<?xml version="1.0" encoding="UTF-8"?><metadata xml:lang="en">
<Esri>
<CreaDate>20250912</CreaDate>
<CreaTime>10122600</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>FALSE</SyncOnce>
<DataProperties>
<itemProps>
<itemName Sync="FALSE">USFS_EDW_RAVG_CompositeBurnIndex</itemName>
<nativeExtBox>
<westBL Sync="TRUE">-14125823.192200</westBL>
<eastBL Sync="TRUE">-8078393.192200</eastBL>
<southBL Sync="TRUE">3195967.621400</southBL>
<northBL Sync="TRUE">6670777.621400</northBL>
<exTypeCode Sync="TRUE">1</exTypeCode>
</nativeExtBox>
<imsContentType export="False"/>
</itemProps>
<coordRef>
<type Sync="TRUE">Projected</type>
<geogcsn Sync="TRUE">GCS_WGS_1984</geogcsn>
<csUnits Sync="TRUE">Linear Unit: Meter (1.000000)</csUnits>
<projcsn Sync="TRUE">WGS_1984_Web_Mercator_Auxiliary_Sphere</projcsn>
<peXml Sync="TRUE">&lt;ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.5.0'&gt;&lt;WKT&gt;PROJCS[&amp;quot;WGS_1984_Web_Mercator_Auxiliary_Sphere&amp;quot;,GEOGCS[&amp;quot;GCS_WGS_1984&amp;quot;,DATUM[&amp;quot;D_WGS_1984&amp;quot;,SPHEROID[&amp;quot;WGS_1984&amp;quot;,6378137.0,298.257223563]],PRIMEM[&amp;quot;Greenwich&amp;quot;,0.0],UNIT[&amp;quot;Degree&amp;quot;,0.0174532925199433]],PROJECTION[&amp;quot;Mercator_Auxiliary_Sphere&amp;quot;],PARAMETER[&amp;quot;False_Easting&amp;quot;,0.0],PARAMETER[&amp;quot;False_Northing&amp;quot;,0.0],PARAMETER[&amp;quot;Central_Meridian&amp;quot;,0.0],PARAMETER[&amp;quot;Standard_Parallel_1&amp;quot;,0.0],PARAMETER[&amp;quot;Auxiliary_Sphere_Type&amp;quot;,0.0],UNIT[&amp;quot;Meter&amp;quot;,1.0],AUTHORITY[&amp;quot;EPSG&amp;quot;,3857]]&lt;/WKT&gt;&lt;XOrigin&gt;-20037700&lt;/XOrigin&gt;&lt;YOrigin&gt;-30241100&lt;/YOrigin&gt;&lt;XYScale&gt;148923141.92838538&lt;/XYScale&gt;&lt;ZOrigin&gt;-100000&lt;/ZOrigin&gt;&lt;ZScale&gt;10000&lt;/ZScale&gt;&lt;MOrigin&gt;-100000&lt;/MOrigin&gt;&lt;MScale&gt;10000&lt;/MScale&gt;&lt;XYTolerance&gt;0.001&lt;/XYTolerance&gt;&lt;ZTolerance&gt;0.001&lt;/ZTolerance&gt;&lt;MTolerance&gt;0.001&lt;/MTolerance&gt;&lt;HighPrecision&gt;true&lt;/HighPrecision&gt;&lt;WKID&gt;102100&lt;/WKID&gt;&lt;LatestWKID&gt;3857&lt;/LatestWKID&gt;&lt;/ProjectedCoordinateSystem&gt;</peXml>
</coordRef>
<RasterProperties>
<General>
<PixelDepth Sync="TRUE">8</PixelDepth>
<HasColormap Sync="TRUE">FALSE</HasColormap>
<CompressionType Sync="TRUE">None</CompressionType>
<NumBands Sync="TRUE">1</NumBands>
<Format Sync="TRUE">AMD</Format>
<HasPyramids Sync="TRUE">TRUE</HasPyramids>
<SourceType Sync="TRUE">continuous</SourceType>
<PixelType Sync="TRUE">unsigned integer</PixelType>
<NoDataValue Sync="TRUE"/>
</General>
<Properties>
<MaxImageHeight Sync="TRUE">100000</MaxImageHeight>
<MaxImageWidth Sync="TRUE">100000</MaxImageWidth>
<MaxRecordCount Sync="TRUE">1000</MaxRecordCount>
<MaxDownloadImageCount Sync="TRUE">20</MaxDownloadImageCount>
<MaxDownloadSizeLimit Sync="TRUE">2048</MaxDownloadSizeLimit>
<MaxMosaicImageCount Sync="TRUE">100</MaxMosaicImageCount>
<DefaultCompressionQuality Sync="TRUE">75</DefaultCompressionQuality>
<DefaultCompressionTolerance Sync="TRUE">0.01</DefaultCompressionTolerance>
<BlendWidth Sync="TRUE">10</BlendWidth>
<ViewpointSpacingX Sync="TRUE">600</ViewpointSpacingX>
<ViewpointSpacingY Sync="TRUE">300</ViewpointSpacingY>
<AllowedItemMetadata Sync="TRUE">Basic</AllowedItemMetadata>
<DefaultResamplingMethod Sync="TRUE">0</DefaultResamplingMethod>
<AllowedMosaicMethods Sync="TRUE">NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None</AllowedMosaicMethods>
