Comparing methods used by the U.S. Geological Survey Coastal and Marine Geology Program for deriving shoreline position from lidar data

Farris, Amy S.; Weber, Kathryn M.; Doran, Kara S.; List, Jeffrey H.

doi:10.3133/ofr20181121

Cited by 13 publications

(26 citation statements)

References 2 publications

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“…Foreshore beach slope data were obtained from the USGS National Assessment of Coastal Change Hazards (Doran et al, 2020;Kratzmann et al, 2017). The dataset consists of foreshore slopes along pre-defined transects spaced approximately 50 m in the alongshore direction (Farris et al, 2018). Transects were resampled from 50-m spacing to 1-km spacing alongshore.…”

Section: Wave Setupmentioning

confidence: 99%

Relative contributions of water level components to extreme water levels along the United States Southeast Atlantic Coast from a regional-scale water level hindcast

Parker¹,

Erikson²,

Thomas³

et al. 2023

Preprint

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A 38-year hindcast water level product is developed for the United States Southeast Atlantic coastline from the entrance of Chesapeake Bay to the southeast tip of Florida. The water level modelling framework utilized in this study combines a global-scale hydrodynamic model (Global Tide and Surge Model, GTSM-ERA5), a novel ensemble-based tide model, a parameterized wave setup model, and statistical corrections applied to improve modelled water level components. Corrected water level data are found to be skillful, with an RMSE of 13 cm, when compared to observed water level measurement at tide gauge locations. The largest errors in the hindcast are location-based and typically found in the tidal component of the model. Extreme water levels across the region are driven by compound events, in this case referring to combined surge, tide, and wave forcing. However, the relative importance of water level components varies spatially, such that tides are found to be more important in the center of the study region, non-tidal residual water levels to the north, and wave setup in the north and south. Hurricanes drive the most extreme water level events within the study area, but non-hurricane events define the low to mid-level recurrence interval water level events. This study presents a robust analysis of the complex oceanographic factors that drive coastal flood events. This dataset will support a variety of critical coastal research goals including research related to coastal hazards, landscape change, and community risk assessments.

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Section: Wave Setupmentioning

confidence: 99%

Relative contributions of water level components to extreme water levels along the United States Southeast Atlantic Coast from a regional-scale water level hindcast

Parker¹,

Erikson²,

Thomas³

et al. 2023

Preprint

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“…Nine methods for extracting the coastline were selected using the above-mentioned criteria, originating from seven scientific publications from the years 2011-2021 [80][81][82][83][84][85][86]. Among the analysed papers, there are those that carried out very extensive method validation using many different types of the coastline and waterbodies with different geometric and optical characteristics [87], as well as those in which the method is only tested on a single dataset [81].…”

Section: Coastline Extraction Componentmentioning

confidence: 99%

Integration Data Model of the Bathymetric Monitoring System for Shallow Waterbodies Using UAV and USV Platforms

Lewicka

Specht²,

Stateczny

et al. 2022

Remote Sensing

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Changes in the seafloor relief are particularly noticeable in shallow waterbodies (at depths up to several metres), where they are of significance for human safety and environmental protection, as well as for which the highest measurement accuracy is required. The aim of this publication is to present the integration data model of the bathymetric monitoring system for shallow waterbodies using Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). As part of this model, three technology components will be created: a hydroacoustic and optoelectronic data integration component proposed by Dąbrowski et al., a radiometric depth determination component based on optoelectronic data using the Support Vector Regression (SVR) method, and a coastline extraction component proposed by Xu et al. Thanks to them, it will be possible to cover the entire area with measurements in the coastal zone, in particular between the shallow waterbody coastline and the min. isobath recorded by the echo sounder (the area is lacking actual measurement data). Multisensor data fusion obtained using Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), Light Detection And Ranging (LiDAR), Real Time Kinematic (RTK), UAV, and USV will allow to meet the requirements provided for the International Hydrographic Organization (IHO) Special Order (horizontal position error ≤ 2 m (p = 0.95), vertical position error ≤ 0.25 m (p = 0.95)). To this end, bathymetric and photogrammetric measurements shall be carried out under appropriate conditions. The water transparency in the tested waterbody should be at least 2 m. Hydrographic surveys shall be performed in windless weather and the water level is 0 in the Douglas sea scale (no waves or sea currents). However, the mission with the use of an UAV should take place in appropriate meteorological conditions, i.e., no precipitation, windless weather (wind speed not exceeding 6–7 m/s), sunny day.

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“…Following Farris et al (2018), the calculated MHWL of +2.234 m was then contoured onto the LiDAR DEM. To filter out noise introduced by the LiDAR resolution, the contoured MHWL was smoothed using a PAEK (Polynomial Approximation with Exponential Kernel) smoothing algorithm with a smoothing window of 10 m (Farris et al, 2018).…”

Section: Shoreline Extractionmentioning

confidence: 99%

Understanding spatio-temporal barrier dynamics through the use of multiple shoreline proxies

et al. 2020

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Comparing methods used by the U.S. Geological Survey Coastal and Marine Geology Program for deriving shoreline position from lidar data

Cited by 13 publications

References 2 publications

Relative contributions of water level components to extreme water levels along the United States Southeast Atlantic Coast from a regional-scale water level hindcast

Relative contributions of water level components to extreme water levels along the United States Southeast Atlantic Coast from a regional-scale water level hindcast

Integration Data Model of the Bathymetric Monitoring System for Shallow Waterbodies Using UAV and USV Platforms

Understanding spatio-temporal barrier dynamics through the use of multiple shoreline proxies

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