https://github.com/geoscienceaustralia/dea-waterbodies

DEA Waterbodies is a product that maps and monitors open waterbodies across Australia. Once a polygon set has been generated corresponding to open waterbodies, each waterbody is tracked over time to record the change in wet surface area over time.

https://github.com/geoscienceaustralia/dea-waterbodies

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 2 DOI reference(s) in README
  • Academic publication links
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (11.8%) to scientific vocabulary

Keywords

digitalearthaustralia landsat satellites water

Keywords from Contributors

earth-observation earthobservation geoscienceaustralia geospatial-data opendatacube remotesensing sentinel-2
Last synced: 6 months ago · JSON representation

Repository

DEA Waterbodies is a product that maps and monitors open waterbodies across Australia. Once a polygon set has been generated corresponding to open waterbodies, each waterbody is tracked over time to record the change in wet surface area over time.

Basic Info
Statistics
  • Stars: 14
  • Watchers: 9
  • Forks: 9
  • Open Issues: 23
  • Releases: 0
Topics
digitalearthaustralia landsat satellites water
Created over 5 years ago · Last pushed about 3 years ago
Metadata Files
Readme License

README.rst

.. image:: figures/dea_logo_wide.jpg
  :width: 900
  :alt: Digital Earth Australia logo

Digital Earth Australia Waterbodies
###################################

.. image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg
  :target: https://opensource.org/licenses/Apache-2.0
  :alt: Digital Earth Australia logo
  
.. image:: https://github.com/GeoscienceAustralia/dea-waterbodies/actions/workflows/lint.yml/badge.svg
  :target: https://github.com/GeoscienceAustralia/dea-waterbodies/actions/workflows/lint.yml
  :alt: Linting status
  
.. image:: https://github.com/GeoscienceAustralia/dea-waterbodies/actions/workflows/test.yml/badge.svg
  :target: https://github.com/GeoscienceAustralia/dea-waterbodies/actions/workflows/test.yml
  :alt: Testing status

**License:** The code in this repository is licensed under the `Apache License, Version 2.0 `_. Digital Earth Australia data is licensed under the `Creative Commons by Attribution 4.0 license `_.

**Contact:** If you need assistance with any of the Jupyter Notebooks or Python code in this repository, please post a question on the `Open Data Cube Slack channel `_. If you would like to report an issue with this repo, or suggest feature requests, you can `open an issue on this repository `_. Non-technical questions about Digital Earth Australia Waterbodies can be sent to dea@ga.gov.au. 

**Citing Digital Earth Australia Waterbodies:**

    Krause, Claire E.; Newey, Vanessa; Alger, Matthew J.; Lymburner, Leo. 2021. "Mapping and Monitoring the Multi-Decadal Dynamics of Australia’s Open Waterbodies Using Landsat" Remote Sens. 13, no. 8: 1437. https://doi.org/10.3390/rs13081437

----------

Up to date information about the extent and location of surface water provides all Australians with a common understanding of this valuable and increasingly scarce resource. Water detection algorithms are now being routinely applied to continental and global archives of satellite imagery. However, water resource management decisions typically take place at the waterbody rather than pixel scale. 

This repository presents a workflow for generating polygons of persistent waterbodies from Landsat observations, enabling improved monitoring and management of water assets across Australia. We use `Digital Earth Australia’s (DEA) Water Observations from Space (WOfS) water classifier `_, which provides a water classified output for every available Landsat scene, to determine the spatial locations and extents of waterbodies across Australia. DEA Waterbodies uses Geoscience Australia’s archive of over 30 years of Landsat satellite imagery to identify where almost 300,000 waterbodies are in the Australian landscape. 

.. image:: figures/WorkflowDiagram.JPG
  :width: 900
  :alt: Digital Earth Australia Waterbodies workflow diagram

*Digital Earth Australia Waterbodies workflow*

Each polygon was then used to generate a time series of WOfS, providing a history of the change in the wet surface area of each waterbody every ~16 days since 1987.

.. image:: figures/DEAWaterbodiesESRIBasemap.jpeg
  :width: 900
  :alt: Digital Earth Australia Waterbodies

*Digital Earth Australia Waterbodies. Waterbody polygons mapped by this product are shown in blue. There are almost 300,000 across Australia.*

DEA Waterbodies supports users to understand and manage water across Australia. DEA Waterbodies provides new insights into local through to national-scale surface water spatio-temporal dynamics by enabling the monitoring of important landscape features such as lakes and dams, improving our ability to use earth observation data to make meaningful decisions. It can be used to gain insights into the severity and spatial distribution of drought, or identify potential water sources for aerial firefighting during bushfires. 

For more information about the DEA Waterbodies product, including instructions for accessing the product, frequently asked questions and data download links, see the `Digital Earth Australia website `_.

Installation
------------

DEA Waterbodies has some requirements which can be installed with pip:

.. code-block:: bash

    pip install --extra-index-url="https://packages.dea.ga.gov.au" -r requirements.txt
    
Once you have installed the requirements for DEA Waterbodies, install the module locally:

.. code-block:: bash

    pip install -e .
    
This command installs an editable version of the module in the current location.

A command line interface is available for generating wet area time series for a given shapefile. You can call the help for this interface from the command line using:

.. code-block:: bash

    waterbodies-ts --help

Owner

  • Name: Geoscience Australia
  • Login: GeoscienceAustralia
  • Kind: organization
  • Location: Canberra, Australia

GitHub Events

Total
  • Watch event: 2
Last Year
  • Watch event: 2

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 306
  • Total Committers: 7
  • Avg Commits per committer: 43.714
  • Development Distribution Score (DDS): 0.467
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Matthew Alger M****r@g****u 163
Matthew Alger m****r@g****m 82
Vanessa Newey v****y 31
Matthew Alger m****r@g****u 15
Claire Krause c****e@g****u 7
Claire 1****e 5
Vanessa Newey v****7@g****u 3
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: about 1 year ago

All Time
  • Total issues: 25
  • Total pull requests: 37
  • Average time to close issues: 29 days
  • Average time to close pull requests: about 19 hours
  • Total issue authors: 3
  • Total pull request authors: 3
  • Average comments per issue: 0.92
  • Average comments per pull request: 0.43
  • Merged pull requests: 33
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • MatthewJA (18)
  • CEKrause (6)
  • whatnick (1)
Pull Request Authors
  • MatthewJA (28)
  • CEKrause (5)
  • vnewey (4)
Top Labels
Issue Labels
enhancement (6) bug (4) documentation (1)
Pull Request Labels

Dependencies

requirements.txt pypi
  • boto3 ==1.17.49
  • datacube *
  • dea-tools *
  • flake8 ==3.9.2
  • fsspec *
  • geopandas >=0.9.0
  • moto ==2.2.6
  • numpy >=1.18.5
  • pytest ==6.2.4
  • python-geohash ==0.8.5
  • rasterstats >=0.15.0
  • rioxarray >=0.3.1
  • rtree *
  • s3fs *
.github/workflows/lint.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.github/workflows/push.yml actions
  • actions/checkout v2 composite
  • aquasecurity/trivy-action master composite
  • whoan/docker-build-with-cache-action v4 composite
.github/workflows/test.yml actions
  • actions/checkout v2 composite
  • satackey/action-docker-layer-caching v0.0.8 composite
environment.yml conda
  • boto3
  • gdal
  • geopandas
  • numpy
  • s3fs
Dockerfile docker
  • opendatacube/geobase-builder ${V_BASE} build
  • opendatacube/geobase-runner ${V_BASE} build
docker-compose.yml docker
  • opendatacube/datacube-index 0.0.13
  • postgres 11.5-alpine