Identification Information Citation Originator: USDA Forest Service, Geospatial Technology and Applications Center, BAER Imagery Support Program Publication Date: 2017-07-03 Title: Burned Area Reflectance Classification (BARC) Data Bundle for the FRAGUITA Fire occurring on the Coronado National Forest - 2017 Publication Information Publication Place: Salt Lake City, Utah, USA Publisher: USDA Forest Service Geospatial Data Presentation Form: raster digital data Online Linkage: http://activefiremaps.fs.fed.us/baer/download.php?year=2017 Description Abstract: These data products are preliminary burn severity assessments derived from Landsat 8 OLI and Landsat 7 ETM+ data. The pre-fire and post-fire subsets included were used to create a differenced Normalized Burn Ratio (dNBR) image. The dNBR image attempts to portray the variation of burn severity within a fire. The severity ratings are influenced by the effects to the canopy. The severity rating is based upon a composite of the severity to the understory (grass, shrub layers), midstory trees and overstory trees. Because there is often a strong correlation between canopy consumption and soil effects, this algorithm works in many cases for BAER teams whose objective is a soil burn severity assessment. It is not, however, appropriate in all ecosystems or fires. It is expected that BAER teams will adjust the thresholds to match field observations to produce a soil burn severity. Purpose: These data were created by the USDA Forest Service Geospatial Technology and Applications Center (GTAC) to support Burned Area Emergency Response (BAER) teams. Supplemental Information Fire Name: FRAGUITA Agency: USDA Forest Service Land Management Unit: Coronado National Forest Type of assessment: Emergency Fire size (as mapped): 2766 acres Pre-Fire Imagery Date and Path/Row: 2016-06-19; 036/038 Post-Fire Imagery Date and Path/Row: 2017-06-30; 036/038 Output Dataset Projection: NAD_1983_UTM_Zone_12N Datum: D_North_American_1983 Spheroid Name: GRS_1980 Mapping Comments: None. Product List az3150711133220170626_20160619_l8_refl_utm.tif = Pre-Fire Landsat 8 OLI reflectance at sensor data subset, scaled by 400 and converted to integer (band order (1-8): B, G, R, NIR, SWIR-1, SWIR-2, Coastal Blue, Cirrus). For best viewing set RGB to bands 6, 4, 3. az3150711133220170626_20170630_l7_refl_utm.tif = Post-Fire Landsat 7 ETM+ reflectance at sensor data subset, scaled by 400 and converted to integer (band order (1-6): B, G, R, NIR, SWIR-1, SWIR-2). For best viewing set RGB to bands 6, 4, 3. az3150711133220170626_20160619_20170630_dNBR.tif = Continuous dNBR product scaled by 1000; theoretical range of values is -2000 to 2000 az3150711133220170626_20160619_20170630_dNBR_barc256_utm.tif = BARC256, 256-class (0-255); continuous image representing preliminary estimate of burn severity. This dataset can be adjusted by the user, if needed, to refine the thematic BARC4 product and/or define a new BARC4. It is anticipated that users will adjust the breakpoints between classes, then recode to their desired 3 or 4 classes. az3150711133220170626_20160619_20170630_dNBR_barc4_utm.tif = BARC4, Four category preliminary estimate severity classification. Subset values: 1 = unburned / undetectable (Dark Green) 2 = low severity (Cyan) 3 = moderate severity (Yellow) 4 = high severity (Red) Time Period of Content Multiple Dates/Times Single Date/Time Calendar Date: 2016-06-19 (pre-fire image) Single Date/Time Calendar Date: 2017-06-30 (post-fire image) Currentness Reference: Severity data correspond to the date of the post-fire image. Status Progress: Evaluation of methods in process Maintenance and Update Frequency: As needed Spatial Domain Bounding Coordinates West Bounding Coordinate: 462199.856056 East Bounding Coordinate: 473629.856056 North Bounding Coordinate: 3491746.6144 South Bounding Coordinate: 3480076.6144 Keywords Theme Theme Keyword Thesaurus: none Theme Keyword: Wildland Fire Theme Keyword: Wildfire Theme Keyword: Burned Area Emergency Response Theme Keyword: BAER Theme Keyword: differenced Normalized Burn Ratio Theme Keyword: dNBR Theme Keyword: Fire Severity Theme Keyword: Burn Severity Theme Keyword: USDA Forest Service Theme Keyword: Landsat 8 OLI Theme Keyword: Landsat 7 ETM+ Place Place Keyword Thesaurus: none Place Keyword: Coronado National Forest Place Keyword: FRAGUITA Fire Access Constraints: Thresholded, preliminary severity estimates (BARC4 and BARC256) are only delivered to BAER teams. Further distribution is at the discretion of the BAER team leader. Pre-imagery, post-imagery, and unthresholded data are publicly available on http://activefiremaps.fs.fed.us/baer/download.php. Access to pre-imagery and post-imagery may be limited when sensors other than Landsat or other government remote sensing assets are used due to product licensing restrictions. Use Constraints: There are no restrictions on use, except for reasonable and proper acknowledgement of information sources and limitations as preliminary/draft data. Data Set Credit: USDA Forest Service Native Data Set Environment: ERDAS Imagine, ESRI ArcGIS Data Quality Information Positional Accuracy Horizontal Positional Accuracy Horizontal Positional Accuracy Report: Landsat data are terrain corrected using a USGS digital elevation model with less than 1/2 pixel RMS