20250804 16572900 1.0 FALSE USFS_EDW_RMRS_WRC_CommunityWildfireRiskReductionZones4 -20011168.152700 -7130458.152700 2136104.661300 11596994.661300 1 Projected GCS_WGS_1984 Linear Unit: Meter (1.000000) WGS_1984_Web_Mercator_Auxiliary_Sphere <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'><WKT>PROJCS[&quot;WGS_1984_Web_Mercator_Auxiliary_Sphere&quot;,GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Mercator_Auxiliary_Sphere&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,0.0],PARAMETER[&quot;Standard_Parallel_1&quot;,0.0],PARAMETER[&quot;Auxiliary_Sphere_Type&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,3857]]</WKT><XOrigin>-20037700</XOrigin><YOrigin>-30241100</YOrigin><XYScale>148923141.92838538</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>102100</WKID><LatestWKID>3857</LatestWKID></ProjectedCoordinateSystem> 16 FALSE None 1 AMD TRUE continuous unsigned integer 100000 100000 1000 20 2048 20 75 0.01 10 600 300 Basic 0 NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None None,JPEG,LZ77,LERC Basic None,Basic,Base-Top Height,Top-Top Shadow Height,Base-Top Shadow Height,3D -1 -1 0.8 1 NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None None,JPEG,LZ77,LERC None,Basic,Full NONE <_docversion_ Sync="TRUE">10 name,minps,maxps,lowps,highps,tag,groupname,productname,centerx,centery,zorder,st_area(shape),st_perimeter(shape) 307200 30 30720 name,minps,maxps,lowps,highps,tag,groupname,productname,centerx,centery,zorder,st_area(shape),st_perimeter(shape) minps,maxps,lowps,highps,centerx,centery,zorder,st_area(shape),st_perimeter(shape) -1 1 1 0 0 0 0 0 0 20250818 15180100 20250819 12042200 150000000 5000 ISO19115_3 USDA Forest Service, Fire Modeling Institute (FMI) 406-329-4836 Missoula Fire Sciences Laboratory 5775 US Hwy 10 W Missoula MT 59808 US eva.c.karau@usda.gov This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS. 20250819 ArcGIS Metadata 1.0 USDA Forest Service, Research and Development Research Data Archivist see Contact Instructions 240 West Prospect Road Fort Collins CO 80526 US This contact information was current as of May 2024. For current information see Contact Us page on: https://doi.org/10.2737/RDS. None TIFF 2024 Georeferenced Tagged Image Format File; 8 Bit unsigned integer; LZW compression; pyramids: levels 5, Nearest Neighbor resampling https://doi.org/10.2737/RDS-2024-0030 USDA Forest Service, Research and Development Research Data Archivist see Contact Instructions 240 West Prospect Road Fort Collins CO 80526 US This contact information was current as of May 2024. For current information see Contact Us page on: https://doi.org/10.2737/RDS. None CSV see Format Specification Comma-separated values file https://doi.org/10.2737/RDS-2024-0030 USDA Forest Service, Research and Development Research Data Archivist see Contact Instructions 240 West Prospect Road Fort Collins CO 80526 US This contact information was current as of May 2024. For current information see Contact Us page on: https://doi.org/10.2737/RDS. None XLSX see Format Specification Microsoft Excel Open XML spreadsheet file https://doi.org/10.2737/RDS-2024-0030 USDA Forest Service, Research and Development Research Data Archivist see Contact Instructions 240 West Prospect Road Fort Collins CO 80526 US This contact information was current as of May 2024. For current information see Contact Us page on: https://doi.org/10.2737/RDS. None PDF see Format Specification Portable Document Format file https://doi.org/10.2737/RDS-2024-0030 https://doi.org/10.2737/RDS-2024-0030 USFS_EDW_RMRS_WRC_CommunityWildfireRiskReductionZones4 2024 Dillon, Gregory K. Lazarz, Mitchell T. Karau, Eva C. Story, Scott J. Pohl, Kelly A. Forest Service Research Data Archive Fort Collins, CO raster digital data <div style='text-align:Left;'><div><div><p><span>This data publication includes Community Wildfire Risk Reduction Zone rasters, products delivered as part of the Wildfire Risk to Communities project. There are two types of data included: 1) raster spatial data that delineate Community Wildfire Risk Reduction Zones for all populated areas in the continental United States (CONUS), Alaska, and Hawaii; and 2) tabular summaries by communities, counties, tribal areas, and states of