CoastSat is an open-source software toolkit written in Python that enables users to obtain time-series of shoreline position at any coastline worldwide from 40 years (and growing) of publicly available satellite imagery (Landsat and Sentinel-2).
Finding CoastSat useful? Show your support with a Github star — it’s a simple click that helps others discover it ⭐️
👉 Visit the CoastSat website to explore and download existing satellite-derived shoreline datasets generated with CoastSat in the Pacific and Atlantic basins.
Latest updates
matplotlib and numpy from @2320sharon and @thekester.
Additional toolkits and documentation
👉 Other repositories and extensions related to CoastSat:
- CoastSeg: an interactive toolbox for downloading satellite imagery, applying image segmentation models, mapping shoreline positions and more.
- CoastSat.slope: estimates the beach-face slope from the satellite-derived shorelines obtained with CoastSat.
- CoastSat.PlanetScope: shoreline extraction for PlanetScope Dove imagery (near-daily since 2017 at 3m resolution).
- SDS_Benchmark: testbed for satellite-derived shorelines mapping algorithms and validation against benchmark datasets.
- CoastSat.islands: 2D planform measurements for small reef islands.
- CoastSat.Maxar: shoreline extraction on Maxar World-View images (in progress)
- InletTracker: monitoring of intermittent open/close estuary entrances.
- VedgeSat: monitoring vegetation lines.
👉 Publications describing the CoastSat satellite-derived shorelines:
- Shoreline detection algorithm: https://doi.org/10.1016/j.envsoft.2019.104528 (Open Access)
- Accuracy assessment: https://doi.org/10.1016/j.coastaleng.2019.04.004
- Challenges in meso-macrotidal environments: https://doi.org/10.1016/j.geomorph.2021.107707
- Basin-scale shoreline mapping (Paficic): https://www.nature.com/articles/s41561-022-01117-8 (The Conversation article here)
- Beach slope estimation: https://doi.org/10.1029/2020GL088365 (preprint here)
- Beach slope dataset for Australia: https://doi.org/10.5194/essd-14-1345-2022
Project description
Satellite remote sensing can provide low-cost long-term shoreline data capable of resolving the temporal scales of interest to coastal scientists and engineers at sites where no in-situ field measurements are available. CoastSat enables the non-expert user to extract shorelines from Landsat 5, Landsat 7, Landsat 8, Landsat 9 and Sentinel-2 images. The shoreline detection algorithm implemented in CoastSat is optimised for sandy beach coastlines. It combines a sub-pixel border segmentation and an image classification component, which refines the segmentation into four distinct categories such that the shoreline detection is specific to the sand/water interface.
The toolbox has the following functionalities:
- easy retrieval of satellite imagery spanning the user-defined region of interest and time period from Google Earth Engine, including state-of-the-art pre-processing steps (re-projecting the different bands, pansharpening, advanced cloud masking).
- automated extraction of shorelines from all the selected images using a sub-pixel resolution technique and options for quality-control.
- intersection of the 2D shorelines with user-defined shore-normal transects.
- tidal correction using tide/water levels and an estimate of the beach slope.
- post-processing of the shoreline time-series, despiking and seasonal averaging.
- Beach slope estimation using satellite-derived shorelines and predicted tides
1.1 Create an environment with Mamba/Anaconda:
To run the toolbox you first need to install the required Python packages in an environment.You can do this with Anaconda or with Mamba. It is highly recommended to use Mamba. Go to https://github.com/conda-forge/miniforge and download Miniforge for your operating system.