error. Accuracy may vary for other sensors. Lineage Process Step Process Description: These data products are derived from Landsat 8 OLI and Landsat 7 ETM+ data. Pre-fire and post-fire scenes are analyzed to create a differenced Normalized Burn Ratio (dNBR) image. The dNBR image portrays the variation of burn severity within the fire. The pre- and post-fire images are terrain corrected and further processed to convert top of atmosphere reflectance. The Normalized Burn Ratio (NBR) is computed for the pre- and post-fire images using the following formula: (NIR Band - SWIR Band) / (NIR Band + SWIR Band) = NBR The Differenced NBR is computed to determine severity by subtracting the post-fire NBR from the pre-fire NBR: (PreNBR - PostNBR) = dNBR The BARC products are a generalization of the raw, continuous dNBR dataset. Both BARC products have been resampled to unsigned 8-bit GeoTIFF and are easily viewed and edited within ArcGIS. The classes represented on the BARC products are created with thresholds chosen by an analyst at GTAC. These thresholds can be roughly related back to original dNBR values by multiplying by 5 and then subtracting 275 (for example, a BARC256 value of 100 would relate to a dNBR value of 225). The BARC thresholds used on this particular fire are: Unburned / Undetectable: All values less than or equal to 71. Low: All values greater than 71 and less than or equal to 105. Moderate: All values greater than 105 and less than or equal to 183. High: All values greater than 183. General descriptions of the severity classes are below: Unburned / Undetectable: This means the area after the fire was indistinguishable from pre-fire conditions. This does not always indicate the area did not burn (i.e. canopy may be occluding the burn signal). Low: This severity class represents areas of surface fire with little detected change in cover and little detected mortality of the dominant vegetation. Moderate: This severity class is between low and high and means there is a mixture of detected effects on the dominant vegetation. High: This severity class represents areas where the canopy has high to complete consumption. Spatial Data Organization Information Direct Spatial Reference Method: Raster Raster Object Information Vertical Count: 1 Spatial Reference Information Horizontal Coordinate System Definition Planar Grid Coordinate System Grid Coordinate System Name: NAD_1983_UTM_Zone_12N Universal Transverse Mercator UTM Zone Number: 12N Transverse Mercator Scale Factor at Central Meridian: 0.9996 Longitude of Central Meridian: -111.0 Latitude of Projection Origin: 0.0 False Easting: 500000.0 False Northing: 0.0 Planar Coordinate Information Planar Coordinate Encoding Method: row and column Coordinate Representation Abscissa Resolution: 30.0 Ordinate Resolution: 30.0 Planar Distance Units: Meter Geodetic Model Horizontal Datum Name: D_North_American_1983 Ellipsoid Name: GRS_1980 Semi-major Axis: 6378137.0 Denominator of Flattening Ratio: 298.257222101 Distribution Information Resource Description: Downloadable Data Metadata Reference Information Metadata Date: 2017-07-03 Metadata Contact Contact Information Contact Organization Primary Contact Organization: USDA Forest Service, Geospatial Technology and Applications Center (GTAC) Contact Person: Justin Epting Contact Address Address Type: mailing address Address: 2222 W. 2300 S. City: Salt Lake City State or Province: UT Postal Code: 84119 Contact Voice Telephone: 801 975 3755 Contact Electronic Mail Address: baerimagery@fs.fed.us ******************************************************************************** The following section should be completed after the BARC has been field validated by the BAER team and this entire metadata file should be returned to GTAC with the Soil Burn Severity data. BAER Assessment Completion Date:07/21/2017 BAER Team GIS Analyst:Brian Park BAER Team Leader:Salek Shafiqullah Soil Burn Severity Analyst(s):Mike Natharius Original BARC256 thresholds: Unburned / Undetectable: <=71 Low: >71 and <=105 Moderate: >105 and <=183 High: >183 Soil Burn Severity thresholds: Unburned / Undetectable:33-71 Low:72-163 Moderate: High: Sequence of steps used to create Soil Burn Severity data: The "FraguitasSeverity_BARC201700630Derived_20170720" feature class is derived from the "az3150711133220170626_20160619_20170630_dnbr_barc256_utm.tif" raster dataset. The BARC 256 values were classified into 2 classes based on field observations to represent levels of burn severity. The RSAC generated BARC4 originally had only 3 classes: unchanged, low and moderate; field observation concluded that the moderate was burned grass that was actually low severity. The final soil burn severity classes of the BARC256 were: 33-71 Unchanged and 72-163 Low Severity. Process steps are as follows:Clipped the BARC256 to a 30-meter buffer of the final Fraguitas Fire perimeter (06/26/2017 1400hrs); used the Spatial Analyst "Nibble" tool to fill the NoData Landsat-7 gaps; reclassed the raster using the classes mentioned above; applied Majority Filter to the reclassed raster (number of neighbors = 8, replacement threshold = half); converted the raster to a polygon feature class (without simplifying polygons); clipped the polygon feature class to the final perimeter -- without a 30-meter buffer. Additional Comments: