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Paul J. Kinzel

Publications and source records attributed to Paul J. Kinzel.

At least 19 recordsLinked to original sources
USGS2026

Refinement of a framework for Moving Aircraft River Velocimetry (MARV) and application to particle tracking along Alaskan rivers

Information on river velocities enhances understanding flood hazards, evaluating habitat conditions, and predicting the transport of floating materials. In this follow-up study, we used data from two new sites, one with a more complex morphology and the other with a lower suspended sediment concentration, to provide further evidence that Moving Aircraft River Velocimetry (MARV) can yield accurate velocity estimates ( R 2 up to 0.87 when compared to field measurements) for long segments of large, turbid rivers. The MARV workflow is packaged in freely available software and is robust to implementation details; neither buffering to mitigate edge effects nor a new approach to aggregating velocity vectors improved performance. MARV was not sensitive to parameters used to establish overlapping image sequences, but combining a long window with a short jump between consecutive windows was the optimal configuration. Although accuracy varied from one cross section to the next, agreement between remotely sensed velocities and those measured in the field was independent of position within a frame range. As an initial step toward application of the approach to help address practical problems, we showed how MARV can drive particle tracking models. Our first-order simulations suggest that channel morphology and flow velocity are the primary controls on travel time and particle fate, with diffusive processes playing a lesser role. Although MARV can be used to characterize an instantaneous flow field, a more comprehensive framework that accounts for other physical processes would be required to model specific types of events like oil spills.

Alaska↗
USGS2026

Evaluating approximations of river channel shape using a national cross section database

Many hydrologic applications require basic information on the size and shape of river channels, but measuring cross section (XS) geometry in the field or via remote sensing can be costly and often provides only partial coverage. Given these challenges, we capitalized upon an existing data set of 46,971 XS from gaging stations to evaluate various approximations of channel shape. After screening and pre-processing these data, we fit four model types to each XS, including a new approach that involves Stacking PDFs (probability density functions) to Approximate River Channel Shapes (SPARCS). This framework produced depth estimates that closely matched field measurements, with typical cross-sectional area errors <1 % and a median R 2 of 0.77 for comparison of observed and predicted depths. SPARCS model parameters can be interpreted in terms of channel characteristics: mean depth, asymmetry, bar convexity, and flatness of the bed. The model performed well for the XS included in the database, which was biased toward straight, uniform channels conducive to operational streamflow measurement. Neither model parameters nor accuracy were dependent on discharge. We also assessed the potential of SPARCS to fill in measurement gaps and found that although the model can help, the accuracy of inferred depths decreased as the observable fraction of the channel decreased. An important limitation of SPARCS is that mid-channel bars or multi-threaded morphologies cannot be produced. Graphical tools can help visualize how model parameters affect simulated river forms. SPARCS could facilitate satellite-based discharge estimation by providing prior information on channel shape.

Water Resources Research↗
USGS2026

Development and field testing of a UAS-based software-defined radar for measuring freshwater bathymetry

We provide an overview of an uncrewed aircraft system (UAS)-based software-defined radar (SDRadar) system for high-resolution geophysical observations. The radar transceiver is implemented on a Radio Frequency System-on-Chip (RFSoC) platform, along with an ultra-wideband Vivaldi antenna that has a starting operating frequency of 150 MHz, enabling the system to be used across different applications, including measurements of freshwater bathymetry. In addition to system design and subsystem performance assessments, this paper presents the results of field testing conducted along the Sacramento River near Glenn, California, USA. The radar-derived river depth measurements are compared with ground truth data collected from a crewed boat using an acoustic Doppler current profiler (ADCP). The results show good agreement, with a root mean square error (RMSE) of 0.079 m.

California↗
USGS2025

Toward an efficient framework for remote sensing of river bathymetry: Comparing sensors and algorithms on an inaccessible proglacial river in Alaska

Remote sensing can provide reliable information on river depths and this approach might be particularly valuable in areas that are difficult to survey via conventional field methods. In this study, we assessed the potential to map the bathymetry of an inaccessible proglacial river in Alaska from both aerial orthophotos and a multispectral satellite image. In addition, we evaluated a variety of depth retrieval algorithms with different input data requirements, including some methods that require field measurements of water depth for calibration and other techniques that can be applied even when such field data are not available. These approaches might enable more efficient use of remote sensing methods by resource management agencies. Our results suggest that bathymetric mapping along the turquoise-colored river we examined was not only feasible but highly accurate ( R 2 up to 0.94) for both types of image data. Algorithms that use paired observations of depth and reflectance to train depth retrieval models were the most accurate, with errors on the order of 15%–20% and little or no bias. Alternative techniques based on hydraulic and statistical concepts also led to strong agreement between predicted and observed depths but were more susceptible to systematic biases toward under- or over-estimation of depth. In contrast to clear-flowing streams, bathymetric mapping in this environment was enabled by a direct relationship between the depth and brightness of the water due to scattering by suspended sediment. In selecting an appropriate depth retrieval method, a compromise might need to be reached between the level of field effort invested and the accuracy of the resulting image-derived bathymetry. Standalone software for implementing these techniques is freely available.