Once you have it installed on your PC, open the Miniforge Prompt (in Mac and Linux, open a terminal window) and run the following commands to install the coastsat environment:
mamba create -n coastsat
mamba activate coastsat
mamba install python=3.11 geopandas gdal -y
mamba install earthengine-api scikit-image matplotlib astropy notebook -y
pip install pyqt5 imageio-ffmpeg
mamba install pyfes -y
All the required packages have now been installed and are self-contained in an environment called coastsat. Always make sure that the environment is activated with:
mamba activate coastsat
To confirm that you have successfully activated CoastSat, your terminal command line prompt should now start with (coastsat).
coastsat again:
mamba clean --all
mamba update conda
If you are more used to Anaconda you can run the same commands in the Anaconda Prompt by replacing mamba with conda.
If any problems with the installation, raise an issue.
1.2 Activate Google Earth Engine Python API:
First, create a Google Earth Engine project at https://signup.earthengine.google.com/.
Then, go to https://cloud.google.com/sdk/docs/install and install the gcloud CLI. After you have installed it will automatically launch and let you authenticate with your GEE account (or personal gmail).
gcloud auth application-default login to set a default authentication on your machine.
✅ If you completed those two steps you are ready to start using CoastSat!
An example of how to run the software in a Jupyter Notebook is provided in example_jupyter.ipynb.
If you prefer to use Spyder or other integrated development environments (IDEs), a Python script named example.py is also included in the repository. If using Spyder, make sure that the Graphics Backend is set to Automatic and not Inline (as this mode doesn't allow to interact with the figures). To change this setting go under Preferences>IPython console>Graphics.
How to run Jupyter Notebooks
To run a Jupyter Notebook, open the Miniforge prompt, activate your `coastsat` environment with `mamba activate coastsat`, and then run type `jupyter lab`.A web browser will open with the JupyterLab interface. Browse to the directory where you downloaded CoastSat and click on the file example_jupyter.ipynb. The notebook will open. A Jupyter Notebook combines formatted text and code. To run the code, place your cursor inside one of the code sections and click on the run cell button (or press Shift + Enter) and progress forward.
The following sections show an example of how to run the full CoastSat workflow at Narrabeen-Collaroy beach (Australia).
To retrieve from the GEE server the available satellite images cropped around the user-defined region of coastline for the particular time period of interest, the following variables are required:
polygon: the coordinates of the region of interest (longitude/latitude pairs in WGS84), do not exceed 100 sqkm or GEE will return memory error (for long beaches split in smaller polygons).dates: dates over which the images will be retrieved (e.g.,dates = ['2017-12-01', '2018-01-01'])sat_list: satellite missions to consider (e.g.,sat_list = ['L5', 'L7', 'L8', 'L9', 'S2']for Landsat 5, 7, 8, 9 and Sentinel-2 collections)sitename: name of the site (this is the name of the subfolder where the images and other accompanying files will be stored)filepath: filepath to the directory where the data will be stored- (optional)
S2tile: for Sentinel-2 only, this parameter let's you specify the tile from which you'd like to crop your ROI, this avoids having many duplicates in the Sentinel-2 collection. For exampleinputs['S2tile'] = '56HLH'is the S2 tile for Sydney, check this website to view all tiles and find the one covering your ROI.
The call metadata = SDS_download.retrieve_images(inputs) will launch the retrieval of the images and store them as .TIF files (under /filepath/sitename). The metadata contains the exact time of acquisition (in UTC time) of each image, its projection and its geometric accuracy. If the images have already been downloaded previously and the user only wants to run the shoreline detection, the metadata can be loaded directly by running metadata = SDS_download.get_metadata(inputs).
The inputs below will download all the images of Narrabeen acquired by since the start of 2024 by Landsat and Sentinel-2.
