This dataset contains shoreline positions derived from available Landsat satellite imagery for five states (Delaware, Maryland, Viginia, Georgia, and Florida) along the U.S. Atlantic coast for the time period 1984 to 2021. An open-source toolbox, CoastSat (Vos and others, 2019a and 2019b), was used to classify coastal Landsat imagery and detect shorelines at the sub-pixel scale. Resulting shorelines are presented in KMZ format. Significant uncertainty is associated with the locations of shorelines in extremely dynamic regions, including at the locations of river mouths, tidal inlets, capes, and ends of spits. These data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D viewing. For technical users and researchers, data can be ingested into Global Mapper or QGIS for more detailed analysis. Similar shoreline positions for North Carolina and South Carolina are available from Barnard and others, 2023 at https://doi.org/10.5066/P9W91314.

The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

Reference literature:

Palomar-Vázquez, J.; Pardo-Pascual, J.E.; Almonacid-Caballer, J.; Cabezas-Rabadán, C. Shoreline Analysis and Extraction Tool (SAET): A New Tool for the Automatic Extraction of Satellite-Derived Shorelines with Subpixel Accuracy. Remote Sens. 2023, 15, 3198. https://doi.org/10.3390/rs15123198

J.E. Pardo-Pascual, J. Almonacid-Caballer, C. Cabezas-Rabadán, A. Fernández-Sarría, C. Armaroli, P. Ciavola, J. Montes, P.E. Souto-Ceccon, J. Palomar-Vázquez: Assessment of satellite-derived shorelines automatically extracted from Sentinel-2 imagery using SAET. Coastal Engineering, 2023, 104426, ISSN 0378-3839, https://doi.org/10.1016/j.coastaleng.2023.104426.

Pardo-Pascual, J. E., Cabezas-Rabadán, C., Palomar-Vázquez, J., Fernández-Sarría, A., Almonacid-Caballer, J., Souto-Ceccon, P. E., Montes, J., Armaroli, C., and Ciavola, P.: Satellite-derived shorelines extracted using SAET for characterizing the effect of Storm Gloria in the Ebro Delta (W Mediterranean), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9856, https://doi.org/10.5194/egusphere-egu22-9856, 2022.

In relation to SAET, additional information, instructions and the open-source code are available here https://zenodo.org/records/10256957 and also in GitHub here https://github.com/jpalomav/SAET_master

Description of the containing files inside the Dataset

The deliverable includes two different files: the dataset of shorelines and the accompanying report.

The report describes the structure of the dataset of shorelines produced in Task 3.2 - Shoreline mapping validation and calibration and described in Deliverable 3.2 - Algorithms for satellite derived shoreline mapping and shorelines dataset (DOI: 10.5281/zenodo.5807711).

The dataset is composed of three folders.

The first folder ("SDS_vs_VHR_shoreline") contains the SDSs extracted at each test site by the different algorithms tested in ECFAS (SHOREX, Cabezas-Rabadán et al., 2021; Sánchez-García et al., 2020; CoastSat, Vos et al., 2019a,b and SAET, Palomar-Vázquez et al., 2021; Pardo-Pascual et al., 2021) to assess their performance through the comparison with shorelines photo-interpreted on coincident VHR satellite images. The SDSs obtained at all the test sites using SAET and CoastSAT are included, as well the SDSs obtained using SHOREX at the Spanish test sites. The photo-interpreted shorelines are also provided. For all the test sites it is provided the line separating the instantaneous shoreline (water/land boundary), obtained by photo-interpretation. For the sites in the Netherlands, also the wet/dry line is provided.

The second folder ("SDS_vs_video-monitored_shorelines") presents for each of the three test sites the SDSs obtained using the different algorithms and video-derived shorelines. The SDSs at the beaches of la Victoria and Cala Millor were obtained using SAET, SHOREX and CoastSAT. In the case of Gerakas Beach, the shorelines were obtained using SAET and CoastSAT.

The third folder ("SDS_SAET_Storm_cases") includes eight examples in different European coasts in which the SDSs before and after a storm have been obtained.

This ECFAS dataset of shorelines is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the ECFAS dataset of shorelines are licensed under the Open Database License: http://opendatacommons.org/licenses/dbcl/1.0/.

This Report is made available under the Creative Commons Attribution 4.0 International License.

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

The European Copernicus Coastal Flood Awareness System (ECFAS) project will contribute to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS will provide a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development will run from January 2021-December 2022. The ECFAS project is a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and is funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

This project has received funding from the European Union’s Horizon 2020 programme

VERSION 2 OF THE PRODUCT IS OPEN ACCESS, PLEASE CHECK THE LATEST VERSION

Reference literature:

Palomar-Vázquez, J.; Pardo-Pascual, J.E.; Almonacid-Caballer, J.; Cabezas-Rabadán, C. Shoreline Analysis and Extraction Tool (SAET): A New Tool for the Automatic Extraction of Satellite-Derived Shorelines with Subpixel Accuracy. Remote Sens. 2023, 15, 3198. https://doi.org/10.3390/rs15123198

J.E. Pardo-Pascual, J. Almonacid-Caballer, C. Cabezas-Rabadán, A. Fernández-Sarría, C. Armaroli, P. Ciavola, J. Montes, P.E. Souto-Ceccon, J. Palomar-Vázquez: Assessment of satellite-derived shorelines automatically extracted from Sentinel-2 imagery using SAET. Coastal Engineering, 2023, 104426, ISSN 0378-3839, https://doi.org/10.1016/j.coastaleng.2023.104426.

Pardo-Pascual, J. E., Cabezas-Rabadán, C., Palomar-Vázquez, J., Fernández-Sarría, A., Almonacid-Caballer, J., Souto-Ceccon, P. E., Montes, J., Armaroli, C., and Ciavola, P.: Satellite-derived shorelines extracted using SAET for characterizing the effect of Storm Gloria in the Ebro Delta (W Mediterranean), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9856, https://doi.org/10.5194/egusphere-egu22-9856, 2022.

Description of the containing files inside the Dataset

The deliverable includes two different files: the dataset of shorelines and the accompayining report.

The report describes the structure of the dataset of shorelines produced in Task 3.2 - Shoreline mapping validation and calibration and described in Deliverable 3.2 - Algorithms for satellite derived shoreline mapping and shorelines dataset (DOI: 10.5281/zenodo.5807711).

The dataset is composed of three folders.

The first folder (“SDS_vs_VHR_shoreline”) contains the SDSs extracted at each test site by the different algorithms tested in ECFAS (SHOREX, Cabezas-Rabadán et al., 2021; Sánchez-García et al., 2020; CoastSat, Vos et al., 2019a,b and SAET, Palomar-Vázquez et al., 2021; Pardo-Pascual et al., 2021) to assess their performance through the comparison with shorelines photo-interpreted on coincident VHR satellite images. The SDSs obtained at all the test sites using SAET and CoastSAT are included, as well the SDSs obtained using SHOREX at the Spanish test sites. The photo-interpreted shorelines are also provided. For all the test sites it is provided the line separating the istantaneuos shoreline (water/land boundary), obtained by photo-interpretation. For the sites in the Netherlands, also the wet/dry line is provided.

The second folder (“SDS_vs_video-monitored_shorelines”) presents for each of the three test sites the SDSs obtained using the different algorithms and video-derived shorelines. The SDSs at the beaches of la Victoria and Cala Millor were obtained using SAET, SHOREX and CoastSAT. In the case of Gerakas Beach, the shorelines were obtained using SAET and CoastSAT.

The third folder (“SDS_SAET_Storm_cases”) includes eight examples in different European coasts in which the SDSs before and after a storm have been obtained.

