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.
The underlying approach and application of the CoastSat toolkit are described in detail in:
The underlying approach of the CoastSat toolkit are described in detail in:
* 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
*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*.
Example applications and accuracy of the resulting satellite-derived shorelines are discussed in:
* 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
### Description
There are three main steps:
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 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 three main functionalities:
- 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.
**If you like the repo put a star on it!**
## 1. Installation
@ -44,21 +45,17 @@ 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.
**In case errors are raised:**: open the **Anaconda Navigator**, in the *Environments* tab click on *Import* and select the *environment.yml* file. For more details, 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).
Once you have created a Google Earth Engine account, go back to the Anaconda prompt and link your GEE credentials to the Python API:
With the `coastsat` environment activated, run the following command on the Anaconda Prompt to link your environment to the GEE server:
```
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.
A web browser will open, login with a gmail account 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!
@ -78,9 +75,9 @@ The following sections guide the reader through the different functionalities of
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).
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 (shown below) and progress forward.
@ -104,24 +101,22 @@ The screenshot below shows an example of inputs that will retrieve all the image
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.
- `check_detection`: if set to `True` the user can quality control each shoreline detection interactively (recommended when mapping shorelines for the first time).
- `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.
There are additional parameters (`min_beach_size`, `buffer_size`, `min_length_sl`, `cloud_mask_issue` and `sand_color`) 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.
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).
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 **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).
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.
@ -133,7 +128,7 @@ The figure below shows how the satellite-derived shorelines can be opened in a G
#### 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.
Before running the batch shoreline detection, there is the option to manually digitize a reference shoreline on one cloud-free image. 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 defined distance from this reference shoreline.
The user can manually digitize a reference shoreline on one of the images by calling:
```
@ -142,9 +137,9 @@ settings['max_dist_ref'] = 100 # max distance (in meters) allowed from the refer
```
This function allows the user to click points along the shoreline on one of the 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 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.
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
@ -153,7 +148,7 @@ As mentioned above, there are some additional parameters that can be modified to
- `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).
- `sand_color`: this parameter can take 3 values: `default`, `dark` or `bright`. Only change this parameter if you are seing that with the `default` the 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 to `dark` and the classifier will be able to pick up the dark sand. On the other hand, if your beach has white sand and the `default` classifier is not picking it up, switch this parameter to `bright`. At this stage this option is only available for Landsat images (soon for Sentinel-2 as well).
### 2.3 Shoreline change analysis
@ -191,12 +186,17 @@ An example is shown in the animation below:
Having a problem? Post an issue in the [Issues page](https://github.com/kvos/coastsat/issues) (please do not email).
## Contributing
If you are willing to contribute, check out our todo list in the [Projects page](https://github.com/kvos/CoastSat/projects/1).
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
4. When the branch is ready to be merged, create a Pull Request (how to make a clean pull request explained [here](https://gist.github.com/MarcDiethelm/7303312))
## References
1. 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
Check the following link for more information on how to make a clean pull request: https://gist.github.com/MarcDiethelm/7303312).
2. 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
If you like the repo put a star on it!
3. Training dataset used for pixel classification in CoastSat: https://doi.org/10.5281/zenodo.3334147
"# *CoastSat*: example at Narrabeen-Collaroy, Australia\n",
"\n",
"This software is described in *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*. It enables the users to extract time-series of shoreline change over the last 30+ years at their site of interest.\n",
"This software is described in details in:\n",
"* 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\n",
"\n",
"It enables the users to extract time-series of shoreline change over the last 30+ years at their site of interest.\n",
"There are three main steps:\n",
"- retrieval of the satellite images of the region of interest from Google Earth Engine\n",
"- extraction of the shorelines from the images using a sub-pixel resolution technique\n",
"- intersection of the 2D shorelines with shore-normal transects\n",
"1. Retrieval of the satellite images of the region of interest from Google Earth Engine\n",
"2. Shoreline extraction at sub-pixel resolution\n",
"3. Intersection of the shorelines with cross-shore transects\n",
"\n",
"## 1. Initial settings\n",
"## Initial settings\n",
"\n",
"Refer to the **Installation** section of the README for instructions on how to install the Python packages necessary to run the software, including Google Earth Engine Python API. If that step has been completed correctly, the following packages should be imported without any problem."
]
@ -37,7 +39,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Retrieval of the images from GEE\n",
"## 1. Retrieval of the images from GEE\n",
"\n",
"Define the region of interest (`polygon`), the date range (`dates`) and the satellite missions (`sat_list`) from which you wish to retrieve the satellite images. The images will be cropped on the Google Earth Engine server and only the region of interest will be downloaded as a .tif file. The files will stored in the directory defined in `filepath`.\n",
"\n",
@ -104,7 +106,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Shoreline extraction\n",
"## 2. Shoreline extraction\n",
"\n",
"This section maps the position of the shoreline on the satellite images. The user can define the cloud threhold (`cloud_thresh`) and select the spatial reference system in which to output the coordinates of the mapped shorelines (`output_epsg`). See http://spatialreference.org/ to find the EPSG number corresponding to your local coordinate system. Make sure that your are using cartesian coordinates and not spherical coordinates (lat,lon) like WGS84. \n",
"\n",
@ -133,7 +135,7 @@
" 'buffer_size': 150, # radius (in metres) of the buffer around sandy pixels considered in the shoreline detection\n",
" 'min_length_sl': 200, # minimum length (in metres) of shoreline perimeter to be valid\n",
" 'cloud_mask_issue': False, # switch this parameter to True if sand pixels are masked (in black) on many images \n",
" 'dark_sand': False, # only switch to True if your site has dark sand (e.g. black sand beach)\n",
" 'sand_color': 'default', # 'default', 'dark' (for grey/black sand beaches) or 'bright' (for white sand beaches)\n",
"}"
]
},
@ -178,7 +180,9 @@
"metadata": {},
"source": [
"### Batch shoreline detection\n",
"Extracts the 2D shorelines from the images in the spatial reference system specified by the user in `'output_epsg'`. The mapped shorelines are saved into `output.pkl` (under *./data/sitename*) and `output.geojson` to use in GIS softwares."
"Extracts the 2D shorelines from the images in the spatial reference system specified by the user in `'output_epsg'`. The mapped shorelines are saved into `output.pkl` (under *./data/sitename*) and `output.geojson` (to be used in a GIS software).\n",
"\n",
"If you see that the sand pixels on the images are not being identified, change the parameter `sand_color` from `default` to `dark` or `bright` depending on the color of your beach. "
]
},
{
@ -225,7 +229,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Shoreline analysis\n",
"## 3. Shoreline analysis\n",
"\n",
"In this section we show how to compute time-series of cross-shore distance along user-defined shore-normal transects."
Kilian Vos
5 years ago
