![](\"../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_s2am_ard_3](https://explorer.dea.ga.gov.au/ga_s2am_ard_3), \n",
"[ga_s2bm_ard_3](https://explorer.dea.ga.gov.au/ga_s2bm_ard_3), \n",
"[ga_s2cm_ard_3](https://explorer.dea.ga.gov.au/ga_s2cm_ard_3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Background\n",
"Effective management of Australia's forests is critical for balancing environmental protection and sustainable growth of the industry.\n",
"Methods for detecting meaningful and significant change in forests are important for those who manage and monitor large areas of forest.\n",
"\n",
"On-the-ground monitoring can be expensive and time-consuming, especially when forests are in difficult-to-navigate terrain.\n",
"Aerial photography and LiDAR can provide detailed information about forests, but are often extremely expensive to acquire, even over small areas.\n",
"\n",
"### Sentinel-2 use case\n",
"Satellite imagery from the [EU Copernicus Sentinel-2 mission](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) is freely available and has a revisit time over Australia of ~5 days.\n",
"Its 10 metre resolution makes it perfect for monitoring fine changes over very large areas of land.\n",
"The archive of Sentinel-2 data stretches back to 2015, meaning that there is a good amount of data for change detection, allowing us to average out or focus on seasonal changes.\n",
"\n",
"## Description\n",
"In this example, we measure the presence of vegetation from Sentinel-2 imagery and apply a hypothesis test to identify areas of significant change (along with the direction of the change).\n",
"The worked example takes users through the code required to do the following:\n",
"\n",
"1. Load cloud-free Sentinel-2 images for an area of interest\n",
"2. Compute an index to highlight presence of vegetation (NDVI)\n",
"3. Apply a statistical hypothesis test to find areas of significant change\n",
"4. Visualise the statistically significant areas.\n",
"\n",
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting started\n",
"\n",
"**To run this analysis**, run all the cells in the notebook, starting with the \"Load packages\" cell.\n",
"\n",
"**After finishing the analysis**, return to the \"Analysis parameters\" cell, modify some values (e.g. choose a different location or time period to analyse) and re-run the analysis.\n",
"There are additional instructions on modifying the notebook at the end."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load packages\n",
"Load key Python packages and any supporting functions for the analysis."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import datacube\n",
"import numpy as np\n",
"import xarray as xr\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"from datacube.utils.cog import write_cog\n",
"from datacube.utils.geometry import CRS\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",
"To use Dask, set up the local computing cluster by running the cell below."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [
{
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