Inputs download example
# region of interest (longitude, latitude)
polygon = [[[151.2957545, -33.7012561],
[151.297557, -33.7388075],
[151.312234, -33.7390216],
[151.311204, -33.701399],
[151.2957545, -33.7012561]]]
# date range
dates = ['2024-01-01', '2025-01-01']
# satellite missions ['L5','L7','L8','L9','S2']
sat_list = ['L8','L9','S2']
# name of the site
sitename = 'NARRA'
# directory where the data will be stored
filepath = os.path.join(os.getcwd(), 'data')
# put all the inputs into a dictionnary
inputs = {'polygon': polygon, 'dates': dates, 'sat_list': sat_list,
'sitename': sitename, 'filepath':filepath}
# download images
metadata = SDS_download.retrieve_images(inputs)
Once the images have been downloaded, the shorelines can be mapped. The following user-defined settings are needed:
cloud_thresh: threshold on maximum cloud cover that is acceptable on the images (value between 0 and 1 - this may require some initial experimentation).dist_clouds: buffer around cloud pixels where shoreline is not mapped (in metres)output_epsg: epsg code defining the spatial reference system of the shoreline coordinates. It has to be a cartesian coordinate system (i.e. projected) and not a geographical coordinate system (in latitude and longitude angles). See http://spatialreference.org/ to find the EPSG number corresponding to your local coordinate system. If you do not use a local projection your results will not be accurate.check_detection: if set toTruethe user can quality control each shoreline detection interactively (recommended when mapping shorelines for the first time) and accept/reject each shoreline.adjust_detection: in case users wants more control over the detected shorelines, they can set this parameter toTrue, then they will be able to manually adjust the threshold used to map the shoreline on each image.save_figure: if set toTruea figure of each mapped shoreline is saved under /filepath/sitename/jpg_files/detection, even if the two previous parameters are set toFalse. Note that this may slow down the process.
There are additional parameters (min_beach_size, min_length_sl, cloud_mask_issue, sand_color, pan_off, s2cloudless_prob) that can be fine-tuned to optimise the detection (for Advanced users). For the moment leave these parameters on their default values, we will see later how they can be edited and what they do.
An example of settings for Narrabeen beach is provided below.
Settings example
settings = {
# general parameters:
'cloud_thresh': 0.5, # threshold on maximum cloud cover
'dist_clouds': 300, # ditance around clouds where shoreline is not mapped
'output_epsg': 28356, # epsg code of spatial reference system for the output
# quality control:
'check_detection': True, # if True, shows each shoreline detection to the user for validation
'adjust_detection': False, # if True, allows user to adjust the postion of each shoreline by changing the threhold
'save_figure': True, # if True, saves a figure showing the mapped shoreline for each image
# [ONLY FOR ADVANCED USERS] advanced detection parameters:
'min_beach_area': 1000, # minimum area (in metres^2) for an object to be labelled as a beach
'min_length_sl': 500, # minimum length (in metres) of shoreline perimeter to be valid
'cloud_mask_issue': False, # switch this parameter to True if sand pixels are masked as clouds
'sand_color': 'default', # 'default', 'latest', 'dark' (for grey/black sand beaches) or 'bright' (for white beaches)
'pan_off': False, # True to switch pansharpening off for Landsat 7/8/9 imagery
's2cloudless_prob': 60, # probability to mask cloudy pixels in s2cloudless
'inputs': inputs, # add the inputs defined previously
}
Before mapping the shorelines, it is HIGHLY RECOMMENDED to digitize a reference shoreline in order to improve the detection.
This can be done by calling settings['reference_shoreline'] = SDS_preprocess.get_reference_sl_manual(metadata, settings), which allows the user to manually digitize the reference shoreline on a cloud-free image. Then you can set the maximum distance from the reference shoreline where shoreline points can be detection using settings['max_dist_ref'].
This reference shoreline helps to reject outliers and false detections when mapping shorelines. See below how to accurately add this reference shoreline.
Digitize reference shoreline
The user can manually digitize one or several reference shorelines on one of the images by calling:
settings['reference_shoreline'] = SDS_preprocess.get_reference_sl_manual(metadata, settings)
settings['max_dist_ref'] = 100 # max distance (in meters) allowed from the reference shoreline
This function allows the user to click points along the shoreline on cloud-free satellite images, as shown in the animation below.
