74 research outputs found
Assessing the use of a camera system within an autonomous underwater vehicle for monitoring the distribution and density of sea scallops (Placopecten magellanicus) in the Mid-Atlantic Bight
No full textPublisher's PDFThe sea scallop (Placopecten
magellanicus) fishery in the
Atlantic is assessed during annual
surveys by using both dredging
and surface-deployed imaging techniques.
In this pilot study in the
Mid-Atlantic Bight, we used an autonomous
underwater vehicle (AUV)
to photograph the seafloor and to
evaluate its use for determining
scallop density and size. During 22
surveys in 2011, 257 km of seafloor
were photographed, resulting in over
203,000 color images. Using trained
annotators and photogrammetric
software, we determined scallop
density and shell heights for 15,252
scallops. The inshore scallop grounds
near Long Island (at depths <40 m)
had a density of 0.077 scallops per
m2, whereas the inshore grounds
of the New York Bight had a density
of 0.012 scallops per m2. Shell
heights derived from images were
found to agree well with measurements
from scallops collected with
a commercial dredge. We show that
images obtained with an AUV can be
used to reliably estimate both density
and shell height consistent with
direct sampling from the same area.
Moreover, side-scan sonar images
obtained with an AUV can be used
to detect dredge scars and, therefore,
can provide a simultaneous,
relative estimate of fishing effort in
that area. AUVs provide a highly accurate
suite of data for each survey
site and therefore allow the design
of experimental studies of fishing
practices.University of Delaware. Department of Geological Sciences.University of Delaware. School of Marine Science and Policy
Esame della fascia costiera Adriatica (settore italiano). Studio di dettaglio sulle aree che presentano le condizioni migliori per avviare lo studio avanzato sulla fattibilità del “sand engineering”. FASE 2
No full textConvenzione di ricerca tra Eni s.p.a. e il Dipartimento di Fisica e Scienze della Terra dell'Università di Ferrara (responsabile scientifico prof. Paolo Ciavola) - Le ben note problematiche legate ai processi di erosione costiera mantengono alta l’attenzione sulle aree litorali, spesso investite da fenomeni di arretramento della linea di riva. Molteplici sono gli interventi adottati per ridurne l’impatto negativo, ma non sempre l’esito è risultato essere quello atteso o, altrimenti, un mero palliativo. Per questo motivo ultimamente è stata concepita l’idea di realizzare sulla costa emiliano-romagnola un ripascimento artificiale caratterizzato da quantitativi di sabbia molto maggiori rispetto a quelli effettuati sinora, secondo caso in Europa dopo quello sulla costa olandese: il cosiddetto Sand Engine. Dopo un primo studio di fattibilità volto alla selezione dei siti idonei all’attuazione del progetto, il presente lavoro è finalizzato a produrre una piena caratterizzazione delle dinamiche sedimentarie e morfologiche dei due tratti di litorale scelti come potenziali Sand Motors, ovvero Lido di Spina – Bellocchio a nord e Lido di Dante – Lido di Classe a sud. Report finale
Carolina Bay Synthesis Dataset
No full text<p>Data used in the manuscript "Investigating the Origin and Dynamics of Carolina Bays".</p>
<ul>
<li>Aqudopp: contains raw ADCP data for each deployment.</li>
<li>Carolina Bay Detections: contains shapefiles for the detections obtained with the detector developed in Lundine and Trembanis 2021.</li>
<li>Detection Maps: contains several maps used to visualize the detections and spatial statistics.</li>
<li>ElevationProfilesForPreviousStudies: contains elevation profiles from National Map data used in construction of the figures illustrating previous sediment coring work in Carolina Bays.</li>
<li>HumminbirdSidescanTonysPond: contains the raw sidescan data collected at Tonys Pond, Assawoman Wildlife Area, Delaware.</li>
<li>Maps contains two folders
<ul>
<li>CarolinaBays_and_Similar_Features: contains maps made with National Map data and satellite imagery of similar features to Carolina Bays around the world.</li>
