Geology topics

Carl J. Legleiter

Publications and source records attributed to Carl J. Legleiter.

At least 55 records · Page 3Linked to original sources

USGS2021

Surface flow velocities from space: Particle image velocimetry of satellite video of a large, sediment-laden river

Conventional, field-based streamflow monitoring in remote, inaccessible locations such as Alaska poses logistical challenges. Safety concerns, financial considerations, and a desire to expand water-observing networks make remote sensing an appealing alternative means of collecting hydrologic data. In an ongoing effort to develop non-contact methods for measuring river discharge, we evaluated the potential to estimate surface flow velocities from satellite video of a large, sediment-laden river in Alaska via particle image velocimetry (PIV). In this setting, naturally occurring sediment boil vortices produced distinct water surface features that could be tracked from frame to frame as they were advected by the flow, obviating the need to introduce artificial tracer particles. In this study, we refined an end-to-end workflow that involved stabilization and geo-referencing, image preprocessing, PIV analysis with an ensemble correlation algorithm, and post-processing of PIV output to filter outliers and scale and geo-reference velocity vectors. Applying these procedures to image sequences extracted from satellite video allowed us to produce high resolution surface velocity fields; field measurements of depth-averaged flow velocity were used to assess accuracy. Our results confirmed the importance of preprocessing images to enhance contrast and indicated that lower frame rates (e.g., 0.25 Hz) lead to more reliable velocity estimates because longer capture intervals allow more time for water surface features to translate several pixels between frames, given the relatively coarse spatial resolution of the satellite data. Although agreement between PIV-derived velocity estimates and field measurements was weak ( R 2 = 0.39) on a point-by-point basis, correspondence improved when the PIV output was aggregated to the cross-sectional scale. For example, the correspondence between cross-sectional maximum velocities inferred via remote sensing and measured in the field was much stronger ( R 2 = 0.76), suggesting that satellite video could play a role in measuring river discharge. Examining correlation matrices produced as an intermediate output of the PIV algorithm yielded insight on the interactions between image frame rate and sensor spatial resolution, which must be considered in tandem. Although further research and technological development are needed, measuring surface flow velocities from satellite video could become a viable tool for streamflow monitoring in certain fluvial environments.

Alaska↗

USGS2021

The optical river bathymetry toolkit

Spatially distributed information on water depth is essential for many applications in river research and management and, under certain circumstances, can be inferred from remotely sensed data. Although fluvial remote sensing has emerged as a rapidly developing subdiscipline of the riverine sciences, more widespread adoption of these techniques has been hindered by a lack of accessible software. The Optical River Bathymetry Toolkit (ORByT) fills this void by providing a standalone package for mapping water depth from passive optical image data. The ORByT interface enables end users to import images and field‐based depth measurements, create and refine water masks, and perform spectrally based depth retrieval via an Optimal Band Ratio Analysis algorithm. The resulting bathymetric map can be exported as an image file, point cloud, and/or cross section; a thorough accuracy assessment also is incorporated into the workflow. In addition, image‐derived depth estimates can be subtracted from water surface elevations to obtain bed elevations suitable for input to a hydrodynamic model. Potential users of ORByT must bear in mind the inherent limitations of passive optical remote sensing: reliable bathymetry can only be inferred in clear‐flowing, shallow streams; this approach is not appropriate for more turbid, deeper rivers.

River Research and Applications↗

USGS2021

Field evaluation of a compact, polarizing topo‐bathymetric lidar across a range of river conditions

This paper summarizes field trials to evaluate the performance of a prototype compact topo‐bathymetric lidar sensor for surveying rivers. The sensor uses a novel polarization technique to distinguish between laser returns from the water surface and streambed and its size and weight permit deployment from a small unmanned aerial system (sUAS) or a boat. Field testing was designed to identify the range of operational conditions under which the sensor can provide accurate information on river depths. For accuracy assessment, conventional, field‐based depth measurements were collected by wading and sonar. Additionally, optical properties of the rivers were measured in situ. Wading and lidar bathymetry comparisons in relatively shallow channels yielded observed versus predicted (OP) regression R 2 values ranging from 0.60 to 0.97. A comparison between sonar and lidar bathymetry in a deeper river resulted in an OP R 2 of 0.72. Absorption and attenuation coefficients at the 532 nm wavelength of the lidar were recorded in the field and the highest values of these inherent optical properties were at sites with the highest turbidity and highest concentrations of colored dissolved organic matter, chlorophyll, and suspended sediment. At these sites, which included both sand and gravel/cobble beds, the point density of riverbed returns was not uniform, with areas of sparse coverage occurring primarily in deeper water. However, submerged objects and slopes could be resolved in the lidar point clouds.

