![](\"../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 compatible with 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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Background\n",
"\n",
"### Normalized Burn Ratio\n",
"\n",
"The Normalized Burn Ratio (NBR) is an index that uses the differences in the way healthy green vegetation and burnt vegetation reflect light to find burnt area. \n",
"It is calculated using the following Sentinel-2 bands: Near Infrared/Band 8 and Shortwave Infrared/Band 12. The equation is defined below: \n",
"\n",
"\\begin{equation}\n",
"NBR = \\frac{(NIR - SWIR)}{(NIR + SWIR)}\n",
"\\end{equation}\n",
"\n",
"NBR returns values between -1 and 1. \n",
"**Healthy green vegetation will have a high NBR value while burnt vegetation will have a low value**. \n",
"Areas of dry, brown vegetation or bare soil will also return lower NBR values than green vegetation. \n",
"\n",
"### Delta Normalized Burn Ratio\n",
"\n",
"Change in Normalized Burn Ratio - also called Delta Normalized Burn Ratio (dNBR) - is calculated by subtracting the post-fire NBR value from the baseline NBR value as defined in this equation:\n",
"\n",
"\\begin{equation}\n",
"dNBR = NBR_{baseline} - NBR_{post fire}\n",
"\\end{equation}\n",
"\n",
"The dNBR value can be more useful than the NBR alone to determine what is burnt as it shows change from the baseline state. \n",
"**A burnt area will have a positive dNBR value** while an unburnt area will have a negative dNBR value or a value close to zero.\n",
"\n",
"dNBR can also be used to describe burn severity (although this notebook does not look at burn severity). \n",
"A higher severity fire will burn more vegetation, resulting in a higher dNBR. More information on NBR, dNBR and using it to measure burn severity can be found [on the UN-SPIDER knowledge portal](http://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio).\n",
"\n",
"### Defining Burnt From Unburnt Areas\n",
"\n",
"[Rahman et al. 2018](https://doi.org/10.1109/IGARSS.2018.8518449) found a dNBR threshold value of +0.1 appropriate for differentiating burnt from unburnt areas when using Sentinel-2. \n",
"However, some exploration with different threshold levels may be needed to get a good result in areas with different vegetation types. \n",
"\n",
"In the example presented in this notebook, which covers part of the Clyde Mountain fire in the area north of Batemans Bay, the fire occurred in heavily forested area, which returns a very strong dNBR result. \n",
"Using +0.1 as a threshold here results in many false positives being picked up in the unburnt urban and forest areas where vegetation drying has occurred prior to the fire. \n",
"A much more conservative threshold here of +0.3 produces a better result. \n",
"Keep in mind the limitations of remote sensing and that in an ideal situation ground truth data collected in the location of interest would be used to assist in selecting a threshold.\n",
"\n",
"Some care should also be taken when interpreting results as a number of possible false positives can return a positive dNBR result:\n",
"\n",
"* A lot of smoke in the post burn image can interfere with the dNBR value\n",
"* Areas that have been cleared of vegetation by some other means (logging, harvesting, and landslides) towards the end of the baseline period may incorrectly show up as burnt\n",
"* Drying out of bright green vegetation such as grasses. \n",
"If a fire event has been preceded by a rapid drying out of vegetation this can result in a low positive dNBR value in areas that have not burnt.\n"
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Description\n",
"\n",
"This notebook calculates the change in Normalized Burn Ratio between a baseline composite image of the pre-fire condition of a selected area and a post-fire event image, in order to find burnt area extent. \n",
" \n",
"The user can change the location over which this notebook is run and specify a different date between which pre and post fire condition will be compared. \n",
"The length of time over which the baseline composite image will be generated can be specified as 3, 6 or 12 months. \n",
"The code in this notebook will automatically generate the composite image over the set length of time using Sentinel-2 data..\n",
"\n",
"The notebook contains the following steps:\n",
"\n",
"1. Select a location for the analysis\n",
"2. Define fire event date and length of composite image\n",
"3. Load all baseline data\n",
"4. Generate Normalized Burn Ratio for baseline period\n",
"5. Load post-fire data\n",
"6. Generate Normalized Burn Ratio for post fire image\n",
"7. Calculate Delta Normalized Burn Ratio\n",
"8. Apply threshold to Delta Normalized Burn Ratio\n",
"9. Calculate the area burnt\n",
"10. Export results as a GeoTIFF\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",
"Import Python packages that are used for the analysis."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
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