Alaska↗
USGS2025

Hyperspectral imaging of river bathymetry using an ensemble of regression trees

Remote sensing has emerged as an effective tool for characterizing river systems, and machine learning (ML) techniques could make this approach even more powerful. To explore this possibility, we developed an ML-based workflow for hyperspectral imaging of river bathymetry using an ensemble of regression trees (HIRBERT). This approach involves using paired observations of depth and reflectance to select wavelength bands as predictors and then train a depth retrieval model; applying the model to the image yields a spatially continuous bathymetric map. We used data from five rivers with diverse morphologies and optical characteristics to assess whether HIRBERT can (1) provide more accurate depth estimates than a band ratio-based algorithm and (2) extend the range of depths detectable via remote sensing. Relative to single band combinations identified via optimal band ratio analysis (OBRA), regression tree ensembles improved depth retrieval performance, with observed versus predicted (OP) regression R 2 values increasing for all five sites. Similarly, HIRBERT provided more reliable depth estimates than OBRA over the full range of depths present along each river. These results suggest that by incorporating additional spectral information from multiple wavelength bands, ML could enhance bathymetric mapping across a range of river environments. In addition, we show how graphical tools can facilitate interpretation of ML-based depth retrieval models and yield insight regarding relationships between depth and reflectance. The HIRBERT workflow is packaged in free, standalone software developed to support applications in river research and management. Although ML can enhance remote sensing of river bathymetry, the limitations of this approach must also be acknowledged: Field measurements of water depth are required to train a depth retrieval model and the resulting model should only be applied to the image from which the training data were derived. The inherently image-specific nature of this approach implies that developing generalized regression tree ensembles that could be applied at larger scales would require additional research.

California, Idaho, Nebraska, Oregon, Wyoming↗
USGS2025

A generalized framework for inferring river bathymetry from image-derived velocity fields

Although established techniques for remote sensing of river bathymetry perform poorly in turbid water, image velocimetry can be effective under these conditions. This study describes a framework for mapping both of these attributes: Depths Inferred from Velocities Estimated by Remote Sensing, or DIVERS. The workflow involves linking image-derived velocities to depth via a flow resistance equation and invoking an optimization algorithm. We generalized an earlier formulation of DIVERS by: (1) using moving aircraft river velocimetry (MARV) to obtain a continuous, spatially extensive velocity field; (2) working within a channel-centered coordinate system; (3) allowing for local optimization of multiple parameters on a per-cross section basis; and (4) introducing a second objective function that can be used when discharge is not known. We also quantified the sensitivity of depth estimates to each parameter and input variable. MARV-based velocity estimates agreed closely with field measurements ( R 2 = 0.81 "> R 2 =0.81 ) and the use of DIVERS led to cross-sectional mean depths that were correlated with in situ observations ( R 2 = 0.75 "> R 2 =0.75 ). Errors in the input velocity field had the greatest impact on depth estimates, but the algorithm was not highly sensitive to initial parameter estimates when a known discharge was available to constrain the optimization. The DIVERS framework is predicated upon a number of simplifying assumptions — steady, uniform, one-dimensional flow and a strict, purely local proportionality between depth and velocity — that impose important limitations, but our results suggest that the approach can provide plausible, first-order estimates of river depths.

