Geology topics

Margaret L. Palmsten

Publications and source records attributed to Margaret L. Palmsten.

26 records · Page 2Linked to original sources

USGS2022

On the potential for remote observations of coastal morphodynamics from surf-cameras

Recreational surf-cameras (surfcams) are ubiquitous along many coastlines, and yet are a largely untapped source of coastal morphodynamic observations. Surfcams offer broad spatial coverage and flexibility in data collection, but a method to remotely acquire ground control points (GCPs) and initial camera parameter approximations is necessary to better leverage this existing infrastructure to make quantitative measurements. This study examines the efficacy of remotely monitoring coastal morphodynamics from surfcams at two sites on the Atlantic coast of Florida, U.S.A., by leveraging freely available airborne lidar observations to acquire remote-GCPs and open-source web tools for camera parameter approximations, ignoring lens distortion. Intrinsic and extrinsic camera parameters are determined using a modified space resection procedure, wherein parameters are determined using iterative adjustment while fitting to remote-GCPs and initial camera parameter approximations derived from justified assumptions and Google Earth. This procedure is completed using the open-source Surf-Camera Remote Calibration Tool (SurfRCaT). The results indicate root mean squared horizontal reprojection errors at the two cameras of 3.43 m and 6.48 m. Only immobile hard structures such as piers, jetties, and boulders are suitable as remote-GCPs, and the spatial distribution of available points is a likely reason for the higher accuracy at one camera relative to the other. Additionally, lens distortion is not considered in this work. This is another important source of error and including it in the methodology is highlighted as a useful avenue for future work. Additional factors, such as initial camera parameter approximation accuracy, likely play a role as well. This work illustrates that, provided there is sufficient remote-GCP availability and small lens distortion, remote video monitoring of coastal areas with existing surfcams could provide a usable source of coastal morphodynamic observations. This is further explored with a shoreline change analysis from the higher-accuracy camera. It was found that only the largest (>6 m) magnitude shoreline changes exceed the observational uncertainty driven by shoreline mapping error and reprojection error, indicating that remotely calibrated surfcams can provide observations of seasonal or storm-driven signals.

Florida↗

USGS2022

The Coastal Imaging Research Network (CIRN)

The Coastal Imaging Research Network (CIRN) is an international group of researchers who exploit signatures of phenomena in imagery of coastal, estuarine, and riverine environments. CIRN participants develop and implement new coastal imaging methodologies. The research objective of the group is to use imagery to gain a better fundamental understanding of the processes shaping those environments. Coastal imaging data may also be used to derive inputs for model boundary and initial conditions through assimilation, to validate models, and to make management decisions. CIRN was officially formed in 2016 to provide an integrative, multi-institutional group to collaborate on remotely sensed data techniques. As of 2021, the network is a collaboration between researchers from approximately 16 countries and includes investigators from universities, government laboratories and agencies, non-profits, and private companies. CIRN has a strong emphasis on education, exemplified by hosting annual “boot camps” to teach photogrammetry fundamentals and toolboxes from the CIRN code repository, as well as hosting an annual meeting for its members to present coastal imaging research. In this review article, we provide context for the development of CIRN as well as describe the goals and accomplishments of the CIRN community. We highlight components of CIRN’s resources for researchers worldwide including an open-source GitHub repository and coding boot camps. Finally, we provide CIRN’s perspective on the future of coastal imaging.

Remote Sensing↗

USGS2022

A machine learning approach to predicting equilibrium ripple wavelength

Sand ripples are geomorphic features on the seafloor that affect bottom boundary layer dynamics including wave attenuation and sediment transport. We present a new equilibrium ripple predictor using a machine learning approach that outputs a probability distribution of wave-generated equilibrium wavelengths and statistics including an estimate of ripple height, the most probable ripple wavelength, and sediment and flow parameterizations. The Bayesian Optimal Model System (BOMS) is an ensemble machine learning system that combines two machine learning algorithms and two deterministic empirical ripple predictors with a Bayesian meta-learner to produce probabilistic wave-generated equilibrium ripple wavelength estimates in sandy locations. A ten-fold cross validation of BOMS resulted in an adjusted R-squared value of 0.93 and an average root mean square error (RMSE) of 8.0 cm. During both cross validation and testing on three unique field datasets, BOMS provided more accurate wavelength predictions than each individual base model and other common ripple predictors.

Environmental Modeling and Software↗

USGS2021

Sea state from single optical images: A methodology to derive wind-generated ocean waves from cameras, drones and satellites

Sea state is a key variable in ocean and coastal dynamics. The sea state is either sparsely measured by wave buoys and satellites or modelled over large scales. Only a few attempts have been devoted to sea state measurements covering a large domain; in particular its estimation from optical images. With optical technologies becoming omnipresent, optical images offer incomparable spatial resolution from diverse sensors such as shore-based cameras, airborne drones (unmanned aerial vehicles/UAVs), or satellites. Here, we present a standalone methodology to derive the water surface elevation anomaly induced by wind-generated ocean waves from optical imagery. The methodology was tested on drone and satellite images and compared against ground truth. The results show a clear dependence on the relative azimuth view angle in relation to the wave crest. A simple correction is proposed to overcome this bias. Overall, the presented methodology offers a practical way of estimating ocean waves for a wide range of applications.

