Search USGS⌕ Search

Geology topics

Carl J. Legleiter

Publications and source records attributed to Carl J. Legleiter.

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

Estimating discharge from undular hydraulic jumps: Feasibility assessment based on flume experiments

Rapids are common in steep rivers, often forming where flow transitions from supercritical (Froude number, Fr > 1) to subcritical ( Fr < 1) through a hydraulic jump. When upstream Fr is supercritical but close to 1, this transition may occur as an undular hydraulic jump, exhibiting a train of stationary waves downstream of the jump toe. Previous studies proposed a method to estimate discharge using only UHJ wave spacing and channel width combined with a wave dispersion equation for large water depths relative to the UHJ wavelength. This method is based on the hypotheses that, by their presence, UHJs indicate near-critical flow conditions ( Fr ≈ 1) and that wave celerity c is equal to and opposite the cross-sectionally averaged flow velocity U . However, these hypotheses have not been thoroughly tested. We used data from published UHJ flume experiments to test the hypotheses that Fr ≈ 1 and c = U , compare the deep-water and general wave dispersion equations, and evaluate the accuracy of discharge estimates. In these experiments, the stationary waves exhibited shallow depths relative to wavelength and flow was subcritical ( Fr < 1) when averaged across multiple wavelengths. Additionally, wave celerity more closely approximated the surface flow velocity than U . By using a Fr representative of actual conditions and applying a coefficient to correct for c ≠ U , the accuracy of the discharge estimates improved. This finding suggests that the critical flow-based method is robust and can produce reliable streamflow estimates if the remotely observed wave trains are correctly interpreted as UHJs, without requiring in situ measurements.

Water Resources Research↗
USGS2026

Demonstration, validation, and application of hyperspectral microscopy for the collection of cyanobacterial spectral signatures

Cyanobacterial and other algal blooms are an environmental concern in waterbodies worldwide. While these blooms are a nuisance for recreational activities, they can also be harmful to human and wildlife health when the algae produce and release toxins. Algal community composition can be monitored and analyzed by acquiring hyperspectral images that provide information on various photosynthetic and accessory pigments. Validated, traceable measurements are needed to compare data collected by different hyperspectral instruments. In this proof-of-concept study, we detail the development and validation of a custom hyperspectral microscopy imaging system and assess whether this technology can differentiate between cyanobacteria genera based on differences in their reflectance characteristics. As not all cyanobacteria produce toxins, the ability to distinguish among taxa could be used to identify potential toxin-producers and guide field sampling and further research. Spectral characterization of these taxa contributes to remote sensing efforts to characterize and identify cyanobacterial genera at larger spatial scales.

Oregon↗
USGS2026

Compilation of a nationwide river image dataset for identifying river channels and river rapids via deep learning

Remote sensing enables large-scale, image-based assessments of river dynamics, offering new opportunities for hydrological monitoring. We present a publicly available dataset consisting of 281,024 satellite and aerial images of U.S. rivers, constructed using an Application Programming Interface (API) and the U.S. Geological Survey’s National Hydrography Dataset. The dataset includes images, primary keys, and ancillary geospatial information. We use a manually labeled subset of the images to train models for detecting rapids, defined as areas where high velocity and turbulence lead to a wavy, rough, or even broken water surface visible in the imagery. To demonstrate the utility of this dataset, we develop an image segmentation model to identify rivers within images. This model achieved a mean test intersection-over-union ( 𝐼 𝑜 𝑈 ) of 0.57, with performance rising to an actual 𝐼 𝑜 𝑈 of 0.89 on the subset of predictions with high confidence (predicted 𝐼 𝑜 𝑈 > 0.9). Following this initial segmentation of river channels within the images, we trained several convolutional neural network (CNN) architectures to classify the presence or absence of rapids. Our selected model reached an accuracy and F1 score of 0.93, indicating strong performance for the classification of rapids that could support consistent, efficient inventory and monitoring of rapids. These data provide new resources for recreation planning, habitat assessment, and discharge estimation. Overall, the dataset and tools offer a foundation for scalable, automated identification of geomorphic features to support riverine science and resource management.