Alaska↗
USGS2024

Mapping river flow from thermal images in approximately real time: Proof of concept on the Sacramento River, California, USA

Image velocimetry has become an effective method of mapping flow conditions in rivers, but this analysis is typically performed in a post-processing mode after data collection is complete. In this study, we evaluated the potential to infer flow velocities in approximately real time as thermal images are being acquired from an uncrewed aircraft system (UAS). The sensitivity of thermal image velocimetry to environmental conditions was quantified by conducting 20 flights over four days and assessing the accuracy of image-derived velocity estimates via comparison to direct field measurements made with an acoustic Doppler current profiler (ADCP). This analysis indicated that velocity mapping was most reliable when the air was cooler than the water. We also introduced a workflow for River Velocity Measurement in Approximately Real Time (RiVMART) that involved transferring brief image sequences from the UAS to a ground station as distinct data packets. The resulting velocity fields were as accurate as those generated via post-processing. A new particle image velocimetry (PIV) algorithm based on staggered image sequences increased the number of image pairs available for a given image sequence duration and slightly improved accuracy relative to a standard PIV implementation. Direct, automated geo-referencing of image-derived velocity vectors based on information on the position and orientation of the UAS acquired during flight led to poor alignment with vectors that were geo-referenced manually by selecting ground control points from an orthophoto. This initial proof-of-concept investigation suggests that our workflow could enable highly efficient characterization of flow fields in rivers and might help support applications that require rapid response to changing conditions.

California↗
USGS2024

Integrating depth measurements from gaging stations with image archives for spectrally based remote sensing of river bathymetry

Remote sensing can be an effective tool for mapping river bathymetry, but the need for direct measurements to calibrate image-derived depth estimates impedes broader application of this approach. One way to circumvent the need for field campaigns dedicated to calibration is to capitalize upon existing data. In this study, we introduce a framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID). This workflow involves retrieving depth measurements made during gaging station site visits, downloading archived multispectral images, and then combining these two data sets to establish a relationship between depth and reflectance. We developed a processing chain that involves using application programming interfaces to obtain both depth measurements made during site visits and images centered on the gage and then linking depth to reflectance via an optimal band ratio analysis (OBRA) algorithm modified for small sample sizes. Applying this workflow to selected gages within two river basins indicated that depth retrieval from multispectral satellite images could be highly accurate, but with variable results from one image to the next at a given site. High resolution aerial photography was less conducive to bathymetric mapping in one of the basin considered. Of the four predictors of depth retrieval performance we evaluated (mean and standard deviation of depth, width, and an index of water clarity), only width was consistently significantly correlated with OBRA R 2 ( p < 0.026). Currently, BaMGRID is best-suited for site-by-site analysis to support practical applications at the reach scale; continuous, basin-wide mapping of river bathymetry will require additional research.

Willamette River basin, Delaware River basin↗
USGS2024

Reach-scale mapping of surface flow velocities from thermal images acquired by an uncrewed aircraft system along the Sacramento River, California, USA

An innovative payload containing a sensitive mid-wave infrared camera was flown on an uncrewed aircraft system (UAS) to acquire thermal imagery along a reach of the Sacramento River, California, USA. The imagery was used as input for an ensemble particle image velocimetry (PIV) algorithm to produce near-continuous maps of surface flow velocity along a reach approximately 1 km in length. To assess the accuracy of PIV velocity estimates, in situ measurements of flow velocity were obtained with an acoustic Doppler current profiler (ADCP). ADCP measurements were collected along pre-planned cross-section lines within the area covered by the imagery. The PIV velocities showed good agreement with the depth-averaged velocity measured by the ADCP, with R 2 ">

Water↗
USGS2024

An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)

Detailed, accurate information on flow patterns in river channels can improve understanding of habitat conditions, geomorphic processes, and potential hazards to help inform water management. Data describing flow patterns in river channels can be obtained efficiently via image-based techniques that have become more widely used in recent years as the number of platforms for acquiring images has expanded and the number of algorithms for inferring velocities has grown. Image-based techniques have been incorporated into various software packages, including the Toolbox for River Velocimetry using Images from Aircraft (TRiVIA). TRiVIA is a freely available, standalone computer program that provides a comprehensive workflow for performing particle image velocimetry (PIV)-based analyses within a graphical interface. This paper summarizes major enhancements incorporated into the latest release of TRiVIA, version 2.1. For example, a new Tool for Input Parameter Selection (TIPS) provides guidance for specifying key inputs to the PIV algorithm by allowing users to explore relationships between flow velocity, pixel size, output vector spacing, and frame interval. Improved visualization capabilities include the ability to create streamlines and display PIV output on an interactive web map. The program now provides greater flexibility for importing field data in various formats and selecting which observations to use for accuracy assessment. The most substantial additions to TRiVIA 2.1 are the ability to integrate bathymetric information with image-derived velocity estimates to calculate river discharge and to use images acquired from moving aircraft to efficiently map long segments of large rivers to support habitat assessment, contaminant transport studies, and a range of other applications.