The maximum distance (in metres) allowed from the reference shoreline is defined by the parameter max_dist_ref. This parameter is set to a default value of 100 m. If you think that 100 m buffer from the reference shoreline will not capture the shoreline variability at your site, increase the value of this parameter. This may be the case for large nourishments or eroding/accreting coastlines.
Additionally, there is the option to visualise the images and create a timelapse (MP4) using the code below:
Timelapse animation
You can save JPEGs of each satellite image using the function SDS_preprocess.save_jpg(metadata, settings) and create a timelapse using SDS_tools.make_animation_mp4(fp_images, fps, fn_animation) (also shown in the notebook). This can be handy to visualise your data before processing the shorelines and make sure cloud masking and preprocessing are fine.
# preprocess images and save as jpg
SDS_preprocess.save_jpg(metadata, settings, use_matplotlib=True)
# create MP4 timelapse animation
fn_animation = os.path.join(inputs['filepath'],inputs['sitename'], '%s_animation_RGB.mp4'%inputs['sitename'])
fp_images = os.path.join(inputs['filepath'], inputs['sitename'], 'jpg_files', 'preprocessed')
fps = 4 # frames per second in animation
SDS_tools.make_animation_mp4(fp_images, fps, fn_animation)
Once all the settings have been defined, the batch shoreline detection can be launched by calling:
output = SDS_shoreline.extract_shorelines(metadata, settings)
To quality-control the detections manually, set check_detection to True, and a figure like the one below will pop up and let the user manually accept/reject each detection by pressing on the keyboard the right arrow (⇨) to keep the shoreline or left arrow (⇦) to skip the mapped shoreline. The user can break the loop at any time by pressing escape (nothing will be saved though).
For further control on the detections (especially in meso/macrotidal coastal environments), the user can set adjust_detection to True. In that case, the threshold used to define the shoreline can be manually adjusted on each image. See the animation below that shows how the shoreline position can be adjusted in a gentle-sloping macrotidal beach in France (Truc Vert).
Adjust thresholds (Truc Vert, France)
If adjust_detection was set to True in settings, a figure like the one below appears and let the user adjust the position of the shoreline by clicking on the histogram of MNDWI pixel intensities. Once the threshold has been adjusted, press Enter and then accept/reject the image with the keyboard arrows. This is very time-consuming but allows more control on the mapped shorelines and is faster that digitising in a GIS environment.
Once all the shorelines have been mapped, the output is saved in two different formats (under /filepath/data/SITENAME):
SITENAME_output.pkl: contains a list with the shoreline coordinates, the exact timestamp at which the image was captured (UTC time), the geometric accuracy and the cloud cover of each individual image. This list can be manipulated with Python, a snippet of code to plot the results is provided in the example script.SITENAME_output.geojson: this output can be visualised in a GIS software (e.g., QGIS, ArcGIS).
The GeoJSON shorelines can be opened in a GIS software (QGIS) as shown below.
While the default settings work in most cases, there are instances where you will need to adjust the Advanced Parameters described below.