<li>CarolinaBays_NorthAmerica_Geology: contains maps of Carolina Bays (Lundine and Trembanis, 2021), Great Plains Playas (Playa Lakes Joint Venture, 2022), INQUA OSL dates (Lancaster <em>et al.,</em> 2016), Last Glacial Maximum Ice Extent (Batchelor <em>et al.</em>, 2019), Last Glacial Maximum permafrost extent (Lindgren <em>et al.</em>, 2016), contiguous USA surficial geology (Horton <em>et al.,</em> 2017).</li>
</ul>
</li>
<li>PreviouslyPublishedGeochron: contains OSL and radiocarbon dates from North American dunes and Carolina Bays from previous studies (see the in-prep manuscript for references).</li>
<li>Sedimentology: contains all of the sediment sample and vibracore logs collected at various Delmarva Bays and Tonys Pond.</li>
<li>subbottomTonysPond: contains the raw data for the sub-bottom profiles collected at Tonys Pond.</li>
<li>Waves: contains the raw data from the Spotter wave buoy deployments at Tonys Pond.</li>
<li>WeatherData: contains weather data compiled from the DEOS Bethany Beach station. </li>
</ul>
Local-scale post-event assessments with GPS and UAV-based quick-response surveys: a pilot case from the Emilia–Romagna (Italy) coast
No full textCoastal communities and assets
are exposed to flooding and erosion hazards due to extreme storm events,
which may increase in intensity due to climatological factors in the incoming
future. Coastal managers are tasked with developing risk-management plans
mitigating risk during all phases of the disaster cycle. This necessitates
rapid, time-efficient post-event beach surveys that collect physical data in
the immediate aftermath of an event. Additionally, the inclusion of local
stakeholders in the assessment process via personal interviews captures the
social dimension of the impact of the event. In this study, a local protocol
for post-event assessment, the quick-response protocol, was tested on a pilot
site on the Emilia–Romagna (Italy) coast in the aftermath of an extreme
meteorological event that occurred in February 2015. Physical data were
collected using both real-time kinematic Geographical Positions Systems and
unmanned aerial vehicle platforms. Local stakeholders were interviewed by
collecting qualitative information on their experiences before, during, and
after the event. Data comparisons between local and regional surveys of this
event highlighted higher data resolution and accuracy at the local level,
enabling improved risk assessment for future events of this magnitude. The
local survey methodology, although improvable from different technical
aspects, can be readily integrated into regional surveys for improved data
resolution and accuracy of storm impact assessments on the regional scale to
better inform coastal risk managers during mitigation planning.</p
Carolina Bay Object Detector Images and Labels
No full text<p>Contained here (in CarolinaBayTrainingData.zip) are the images (jpegs) and labels (csv) that were used to train a bounding box object detector for Carolina Bays detailed in the following paper:</p>
<p>Lundine, M., Trembanis, A., Using Convolutional Neural Networks for Detection and Morphometric Analysis of Carolina Bays from Publicly Available Digital Elevation Models, Remote Sensing, 2021, Volume 13(18), 3770, <a href="https://doi.org/10.3390/rs13183770">https://doi.org/10.3390/rs13183770</a>.</p>
<p>Each image is a single channel jpeg. The channel corresponds to gridded LiDAR elevation values remapped to 256 values. Each image was normalized individually for maximum contrast.</p>
<p>The labels csv contains the coordinates for each bounding box annotation of Carolina Bays in each available image. The columns are: filename, width, height, label, xmin, ymin, xmax, ymax, label_value. Each row is an annotation of a Carolina Bay. Filename corresponds to the image, width is the width in pixels of that image, height is the height in pixels of that image. xmin, ymin, xmax, ymax are the bounding box coordinates for the annotation. Label is 'bay' for each annotation, with a label_value of 1.</p>
<p>Feel free to experiment with this dataset, add to it, and improve upon the results.</p>
<p>Also contained here (in CarolinaBayDetections.zip) are the detection results (as unaggregated polygons, as aggregated polygons, as smooth polygons, and as points).</p>
Hurricane Sandy’s Fingerprint: Ripple Bedforms at an Inner Continental Shelf Sorted Bedform Field Site
