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fmemuir / COASTGUARD

Coastal Observation + Analysis using Satellite-derived Timeseries, Generated Using AI + Real-time Data
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coastal-change coastal-modelling machine-learning remote-sensing satellite-imagery

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COASTGUARD

$\textcolor{#00B0B7}{\textsf{C}}$ oastal $\textcolor{#00B0B7}{\textsf{O}}$ bservation + $\textcolor{#00B0B7}{\textsf{A}}$ nalysis using $\textcolor{#00B0B7}{\textsf{S}}$ atellite-derived $\textcolor{#00B0B7}{\textsf{T}}$ imeseries,

$\textcolor{#00B0B7}{\textsf{G}}$ enerated $\textcolor{#00B0B7}{\textsf{U}}$ sing $\textcolor{#00B0B7}{\textsf{A}}$ I + $\textcolor{#00B0B7}{\textsf{R}}$ eal-time $\textcolor{#00B0B7}{\textsf{D}}$ ata

is a Python toolkit for coastal monitoring and modelling using machine learning approaches.

https://github.com/fmemuir/COASTGUARD/assets/22475417/0ffaeea4-adeb-41c7-9936-937d9899df6c

Currently, the main toolset VedgeSat is for extracting $\textcolor{#2EA043}{\textsf{coastal vegetation edges}}$ from satellite imagery, which is partially built on the CoastSat toolbox (https://github.com/kvos/CoastSat).

:warning: PATCH NOTES :wrench:

Description and Scope

The goal of this toolkit is to have a fully operational framework for predicting coastal change, using machine learning techniques that are trained with satellite observations. With just one satellite image, multiple indicators of coastal change can be automatically extracted such as wave breaking zones, wet-dry boundaries, high water marks and vegetation edges. These automatically extracted indicators can then be fed into a machine learning network which makes future predictions based on the past changes and relationships between these indicators. The result is an automated, early warning system for coastal erosion at a potentially global scale.

https://github.com/fmemuir/COASTGUARD/assets/22475417/cb27e704-f361-4f34-b999-dcd5c990816c

Enhancements

Various improvements have been made to the toolkit to address more accurate approaches recently reported on, and to incorporate new Python packages and tools for more seamlessness. These are detailed further in the methods paper, but include:

Installation

INSTALL QUICK VERSION

  1. Download repo: $ git clone https://github.com/fmemuir/COASTGUARD.git
  2. Create conda environment: conda env create -f coastguard_env.yml
  3. Activate env: conda activate coastguard
  4. Authenticate GEE: earthengine authenticate

Remember!: Always run conda activate coastguard each time you want to use the toolbox. You should not need to authenticate earthengine each time, just the once when installing.

1.1 Download the code

The Python tool relies on packages downloaded through Anaconda and the Google Earth Engine API to run. The preliminary step is downloading this repository. You can do this either by clicking the Code button at the top and downloading + extracting the zipped folder, or by navigating to where you want to download it on your local machine and running 

git clone https://github.com/fmemuir/COASTGUARD.git

from a command line (if you have git command line tools installed).

If you downloaded the code zip file manually, it's recommended you extract the files to a new local folder rather than keeping it in your Downloads!

1.2 Create a conda enviroment

To run the toolbox you first need to install the required Python packages in an environment. If you don't already have it, Anaconda can be downloaded freely here.

Once you have Anaconda installed on your PC:

and navigate to the folder with the repository files using cd.

Navigate to the COASTGUARD repository folder (cd COASTGUARD) and then create a new conda environment named coastguard with all the required packages by entering this command (make sure you're in the repo folder!):


conda update -n base conda

conda env create --file coastguard_env.yml

Note: the Python version listed in the .yml file is a dependent of the pyfes package (which is needed for tidal corrections of waterlines), see these issues here for details.

Then run this command to install the remaining packages:

conda install -c conda-forge earthengine-api pandas=2.0.3 geopandas spyder=5.5.0 geemap scikit-image matplotlib rasterio seaborn astropy geopy notebook netcdf4 arosics utm

Please note that solving and building the environment can take some time (minutes to hours depending on the the nature of your base environment). If you want to make things go faster, it's recommended you solve the conda environment installation with Mamba. You can set Mamba as the default conda solver with these steps:

conda update -n base conda

conda install -n base conda-libmamba-solver
conda config --set solver libmamba

Once the installation steps are complete, all the required packages will have been installed in an environment called coastguard. Always make sure that the environment is activated with:

conda activate coastguard

before you start working with the tools each time.

1.3 Activate Google Earth Engine API

This tool uses Google Earth Engine (GEE) API to access satellite image metadata. You need to request access to GEE API by signing up at https://signup.earthengine.google.com/ with a Google account and filling in a few questions about your intended usage (the safest bet is 'research'). It can take up to 24 hours to approve a request, but it's usually fairly quick.

In the meantime, you will also need to install a program called Google Cloud Command Line Interface (gcloud CLI). It shouldn't matter where you download this to. Find installation instructions here: https://cloud.google.com/sdk/docs/install.

