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6 results for “shoreline change”
Time-series of shoreline change along the Pacific Rim
<p>This repository contains 40 years of tidally-corrected shoreline change time-series for most sandy coastlines around the Pacific Rim derived from Landsat imagery. <br><br><strong>The time-series were last updated in May 2025. For the latest data always refer to <a href="http://coastsat.space/">http://coastsat.space/</a>.</strong></p> <p>The dataset was used to investigate the impact of ENSO on beach erosion and accretion in:<br>- Vos, K., Harley, M.D., Turner, I.L. <em>et al.</em> Pacific shoreline erosion and accretion patterns controlled by El Niño/Southern Oscillation. <em>Nat. Geosci.</em> <strong>16</strong>, 140–146 (2023). <a href="https://doi.org/10.1038/s41561-022-01117-8">https://doi.org/10.1038/s41561-022-01117-8</a><em> </em></p> <p><em>CoastSat </em>was used to map shoreline changes on Landsat 5, Landsat 7 and Landsat 8 imagery between 1984 and 2025. The <em>Coastsat </em>toolbox is publicly available at https://github.com/kvos/CoastSat and described in <em>Vos et al. 2019, </em><a href="https://doi.org/10.1016/j.envsoft.2019.104528">https://doi.org/10.1016/j.envsoft.2019.104528</a>. The time-series of shoreline change were tidally-corrected along cross-shore transects using tide levels from a global tide model (FES2022) and a satellite-derived estimate of the beach slope (as described in <em>Vos et al. 2020, "Beach slopes from satellite-derived shorelines", </em><a href="https://doi.org/10.1029/2020GL088365">https://doi.org/10.1029/2020GL088365</a><em>)</em>.</p> <p>This dataset covers wave-dominated sandy coasts in the Pacific basin where Landsat imagery was available, including a total of 3,000 beaches and more than 100,000 cross-shore transects (100-m alongshore spaced). This includes coastlines in Australia, New Zealand, Japan, Chile , Peru, Mexico and USA (California and Hawaii only).</p> <p>The data is structured as follows:</p> <ul> <li>There is a folder for each country (e.g. Australia)</li> <li> In the country folder, there is a folder for each site (e.g. aus0001, aus0002 etc)</li> <li>In the site folder, there are 4 CSV files: <ul> <li><em>time_series_tidally_corrected.csv</em>: this file contains the tidally-corrected time-series of shoreline change along each transect belonging to the site (e.g. aus0001-0001, aus0001-0002 etc). This is the final product used for coastal change analyses.</li> <li><em>time_series_raw.csv</em>: 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.</li> <li><em>tide_levels_fes2022</em>: this file contains the tide levels at the time of image acquisition extracted from FES2022 (global tide model publicly available on AVISO+).</li> <li><em>transect_coordinates_and_beach_slopes.csv</em>: this file contains the coordinates (in WGS84 lat/lon coordinates) as well as the estimated beach slope for each transect, including confidence intervals.</li> </ul> </li> </ul> <p> In addition, there are four geospatial layers (.GEOJSON) which contain important spatial information:</p> <ul> <li> <em>polygons.geojson</em>: this layer contains the polygons that were used to run CoastSat for each beach.</li> <li><em>shorelines.geojson</em>: this layer contains the sandy shorelines that were used to generate the cross-shore transects (also used as reference shorelines in CoastSat). Each beach has the following attributes: beach length, median orientation, median slope, and mean springs tidal range.</li> <li><em>transects.geojson</em>: this layer contains the cross-shore transects, which are spaced 100 m along each beach. Each transect has the following attributes: orientation, beach slope, linear trend (in m/year), alongshore distance relative to the northern end of the beach (absolute and normalised).</li> <li><em>transects_edit.geojson</em>: this layer is the same as transects.geojson but the transects that are not suitable for shoreline mapping were manually deleted (rocky shores, submerged reef, coastal lagoons and inlets, coastal defences etc...).</li> <li><em>transects_ENSO.geojson</em>: this layer (similar to transects.geojson) contains the transects that were used to analyse ENSO effects on shoreline changes in the Pacific (a total of 83,000).</li> </ul> <p> </p>
Time-series of shoreline change for the Klamath River Littoral Cell (California)
