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DISASTERS_202501_FIRE_CA/2501_sentinel2_dnbr (ImageServer)

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Service Description:

Date of Image(s):

12/18/2024; UTC: 183709

1/12/25; UTC: 183731

Summary:

These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input imagers were generated from the ESA’s Sentinel-2’s Multi-Spectral imager. They were created from the Level 2A products which have been atmospherically corrected and orthorectified.

NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. For Sentinel 2, NIR = Band 8 and SWIR = Band 12. Using these definitions, the 20-meter resolution Band 8 and Band 12 images were used to generate these products. Sentinel-2 NBR methods: https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/nbr/

Finally dNBR is simply the difference between the pre-fire NBR and the post-fire NBR. Thus, we calculated dNBR as dNBR = NBR(12/18/2024) - NBR(01/12/2025).

Suggested Use:

NBR is commonly used as a proxy to indicate areas which have charred vegetation. Darker areas (more negative values) in the NBR image more strongly represent the presence of burned vegetation. Since the dNBR considers the condition of the scene before the fire occurred, the resulting value has been used as a proxy for burn severity. More positive values in the dNBR analysis represent a proxy for greater burn severity. Negative values in dNBR represent a re-greening of or growth of vegetation in between pre and post imagery.

The use of this dNBR product as a quantitative metric of burn severity of the Los Angeles Fires at the time of posting this dataset should be strongly caveated. This is due to several dNBR Limitations:

The spectral band selections used in the NBR and dNBR calculations, and the implication of changes observed following fire in those wavelengths, primarily pertain to how vegetation spectral signatures change in NIR and SWIR wavelengths following charring. Because of this, dNBR may not accurately describe burned surfaces that are not vegetation (e.g. human built infrastructure).

This dataset has not been validated by independent burn severity assessments.

The degree to which dNBR is accurately determined depends on careful selection of pre and post event imagery. An effort was made to use the highest quality imagery (i.e. cloud free) with the shortest time difference possible in the image pair; however, it is unknown at the time of this posting how selection of different pre/post image pairs could affect the derived dNBR values.

Satellite/Sensor:

ESA Copernicus Sentinel-2

Resolution:

20 m in infrared bands

Credits:

Copernicus Sentinel-2 data was downloaded from the Sentinel-2 data explorer (https://dataspace.copernicus.eu/explore-data/data-collections/sentinel-data/sentinel-2)

Data processed by Kyle Kabasares (ARC; kyle.k.kabasares@nasa.gov)

References:

Normalize Burn Ratio theory: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio

Service URL:

https://gis.earthdata.nasa.gov/portal/home/item.html?id=3bc872886af24b799acd2ef881691a6a

WMS Endpoint:

https://gis.earthdata.nasa.gov/gis05/services/DISASTERS_202501_FIRE_CA/SENTINEL2_DNBR/ImageServer/WMSServer



Name: DISASTERS_202501_FIRE_CA/2501_sentinel2_dnbr

Description:

Date of Image(s):

12/18/2024; UTC: 183709

1/12/25; UTC: 183731

Summary:

These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input imagers were generated from the ESA’s Sentinel-2’s Multi-Spectral imager. They were created from the Level 2A products which have been atmospherically corrected and orthorectified.

NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. For Sentinel 2, NIR = Band 8 and SWIR = Band 12. Using these definitions, the 20-meter resolution Band 8 and Band 12 images were used to generate these products. Sentinel-2 NBR methods: https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/nbr/

Finally dNBR is simply the difference between the pre-fire NBR and the post-fire NBR. Thus, we calculated dNBR as dNBR = NBR(12/18/2024) - NBR(01/12/2025).

Suggested Use:

NBR is commonly used as a proxy to indicate areas which have charred vegetation. Darker areas (more negative values) in the NBR image more strongly represent the presence of burned vegetation. Since the dNBR considers the condition of the scene before the fire occurred, the resulting value has been used as a proxy for burn severity. More positive values in the dNBR analysis represent a proxy for greater burn severity. Negative values in dNBR represent a re-greening of or growth of vegetation in between pre and post imagery.