Arizona, Colorado↗

USGS2021

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 effects by Inferring Bathymetry from Averaged River Images (IBARI). This workflow produces time‐averaged images from video frames stabilized to account for platform motion and/or computes a spatial average from an ensemble simulated by randomly shifting images relative to themselves. We used field observations of water depth and helicopter‐based videos from a clear‐flowing river to assess the potential of this approach to improve depth retrieval. Our results indicated that depths inferred from averaged images were more accurate and precise than those inferred from single frames; observed versus predicted regression R 2 increased from 0.80 to 0.88. In addition, IBARI significantly enhanced the texture of image‐derived depth maps, leading to smoother, more coherent representations of channel morphology. Depth retrieval improved with image sequence duration, but the number of images was more important than the length of time encompassed; shorter acquisitions at higher frame rates would economize data collection. We also demonstrated the potential to scale up the IBARI workflow by producing a mosaic of bathymetric maps derived from averaged images acquired at several hovering waypoints distributed along a 2.36 km reach. This approach is well‐suited to data collected from helicopters and small unmanned aircraft systems.

Water Resources Research↗

USGS2020

Assessing the potential for spectrally based remote sensing of salmon spawning locations

Remote sensing tools are increasingly used for quantitative mapping of fluvial habitats, yet few techniques exist for continuous sampling of aquatic organisms, such as spawning salmonids. This study assessed the potential for spectrally based remote sensing of salmon spawning locations (i.e., redds) using data acquired from unmanned aircraft systems (UAS) along a large, gravel‐bed river. We developed a novel, semi‐automated approach for detecting salmon redds by applying machine learning classification and object detection techniques to UAS‐based imagery. We found that both true colour (RGB) and hyperspectral imagery could be used to identify salmon redds, though with varying degrees of accuracy. Redds were mapped with accuracies of ~0.75 from RGB imagery using logistic regression and support vector machines (SVM) classification algorithms, but this type of data could not be used to identify redds using Object‐based Image Analysis (OBIA). The hyperspectral imagery was more useful for mapping salmon redds, with accuracies greater than 0.9 for both logistic regression and SVM classifiers; OBIA of the hyperspectral data resulted in redd detection accuracies up to 0.86. The hyperspectral imagery also yielded complementary physical habitat information including water depth and substrate composition, which we quantified on the basis of a spectrally based chlorophyll absorption ratio. Overall, the hyperspectral imagery more effectively identified salmon spawning locations than RGB images and was more conducive to the classification approaches we evaluated. Each type of remotely sensed data had advantages and limitations, which are important for potential users to understand when incorporating UAS‐based data collection into river ecosystem studies.

California↗

USGS2020

Measuring channel planform change from image time series: A generalizable, spatially distributed, probabilistic method for quantifying uncertainty

Channels change in response to natural or anthropogenic fluctuations in streamflow and/or sediment supply and measurements of channel change are critical to many river management applications. Whereas repeated field surveys are costly and time‐consuming, remote sensing can be used to detect channel change at multiple temporal and spatial scales. Repeat images have been widely used to measure long‐term channel change, but these measurements are only significant if the magnitude of change exceeds the uncertainty. Existing methods for characterizing uncertainty have two important limitations. First, while the use of a spatially variable image co‐registration error avoids the assumption that errors are spatially uniform, this type of error, as originally formulated, can only be applied to linear channel adjustments, which provide less information on channel change than polygons of erosion and deposition. Second, previous methods use a level‐of‐detection (LoD) threshold to remove non‐significant measurements, which is problematic because real changes that occurred but were smaller than the LoD threshold would be removed. In this study, we present a new method of quantifying uncertainty associated with channel change based on probabilistic, spatially varying estimates of co‐registration error and digitization uncertainty that obviates a LoD threshold. The spatially distributed probabilistic (SDP) method can be applied to both linear channel adjustments and polygons of erosion and deposition, making this the first uncertainty method generalizable to all metrics of channel change. Using a case study from the Yampa River, Colorado, we show that the SDP method reduced the magnitude of uncertainty and enabled us to detect smaller channel changes as significant. Additionally, the distributional information provided by the SDP method allowed us to report the magnitude of channel change with an appropriate level of confidence in cases where a simple LoD approach yielded an indeterminate result.