Advanced shoreline detection parameters
There are some additional parameters that can be modified to optimise the shoreline detection:
min_beach_area: minimum allowable object area (in metres^2) for the class 'sand'. During the image classification, some features (for example, building roofs) may be incorrectly labelled as sand. To correct this, all the objects classified as sand containing less than a certain number of connected pixels are removed from the sand class. The default value is 4500 m^2, which corresponds to 20 connected pixels of 15 m^2. If you are looking at a very small beach (<20 connected pixels on the images), try decreasing the value of this parameter.min_length_sl: minimum length (in metres) of shoreline perimeter to be valid. This can be used to discard small features that are detected but do not correspond to the actual shoreline. The default value is 500 m. If the shoreline that you are trying to map is shorter than 500 m, decrease the value of this parameter.cloud_mask_issue: the cloud mask algorithm applied to Landsat images by USGS, namely CFMASK, does have difficulties sometimes with very bright features such as beaches or white-water in the ocean. This may result in pixels corresponding to a beach being identified as clouds and appear as masked pixels on your images. If this issue seems to be present in a large proportion of images from your local beach, you can switch this parameter toTrueand CoastSat will remove from the cloud mask the pixels that form very thin linear features, as often these are beaches and not clouds. Only activate this parameter if you observe this very specific cloud mask issue, otherwise leave to the default value ofFalse.sand_color: this parameter can take 3 values:default,latest,darkorbright. Only change this parameter if you are seing that with thedefaultthe sand pixels are not being classified as sand (in orange). If your beach has dark sand (grey/black sand beaches), you can set this parameter todarkand the classifier will be able to pick up the dark sand. On the other hand, if your beach has white sand and thedefaultclassifier is not picking it up, switch this parameter tobright. Thelatestclassifier contains all the training data and can pick up sand in most environments (but not as accurately). At this stage the different classifiers are only available for Landsat images (soon for Sentinel-2 as well).pan_off: by default Landsat 7, 8 and 9 images are pan-sharpened using the panchromatic band and a PCA algorithm. If for any reason you prefer not to pan-sharpen the Landsat images, switch it off by settingpan_offtoTrue.s2cloudless_prob: by default set to 60, this is the threshold to identify cloudy pixels in the s2cloudless probability mask. If you see that too many cloudy pixels appear on the image increase the threshold, if too many cloudy pixels are missed lower the threshold (reasonable range between 20 and 80).
Finally, the provided classifiers may not be able to detect sand accurately at certain beaches so you have the option to re-train your own classifier in a separate notebook.
Re-training the classifier
CoastSat's shoreline mapping alogorithm uses an image classification scheme to label each pixel into 4 classes: sand, water, white-water and other land features. While this classifier has been trained using a wide range of different beaches, it may be that it does not perform very well at specific sites that it has never seen before. You can try the different classifiers already available in the /classification folder by changing the settings['sand_color'] parameter to latest, dark or bright, but if none of those fit your data you can label images of your own site and uses those labels to train a new classifier specifically for your site. This process is implemented in a separate Jupyter notebook re-train CoastSat classifier located in the /classification folder.
Once you have trained your new classifier, a new .pkl file will be created in /classification/models. In order to use it for shoreline mapping, you will need to open the script SDS_shorelines located in /coastsat and go to lines 132-134 and change the filename to match your new classifier .pkl file.
This section shows how to process the satellite-derived shoreline to obtain time-series along shore-normal transects.
3 options are provided to define the coordinates of the transects:
- Interactively draw shore-normal transects along the mapped shorelines:
transects = SDS_transects.draw_transects(output, settings)
- Load the transect coordinates from a .geojson file:
transects = SDS_tools.transects_from_geojson(path_to_geojson_file)
- Create the transects by manually providing the coordinates of two points:
transects = dict([])
transects['Transect 1'] = np.array([[342836, ,6269215], [343315, 6269071]])
transects['Transect 2'] = np.array([[342482, 6268466], [342958, 6268310]])
transects['Transect 3'] = np.array([[342185, 6267650], [342685, 6267641]])
settings['output_epsg'], otherwise they won't match the shorelines.
An example of how to draw the transects is shown below.
To calculate the intersections between the shorelines and the transects, there are 2 modes: simple and quality-controlled.
Simple intersections
This simple intersections are computed by the following function:
settings['along_dist'] = 25
cross_distance = SDS_transects.compute_intersection(output, transects, settings)
The parameter along_dist defines the along-shore distance around the transect over which shoreline points are selected to compute the intersection. The default value is 25 m, which means that the intersection is computed as the median of the points located within 25 m of the transect (50 m alongshore-median). This helps to smooth out localised water levels in the swash zone.