wildfire hazard and risk produced as part of the Wildfire Risk to Communities (WRC) project. </span></p><p><span>The Community Wildfire Risk Reduction Zones (CWiRRZ) product is a 30-m raster delineating areas where mitigation activities will be most effective at protecting homes from most types of wildfire. The zones are determined by the spatial coincidence of wildfire likelihood (Burn Probability), and populated areas. There are four Risk Reduction Zones: Minimal Exposure Zone, Indirect Exposure Zone, Direct Exposure Zone, and Wildfire Transmission Zone. However, the CWiRRZ raster can be further deconstructed into ten zones, wherein the Wildfire Transmission Zone is separated into the following surface fuel types: Tree, Shrub, Grass, Agriculture, Non-Vegetated, Water, and Outlying Wildlands (area beyond 2400-m from buildings).</span></p><div><div><p><span>Additional methodology documentation is provided with the data publication download. (https://www.fs.usda.gov/rds/archive/Catalog/RDS-2024-0030)</span></p><p><span>Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.</span></p></div></div></div></div></div> The geospatial data products described and distributed here are part of the Wildfire Risk to Communities project. This project was directed by Congress in the 2018 Consolidated Appropriations Act (i.e., 2018 Omnibus Act, H.R. 1625, Section 210: Wildfire Hazard Severity Mapping) to help U.S. communities understand components of their relative wildfire risk profile, the nature and effects of wildfire risk, and actions communities can take to mitigate risk. These data serve two purposes: 1) provide nationally-consistent spatial data that could be used to summarize hazard and risk to populated areas and take into consideration areas with housing units as well as adjacent areas with wildland fuels; and 2) provide communities with a way to spatially identify where different types of risk mitigation activities are likely to be most effective. Funding for this project provided by USDA Forest Service, Fire and Aviation Management. Funding also provided by USDA Forest Service, Fire Modeling Institute, which is part of the Rocky Mountain Research Station, Fire, Fuel and Smoke Science Program. Work on data development was primarily completed by the USDA Forest Service, Fire Modeling Institute (FMI). Some salary was provided by FMI through an ORISE agreement under the U.S. Department of Energy (DE-SC0014664). Gregory K. Dillon USDA Forest Service, Rocky Mountain Research Station https://orcid.org/0009-0006-6304-650X Mitchell T. Lazarz USDA Forest Service, Rocky Mountain Research Station https://orcid.org/0000-0002-4558-4949 Eva C. Karau USDA Forest Service, Rocky Mountain Research Station https://orcid.org/0009-0009-6776-9387 Scott J. Story Headwaters Economics Kelly A. Pohl Headwaters Economics https://orcid.org/0009-0003-3876-5121 USDA Forest Service, Fire Modeling Institute (FMI) 406-329-4836 Missoula Fire Sciences Laboratory 5775 US Hwy 10 W Missoula MT 59808 US eva.c.karau@usda.gov This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS. conterminous United States CONUS Alaska Hawaii ISO 19115 Topic Category environment geoscientificInformation society structure National Research & Development Taxonomy Ecology, Ecosystems, & Environment Fire Fire detection Fire ecology Fire effects on environment Fire suppression, pre-suppression Prescribed fire Environment and People Forest management Landscape management burn probability hazard fuels management fire suppression fire likelihood fire planning risk assessment wildfire hazard potential environment geoscientificInformation society structure Ecology Ecosystems & Environment Fire Fire detection Fire ecology Fire effects on environment Fire suppression pre-suppression Prescribed fire Environment and People Forest management Landscape management burn probability hazard fuels management fire suppression fire likelihood fire planning risk assessment wildfire hazard potential conterminous United States CONUS Alaska Hawaii <div style='text-align:Left;'><div><div><p><span>These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation: </span></p><p><span>Dillon, Gregory K.; Lazarz, Mitchell T.; Karau, Eva C.; Story, Scott J.; Pohl, Kelly A. 2024. Wildfire Risk to Communities: Community Wildfire Risk Reduction Zones for the United States. