Geology topics

Jenna A. Brown

Publications and source records attributed to Jenna A. Brown.

11 recordsLinked to original sources

USGS2026

Measuring storm waves and water levels from a fixed structure with a rapidly deployable oceanographic radar

A new oceanographic radar instrument package was developed by the U.S. Geological Survey (USGS) to measure storm waves and water levels in the nearshore, capable of being deployed rapidly and transmitting data in near real-time. To test the performance and accuracy of the sensor, multiple years of data were collected over various hydrodynamic conditions and compared to long-term monitoring data collected at the U.S. Army Corps of Engineers (USACE) Field Research Facility in Duck, North Carolina, USA. The oceanographic radars were highly reliable, with less than 1% of the record being erroneous spikes or missing data points. At the end of the pier, the radar was highly accurate, with nearly perfect agreement in water level ( r 2 = 0.997) compared to a nearby National Oceanic and Atmospheric Administration (NOAA) tide gauge, and good agreement in significant wave height ( r 2 = 0.98) and peak wave period ( r 2 = 0.65) compared to a nearby USACE sensor. This work demonstrates the potential of the USGS radar for rapid response storm deployments and collecting reliable and accurate hydrodynamic measurements in the nearshore for validating coastal impact models.

North Carolina↗

USGS2024

Wave runup and total water level observations from time series imagery at several sites with varying nearshore morphologies

Coastal imaging systems have been developed to measure wave runup and total water level (TWL) at the shoreline, which is a key metric for assessing coastal flooding and erosion. However, extracting quantitative measurements from coastal images has typically been done through the laborious task of hand-digitization of wave runup timestacks. Timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. We utilize over 7000 hand-digitized timestacks from six diverse locations to train and validate machine learning models to automate the process of TWL extraction. Using these data, we evaluate two deep learning model architectures for the task of runup detection. One is based on a fully convolutional architecture trained from scratch, and the other is a transformer-based architecture trained using transfer learning. The deep learning models provide a probability of each pixel being either wet or dry. When contoured at the 50% level (equal chance of being wet or dry), the deep learning models more accurately identified TWL maxima than minima at all sites. This resulted in accurate predictions of 2% exceedance runup, but under predictions of significant swash and over predictions of wave setup. Improved agreement with the complete TWL time series was obtained through post-processing by utilizing the wet/dry probability of each pixel to weight the contouring toward lower dryness probabilities for runup minima (maxima agreed well with observations without tuning). Overall, a transformer-based model using transfer learning provided the best agreement with wave runup statistics, including a) the 2% exceedance runup, b) significant swash, and c) wave setup at the shoreline. For a random subset of images, the model was found to be within the uncertainty range of hand-digitization. The relative success of the transfer learning model suggests that fine-tuning a large model has advantages compared to training a smaller model from scratch. Models provide per-pixel probabilistic estimates in less than 10 s per timestack on a single computational unit, versus the more than 5 min required for hand-digitization. The model is therefore well-suited for near real-time applications, allowing for the development of early warning systems for difficult to forecast events. Real-time wave runup and total water level observations can also be incorporated into coastal hazards forecasts for data assimilation and continual model validation and improvement.

Coastal Engineering↗

USGS2023

The During Nearshore Event Experiment (DUNEX): A collaborative coastal community experiment to address coastal resilience

The During Nearshore Event Experiment (DUNEX) was a large-scale coastal field effort focused on improving understanding of during-storm nearshore processes to ultimately develop predictive technologies, engineering solutions, and actions to enhance coastal resilience. The experiments were conducted on the North Carolina coast by a multidisciplinary group of over 30 research scientists from 18 academic and federal institutions supporting over 30 graduate students and deploying over 300 instruments from 2019 to 2021. The overarching goal of DUNEX was to gather information collaboratively to improve understanding of the interactions of coastal water levels, waves, currents, beach and dune evolution, soil behavior, vegetation, and groundwater during major coastal storms that affect infrastructure, habitats, and communities. In the short term, these high-quality field measurements will lead to better understanding of during-storm processes and impacts and will enhance U.S. academic coastal research programs by providing opportunities for students to learn about field data collection and to potentially analyze data as part of their studies. Longer-term, DUNEX data and outcomes will improve the ability to predict extreme event physical processes and impacts, validate coastal processes numerical models, and improve coastal resilience strategies and communication methods for coastal communities impacted by storms. The purpose of this paper is to describe the motivation for and science goals of the experiment, how stakeholder needs led to these goals, collaborations amongst researchers, and the knowledge gained that will lead to tools to improve coastal resilience. Herein, we first describe how researchers worked with stakeholders to structure their community-driven needs into science-based requirements. Next, we summarize how federal, academic, and stakeholder researchers worked together to design and execute a multi-organizational experiment aligned with those requirements. Finally, we articulate early findings and lessons learned from the experiment. This paper does not summarize all the research findings from DUNEX, as analyses are still ongoing. An American Geophysical Union (AGU) Special Collection on Coastal Storm Research will be published in 2025 including outcomes from DUNEX research.