<AllowedCompressions Sync="TRUE">None,JPEG,LZ77,LERC</AllowedCompressions>
<AllowedMensurationCapabilities Sync="TRUE">Basic</AllowedMensurationCapabilities>
<AvailableMensurationCapabilities Sync="TRUE">None,Basic,Base-Top Height,Top-Top Shadow Height,Base-Top Shadow Height,3D</AvailableMensurationCapabilities>
<ClipToBoundary Sync="TRUE">-1</ClipToBoundary>
<FootprintMayContainNoData Sync="TRUE">-1</FootprintMayContainNoData>
<CellSizeTolerance Sync="TRUE">0.8</CellSizeTolerance>
<MinimumPixelContribution Sync="TRUE">1</MinimumPixelContribution>
<AvailableMosaicMethods Sync="TRUE">NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None</AvailableMosaicMethods>
<AvailableCompressionMethods Sync="TRUE">None,JPEG,LZ77,LERC</AvailableCompressionMethods>
<AvailableItemMetadataLevels Sync="TRUE">None,Basic,Full</AvailableItemMetadataLevels>
<BandDefinitionKeyword Sync="TRUE">NONE</BandDefinitionKeyword>
<_docversion_ Sync="TRUE">10</_docversion_>
<AllowedFields Sync="TRUE">name,minps,maxps,lowps,highps,tag,groupname,productname,centerx,centery,zorder,st_area(shape),st_perimeter(shape),beginyear,endyear</AllowedFields>
<MaxCellSize Sync="TRUE">153600</MaxCellSize>
<LowCellSize Sync="TRUE">30</LowCellSize>
<HighCellSize Sync="TRUE">15360</HighCellSize>
<TimeIntervalUnits Sync="TRUE">Years</TimeIntervalUnits>
<AvailableFields Sync="TRUE">name,minps,maxps,lowps,highps,tag,groupname,productname,centerx,centery,zorder,beginyear,endyear,st_area(shape),st_perimeter(shape)</AvailableFields>
<SortableFields Sync="TRUE">minps,maxps,lowps,highps,centerx,centery,zorder,st_area(shape),st_perimeter(shape),beginyear,endyear</SortableFields>
<SortAscending Sync="TRUE">-1</SortAscending>
<MosaicOperator Sync="TRUE">1</MosaicOperator>
<BlendWidthUnits Sync="TRUE">1</BlendWidthUnits>
<ClipToFootprint Sync="TRUE">0</ClipToFootprint>
<ClipOverviewsToFootprint Sync="TRUE">0</ClipOverviewsToFootprint>
<HasDodgingTable Sync="TRUE">0</HasDodgingTable>
<ApplyColorCorrection Sync="TRUE">0</ApplyColorCorrection>
<UseTime Sync="TRUE">-1</UseTime>
<StartTimeFieldName Sync="TRUE">beginyear</StartTimeFieldName>
<EndTimeFieldName Sync="TRUE">endyear</EndTimeFieldName>
<TimeValueFormat Sync="TRUE">YYYY</TimeValueFormat>
<UseRange Sync="TRUE">0</UseRange>
<TimeInterval Sync="TRUE"/>
</Properties>
<Functions Sync="TRUE">
<Function Description="Performs on-the-fly mosaic on a raster catalog." Name="Mosaic Function"/>
</Functions>
</RasterProperties>
</DataProperties>
<SyncDate>20250915</SyncDate>
<SyncTime>09013300</SyncTime>
<ModDate>20250915</ModDate>
<ModTime>9053800</ModTime>
<scaleRange>
<minScale>150000000</minScale>
<maxScale>5000</maxScale>
</scaleRange>
<ArcGISProfile>ISO19139</ArcGISProfile>
</Esri>
<mdLang>
<languageCode Sync="TRUE" value="eng"/>
<countryCode Sync="TRUE" value="USA"/>
</mdLang>
<mdChar>
<CharSetCd value="004"/>
</mdChar>
<mdHrLv>
<ScopeCd value="005"/>
</mdHrLv>
<mdContact>
<rpOrgName>USFS Chief Information Office, Enterprise Data Warehouse</rpOrgName>
<rpCntInfo>
<cntPhone>
<voiceNum>Please send an e-mail to the address below.</voiceNum>
</cntPhone>
<cntAddress addressType="physical">
<city>Washington</city>
<adminArea>DC</adminArea>
<postCode>20250</postCode>
<eMailAdd>data@fs.fed.us</eMailAdd>
</cntAddress>
</rpCntInfo>
<role>
<RoleCd value="007"/>
</role>
</mdContact>
<mdDateSt Sync="TRUE">20250915</mdDateSt>
<mdStanName>ArcGIS Metadata</mdStanName>
<mdStanVer>1.0</mdStanVer>
<distInfo>
<distTranOps>
<onLineSrc>
<linkage>withheld</linkage>
</onLineSrc>
</distTranOps>
</distInfo>
<dataIdInfo>
<idCitation>
<resTitle>Rapid Assessment of Vegetation Condition after Wildfire (RAVG) Composite Burn Index</resTitle>
<date>
<pubDate>2022-02-01</pubDate>
</date>
<citRespParty>
<rpOrgName>U.S. Forest Service</rpOrgName>
<role>
<RoleCd value="006"/>
</role>
</citRespParty>
<presForm>
<PresFormCd value="raster dataset"/>
</presForm>
<presForm>
<fgdcGeoform>raster dataset</fgdcGeoform>
</presForm>
</idCitation>
<idAbs>&lt;DIV STYLE="text-align:Left;"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ("initial assessments"). Late-season fires, however, may be deferred until the following spring or summer ("extended assessments"). National mosaics of each thematic product are prepared annually. The associated burned area perimeters are available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php). Assessment type (initial or extended assessment) for each fire is included as an attribute in the perimeter dataset.&lt;/SPAN&gt;&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</idAbs>
<idPurp>RAVG data are produced to assist in post-fire vegetation management planning. They are intended to enhance decision-making capabilities and reduce planning and implementation costs associated with post-fire vegetation management. The primary benefit is cost-effective, efficient, and precise identification of potential areas of resource concern following wildfire. RAVG complements the Burned Area Emergency Response (BAER) Imagery Support program--which provides information integral to determining fire effects on soils--by providing information about fire effects on existing vegetation. RAVG analysis provides a first approximation of areas that may require reforestation treatments after a fire in order to re-establish forest cover and restore associated ecosystem services. This initial approximation may be followed by site-specific diagnosis and development of a silvicultural prescription to more precisely identify reforestation needs.</idPurp>
<idStatus>
<ProgCd value="007"/>
</idStatus>
<idPoC>
<rpOrgName>U.S. Forest Service</rpOrgName>
<rpCntInfo>
<cntPhone>
<voiceNum>(801) 975-3800</voiceNum>
</cntPhone>
<cntAddress addressType="physical">
<city>Washington</city>
<adminArea>DC</adminArea>
<postCode>20250</postCode>
<eMailAdd>SM.FS.PostFireRAVG@usda.gov</eMailAdd>
</cntAddress>
<cntInstr>Questions or comments may be submitted by phone, by email or via the RAVG website: &lt;https://fsapps.nwcg.gov/ravg/contact&gt;</cntInstr>
</rpCntInfo>
<role>
<RoleCd value="007"/>
</role>
</idPoC>
<resMaint>
<maintFreq>
<MaintFreqCd value="published layers are considered complete with the possible exception of the most recent year. however, a layer may be updated if additional fires are assessed for the given year or if data for constituent fires are modified. layers for additional annual national mosaics will be added as they become available. annual mosaics for the most recent year may be expected early in the next calendar year. if any extended assessments are to be included, an updated mosaic will be provided near the middle of the year. mosaics for 2007 to 2011 will be added at a future date as well."/>
</maintFreq>
</resMaint>
<placeKeys>
<keyword>United States</keyword>
<keyword>CONUS</keyword>
</placeKeys>
<themeKeys>
<thesaName>
<resTitle>ISO 19115 Topic Category</resTitle>
</thesaName>
<keyword>Biota</keyword>
</themeKeys>
<themeKeys>
<keyword>RAVG</keyword>
<keyword>Burn Severity</keyword>
<keyword>Vegetation Condition</keyword>