Quality-controlled intersections (RECOMMENDED)
There is also a more advanced function to compute the intersections SDS_transects.compute_intersection_QA(), which provides more quality-control and can deal with small loops, multiple intersections, false detections etc. It is recommended to use this function as it can provide cleaner shoreline time-series. An example of parameter values is provided below, the default parameters should work in most cases (leave as it is if unsure).
settings_transects = { # parameters for computing intersections
'along_dist': 25, # along-shore distance to use for computing the intersection
'min_points': 3, # minimum number of shoreline points to calculate an intersection
'max_std': 15, # max std for points around transect
'max_range': 30, # max range for points around transect
'min_chainage': -100, # largest negative value along transect (landwards of transect origin)
'multiple_inter': 'auto', # mode for removing outliers ('auto', 'nan', 'max')
'auto_prc': 0.1, # percentage of the time that multiple intersects are present to use the max
}
cross_distance = SDS_transects.compute_intersection_QC(output, transects, settings_transects)
along_dist: (in metres), alongshore distance to caluclate the intersection (median of points within this distance).min_points: minimum number of shoreline points to calculate an intersection.max_std: (in metres) maximum STD for the shoreline points within the alongshore range, if STD is above this value a NaN is returned for this intersection.max_range: (in metres) maximum RANGE for the shoreline points within the alongshore range, if RANGE is above this value a NaN is returned for this intersection.min_chainage: (in metres) furthest distance landward of the transect origin that an intersection is accepted, beyond this point a NaN is returned.multiple_inter: ('auto','nan','max') defines how to deal with multiple shoreline intersectionsauto_prc: (value between 0 and 1) by default 0.1, percentage of the time that a multiple intersection needs to be present to use the max in auto mode
The multiple_inter setting helps to deal with multiple shoreline intersections along the same transect. This is quite common, for example when there is a lagoon behind the beach and the transect crosses two water bodies. The function will try to identify this cases and the user can choose whether to:
'nan': always assign a NaN when there are multile intersections.'max': always take the max (intersection the furtherst seaward).'auto': let the function decide transect by transect, and if it thinks there are two water bodies, take the max. If'auto'is chosen, theauto_prcparameter will define when to use the max, by default it is set to 0.1, which means that the function thinks there are two water bodies if 10% of the time-series show multiple intersections.
Each satellite image is captured at a different stage of the tide, therefore a tidal correction is necessary to remove the apparent shoreline changes cause by tidal fluctuations.
In order to tidally-correct the time-series of shoreline change, two inputs are needed: 1) tide level time-series and 2) an estimate of the beach slope.
In the notebook, the user has two options for the tide level time-series:
- Option 1: use a CSV file with the time-series of water levels (at least 15/30 min timestep). Note that this file should be formatted as the one provided in /examples NARRA_tides.csv. Dates should be in UTC time and tides in metres above mean sea level.
- Option 2: use the FES2022 global tide model to predict tide levels at your beach. This requires you to have FES2022 setup, follow the instructions in this document. Once installed, you can predict tides for any dates at any location in the world!
Once you have the tide levels, you need an estimate of the beach slope. You can provide this manually (e.g., 0.1 for all transects) or you can also estimate it using the satellite-derived shorelines and tide levels inside the notebook, see the Beach Slope estimation scetion. This parts uses the CoastSat.slope repository, for more details on the methodology see Vos et al. 2020 (preprint available here).
The tidally-corrected time-series can be post-processed to remove outliers with a despiking algorithm SDS_transects.reject_outliers(). This function was developed to remove obvious outliers in the time-series by removing the points that do not make physical sense in a shoreline change setting. For example, the shoreline can experience rapid erosion after a large storm, but it will then take time to recover and return to its previous state. Therefore, if the shoreline erodes/accretes suddenly of a significant amount (max_cross_change) and then immediately returns to its previous state, this spike does not make any physical sense and can be considered an outlier.
settings_outliers = {'max_cross_change': 40, # maximum cross-shore change observable between consecutive timesteps
'otsu_threshold': [-.5,0], # min and max intensity threshold use for contouring the shoreline
'plot_fig': True, # whether to plot the intermediate steps
}
cross_distance = SDS_transects.reject_outliers(cross_distance,output,settings_outliers)
Additionally, this function also checks that the Otsu thresholds used to map the shoreline are within the typical range defined by otsu_threshold, with values outside this range (typically -0.5 to 0) identified as outliers.
