20220516 08082300 1.0 FGDC CSDGM Metadata FALSE Conus_BuildingExposure_by_90mPixel -2356193.911200 2256396.088800 269497.141600 3172627.141600 1 002 Projected GCS_North_American_1983 Linear Unit: Meter (1.000000) USA_Contiguous_Albers_Equal_Area_Conic_USGS_version <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/10.5'><WKT>PROJCS[&quot;USA_Contiguous_Albers_Equal_Area_Conic_USGS_version&quot;,GEOGCS[&quot;GCS_North_American_1983&quot;,DATUM[&quot;D_North_American_1983&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Albers&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-96.0],PARAMETER[&quot;Standard_Parallel_1&quot;,29.5],PARAMETER[&quot;Standard_Parallel_2&quot;,45.5],PARAMETER[&quot;Latitude_Of_Origin&quot;,23.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;Esri&quot;,102039]]</WKT><XOrigin>-16901100</XOrigin><YOrigin>-6972200</YOrigin><XYScale>266467840.99085236</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102039</WKID><LatestWKID>102039</LatestWKID></ProjectedCoordinateSystem> 20220523 13572300 20230908 11192100 150000000 5000 FGDC dataset Michelle Day USDA Forest Service, Rocky Mountain Research Station Biological Scientist 3200 SW Jefferson Way Corvallis Oregon 97331 michelle.day@usda.gov US 541-758-7758 Michelle Day 20230908 File Geodatabase Raster Dataset Smoothed raster of wildfire transmission to buildings in the continental United States raster digital data 2021-12-07T00:00:00 Michelle Day USDA Forest Service, Rocky Mountain Research Station Biological Scientist 3200 SW Jefferson Way Corvallis Oregon 97331 michelle.day@usda.gov US 541-758-7758 Spatial extent is the Continental United States. 2020-01-01T00:00:00 2020-12-31T00:00:00 Wildfire impacts to developed areas have stimulated wide-ranging policy discussions about the role of active forest management to reduce hazardous fuels on federal and private wildlands. An assessment of areas that have the highest likelihood to ignite fires that spread to buildings in developed areas are needed. These spatial data were used to estimate sources of wildfire ignitions that spread to and expose communities to help highlight areas that could be targeted for fuels reduction treatments and other risk mitigation efforts. <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Recent fire seasons in the continental United States have motivated federal agencies to explore scenarios for augmenting current fuel management and forest restoration in areas where fires ignite and spread to buildings in adjacent communities. This data publication contains one (1) geodatabase raster representing a spatial assessment within the continental U.S. of areas at high risk of igniting fires that spread to and expose buildings as a smoothed raster of sources of annual building exposure from wildfire. This raster was created by intersecting simulated wildfire perimeters with building location data over 10,000 to 100,000 fire seasons. These data can be used in scenario planning to helps design fuel treatment program that target wildfire exposure to developed areas. The continental scale of the data support expanded use of scenario planning science to analyze and communicate large scale expansion of current forest and fuel management initiatives.</SPAN></P></DIV></DIV></DIV> Authors: Ken J. Bunzel, Kingbird Software Alan A. Ager, USDA Forest Service, Rocky Mountain Research Station Michelle A. Day, USDA Forest Service, Rocky Mountain Research Station Cody R. Evers, Portland State University Chris D. Ringo, Oregon State University Wildfire Wildfire exposure Wildfire transmission Wildfire management National Research & Development Taxonomy Fire Fire effects on environment Wildland/urban interface Natural Resource Management & Use Forest management 90 Michelle Day USDA Forest Service, Rocky Mountain Research Station Biological Scientist 3200 SW Jefferson Way Corvallis Oregon 97331 michelle.day@usda.gov US 541-758-7758 Michelle Day Michelle Day USDA Forest Service, Rocky Mountain Research Station Biological Scientist 3200 SW Jefferson Way Corvallis Oregon 97331 michelle.day@usda.gov US 541-758-7758 Michelle Day Funding for this project was provided by the US Forest Service, Rocky Mountain Research Station. US Forest Service State & Private Forestry. Additional acknowledgments are extended to Portland State University, Oregon State University, USDA Forest Service International Visitor Program, and Kingbird Software. Development and application of the Fireshed Registry US Forest Service Gen. Tech. Rep. RMRS-GTR-425 2021-05-01T00:00:00 