<keyword>Wildland Fire</keyword>
<keyword>Wildfire</keyword>
<keyword>Landsat</keyword>
<keyword>Normalized Burn Ratio</keyword>
<keyword>Differenced Normalized Burn Ratio</keyword>
<keyword>Relative Differenced Normalized Burn Ratio</keyword>
<keyword>NBR</keyword>
<keyword>dNBR</keyword>
<keyword>RNBR</keyword>
<keyword>Basal Area</keyword>
<keyword>Canopy Cover</keyword>
<keyword>Composite Burn Index</keyword>
<keyword>CBI</keyword>
</themeKeys>
<searchKeys>
<keyword>Biota</keyword>
<keyword>RAVG</keyword>
<keyword>Burn Severity</keyword>
<keyword>Vegetation Condition</keyword>
<keyword>Wildland Fire</keyword>
<keyword>Wildfire</keyword>
<keyword>Landsat</keyword>
<keyword>Normalized Burn Ratio</keyword>
<keyword>Differenced Normalized Burn Ratio</keyword>
<keyword>Relative Differenced Normalized Burn Ratio</keyword>
<keyword>NBR</keyword>
<keyword>dNBR</keyword>
<keyword>RNBR</keyword>
<keyword>Basal Area</keyword>
<keyword>Canopy Cover</keyword>
<keyword>Composite Burn Index</keyword>
<keyword>CBI</keyword>
<keyword>United States</keyword>
<keyword>CONUS</keyword>
</searchKeys>
<resConst>
<Consts>
<useLimit>&lt;DIV STYLE="text-align:Left;"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. &lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN /&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;Additionally, the U.S. Forest Service waives copyright and related rights in the work worldwide through the CC0 (which can be found at https://creativecommons.org/public-domain/cc0/). &lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN /&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and institutions participating in or administering USDA programs are prohibited from discriminating based on race, color, national origin, religion, sex, disability, age, marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal or retaliation for prior civil rights activity, in any program or activity conducted or funded by USDA (not all bases apply to all programs). Remedies and complaint filing deadlines vary by program or incident. &lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN /&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, American Sign Language, etc.) should contact the State or local Agency that administers the program or contact USDA through the Telecommunications Relay Service at 711 (voice and TTY). Additionally, program information may be made available in languages other than English. &lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN /&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at How to File a Program Discrimination Complaint and at any USDA office or write a letter addressed to USDA and provide in the letter all of the information requested in the form. To request a copy of the complaint form, call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, SW, Mail Stop 9410, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: program.intake@usda.gov. &lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN /&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;USDA is an equal opportunity provider, employer, and lender. &lt;/SPAN&gt;&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</useLimit>
</Consts>
</resConst>
<resConst>
<LegConsts>
<useLimit>The U.S. Forest Service makes no warranty, express or implied, nor assumes any liability or responsibility for the accuracy, reliability, completeness, or utility of these geospatial data or for the improper or incorrect use of those data. The data are dynamic and may change over time. The user is responsible for verifying the limitations of the geospatial data and for using the data accordingly.</useLimit>
