<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20250723</CreaDate>
<CreaTime>14455700</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
</Esri>
<dataIdInfo>
<idCitation>
<resTitle>EDW_BurnedAreaEmergencyResponse_01</resTitle>
</idCitation>
<searchKeys>
<keyword>BAER</keyword>
<keyword>Burned Area Emergency Response</keyword>
<keyword>Post-fire</keyword>
<keyword>USDA Forest Service</keyword>
<keyword>assessment</keyword>
<keyword>biota</keyword>
<keyword>climatology/atmosphere</keyword>
<keyword>environment</keyword>
<keyword>geoscience</keyword>
<keyword>boundary</keyword>
<keyword>critical values</keyword>
<keyword>wildfire</keyword>
<keyword>wildland fire</keyword>
</searchKeys>
<idPurp>Soil Burn Severity is a measure of a fire's effects on the ground surface and soil condition. The map identifies the fire-induced changes in soil and ground surface properties that may affect infiltration, runoff, and erosion potential. The BAER Team uses this map to identify areas of unacceptable risk to a critical value and where mitigating treatments may be most effective. This product is appropriate for wildland landscapes and does not represent fire effects in developed areas.</idPurp>
<idAbs>&lt;div style='text-align:Left;font-size:12pt'&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;&lt;span&gt;An initial Burned Area Reflectance Classification (BARC) dataset is created by analyzing a differenced Normalized Burn Ratio (dNBR) image, which was derived from the paired pre- and post-fire images USDA Forest Service, Geospatial Technology and Applications Center, BAER Imagery Support Program. The dNBR attempts to portray the variation in burn severity within a burned area, capturing the combined effects of the fire on the vegetation and soil components of the ecosystem. The preliminary BARC dataset is then assessed, and field validated by a Forest Service Burned Area Emergency Response (BAER) team. The Classified BARC image is adjusted and modified, as needed, based on field observations. The resulting product is a Final Soil Burn Severity. Severity Indicators High soil burn severity: Most or all pre-fire ground cover and surface organic matter (litter, duff, and fine roots) is generally consumed, and charring may be visible on larger roots. Soil is often gray, orange, or reddish at the ground surface where large or dense fuels were concentrated and consumed. Soil structure is often altered and less stable at the surface. Moderate soil burn severity: Up to 80 percent of the pre-fire ground cover may be consumed but generally not all of it. There may be potential for recruitment of effective ground cover from scorched needles or leaves remaining in the canopy that will soon fall to the ground. Soil structure is generally unchanged. Low soil burn severity: The ground surface, including any exposed mineral soil, may appear brown or black (lightly charred), and surface organic layers are not completely consumed. The canopy and understory vegetation will likely appear “green.” Very low soil burn severity or Unburned: Little to no burn expected within these areas except in small patches, or where fuels were sparse. Canopy and ground litter almost completely intact. Little to no vegetation mortality expected.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</idAbs>
<idCredit/>
<resConst>
<Consts>
<useLimit/>
</Consts>
</resConst>
</dataIdInfo>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO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==</Data>
</Thumbnail>
</Binary>
</metadata>