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

This dataset contains shoreline positions derived from available Landsat satellite imagery for North Carolina and South Carolina for the time period of 1984 to 2021. Positions were determined using CoastSat (Vos and others, 2019a and 2019b), an open-source mapping toolbox, was used to classify coastal Landsat imagery and detect shorelines at the sub-pixel scale. To understand shoreline evolution in complex environments and operate long-term simulations illustrating potential shoreline positions in the next century (Vitousek and others, 2017, 2021), robust historical shoreline data is necessary. Satellite-derived shorelines (SDS) offer expansive shoreline observational data over large geographic and temporal scales. Resulting shorelines for the period of 1984-2021 are presented in KMZ format. Significant uncertainty is associated with the locations of shorelines in extremely dynamic regions, including at the locations of river mouths, tidal inlets, capes, and ends of spits. These data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D viewing. For technical users and researchers, data can be ingested into Global Mapper or QGIS for more detailed analysis.

This dataset contains shoreline positions derived from available Landsat satellite imagery for four states (Texas, Louisiana, Mississippi, and Florida) along the U.S. Gulf coast for the time period 1984 to 2022. An open-source toolbox, CoastSat (Vos and others, 2019a and 2019b), was used to classify coastal Landsat imagery and detect shorelines at the sub-pixel scale. Resulting shorelines are presented in CSV format. Significant uncertainty is associated with the locations of shorelines in extremely dynamic regions, including at the locations of river mouths, tidal inlets, capes, and ends of spits. These data are readily viewable in a text or spreadsheet editor. For technical users and researchers, data can be ingested into Global Mapper or QGIS or similar for more detailed analysis. Similar shoreline positions for North Carolina and South Carolina are available from Barnard and others, 2023 at https://doi.org/10.5066/P9W91314.

This dataset accompanies the article by Konstantinou et al. (2022) titled ‘Satellite-based shoreline detection: macrotidal high-energy coasts’. This study assesses the ability of existing satellite image analysis technology to capture shoreline position change at relevant magnitudes and timescales for two different coastal environments in the United Kingdom. It addresses the influence of tidal elevation and wave-induced water-level fluctuations at two sites representing end members of beach morphological type in a region of low satellite useability (high cloud cover combined with low image availability). The study uses 14 years of monthly topographic surveys at two macrotidal sites in the UK, combined with modelled wave data and harmonic tidal predictions to investigate the influence of tidal elevation and wave action on SDS accuracy.

Description

The dataset consists of two matlab files each containing the information listed below for the two test sites (Slapton Sands (SLSdata); Perranporth (PPTdata)):

dnum: date of satellite image capture in matlab datenum format

sat: name of satellite (L5=Landsat 5; L7=Landsat 7; L8=Landsat 8; S2=Sentinel-2)

transects: a structure containing the following variables:

profName – the name of the survey profile

startEast; startNorth; endEast; endNorth: OSGB coordinates of the start (landward) and end (seaward) of the transect line

startUTMlat; startUTMlon; endUTMlat; endUTMlon: WGS84-UTM30 coordinates of the start (landward) and end (seaward) of the transect line

orientation: profile orientation

shoreOrient: shoreline orientation

transAngle: profile angle to shore-normal.

transTimeSeries: time series of the intersection of the SDW at each transect in three columns that include chainage (m), longitude, latitude.

profData: a nested structure containing the survey data at organised by profile including survey date, OSGB and WGS84-UTM30 coordinates of surveyed points.

