Abstract
Passive optical remote sensing of river bathymetry involves establishing a relation between depth and reflectance that can be applied throughout an image to produce a depth map. Building upon the Optimal Band Ratio Analysis (OBRA) framework, we introduce sampling strategies for constructing calibration data sets that lead to strong relationships between an image-derived quantity and depth across a range of depths. Progressively excluding observations that exceed a series of cutoff depths from the calibration process improved the accuracy of depth estimates and allowed the maximum detectable depth (dmax) to be inferred directly from an image. Depth retrieval in two distinct rivers also was enhanced by a stratified version of OBRA that partitions field measurements into a series of depth bins to avoid biases associated with under-representation of shallow areas in typical field data sets. In the shallower, clearer of the two rivers, including the deepest field observations in the calibration data set did not compromise depth retrieval accuracy, suggesting that dmax was not exceeded and the reach could be mapped without gaps. Conversely, in the deeper and more turbid stream, progressive truncation of input depths yielded a plausible estimate of dmax consistent with theoretical calculations based on field measurements of light attenuation by the water column. This result implied that the entire channel, including pools, could not be mapped remotely. However, truncation improved the accuracy of depth estimates in areas shallower than dmax, which comprise the majority of the channel and are of primary interest for many habitat-oriented applications.
Highlights
Beginning in the 1990s, remote sensing emerged as a powerful means of characterizing river channel morphology and in-stream habitat [1,2,3,4,5,6]
Because most of the field data were retained for the largest cutoff depth of 8.57 m, the Optimal Band Ratio Analysis (OBRA) R2 decreased to a value similar to that for the original data set
We introduced a pair of depth retrieval algorithms that build upon the Optimal Band Ratio Analysis (OBRA) framework: OBRA of Progressively Truncated Input Depths (OPTID) and Stratified Optimal Band Ratio Analysis (SOBRA)
Summary
Beginning in the 1990s, remote sensing emerged as a powerful means of characterizing river channel morphology and in-stream habitat [1,2,3,4,5,6]. Whereas earlier methodological studies focused on algorithm development and testing at the reach scale [9,10,11,12], more recent work is broader in scope, encompassing long river segments or entire watersheds [13,14,15]. This kind of large-scale remote sensing could support a broad range of applications, such as assessing geomorphic impacts of extreme flow events [16] and quantifying habitat availability for critical species [17]. Considerable progress has been made in remote sensing of rivers, but further improvements are needed to provide reliable data products capable of supporting widespread, cost-effective application of imaging technologies to the fluvial domain
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