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2024-0030 </span></p><p><span>The data presented here are the product of modeling, and as such carry an inherent degree of error and uncertainty. Users are strongly encouraged to read and fully comprehend the metadata and other available documentation prior to data use. No warranty is made by the Originator as to the accuracy, reliability, or completeness of these data for individual use or aggregate use with other data, or for purposes not intended by the Originator. These data are intended to provide nationally-consistent information for the purpose of comparing relative wildfire risk among communities nationally or within a state or county. Data included here are not intended to replace locally-calibrated state, regional, or local risk assessments where they exist. It is the responsibility of the user to be familiar with the value, assumptions, and limitations of these national data publications. Managers and planners must evaluate these data according to the scale and requirements specific to their needs. Spatial information may not meet National Map Accuracy Standards. This information may be updated without notification.</span></p><p><span>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. </span></p><p><span>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/). </span></p><p><span>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. </span></p><p><span>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. </span></p><p><span>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. </span></p><p><span>USDA is an equal opportunity provider, employer, and lender. </span></p></div></div></div> Metadata documents have been reviewed for accuracy and completeness. Unless otherwise stated, all data and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. However, neither the author, the Archive, nor any part of the federal government can assure the reliability or suitability of these data for a particular purpose. The act of distribution shall not constitute any such warranty, and no responsibility is assumed for a user's application of these data or related materials. The metadata, data, or related materials may be updated without notification. If a user believes errors are present in the metadata, data or related materials, please use the information in (1) Identification Information: Point of Contact, (2) Metadata Reference: Metadata Contact, or (3) Distribution Information: Distributor to notify the author or the Archive of the issues. Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States 2024 2nd Scott, Joe H. Dillon, Gregory K. Jaffe, Melissa R. Vogler, Kevin C. Olszewski, Julia H. Callahan, Michael N. Karau, Eva C. Lazarz, Mitchell T. Short, Karen C. Riley, Karin L. Finney, Mark A. Grenfell, Isaac C. Forest Service Research Data Archive Fort Collins, CO raster digital data https://doi.org/10.2737/RDS-2020-0016-2 Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States 2024 2nd Jaffe, Melissa R. Scott, Joe H. Callahan, Michael N. Dillon, Gregory K. Karau, Eva C. Lazarz, Mitchell T. Forest Service Research Data Archive Fort Collins, CO raster digital data https://doi.org/10.2737/RDS-2020-0060-2 true -180.00000 -67.93318 63.90442 18.85415 Ground condition 2020-12-31 See the Wildfire Risk to Communities (WRC) website at https://www.wildfirerisk.org for complete project information and an interactive web application for exploring some of the data published here. These data are considered part of WCR 2.0 which also includes: 1) Scott et al. (2024) containing data regarding wildfire risk across all lands, and 2) Jaffe et al. (2024) containing wildfire risk across lands where only housing units current exist. Esri ArcGIS 13.5.2.57366 1 -179.763382 -64.053995 71.561341 18.839976 The raster data and tabular summaries described here are derived from wildfire simulation modeling and GIS processing, and their exact accuracy cannot be measured. They are intended to depict wildfire exposure zones and provide relative measures of wildfire risk for planning purposes. Quantitative accuracy cannot be evaluated. Unknown The