Remote sensing of tracer dye concentrations to support dispersion studies in river channels

Legleiter, Carl J.; McDonald, Richard R.; Nelson, Jonathan M.; Kinzel, Paul J.; Perroy, Ryan L.; Baek, Donghae; Seo, Il Won

doi:10.1080/24705357.2019.1662339

Cited by 20 publications

(33 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. In addition, Legleiter et al [10] showed that X vs. C relations derived from field spectra could be applied to hyperspectral images and that concentrations could be estimated nearly as reliably from RGB images as from hyperspectral data.…”

Section: Introductionmentioning

confidence: 96%

“…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. In addition, Legleiter et al [10] showed that X vs. C relations derived from field spectra could be applied to hyperspectral images and that concentrations could be estimated nearly as reliably from RGB images as from hyperspectral data.…”

Section: Introductionmentioning

confidence: 96%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

The movement of contaminants and biota within river channels is influenced by the flow field via various processes of dispersion. Understanding and modeling of these processes thus can facilitate applications ranging from the prediction of travel times for spills of toxic materials to the simulation of larval drift for endangered species of fish. A common means of examining dispersion in rivers involves conducting tracer experiments with a visible tracer dye. Whereas conventional in situ instruments can only measure variations in dye concentration over time at specific, fixed locations, remote sensing could provide more detailed, spatially-distributed information for characterizing dispersion patterns and validating two-dimensional numerical models. Although previous studies have demonstrated the potential to infer dye concentrations from remotely sensed data in clear-flowing streams, whether this approach can be applied to more turbid rivers remains an open question. To evaluate the feasibility of mapping spatial patterns of dispersion in streams with greater turbidity, we conducted an experiment that involved manipulating dye concentration and turbidity within a pair of tanks while acquiring field spectra and hyperspectral and RGB (red, green, blue) images from a small Unoccupied Aircraft System (sUAS). Applying an optimal band ratio analysis (OBRA) algorithm to these data sets indicated strong relationships between spatially averaged reflectance (i.e., water color) and Rhodamine WT dye concentration across four different turbidity levels from 40-60 NTU. Moreover, we obtained high correlations between spectrally based quantities (i.e., band ratios) and dye concentration for the original, essentially continuous field spectra; field spectra resampled to the bands of a five-band imaging system and an RGB camera; and both hyperspectral and RGB images acquired from an sUAS during the experiment. The results of this study thus confirmed the potential to map dispersion patterns of tracer dye via remote sensing and suggested that this empirical approach can be extended to more turbid rivers than those examined previously.

show abstract

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

Remote sensing of laboratory rivers

Leenman

Eaton

2023

Earth Surf Processes Landf

View full text Add to dashboard Cite

Remote sensing enables us to measure fluvial systems without disrupting their dynamics. Small‐scale physical models of rivers allow us to observe their geomorphic evolution, but we need remote sensing methods to monitor these laboratory landscapes without altering their flow or topography, just as with field‐scale rivers. In this paper, we review how experimental geomorphologists have adapted remote sensing for the laboratory. We consider how remote methods to monitor model topography, flow depth, velocity and planform have been employed, enabling uninterrupted experimental evolution. We also explore the transfer of techniques between field‐scale and experimental remote sensing; the controlled conditions in the lab aided the development of some methods, while others benefited from airborne deployment. We consider recent developments offered by laboratory remote sensing, including through‐water laser scanning and adaptations of structure‐from‐motion photogrammetry; we also consider new challenges associated with these developments, such as computational power. Finally, we discuss new research problems that laboratory remote sensing is opening up to geomorphology. We hope this review will be useful for experimentalists seeking to collect data remotely, continuously and/or cost‐effectively.

show abstract

Preferential behavior of Tribolodon hakonensis for fishways according to biological characteristics

Masumoto

Nakai²,

Asaeda

et al. 2023

River Research & Apps

View full text Add to dashboard Cite

In 2012, the fishway in the Miyanaka Intake Dam in Niigata Prefecture, Japan, was reconstructed and divided into three fishways (ice‐harbor‐, stair‐, and rock‐ramp‐type) with different flow velocities and water depths. We conducted an adaptive monitoring survey in these fishways from 2012 to 2020, and 33 fish types were found, including migratory, swimming, small swimming, and benthic fishes. Many species displayed a preference for specific fishways. The run‐up for Tribolodon hakonensis was studied to determine the effects of physical parameters such as body length, rush speed for instantaneous swimming, water temperature, and presence/absence of nuptial coloration on fishway preference. T. hakonensis with nuptial coloration had greater body length and higher rush speeds than those without it. They also preferred the ice‐harbor fishway, whereas their shorter, uncolored counterparts preferred the stair and rock‐ramp alternatives. The environmental conditions selected by T. hakonensis changed according to their growth stage, as did fishway preference. Changes in fishway environmental conditions may have influenced the observed differences in fishway populations. It was thus confirmed that developing fishways with different flow velocities and water depths is effective in supporting the various life stages of T. hakonensis.

show abstract

Remote sensing of tracer dye concentrations to support dispersion studies in river channels

Cited by 20 publications

References 12 publications

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

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

Remote sensing of laboratory rivers

Preferential behavior of Tribolodon hakonensis for fishways according to biological characteristics

Contact Info

Product

Resources

About