:Category:Devices|Device]], [[:Category:Tools|Tool]]

=Introduction=
The technique of structure from motion was developed for the video-game industry to allow fast and easy 3D detection and evaluation of bodies. With this technique it is possible, based on pictures of an object taken with a camera, to combine these pictures. There are several approaches to generate a 3D model from SfM. In incremental SFM (Schönberger & Frahm 2016) camera poses are solved and added one by one. In global SFM (Govindu 2001) the poses of all cameras are solved at the same time.

The best result can be achieved when taking pictures from as many positions as possible 360 degree around the object of interest. However, this is not possible for rivers. Depending on the riverbank vegetation and the specific location of each river (such as located in a canyon etc.) only a flight directly above, but with no relevant angle to the sides, might be possible (Figure 1).
The same problem might appear when using the technique in the lab (Figure 2).

For a sufficient positioning accuracy of the results, at least a camera with a high-quality GPS sensor needs to be used. The use of targets or even coded targets to allow a more accurate positioning would be more favourable, however. Without such an accurate positioning of the pictures in the space, the result would not be correct as the cameras could not be located correctly in dependency to each other. Targets on the ground, especially for field measurements, are highly recommended. The position of the targets can be measured with a GPS and the targets can be redetected later in the pictures.

Depending on the software used this process of “re-finding” can be done automatically or needs to be done manually.
In general are there different types of software available, commercial and non-commercial ones. As there is a very fast development and most non-commercial tools need a lot of experience with picture modifications etc. it is recommended, despite the costs, to use a commercial one such as Agisoft Photoscan for instance. In this software, the user can follow relatively easy the workflow, provided by the program (Figure 3), to produce a DEM. It further offers a quite comprehensive manual.

=Application=
As for other picture-based evaluation methods, the data processing is the most demanding part of the measurement. When starting with the measurements in the field, it is highly recommended to use targets. The rough workflow is then as follows:

* Putting targets on the ground with sufficient visibility (from an aerial view). The quality of the results increases in case there are two to three targets visible in every picture. Hence the distribution of the targets depends on the flight height of the camera.
* The camera is usually mounted on a drone, however other constructions as a crane etc. are possible.
* Speed and height over ground depend on the area to be evaluated.
* In case large areas need to be covered an automated flight route programmed for the drone would be useful.
* Starting and landing the drone can be a crucial point, especially in the case this is done automatic and the connection is nut sufficient.
* Further problems can be caused by wind (for the flying performance) and by reflections (for the resulting pictures).
* Always be aware of any kind of regulation, law etc. which might restrict or limit the use and handling of a drone!

Once the measurements are done the evaluation is the most crucial point. Depending on the number of pictures evaluated the result quality de- or increases and in a reverse way the computer performance and power needed in- or decreases (Figure 4).

=Relevant mitigation measures and test cases=
{{Suitable measures for 3D fish tracking system}}

=Other information=
The highest cost is the software license in case a commercial product is used.