Remote Sensing↗
USGS2026

Software to support remote sensing of river discharge based on critical flow theory

Water resource management requires accurate observations of streamflow but standard field methods for measuring river discharge ( Q ) are costly and can be hazardous for equipment and personnel. Remote sensing has become a viable alternative, but many image-based techniques require field data for calibration and depth and velocity can seldom be mapped with a single sensor. A new approach based on critical flow theory, in contrast, allows both of these attributes to be inferred from readily available image data. This technique only pertains to sites with standing waves, called undular hydraulic jumps (UHJs), but a recent investigation demonstrated its potential to provide accurate discharge estimates. This paper introduces software designed to facilitate Inferring Q from UHJs Identified in River Images (InQUIRI). The package includes modules for retrieving data from image servers, making the measurements of wavelength and width required to calculate discharge, inferring a representative wavelength from a profile digitized along a wave train, combining multiple estimates to obtain an ensemble median discharge, and assessing accuracy via comparison to gage records from the U.S. Geological Survey. By making these steps easier to implement, InQUIRI enables users to apply the workflow to a variety of UHJ-containing images. Accumulating more case studies, some successful and others less so, would help constrain the range of applicability of the critical flow approach and foster development of refined guidelines for selecting and measuring waves. The software described herein could play an important role in promoting informed use of this new technique for non-contact streamflow measurement.

Arizona, Colorado, New Mexico, Utah↗
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

Sundial: A method for inferring image acquisition time from shadow orientation

Aerial photography and satellite imagery can be used to characterize landscape change over time and help to understand how these changes are related to climate and hydrology. Publicly available optical imagery from sources such as the United States National Agricultural Imagery Program (NAIP) is particularly valuable in this context due to its high temporal and spatial resolution. However, the exact time an image was acquired is often unknown, which complicates, if not precludes, linking images with other types of high temporal resolution data, such as streamflow records. In this letter, we propose a ‘sundial method’ to infer image acquisition time from shadow orientation. This approach involves measuring the direction of a shadow on the image and using solar geometry calculated for the known image date and location to infer the former sun position. Time estimates for 16 Worldview satellite and six NAIP aerial images based on 407 independent measurements of shadow orientation demonstrate the sundial method had an error of 2.1 ± 3.4 min, indicating that image acquisition times can be inferred with a high degree of accuracy and precision. Sensitivity analyses confirm the robustness of the method across different object types, shadow lengths, and solar zenith angles, while also providing practical guidelines regarding the number of measurements required and errors associated with uncertainty in the image date.

Earth Surface Processes and Landforms↗
USGS2025

Remote sensing of river discharge based on critical flow theory

Critical flow theory provides a physical foundation for inferring discharge from measurements of wavelength and channel width made from images. In rivers with hydraulically steep local slopes greater than ∼0.01, flow velocities are high and the Froude number F r (ratio of inertial to gravitational forces) can approach 1.0 (critical flow) or greater. Under these conditions, undular hydraulic jumps (UHJ's) can form as standing wave trains at slope transitions or constrictions. The presence of UHJ's indicates that mean F r ≈ 1, implying that the velocity and depth of the flow and the spacing of the waves are uniquely related to one another. Discharges estimated from 82 Google Earth images agreed closely with discharges recorded at gaging stations ( R 2 = 0.98), with a mean bias of 1% ± 11%. This approach could provide reliable discharge information in many fluvial environments where critical flow occurs, which tend to be underrepresented in gage networks

Geophysical Research Letters↗
USGS2025

Neural network-based temporal ensembling of water depth estimates derived from SuperDove Images

CubeSats provide a wealth of high-frequency observations at a meter-scale spatial resolution. However, most current methods of inferring water depth from satellite data consider only a single image. This approach is sensitive to the radiometric quality of the data acquired at that particular instant in time, which could be degraded by various confounding factors, such as sun glint or atmospheric effects. Moreover, using single images in isolation fails to exploit recent improvements in the frequency of satellite image acquisition. This study aims to leverage the dense image time series from the SuperDove constellation via an ensembling framework that helps to improve empirical (regression-based) bathymetry retrieval. Unlike previous studies that only ensembled the original spectral data, we introduce a neural network-based method that instead ensembles the water depths derived from multi-temporal imagery, provided the data are acquired under steady flow conditions. We refer to this new approach as NN-depth ensembling. First, every image is treated individually to derive multitemporal depth estimates. Then, we use another NN regressor to ensemble the temporal water depths. This step serves to automatically weight the contribution of the bathymetric estimates from each time instance to the final bathymetry product. Unlike methods that ensemble spectral data, NN-depth ensembling mitigates against propagation of uncertainties in spectral data (e.g., noise due to sun glint) to the final bathymetric product. The proposed NN-depth ensembling is applied to temporal SuperDove imagery of reaches from the American, Potomac, and Colorado rivers with depths of up to 10 m and evaluated against in situ measurements. The proposed method provided more accurate and robust bathymetry retrieval than single-image analyses and other ensembling approaches.