Most South African beaches lack sufficient monitoring, which impedes a holistic understanding of shoreline dynamics amid increasing environmental and anthropogenic pressures. This study addressed this critical knowledge gap by utilising a satellite-derived shoreline algorithm (CoastSat) to rectify years of inadequate monitoring and to contribute to a thorough understanding of South African shoreline dynamics. Enhancements were made to the open-source CoastSat algorithm to enable a semi-automated, nationwide application. As a result, a pioneering database was created, spanning from 1984 to 2023 and covering nearly all sandy areas of the South African coastline. This extensive and coherent database represents the first of its kind for South Africa. The accuracy of the satellite-derived shoreline data (SDS) was assessed by comparing it with Lidar-surveyed data from 27km of beach area across six different beaches in the eThekwini Municipality. The results showed a very strong correlation (R = 0.95) between the SDS and the surveyed data, although an overall landward bias of 11.2m was observed. By incorporating wave runup in the analysis the accuracy was significantly improved, reducing bias by up to 79%. These findings were consistent with previous CoastSat studies from abroad.In addition to developing this extensive shoreline dynamics database, four local case studies and four regional assessments were carried out. These efforts served two primary objectives: to further the understanding of South African coastal dynamics both locally and regionally, and to demonstrate the utility of the database. For example, (i) A study of the Tugela River Mouth revealed shoreline erosion of several hundred metres from 2005 to 2023, which is important information for ongoing and planned catchment projects, such as large dams, that impact fluvial sand yield to the coast. (ii) The consistent extreme accretion south of the Richards Bay port entrance sharply contrasted with the extreme erosion to the north. This highlighted the impacts of various coastal engineering interventions, providing valuable insights into their effectiveness and guiding future coastal management strategies based on the lessons learned. (iii) Studies of the seasonal shoreline responses at St Helena Bay and Cape Town bays (Table Bay and False Bay) showed how the magnitude of these responses was related to the degree of wave exposure. (iv) Regional investigations found interesting distinctions in shoreline evolution: for instance, the west coast typically experienced shoreline retreat during winter, the south coast had less extreme winter erosion, and the east coast, particularly from Port St Johns northward, saw the greatest erosion shifting from winter to spring. This information is invaluable for informing local, regional, and provincial vulnerability assessments and guiding resource allocation more effectively.This study successfully established the first comprehensive database of shoreline dynamics for the entire South African sandy coastline. The data and insights provided could serve as a valuable resource for coastal managers, policymakers, engineers, researchers, and other stakeholders, facilitating the development of informed, effective, and sustainable coastal management strategies that address both current and future challenges. Future research can build on these data and insights by exploring new, unresearched avenues or enhancing methods and technologies to mitigate the identified errors and limitations.

This is an archived copy of the following Github repository: https://github.com/SatelliteShorelines/SDS_Benchmark

This dataset contains shorelines (as vectors, where vertices are positions determined along transects) derived from available satellite imagery for multiple locations (Barter Island, Alaska; Elwha, Washington; Cape Cod, Massachusetts; Madeira Beach, Florida; and Rincon, Puerto Rico) and associated settings used to derive the data across the United States for the time period 1984 to 2023. An open-source toolbox, CoastSeg (Fitzpatrick and others, 2024a; Fitzpatrick and others, 2024b), was used to classify coastal Landsat and Sentinel imagery and detect shorelines at the sub-pixel scale, using the CoastSat (Vos and others, 2019) methodology. Shorelines are derived for multiple slope values, representing the spatial and temporal variance of slope conditions at each site. Resulting shorelines from transect-based derivation are presented in GeoJSON format. Significant uncertainty is associated with the locations of shorelines in extremely dynamic regions at all sites, including at the locations of river mouths, tidal inlets, capes, ends of spits, and adjacent to wetlands at the Barter Island site. For technical users and researchers, data can be ingested into geospatial platforms (for example, QGIS or GlobalMapper) for more detailed analysis.

This dataset contains shoreline positions derived from available Landsat satellite imagery (1984 to 2023) for the Romanian Black Sea coast. The shoreline positions were derived on transects 50 m apart using the open-source toolbox, CoastSat described in Vos and others, 2019a and 2019b. Landsat coastal imagery was classified, and shoreline positions were detected at the sub-pixel scale. Transects starting and end coordinates as well as all shoreline points along the transects for each date for which a Landsat image was processed are delivered in a comma delimited CSV table. Significant uncertainty can be associated with the locations of shorelines in extremely dynamic regions, including at the locations of river mouths, tidal inlets, capes, and ends of spits. Since the Black Sea is a micro-tidal environment (less than 10 cm tidal range), the expected positional shoreline root means square error (RMSE) is less than 10% of Landsat pixel size of 30 m. These data can be used with any GIS software for shoreline evolution analysis, or other software to assist identifying and assessing possible areas of change and vulnerability, along with appropriate inclusion of uncertainty.

Access

Full dataset can be accessed and downloaded from the directory via: https://arcticdata.io/data/10.18739/A2610VT7V/.

