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Jupyter Notebook","category":"Hydrosphere","sub_category":"Coastal and Reefs","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"\u003e 🌏 **Note: This branch contains the Australia-specific implementation of DEA Coastlines.** For a more flexible implementation adapted to globally available USGS Collection 2 Level 2 Landsat data, we recommend adapting the [Digital Earth Africa Coastlines](https://github.com/digitalearthafrica/deafrica-coastlines/) implementation instead.\n\n![Digital Earth Australia Coastlines](visualisation/images/DEACoastlines_header.gif)\n\n# Digital Earth Australia Coastlines\n\n[![DOI](https://img.shields.io/badge/DOI-10.1016/j.rse.2021.112734-0e7fbf.svg)](https://doi.org/10.1016/j.rse.2021.112734)\n[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n[![codecov](https://codecov.io/gh/GeoscienceAustralia/dea-coastlines/branch/develop/graph/badge.svg?token=7HXSIPGT5I)](https://codecov.io/gh/GeoscienceAustralia/dea-coastlines)\n[![example workflow](https://github.com/GeoscienceAustralia/dea-coastlines/actions/workflows/dea-coastlines-image.yaml/badge.svg)](https://github.com/GeoscienceAustralia/dea-coastlines/actions/workflows/dea-coastlines-image.yaml)\n\n**License:** The code in this repository is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). Digital Earth Australia data is licensed under the [Creative Commons by Attribution 4.0 license](https://creativecommons.org/licenses/by/4.0/).\n\n**Contact:** For assistance with any of the Python code or Jupyter Notebooks in this repository, please post a [Github issue](https://github.com/GeoscienceAustralia/dea-coastlines/issues/new). For questions or more information about this workflow, email Robbi.BishopTaylor@ga.gov.au.\n\n**To cite:**\n\u003e Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. (2021). Mapping Australia's dynamic coastline at mean sea level using three decades of Landsat imagery. _Remote Sensing of Environment_, 267, 112734. Available: https://doi.org/10.1016/j.rse.2021.112734\n\n\u003e Nanson, R., Bishop-Taylor, R., Sagar, S., Lymburner, L., (2022). Geomorphic insights into Australia's coastal change using a national dataset derived from the multi-decadal Landsat archive. _Estuarine, Coastal and Shelf Science_, 265, p.107712. Available: https://doi.org/10.1016/j.ecss.2021.107712\n\n\u003e Bishop-Taylor, R., Sagar, S., Lymburner, L., Alam, I., Sixsmith, J. (2019). Sub-pixel waterline extraction: characterising accuracy and sensitivity to indices and spectra. _Remote Sensing_, 11 (24):2984. Available: https://doi.org/10.3390/rs11242984\n\n---\n\n[**Digital Earth Australia Coastlines**](https://maps.dea.ga.gov.au/story/DEACoastlines) is a continental dataset that includes annual shorelines and rates of coastal change along the entire Australian coastline from 1988 to the present.\n\nThe product combines satellite data from Geoscience Australia's [Digital Earth Australia program](https://www.ga.gov.au/dea) with tidal modelling to map the typical location of the coastline at mean sea level for each year. The product enables trends of coastal erosion and growth to be examined annually at both a local and continental scale, and for patterns of coastal change to be mapped historically and updated regularly as data continues to be acquired. This allows current rates of coastal change to be compared with that observed in previous years or decades.\n\nThe ability to map shoreline positions for each year provides valuable insights into whether changes to our coastline are the result of particular events or actions, or a process of more gradual change over time. This information can enable scientists, managers and policy makers to assess impacts from the range of drivers impacting our coastlines and potentially assist planning and forecasting for future scenarios.