Earth Surface Processes and Landforms↗

USGS2020

Inferring surface flow velocities in sediment-laden Alaskan rivers from optical image sequences acquired from a helicopter

The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement ( R 2 > 0 . 9 "> R 2 > 0 . 9 ) between remotely sensed velocities and field measurements. Similarly, analysis of the sensitivity of PIV accuracy to image sequence duration showed that dwell times as short as 16 s would be sufficient at a frame rate of 1 Hz and could be cut in half if the frame rate were doubled. The results of this investigation indicate that helicopter-based remote sensing of velocities in sediment-laden rivers could contribute to noncontact streamgaging programs and enable reach-scale mapping of flow fields.

Alaska↗

USGS2019

An experimental evaluation of the feasibility of inferring concentrations of a visible tracer dye from remotely sensed data in turbid rivers

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 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 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 a 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 approach can be extended to more turbid rivers than those examined previously.

Missouri↗

USGS2019

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

In river channels the flow field influences the dispersion of biota, contaminants, and other suspended or dissolved materials. Insight on patterns and rates of dispersion can be gained by injecting a pulse of visible dye and observing spatial and temporal variations in dye concentration as the pulse moves downstream. We evaluated the potential of passive optical remote sensing to enhance such tracer experiments by providing spatially distributed concentration information. During tests performed in both an experimental flume facility and a large natural channel, we made field measurements of Rhodamine WT dye concentration and above-water spectral reflectance. At Korea's River Experiment Center, a small unmanned aircraft system (sUAS) was used to acquire hyperspectral images of a sinuous outdoor flume. On the Kootenai River in northern Idaho, USA, field spectra were collected from a boat and hyperspectral image data and high resolution aerial photographs were obtained from manned aircraft. We modified an Optimal Band Ratio Analysis (OBRA) algorithm to identify wavelength combinations that yielded strong correlations between a spectrally based quantity X and dye concentration C. For both the flume and field tests, we obtained very strong (R^2 from 0.94 to 0.99) relationships between X and C across a broad range of visible wavelengths. On the Kootenai, we found that X vs. C relations derived from field spectra could be applied to airborne hyperspectral images and that dye concentrations could be estimated nearly as reliably from relatively simple three-band images as from hyperspectral data. These results imply that remote sensing could become a powerful tool for mapping dye patterns. Such a capability would advance our understanding of dispersion processes by enabling more rigorous testing of numerical flow models.

Journal of Ecohydraulics↗

USGS2019

Physical controls on salmon redd site selection in restored reaches of a regulated, gravel-bed river

Large‐scale river restoration programs have emerged recently as a tool for improving spawning habitat for native salmonids in highly altered river ecosystems. Few studies have quantified the extent to which restored habitat is utilized by salmonids, which habitat features influence redd site selection, or the persistence of restored habitat over time. We investigated fall‐run Chinook salmon spawning site utilization and measured and modeled corresponding habitat characteristics in two restored reaches: a reach of channel and floodplain enhancement completed in 2013 and a reconfigured channel and floodplain constructed in 2002. Redd surveys demonstrated that both restoration projects supported a high density of salmon redds, 3 and 14 years following restoration. Salmon redds were constructed in coarse gravel substrates located in areas of high sediment mobility, as determined by measurements of gravel friction angles and a grain entrainment model. Salmon redds were located near transitions between pool‐riffle bedforms in regions of high predicted hyporheic flows. Habitat quality (quantified as a function of stream hydraulics) and hyporheic flow were both strong predictors of redd occurrence, though the relative roles of these variables differed between sites. Our findings indicate that physical controls on redd site selection in restored channels were similar to those reported for natural channels elsewhere. Our results further highlight that in addition to traditional habitat criteria (e.g., water depth, velocity, and substrate size), quantifying sediment texture and mobility, as well as intragravel flow, provides a more complete understanding of the ecological benefits provided by river restoration projects.