Community Wildfire Risk Reduction Zones (CWiRRZ) raster is delivered in TIFF format with an attribute table with the following fields: 10 Class CWiRRZ: 1. Minimal Exposure 2. Indirect Exposure 3. Direct Exposure 4. Wildfire Transmission Zone: Tree 5. Wildfire Transmission Zone: Shrub 6. Wildfire Transmission Zone: Grass 7. Wildfire Transmission Zone: Agriculture 8. Wildfire Transmission Zone: Non-Vegetated 9. Outlying Wildlands 10. Water 4 Class CWiRRZ: 1. Minimal Exposure 2. Indirect Exposure 3. Direct Exposure 4. Wildfire Transmission Zone All pixels that are part of the land and water of the United States have valid non-negative values. The Wildfire Risk to Communities (WRC) datasets are based on wildfire simulation modeling. Given the relatively short time available for analysis and production of the WRC datasets, the methods for this project were designed to leverage the existing national wildfire simulation data from Dillon et al. (2023) without further local calibration or modeling work, although one minor edit to the off-the-shelf fuel data was required to calibrate fire occurrence in northern Minnesota. To make the WRC data most useful to communities, we implemented a process to downscale the national burn probability data from their native 270-m cell size to the native 30-m resolution of the nationally available LANDFIRE fuels and vegetation data. Through this process, we also used geospatial smoothing techniques to account for wildfire hazard in parts of communities adjacent to wildland vegetation that may have indirect exposure to wildland fire. Dillon, Gregory K. 2023. Wildfire Hazard Potential for the United States (270-m), version 2023. 4th Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2015-0047-4 The overall process is defined below. 2023 1. Remove potentially erroneously mapped buildings. To create wildfire exposure zones, we started with the Building Count raster. But because there are some pixels mapped erroneously as buildings (“false positives”) in the Building Count raster, we removed any pixels mapped within USDA or USDOI Wilderness and Roadless areas, while including buildings mapped within 200-m from the inside edge of those boundaries. We also used the following logic to further minimize false positives: Building Count pixels were eliminated if they were greater than 500-m from a road or greater than 2400-m from a housing unit as mapped in the HUCount raster. 2023 WRC 2.0 Building Count, WRC 2.0 Housing Unit Count 2. Restore potential buildings within Census Places. Because Step 1 removed several building pixels that were true buildings, we checked to see if any Census Designated Places (CDPs) have no buildings after that step, and if so, we added them back into the set of filtered building pixels within that CDP. This ensured that we include buildings within communities where roads were sparsely mapped, and Census data does not adequately count population. 2023 WRC 2.0 Building Count, Census Place boundaries 3. Create exposure area around buildings. We buffered filtered buildings by 200-m to approximate an exposure area (approximately 30 acres) surrounding structures where wildfire risk reduction actions may be warranted. 2023 WRC 2.0 Building Count 4. Characterize exposure zones. Within the buffered area surrounding buildings, we reclassified the Exposure Type raster where: Exposure Type is 0 = Minimal Exposure, Exposure type is between 0 and 1 = Indirect Exposure, and Exposure Type is 1 = Direct Exposure. We then used a 200-m radius moving window majority filter on the classified Exposure Types to define boundaries between exposure zones and represent the influence of adjacent fuels on a pixel’s exposure type. To maintain the spatial precision of the Direct Exposure class, we returned pixels originally in the Direct Exposure class to their Direct Exposure in the final exposure zone raster. 