https://doi.org/10.2737/RMRS-GTR-425 Planning for future fire: scenario analysis of an accelerated fuel reduction plan for the western United States 2021-11-01T00:00:00 https://www.fs.usda.gov/treesearch/pubs/63129 Version 6.2 (Build 9200) ; Esri ArcGIS 10.5.1.7333 1 -127.888549 -65.368682 51.605331 22.873717 Michelle Day USDA Forest Service, Rocky Mountain Research Station Biological Scientist 3200 SW Jefferson Way Corvallis Oregon 97331 michelle.day@usda.gov US 541-758-7758 Michelle Day The fuels data used in this prediction are not current, due to the data vintage of fuel conditions used in the available FSim simulations. Thus our predictions in areas that have had disturbance and management since 2014 should be used with caution, with consideration of finer scaled data. As with all risk products there is substantial uncertainty associated with these estimates. This assessment concerns exposure and not risk as we make no predictions about loss. In addition, building footprint data represent 2015 conditions and do not consider more recent development. Data estimate exposure and not loss. No prediction is made about the potential loss from wildfire. Note that due to the smoothing process there may be unburnable areas where exposure is predicted; this will need to be accounted for during the use of these data. Building exposure was mapped using the wildfire simulation data from the national FSim library (Short et al. 2020; fire perimeters not publicly available) with 79 million simulated fires covering the continental US and Microsoft building footprint data (Microsoft 2020) as described in Ager et al. (2021). The Short et al. (2020) wildfire simulations used LANDFIRE 2014 fuel conditions (LANDFIRE, 2017) and the fire season scenarios were based on surface weather data for windspeed and direction, and national gridded weather data from North American Land Data Assimilation System (NLDAS; 1979–2012)(Abatzoglou 2013). In contrast to prior versions of the library, the newer fire simulations used synchronized Energy Release Component (ERC) streams as described in Grenfell et al. (2010), which retain the spatial covariance structure and temporal auto-correlation of the NLDAS weather inputs. FSim simulates wildfires within large geographic units or “pyromes”, regions of relatively homogenous fire regimes. Between 10,000 and 100,000 hypothetical fire season scenarios are simulated for each pyrome depending on the historical large fire frequency. We created a building centroid point file from individual Microsoft building footprint polygons (n = 125 million) (Microsoft 2020), then intersected FSim fire perimeters with the building footprint data in ArcGIS, to tabulate the total number of buildings within each perimeter. Although FSim models firebrands and thus potential exposure to buildings outside of the fire perimeters, we excluded firebrand exposure due to limitations in the fire modeling system. The resulting exposure values were attributed to FSim ignition points to predict exposure from ignitions at that location. The attributed ignition dataset was used to create a 90 m smoothed building exposure raster using inverse distance weighting in ArcGIS with a search radius of 2500 m and a power of 0.5, creating a building exposure grid based on the sum total of the ignitions over the 10,000 + fire seasons. To annualize the exposure data an ignition probability raster was created (ignitions/acre/yr) using the ArcGIS point density tool, with a 2500 m circular search radius and the population field set to ignitions per year. The exposure raster and ignition probability raster were then multiplied to create a final smoothed building exposure raster, in which each cell represents the expected annual number of buildings exposed by wildfire igniting in the surrounding acre per year. Data were smoothed to account for the inherent wildfire stochasticity to wildfire ignitions and to account for exposure in areas that did not by chance experience an ignitions but could given landscape conditions. As a final processing step, the raster was multiplied by 2.00115 to convert from per-acre exposure values to per-pixel values. Note that because the building exposure values are a per-90m-pixel value, any processing of the raster that modifies the pixel size would also need to modify the raster values accordingly. Esri 8.1.2 The raster value is the expected annual average number of buildings exposed to wildfire from ignitions within the 90m pixel. 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