Field measurements of flow depth and optical image sequences acquired from the Salcha River, Alaska, on July 25, 2019
March 3, 2021
This data release includes field measurements of flow depth and optical image sequences acquired from the Salcha River in Alaska on July 25, 2019. These data were used to develop and test a spectrally based remote sensing technique for estimating water depth from passive optical image data. The purpose of this study was to assess the feasibility of inferring water depths from optical image sequences acquired from a helicopter hovering above the river by averaging the images over time and then establishing a correlation between a spectral band ratio and field measurements of depth, and to develop a modular workflow for performing this type of analysis. Remote sensing of river bathymetry (depth) could provide a more efficient, cost-effective alternative to conventional field-based methods of measuring depth and become an important component of non-contact approaches to streamgaging, geomorphic characterization, and habitat assessment. This parent data release includes links to child pages for several data sets produced during the study: Acoustic Doppler Current Profiler (ADCP) field measurements of flow depth from the Salcha River collected on July 25, 2019. High frame rate video acquired from the Salcha River on July 25, 2019, and used to estimate water depth. High spatial resolution orthophotos produced from images acquired from the Salcha River on July 25, 2019, and used as a base for geo-refreencing images extracted from the video. Please refer to the individual child pages for further detail about each data set. Overall, these data were used to assess the potential to estimate water depth in relatively clear-flowing, shallow rivers from helicopter-based, hovering image sequences using a spectrally based remote sensing technique.
Citation Information
Publication Year | 2021 |
---|---|
Title | Field measurements of flow depth and optical image sequences acquired from the Salcha River, Alaska, on July 25, 2019 |
DOI | 10.5066/P9S4T8YM |
Authors | Carl J Legleiter, Paul J Kinzel |
Product Type | Data Release |
Record Source | USGS Asset Identifier Service (AIS) |
USGS Organization | Water Resources Mission Area - Headquarters |
Rights | This work is marked with CC0 1.0 Universal |
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Improving remotely sensed river bathymetry by image-averaging
Basic data on river bathymetry is critical for numerous applications in river research and management and is increasingly obtained via remote sensing, but the noisy, pixelated appearance of image‐derived depth maps can compromise subsequent analyses. We hypothesized that this noise originates from reflectance from an irregular water surface and introduced a framework for mitigating these...
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Improving remotely sensed river bathymetry by image-averaging
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