River Research and Applications↗
USGS2024

A two-dimensional, reach-scale implementation of space-time image velocimetry (STIV) and comparison to particle image velocimetry (PIV)

Image-based algorithms have become a powerful tool for estimating flow velocities in rivers. In this study, we generalize the space-time image velocimetry (STIV) framework for reach-scale application rather than along a cross section. The new algorithm provides information on both the magnitude and orientation of velocity vectors, and we refer to the algorithm as two-dimensional STIV, or 2D-STIV. The workflow involves setting up a grid, using centreline tangent vectors as initial estimates of flow direction, and then extracting space-time images (STIs) along search lines radiating from each grid node. The autocorrelation function is used to infer the inclination of streak lines present in STIs, which represents the advection of water surface features. Information on flow direction is obtained by evaluating various candidate search lines and identifying that which yields the highest velocity. This search can be performed exhaustively or via optimization. We applied the new 2D-STIV algorithm to three test cases, one simulated data set and two natural channels, and compared image-derived velocities to modelled or measured values. We also applied two established particle image velocimetry (PIV) algorithms to the same data sets. 2D-STIV performed as well as the two PIV algorithms for simulated images. For a natural river with distinct water surface features, 2D-STIV was effective for much of the channel but also led to a more patchy, irregular velocity field than the two PIV algorithms. For a site lacking obvious surface features, exhaustive 2D-STIV led to velocity estimates uncorrelated with field data while the optimization-based version produced erratic flow directions. 2D-STIV also required greater image sequence durations, higher frame rates, and generally longer computational run times. Overall, ensemble PIV was the most reliable algorithm.

Earth Surface Processes and Landforms↗
USGS2024

A framework to facilitate development and testing of image-based river velocimetry algorithms

Image-based methods have compelling, demonstrated potential for characterizing flow fields in rivers, but algorithms like particle image velocimetry (PIV) must be further tested and improved to enable more effective use of these techniques. This paper presents a framework designed for this exact purpose: Simulating Hydraulics and Images for Velocimetry Evaluation and Refinement (SHIVER). The approach involves coupling a hydrodynamic model with a synthetic particle generator to advect particles between frames, as dictated by local velocity vectors and thus construct a plausible image sequence specific to the reach of interest. The resulting time series can then be used as input to a velocimetry algorithm to compare image-derived estimates with known (modelled) velocities to perform an exhaustive, spatially distributed accuracy assessment. As an example application of SHIVER, we examined the effects of interrogation area (IA) size, frame rate, flow velocity, and image sequence duration on the performance of a standard PIV algorithm. This analysis indicated that image-derived velocities were generally in close agreement with those from the flow model (root mean square error <10% and mean bias <3%), except when small IAs were coupled with low frame rates. Velocity estimates were most accurate for the lowest modelled discharge ( R 2 =0.97 at baseflow) and became less reliable as the mean flow velocity increased ( R 2 =0.92 for an intermediate discharge and R 2 =0.86 at bankfull). Accuracy was essentially independent of image sequence duration, implying that long occupations might not be necessary. Errors were concentrated along channel margins, where PIV-based velocities tended to be greater than those from the flow model. Small IAs led to underpredictions of velocity, while larger IAs led to overpredictions. SHIVER is highly modular and could be updated to make use of different hydrodynamic models or image simulators. The framework could also facilitate more thorough sensitivity analyses and comparison of various velocimetry algorithms.

Earth Surface Processes and Landforms↗
USGS2023

The Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)

Accurate knowledge of the speed at which water moves along a river is essential for understanding ecohydraulic processes and managing natural resources. Measuring flow velocity via remote sensing can be more efficient than conventional field methods, and powerful computational techniques for inferring velocity fields from videos or image time series have been developed. The development of dedicated software tools for particle image velocimetry (PIV) could facilitate greater use of these methods by the river community. This paper introduces a standalone app designed for this exact purpose: the Toolbox for River Velocimetry using Images from Aircraft, or TRiVIA. The program provides a complete workflow for producing spatially distributed velocity vectors from a video or sequence of images, all within an accessible graphical user interface. TRiVIA includes modules for extracting and resampling frames, stabilization and geo-referencing images, defining a region of interest, enhancing images, performing PIV with an efficient ensemble correlation algorithm, visualizing results, assessing accuracy assessment, and exporting PIV output. We illustrate the software's capabilities using an example data set from a large river in Alaska. The initial release of the toolbox is now freely available. Augmenting TRiVIA to incorporate bathymetric information could enable discharge calculation functionality.