North Carolina↗

USGS2023

Modeling total water level and coastal change at Pea Island, North Carolina, USA

The DUring Nearshore Event eXperiment (DUNEX) was carried out on Pea Island, North Carolina, USA between September-October 2021. We use a coupled numerical model (Windsurf) to hindcast the evolution of the DUNEX transect and produce a time series of hourly water levels at the shoreline from the model output. In addition to assessing the ability of Windsurf to reproduce TWL, we use model output paired with an ensemble of empirical models to assess how TWL forecasts can be improved by incorporating dynamic morphology. The morphological hindcast achieved an RMSE of 0.10 m and a BSS of 0.47, while the total water level (TWL) time series from the model correlates with the U.S. Geological Survey forecast (r 2 = 0.61) for the study period but with a 0.47 m bias that is primarily due to the much steeper beach slope used to produce the forecast compared to that surveyed at the start of the study period. We find that using dynamic morphology provides a small but statistically significant (α = 0.05) improvement in predicting TWL versus applying the pre-storm beach slope to the whole time series.

North Carolina↗

USGS2023

Sound-side inundation and seaward erosion of a barrier island during hurricane landfall

Barrier islands are especially vulnerable to hurricanes and other large storms, owing to their mobile composition, low elevations, and detachment from the mainland. Conceptual models of barrier-island evolution emphasize ocean-side processes that drive landward migration through overwash, inlet migration, and aeolian transport. In contrast, we found that the impact of Hurricane Dorian (2019) on North Core Banks, a 36-km barrier island on the Outer Banks of North Carolina, was primarily driven by inundation of the island from Pamlico Sound, as evidenced by storm-surge model results and observations of high-water marks and wrack lines. Analysis of photogrammetry products from aerial imagery collected before and after the storm indicate the loss of about 18% of the subaerial volume of the island through the formation of over 80 erosional washout channels extending from the marsh and washover platform, through gaps in the foredunes, to the shoreline. The washout channels were largely co-located with washover fans deposited by earlier events. Net seaward export of sediment resulted in the formation of deltaic bars offshore of the channels, which became part of the post-storm berm recovery by onshore bar migration and partial filling of the washouts with washover deposits within 2 months. This event represents a volumetric setback in the overwash/rollover behavior required for barrier transgression, but the new ponds and lowland habitats may provide beneficial habit for endangered species and will likely persist for years.

North Carolina↗

USGS2022

Human-in-the-Loop segmentation of earth surface imagery

Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. Manual methods are often prohibitively time-consuming, especially those images consisting of small objects and/or significant spatial heterogeneity of colors or textures. Labeling complicated regions of transition that in Earth surface imagery are represented by collections of mixed-pixels, -textures, and -spectral signatures, can be especially error-prone because it is difficult to reliably unmix, identify and delineate consistently. However, the success of supervised machine learning (ML) approaches is entirely dependent on good label data. We describe a fast, semi-automated, method for interactive segmentation of N-dimensional (x, y, N) images into two-dimensional (x, y) label images. It uses human-in-the-loop ML to achieve consensus between the labeler and a model in an iterative workflow. The technique is reproducible; the sequence of decisions made by human labeler and ML algorithms can be encoded to file, so the entire process can be played back and new outputs generated with alternative decisions and/or algorithms. We illustrate the scientific potential of segmentation of imagery of diverse settings and image types using six case studies from river, estuarine, and open coast environments. These photographic and non-photographic imagery consist of 1- and 3-bands on regular and irregular grids ranging from centimeters to tens of meters. We demonstrate high levels of agreement in label images generated by several labelers on the same imagery, and make suggestions to achieve consensus and measure uncertainty, ideal for widespread application in training supervised ML for image segmentation.

Earth and Space Science↗

USGS2021

Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation

Introduction Structure from motion (SFM) has become an integral technique in coastal change assessment; the U.S. Geological Survey (USGS) used Agisoft Metashape Professional Edition photogrammetry software to develop a workflow that processes coastline aerial imagery collected in response to storms since Hurricane Florence in 2018. This report details step-by-step instructions to create three-dimensional (3D) spatial products from both singular and repeated collections of shoreline aerial imagery. The products can be used for real-time hazard guidance and future forecasting and recovery endeavors.

