![](\"../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",
"* **Compatibility:** Notebook currently compatible with both the `NCI` and `DEA Sandbox` environments\n",
"* **Products used:** \n",
"[ga_ls8c_ard_3](https://explorer.dea.ga.gov.au/ga_ls8c_ard_3)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Background\n",
"\n",
"The rate at which cities and towns grow, or the urbanisation rate, is an important indicator of the sustainability of towns and cities. \n",
"Rapid, unplanned urbanisation can result in poor social, economic, and environmental outcomes due to inadequate and overburdened infrastructure and services creating congestion, worsening air pollution, and leading to a shortage of adequate housing.\n",
"\n",
"The first requirement for addressing the impacts of rapid urbanisation is to accurately and regularly monitor urban expansion in order to track urban development over time. \n",
"Earth Observation datasets, such as those available through the Digital Earth Australia platform provide a cost-effective and accurate means of mapping the urban extent of cities. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Description\n",
"\n",
"This notebook will use Landsat satellite images to examine the change in urban extent between a baseline period and a more recent period. \n",
"The difference in urban extent (area is square kilometres) between the two periods is calculated, along with a map highlighting the location of urban growth hotspots.\n",
"\n",
"This notebook conducts the following analysis:\n",
"\n",
"1. Load Landsat 8 data over the city/region of interest\n",
"2. Generate geomedian composites for the baseline and more recent year.\n",
"3. Calculate the Enhanced Normalised Difference Impervious Surfaces Index (ENDISI)\n",
"4. Threshold the ENDISI plots to delineate urban extent\n",
"5. Compare the urban extent in the baseline year to the more recent urban extent\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\n",
"Import Python packages that are used for the analysis."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/env/lib/python3.8/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.9.1-CAPI-1.14.2). Conversions between both will be slow.\n",
" warnings.warn(\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import datacube\n",
"import numpy as np\n",
"import xarray as xr\n",
"import matplotlib.pyplot as plt\n",
"from odc.algo import xr_geomedian\n",
"from matplotlib.patches import Patch\n",
"from matplotlib.colors import ListedColormap\n",
"\n",
"import sys\n",
"sys.path.insert(1, '../Tools/')\n",
"from dea_tools.datahandling import load_ard\n",
"from dea_tools.plotting import rgb, display_map\n",
"from dea_tools.bandindices import calculate_indices\n",
"from dea_tools.dask import create_local_dask_cluster\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set up a Dask cluster\n",
"\n",
"Dask can be used to better manage memory use down and conduct the analysis in parallel. \n",
"For an introduction to using Dask with Digital Earth Australia, see the [Dask notebook](../Beginners_guide/09_Parallel_processing_with_Dask.ipynb).\n",
"\n",
">**Note**: We recommend opening the Dask processing window to view the different computations that are being executed; to do this, see the *Dask dashboard in DEA* section of the [Dask notebook](../Beginners_guide/09_Parallel_processing_with_Dask.ipynb).\n",
"\n",
"To use Dask, set up the local computing cluster using the cell below."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"Cluster\n", "
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