Water Resources Research↗

USGS2019

sUAS-based remote sensing of river discharge using thermal particle image velocimetry and bathymetric lidar

This paper describes a non-contact methodology for computing river discharge based on data collected from small Unmanned Aerial Systems (sUAS). The approach is complete in that both surface velocity and channel geometry are measured directly under field conditions. The technique does not require introducing artificial tracer particles for computing surface velocity, nor does it rely upon the presence of naturally occurring floating material. Moreover, no prior knowledge of river bathymetry is necessary. Due to the weight of the sensors and limited payload capacities of the commercially available sUAS used in the study, two sUAS were required. The first sUAS included mid-wave thermal infrared and visible cameras. For the field evaluation described herein, a thermal image time series was acquired and a particle image velocimetry (PIV) algorithm used to track the motion of structures expressed at the water surface as small differences in temperature. The ability to detect these thermal features was significant because the water surface lacked floating material (e.g., foam, debris) that could have been detected with a visible camera and used to perform conventional Large-Scale Particle Image Velocimetry (LSPIV). The second sUAS was devoted to measuring bathymetry with a novel scanning polarizing lidar. We collected field measurements along two channel transects to assess the accuracy of the remotely sensed velocities, depths, and discharges. Thermal PIV provided velocities that agreed closely (R^2 = 0.82 and 0.64) with in situ velocity measurements from an acoustic Doppler current profiler (ADCP). Depths inferred from the lidar closely matched those surveyed by wading in the shallower of the two cross sections (R^2 = 0.95) but the agreement was not as strong for the transect with greater depths (R^2 = 0.61). Incremental discharges computed with the remotely sensed velocities and depths were greater than corresponding ADCP measurements by 22% at the first cross section and < 1% at the second.

Colorado↗

USGS2019

Remote sensing of river flow in Alaska—New technology to improve safety and expand coverage of USGS streamgaging

The U.S. Geological Survey monitors water level (water surface elevation relative to an arbitrary datum) and measures streamflow in Alaska rivers to compute and compile river flow records for use by water resource planners, engineers, and land managers to design infrastructure, manage floodplains, and protect life, property, and aquatic resources. Alaska has over 800,000 miles of rivers including the Yukon River, the third longest river in the United States. These rivers are home to rare and important ecosystems and are used for recreation, hydropower generation, commercial fishing, and transportation. River flow measurements are essential for wise and safe development and use of Alaska rivers.

Alaska↗

USGS2019

Remote sensing of river bathymetry: Evaluating a range of sensors, platforms, and algorithms on the upper Sacramento River, California, USA

Remote sensing has become an increasingly viable tool for characterizing fluvial systems. In this study, we used field measurements from a 1.6 km reach of the upper Sacramento River, CA, to evaluate the potential of mapping water depths from a range of platforms, sensors, and depth retrieval methods. Field measurements of water column optical properties also were compared to similar data sets from other rivers to provide context for our results. We considered field spectra, a multispectral satellite image, hyperspectral data collected from conventional and unmanned aircraft, and a bathymetric LiDAR and applied a generalized version of Optimal Band Ratio Analysis (OBRA) and the K nearest neighbors regression (KNN) machine learning algorithm. Linear, quadratic, exponential, power, and lowess OBRA models enabled more flexible curve-fitting in calibrating spectrally based quantities to depth; an exponential formulation avoided artifacts associated with other model types. KNN increased observed vs. predicted R2 values, particularly for the satellite image; we also found that pre-processing of satellite images was unnecessary and that a basic data product could be used for depth retrieval. Bathymetric LiDAR was highly accurate and precise in shallow water, but a lack of bottom returns from areas greater than 2 m deep resulted in large gaps in coverage. The maximum detectable depth imposes an important constraint on fluvial remote sensing and a hybrid approach combined with field surveys of deep areas might be a more realistic operational strategy for bathymetric mapping. Future work will focus on scaling up from short reaches to long river segments.