Overview

This research is motivated by the limited resolution of existing global coastline datasets and the growing availability of high-resolution multispectral satellite imagery. We retrieve coastlines using a water probability algorithm, which stacks water masks generated from the Normalized Difference Water Index (NDWI) method. This dataset provides global coastlines, water probability maps, and intertidal zones derived from multispectral images captured by Maxar satellites (2009–2023) at a high spatial resolution of 2 meters (m). Coastlines represent the median tidal height of image acquisitions, with modeled tidal heights included. The intertidal zones derived from water probability maps represent dynamic regions sensitive to tidal variations. These high-resolution products support applications in coastal resource management, sea level rise analysis, and coastal habitat loss and migration.

A dataset of Landsat, Sentinel, and Planetscope satellite images of coastal shoreline regions, and corresponding semantic segmentations. The dataset consists of folders of images and label images. Label images are images where each pixel is given a discrete class by a human annotator, among the following classes: a) water, b) whitewater/surf, c) sediment, and d) other. These data are intended only to be used as a training and validation dataset for a machine learning based image segmentation model that is specifically designed for the task of coastal shoreline satellite image semantic segmentation.

This repository contains 35 years of tidally-corrected shoreline change data at the Klamath River Littoral Cell in northern California. This dataset was used in Warrick et al. 2023, "A Large Sediment Accretion Wave Along a Northern California Littoral Cell", to investigate and track the movement of a large sediment wave.

CoastSat was used to map shoreline changes on Landsat 5, Landsat 7 and Landsat 8 imagery between 1984 and 2022. The Coastsat toolbox is publicly available at https://github.com/kvos/CoastSat and described in Vos et al. 2019, https://doi.org/10.1016/j.envsoft.2019.104528. The time-series of shoreline change were tidally-corrected along cross-shore transects using tide levels from a global tide model (FES2014) and a satellite-derived estimate of the beach slope (as described in Vos et al. 2020, "Beach slopes from satellite-derived shorelines", https://doi.org/10.1029/2020GL088365).

The data is located in the /shoreline_data folder and structured as follows:

The littoral cell is divided in 4 sections (kmt_01, kmt_02, kmt_03, kmt_04)

For each section there is a folder with 4 CSV files:

time_series_tidally_corrected.csv: this file contains the tidally-corrected time-series of shoreline change along each transect belonging to the site (e.g. kmt01-000, kmt01-001 etc). This is the final product used for coastal change analyses.

time_series_raw.csv: this file contains the raw time-series of shoreline change, which have not be tidally-corrected. Note that each image is taken at a different stage of the tide.

tide_levels_fes2014: this file contains the tide levels at the time of image acquisition extracted from FES2014 (global tide model publicly available on AVISO+).

transect_coordinates_and_beach_slopes.csv: this file contains the coordinates (in WGS84 lat/lon coordinates) as well as the estimated beach slope for each transect.

In addition, there are 3 geospatial layers (.GEOJSON) which contain important spatial information. All the geospatial layers are in EPSG:2163 - US National Atlas Equal Area:

Klamath_polygons.geojson: this layer contains the polygons that were used to run CoastSat for each section of the littoral cell.

Klamath_shorelines.geojson: this layer contains the sandy shorelines that were used to generate the cross-shore transects (also used as reference shorelines in CoastSat).

transects.geojson: this layer contains the cross-shore transects, which are spaced 100 m alongshore.

Finally, in the /animations folder, there is a clip showing the mapped shorelines on the satellite imagery.

This dataset contains shoreline positions derived from available satellite imagery for multiple locations (Barter Island, Alaska; Elwha, Washington; Cape Cod, Massachusetts; Madeira Beach, Florida; and Rincon, Puerto Rico) across the United States for the time period 1984 to 2023. An open-source toolbox, CoastSeg (Fitzpatrick and others, 2024a; Fitzpatrick and others, 2024b), was used to classify coastal Landsat and Sentinel imagery and detect shorelines at the sub-pixel scale, using the CoastSat (Vos and others, 2019) methodology. Shorelines are derived for multiple slope values, representing the spatial and temporal variance of slope conditions at each site. Resulting shoreline positions are presented as discrete points in comma-separated value (CSV) format. Significant uncertainty is associated with the locations of shorelines in extremely dynamic regions at all sites, including at the locations of river mouths, tidal inlets, capes, ends of spits, and adjacent to wetlands at the Barter Island site. For technical users and researchers, data can be ingested into geospatial platforms (for example, QGIS or GlobalMapper) for more detailed analysis.