2023 WRC 2.0 Exposure Type 5. Identify building clusters. Though we build an exposure zone around every building across the landscape, the Wildfire Transmission Zone is created only around building clusters. To implement this logic, we create a raster processing mask extending from buildings where either of the following conditions apply: 1. A building is within a 227 m radius of one or more buildings, OR 2. A building is within a 908 m radius of 16 or more buildings. Both conditions, when implemented in a raster focal sum operation, generates an area around a building equivalent to 1 building per 40 acres, however the first condition captures buildings that are within 40 acres of another building but in an otherwise sparsely populated area, while the second condition captures buildings near the edges of more densely populated areas. We combined the masks created from these two conditions and selected buildings that fell within the combined area as the starting point from which to build the Wildfire Transmission Zone. We identified the buildings selected with this process as belonging to a “building cluster”, as each one is in a group with other buildings. 2023 6. Create buffer surrounding building clusters. From the building clusters selected in Step 2, we created a 2.4 kilometer (km) buffer to delineate the Wildfire Transmission Zone. 2023 7. Define fuel categories within Wildfire Transmission Zone. Several geoprocessing steps were required to define each of the categories within the Wildfire Transmission Zone (WTZ): WTZ-Tree is mapped as the area 200 m – 2400 m from building clusters, where LANDFIRE FBFM40 is equal to either a Timber Understory (161, 162, 163, 164, 165), Timber Litter (181, 182, 183, 184, 185, 186, 187, 188, 189), or Slash-Blowdown (201, 202, 203, 204) fuel model. WTZ-Shrub is mapped as the area 200 m – 2400 m from building clusters, where LANDFIRE FBFM40 is equal to either a Grass-Shrub (121, 122, 123, 124) or Shrub (141, 142, 143, 144, 145, 146, 147, 148, 149) fuel model. WTZ-Grass is mapped as the area 200 m – 2400 m from building clusters, where LANDFIRE FBFM40 is equal to a Grass fuel model (101, 102, 103, 104, 105, 106, 107, 108, 109). WTZ-Agriculture is mapped as the area 200 m – 2400 m from building clusters, where LANDFIRE FBFM40 is equal to the non-burnable Agricultural fuel model (93); or where exposure is minimal, FBFM40 is 93, and there are no buildings. The second part of this mapping rule eliminated areas of minimal exposure around buildings surrounded by larger regions of non-burnable agriculture, resulting in a more conservative and intuitive characterization for this Wildfire Transmission Zone category. WTZ-Non-Vegetated is mapped as the area 200 m – 2400 m from building clusters, where LANDFIRE FBFM40 is equal to a non-burnable Urban, Snow/Ice, or Barren fuel model (91, 92, 99); or where exposure is minimal, FBFM40 is either 91, 92, or 99, there are no buildings, and the majority of pixels within a 200-m radius are non-burnable Agriculture. The second part of this mapping rule is a minor fix that maintains logical consistency by stamping in the WTZ-Non-Vegetated class where Minimal Exposure pixels are surrounded by agriculture. 2023 LANDFIRE FBFM40 8. Delineate ancillary zones.Outlying Wildlands is mapped as the area beyond 2400 m from building clusters, where an Exposure Zone is not assigned, or water is not present. Water is mapped as any area where LANDFIRE FBFM40 is water (98). 2023 LANDFIRE FBFM40 9. Create final CWiRRZ raster. The final step in creating the CWiRRZ product is to stamp the Exposure Zone raster on top of the Wildfire Transmission Zone and ancillary zones resulting in a raster with the final classes. The raster attribute table includes all information needed to display either the 4 Class or 10 Class CWiRRZ versions: 4-class CWiRRZ – includes the three exposure zones and the entire Wildfire Transmission Zone mapped with a single value. 10-class CWiRRZ – includes the three exposure zones and separate values for each of the fuel categories in the Wildfire Transmission Zone. It also includes the Outlying Wildlands and Water categories. 