\n\n#### Applications\n* Monitoring and mapping rates of coastal erosion along the Australian coastline\n* Prioritise and evaluate the impacts of local and regional coastal management based on historical coastline change\n* Modelling how coastlines respond to drivers of change, including extreme weather events, sea level rise or human development\n* Supporting geomorphological studies of how and why coastlines have changed across time\n\n---\n\n## Table of contents\n- [Digital Earth Australia Coastlines](#digital-earth-australia-coastlines)\n      - [Applications](#applications)\n  - [Table of contents](#table-of-contents)\n  - [Repository code](#repository-code)\n      - [Getting started](#getting-started)\n        - [Setting up global ocean tide models](#setting-up-global-ocean-tide-models)\n      - [Python modules](#python-modules)\n      - [Jupyter notebooks](#jupyter-notebooks)\n    - [Running a DEA Coastlines analysis using the command-line interface (CLI)](#running-a-dea-coastlines-analysis-using-the-command-line-interface-cli)\n      - [Analysis outputs](#analysis-outputs)\n    - [Data access](#data-access)\n      - [Data download](#data-download)\n      - [Interactive map](#interactive-map)\n      - [Loading DEA Coastlines data from the Web Feature Service (WFS) using Python](#loading-dea-coastlines-data-from-the-web-feature-service-wfs-using-python)\n    - [Loading DEA Coastlines data from the Web Feature Service (WFS) using R](#loading-dea-coastlines-data-from-the-web-feature-service-wfs-using-r)\n      - [Jupyter Notebook](#jupyter-notebook)\n  - [Credits](#credits)\n  - [References](#references)\n\n---\n\n## Repository code\nThe code in this repository is built on the Digital Earth Australia implementation of the [Open Data Cube](https://www.opendatacube.org/) software for accessing, managing, and analyzing large quantities of Earth observation (EO) data.\nThe code currently runs on the [Digital Earth Australia Sandbox](https://app.sandbox.dea.ga.gov.au/) infrastructure.\n\n#### Getting started\n\nClone the `dea-coastlines` repository and checkout the `develop` branch:\n```\ngit clone https://github.com/GeoscienceAustralia/dea-coastlines.git\ngit checkout --track origin/develop\n```\n\n##### Setting up global ocean tide models\nTide modelling is a core part of the DEA Coastlines workflow. DEA Coastlines can be run using any global ocean tide model supported by the `eo-tides` and `pyTMD` Python packages, including:\n\n- [Empirical Ocean Tide model](https://doi.org/10.5194/essd-13-3869-2021) (EOT20)\n- [Finite Element Solution tide models](https://doi.org/10.5194/os-2020-96) (FES2022, FES2014, FES2012)\n- [TOPEX/POSEIDON global tide models](https://www.tpxo.net/global) (TPXO10, TPXO9, TPXO8)\n- [Global Ocean Tide models](https://doi.org/10.1002/2016RG000546) (GOT5.6, GOT5.5, GOT4.10, GOT4.8, GOT4.7)\n- [Hamburg direct data Assimilation Methods for Tides models](https://doi.org/10.1002/2013JC009766) (HAMTIDE11)\n- [Technical University of Denmark tide models](https://doi.org/10.11583/DTU.23828874) (DTU23)\n\nFor detailed instructions on setting up tide models, follow the [Setting up tidal models guide]([https://github.com/GeoscienceAustralia/dea-coastlines/wiki/Setting-up-tidal-models-for-DEA-Coastlines](https://geoscienceaustralia.github.io/eo-tides/setup/).\n\n#### Python modules\n\nCode in this repository is included in the `coastlines` Python package which contains three main modules. These are intended to be run in the following order:\n\n1. [`coastlines.raster`](coastlines/raster.py): This module conducts raster generation for DEA Coastlines. This analysis is processed on individual study area tiles to minimise peak memory usage.\n\n    * Load stack of all available Landsat 5, 7, 8 and 9 satellite imagery for a location using [ODC Virtual Products](https://docs.dea.ga.gov.au/notebooks/Frequently_used_code/Virtual_products.html)\n    * Convert each satellite image into a remote sensing water index (e.g. MNDWI)\n    * For each satellite image, model ocean tides into a tidal modelling grid based on exact time of image acquisition\n    * Interpolate tide heights into spatial extent of image stack\n    * Mask out high and low tide pixels by removing all observations acquired outside of 50 percent of the observed tidal range centered over mean sea level\n    * Combine tidally-masked data into annual median composites representing the most representative position of the shoreline at approximately mean sea level each year\n\n2. [`coastlines.vector`](coastlines/vector.py): This module conducts vector subpixel coastline extraction and rates of change statistics calculation. This analysis is processed on individual study area tiles to minimise peak memory usage.