Open-File Report↗

USGS2021

A survey of storm-induced seaward-transport features observed during the 2019 and 2020 hurricane seasons

Hurricanes are known to play a critical role in reshaping coastlines, but often only impacts on the open ocean coast are considered, ignoring seaward-directed forces and responses. The identification of subaerial evidence for storm-induced seaward transport is a critical step towards understanding its impact on coastal resiliency. The visual features, found in the National Oceanic and Atmospheric Administration, National Geodetic Survey Emergency Response Imagery (ERI) collected after recent hurricanes on the U.S. East Atlantic and Gulf of Mexico coasts, include scours and channelized erosion, but also deposition on the shoreface or in the nearshore as deltas and fans of various sizes. We catalog all available ERI and describe recently formed features found on the North Core Banks, North Carolina, after Hurricane Dorian (2019); the Carolina coasts after Hurricane Isaias (2020); the Isles Dernieres, Louisiana, after Hurricane Zeta (2020); and the southwest coast of Louisiana, after Hurricanes Laura and Delta (2020). Hundreds of features were identified over nearly 200 km of coastline with the density of features exceeding 20 per km in some areas. Individual features range in size from 5 m to 500 m in the alongshore, with similar dimensions in the cross-shore direction, including the formation or reactivation of outlets. The extensive occurrence of these storm-induced return-flow and seawardflow morphologic features demonstrates that their role in coastal evolution and resilience may be more prominent than previously thought. Based on these observations we propose clarifying terms for return- and seaward-flow features to distinguish them from more frequently documented landward-flow features and advocate for their inclusion in coastal change hazards classification schemes and coastal evolution morphodynamic models.

southeast United States↗

USGS2021

Developing bare-earth digital elevation models from structure-from-motion data on barrier islands

Unoccupied aerial systems can collect aerial imagery that can be used to develop structure-from-motion products with a temporal resolution well-suited to monitoring dynamic barrier island environments. However, topographic data created using photogrammetric techniques such as structure-from-motion represent the surface elevation including the vegetation canopy . Additional processing is required for estimating bare-earth elevation, which is critical for understanding the underlying geomorphology of these islands. In this study, we used a vegetation and elevation survey to produce bare-earth digital elevation models from structure-from-motion-derived elevation products for two sites on Dauphin Island, Alabama (USA). One site was exposed to high wave energy and included a mix of beach, dune, and barrier flat habitats that were dominated by supratidal/upland herbaceous vegetation. The second site was exposed to low wave energy and was dominated by intertidal marsh. Aerial imagery was collected in late fall of 2018 and spring of 2019. We tested several machine learning algorithms for predicting and removing elevation bias for vegetated areas using predictors that included spectral indices from unoccupied aerial systems-based multispectral imagery and landscape position information (e.g., relative topography and distance from shore). Models were developed for each site and season. We also explored how well the model from one season generalized to data from a different season for the same site. For developing initial digital surface models, we found that utilizing a minimum bin algorithm, as opposed to interpolation, led to lower elevation bias. For bias removal, Gaussian process regression performed the best and led to a root mean square error for the bare-earth digital elevation models of around 0.10 m for the high energy site and 0.15 m for the low energy site. Compared to the digital surface models, the root mean square error for the bare-earth digital elevation models was reduced by at least 29 percent for the high energy site and 69 percent for the low energy site. For all models, common predictors included surface elevation, vegetation greenness, and distance from the shoreline. The models produced comparable results when trained using data from a different season. The error estimates for all analyses were within published elevation standards for lidar data for vegetated areas. With calibration, this approach could be portable to other areas or data, such as aerial lidar (conventional or unoccupied), to provide an efficient and repeatable framework for monitoring geomorphology or provide baseline elevations for predicting changes to these environments under future conditions.

Alabama↗

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↗

USGS2019

Observations of mixing and transport on a steep beach

Surfzone mixing and transport on a sandy, steep (∼1/8 slope), reflective beach at Carmel River State Beach, California, are described for a range of wave and alongshore flow conditions. Depth-limited wave breaking occurred close to the shore due to the steepness of the beach, creating a narrow surf/swash zone (∼10 m wide). Fluorescent Rhodamine dye was released as a slug in the surfzone, and the temporal and spatial evolution was measured using in-situ dye sensors. Dye concentration measured as a function of time reveals sharp fronts that quickly decay resulting in narrow peaks near the dye release, which subsequently broaden and decrease in peak concentration with alongshore distance. The measurements indicate two stages of mixing and transport occur inside the surfzone on the steep beach. 1) In the near-field (<50 m downstream of the dye release location), the dye fully mixed throughout the water column after a few incident waves then continued to disperse in two dimensions, with both advection and diffusion processes being important. 2) In the far-field (>50 m downstream from the dye release location), the mass transport was dominated by advection. The distance to the far-field is much shorter in the alongshore on a steep beach compared with a dissipative beach. Estimates of cross-shore and alongshore diffusion coefficients ( κ x , κ y ) were found to be similar in magnitude within the surfzone. Outside the surfzone in the far-field, the results suggest that the mixing processes are independent of those inside the surfzone. The mixing and transport of material observed on this steep beach are found to be analogous to that previously measured on dissipative beaches, however the diffusion coefficients within and outside the surfzone were found to be smaller on this steep beach.

California↗