BAS service specifications
This service takes in input a pair of EO data products in native format and estimates burnt areas using the Normalized Burn Ratio (NBR) index.
BAS requires in input native EO data. The EO data calibration is handled as pre-processing within the service.
Service Description
The Burned Area Severity Analysis (BAS) service takes in input optical EO data in native format and estimates the severity of burnt areas by using pairs of swir22
and nir
images.
The burn severity describes how the fire intensity affects the functioning of the ecosystem in the area that has been burnt. The observed effects often vary within the area and between different ecosystems (Keeley, 2009)1. The burn severity can also be described as the degree to which an area has been altered or disrupted by the fire2.
The Burned Area Severity Analysis methodology integrated into this service is based on the Normalized Burn Ratio (NBR) index (Key and Benson, 2006)3 and its derivatives: the delta Normalized Burn Ratio (dNBR) from Miller and Thode, 20144, and the Relativized Burn Ratio (RBR) from Parks et al., 20145. Such indexes are described below.
Normalized Burn Ratio and delta Normalized Burn Ratio
The Normalized Burn Ratio (NBR) index developed by Key and Benson, 20063 is defined as the normalized difference between the nir and swir22:
The delta Normalized Burn Ratio (dNBR) from Miller and Thode (2014) is defined as:
Relativized Burn Ratio
The Relativized Burn Ratio (RBR) from Parks et al.,20145 is defined as:
Also, the processor generates from the pair of input optical images a couple of Shortwave infrared (swir22, nir, red) RGB composites, for both pre- and post-event images.
Workflow
The first step of the BAS processing service is dedicated to the calibration of input EO data. Later it creates a collocated stack between the pre and post-event acquisitions of the assets red, nir and swir22.
This collocated stack covers the common area between the pre and the post-event acquisitions.
In case of inputs with different spatial resolution, the higher resolution is used.
The second step applies the band expressions listed below:
The third step generates an RGB composite with:
Warning
In this service water and cloud masking are not performed.
Note
Output products, such as the RBR index, can be masked in a following post-processing step (e.g. in QGIS using external available water masks).
Input
The BAS processor uses as input geocoded L1 or L2 products given in native format that include at least the following multispectral bands:
- red
- nir
- swir22
Examples of such missions are:
- Sentinel-2
- Landsat-8
- Worldview-3 (only if swir bands are given)
The pair is typically selected as the pre and post-event acquisition.
Parameters
The BAS service requires a specified number of mandatory and optional parameters. All service parameters are listed in the below Table 1.
Parameter | Description | Required | Default value |
---|---|---|---|
Pre-event optical EO data | Product reference to input pre-event optical EO data in native format including red, nir and swir22 MS bands | YES | |
Post-event optical EO data | Product reference to input post-event optical EO data in native format including red, nir and swir22 MS bands | YES | |
Area of Interest | Area of interest expressed in WKT | NO |
Table 1 - Service parameters for the BAS processor.
Output
The BAS processor provides as output the following products:
-
N=6 single-band TOA/BOA reflectance products from input EO data and their assets (red, nir, swir22).
-
Single band NBR index (pre-event)
-
Single band NBR index (post-event)
-
Single band dNBR index
-
Single band RBR index
-
BAS overview as 4 bands RGBA
-
SIR overviews for pre- and post-event as 4 bands RGBA, plus the three single-bands for Red-, Green-, and blue channels.
BAS Product Specifications can be found in the below tables.
Long Name | Normalized Burn Ratio pre-event |
Short Name | nbr_pre |
Description | Normalized difference between nir and swir22 assets of the pre-event acquisition |
Processing level | L1 / L2 |
Data Type | Float32 |
Band | Single |
Format | COG |
Projection | EPSG:4326 - WGS84 |
Units | N/A |
Valid Range | [-1 , 1] |
Fill Value | N/A |
Long Name | Normalized Burn Ratio post-event |
Short Name | nbr_post |
Description | Normalized difference between nir and swir22 assets of the post-event acquisition |
Processing level | L1 / L2 |
Data Type | Float32 |
Band | Single |
Format | COG |
Projection | EPSG:4326 - WGS84 |
Units | N/A |
Valid Range | [-1 , 1] |
Fill Value | N/A |
Long Name | Delta Normalized Burn Ratio |
Short Name | dnbr |
Description | Difference between the pre and post Normalized Burn Ratio |
Processing level | L1 / L2 |
Data Type | Float32 |
Band | Single |
Format | COG |
Projection | EPSG:4326 - WGS84 |
Units | N/A |
Valid Range | [-2 , 2] |
Fill Value | N/A |
Long Name | Relativized Burn Ratio |
Short Name | rbr |
Description | Relativized Burn Ratio using the pre and post Normalized Burn Ratio |
Processing level | L1 / L2 |
Data Type | Float32 |
Band | Single |
Format | COG |
Projection | EPSG:4326 - WGS84 |
Units | N/A |
Valid Range | [-2 , 2] |
Fill Value | N/A |
Long Name | Burned Area Severity overview |
Short Name | overview-bas |
Description | RGBA composite from a reclassification of RBR index using predefined color operations (from yellow to red shades), including transparency. |
Processing level | L1 / L2 |
Data Type | Unsigned 8-bit Integer (UInt 8) |
Band | 4 |
Format | COG |
Projection | EPSG:4326 - WGS84 |
Units | N/A |
Valid Range | [1 - 255] |
Fill Value | 0 |
Long Name | Shortwave Infrared RGB composite |
Short Name | overview-sir-pre, overview-sir-post |
Description | RGBA composite (Shortwave Infrared), including transparency, for both pre- and post-event images. |
Processing level | L1 / L2 |
Data Type | Unsigned 8-bit Integer (UInt 8) |
Band | 4 |
Format | COG |
Projection | EPSG:4326 - WGS84 |
Units | N/A |
Valid Range | [1 - 255] |
Fill Value | 0 |
References
-
Keeley, J. E. (2009), "Fire intensity, fire severity and burn severity: A brief review and suggested usage", International Journal of Wildland Fire, 18(1), 116–126. Available at: https://pubs.er.usgs.gov. ↩
-
Key, C. H. and Benson, N. C. (2006), “Landscape Assessment (LA): Sampling and Analysis Methods”, USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. FIREMON Fire effects monitoring and inventory System. Available at: https://www.fs.usda.gov. ↩↩
-
Miller J. and Thode A. (2007), “Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (RdNBR)”, Remote Sensing of Environment, 109, 66-80. DOI: 10.1016/j.rse.2006.12.006. ↩
-
Parks S. A., Dillon G. K., Miller C. A. (2014) "A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio" Remote Sens. 6, no. 3: 1827-1844. DOI: 10.3390/rs6031827. ↩↩