<p>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 <em>Warrick et al. 2023, "</em><strong>A Large Sediment Accretion Wave Along a Northern California Littoral Cell</strong>"<em>, </em>to investigate and track the movement of a large sediment wave.</p> <p><em>CoastSat </em>was used to map shoreline changes on Landsat 5, Landsat 7 and Landsat 8 imagery between 1984 and 2022. The <em>Coastsat </em>toolbox is publicly available at https://github.com/kvos/CoastSat and described in <em>Vos et al. 2019, </em><a href="https://doi.org/10.1016/j.envsoft.2019.104528">https://doi.org/10.1016/j.envsoft.2019.104528</a>. 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 <em>Vos et al. 2020, "Beach slopes from satellite-derived shorelines", </em><a href="https://doi.org/10.1029/2020GL088365">https://doi.org/10.1029/2020GL088365</a><em>)</em>.</p> <p>The data is located in the <em>/shoreline_data</em> folder and structured as follows:</p> <ul> <li>The littoral cell is divided in 4 sections (kmt_01, kmt_02, kmt_03, kmt_04)</li> <li>For each section there is a folder with 4 CSV files: <ul> <li><em>time_series_tidally_corrected.csv</em>: 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.</li> <li><em>time_series_raw.csv</em>: 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.</li> <li><em>tide_levels_fes2014</em>: this file contains the tide levels at the time of image acquisition extracted from FES2014 (global tide model publicly available on AVISO+).</li> <li><em>transect_coordinates_and_beach_slopes.csv</em>: this file contains the coordinates (in WGS84 lat/lon coordinates) as well as the estimated beach slope for each transect.</li> </ul> </li> </ul> <p>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:</p> <ul> <li> <em>Klamath_polygons.geojson</em>: this layer contains the polygons that were used to run CoastSat for each section of the littoral cell.</li> <li><em>Klamath_shorelines.geojson</em>: this layer contains the sandy shorelines that were used to generate the cross-shore transects (also used as reference shorelines in CoastSat).</li> <li><em>transects.geojson</em>: this layer contains the cross-shore transects, which are spaced 100 m alongshore.</li> </ul> <p>Finally, in the<em> /animations</em> folder, there is a clip showing the mapped shorelines on the satellite imagery.</p>
Dynamics of Shoreline Change in the Coastal Region of Rupat Island, Riau, Indonesia
<p>shoreline changes in coastal of Rupat Island</p>
CoastSeg: estimate of zone of potential shoreline change, California and southeast USA Atlantic (FL, GA, SC, NC), in geoJSON format.
<p><em><strong>CoastSeg: estimate of zone of potential shoreline change, California and southeast USA Atlantic (FL, GA, SC, NC), in geoJSON format.</strong></em></p> <ul> <li>Data have been made by Daniel Buscombe, Marda Science.</li> <li>Data cover the shorelines of five states</li> <li>A single geoJSON file per region. Data outline the extent of potential shoreline change.</li> <li>A 30-m vector defining the average shoreline, and a 30-m vector defining the limit of erodible material, were constructed and merged, then buffered, and manually edited.</li> <li>It is designed to be used in conjunction with the program CoastSeg https://github.com/Doodleverse/CoastSeg , for masking shoreline estimates outside of reasonable spatial bounds.</li> </ul> <p> </p>
Training dataset for Nile delta shoreline change prediction until 2050 using machine learning
Open the record for dataset details and reuse information.
Shoreline change in coastal Natural World Hertiage Sites
<p>This repository contains the data associated with the paper: Multi-decadal shoreline change in coastal Natural World Heritage Sites - a global assessment (<a href="http://doi.org/10.1088/1748-9326/ab968f">http://doi.org/10.1088/1748-9326/ab968f</a>).</p> <p>It contains:</p> <p>- 2020_transects_data_shorelines_linearity_geomorphology_final.csv: a dataset of transect-based shoreline and geomorphological datasets in 67 coastal Natural World Heritage Sites with available shoreline data.</p> <p>-2020_transects_data_shorelines_strong_linear_geomorphology_final.csv: a dataset of transect-based strong linear trends (recessional, depositional and stable) and geomorphological datasets in 59 coastal Natural World Heritage Sites with strong linear behaviour.</p> <p>Shorelines were derived from a global assessment of derived Landsat images (Luijendijk, A. <em>et al.</em>,2018,<em> </em> https://doi.org/10.1038/s41598-018-24630-6, Hagenaars, G. <em>et al., </em>2018, <a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.coastaleng.2017.12.011?_sg%5B0%5D=e7jxl0sHv0Rk-vxjjQWhconm1RtF5itcOHzqmZiTCaB0Jn7eujgLvuHi94LPIFeL3E_yP8wB-_F1qUjL9eidIw8eJw.qTorN3HHl-zPzbBg0YIpOUil3-oAv0JrEaaHD7qmCOA89tuoCGo70cV3H6QHsTerBHY5M7tjQZebo2GBybINVw">10.1016/j.coastaleng.2017.12.011</a> and Hagenaars, G. <em>et al., </em>2017, <a href="http://resolver.tudelft.nl/uuid:34a0114b-5e39-4b52-9940-3a7e9f5a2982">http://resolver.tudelft.nl/uuid:34a0114b-5e39-4b52-9940-3a7e9f5a2982</a>). Conditional and outlier data cleaning were undertaken on the raw shoreline dataset for further analysis.</p> <p>The geomorphological data have been obtained from the following datasets:</p> <ul> <li><strong>Topography:</strong> Global Map DEM (2017) - <a href="https://globalmaps.github.io/">https://globalmaps.github.io/</a></li> <li><strong>Land cover:</strong> Global Land Cover by National Mapping Organisations - GLCNMO (Kobayashi, T. <em>et al.</em>, 2013, <a href="https://doi.org/10.5539/jgg.v9n3p1">10.5539/jgg.v9n3p1</a>)</li> <li><strong>Coastal type:</strong> Worldwide Typology of Nearshore Coastal Systems (Dürr, H. H. <em>et al.</em>, 2011, https://doi.org/10.1007/s12237-011-9381-y)</li> <li><strong>Lithology:</strong> Global Lithological Map - GliM (Hartmann, J. & Moosdorf, N., 2012, <a href="https://doi.org/10.1029/2012GC004370">https://doi.org/10.1029/2012GC004370</a>)</li> </ul> <p>For each perpendicular transect to the satellite-derived shorelines, a set of data is provided in the table columns:</p> <ul> <li>transect_id: unique identifier of the transect</li> <li>Continent: continent of the transect</li> <li>country_name: country of the transect</li> <li>Intersect_lon: the longitude of the middle point of the transect drawn between the first available shoreline (1984 or more) and the latter available shoreline (2016).</li> <li>Intersect_lon: the latitude of the middle point of the transect drawn between the first available shoreline (1984 or more) and the latter available shoreline (2016).</li> <li>NAME: name of the coastal Natural World Heritage Site</li> <li>ORIG_NAME: the original name of the coastal Natural World Heritage Site</li> <li>INT_CRIT: criteria of selection of the coastal Natural World Heritage Site</li> <li>dt_max: is the latest year for which SDS data points are available (2016)</li> <li>dt_min: is the first year for which SDS data points are available (starts from 1984)</li> <li>nb_shorelines: the number of shorelines available from dt_min to dt_max</li> <li>cor: Pearson’s correlation coefficient (<em>r</em>)</li> <li>p.value: Pearson’s correlation coefficient (<em>r</em>) p-value</li> <li>cor_fact: classification of Pearson’s correlation coefficient (<em>r</em>) <ul> <li>0: Non-linear (-0.3 to 0.3).</li> <li>1: Weak linear ( -0.7 to -0.3 or 0.3 to 0.7)</li> <li>2: Strong linear (less than -0.7 or greater than 0.7)</li> </ul> </li> <li>coeflm: Ordinary Least Square linear regression rate (m yr-1)</li> <li>std: standard deviation of the Ordinary Least Square linear regression rate (m yr-1)</li> <li>Mean_DEM_Class: topography <ul> <li>1: 0 ≤ elevation ≤ 1 m (extremely low-lying)</li> <li>2: 1 < elevation ≤ 10 m (low-lying)</li> <li>3: 10 < elevation ≤ 50 m (middle)</li> <li>4: 50 < elevation ≤ 400 m (high)</li> <li>5: No data (transects without available elevation)</li> </ul> </li> <li>GLIM_categories: lithology <ul> <li>ev: Evaporites</li> <li>ig: Polar ice and Glaciers</li> <li>pa: Acid Plutonic Rocks</li> <li>pb: Basic-Ultrabasic Plutonic Rocks</li> <li>pi: Intermediate Plutonic Rocks</li> <li>mt: Metamorphic Rocks</li> <li>sc: Carbonate Sedimentary Rocks</li> <li>sm: Mixed Sedimentary Rocks</li> <li>ss: Siliciclastic Sedimentary Rocks</li> <li>su: Unconsolidated Sediments</li> <li>py Pyroclastic</li> <li>va: Acid Volcanic Rocks</li> <li>vb: Basic Volcanic Rocks</li> <li>vi: Intermediate Volcanic Rocks</li> </ul> </li> <li>Land_Cover_Class: land cover <ul> <li>1: Coral reefs</li> <li>2: Mangroves</li> <li>3: Marshes</li> <li>4: Vegetated</li> <li>5: Non-vegetated</li> <li>6: Urban areas</li> </ul> </li> <li>Coastal_type_Class_1: coastal types <ul> <li>1: Small deltas</li> <li>2: Tidal systems</li> <li>3: Lagoons</li> <li>4: Fjords and fjärds</li> <li>5: Large rivers</li> <li>6: Large rivers with tidal influence</li> <li>7: Karst-dominated stretches of coasts</li> <li>8: Arheic (dry areas)</li> <li>9: Islands</li> </ul> </li> <li>cor_classes_1: classification of Pearson’s correlation coefficient (<em>r</em>) <ul> <li>StrongLin: Strong linear (less than -0.7 or greater than 0.7)</li> <li>WeakLin: Weak linear ( -0.7 to -0.3 or 0.3 to 0.7)</li> <li>NonLin: Non-linear (-0.3 to 0.3).</li> </ul> </li> <li>shoreline_trend_classes: Ordinary Least Square linear regression rate (m/y) for strong linear shoreline behaviours <ul> <li>StrongLinSta: stable - shoreline change rate between -0.5 and 0.5 m yr-1 </li> <li>StrongLinAcr: depositional - shoreline change rate >0.5 m yr-1</li> <li>StrongLinErr: recessional - shoreline change rate <-0.5 m yr-1</li> </ul> </li> </ul>
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