Spectrally based bathymetric mapping of a dynamic, sand‐bedded channel: Niobrara River, Nebraska, USA

Abstract

Methods for spectrally based mapping of river bathymetry have been developed and tested in clear‐flowing, gravel‐bed channels, with limited application to turbid, sand‐bed rivers. This study used hyperspectral images and field surveys from the dynamic, sandy Niobrara River to evaluate three depth retrieval methods. The first regression‐based approach, optimal band ratio analysis (OBRA), paired in situ depth measurements with image pixel values to estimate depth. The second approach used ground‐based field spectra to calibrate an OBRA relationship. The third technique, image‐to‐depth quantile transformation (IDQT), estimated depth by linking the cumulative distribution function (CDF) of depth to the CDF of an image‐derived variable. OBRA yielded the lowest depth retrieval mean error (0.005 m) and highest observed versus predicted R2 (0.817). Although misalignment between field and image data did not compromise the performance of OBRA in this study, poor georeferencing could limit regression‐based approaches such as OBRA in dynamic, sand‐bedded rivers. Field spectroscopy‐based depth maps exhibited a mean error with a slight shallow bias (0.068 m) but provided reliable estimates for most of the study reach. IDQT had a strong deep bias but provided informative relative depth maps. Overprediction of depth by IDQT highlights the need for an unbiased sampling strategy to define the depth CDF. Although each of the techniques we tested demonstrated potential to provide accurate depth estimates in sand‐bed rivers, each method also was subject to certain constraints and limitations.

The optical river bathymetry toolkit

“…More specifically, Optimal Band Ratio Analysis (OBRA) identifies the numerator and denominator wavelengths that yield the strongest relationship between an imagederived quantity and water depth (Legleiter, Roberts, & Lawrence, 2009). OBRA has been applied in numerous contexts, ranging from sand-bed rivers (Dilbone, Legleiter, Alexander, & McElroy, 2018) to supraglacial channels (Legleiter, Tedesco, Smith, Behar, & Overstreet, 2014), and proven robust to variations in substrate composition, water column characteristics, and water surface roughness. Moreover, accurate bathymetric maps have been produced from various types of images.…”

sUAS-Based Remote Sensing of River Discharge Using Thermal Particle Image Velocimetry and Bathymetric Lidar

“…This process served to project the three-dimensional lidar data onto a cross-sectional plane. We used a lowess (locally weighted scatterplot smoothing, [46,47]) fit to create a smooth line through the resulting two-dimensional point cloud of lidar depths. The lowess procedure was implemented with a span parameter of 0.1, specified as percentage of the total number of data points.…”

Defining the Limits of Spectrally Based Bathymetric Mapping on a Large River

“…Because the physical basis for passive optical depth retrieval is the attenuation of solar radiation propagating from the water surface to the streambed and back upward toward the sensor, quantitative information on water column characteristics from different river environments can yield insight on how these radiative transfer processes affect bathymetric mapping performance and limit d max . To augment the database we have assembled through several previous studies, we made field measurements of water column optical properties from a single location at the upper end of our study area on the Kootenai River and compared these observations to similar data sets from seven other rivers in the western United States: California's upper Sacramento River [17], the Deschutes River in Oregon [24], Nebraska's Niobrara River [20], the Snake River in Grand Teton National Park, Wyoming, and the Blue and Colorado Rivers and Muddy Creek where the three streams come together in Colorado [14]. In this study, we employed the same field methods as in Legleiter and Harrison [17], which provides further detail.…”

“…Although previous research has demonstrated the potential to acquire reliable depth information in many clear-flowing gravel-bed rivers (e.g., [11,18]), and even some more turbid sand-bed channels (e.g., [19,20]), additional testing across a broader range of river environments is needed to identify portions of the overall fluvial parameter space where remote sensing methods might play a useful role. More specifically, because earlier studies tended to focus on shallow streams conducive to remote sensing, the limits of spectrally based depth retrieval remain poorly constrained.…”