</LegConsts>
</resConst>
<resConst>
<SecConsts>
<class>
<ClasscationCd value="001"/>
</class>
<classSys>None</classSys>
<handDesc>None</handDesc>
</SecConsts>
</resConst>
<spatRpType>
<SpatRepTypCd value="002"/>
</spatRpType>
<envirDesc>ERDAS Imagine 14.00.0100, Build 745; Esri ArcCatalog 10.2.2.3552</envirDesc>
<dataExt>
<geoEle>
<GeoBndBox>
<exTypeCode>true</exTypeCode>
<westBL>-126.882</westBL>
<eastBL>-72.763</eastBL>
<northBL>50.019</northBL>
<southBL>29.174</southBL>
</GeoBndBox>
</geoEle>
</dataExt>
<dataExt>
<exDesc>Ground condition</exDesc>
<tempEle>
<TempExtent>
<exTemp>
<TM_Period>
<tmBegin>2012</tmBegin>
<tmEnd>2021</tmEnd>
</TM_Period>
</exTemp>
</TempExtent>
</tempEle>
</dataExt>
<dataLang>
<languageCode Sync="TRUE" value="eng"/>
<countryCode Sync="TRUE" value="USA"/>
</dataLang>
<dataExt>
<geoEle>
<GeoBndBox esriExtentType="search">
<exTypeCode Sync="TRUE">1</exTypeCode>
<westBL Sync="TRUE">-126.894429</westBL>
<eastBL Sync="TRUE">-72.569441</eastBL>
<northBL Sync="TRUE">51.278904</northBL>
<southBL Sync="TRUE">27.578756</southBL>
</GeoBndBox>
</geoEle>
</dataExt>
<idCredit>Geospatial Technology and Applications Center - Disturbance Assessment and Services</idCredit>
<tempKeys>
<keyword>2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2012-2023</keyword>
</tempKeys>
<tpCat>
<TopicCatCd value="002"/>
</tpCat>
</dataIdInfo>
<dqInfo>
<dqScope>
<scpLvl>
<ScopeCd value="005"/>
</scpLvl>
</dqScope>
<report type="DQConcConsis">
<measDesc>No tests for logical consistency have been performed on these data. Each layer is a mosaic of numerous individual raster datasets, which were produced using a standardized process; however, some variability is expected due to the many factors that affect the underlying imagery as a function of time, location, and natural environment.</measDesc>
</report>
<report type="DQCompOm">
<measDesc>This dataset is intended to include burn severity data for all fires that meet standard RAVG criteria for the given years; namely, wildfires reported within CONUS that include at least 1000 acres of forested National Forest System land (at least 500 acres for Regions 8 and 9 as of 2016). Exceptions (omissions) may include fires for which suitable imagery was not available in a timely manner (i.e., within 45 days after fire containment for initial assessments).</measDesc>
</report>
<report dimension="horizontal" type="DQAbsExtPosAcc">
<measDesc>The target scale of this dataset is 1:24,000. The horizontal accuracy of this dataset is determined by that of the moderate-resolution multi-spectral imagery from which it is derived.</measDesc>
</report>
<dataLineage>
<prcStep>
<stepDesc>RAVG burn severity data are derived from multi-spectral imagery. A Relative Differenced Normalized Burn Ratio (RdNBR) image, which portrays the variation of burn severity within a burn area, is derived from a pair of multi-spectral images: a post-fire scene and a corresponding pre-fire scene. Whenever possible, the RAVG project uses Landsat images that have been geometrically rectified, terrain corrected, and converted to top-of-atmosphere (at-satellite) reflectance. The following process was developed for Landsat imagery but applies to other multi-spectral satellite imagery that includes a near-infrared (NIR) band and a short-wave infrared (SWIR) band (approximately 2.2 micrometers).
