RDW_Wildfire/BAER_SoilBurnSeverityClassification (ImageServer)

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Service Description: The USDA Forest Service Burned Area Emergency Response (BAER) Imagery Support program produces geospatial data of post-fire burn severities using standardized change detection methods based on Landsat, Sentinel-2, or similar multispectral satellite imagery. The BAER Imagery Support program provides an initial Burned Area Reflectance Classification (BARC) product to BAER teams. The BARC is a preliminary vegetation burn severity product which assesses severity based on the magnitude of detectable change to above ground biomass. The BAER teams collect field data on post-fire soil conditions that is used with the BARC to create the final soil burn severity (SBS) classification product for each BAER assessment. The SBS data is preferred for sharing publicly since it has been validated by the BAER team in the field. BAER teams use the soil burn severity product to assess the need for and application of emergency soil stabilization treatments following a wildfire. National mosaics compiling SBS classification products from individual BAER assessments are prepared on an annual basis. This SBS dataset is a compilation of final burn severity data products created by USDA Forest Service BAER teams from 2017 through the most recent completed fire year. It is a thematic raster dataset with four burn severity classes: unburned to very low, low, moderate, and high. The spatial resolution is variable based on the imagery used to create the initial BARC product and subsequent processing by the BAER teams. Datasets are typically 30m for Landsat assessments and 20m for Sentinel-2 assessments.

Name: RDW_Wildfire/BAER_SoilBurnSeverityClassification

Description: The USDA Forest Service Burned Area Emergency Response (BAER) Imagery Support program produces geospatial data of post-fire burn severities using standardized change detection methods based on Landsat, Sentinel-2, or similar multispectral satellite imagery. The BAER Imagery Support program provides an initial Burned Area Reflectance Classification (BARC) product to BAER teams. The BARC is a preliminary vegetation burn severity product which assesses severity based on the magnitude of detectable change to above ground biomass. The BAER teams collect field data on post-fire soil conditions that is used with the BARC to create the final soil burn severity (SBS) classification product for each BAER assessment. The SBS data is preferred for sharing publicly since it has been validated by the BAER team in the field. BAER teams use the soil burn severity product to assess the need for and application of emergency soil stabilization treatments following a wildfire. National mosaics compiling SBS classification products from individual BAER assessments are prepared on an annual basis. This SBS dataset is a compilation of final burn severity data products created by USDA Forest Service BAER teams from 2017 through the most recent completed fire year. It is a thematic raster dataset with four burn severity classes: unburned to very low, low, moderate, and high. The spatial resolution is variable based on the imagery used to create the initial BARC product and subsequent processing by the BAER teams. Datasets are typically 30m for Landsat assessments and 20m for Sentinel-2 assessments.

Single Fused Map Cache: false

Extent:

Initial Extent:

Full Extent:

Time Info:

[2017/01/01 00:00:00 UTC, 2024/01/01 00:00:00 UTC]

Pixel Size X: 8.2

Pixel Size Y: 8.2

Band Count: 1

Pixel Type: U8

RasterFunction Infos: {"rasterFunctionInfos": [ { "name": "BAER SBS Colormap", "description": "RDW BAER Soil Burn Severity Color", "help": "" }, { "name": "None", "description": "Make a Raster or Raster Dataset into a Function Raster Dataset.", "help": "" } ]}

Mensuration Capabilities: Basic

Inspection Capabilities:

Has Histograms: true

Has Colormap: false

Has Multi Dimensions : false

Rendering Rule:

Min Scale: 0

Max Scale: 0

Resampling: false

Copyright Text: BAER Imagery Support Program: baerimagery@fs.fed.us

Service Data Type: esriImageServiceDataTypeThematic

Min Values: 0

Max Values: 255

Mean Values: 68.08969816767308

Standard Deviation Values: 111.61917254589129

Object ID Field: OBJECTID

Fields:

OBJECTID ( type: esriFieldTypeOID, alias: OBJECTID )
Shape ( type: esriFieldTypeGeometry, alias: Shape )
Name ( type: esriFieldTypeString, alias: Name, length: 200 )
MinPS ( type: esriFieldTypeDouble, alias: MinPS )
MaxPS ( type: esriFieldTypeDouble, alias: MaxPS )
LowPS ( type: esriFieldTypeDouble, alias: LowPS )
HighPS ( type: esriFieldTypeDouble, alias: HighPS )
Category ( type: esriFieldTypeInteger, alias: Category , Coded Values: [0: Unknown] , [1: Primary] , [2: Overview] , ...6 more... )
Tag ( type: esriFieldTypeString, alias: Tag, length: 100 )
GroupName ( type: esriFieldTypeString, alias: GroupName, length: 100 )
ProductName ( type: esriFieldTypeString, alias: ProductName, length: 100 )
CenterX ( type: esriFieldTypeDouble, alias: CenterX )
CenterY ( type: esriFieldTypeDouble, alias: CenterY )
ZOrder ( type: esriFieldTypeInteger, alias: ZOrder )
Shape_Length ( type: esriFieldTypeDouble, alias: Shape_Length )
Shape_Area ( type: esriFieldTypeDouble, alias: Shape_Area )
Year ( type: esriFieldTypeSmallInteger, alias: Year )

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: Nearest

Max Record Count: 1000

Max Image Height: 50000

Max Image Width: 50000

Max Download Image Count: 20

Max Mosaic Image Count: 80

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