Remote Sensing↗
USGS2025

Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry

Continuous, high-resolution data for characterizing freshwater habitat conditions can support successful management of endangered salmonids. Uncrewed aircraft systems (UAS) make acquiring such fine-scale data along river channels more feasible, but workflows for quantifying reach-scale salmon habitats are lacking. We evaluated the potential for UAS-based mapping of hydraulic habitats using spectrally based depth retrieval and particle image velocimetry (PIV) by comparing these methods to a more well-established flow modeling approach. Our results indicated that estimates of water depth, depth-averaged velocity, and flow direction derived via remote sensing and modeling techniques were comparable and in good agreement with field measurements. Predictions of spring-run Chinook salmon ( Oncorhynchus tshawytscha ) juvenile rearing habitat produced from PIV and model output were similar, with small errors relative to direct field observations. Estimates of hydraulic heterogeneity based on kinetic energy gradients in the flow field were generally consistent between PIV and flow modeling, but errors relative to field measurements were larger. PIV results were sensitive to the velocity index ( α ) used to convert surface velocities to depth-averaged velocities. Sun glint precluded PIV analysis along the margins of some images and a large degree of overlap between frames was thus required to obtain continuous coverage of the reach. Similarly, shadows cast by riparian vegetation caused gaps in spectrally based bathymetric maps. Despite these limitations, our results suggest that for sites with sufficient water surface texture, UAS-based PIV can provide detailed hydraulic habitat information at the reach scale, with accuracies comparable to traditional field methods and multidimensional flow modeling.

Oregon↗
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↗
USGS2025

Monitoring cyanobacteria temporal trends in a hypereutrophic lake using remote sensing: From multispectral to hyperspectral

Cyanobacterial harmful algal blooms (cyanoHABs) and associated cyanotoxins are a concern for inland waters. Due to the extensive spatial coverage and frequent availability of satellite images, multispectral remote sensing tools demonstrate utility for monitoring these blooms. The next frontier for remote sensing of cyanoHABs in inland waters is hyperspectral data. Recent and upcoming hyperspectral satellite missions using narrow wavelength imaging spectrometers could have a major impact on advancing our ability to detect, quantify, and characterize cyanobacterial blooms. This study compares multispectral and hyperspectral remote sensing capabilities and processing tools for monitoring cyanoHAB dynamics. We evaluated the temporal trends of cyanoHABs in Clear Lake, California, a hypereutrophic lake with diverse cyanobacteria genera based on 38 sampling events over a five-year monitoring period (2019–2023). We validated the Sentinel-3 Ocean and Land Color Instrument (multispectral) Cyanobacteria Index algorithm for Clear Lake using in situ cyanobacteria measurements, which complemented our field-based evaluation of cyanobacteria trends in Clear Lake. We then demonstrate the advantages of hyperspectral data from both in situ spectroradiometer measurements and full-lake hyperspectral satellite images. We apply the Spectral Mixture Analysis for Surveillance of HABs (SMASH) workflow, a Multiple Endmember Spectral Mixture Analysis (MESMA) algorithm, to the hyperspectral images to assess the potential of satellite imaging spectrometer data to identify cyanobacteria genera – the first study to test this tool outside its original study sites. We developed a Clear Lake-specific cyanobacteria spectral library using our field spectroradiometer measurements to improve SMASH performance in Clear Lake, which supports the continued development of this tool.

California↗
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

Hyperspectral Image Transects during Transient Events in Rivers (HITTER): Framework development and application to a tracer experiment on the Missouri River, USA

Rivers convey a broad range of materials, such as sediment, nutrients, and contaminants. Much of this transport can occur during or immediately after an episodic, pulsed event like a flood or an oil spill. Understanding the flow processes that influence the motion of these substances is important for managing water resources and conserving aquatic ecosystems. This study introduces a new remote sensing framework for characterizing dynamic phenomena at the scale of a channel cross-section: Hyperspectral Image Transects during Transient Events in Rivers (HITTER). We present a workflow that uses repeated hyperspectral scan lines acquired from a hovering uncrewed aircraft system (UAS) to quantify how a water attribute of interest varies laterally across the river and evolves over time. Data from a tracer experiment on the Missouri River are used to illustrate the components of the end-to-end processing chain we used to quantify the passage of a visible dye. The framework is intended to be flexible and could be applied in a number of different contexts. The results of this initial proof-of-concept investigation suggest that HITTER could potentially provide insight regarding the dispersion of a range of materials in rivers, which would facilitate ecological and geomorphic studies and help inform management.

Remote Sensing↗
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↗