The Normalized Burn Ratio (NBR) is computed for each image using the NIR and SWIR bands:
NBR = (NIR - SWIR) / (NIR + SWIR)
The Differenced NBR (dNBR) is computed as the change in NBR, scaled by 1000 (for representation as integer values):
dNBR = (PreNBR - PostNBR)*1000
The Relative dNBR (RdNBR) includes an additional scale factor designed to reduce the correlation with pre-fire vegetation density. It also includes an offset calculated from the dNBR in an unburned area in the vicinity of the fire having vegetation cover similar to that of the burned area. The offset is intended to account for seasonal or interannual differences between the two images.
RdNBR = (dNBR - offset)/sqrt(abs(PreNBR/1000))
RdNBR is the index used for vegetation burn severity assessments in the RAVG program. Higher RdNBR values are correlated with more severe burns.
Three burn severity indices form the complement of RAVG products: Composite Burn Index (CBI), percent change in basal area (BA), and percent change in canopy cover (CC). Values are estimated from RdNBR via regression models, which were developed from field data and Landsat imagery, both acquired approximately one year post-fire. The field data were collected from a number of fires that burned in the Sierra Nevada and Klamath Mountains in California between 1999 and 2006. RdNBR values were derived from post-fire imagery acquired at approximately the same time, and seasonally matching imagery from before each fire.
The CBI is a composite linear combination of fire effects experienced at a site and encompasses all vegetation strata from the forest floor to the upper canopy. The composite value ranges from 0 to 3. Thematic CBI-based severity classes were "calibrated" from field data collected about one year post-fire, meaning that field data collected on a given fire or fires that occurred in similar vegetation types were used to classify burn severity as unchanged, low, moderate or high severity. CBI values of 0.1, 1.25, and 2.25 were selected as the breakpoints between the successive classes, which are defined as follows:
Unchanged: Areas indistinguishable from pre-fire conditions. This does not necessarily indicate that the area was unburned.
Low: Areas of surface fire with little change in cover and little mortality of the structurally dominant vegetation.
Moderate: Areas between low and high severity, with a mixture of effects on the structurally dominant vegetation.
High: Areas where the dominant vegetation has high to complete mortality.
Percent change in basal area was calibrated through regression analysis of the RdNBR index to basal area change as measured in the same field plots where the CBI data were collected. For this measure, a tree with no green foliage one year post-fire was defined as being dead. Percent change in canopy cover was likewise calibrated through regression analysis of the RdNBR index to measured or calculated tree canopy cover change. Pre-fire canopy cover was determined through photo interpretation of pre-fire digital orthophotos or by way of the Forest Vegetation Simulator (FVS) (https://www.fs.fed.us/fmsc/fvs/).