No full textThe hydrodynamics and seabed morphodynamics on the inner continental shelf and near shore environments have increasing relevance with continued development of near shore structures, offshore energy technologies and artificial reef construction. Characterizing the stresses on and response of the seabed near and around seabed objects will inform best practices for structural design, seabed mine and unexploded ordnance detection, and archaeological and benthic habitat studies. As part of an ONR funded project, Delaware’s Redbird Reef is being studied for object scour and sorted bedform morphodynamics (Trembanis et al., in press). Central to this study are the effects of large storm events, such as Hurricane Sandy, which have had significant impact on the seafloor. Previous studies of inner shelf bedform dynamics have typically focused on near bed currents and bed stressors (e.g. Trembanis et al., 2004), sorted bedforms (e.g. Green et al., 2004) and object scour (e.g. Quinn, 2006; Trembanis et al., 2007; Mayer et al., 2007), but our understanding of the direct effects of objects and object scour on bedform morphodynamics is still incomplete. With prominent sorted bedform ripple fields, the Delaware Redbird artificial reef site, composed of 997 former New York City subway cars, as well as various military vehicles, tugboats, barges and ballasted tires, has made an ideal study location (Raineault et al., 2013 and 2011). Acoustic mapping of the Redbird reef three days prior to Sandy and two days after the following nor’easter, captured the extensive effects of the storms to the site, while acoustic Doppler current profilers characterized both the waves and bottom currents generated by the storm events. Results of the post-Sandy survey support the theory of sorted bedform evolution proposed by Murray and Thieler (2004). Acoustic imagery analysis indicates a highly energized and mobile bed during the storms, leading to self-organization of bedforms and creation of large orbital ripples. Using the Fingerprint Algorithm technique developed by Skarke and Trembanis (2011), sonar images have been analyzed to quantify ripple orientation, wavelength and defects (e.g. bifurcation and terminations). Correlation to time-series current and wave data shows strong agreement between peak-storm ripple wavelength scaling predictions and Fingerprint Algorithm wavelength measurements of relict ripples, indicating a non-equilibrated response of ripple bedforms to near bed orbital currents. Preliminary results further indicate an increase of ripple bedform defects near seabed objects, and deviations in ripple orientation and wavelength possibly related to current steering and vortices shed from nearby objects. Subsequent surveys and instrument deployments at the site have recorded the burial of these ripple bedforms during low-energy conditions, typical with the cyclical evolution of sorted bedform sites
Pockmark Bounding Box Detection and Segmentation Labels
No full text<p>This dataset contains 256x256 pixel jpeg images of gridded depth values as well as binary masks for pixels containing pockmarks (these jpegs are merged together with the depth image on the left and the mask on the right, making a 512x256 image). These are contained within the subdirectory 'PockmarkMaskAnnotations'.</p>
<p>Additionally, this dataset contains a csv containing bounding box annotations (label, bounding box coordinates in terms of image pixels, and a unique integer for the label) of pockmarks in each image. These are contained within the subdirectory 'PockmarkBoxAnnotations'.</p>
<p>Together, these annotations can be used to construct either a bounding box object detector, a bounding box and mask object detector, or a semantic segmentation model.</p>
<p>These were the labels used for the experiments described in Lundine et al., 2023. See this reference to find original data sources to the collected bathymetry data.</p>
<p>Lundine, M., Brothers, L., Trembanis, A., Deep learning-based pockmark detection: implications for quantitative seafloor characterization, Geomorphology, 2023, Volume 421, 108524, <a href="https://doi.org/10.1016/j.geomorph.2022.108524">https://doi.org/10.1016/j.geomorph.2022.108524</a>.</p>
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