Once your GEE request has been approved, you should get a confirmation email. Open a prompt/terminal window and activate coastguard environment. Run this command to link your conda environment to the GEE server:

earthengine authenticate

A web browser will open; log in with the GMail account you used to sign up to GEE. The authenticator should then redirect back to your terminal window. If it doesn't, copy+paste the authorization code into the terminal.

Getting Started

The process of extracting coastal vegetation edges from satellite data is run through a driver file. Driver files can be customised for your own site of interest. There are a couple of template examples in the repository to help you get started. The interactive python notebook VedgeSat_DriverTemplate.ipynb can be viewed and executed in an interactive notebook environment such as jupyter-notebook which can be launched at the command line:

(coastguard) $ jupyter-notebook VedgeSat_DriverTemplate.ipynb

Alternatively, you can customise and run the standard python script VedgeSat_DriverTemplate.py using a python IDE such as spyder:

(coastguard) $ spyder VedgeSat_DriverTemplate.py

https://github.com/fmemuir/COASTGUARD/assets/22475417/1bd4722b-ece9-4ed9-a9ac-104f71c241d7

There are 7 main steps to setting up the vegetation extraction tool. These steps are run from a driver file which takes care of all the user-driven params when setting up a new site. The main steps found in a driver file are:

  1. Import relevant packages (including initialising the earthengine tools);
  2. Define an area of interest. For the time being, this must be smaller than 262144 (512 x 512) pixels, equivalent to 5.12 x 5.12 km for Sentinel and 7.68 x 7.68 km for Landsat;
  3. Define image parameters (start and end date, satellites, CRS/projections, sitename);
  4. Retrieve and save image collection metadata*;
  5. Set coastal boundary parameters (cloud cover threshold, plotting flags, minimum area for contouring);
  6. Define a reference shore along which to create a buffer (boundaries will only be extracted along here);
  7. Run the main edge extraction function.

*This is an update from the original CoastSat toolkit! Raw satellite images will not be downloaded, but merely the filenames will be passed to geemap and converted from the cloud server straight to numpy arrays. This is to save time and bandwidth. TIFs of true colour images and their classified and NDVI counterparts will however be exported throughout the process to be explored in a GIS environment.

The tool takes all the input settings the user has defined, and performs these steps:

  1. Preprocess each image in the metadata collection (downsample or pansharpen, mask clouds, clean nodata);
  2. Create buffer around reference shoreline (or most recent shore extracted, useful for dynamic shores and image collections over a long period);
  3. Classify image using the pre-trained neural network;
  4. Show/adjust detected boundary between image classes (depending on if user has requested to be shown the interactive plot window);
  5. Export boundaries and relevant metadata to a .pkl file and a shapefile of lines.

Extracting Waterlines Alongside Vegetation Edges

As this tool is built from the original CoastSat toolkit, it is possible to extract instantaneous waterlines as well as vegetation edges from each satellite image. To do this, change the wetdry flag in the user requirements to True. Any tidal correction on the extracted waterlines is performed using the FES2014 tidal model. You will need to use pyFES and the Aviso FES2014 repo for this. To get the tide data all set up:

  1. Clone the repo from the aviso-fes github.
  2. You will notice the folders in /aviso-fes/data/fes2014 are empty. You need to get the actual tide data from the AVISO file transfer service by signing up to their FTP subscription here and ticking "FES2014 / FES2012 (Oceanic Tides Heights)" under the Auxiliary products requested.
  3. Once you have been approved and given access to their file transfer service, you can use whichever file transfer program (we like FileZilla, or you can use ftp at the command line) to download the files below:
    • eastward_velocity.tar.xz
    • load_tide.tar.xz
    • northward_velocity.tar.xz
    • ocean_tide.tar.xz
    • ocean_tide_extrapolated.tar.xz You'll find these in the AVISO Altimetry database under /auxiliary/tide_model/fes2014_elevations_and_load/
  4. Decompress the archives (using 7zip or alternatives) and move them into the relevant folders in your local /aviso-fes/data/fes2014 directory (that you cloned in Step 1).

When loading in the tidal data in the driver file, you should change the tidal files path to wherever you have cloned the FES2014 repo to on your machine.

Roadmap

This code is live and the master branch is being updated often (daily to weekly). If you clone this repo, please update it regularly with git pull! June 2024: New functionality is coming to run timeseries predictions based on the vegetation edge and waterline timeseries that are generated from this tool!

Contributions

We welcome any enhancements! Please open an issue if you have any contributions or questions.

Authors and acknowledgements

This tool is based on work by Kilian Vos (github: kvos) at University of New South Wales. The veg adaptation for the tool was originally conceived by Freya Muir, Luke Richardson-Foulger and Martin Hurst, and was executed, tested and refined by Freya Muir and Luke Richardson-Foulger.

If you would like to share your use of this toolkit, please cite it as appropriate: