@ -9,7 +9,7 @@ The underlying approach and application of the CoastSat toolkit are described 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*.
*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 two main steps:
There are two main steps:
- assisted retrieval from from Google Earth Engine of all avaiable satellite images spanning the user-defined region of interest and time period
- assisted retrieval from Google Earth Engine of all avaiable 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
- automated extraction of shorelines from all the selected images using a sub-pixel resolution technique
@ -105,7 +105,7 @@ jupyter notebook
```
```
A web browser window will open. Point to the directory where you downloaded/cloned this repository and click on `example_jupyter.ipynb`.
A web browser window will open. Point 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-Collaroy beach (Australia). If you prefer to use Spyder or PyCharm or other integrated development environments (IDEs), a Python script is also included `main.py` in the repository.
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 or PyCharm or other integrated development environments (IDEs), a Python script `main.py` is also included in the repository. If using `main.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 clikc on the 'run' button up in the top menu to run that section and progress forward (as shown in the animation below).
To run a Jupyter Notebook, place your cursor inside one of the code sections and then clikc on the 'run' button up in the top menu to run that section and progress forward (as shown in the animation below).
@ -129,16 +129,16 @@ It is now time to map the sandy shorelines!
The following user-defined settings are required:
The following user-defined settings are required:
- `cloud_thresh`: threshold on maximum cloud cover that is acceptable on the images (value between 0 and 1 - this may require some initial experimentation)
- `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
- `output_epsg`: epsg code defining the spatial reference system of the shoreline coordinates. It has to be a cartesion coordinate system (i.e. projected) and not a geographical coordinate system (in latitude and longitude angles).
- `check_detection`: if set to `True` allows the user to quality control each shoreline detection
- `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`.
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 these parameters set to their default values, we will see later how they can be modified.
In addition, there are extra parameters (`min_beach_size`, `buffer_size`, `min_length_sl`, `cloud_mask_issue`) 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.
Once all the settings have been defined, the batch shoreline detection can be launched by calling:
Once all the settings have been defined, the batch shoreline detection can be launched by calling:
```
```
@ -162,6 +162,7 @@ As mentioned above, there are some additional parameters that can be modified to
- `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 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),trydecreasingthevalueofthisparameter.
- `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 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),trydecreasingthevalueofthisparameter.
- `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.
- `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 can be used to discard small features that are detected but do not correspond to the sand-water 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.
- `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 sand-water 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 in the cloud mask (appear as black 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`.
#### Reference shoreline
#### Reference shoreline
@ -180,13 +181,25 @@ The maximum distance (in metres) allowed from the reference shoreline is defined
### 2.3 Shoreline change analysis
### 2.3 Shoreline change analysis
This section shows how to obtain time-series of shoreline change along shore-normal transects.
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 orientation. The parameter `transect_length` determines how far (in metres) from the origin the transect will span. There are 3 options to define the coordinates of the transects:
1. The user can interactively draw shore-normal transects along the beach:
The user can draw shore-normal transects by calling:
```
```
settings['transect_length'] = 500 # defines the length of the transects in metres
**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:
Once the shore-normal transects have been defined, the intersection between the 2D shorelines and the transects is computed with the following function:
" 'min_beach_area': 4500, # minimum area (in metres^2) for an object to be labelled as a beach\n",
" 'min_beach_area': 4500, # minimum area (in metres^2) for an object to be labelled as a beach\n",
" 'buffer_size': 150, # radius (in metres) of the buffer around sandy pixels considered in the shoreline detection\n",
" '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",
" '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",
"}"
"}"
]
]
},
},
@ -218,7 +220,7 @@
"source": [
"source": [
"## 4. Shoreline analysis\n",
"## 4. Shoreline analysis\n",
"\n",
"\n",
"Shows how to plot the mapped shorelines and draw shore-normal transects and compute time-series of cross-shore distance along the transects."
"In this section we show how to compute time-series of cross-shore distance along user-defined shore-normal transects."
]
]
},
},
{
{
@ -243,7 +245,25 @@
"cell_type": "markdown",
"cell_type": "markdown",
"metadata": {},
"metadata": {},
"source": [
"source": [
"Create shore-normal transects along the beach"
"The shore-normal transects are defined by two points, the origin of the transect and a second point that defines its orientaion. The parameter *transect_length* determines how far from the origin the transects span."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"settings['transect_length'] = 500 # defines the length of the transects in metres"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 3 options to define the coordinates of the shore-normal transects:\n",
"\n",
"**Option 1**: the user can interactively draw the shore-normal transects along the beach by calling:"
]
]
},
},
{
{
@ -253,7 +273,6 @@
"outputs": [],
"outputs": [],
"source": [
"source": [
"%matplotlib qt\n",
"%matplotlib qt\n",
"settings['transect_length'] = 500 # defines the length of the transects in metres\n",
"Intersect the transects with the 2D shorelines to obtain time-series of cross-shore distance"
"**Option 2**: the user can load the transect coordinates (make sure the spatial reference system is the same as defined by *output_epsg* previously) from a .kml file by calling:"
Kilian Vos
6 years ago