Publications

An Evaluation of Image Velocimetry Techniques under Low Flow Conditions and High Seeding Densities Using Unmanned Aerial Systems.

Image velocimetry has proven to be a promising technique for monitoring river flows using remotely operated platforms such as Unmanned Aerial Systems (UAS). However, the application of various image velocimetry algorithms has not been extensively assessed. Therefore, a sensitivity analysis has been conducted on five different image velocimetry algorithms including Large Scale Particle Image Velocimetry (LSPIV), Large-Scale Particle Tracking Velocimetry (LSPTV), Kanade–Lucas Tomasi Image Velocimetry (KLT-IV or KLT), Optical Tracking Velocimetry (OTV) and Surface Structure Image Velocimetry (SSIV), during low river flow conditions (average surface velocities of 0.12–0.14 ms-1 , Q60) on the River Kolubara, Central Serbia. A DJI Phantom 4 Pro UAS was used to collect two 30-second videos of the surface flow. Artificial seeding material was distributed homogeneously across the rivers surface, to enhance the conditions for image velocimetry techniques. The sensitivity analysis was performed on comparable parameters between the different algorithms, including the particle identification area parameters (such as Interrogation Area (LSPIV, LSPTV and SSIV), Block Size (KLT-IV) and Trajectory Length (OTV)) and the feature extraction rate. Results highlighted that KLT and SSIV were sensitive to changing the feature extraction rate; however, changing the particle identification area did not affect the surface velocity results significantly. OTV and LSPTV, on the other hand, highlighted that changing the particle identification area presented higher variability in the results, while changing the feature extraction rate did not affect the surface velocity outputs. LSPIV proved to be sensitive to changing both the feature extraction rate and the particle identification area. This analysis has led to the conclusions that for surface velocities of approximately 0.12 ms-1 image velocimetry techniques can provide results comparable to traditional techniques such as ADCPs. However, LSPIV, LSPTV and OTV require additional effort for calibration and selecting the appropriate parameters when compared to KLT-IV and SSIV. Despite the varying levels of sensitivity of each algorithm to changing parameters, all configuration image velocimetry algorithms provided results that were within 0.05 ms-1 of the ADCP measurements, on average.

Berührlose optische Durchflussmessung unter hochalpinen Verhältnissen.

Dieser Beitrag stellt ein bildgebendes Messsystem vor, mit dem Wasserstand und Durchfluss in einem alpinen Wildbach gemessen werden. Das System ist energetisch autark und kann in Echtzeit zur Überwachung eines Wildbaches und dessen Umgebung abgerufen werden. Wasserstand, Fließgeschwindigkeit und Durchfluss werden automatisch bestimmt und auf einen Server übertragen. Die gemessenen Durchflusswerte wurden durch Tracerversuche validiert und das Messsystem erwies sich als zuverlässig über vier Schmelzsaisons bei alpinen Wetterverhältnissen.

Abflussmessungen Mittels Videos.

Mit kurzen Videosequenzen von Überwachungskameras und Smartphones können Oberflächengeschwindigkeiten in Abwasserkanälen gemessen werden. Eingesetzt wird dabei die bildbasierte Methode namens Surface Structure Image Velocimetry, kurz SSIV. Die resultierenden Ergebnissestimmen gut mit Ultraschall-Referenzmessungen überein und unterstreichen das Potenzial der innovativen Methode.

Oberflächenabflussmessungen im Urbanen raum mittels Videomaterial von Überwachungskameras.

Messungen von Oberflächenabflüssen werden selten im urbanen Raum durchgeführt, weil tradi­tionelle Messgeräte zu teuer sind oder direkt über oder innerhalb des Fliessgewässers installiert werden müssen. Deswegen sind Daten über Überschwemmungen im urbanen Raum nur selten vorhanden, was die Kalibrierung und Validierung von Überflutungsmodellen erschwert. In dieser Studie wird ein Ansatz vorgestellt, bei dem marktübliche Überwachungskameras zur Durchfluss­ messung eingesetzt werden.

Evaluation of the DischargeApp: a smartphone application for discharge measurements.

Herein we present a study that evaluates the DischargeApp to gauge water flow rates at 20–120 L/s in a clear water laboratory flume. In comparison to gauges of a magnetic flow meter the resulting absolute measurement error shows to be ±10 L/s, while for more of 85% of the measurements, the relative error is below 15%. This acceptable error, together with its simplicity and low cost characteristic, rank the DischargeApp as an ideal device for fast measuring of discharges. The DischargeApp has, therefore, the potential to gather useful and much needed hydrometric data in order to globally improve water resources management.

Urban overland runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry.

Here we investigated the potential of using surveillance camera footage to measure surface flow velocity thanks to the Surface Structure Image Velocimetry (SSIV). Seven real-scale experiments conducted in a specialized flood training facility were used to test the SSIV method under varied and challenging conditions. In the best conditions tested, SSIV and conventional flow sensors differed by only 1.7% (0.1% standard deviation). While the method proved sensitive to light conditions, our results suggest that infrared lighting could be used to increase measurement consistency. Our study concludes that for measuring overland flow velocity in urban areas, surveillance and traffic cameras can be used.

Mobile device app for small open-channel flow measurement.

We present a mobile device app to measure discharge in open channels. With this tool flow data can be collected reliably and cheaply. The technology, on which the app is based, is derived from an already implemented and tested similar webcam application. The methodology has been tested for the webcam application in a pilot project in Switzerland. The results show that it is capable to produce continuous and reliable data for water level, surface velocity and runoff. The accuracy is within 5% of data obtained from a commercial radar sensor. The existing preliminary results for the smartphone application indicate similar accuracy. The tool has the potential to become the state of the art method for mobile runoff measurements, as it is truly high-tech and low-cost.