Remote Sensing↗

USGS2021

Hotspot dune erosion on an intermediate beach

A large, low pressure Nor’easter storm and Hurricane Joaquin contributed to multiple weeks of sustained, elevated wave and water level conditions along the southeastern Atlantic coast of the United States in Fall 2015. Sea level anomalies in excess of 1 m and offshore wave heights of up to 4 m were recorded during these storms, as observed at the U.S. Army Corps of Engineers’ Field Research Facility in Duck, NC, USA. In response to these energetic oceanographic conditions, there were highly variable morphologic changes to the dune over short spatial scales (<km) which included a range of responses from vertical dune scarping to no measureable response. The portion of the study area with the largest dune erosion occurred at a location fronted by an abnormally deep nearshore bathymetric feature, which altered surf-zone waves and hydrodynamics. The pre-storm beach and dune topography also varied throughout the study area, additionally influencing the frequency of dune collision and contributing to the spatially variable erosion patterns. This work uses field datasets and numerical modeling tools to investigate the causation of hotspot dune erosion at the Field Research Facility. Three different numerical models were tested against the available data in order to assess model skill at resolving complex spatial dune erosion patterns. The three models successfully reproduce the general spatial trends in alongshore variable responses, although not necessarily the details of profile response or net erosion magnitude. Analysis of the model outputs, in conjunction with the available field data, suggests that the observed hotspot dune erosion is related to a complex combination of both topographic and bathymetric controls on the processes driving dune erosion. Therefore, the most simplistic model tested, which only accounts for alongshore variations in topographic profile details, can only predict hotspot dune erosion in locations where steep beach and/or dune topography is the primary control on collisional dune impacts. The higher fidelity models , which account for feedback effects from subaqueous morphology, are similarly able to predict the locations of maximum hotspot erosion, but are sensitive to beach over-steepening and/or errors in wave runup calculations that can lead to over-prediction of simulated dune erosion. This work highlights that numerous existing tools are capable of identifying the foredune regions at most risk from hotspot erosion, as well as the need for continued research to improve representation of all relevant intra-storm morphodynamic processes.

Coastal Engineering↗

USGS2020

SurfRCaT: A tool for remote calibration of pre-existing coastal cameras to enable their use as quantitative coastal monitoring tools

The Surf-camera Remote Calibration Tool (SurfRCaT) is a Python-based software application to calibrate and rectify images from pre-existing video cameras that are operating at coastal sites in the United States. The software enables remote camera calibration and subsequent image rectification by facilitating the remote-extraction of ground control points using airborne lidar observations, and guides the user through the entire process. No programming or code interaction are necessary to use the software. Calibration parameters and subsequent rectified image products derived from the software are saved locally. Users can apply SurfRCaT to any camera imagery that has stationary structures within the camera’s field of view. Given current recreational camera infrastructure, SurfRCaT could increase the number of potential quantitative coastal video monitoring stations in the United States by an order of magnitude.

SoftwareX↗

USGS2014

Probabilistic estimation of dune retreat on the Gold Coast, Australia

Sand dunes are an important natural buffer between storm impacts and development backing the beach on the Gold Coast of Queensland, Australia. The ability to forecast dune erosion at a prediction horizon of days to a week would allow efficient and timely response to dune erosion in this highly populated area. Towards this goal, we modified an existing probabilistic dune erosion model for use on the Gold Coast. The original model was trained using observations of dune response from Hurricane Ivan on Santa Rosa Island, Florida, USA (Plant and Stockdon 2012. Probabilistic prediction of barrier-island response to hurricanes, Journal of Geophysical Research, 117(F3), F03015). The model relates dune position change to pre-storm dune elevations, dune widths, and beach widths, along with storm surge and run-up using a Bayesian network. The Bayesian approach captures the uncertainty of inputs and predictions through the conditional probabilities between variables. Three versions of the barrier island response Bayesian network were tested for use on the Gold Coast. One network has the same structure as the original and was trained with the Santa Rosa Island data. The second network has a modified design and was trained using only pre- and post-storm data from 1988-2009 for the Gold Coast. The third version of the network has the same design as the second version of the network and was trained with the combined data from the Gold Coast and Santa Rosa Island. The two networks modified for use on the Gold Coast hindcast dune retreat with equal accuracy. Both networks explained 60% of the observed dune retreat variance, which is comparable to the skill observed by Plant and Stockdon (2012) in the initial Bayesian network application at Santa Rosa Island. The new networks improved predictions relative to application of the original network on the Gold Coast. Dune width was the most important morphologic variable in hindcasting dune retreat, while hydrodynamic variables, surge and run-up elevation, were also important

Queensland↗

USGS2012

Sediment transport on a high-energy ebb-tidal delta

Six tripods were deployed at shallow (~14-m) and deep (~24-m) sites on the northern, middle, and southern flanks of the Grays Harbor, Washington, U.S.A. ebb-tidal delta from early October through December, 1999 to measure waves, currents, temperature, and suspended-sediment concentrations as part of a wave-refraction and sediment-transport experiment. Directional wave spectra show that the general direction of wave approach shifted from WNW to WSW as the North Pacific weather pattern shifted from summer to winter, and we were fortunate enough to capture a large storm (offshore significant wave heights of ~8 m) in late October and a sequence of about 8 smaller events with ~4 to 6-m waves in November and December. As expected, wave directions indicated refraction around the ebb-tidal delta, and varied with incident wave period and direction. Direct estimates of sediment flux about 0.4–0.6 m above the bottom, and modeled estimates of depth-integrated suspended sediment flux indicate net offshore and northward transport. By comparison, estimated net bedload flux was onshore, but at much lower rates. These results indicate that sand on the ebb-tidal delta can be mobilized frequently under winter conditions, and can bypass the inlet at depths of at least 24 m. The data also suggest that significant offshore transport occurs at these depths and that offshore suspended-sediment transport during winter is so great that it might not be balanced by onshore bedload transport.

Washington↗