Evaluating Image Tracking Approaches for Surface Velocimetry With Thermal Tracers

Abstract

In this paper an automatic approach is proposed to measure flow velocity with an uncooled thermal camera. Hot water is used as thermal tracer. The introduced tracking algorithm utilizes the pyramidal Lucas‐Kanade method and is especially suitable for thermal image data. The performance of the new tool is compared to traditional image‐based tracking tools, that is, PIVlab and PTVlab. Experiments are performed in the laboratory for three different flow velocities and tests are conducted in a small stream to illustrate the suitability of the tool for field measurements. Results of the laboratory experiments as well as of the field experiments show that our tracking algorithm, applied to imagery from a thermal camera, outperforms commonly used tracking methods. Our tool provides velocity fields with very high resolution and is in close agreement with reference measurements, whereas PTVlab and PIVlab tend to overestimate and underestimate flow velocities, respectively.

River-ice and water velocities using the Planet optical cubesat constellation

“…ice-free conditions) is employed to automatically delineate the river water surface over the Yukon River study reach used in this work. Indexes used for the purpose of mapping water areas from multispectral satellite data are typically based on the reflectance contrast of water between blue (high reflectance) and near-infrared wavelengths (low reflectance) (McFeeters, 1996;Pekel et al, 2016). However, for our study site and conditions, we find that the contrast between the blue and thermal infrared Landsat bands is larger than the blue vs. infrared contrast due to a high suspended sediment concentration that increases the near-infrared reflectance and, thus, reduces the contrast to reflectance at blue wavelengths.…”

“…However, for our study site and conditions, we find that the contrast between the blue and thermal infrared Landsat bands is larger than the blue vs. infrared contrast due to a high suspended sediment concentration that increases the near-infrared reflectance and, thus, reduces the contrast to reflectance at blue wavelengths. To increase index sensitivity compared with the commonly used normalized difference indexes (McFeeters, 1996), we apply a band ratio. Thus, river outlines were obtained from a rasterto-vector conversion of a noise-filtered (3 × 3 median filter) and thresholded band ratio image (Paul et al, 2002) between the blue and thermal infrared bands of Landsat 8.…”

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

“…The third possibility is the usage of optical flow algorithms developed in the computer vision community. For instance, the Lucas-Kanade (Lucas and Kanade, 1981) operation has been utilised to measure surface velocities of large floods or small rivers (Perks et al, 2016or Lin et al, 2019. The method aims to minimise greyscale value differences between the template and search area adapting the parameters of an affine transformation within an optimisation procedure.…”

sUAS-Based Remote Sensing of River Discharge Using Thermal Particle Image Velocimetry and Bathymetric Lidar

“…A thorough review of the extensive literature on and rapid technological advances in remote sensing of streamflow is beyond the scope of this paper. To summarize in brief, research on remote sensing of discharge and other river characteristics is being actively pursued by many academic, governmental, and commercial institutions using a wide variety of platforms and sensors including ground-based approaches [7,8], manned [9,10] and unmanned aerial vehicles [11][12][13], and satellites [14,15].…”