The following models were used for extended assessments; that is, assessments based on post-fire imagery acquired during the growing season one year after the fire:
CBI = (1/0.6124)*LN((RdNBR + 123.3)/196.8) [2016 and earlier]
CBI = (1/0.3890)*LN((RdNBR + 369.0)/421.7) [2017 and later]
BA Percent Change = 100*(SIN((RdNBR-166.5)/389))^2
CC Percent Change = 100*(SIN((RdNBR-161.0)/392.6))^2
Calibrated thresholds for initial assessments were derived through regression modeling of satellite reflectance values from imagery collected one year post-fire to imagery acquired immediately post-fire. Application of the calculated correction factor yields the following initial assessment models:
CBI = (1/0.6124)*LN((RdNBR/1.1438 + 123.3)/196.8) [2016 and earlier]
CBI = (1/0.3890)*LN((RdNBR/1.1438 + 369.0)/421.7) [2017 and later]
BA Percent Change = 100*(SIN((RdNBR/1.1438-166.5)/389))^2
CC Percent Change = 100*(SIN((RdNBR/1.1438-161.0)/392.6))^2
A thematic version of each continuous burn severity raster was created based on the following class breaks:
Four-category severity classification based on CBI:
0 = outside perimeter
1 = unchanged (0 &lt;= CBI &lt; 0.1)
2 = low severity (0.1 &lt;= CBI &lt; 1.25)
3 = moderate severity (1.25 &lt;= CBI &lt; 2.25)
4 = high severity (2.25 &lt;= CBI &lt;= 3.0)
9 = unmappable
Seven-category percent change in basal area (BA):
0 = outside perimeter
1 = 0% BA loss
2 = 0% &lt; BA loss &lt; 10%
3 = 10% &lt;= BA loss &lt; 25%
4 = 25% &lt;= BA loss &lt; 50%
5 = 50% &lt;= BA loss &lt; 75%
6 = 75% &lt;= BA loss &lt; 90%
7 = BA loss &gt;= 90%
9 = unmappable
Five-category percent change in canopy cover (CC):
0 = outside perimeter
1 = 0% CC loss
2 = 0% &lt; CC loss &lt; 25%
3 = 25% &lt;= CC loss &lt; 50%
4 = 50% &lt;= CC loss &lt; 75%
5 = CC loss &gt;= 75%
9 = unmappable
When a perimeter for a given fire was available from the incident management team or local unit, it was used as the initial approximation for the RAVG burned area perimeter. In many cases, supplied perimeters were edited to account for obvious discrepancies with the visible burned area (based on the post-fire imagery). If no perimeter was provided, the burned area boundary was delineated from the dNBR and post-fire reflectance imagery. Open water and areas obscured by clouds, shadows, active fire, smoke, or snow were masked and identified as "unmappable".
Note that derived burn severity products represent a snapshot in time and that the models are applied to the entire extent of each burned area without regard to vegetation type. Factors such as delayed mortality, resprouting, the presence of non-tree vegetation, and the occurrence of non-fire disturbances can contribute to errors in the burn severity estimates.</stepDesc>
<stepDateTm>2021</stepDateTm>
</prcStep>
<prcStep>
<stepDesc>All thematic burn severity images of a given severity type (percent change in basal area, percent change in canopy cover, or composite burn index) for a given year have been mosaicked into a national burn severity raster dataset. If needed, constituent raster datasets were resampled to a common resolution (30 m) and snapped to a common grid (the 2011 National Land Cover Database (NLCD)). Note that initial and extended assessments, if any, are combined in the mosaics. Assessment type for each fire is included as an attribute in the associated burned area perimeter dataset, available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php).</stepDesc>
<stepDateTm>2021</stepDateTm>
</prcStep>
</dataLineage>
</dqInfo>
<spatRepInfo>
<Georect>
<numDims>0</numDims>
<cellGeo>
