*Vos K., Splinter K.D., Harley M.D., Simmons J.A., Turner I.L. (submitted). CoastSat: a Google Earth Engine-enabled software to extract shorelines from publicly available satellite imagery, Environmental Modelling and Software*.
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 measurements are available. Satellite imagery spanning the last 30 years with constant revisit periods is publicly available and suitable to extract repeated measurements of the shoreline position.
CoastSat is an open-source Python module that allows the user to extract shorelines from Landsat 5, Landsat 7, Landsat 8 and Sentinel-2 images.
The shoreline detection algorithm implemented in CoastSat 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.
If you are not a regular Python user and are not sure how to install these packages from *conda-forge* and *PyPi*, the section below shows how to install them step-by-step using Anaconda. Otherwise, install the packages and go directly to section **1.2 Activating Google Earth Engine Python API**.
To know if you have activated coastsat, your terminal command line prompt should now start with (coastsat) when it is activated.
Now you need to populate the environment with the packages needed to run CoastSat. All the necessary packages are contained in three platform specific files: `requirements_win64.txt`, `requirements_osx64.txt`, `requirements_linux64.txt`. To install the packages, run one of the following commands, depending on which platform you are operating:
Once you have created a Google Earth Engine account, go back to Anaconda and link your GEE credentials to the Python API:
```
earthengine authenticate
```
A web browser will open, login with your GEE credentials, accept the terms and conditions and copy the authorization code into the Anaconda terminal.
Now you are ready to start using the CoastSat toolbox!
## 2. Usage
**Note**: remeber to always activate the `coastsat` environment with `conda activate coastsat` each time you wish to use it.
Your terminal command line prompt should start with (coastsat) when it is activated.
An example of how to run the software in a Jupyter Notebook is provided in the repository (`example_jupyter.ipynb`). To run it, first activate your `coastsat` environment with `conda activate coastsat` (if not already active), and then type:
```
jupyter notebook
```
A web browser window will open, drive to the directory where you downloaded/cloned this repository and click on `example_jupyter.ipynb`.
The following sections guide the reader through the different functionalities of CoastSat with an example at Narrabeen beach (Australia).
To run a Jupyter Notebook, put your cursor inside one of the code sections and then hit the 'run' button up in the top menu to run that section and progress forward (as shown in the animation below).
To retrieve the satellite images cropped around the the region of interest from Google Earth Engine servers the following user-defined variables are needed:
-`polygon`: the coordinates of the region of interest (longitude/latitude pairs)
-`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', 'S2']` for Landsat 5, 7, 8 and Sentinel-2 collections).
-`sitename`: name of the site (defines the name of the subfolder where the files will be stored)
The call `metadata = SDS_download.retrieve_images(inputs)` will launch the retrieval of the images and store them as .TIF files (under *.data\sitename*). The metadata contains the exact time of acquisition (UTC) and geometric accuracy of each downloaded image and is saved as `metadata_sitename.pkl`. If the images have already been downloaded previously and the user only wants to run the shoreline detection, the metadata can be loaded directly from this file. The screenshot below shows an example where all the images of Narrabeen-Collaroy (Australia) acquired in December 2017 are retrieved.
-`cloud_thresh`: threshold on maximum cloud cover that is acceptable on the images (value between 0 and 1)
-`output_epsg`: epsg code defining the spatial reference system of the shoreline coordinates
-`check_detection`: if set to `True` allows the user to quality control each shoreline detection
See http://spatialreference.org/ to find the EPSG number corresponding to your local coordinate system. If the user wants to quality control the mapped shorelines and manually validate each detection, the parameter `check_detection` should be set to `True`.
In addition, there are extra parameters (`min_beach_size`, `buffer_size`, `min_length_sl`) that can be tuned to optimise the shoreline detection (for Advanced users only). For the moment leave those parameters to their default values, we will see later how they can be modified.
When `check_detection` is set to `True`, a figure like the one below appears and asks the user to manually accept/reject each detection by clicking on `keep` or `skip`.
Once all the shorelines have been mapped, the output is available in two different formats (saved under *.\data\sitename*):
-`sitename_output.pkl`: contains a list with the shoreline coordinates and the exact timestamp at which the image was captured (UTC time) as well as the geometric accuracy and the cloud cover of the image. The list can be manipulated with Python, a snippet of code to plot the results is provided in the main script.
-`sitename_output.kml`: this output can be visualised in a GIS software (e.g., QGIS, ArcGIS).
The figure below shows how the satellite-derived shorelines can be opened in GIS software using the `.kml` output.
As mentioned above, there are extra 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 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 of `min_beach_area` is 4500 m^2, which corresponds to 20 connected pixels of 15 m^2. If you are looking at a very small beach (<20connectedpixelsontheimages),decreasethevalueofthisparameter.
-`buffer_size`: radius (in metres) that defines the buffer around sandy pixels that is considered for the shoreline detection. The default value of `buffer_size` is 150 m. This parameter should be increased if you have a very wide (>150 m) surf zone or inter-tidal zone.
-`min_length_sl`: minimum length (in metres) of shoreline perimeter to be valid. This allows to discard small contours that are detected but do not correspond to the actual shoreline. The default value is 200 m. If the shoreline that you are trying to map is shorter than 200 m, decrease the value of this parameter.
### Reference shoreline
There is also an option to manually digitize a reference shoreline before running the batch shoreline detection on all the images. This reference shoreline helps to reject outliers and false detections when mapping shorelines as it only considers as valid shorelines the points that are within a distance from this reference shoreline.
The user can manually digitize a reference shoreline on one of the images by calling:
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 your shoreline will move more than 100 m, please change this parameter to an appropriate distance. This may be the case for large nourishments or eroding/accreting coastlines.