CoastSeg: Beach transects and beachface slope database v2.0

Coastal shoreline-normal transects, to support shoreline extraction from satellite imagery, and tidal correction of CoastSeg-derived shoreline time-series and other shoreline data, as well as miscellaneous analyses of coastal shoreline data.

These data work with the software package CoastSeg https://github.com/SatelliteShorelines/CoastSeg. More details are available on the project's website https://satelliteshorelines.github.io/CoastSeg/

These transecst are not comprehensive in coverage, representing the best available data known to us at this time, and are provided to the user as a courtesy, but each user has the option (and is encouraged) to develop and use their own transects.

Transects data

1. id: unique ID code

2. slope: beach face slope, for tidal correction of transect-based data3. distance: distance in degrees between slope datum location and transect location4. feature_x: transect location x5. feature_y: transect location y6. nearest_x: nearest slope location x7. nearest_y: nearest slope location y

Note that beach slopes are not available for every transect location. A value of NULL is used in those (relatively rare) locations.

Beach face slope and transect data have been derived from:

1. Doran, K.S., Long, J.W., Birchler, J.J., Brenner, O.T., Hardy, M.W., Morgan, K.L.M, Stockdon, H.F., and Torres, M.L., 2017, Lidar-derived beach morphology (dune crest, dune toe, and shoreline) for U.S. sandy coastlines (ver. 4.0, October 2020): U.S. Geological Survey data release, https://doi.org/10.5066/F7GF0S0Z.

2. Kilian Vos. (2023). Time-series of shoreline change along the Pacific Rim (v1.4) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7758183

3. Andrew Short. (2022). Sediment size dataset for Australia [Data set]. In Australian Coastal Systems (0.1, p. XXV, 1241). Springer Cham. https://doi.org/10.5281/zenodo.7127186

4. Vos, Kilian, Wen, Deng, Harley, Mitchell D., Turner, Ian L., & Splinter, Kristen D. (2022). Beach-face slope dataset for Australia (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7272538

5. Gibbs, A.E., Ohman, K.A., Coppersmith, R., and Richmond, B.M., 2017, National Assessment of Shoreline Change: A GIS compilation of updated vector shorelines and associated shoreline change data for the north coast of Alaska, U.S. Canadian border to Icy Cape: U.S. Geological Survey data release, https://doi.org/10.5066/F72Z13N1.

6. Himmelstoss, E.A., Kratzmann, M., Hapke, C., Thieler, E.R., and List, J., 2010, The National Assessment of Shoreline Change: A GIS Compilation of Vector Shorelines and Associated Shoreline Change Data for the New England and Mid-Atlantic Coasts: U.S. Geological Survey Open-File Report 2010-1119, available at https://pubs.usgs.gov/of/2010/1119/.

7. Snyder, A.G., and Gibbs, A.E., 2019, National assessment of shoreline change: A GIS compilation of updated vector shorelines and associated shoreline change data for the north coast of Alaska, Icy Cape to Cape Prince of Wales: U.S. Geological Survey data release, https://doi.org/10.5066/P9H1S1PV

8. Romine, B.M., Fletcher, C.H., Genz, A.S., Barbee, M.M., Dyer, Matthew, Anderson, T.R., Lim, S.C., Vitousek, Sean, Bochicchio, Christopher, and Richmond, B.M., 2012, National Assessment of Shoreline Change: A GIS compilation of vector shorelines and associated shoreline change data for the sandy shorelines of Kauai, Oahu, and Maui, Hawaii: U.S. Geological Survey Open-File Report 2011-1009, available online at https://pubs.usgs.gov/of/2011/1009/.