River Research and Applications↗
USGS2023

Moving Aircraft River Velocimetry (MARV): Framework and proof-of-concept on the Tanana River

Information on velocity fields in rivers is critical for designing infrastructure, modeling contaminant transport, and assessing habitat. Although non-contact approaches to measuring flow velocity are well established, these methods assume a stationary imaging platform. This study eliminates this constraint by introducing a framework for moving aircraft river velocimetry (MARV). The workflow takes as input images acquired from an airplane and involves orthorectification, frame overlap analysis, image enhancement, particle image velocimetry (PIV), and aggregation of the resulting velocity vectors onto a prediction grid. We also use new metrics to quantify the agreement between image-derived and field-measured velocity vectors in terms of both orientation and magnitude. The potential of MARV was evaluated using data from two Alaskan rivers: a large, highly turbid channel and its smaller, clearer tributary. Sediment boil vortices on the mainstem provided natural features trackable via PIV and estimated velocities corresponded closely with field measurements ( R 2 up to 0.911). We compared an exhaustive approach that evaluates overlap for all frame combinations to a simpler rolling window implementation and found that the more efficient algorithm did not compromise accuracy. Sensitivity analysis suggested that the method was robust to window parameterization. Comparing PIV output from different flying heights and imaging systems indicated that larger pixels led to higher accuracy and that a more advanced dual-camera system provided superior performance. Results from the tributary were less encouraging, presumably due to a lack of trackable features in visible images. Testing across a range of rivers is needed to assess the generality of MARV.

Water Resources Research↗
USGS2023

Repeat bathymetric surveys and model simulation of sedimentation processes near fish spawning placements, Detroit and St. Clair Rivers, Michigan

Nine rock-rubble fish spawning placements, or artificial reef complexes, constructed in the Detroit and St. Clair Rivers between 2004 to 2018 were surveyed periodically with multibeam sonar. These serial bathymetric surveys, conducted in 2015, 2018, 2021, and 2022, identified active sand bedform fields impinging two reef complexes: Fighting Island in the Detroit River and Middle Channel in the St. Clair River delta. The spatial extent over which the bedforms interacted with these reef complexes differed. The Fighting Island reef complex, which was comprised of twelve reef beds oriented across the river channel, experienced partial sedimentation that can be attributed to the streamwise translation and lateral encroachment of a bedform field on several of the eastern reef beds. The Middle Channel reef complex was comprised of nine reef beds also oriented across the river channel. Sedimentation of the Middle Channel reef complex was more comprehensive compared to the Fighting Island reef complex as most of the beds in the Middle Channel reef complex were within a translating bedform field. We simulated the temporal evolution of reef sedimentation at the Middle Channel reef complex using the Wilcock-Kenworthy (WK) two-fraction sediment transport model. In the WK simulation, sand available upstream of the reef migrated into the 36-meter-long gravel reef beds over 10 days of model simulation. The rate of sediment infill predicted by the model was more rapid than the speed of bedform slip face translation measured in the field, approximately 0.3 meters per day. Further, as the supply of sediment from upstream is continuous, once a reef bed fills with sediment it generally remains in place, although some small variations (+/- 0.2 m) in the elevation of the sand overlying the reef beds were observed. Taken together, bathymetric surveys and modeling could be used to identify, monitor, and simulate potential sources of bedload sediment that could impair the longevity of future spawning reef placements. Efforts directed toward enhancement and/or maintenance of reefs impaired by sedimentation could benefit from continued monitoring through periodic high-resolution bathymetric surveys, detailed inspection by diving, and collection of underwater imagery.

Michigan↗
USGS2021

Depths inferred from velocities estimated by remote sensing: A flow resistance equation-based approach to mapping multiple river attributes at the reach scale

Remote sensing of flow conditions in stream channels could facilitate hydrologic data collection, particularly in large, inaccessible rivers. Previous research has demonstrated the potential to estimate flow velocities in sediment-laden rivers via particle image velocimetry (PIV). In this study, we introduce a new framework for also obtaining bathymetric information: Depths Inferred from Velocities Estimated by Remote Sensing (DIVERS). This approach is based on a flow resistance equation and involves several assumptions: steady, uniform, one-dimensional flow and a direct proportionality between the velocity estimated at a given location and the local water depth, with no lateral transfer of mass or momentum. As an initial case study, we performed PIV and inferred depths from videos acquired from a helicopter hovering at multiple waypoints along a large river in central Alaska. The accuracy of PIV-derived velocities was assessed via comparison to field measurements and the performance of an optimization-based approach to DIVERS specification of roughness

Alaska↗
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↗