# CoastSat

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2779294.svg)](https://doi.org/10.5281/zenodo.2779294)

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 30+ years (and growing) of publicly available satellite imagery.

![Alt text](https://github.com/kvos/CoastSat/blob/development/examples/doc/example.gif)

The underlying approach and application of the CoastSat toolkit are described in detail in:

*Vos K., Splinter K.D., Harley M.D., Simmons J.A., Turner I.L. (submitted). CoastSat: a Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery, Environmental Modelling and Software*.

There are three main steps:
- assisted retrieval from Google Earth Engine of all available satellite images spanning the user-defined region of interest and time period
- automated extraction of shorelines from all the selected images using a sub-pixel resolution technique
- intersection of the 2D shorelines with user-defined shore-normal transects


### 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. 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 enables the non-expert user to extract shorelines from Landsat 5, Landsat 7, Landsat 8 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.

## 1. Installation

### 1.1 Create an environment with Anaconda

To run the toolbox you first need to install the required Python packages in an environment. To do this we will use **Anaconda**, which can be downloaded freely [here](https://www.anaconda.com/download/).

Once you have it installed on your PC, open the Anaconda prompt (in Mac and Linux, open a terminal window) and use the `cd` command (change directory) to go the folder where you have downloaded this repository.

Create a new environment named `coastsat` with all the required packages:

```
conda env create -f environment.yml -n coastsat
```

All the required packages have now been installed in an environment called `coastsat`. Now, activate the new environment:

```
conda activate coastsat
```

To confirm that you have successfully activated CoastSat, your terminal command line prompt should now start with (coastsat).

**In case errors are raised:**: you should create a new environment and manually install the required packages, which are listed in the environment.yml file. The following [link](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands) shows how to create and manage an environment with Anaconda.

### 1.2 Activate Google Earth Engine Python API

Go to https://earthengine.google.com and sign up to Google Earth Engine (GEE).

![gee_capture](https://user-images.githubusercontent.com/7217258/49348457-a9271300-f6f9-11e8-8c0b-407383940e94.jpg)

Once you have created a Google Earth Engine account, go back to the Anaconda prompt and link your GEE credentials to the Python API:

```
earthengine authenticate
```

A web browser will open, login with your GEE credential and accept the terms and conditions. Then copy the authorization code into the Anaconda terminal.

Now you are ready to start using the CoastSat toolbox!

**Note**: remember to always activate the environment with `conda activate coastsat` each time you are preparing to use the toolbox.

## 2. Usage

An example of how to run the software in a Jupyter Notebook is provided in the repository (`example_jupyter.ipynb`). To run this, first activate your `coastsat` environment with `conda activate coastsat` (if not already active), and then type:

```
jupyter notebook
```

A web browser window will open. Point to the directory where you downloaded 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-Collaroy beach (Australia). If you prefer to use **Spyder**, **PyCharm** or other integrated development environments (IDEs), a Python script named `example.py` is also included in the repository.

If using `example.py` on **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.

To run a Jupyter Notebook, place your cursor inside one of the code sections and then click on the `run` button up in the top menu to run that section and progress forward (as shown in the animation below).

![example_jupyter](https://user-images.githubusercontent.com/7217258/49705486-8dc88480-fc72-11e8-8300-c342baaf54eb.gif)

### 2.1 Retrieval of the satellite images

To retrieve from the GEE server the avaiable satellite images cropped around the user-defined region of coasltine 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)
- `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 (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

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) 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 by running `metadata = SDS_download.get_metadata(inputs)`.

The screenshot below shows an example of inputs that will retrieve all the images of Collaroy-Narrrabeen (Australia) acquired by Sentinel-2 in December 2017.

![doc1](https://user-images.githubusercontent.com/7217258/56278746-20f65700-614a-11e9-8715-ba5b8f938063.PNG)

**Note:** The area of the polygon should not exceed 100 km2, so for very long beaches split it into multiple smaller polygons.

### 2.2 Shoreline detection

To map the shorelines, 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).
- `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.
- `check_detection`: if set to `True` the user can quality control each shoreline detection interactively.
- `save_figure`: if set to `True` a figure of each mapped shoreline is saved (under *filepath/sitename/jpg_files/detection*). Note that this may slow down the process.

 The setting `check_detection` is recommended when using the tool for the first time as it will show the user how CoastSat is mapping the shorelines.

There are additional parameters (`min_beach_size`, `buffer_size`, `min_length_sl`, `cloud_mask_issue` and `dark sand`) that can be tuned to optimise the shoreline detection (for Advanced users only). For the moment leave these parameters set to their default values, we will see later how they can be modified.

An example of settings is provided here:

![doc2](https://user-images.githubusercontent.com/7217258/56278918-7a5e8600-614a-11e9-9184-77b69427b834.PNG)

Once all the settings have been defined, the batch shoreline detection can be launched by calling:
```
output = SDS_shoreline.extract_shorelines(metadata, settings)
```
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 pressing 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).

![Alt text](https://github.com/kvos/CoastSat/blob/development/examples/doc/batch_detection.gif)

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, 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 figure below shows how the satellite-derived shorelines can be opened in a GIS software (QGIS) using the `.geojson` output. Note that the coordinates in the `.geojson` file are in the spatial reference system defined by the `output_epsg`.

![gis_output](https://user-images.githubusercontent.com/7217258/49361401-15bd0480-f730-11e8-88a8-a127f87ca64a.jpeg)

#### 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:
```
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 one of the satellite images, as shown in the animation below.

![ref_shoreline](https://user-images.githubusercontent.com/7217258/49710753-94b1c000-fc8f-11e8-9b6c-b5e96aadc5c9.gif)

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 100m 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.

#### Advanced shoreline detection parameters

As mentioned above, 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.
- `buffer_size`: radius (in metres) that defines the buffer around sandy pixels that is considered to calculate the sand/water threshold. 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 can be used to discard small features 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.
- `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 to `True` and 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 of `False`.
- `dark_sand`: if your beach has dark sand (grey/black sand beaches), you can set this parameter to `True` and the classifier will be able to pick up the dark sand. At this stage this option is only available for Landsat images (soon for Sentinel-2 as well).

### 2.3 Shoreline change analysis

This section shows how to obtain time-series of shoreline change along shore-normal transects. Each transect is defined by two points, its origin and a second point that defines its length and orientation. There are 3 options to define the coordinates of the transects:
1. Interactively draw shore-normal transects along the mapped shorelines:
```
transects = SDS_transects.draw_transects(output, settings)
```
2. Load the transect coordinates from a .geojson file:
```
transects = SDS_tools.transects_from_geojson(path_to_geojson_file)
```
3. 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]])
```

**Note:** if you choose option 2 or 3, make sure that the points that you are providing are in the spatial reference system defined by `settings['output_epsg']`.

Once the shore-normal transects have been defined, the intersection between the 2D shorelines and the transects is computed with 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.

An example is shown in the animation below:

![transects](https://user-images.githubusercontent.com/7217258/49990925-8b985a00-ffd3-11e8-8c54-57e4bf8082dd.gif)

## Issues
Having a problem? Post an issue in the [Issues page](https://github.com/kvos/coastsat/issues) (please do not email).

## Contributing
1. Fork the repository (https://github.com/kvos/coastsat/fork).
A fork is a copy on which you can make your changes.
2. Create a new branch on your fork
3. Commit your changes and push them to your branch
4. When the branch is ready to be merged, create a Pull Request

Check the following link for more information on how to make a clean pull request: https://gist.github.com/MarcDiethelm/7303312).

If you like the repo put a star on it!