\n\n    * Apply morphological extraction algorithms to mask annual median composite rasters to a valid coastal region\n    * Extract shoreline vectors using subpixel waterline extraction ([Bishop-Taylor et al. 2019b](https://doi.org/10.3390/rs11242984))\n    * Compute rates of coastal change at every 30 m along the coastline using linear regression\n\n3. [`coastlines.continental`](coastlines/continental.py): This module combines tiled layers into seamless continental-scale vector files:\n\n    * Combines multiple output shoreline and rates of change statistics point vectors into single continental datasets\n    * Aggregates this data to produce moving window coastal change hotspot datasets that summarise coastal change at regional and continental scale.\n\n\n#### Jupyter notebooks\nAn interactive walk-through of each step of the tiled raster and vector DEA Coastlines workflow and the continental layer generation is provided in the following Jupyter Notebooks. These notebooks can be run on the [DEA Sandbox](https://app.sandbox.dea.ga.gov.au/) to assist in prototyping or troubleshooting:\n* [DEA Coastlines raster generation](notebooks/DEACoastlines_generation_raster.ipynb)\n* [DEA Coastlines vector generation](notebooks/DEACoastlines_generation_vector.ipynb)\n* [DEA Coastlines continental hotspots](notebooks/DEACoastlines_generation_continental.ipynb)\n\n### Running a DEA Coastlines analysis using the command-line interface (CLI)\n\nThese three modules have a command-line interface that can be used to automate each stage of the analysis. An example of using these tools is provided in the following Jupyter Notebook:\n* [DEA Coastlines generation using command line tools](notebooks/DEACoastlines_generation_CLI.ipynb)\n\nFor help using these command line tools, run:\n```\npython -m coastlines.raster --help\npython -m coastlines.vector --help\npython -m coastlines.continental --help\n```\n\n#### Analysis outputs\nFiles generated by DEA Coastlines are exported to the `data` directory.\n\nTemporary raster and vector outputs produced by [`coastlines.raster`](coastlines/raster.py) and [`coastlines.vector`](coastlines/vector.py) for each study area grid cell are exported to:\n```\ndata/interim/raster/{unique_analysis_name}/{unique_analysis_name}_{study_area_name}\ndata/interim/vector/{unique_analysis_name}/{unique_analysis_name}_{study_area_name}\n```\n\nOnce all study area grid cells have been processed, these are combined into a continental-scale output GeoPackage vector file and zipped ESRI Shapefiles using [`coastlines.continental`](coastlines/continental.py). These final outputs are exported to:\n```\ndata/processed/{unique_analysis_name}/coastlines_{continental_version}.gpkg\ndata/processed/{unique_analysis_name}/coastlines_{continental_version}.shp.zip\n```\n\n---\n### Data access\n\n#### Data download\n\nTo download DEA Coastlines data for the entire Australian coastline, visit the \"Access\" tab of the [Geoscience Australia DEA Coastlines product description](https://cmi.ga.gov.au/data-products/dea/581/dea-coastlines#access) and follow the instructions under \"Access notes\". Data is available in two formats:\n\n* GeoPackage (recommended): suitable for QGIS; includes built-in symbology for easier interpretation\n* ESRI Shapefiles: suitable for ArcMap and QGIS\n\n#### Interactive map\n\nTo explore DEA Coastlines on an interactive map, visit the [Digital Earth Australia Maps platform](https://maps.dea.ga.gov.au/story/DEACoastlines).\n\n![Zooming to annual rates of change and plotting chart in DEA Maps](https://data.dea.ga.gov.au/projects/coastlines/DEACoastLines_DEAMaps_1.gif)\n\n\n#### Loading DEA Coastlines data from the Web Feature Service (WFS) using Python\n\nDEA Coastlines data can be loaded directly in a Python script or Jupyter Notebook using the DEA Coastlines Web Feature Service (WFS) and `geopandas`:\n\n```\nimport geopandas as gpd\n\n# Specify bounding box\nymax, xmin = -33.65, 115.28\nymin, xmax = -33.66, 115.30\n\n# Set up WFS requests for annual shorelines \u0026 rates of change points\ndeacl_annualshorelines_wfs = f'https://geoserver.dea.ga.gov.au/geoserver/wfs?' \\\n                       f'service=WFS\u0026version=1.1.0\u0026request=GetFeature' \\\n                       f'\u0026typeName=dea:shorelines_annual\u0026maxFeatures=1000' \\\n                       