Shoreline is a library and CLI for generating shorelines over arbitrary regions. It handles retrieval of image data
bf-alg-ndwi is a library and a CLI for running shoreline detection on a directory of input data using an NDWI algorithm. The algorithm performs several steps including masking (if Landsat and BQA mask is provided), band ratioing, thresholding, and vectorizing to create a GeoJSON output of the in-scene coastline.
There are several system libraries required should be installed before installing. On a debian system:
$ apt-get install -y python-setuptools python-numpy python-dev libgdal-dev python-gdal swig git g++ libagg-dev libpotrace-dev
GDAL version 2.1.0 or higher is required, as is Potrace v1.14. Also included is a Dockerfile that contains the necessary environment (see below for more).
Then, from this directory (contaning this repo) install the Python requirements and bfalg-ndwi. The use of a virtual environment is recommended.
$ virtualenv venv
$ source venv/bin/activate
(venv) $ pip install -r requirements.txt
(venv) $ pip install .
While shoreline can be used as a Python library, the more common use is to use the Command Line Interface (CLI). Use the help switch (-h or --help) to display online help:
If a single filename is provided via the INPUT argument, then BANDS needs to be provided to specify which bands in the file should be used, otherwise it defaults to '1, 1', which means it would use the same band for both green and nir. This example uses the 1st band in the file as the green band and the 5th as the nir.
$ bfalg-ndwi -i test1.tif -b 1 5
If the INPUT parameter is provided twice for two filenames, then BANDS is the band number for the first file (green) and the second file (nir). This example uses the second band from the test1.tif as the green band, the first band from test2.tif as the nir band.
$ bfalg-ndwi -i test1.tif -i test2.tif -b 2 1
Input files are all that are absolutely required, but a more typical scenario would look like this:
$ bfalg-ndwi -i scene123.tif -b 1 2 --basename testrun --outdir scene123-output --coastmask
This will apply the included buffered coastline (bfalg_ndwi/coastmask.shp) to the image to mask out non-coastal regions. It will store all output files with with the name 'testrun' (+ additional tag and extension, e.g. testrun.geojson, testrun_otsu.TIF) in the directory 'scene123-output'.
For Landsat8, if the BQA band is available it can be provided which will mask out the clouds from the scene.
$ bfalg-ndwi -i LC80080282016215LGN00_B1.TIF LC80080282016215LGN00_B5.TIF --l8bqa LC80080282016215LGN00_BQA.TIF --basename LC80080282016215LGN00 --outdir LC80080282016215LGN00_test --coastmask
The last three remaining parametes are tuning parameters involving the creation of the vector output.
- minsize (100): The minimum size a linestring should be before being filtered out. This corresponds to the potrace parameter 'turdsize', and is not the length of the line but rather some measure of the extent of it. The default of 10 will not filter out many lines. For Landsat, a value of 100 works well and removing false coasts, but may also remove islands or smaller incomplete shorelines.
- close (5): Linestrings will be closed if their two endpoints are within this number of pixels. The default is 5, and setting it to 0 will turn it off. This should not be set to a value much higher than 10.
- simplify (None): Simplification will not be done by default. If provided it is in units of degrees, and is used to simplify and smooth the output. Simplification is heavily application and source imagery dependent, and is a lossy process. Consider starting points for RapidEye and PlanetScope data to be 0.00035, and for Landsat8, 0.0007.
The traditional NDWI algorithm uses the green and NIR bands to calculate a normalized difference index:
NDWI = (green-nir) / (green+nir)
The band names, green and nir, is what is referenced in the online help. However, other bands can work as well, or even better on some instruments. The 'Green' band is a band that has a high water reflectance, while the NIR band is a band that has a very low water reflectance. The following bands are recommended.
| Sensor | 'Green' band | 'NIR' band |
|---|---|---|
| Landsat8 | 1 (Coastal) | 5 (NIR) |
| RapidEye | 2 (Green) | 4 (NIR) |
| PlanetScope | 2 (Green) | 4 (NIR) |
| Sentinel | 3 (Green) | 8 (NIR) |
The 'develop' branch is the default branch and contains the latest accepted changes to the code base. Changes should be created in a branch and Pull Requests issues to the 'develop' branch. Releases (anything with a version number) should issue a PR to 'master', then tagged with the proper version using git tag. Thus, the 'master' branch will always contain the latest tagged release.
Python nose is used for testing, and any new function added to the main library should have at least one corresponding test in the appropriate module test file in the tests/ directory.
A Dockerfile and a docker-compose.yml are included for ease of development. The built docker image provides all the system dependencies needed to run the library. The library can also be tested locally, but all system dependencies must be installed first, and the use of a virtualenv is recommended.
To build the docker image use the included docker-compose tasks:
$ docker-compose build
Which will build an image called that can be run
# this will run the image in interactive mode (open bash script)
$ docker-compose run bash
# this willl run the tests using the locally available image
$ docker-compose run test
Additionally, if a package had previously been created (see above), the testpackage command can be used to test it on a base Linux image. Note this is different then the test which uses a system (the image built to create the package) where dependencies have been conventionally installed.
Docker can be used to create a deploy package. First the docker image must be built, and then the package command can be called.
$ docker-compose build $ docker-compose run package
$ optionally test package on basic Linux image $ docker-compose run testpackage
All necessary files will be put into the deploy directory, which will then be packaged up into a deploy.zip file. Copy to the target machine and unzip.
$ unzip deploy.zip
There are two environment variables that need to be set prior to calling the CLI.
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$PWD/lib $ export PYTHONPATH=$PYTHONPATH:$PWD/lib/python2.7/dist-packages $ export GDAL_DATA=$PWD/share/gdal