2023 10. Produce tabular summaries of wildfire hazard and risk. This publication includes a set of tables that summarize many of the wildfire hazard and risk raster datasets from Scott et al. (2024, https://doi.org/10.2737/RDS-2020-0016-2) and Jaffe et al. (2024, https://doi.org/10.2737/RDS-2020-0060-2) by jurisdictions – states, counties, tribal areas, and communities. For WRC 1.0 (Scott et. al. 2020), we calculated these summaries as housing unit weighted means within the political boundaries of the different jurisdictions. We summarized risk only where Housing Unit Density is greater than zero, focusing in on the location of homes and the surrounding 200-m radius area used in creating the density raster. For the new summaries in WRC 2.0, we included broader spatial context that captures the hazard characteristics of the surrounding landscape. This was accomplished by summarizing the hazard and risk within three zones defined in the CWiRRZ: the Indirect Exposure Zone, Direct Exposure Zone, and Wildfire Transmission Zone. To capture the full extent of these zones relative to any jurisdiction, we include all area within these three zones that is within 2.4 km of the jurisdictional boundary, such that the wildfire hazard within 2.4 km of all buildings in any jurisdiction is considered in its summary statistics. The metrics reported in the WRC 2.0 tables are also land area based rather than weighted by housing units. This means that averages for this version were calculated with all pixels within the summary area weighted evenly, as opposed to previous calculations that were weighted by the relative housing-unit density at each pixel. Scott, Joe H.; Gilbertson-Day, Julie W.; Moran, Christopher; Dillon, Gregory K.; Short, Karen C.; Vogler, Kevin C. 2020. Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States. Fort Collins, CO: Forest Service Research Data Archive. Updated 25 November 2020. https://doi.org/10.2737/RDS-2020-0016 2024 Census State boundaries, Census County boundaries, Census Tribal Area boundaries, Census Place boundaries, WRC 2.0 Landscape-wide hazard and risk rasters, WRC 2.0 Populated Areas datasets We used the LANDFIRE Fire Behavior Fuel Models layer (FBFM40) to delineate surface fuel categories within the Wildfire Transmission Zone. LANDFIRE 2.2.0 40 Scott and Burgan Fire Behavior Fuel Models layer LANDFIRE FBFM40 2022 2.2.0 U.S. Department of Agriculture, Forest Service U.S. Department of the Interior raster digital data Scott, Joe H.; Burgan, Robert E. 2005. Standard fire behavior fuel models: a comprehensive set for use with Rothermel's surface fire spread model. Gen. Tech. Rep. RMRS-GTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 72 p. https://doi.org/10.2737/rmrs-gtr-153 https://www.landfire.gov/fuel.php Ground Condition 2020 We used the WRC 2.0 Building Count to identify building locations when creating wildfire exposure zones. Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States WRC 2.0 Building Count 2024 2nd Jaffe, Melissa R. Scott, Joe H. Callahan, Michael N. Dillon, Gregory K. Karau, Eva C. Lazarz, Mitchell T. Forest Service Research Data Archive Fort Collins, CO raster digital data https://doi.org/10.2737/RDS-2020-0060-2 Ground Condition 2020-12-31 We used the WRC 2.0 Exposure Type raster to classify the area surrounding buildings into three categories: Minimal Exposure, Indirect Exposure, and Direct Exposure. Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States WRC 2.0 Exposure Type 2024 2nd Scott, Joe H. Dillon, Gregory K. Jaffe, Melissa R. Vogler, Kevin C. Olszewski, Julia H. Callahan, Michael N. Karau, Eva C. Lazarz, Mitchell T. Short, Karen C. Riley, Karin L. Finney, Mark A. Grenfell, Isaac C. Forest Service Research Data Archive Fort Collins, CO raster digital data https://doi.org/10.2737/RDS-2020-0016-2 Ground Condition 2020-12-31 We used the WRC 2.0 Housing Unit Count raster in the process of removing erroneously mapped building pixels before creating wildfire exposure zones. Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States WRC 2.0 Housing Unit Count 2024 2nd Scott, Joe H. Dillon, Gregory K. Jaffe, Melissa R. Vogler, Kevin C. Olszewski, Julia H. Callahan, Michael N. Karau, Eva C. Lazarz, Mitchell T. Short, Karen C. Riley, Karin L. Finney, Mark A. Grenfell, Isaac C. Forest Service Research Data Archive Fort Collins, CO raster digital data https://doi.org/10.2737/RDS-2020-0016-2 Ground Condition 2022 We summarized wildfire hazard and risk metrics within Census state polygons for the state level summary tables. 