![](\"../Supplementary_data/dea_logo.jpg\"){kind=logo}
\n",
+ "\n",
+ "* **[Sign up to the DEA Sandbox](https://app.sandbox.dea.ga.gov.au/)** to run this notebook interactively from a browser\n",
+ "* **Compatability:** Notebook currently compatible with the `DEA Sandbox` environment\n",
+ "* **Products used:** \n",
+ "[ga_ls8cls9c_gm_cyear_3](https://explorer.dea.ga.gov.au/ga_ls8cls9c_gm_cyear_3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Background\n",
+ "\n",
+ "Using the DEA function `xr_animation`, which is based on `matplotlib.animation` and `xarray`, we can take a time series of Digital Earth Australia's [Geomedian](https://github.com/GeoscienceAustralia/dea-notebooks/blob/develop/DEA_products/DEA_GeoMAD.ipynb) annual satellite imagery composites and export a visually appealing time series animation for the **entire Australian continent**.\n",
+ "\n",
+ "Animations can be a powerful method for visualising change in the landscape across time using satellite imagery.\n",
+ "Satellite data from Digital Earth Australia is an ideal subject for animations as it has been georeferenced, processed to analysis-ready surface reflectance, and stacked into a spatio-temporal 'datacube', allowing landscape conditions to be extracted and visualised consistently across time.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Description\n",
+ "This notebook demonstrates how to:\n",
+ "\n",
+ "1. Import a time series of Landsat 8 and 9 geomedians\n",
+ "2. Export true colour animations\n",
+ "\n",
+ "***"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting started\n",
+ "To run this analysis, run all the cells in the notebook, starting with the \"Load packages\" cell. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load packages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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