Author(s): Edwin Cowen; Seth Schweitzer

Linked Author(s): Edwin Cowen, Seth Schweitzer

Keywords: Remote sensing; Turbulence; Quantitative imaging; Surface water; Measurement

Abstract: Water and its ability to transport, stir, mix and disperse mass is fundamental to life. Considering climate change there is a critical need for better measurements of water’s full transport ability, in particular turbulence, for model development and validation. This need spans monitoring small-scale turbulent processes to calibrating and nudging regional scale ocean models. Driven by applications from river discharge to, the near-shore environment of lakes, estuaries and the coasts, remote sensing with quantitative imaging tools is a rapidly expanding field but to date with primary focus on mean transport. In this presentation we focus on using remotely mounted infrared cameras, which can be deployed from fixed platforms, drones, planes and satellites, to measure turbulent processes and turbulence metrics in surface waters. In recent years field-scale applications of image-based velocimetry methods built around visible-light cameras, often referred to as Large Scale Particle Image Velocimetry (LSPIV), which have the advantage of ubiquitous availability and low cost, have been increasingly deployed. However, LSPIV methods can have significant drawbacks. The water surface lacks natural features that can be tracked in the visible and generally requires seeding with tracer particles, adding logistical difficulties, including achieving sufficient and uniform seeding density in all areas of interest, and raising concerns of how well the particles track the flow, especially in regions with appreciable velocity accelerations such as turbulence. In LSPIV, image collection is dependent on available illumination, which generally limits operation to daylight hours, and can suffer from non-uniformity of illumination across the camera’s field-of-view. An alternative imaging technology that avoids these seeding and illumination issues is infrared (IR) imaging, which accurately captures subtle temperature patterns at the water surface with high thermal and spatial resolution. In natural flows small temperature differences exist in the surface skin temperature field due to spatial heterogeneity in turbulent stirring and heat exchange between air and water. These spatial differences in temperature form a rich texture of patterns on the water surface that are observable in IR images. In IR-QIV the advection of these patterns is tracked and the instantaneously velocity field extracted. Utilizing IR imagers removes any dependence on available illumination since they record radiation emitted by the water itself. Since the patterns being tracked are properties of the water itself there are no concerns about the accuracy with which tracer particles track flow features. While both LSPIV and IR-QIV are able to extract the mean surface velocity field at field-scale operation, LSPIV often requires spatio-temporal averaging. IR-QIV is able to extract the instantaneous velocity field reliably and robustly. We will provide an overview of our developed infrared quantitative imaging technique (IR-QIV) with particular emphases on how to optimize IR-QIV for accurate turbulence measurements.

DOI: https://doi.org/10.3850/IAHR-39WC252171192022523

Year: 2022