f'\u0026bbox={ymin},{xmin},{ymax},{xmax},' \\\n                       f'urn:ogc:def:crs:EPSG:4326'\ndeacl_ratesofchange_wfs = f'https://geoserver.dea.ga.gov.au/geoserver/wfs?' \\\n                       f'service=WFS\u0026version=1.1.0\u0026request=GetFeature' \\\n                       f'\u0026typeName=dea:rates_of_change\u0026maxFeatures=1000' \\\n                       f'\u0026bbox={ymin},{xmin},{ymax},{xmax},' \\\n                       f'urn:ogc:def:crs:EPSG:4326'\n\n# Load DEA Coastlines data from WFS using geopandas\ndeacl_annualshorelines_gdf = gpd.read_file(deacl_annualshorelines_wfs)\ndeacl_ratesofchange_gdf = gpd.read_file(deacl_ratesofchange_wfs)\n\n# Ensure CRSs are set correctly\ndeacl_annualshorelines_gdf.crs = 'EPSG:3577'\ndeacl_ratesofchange_gdf.crs = 'EPSG:3577'\n\n# Optional: Keep only rates of change points with \"good\" certainty \n# (i.e. no poor quality flags)\ndeacl_ratesofchange_gdf = deacl_ratesofchange_gdf.query(\"certainty == 'good'\")\n```\n\n### Loading DEA Coastlines data from the Web Feature Service (WFS) using R\nDEA Coastlines data can be loaded directly into `R` using the DEA Coastlines Web Feature Service (WFS) and `sf`:\n\n```\nlibrary(magrittr)\nlibrary(glue)\nlibrary(sf)\n\n# Specify bounding box\nxmin = 115.28\nxmax = 115.30\nymin = -33.66\nymax = -33.65\n\n# Read in DEA Coastlines annual shoreline data, using `glue` to insert our bounding \n# box into the string, and `sf` to  load the spatial data from the Web Feature Service \n# and set the Coordinate Reference System to Australian Albers (EPSG:3577)\ndeacl_annualshorelines = \"https://geoserver.dea.ga.gov.au/geoserver/wfs?service=WFS\u0026version=1.1.0\u0026request=GetFeature\u0026typeName=dea:shorelines_annual\u0026maxFeatures=1000\u0026bbox={ymin},{xmin},{ymax},{xmax},urn:ogc:def:crs:EPSG:4326\" %\u003e% \n  glue::glue() %\u003e%\n  sf::read_sf() %\u003e% \n  sf::st_set_crs(3577)\n\n# Read in DEA Coastlines rates of change points\ndeacl_ratesofchange = \"https://geoserver.dea.ga.gov.au/geoserver/wfs?service=WFS\u0026version=1.1.0\u0026request=GetFeature\u0026typeName=dea:rates_of_change\u0026maxFeatures=1000\u0026bbox={ymin},{xmin},{ymax},{xmax},urn:ogc:def:crs:EPSG:4326\" %\u003e% \n  glue::glue() %\u003e%\n  sf::read_sf() %\u003e% \n  sf::st_set_crs(3577)\n```\n\n#### Jupyter Notebook\nAn [Introduction to DEA Coastlines](https://docs.dea.ga.gov.au/notebooks/DEA_datasets/DEA_Coastlines.html) Jupyter notebook providing additional useful tools for loading and analysing DEA Coastlines data can be found on the [DEA Notebooks repository](https://github.com/GeoscienceAustralia/dea-notebooks). This notebook is available on the interactive [DEA Sandbox learning and analysis environment](https://docs.dea.ga.gov.au/setup/Sandbox/sandbox.html) for easy access via a web browser.\n\n\n\n---\n## Credits\nThe [FES2014 global tidal model](https://www.aviso.altimetry.fr/es/data/products/auxiliary-products/global-tide-fes/description-fes2014.html) was produced by NOVELTIS, LEGOS, CLS Space Oceanography Division and CNES. It is distributed by AVISO, with support from CNES (http://www.aviso.altimetry.fr/).\n\n## References\n\u003e Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. (2021). Mapping Australia's dynamic coastline at mean sea level using three decades of Landsat imagery. _Remote Sensing of Environment_, 267, 112734. Available: https://doi.org/10.1016/j.rse.2021.112734\n\n\u003e Bishop-Taylor, R., Sagar, S., Lymburner, L., \u0026 Beaman, R. J. (2019a). Between the tides: Modelling the elevation of Australia's exposed intertidal zone at continental scale. _Estuarine, Coastal and Shelf Science_, 223, 115-128. Available: https://doi.org/10.1016/j.ecss.2019.03.006\n\n\u003e Bishop-Taylor, R., Sagar, S., Lymburner, L., Alam, I., \u0026 Sixsmith, J. (2019b). Sub-pixel waterline extraction: Characterising accuracy and sensitivity to indices and spectra. _Remote Sensing_, 11(24), 2984. Available: https://doi.org/10.3390/rs11242984\n\n\u003e Nanson, R., Bishop-Taylor, R., Sagar, S., Lymburner, L., (2022). Geomorphic insights into Australia's coastal change using a national dataset derived from the multi-decadal Landsat archive. _Estuarine, Coastal and Shelf Science_, 265, p.107712. 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