Additionally, a set of functions to compute seasonal averages, monthly averages and linear trends on the shoreline time-series are provided.
SDS_transects.seasonal_averages()
SDS_transects.monthly_averages()
This section provides code to compare the satellite-derived shorelines against the survey data for Narrabeen, available at http://narrabeen.wrl.unsw.edu.au/.
Having a problem? Post an issue in the Issues page (please do not email).
If you are willing to contribute, check out our todo list in the Projects page.
- Fork the repository (https://github.com/kvos/coastsat/fork). A fork is a copy on which you can make your changes.
- Create a new branch on your fork
- Commit your changes and push them to your branch
- When the branch is ready to be merged, create a Pull Request (how to make a clean pull request explained here)
This section provides a list of references that use the CoastSat toolbox as well as existing shoreline datasets extracted with CoastSat.
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Vos K., Splinter K.D., Harley M.D., Simmons J.A., Turner I.L. (2019). CoastSat: a Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. Environmental Modelling and Software. 122, 104528. https://doi.org/10.1016/j.envsoft.2019.104528 (Open Access)
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Vos K., Harley M.D., Splinter K.D., Simmons J.A., Turner I.L. (2019). Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery. Coastal Engineering. 150, 160–174. https://doi.org/10.1016/j.coastaleng.2019.04.004
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Vos K., Harley M.D., Splinter K.D., Walker A., Turner I.L. (2020). Beach slopes from satellite-derived shorelines. Geophysical Research Letters. 47(14). https://doi.org/10.1029/2020GL088365 (Open Access preprint here)
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Vos, K. and Deng, W. and Harley, M. D. and Turner, I. L. and Splinter, K. D. M. (2022). Beach-face slope dataset for Australia. Earth System Science Data. volume 14, 3, p. 1345--1357. https://doi.org/10.5194/essd-14-1345-2022
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Vos, K., Harley, M.D., Turner, I.L. et al. Pacific shoreline erosion and accretion patterns controlled by El Niño/Southern Oscillation. Nature Geosciences. 16, 140–146 (2023). https://doi.org/10.1038/s41561-022-01117-8
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Castelle B., Masselink G., Scott T., Stokes C., Konstantinou A., Marieu V., Bujan S. (2021). Satellite-derived shoreline detection at a high-energy meso-macrotidal beach. Geomorphology. volume 383, 107707. https://doi.org/10.1016/j.geomorph.2021.107707
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Castelle, B., Ritz, A., Marieu, V., Lerma, A. N., & Vandenhove, M. (2022). Primary drivers of multidecadal spatial and temporal patterns of shoreline change derived from optical satellite imagery. Geomorphology, 413, 108360. https://doi.org/10.1016/j.geomorph.2022.10836
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Konstantinou, A., Scott, T., Masselink, G., Stokes, K., Conley, D., & Castelle, B. (2023). Satellite-based shoreline detection along high-energy macrotidal coasts and influence of beach state. Marine Geology, 107082. https://doi.org/10.1016/j.margeo.2023.107082
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Time-series of shoreline change along the Pacific Rim (v1.4) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7758183
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Time-series of shoreline change along the U.S. Atlantic coast: U.S. Geological Survey data release, https://doi.org/10.5066/P9BQQTCI.
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Time-series of shoreline change for the Klamath River Littoral Cell (California) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7641757
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Beach-face slope dataset for Australia (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7272538
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Training dataset used for pixel-wise classification in CoastSat (initial version): https://doi.org/10.5281/zenodo.3334147