California↗

USGS2019

Defining the limits of spectrally based bathymetric mapping on a large river

Remote sensing has emerged as a powerful method of characterizing river systems but is subject to several important limitations. This study focused on defining the limits of spectrally based mapping in a large river. We used multibeam echosounder (MBES) surveys and hyperspectral images from a deep, clear-flowing channel to develop techniques for inferring the maximum detectable depth, d m a x "> d m a x , directly from an image and identifying optically deep areas that exceed d m a x "> d m a x . Optimal Band Ratio Analysis (OBRA) of progressively truncated subsets of the calibration data provided an estimate of d m a x "> d m a x by indicating when depth retrieval performance began to deteriorate due to the presence of depths greater than the sensor could detect. We then partitioned the calibration data into shallow and optically deep ( d > d m a x "> d > d m a x ) classes and fit a logistic regression model to estimate the probability of optically deep water, P r ( O D ) "> P r ( O D ) . Applying a P r ( O D ) "> P r ( O D ) threshold value allowed us to delineate optically deep areas and thus only attempt depth retrieval in relatively shallow locations. For the Kootenai River, d m a x "> d m a x reached as high as 9.5 m at one site, with accurate depth retrieval ( R 2 = 0.94 "> R 2 = 0.94 ) in areas with d < d m a x "> d < d m a x . As a first step toward scaling up from short reaches to long river segments, we evaluated the portability of depth-reflectance relations calibrated at one site to other sites along the river. This analysis highlighted the importance of calibration data spanning a broad range of depths. Due to the inherent limitations of passive optical depth retrieval in large rivers, a hybrid field- and remote sensing-based approach would be required to obtain complete bathymetric coverage.

Idaho↗

USGS2019

Near-field remote sensing of Alaskan Rivers

The U.S. Geological Survey (USGS) Geomorphology and Sediment Transport Laboratory (GSTL), in collaboration with the U.S. Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL), acquired remotely sensed data from several Alaskan rivers in 2017 and 2018 with the goal of developing a methodology for measuring streamflow from a helicopter. CRREL operates a custom airborne lidar system that can be deployed in a helicopter-based pod (HeliPod). Data were collected with the HeliPod near existing USGS streamflow information stations on the Knik, Matanuska, Chena, and Salcha Rivers in both 2017 and 2018. Sites on the Tanana and Snow Rivers were added in 2018. In 2018, the HeliPod was modified to accommodate both a thermal infrared and a visible camera. The cameras were integrated with the flight management software to simultaneously acquire imagery with lidar. The Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU) in the HeliPod were used to compute trajectories with precise position and orientation information needed for image orthorectification. The HeliPod sensors provide data for measuring river channel characteristics. Lidar can map the elevation of the water surface and thus be used to measure water-surface slopes and return intensity can be used to delineate the extent of the wetted river channel. Various approaches are currently being evaluated to estimate surface flow velocity from visible and thermal image time series. In this paper, we examine and compare water-surface elevation returns and slopes derived from the HeliPod lidar and found good agreement with measurements made using conventional field-based techniques.

Alaska↗

USGS2018

New methods for predicting and measuring dispersion in rivers

To develop a better predictive tool for dispersion in rivers over a range of temporal and spatial scales, our group has developed a simple Lagrangian model that is applicable for a wide range of coordinate systems and flow modeling methodologies. The approach allows dispersion computations for a large suite of discretizations, model dimensions (1-, 2-, or 3-dimensional), spatial and temporal discretization, and turbulence closures. As the model is based on a discrete non-interacting particle approach, parallelization is straightforward, such that simulations with large numbers of particles are tractable. Results from the approach are compared to dispersion measurements made with conventional Rhodamine WT dye experiment in which typical at-a-point sensors are employed to determine concentration. The model performs well, but spatial resolution for experiments over large and or complex river flows was inadequate for model testing. To address this issue, we explored the idea of measuring spatial concentrations in river flows using hyperspectral remote sensing. Experiments both for idealized channels and real rivers show that this technique is viable and can provide high levels of spatial detail in concentration measurements with quantitatively accurate concentrations.

Conference Paper↗

USGS2018

Sampling strategies to improve passive optical remote sensing of river bathymetry

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 ($d_{max}$) 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 $d_{max}$ 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 $d_{max}$ 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 $d_{max}$, which comprise the majority of the channel and are of primary interest for many habitat-oriented applications.

Remote Sensing↗

USGS2018

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

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 R 2 (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.

Nebraska↗