<CellGeoCd Sync="TRUE" value="002"/>
</cellGeo>
<tranParaAv Sync="TRUE">1</tranParaAv>
<chkPtAv Sync="TRUE">0</chkPtAv>
<cornerPts>
<pos Sync="TRUE">-14125823.192200 3195967.621400</pos>
</cornerPts>
<cornerPts>
<pos Sync="TRUE">-14125823.192200 6670777.621400</pos>
</cornerPts>
<cornerPts>
<pos Sync="TRUE">-8078393.192200 6670777.621400</pos>
</cornerPts>
<cornerPts>
<pos Sync="TRUE">-8078393.192200 3195967.621400</pos>
</cornerPts>
<centerPt>
<pos Sync="TRUE">-11102108.192200 4933372.621400</pos>
</centerPt>
<axisDimension type="002">
<dimSize Sync="TRUE">201581</dimSize>
<dimResol>
<value Sync="TRUE" uom="m">30.000000</value>
</dimResol>
</axisDimension>
<axisDimension type="001">
<dimSize Sync="TRUE">115827</dimSize>
<dimResol>
<value Sync="TRUE" uom="m">30.000000</value>
</dimResol>
</axisDimension>
<ptInPixel>
<PixOrientCd Sync="TRUE" value="001"/>
</ptInPixel>
</Georect>
</spatRepInfo>
<refSysInfo>
<RefSystem>
<refSysID>
<identCode Sync="TRUE" code="3857"/>
<idCodeSpace Sync="TRUE">EPSG</idCodeSpace>
<idVersion Sync="TRUE">6.18.3(9.3.1.2)</idVersion>
</refSysID>
</RefSystem>
</refSysInfo>
<contInfo>
<ImgDesc>
<contentTyp>
<ContentTypCd Sync="TRUE" value="001"/>
</contentTyp>
<covDim>
<Band>
<dimDescrp Sync="TRUE">Band_1</dimDescrp>
<bitsPerVal Sync="TRUE">8</bitsPerVal>
<valUnit>
<UOM type="length"/>
</valUnit>
<maxVal Sync="TRUE">9.000000</maxVal>
<minVal Sync="TRUE">1.000000</minVal>
</Band>
</covDim>
</ImgDesc>
</contInfo>
<eainfo>
<detailed>
<enttyp>
<enttypl>CBI4</enttypl>
<enttypd>RAVG 4-class Composite Burn Index (CBI) class</enttypd>
<enttypds>U.S. Forest Service</enttypds>
</enttyp>
<attr>
<attrlabl>Value</attrlabl>
<attrdef>4-class Composite Burn Index (CBI) Category</attrdef>
<attrdefs>U.S. Forest Service</attrdefs>
<attrdomv>
<edom>
<edomv>0</edomv>
<edomvd>Outside of fire perimeter</edomvd>
<edomvds>Internal</edomvds>
</edom>
</attrdomv>
<attrdomv>
<edom>
<edomv>1</edomv>
<edomvd>Unchanged (0 &lt;= CBI &lt; 0.1)</edomvd>
<edomvds>Internal</edomvds>
</edom>
</attrdomv>
<attrdomv>
<edom>
<edomv>2</edomv>
<edomvd>Low Severity (0.1 &lt;= CBI &lt; 1.25)</edomvd>
<edomvds>Internal</edomvds>
</edom>
</attrdomv>
<attrdomv>
<edom>
<edomv>3</edomv>
<edomvd>Moderate Severity (1.25 &lt;= CBI &lt; 2.25)</edomvd>
<edomvds>Internal</edomvds>
</edom>
</attrdomv>
<attrdomv>
<edom>
<edomv>4</edomv>
<edomvd>High Severity (2.25 &lt;= CBI &lt;= 3.0)</edomvd>
<edomvds>Internal</edomvds>
</edom>
</attrdomv>
<attrdomv>
<edom>
<edomv>9</edomv>
<edomvd>Unmappable</edomvd>
<edomvds>Internal</edomvds>
</edom>
</attrdomv>
</attr>
<attr>
<attrlabl>Red</attrlabl>
<attrdef>Red Color Value (0-255 scale)</attrdef>
<attrdefs>U.S. Forest Service</attrdefs>
<attrdomv>
<rdom>
<rdommin>0</rdommin>
<rdommax>255</rdommax>
</rdom>
</attrdomv>
</attr>
<attr>
<attrlabl>Green</attrlabl>
<attrdef>Green Color Value (0-255 scale)</attrdef>
<attrdefs>U.S. Forest Service</attrdefs>
<attrdomv>
<rdom>
<rdommin>0</rdommin>
<rdommax>255</rdommax>
</rdom>
</attrdomv>
</attr>
<attr>
<attrlabl>Blue</attrlabl>
<attrdef>Blue Color Value (0-255 scale)</attrdef>
<attrdefs>U.S. Forest Service</attrdefs>
<attrdomv>
<rdom>
<rdommin>0</rdommin>
<rdommax>255</rdommax>
</rdom>
</attrdomv>
</attr>
<attr>
<attrlabl>Count</attrlabl>
<attrdef>Pixel count for burn severity category</attrdef>
<attrdefs>U.S. Forest Service</attrdefs>
<attrdomv>
<rdom>
<rdommin>0</rdommin>
<rdommax>Unlimited</rdommax>
</rdom>
</attrdomv>
</attr>
</detailed>
</eainfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsK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</Data>
</Thumbnail>
</Binary>
</metadata>