=Relevant literature=
*Alfredsen, K., Haas, C., Tuthan, J., Zinke, P., (2018). Brief Communication: Mapping river ice using drones and structure from motion.
*Bouhoubeiny, E., Germain, G., Druault, P., (2011). Time-Resolved PIV investigations of the flow field around cod-end net structures. Fisheries Research 108, 344–355. https://doi.org/10.1016/j.fishres.2011.01.010
*Buscombe, D., (2016a). Spatially explicit spectral analysis of point clouds and geospatial data. Computers & Geosciences 86, 92–108. https://doi.org/10.1016/j.cageo.2015.10.004
*Buscombe, D., (2016b). Spatially explicit spectral analysis of point clouds and geospatial data. Computers & Geosciences 86, 92–108. https://doi.org/10.1016/j.cageo.2015.10.004
*Cunliffe, A.M., Brazier, R.E., Anderson, K., (2016). Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sensing of Environment 183, 129–143. https://doi.org/10.1016/j.rse.2016.05.019
*Flammang, B.E., Lauder, G.V., Troolin, D.R., Strand, T.E., (2011). Volumetric imaging of fish locomotion. Biol. Lett. 7, 695–698. https://doi.org/10.1098/rsbl.2011.0282
*Govindu, V.M. (2001). Combining two-view constraints for motion estimation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
*Koci, J., Jarihani, B., Leon, J.X., Sidle, R., Wilkinson, S., Bartley, R., (2017). Assessment of UAV and Ground-Based Structure from Motion with Multi-View Stereo Photogrammetry in a Gullied Savanna Catchment. ISPRS International Journal of Geo-Information 6, 328. https://doi.org/10.3390/ijgi6110328
*Kothnur, P.S., Tsurikov, M.S., Clemens, N.T., Donbar, J.M., Carter, C.D., (2002). Planar imaging of CH, OH, and velocity in turbulent non-premixed jet flames. Proceedings of the Combustion Institute 29, 1921–1927. https://doi.org/10.1016/S1540-7489(02)80233-4
*Langhammer, J., Lendzioch, T., Miřijovský, J., Hartvich, F., (2017). UAV-Based Optical Granulometry as Tool for Detecting Changes in Structure of Flood Depositions. Remote Sensing 9, 240. https://doi.org/10.3390/rs9030240
*Li, S., Cheng, W., Wang, M., Chen, C., (2011). The flow patterns of bubble plume in an MBBR. Journal of Hydrodynamics, Ser. B 23, 510–515. https://doi.org/10.1016/S1001-6058(10)60143-6
*Lükő, G., Baranya, S., Rüther, D.N., (2017). UAV Based Hydromorphological Mapping of a River Reach to Improve Hydrodynamic Numerical Models.19th EGU General Assembly, EGU2017, proceedings from the conference held 23-28 April, 2017 in Vienna, Austria., p.13850
*Morgan, J.A., Brogan, D.J., Nelson, P.A., (2017). Application of Structure-from-Motion photogrammetry in laboratory flumes. Geomorphology 276, 125–143. https://doi.org/10.1016/j.geomorph.2016.10.021
*Paterson, D.M., Black, K.S., (1999). Water Flow, Sediment Dynamics and Benthic Biology, in: D.B. Nedwell and D.G. Raffaelli (Ed.), Advances in Ecological Research. Academic Press, pp. 155–193.
*Schönberger, J.L & Frahm, J.M. (2016). Structure-from-Motion Revisited. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
*Tytell, E.D., (2011). Buoyancy, locomotion and movement fishes | Experimental Hydrodynamics, in: Anthony P. Farrell (Ed.), Encyclopedia of Fish Physiology. Academic Press, San Diego, pp. 535–546.
*Vázquez-Tarrío, D., Borgniet, L., Liébault, F., Recking, A., (2017). Using UAS optical imagery and SfM photogrammetry to characterize the surface grain size of gravel bars in a braided river (Vénéon River, French Alps). Geomorphology 285, 94–105. https://doi.org/10.1016/j.geomorph.2017.01.039
*Westoby, M.J., Brasington, J., Glasser, N.F., Hambrey, M.J., Reynolds, J.M., (2012). ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology 179, 300–314. https://doi.org/10.1016/j.geomorph.2012.08.021

=Contact information=

[[Category:Devices]][[Category:Methods]]

Shelter measurements

2020-09-30T07:10:53Z

Kordula NTNU:

=Quick summary=
[[file:shelter_measurements_salmon.png|thumb|500px|Figure 1: Juvenile salmon in the upper layers of river bed sediment (Forseth et al., 2014).]]
[[file:shelter_measurements_shelter_tube.png|thumb|250px|Figure 2: Example showing hiding depth of 2cm (first marker covered) on a tube (Forseth et al., 2014).]]

Developed by: A. G. Finstad

Date: -

Type: [[:Category:Methods|Method]]

=Introduction=
The technique was developed by Finstad to estimate the shelter availability for juvenile salmon (Finstad et al. 2007a). In this stage of their life cycle, the salmon seeks shelter in the free spaces of the upper layers of the riverbed sediment (Figure 1). The existence and size of such openings to be used as shelter is measured with the described technique. It is relatively easy to be carried out in the field. The method is based on the idea to mimic the small fish by using a plastic tube, which has the approximate diameter of such a fish.

For salmon, Finstad used tubes with five different diameters (5, 10, 13, 16, 22 mm). As a result of their experiments Forseth et al. (2014) suggest the use of a 13mm tube to conduct the tests in an area of about 0.25m2. The tube is marked according to the three different shelter categories at lengths of 2, 5 and 10 cm to be able to detect certain possible hiding depths. Within the fixed framed area one is aiming to find every hole or opening where one can stick the tube into. The length of the tube (defined by the three rings) which is not visible anymore, defines the potential hiding depth (Figure 2).

The post-processing of the data is quite simple and described in the following section.

The method was developed in and for Norwegian rivers with large grain sizes. It is currently (also in FIThydro) under investigation if, when using it in rivers with a smaller D50, i.e. smaller grain sizes, this relation still applies or another relation becomes relevant.