9. Gibbs, A.E., Jones, B.M., and Richmond, B.M., 2020, A GIS compilation of vector shorelines and coastal bluff edge positions, and associated rate-of-change data for Barter Island, Alaska: U.S. Geological Survey data release, https://doi.org/10.5066/P9CRBC5I.

10. Sturdivant, E.J., Zeigler, S.L., Gutierrez, B.T., and Weber, K.M., 2019, Barrier island geomorphology and shorebird habitat metrics–Sixteen sites on the U.S. Atlantic Coast, 2013–2014: U.S. Geological Survey data release, https://doi.org/10.5066/P9V7F6UX.

Additional contributions:

1. Sean Vitousek, USGS

Bounding boxes

These supporting files are the bounding boxes of vector datasets used by the program to attribute transects data

shorelines_bounding_boxes.csv

transects_bounding_boxes.csv

usa_shorelines_bounding_boxes.geojson

world_reference_shorelines_bboxes.geojson

Reference shoreline data is from Sayre et al. (2018)

Sayre, R., Noble, S., Hamann, S., Smith, R., Wright, D., Breyer, S., Butler, K., Van Graafeiland, K., Frye, C., Karagulle, D. and Hopkins, D., 2019. A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units. Journal of Operational Oceanography, 12(sup2), pp.S47-S56.

The U.S. Geological Survey has a long history of responding to and documenting the impacts of storms along the Nation’s coasts and incorporating these data into storm impact and coastal change vulnerability assessments. These studies, however, have traditionally focused on sandy shorelines and sandy barrier-island systems, without consideration of impacts to coastal wetlands. The goal of the Barrier Island and Estuarine Wetland Physical Change Assessment project is to integrate a wetland-change assessment with existing coastal-change assessments for the adjacent sandy dunes and beaches, initially focusing on Assateague Island along the Maryland and Virginia coastline. Assateague Island was impacted by waves and storm surge associated with the passage of Hurricane Sandy in October 2012, including erosion and overwash along the ocean-facing sandy shoreline as well as erosion and overwash deposition in the back-barrier and estuarine bay environments. This report serves as an archive of data that were derived from Landsat 5 and Landsat 8 imagery from 1984 to 2014, including wetland and terrestrial habitat extents; open-ocean, back-barrier, and estuarine mainland shoreline positions; and sand-line positions along the estuarine mainland and barrier shorelines from Assateague Island, Maryland to Metompkin Island, Virginia. The geographic information system data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from http://pubs.usgs.gov/ds/0968/ds968_data.html.

This repository contains the data used to evaluate the performance of a beach nourishment project in three bays of Mar del Plata, Province of Buenos Aires, Argentina. The project was carried out by the Belgian company Dredging International between 1998 and 1999. A total of 2,480,000 m3 of sediments were dredged from the mouth of the local port and deposited on the Playa Grande, Varese and Bristol beaches. CoastSat 2.0 toolkit (https://github.com/kvos/CoastSat), an open-source Python software for shoreline detection was utilized. The toolkit allows users to acquire time series of shoreline positions for any coastal area using available satellite imagery from the Google Earth Engine platform. In this case, it was used with data from the Landsat missions L5 (1986–2012), L7 (1999–2021), L8 (2013–2021), and the Sentinel mission S2 (2015–2021). Top-of-Atmosphere reflectance images from the Landsat missions with a resolution of 30 m and a revisit time of 16 days (Tier 1) were utilized, along with images from the Sentinel 2 mission with a resolution of 10 m and a revisit time of 5 days (Level-1C). Additionally, the toolkit employed spatial resolution enhancement techniques over Landsat images to map the position of the shoreline with an accuracy of ~10 m. In this repository, CoastSat-detected shorelines can be accessed along with the normal to shore transects from which the beach width time series were obtained to analyze beach response to nourishment. Tide-corrected beach width time series are also provided.

This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual deline...

