Quantifying riverine surface currents from time sequences of thermal infrared imagery

Abstract

[1] River surface currents are quantified from thermal and visible band imagery using two methods. One method utilizes time stacks of pixel intensity to estimate the streamwise velocity at multiple locations. The other method uses particle image velocimetry to solve for optimal two-dimensional pixel displacements between successive frames. Field validation was carried out on the Wolf River, a small coastal plain river near Landon, Mississippi, United States, on 26-27 May 2010 by collecting imagery in association with in situ velocities sampled using electromagnetic current meters deployed 0.1 m below the river surface. Comparisons are made between mean in situ velocities and image-derived velocities from 23 thermal and 6 visible-band image sequences (5 min length) during daylight and darkness conditions. The thermal signal was a small apparent temperature contrast induced by turbulent mixing of a thin layer of cooler water near the river surface with underlying warmer water. The visible-band signal was foam on the water surface. For thermal imagery, streamwise velocities derived from the pixel time stack and particle image velocimetry technique were generally highly correlated to mean streamwise current meter velocities during darkness (r 2 typically greater than 0.9) and early morning daylight (r 2 typically greater than 0.83). Streamwise velocities from the pixel time stack technique had high correlation for visible-band imagery during early morning daylight hours with respect to mean current meter velocities (r 2 > 0.86). Streamwise velocities for the particle image velocimetry technique for visible-band imagery had weaker correlations with only three out of six correlations performed having an r 2 exceeding 0.6.

Discharge Measurements of Snowmelt Flood by Space-Time Image Velocimetry during the Night Using Far-Infrared Camera

“…However, this shortcoming was overcome by introducing a far-infrared ray camera in the present study for the first time for flood flow measurement with surface velocities more than 3 m/s. Puelo et al [21] used a thermal infrared camera to measure small stream flows, but the velocity level was low with the maximum velocity less than 0.9 m/s, and it was not a flood measurement. Regarding the traceability of surface features, the assumption that surface features are advected with the surface velocity was verified by directly comparing it with the velocities of floating objects such as driftwood or floats in a space-time image.…”

Analysis of the temperature dynamics of a proglacial river using time-lapse thermal imaging and energy balance modeling

“…Later, techniques developed for particle image velocimetry were applied to high-frequency thermal images to analyze turbulent mixing processes between a jet of hot spring water entering a cold creek (Andrews et al, 2011). A similar approach has been applied to large rivers in order to quantify velocity distributions (Puleo et al, 2012). Some studies have actively used heat as a tracer by introducing warm water into surface water bodies and then conducting time-lapse imaging of the thermal plume to quantify transport behavior (Schuetz et al, 2012).…”

Estimación del caudal a partir de la evolución del nivel del agua en un estuario con onda de marea estacionaria

“…Más recientemente, mediciones continuas de la velocidad superficial a partir de métodos de no contacto mostraron ser útiles para estimar la descarga [25][26][27][28][29][30][31][32][33][34][35][36][37]. El desafío consiste en relacionar la velocidad medida en superficie con el flujo subsuperficial.…”