forked from kilianv/CoastSat_WRL
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
34 lines
3.9 KiB
Markdown
34 lines
3.9 KiB
Markdown
# coastsat
|
|
|
|
Python code to download publicly available satellite imagery with Google Earth Engine API and extract shorelines using a robust sub-pixel resolution shoreline detection algorithm described in *Vos K., Harley M.D., Splinter K.D., Simmons J.A., Turner I.L. (in review). Capturing intra-annual to multi-decadal shoreline variability from publicly available satellite imagery, Coastal Engineering*.
|
|
|
|
Written by *Kilian Vos*.
|
|
|
|
## 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 measurements are available. Satellite imagery spannig the last 30 years with constant revisit periods is publicly available and suitable to extract repeated measurements of the shoreline positon.
|
|
*coastsat* is an open-source Python module that allows to extract shorelines from Landsat 5, Landsat 7, Landsat 8 and Sentinel-2 images.
|
|
The shoreline detection algorithm proposed here 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.
|
|
|
|
## Use
|
|
|
|
A demonstration of the use of *coastsat* is provided in the Jupyter Notebook *shoreline_extraction.ipynb*. The code can also be run in Spyder with *main_spyder.py*.
|
|
|
|
The Python packages required to run this notebook can be installed by running the following anaconda command: *conda env create -f environment.yml*. This will create a new enviroment with all the relevant packages installed. You will also need to sign up for Google Earth Engine (https://earthengine.google.com and go to signup) and authenticate on the computer so that python can access via your login.
|
|
|
|
The first step is to retrieve the satellite images of the region of interest from Google Earth Engine servers by calling *SDS_download.get_images(sitename, polygon, dates, sat_list)*:
|
|
- *sitename* is a string which will define the name of the folder where the files will be stored
|
|
- *polygon* contains the coordinates of the region of interest (longitude/latitude pairs)
|
|
- *dates* defines the dates over which the images will be retrieved (e.g., *dates = ['2017-12-01', '2018-01-01']*)
|
|
- *sat_list* indicates which satellite missions to consider (e.g., *sat_list = ['L5', 'L7', 'L8', 'S2']* will download images from Landsat 5, 7, 8 and Sentinel-2 collections).
|
|
|
|
The images are cropped on the Google Earth Engine servers and only the region of interest is downloaded resulting in low memory allocation (~ 1 megabyte/image for a 5km-long beach). The relevant image metadata (time of acquisition, geometric accuracy...etc) is stored in a file named *sitename_metadata.pkl*.
|
|
|
|
Once the images have been downloaded, the shorelines are extracted from the multispectral images using the sub-pixel resolution technique described in *Vos K., Harley M.D., Splinter K.D., Simmons J.A., Turner I.L. (in review). Capturing intra-annual to multi-decadal shoreline variability from publicly available satellite imagery, Coastal Engineering*.
|
|
The shoreline extraction is performed by the function SDS_shoreline.extract_shorelines(metadata, settings). The user must define the settings in a Python dictionary. To ensure maximum robustness of the algorithm the user can optionally digitize a reference shoreline (byc calling *SDS_preprocess.get_reference_sl(metadata, settings)*) that will then be used to identify obvious outliers and minimize false detections. Since the cloud mask is not perfect (especially in Sentinel-2 images) the user has the option to manually validate each detection by setting the *'check_detection'* parameter to *True*.
|
|
The shoreline coordinates (in the coordinate system defined by the user in *'output_epsg'* are stored in a file named *sitename_out.pkl*.
|
|
|
|
## Issues and Contributions
|
|
|
|
Looking to contribute to the code? Please see the [Issues page](https://github.com/kvos/coastsat/issues).
|