It should further be applied also to other fish species than the salmon. Therefor additional tubes with diameters of 5 mm and 8 mm are used within the project`s measurement campaigns. These results might then be linked to the D5 and D10 grain diameter of the sediment (Szabo-Meszaros et al 2016).

=Application=
For the measurement the following material is needed:
*One tube with a diameter of 13 mm and marks (rings) at 2, 5 and 10 cm
*One frame (metal or wood) to define the size of the area to be counted on
*Something to take notes (Laptop, paper, etc.)

The hiding depth thresholds are defined as 2, 5 and 10 cm and often named as class S1 (2 cm – 5 cm), class S2 (5 cm – 10 cm) and class S3 (> 10 cm).
It is recommended that the same person is carrying out the counting, to minimize the human based systematic error as much as possible.

When all values are collected in the field, the counted numbers for the different classes / hiding depth per spot are weighted using the following empirical formula described in Forseth et al (2014):

<math>S1+S2*2+S3*3</math>

Based on the following matrix the shelter availability can be defined:

<table border="1">
<caption> Table 1: shelter availability depending on the number of shelter spots available. </caption>
<tr style="height: 35px;">
<td style="height: 35px; text-align: center;" width="265">
Shelter Class
</td>
<td style="height: 35px; text-align: center;" width="76">
Low
</td>
<td style="height: 35px; text-align: center;" width="76">
Moderate
</td>
<td style="height: 35px; text-align: center;" width="76">
High
</td>
</tr>
<tr style="height: 35.2px;">
<td style="height: 35.2px; text-align: center;" width="265">
Values for weighted shelter
</td>
<td style="height: 35.2px; text-align: center;" width="76">
< 5
</td>
<td style="height: 35.2px; text-align: center;" width="76">
5 – 10
</td>
<td style="height: 35.2px; text-align: center;" width="76">
> 10
</td>
</tr>
</table>

=Relevant mitigation measures and test cases=
{{Suitable measures for 3D fish tracking system}}

=Other information=

=Relevant literature=
*Finstad, A.G., Einum, S., Forseth, T., Ugedal, O., 2007a. Shelter availability affects behaviour, size-dependent and mean growth of juvenile Atlantic salmon. Freshwater Biology 52, 1710–1718. https://doi.org/10.1111/j.1365-2427.2007.01799.x

*Finstad, A.G., Forseth, T., Ugedal, O., Naesje, T.F., 2007b. Metabolic rate, behaviour and winter performance in juvenile Atlantic salmon. Functional Ecology 21, 905–912. https://doi.org/10.1111/j.1365-2435.2007.01291.x

*Forseth, T., Harby, A., Ugedal, O., Pulg, U., Fjeldstad, H.-P., Robertsen, G., Barlaup, B.T., Alfredsen, K., Sundt, H., Salveit, S.J., Skoglund, H., Kvingedal, E., Sundt-Hansen, L.E.B., Finstad, A., Einum, S., Arnekleiv, J.V., 2014. Handbook for environmental design in regulated salmon rivers (NINA temahefte No. 53). Norsk institutt for naturforskning, Trondheim, Norway.

*Szabo-Meszaros, Marcel, Rüther, N., Alfredsen, K., 2016. Correlation between the shelter of juvenile salmonids and bed substrate, in: Wieprecht, S., Haun, S., Weber, K., Noack, M., Terheiden, K. (Eds.), River Sedimentation: Proceedings of the 13th International Symposium on River Sedimentation (Stuttgart, Germany, 19-22 September, 2016).

*Valdimarsson, S.K., Metcalfe, N.B., 1998. Shelter selection in juvenile Atlantic salmon, or why do salmon seek shelter in winter? Journal of Fish Biology 52, 42–49. https://doi.org/10.1111/j.1095-8649.1998.tb01551.x

=Contact information=

[[Category:Methods]]

Particle image velocimetry (PIV)

2020-09-30T07:02:19Z

Kordula NTNU:

=Quick summary=
[[file:piv_2d_system.png|thumb|250px|Figure 1: Set-up of a standard 2D PIV system (Raffel et al 2007).]]
[[file:piv_large_scale.png|thumb|250px|Figure 2: 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 velocity vectors overlaid on the image (Muste et al. 2008).]]
[[file:piv_airborne.png|thumb|250px|Figure 3: Swiss grid georeferenced surface velocity field and streamlines measured by AIV, including areas of noisy velocity data. (Note that according to Le Coz et al. (2010), depth averaged velocities can be estimated by multiplying the surface velocity by factors 0.79-0.89, with a central value close to 0.85); (Detert et al. 2019).]]

Developed by: -

Date: -

Type: [[:Category:Devices|Device]], [[:Category:Methods|Method]]

=Introduction=

==Particle Image Velocimetry (PIV)==
The particle image velocimetry (PIV), is an optical technique to measure the velocity of a fluid by measuring the velocity of the movement of particles transported in this fluid, assuming that the particles are transported with the fluid without losses or disturbances.

The main devices used for this technique are:
* A source of light (for PIV usually a laser, however strong LED lights become more popular)
* At least one camera (in case of a 3D or volume measurement up to at least 4 cameras)
* Synchronizer to be able to automatically synchronize the camera (times when the pictures are taken) with the laser, otherwise, the pictures would not be properly illuminated
* Software for evaluation of the results
* Mostly also tracer particles as seeding

PIV is an instantaneous method to measure the flow. The main set-up usually includes a laser as a source of light (see Figure 1). With this laser, a so-called laser sheet, a plain area within the fluid, which is enlightened with the laser, is created. All particles transported with the fluid are supposed to reflect the light and become visible like that. This effect can be supported by using special tracer particles (which are quite costly and not necessarily environmentally friendly, so this is only recommended for lab use).

A camera takes pictures of the moving particles in predefined, very short time intervals t. Based on the known t the flow velocity can be calculated using special algorithms to evaluate the movement of the seeding particles visible in the picture. The evaluation of the pictures uses an Eulerian’ approach. To be able to evaluate the shifts on the pictures the system needs to be calibrated. Using the information about the time shift t between two pictures and the calibrated length scale, comparing the shift of the particles on the pictures allow the calculation of the particle movement and hence the fluid velocity.

To be able to achieve good results it is necessary to keep in mind certain rules such as that one particle on the resulting picture should have the size of approx. 2-3 pixel etc. It is highly recommended to get a detailed view into the literature (i.a. Raffel et al 2007) before starting with a measurement.

The PIV can be bought as a complete system, usually in a package with the software. Different companies such as ILA Laser applications, Dantec or LaVision offer this possibility including support. Some research institutions and universities also develop their own codes for post-processing depending on their needs or build the whole laser or camera system on their own.

==Large Scale PIV (LS-PIV) or Airborne Image Velocimetry (AIV)==
Based on the idea of PIV the so-called Large Scale PIV (LS-PIV) or Airborne Image Velocimetry (AIV) was developed. As “Large Scale” with the increasing technical possibilities became a quite wide meaning, the term AIV is more correct nowadays and used hereinafter.

The measurement and evaluation technique is basically the same set-up and underlying theory as PIV and adapts this for use on large scale mainly in the field. As mentioned above the term large scale is not defined, generally it means more than lab size with about max. 1.5m x 1.5m for very good conditions. Such as a PIV the AIV is an optical system used to measure flow velocities, other than the PIV mainly surface velocities in 2D.

Instead of Laser as a source of light, the AIV uses the sun. This might become a problem for the evaluation of the pictures when direct sun is present, as reflections will affect the quality of the results.
The camera is often mounted on a UAV to ensure a sufficient size of the pictures taken as a result of greater distance and angle (Figure 2). Seeding is recommended for a sufficient quality of results. As the seeding, other than for the use with PIV, is used in a natural environment and most likely it will not be possible to collect it back behind the area of interest it is necessary to use seeding which is biodegradable such as wood chips or maize flips (environmentally friendly packing material). Any other material which is biodegradable and available can be used as well, as long as it is following the flow and is visible on the pictures taken.

=Application=
For the application, the study area needs to be well known and targets need to be distributed in the area to ensure a defined distance for calibration on the pictures. As due to the huge amount of seeding needed for one run, not too many tests can be done and it is highly recommended to prepare the experiment properly.
* Set up of targets (either own targets such as wooden crosses or coded targets can be used). The coded targets are especially of interest in case the measurement shall be combined with a Structure from Motion measurement
* Preparation of the drone, best case would be an automated flight route, however, also manual flights are possible without any problem
* Preparation of any other measurement devices to be used at the same time, when the seeding starts floating everything needs to run smoothly
* Preparation of the team which is responsible for spreading the seeding: based on a detailed planning it needs to be ensured, that the seeding is present in a sufficient amount under the drone, hence visible in the picture taken.
* In general, it depends on the camera of the resolution and frequency for pictures or movies is better
*A second run of the experiment would be recommended

After the data collection in the field, the more time-consuming part is the data evaluation. Therefor different software can be used. Usually all variations of commercial products such as the PIV software of DanTec, ILA or LaVision. Further free software such as PIVlab can be used.

Depending on the software different levels of own effort are necessary when it comes to rectification of the pictures (Detert et al. 2015; Patalano et al 2017). The software is, when using PIVlab, MATLAB based and the basic idea is the same as for a standard PIV.

