UAV-based measurements of spatio-temporal concentration distributions of fluorescent tracers in open channel flows

An Experimental Evaluation of the Feasibility of Inferring Concentrations of a Visible Tracer Dye from Remotely Sensed Data in Turbid Rivers

“…However, because prior research was conducted in clear-flowing streams, a key question remains: can this approach also be applied in more turbid settings? Whereas the Kootenai is an exceptionally clear river [14] and the outdoor flume used by Baek et al [11] and Legleiter et al [10] was both clear and shallow, many rivers of interest are large, deep, and, perhaps most importantly, far more turbid. The presence of greater turbidity could pose a challenge to remote sensing of tracer dye concentrations because this approach is based on spectrally exploiting the change in water color caused by the presence of the dye.…”

“…More recently, the potential for remote sensing of tracer dye concentrations in river systems also has been established [10,11]. Using data acquired from a unique experimental facility with a sinuous outdoor flume, Baek et al [11] tested the feasibility of estimating dye concentrations from RGB (red, green, blue) images acquired from a sUAS.…”

“…More recently, the potential for remote sensing of tracer dye concentrations in river systems also has been established [10,11]. Using data acquired from a unique experimental facility with a sinuous outdoor flume, Baek et al [11] tested the feasibility of estimating dye concentrations from RGB (red, green, blue) images acquired from a sUAS. Accurate concentration maps were produced from the relatively basic, three-band image data by training an artificial neural network to account for spatial variations in water depth and bottom composition that modified the relationship between image brightness and dye concentration on a local basis.…”

“…Accurate concentration maps were produced from the relatively basic, three-band image data by training an artificial neural network to account for spatial variations in water depth and bottom composition that modified the relationship between image brightness and dye concentration on a local basis. Legleiter et al [10] developed a more spectrally based approach using hyperspectral images acquired from a sUAS deployed above the same experimental channel as in [11] and field spectra and multi-and hyperspectral image data from the Kootenai River, a large natural river in northern Idaho, USA. An optimal band ratio analysis (OBRA) algorithm [12,13] was modified to identify wavelength combinations that yielded strong correlations between a spectrally based quantity X and dye concentration C. Relative to the machine learning technique employed by Baek et al [11], the OBRA framework provided greater insight as to how reflectance varies with concentration and a more flexible, readily interpretable means of identifying specific wavelength combinations that yield strong relationships between dye concentrations and spectrally based quantities.…”

“…Legleiter et al [10] developed a more spectrally based approach using hyperspectral images acquired from a sUAS deployed above the same experimental channel as in [11] and field spectra and multi-and hyperspectral image data from the Kootenai River, a large natural river in northern Idaho, USA. An optimal band ratio analysis (OBRA) algorithm [12,13] was modified to identify wavelength combinations that yielded strong correlations between a spectrally based quantity X and dye concentration C. Relative to the machine learning technique employed by Baek et al [11], the OBRA framework provided greater insight as to how reflectance varies with concentration and a more flexible, readily interpretable means of identifying specific wavelength combinations that yield strong relationships between dye concentrations and spectrally based quantities. Legleiter et al [10] reported very strong (R 2 from 0.94 to 0.99) relationships between X and C across a broad range of visible wavelengths for all of the data sets considered: sUAS-based hyperspectral images from the outdoor flume, field spectra collected from a boat on the Kootenai River, and hyperspectral images and high-resolution aerial photographs acquired from a manned aircraft along the Kootenai.…”