The use of this dNBR product as a quantitative metric of burn severity of the Los Angeles Fires at the time of posting this dataset should be strongly caveated. This is due to several dNBR Limitations:

The spectral band selections used in the NBR and dNBR calculations, and the implication of changes observed following fire in those wavelengths, primarily pertain to how vegetation spectral signatures change in NIR and SWIR wavelengths following charring. Because of this, dNBR may not accurately describe burned surfaces that are not vegetation (e.g. human built infrastructure).

This dataset has not been validated by independent burn severity assessments.

The degree to which dNBR is accurately determined depends on careful selection of pre and post event imagery. An effort was made to use the highest quality imagery (i.e. cloud free) with the shortest time difference possible in the image pair; however, it is unknown at the time of this posting how selection of different pre/post image pairs could affect the derived dNBR values.

Satellite/Sensor:

ESA Copernicus Sentinel-2

Resolution:

20 m in infrared bands

Credits:

Copernicus Sentinel-2 data was downloaded from the Sentinel-2 data explorer (https://dataspace.copernicus.eu/explore-data/data-collections/sentinel-data/sentinel-2)

Data processed by Kyle Kabasares (ARC; kyle.k.kabasares@nasa.gov)

References:

Normalize Burn Ratio theory: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio

Service URL:

https://gis.earthdata.nasa.gov/portal/home/item.html?id=3bc872886af24b799acd2ef881691a6a

WMS Endpoint:

https://gis.earthdata.nasa.gov/gis05/services/DISASTERS_202501_FIRE_CA/SENTINEL2_DNBR/ImageServer/WMSServer



Single Fused Map Cache: false

Extent: Initial Extent: Full Extent: Pixel Size X: 1.7966305682390478E-4

Pixel Size Y: 1.7966305682390478E-4

Band Count: 1

Pixel Type: F64

RasterFunction Infos: {"rasterFunctionInfos": [{ "name": "None", "description": "", "help": "" }]}

Mensuration Capabilities:

Inspection Capabilities:

Has Histograms: true

Has Colormap: false

Has Multi Dimensions : false

Rendering Rule:

Min Scale: 0

Max Scale: 0

Resampling: false

Copyright Text: Copernicus Sentinel-2, Kyle Kabasares (ARC; kyle.k.kabasares@nasa.gov)

Service Data Type: esriImageServiceDataTypeGeneric

Min Values: -0.633372838459472

Max Values: 0.6502688283835241

Mean Values: 0.02079076646999605

Standard Deviation Values: 0.08100147412259667

Object ID Field: OBJECTID

Fields: Default Mosaic Method: Northwest

Allowed Mosaic Methods: NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None

SortField:

SortValue: null

Mosaic Operator: First

Default Compression Quality: 75

Default Resampling Method: Bilinear

Max Record Count: 1000

Max Image Height: 4100

Max Image Width: 15000

Max Download Image Count: 20

Max Mosaic Image Count: 20

Allow Raster Function: true

Allow Copy: true

Allow Analysis: true

Allow Compute TiePoints: false

Supports Statistics: true

Supports Advanced Queries: true

Use StandardizedQueries: true

Raster Type Infos: Has Raster Attribute Table: false

Edit Fields Info: null

Ownership Based AccessControl For Rasters: null

Child Resources:   Info   Histograms   Statistics   Key Properties   Legend   Raster Function Infos

Supported Operations:   Export Image   Query   Identify   Measure   Compute Histograms   Compute Statistics Histograms   Get Samples   Compute Class Statistics   Query GPS Info   Find Images   Image to Map   Map to Image   Measure from Image   Image to Map Multiray   Query Boundary   Compute Pixel Location   Compute Angles   Validate   Project