The Arctic is warming, but the impacts on its coasts are not well documented. To better understand the reaction of Arctic coasts to increasing environmental pressure, shoreline position changes along a 210 km length of the Yukon Territory mainland coast in north-west Canada were investigated for the time period from 1951 to 2011. Shoreline positions were extracted from georeferenced aerial photographs from 1951, 1953, 1954, 1972, 1976, 1992, 1994, and 1996, and from WorldView and GeoEye satellite imagery from 2011. Shoreline change was then analyzed using the Digital Shoreline Analysis System (DSAS) extension for ESRI ArcGIS. Shoreline change rates decelerated to a mean rate of -0.5 m/a between the 1970s to 1990s, which was followed by a significant increase in coastal erosion to -1.3 m/a in the 1990s to 2011 time period. These observation indicate that the current rate of coastal retreat along the Yukon coast is higher than at any time before in the 60 year long observation record. Total and annual shoreline movements along a 210 km long stretch of the Yukon Territory mainland coast were quantified, using a combination of air photos and satellite imagery. Aerial black and white photographs were obtained from the Canadian National Air Photo Library [NRCan, 2016] for the 1950s (i.e., 1951, 1952, 1953, 1954), the 1970s (i.e., 1972, 1976) and the 1990s (i.e., 1992, 1994, 1996). The usage of pictures from different years within the respective decades assured a complete coverage of the shoreline of the study area in the 1950s and the 1970s and coverage of seven short shoreline sections in the 1990s. The most recent (i.e. 2011) shoreline position was mapped using satellite imagery derived from the GeoEye-1 and WorldView-2 satellites [Digital Globe 2014, 2016]. Orthorectification of all aerial photos was performed by geo-coding all imagery to the 2011 satellite images using PCI Geomatic's Geomatica Orthoengine© software (2014) in the UTM map projection WGS84 Zone 7 North. The Yukon Digital Elevation Model (DEM) (30.0 m ground resolution) [Environment Yukon, 2016], airplane-based LiDAR (Light Detection and Ranging) elevation data (1.0 m ground resolution) [Obu et al., 2016], and the TanDEM-X intermediate DEM (12.0 m ground resolution) [Huber et al., 2012, doi:10.5194/isprsarchives-XXXIX-B7-45-2012] datasets were used to reduce terrain relief displacement. Aerial photographs from the 1950s and 1970s have a ground resolution of 3.5 m and 3.0 m, respectively, meaning that the smallest distinguishable objects are 3.5 m, or 3.0 m apart. Assuming that landscape changes which occurred between the overflights from 1951 until 1954 and from 1972 and 1976 are within the range of the ground resolution, photos from the 1950s and 1970s were treated as two coherent series. The 1990s were treated differently because the ground resolution of the photographs was higher (0.3 m). Photographs taken within one year in the 1990s each display a single site which was spatially separated from pictures from other years. After geocoding of all pictures, the shoreline was digitized at a scale of 1:1 000. A set of indicators like (i.e., vegetation line, wet-dry line, cliff top line and cliff toe line) was used to digitize the shoreline. The usage of different shoreline types was necessary in order to capture the wide variety of landforms along the Yukon coast. The landforms were classified in five geomorphological classes, being: 1) Beach, barrier island, spit, 2) Inundated tundra, 3) Tundra flats, 4) Tundra slopes, and 5) Active tundra cliff. Shoreline change rates were calculated using the Esri ArcGIS extension Digital Shoreline Analysis System (DSAS) version 4.3 [Thieler et al., 2009]. The rates were computed along transects perpendicular to the shoreline, with a transect spacing of 100 m. The data table contains DSAS transect intersection points with each shoreline in UTM Map projection WGS84 Zone 7 North. For each transect and each time step, the total shoreline movement as well as yearly shoreline change rates were extracted. DSAS analyses were conducted for the time steps from the 1950s to the 1970s, the 1970s to 2011 and for the 1950s to 2011 for the whole study area, and for the time steps from the 1970s to the 1990s and from the 1990s to 2011 for the areas for which shoreline position data from the 1990s was available. Detailed information about the methods can be found in the publication to which this dataset is a supplement.

This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual deline...