Note that the results generated are surface velocities in the measured areas (Figure 3).

=Relevant mitigation measures and test cases=
{{Suitable measures for Particle image velocimetry (PIV)}}

=Other information=
Costs depend mainly on the staff needed for conducting the experiment and in case a commercial software is used for evaluation on the license costs.

=Relevant literature=
*Detert, M., & Weitbrecht, V. (2014). Helicopter-based surface PIV experiments at Thur River. Proceedings of the Seventh International Conference on River Flow, Lausanne, Switzerland, 2003–2008.
*Detert, M., & Weitbrecht, V. (2015). Estimation of flow discharge by an airborne velocimetry system. First International SHF-Conference on Drones et hydraulique, Paris-Cachan, France.
*Detert, M., Weitbrecht, V., (2015). A low-cost airborne velocimetry system: proof of concept. Journal of Hydraulic Research 53, 532–539. https://doi.org/10.1080/00221686.2015.1054322
*Detert, M., Cao, L. & Albayrak, I. (2019). Airborne image velocimetry measurements at the hydropower plant Schiffmühle on Limmat river, Switzerland. HydroSenSoft, International Symposium and *Exhibition on Hydro-Environment Sensors and Software, Madrid, Spain, ISBN: 978-90-824846-4-9
*Dramais, G., Le Coz, J., Camenen, B., & Hauet, A. (2011). Advantages of a mobile LSPIV method for measuring flood discharges and improving stage-discharge curves. Journal of Hydroenviromental Research, 5, 301–312.
*Fujita, I., & Hino, T. (2003). Unseeded and seeded PIV measurementsof river flows video from a helicopter. Journal of Visualization, 6, 245–252.
*Fujita, I., & Kunita, Y. (2011). Application of aerial LSPIV to the 2002 flood of the Yodo River using a helicopter mounted high density video camera. Journal of Hydroenvironmental Research, 5, 323–331.
*Gunawan, B., Sun, X., Sterling, M., Shiono, K., Tsubaki, R., Rameshwaran, P., Knight, D.W., Chandler, J.H., Tang, X., Fujita, I., (2012). The application of LS-PIV to a small irregular river for inbank and overbank flows. Flow Measurement and Instrumentation 24, 1–12. https://doi.org/10.1016/j.flowmeasinst.2012.02.001
*Le Coz, J., Hauet, A., Pierrefeu, G., Dramais, G., Camenen, B., 2010. Performance of image-based velocimetry (LSPIV) applied to flash-flood discharge measurements in Mediterranean rivers. Journal of Hydrology 394, 42–52. https://doi.org/10.1016/j.jhydrol.2010.05.049
*Muste, M., Fujita, I., & Hauet, A. (2008). Large-scale particle image velocimetry for measurements in riverine environments. Special Issue on Hydrologic Measurements, Water Resources Research, 44, https://doi.org/10.1029/2008WR006950.
*Muste, M., Hauet, A., Fujita, I., Legout, C., Ho, H.-C., (2014). Capabilities of Large-scale Particle Image Velocimetry to characterize shallow free-surface flows. Advances in Water Resources 70, 160–171. https://doi.org/10.1016/j.advwatres.2014.04.004
*Patalano A, Garcia CM, Rodriguez A (2017) Rectification of image velocity results (river): a simple and user-friendly toolbox for large scale water surface particle image velocimetry (piv) and particle tracking velocimetry (ptv). Comput Geosci 109:323–330 http://dx.doi.org/10.1016/j.cageo.2017.07.009
*Pagano, C., Tauro, F., Porfiri, M., & Grimaldi, S. (2014). Development and testing of an unmanned aerial vehicle for large scale particle image velocimetry. Proceedings of ASEM (2014) Dynamic Systems and Control Conference, San Antonio,USA, DSCC2014-5838.
* Raffel, M., Willert, C., Wereley, S.T., Kompenhans, J. (2007). Particle Image Velocimetry: A Practical Guide. http://dx.doi.org/10.1007/978-3-540-72308-0
*Thielicke, W., & Stamhuis, E. J. (2014). PIVlab: Time-resolved digital Particle Image Velocimetry tool for MATLAB (version: 1.35). http://dx.doi.org/10.6084/m9.figshare.1092508.
*Weitbrecht, V., Kühn, G., & Jirka, G. H. (2002). Large-scale PIV-measurements at the surface of shallow water flows. Flow Measurement and Instrumentation, 13, 237–245.

=Contact information=

[[Category:Devices]][[Category:Methods]]