![](\"../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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Background\n",
"Phenology is the study of plant and animal life cycles in the context of the seasons.\n",
"It can be useful in understanding the life cycle trends of crops and how the growing seasons are affected by changes in climate.\n",
"For more information, see the [USGS page on deriving phenology from NDVI time-series](https://www.usgs.gov/land-resources/eros/phenology/science/deriving-phenological-metrics-ndvi?qt-science_center_objects=0#qt-science_center_objects)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Description\n",
"\n",
"This notebook demonstrates how to calculate vegetation phenology statistics using the DEA function [xr_phenology](../Tools/dea_tools/temporal.py). To detect changes in plant life using satellite images, the script uses either the **Normalized Difference Vegetation Index (NDVI)** or the **Enhanced Vegetation Index (EVI)**, which are common proxies for vegetation growth and health.\n",
"\n",
"The outputs of this notebook can be used to assess spatio-temporal differences in the growing seasons of agriculture fields or native vegetation.\n",
"\n",
"This notebook demonstrates the following steps:\n",
"\n",
"1. Load cloud-masked Sentinel 2 data for an area of interest.\n",
"2. Calculate a vegetation proxy index (NDVI or EVI).\n",
"3. Generate a zonal time series of vegetation health\n",
"4. Complete and smooth the vegetation timeseries to remove gaps and noise.\n",
"5. Calculate phenology statistics on a simple 1D vegetation time series\n",
"6. Calculate per-pixel phenology statistics\n",
"7. Optional: Calculating generic temporal statistics usng the `hdstats` library\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. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load packages\n",
"Load key Python packages and supporting functions for the analysis."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
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