2022 States and Equivalent Entities TIGER/Line Shapefile Census State boundaries 2022 U.S. Census Bureau vector digital data https://www2.census.gov/geo/tiger/TIGER2022/STATE Publication Date 2022 We summarized wildfire hazard and risk metrics within Census county polygons for the county level summary tables. 2022 Counties and Equivalent Entities TIGER/Line Shapefile Census County boundaries 2022 2022 U.S. Census Bureau vector digital data https://www2.census.gov/geo/tiger/TIGER2022/COUNTY Publication Date 2022 We summarized wildfire hazard and risk metrics within Census tribal area polygons for the Tribal Area level summary tables. U.S. Census Bureau: 2022 American Indian/Alaska Native/Native Hawaiian Areas (AIANNH) TIGER/Line Shapefile Census Tribal Area boundaries 2022 2022 U.S. Census Bureau vector digital data https://www2.census.gov/geo/tiger/TIGER2022/AIANNH/ Publication Date 2022 We summarized wildfire hazard and risk metrics within Census place polygons for the community level summary tables. U.S. Census Bureau: 2022 Places TIGER/Line Shapefile Census Place boundaries 2022 2022 U.S. Census Bureau vector digital data https://www2.census.gov/geo/tiger/TIGER2022/PLACE/ Publication Date 2022 WRC 2.0 Landscape-wide hazard and risk rasters were used in the process of summarizing hazard and risk metrics within jurisdictional boundaries (states, counties, tribal areas, and communities). Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States WRC 2.0 Landscape-wide hazard and risk rasters 2024 2nd Scott, Joe H. Dillon, Gregory K. Jaffe, Melissa R. Vogler, Kevin C. Olszewski, Julia H. Callahan, Michael N. Karau, Eva C. Lazarz, Mitchell T. Short, Karen C. Riley, Karin L. Finney, Mark A. Grenfell, Isaac C. Forest Service Research Data Archive Fort Collins, CO raster digital data https://doi.org/10.2737/RDS-2020-0016-2 Publication Date 2024 WRC 2.0 Populated Areas datasets were used in the process of summarizing populated areas metrics within jurisdictional boundaries (states, counties, tribal areas, and communities). Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States WRC 2.0 Populated Areas datasets 2024 2nd Jaffe, Melissa R. Scott, Joe H. Callahan, Michael N. Dillon, Gregory K. Karau, Eva C. Lazarz, Mitchell T. Forest Service Research Data Archive Fort Collins, CO raster digital data https://doi.org/10.2737/RDS-2020-0060-2 Publication Date 2024 0 1 0 -20011168.152700 2136104.661300 -20011168.152700 11596994.661300 -7130458.152700 11596994.661300 -7130458.152700 2136104.661300 -13570813.152700 6866549.661300 429357 30.000000 315363 30.000000 Below is a description of the files included in this data publication. DATA FILES (3) 1. \Data\CWiRRZ_CONUS.tif: Georeferenced TIFF file containing discrete values which characterize Community Wildfire Risk Reduction Zones with a 30 meter (m) pixel size. Mapped for the extent of the conterminous United States (CONUS) 3. \Data\CWiRRZ_AK.tif: Georeferenced TIFF file containing discrete values which characterize Community Wildfire Risk Reduction Zones with a 30 m pixel size. Mapped for the extent of the state of Alaska and available as a downloadable zip file. Referred to in the Wildfire Risk to Communities web application as Risk Reduction Zones. 3. \Data\CWiRRZ_HI.tif: Georeferenced TIFF file containing discrete values which characterize Community Wildfire Risk Reduction Zones with a 30 m pixel size. Mapped for the extent of the state of Hawaii and available as a downloadable zip file. Referred to in the Wildfire Risk to Communities web application as Risk Reduction Zones. These three datasets are referred to in the Wildfire Risk to Communities web application as Risk Reduction Zones. Associated OVR files are included and contain pyramids that allow the raster datasets to draw more quickly in GIS software. Associated XML files contain dataset-specific FGDC-CSDGM metadata containing a description of the content, quality, and other characteristics of the data. Attributes for each raster dataset include: OID = Internal feature number generated automatically Value = Cell value for 10-class attributes (0-9), see 'Zone' for description of each class Count = Number of cells in raster dataset with specified 'Value' Zone = Wildfire Transmission Zone (WTZ) is deconstructed into individual fuel type categories, and two ancillary categories are mapped, resulting in the following 10 classes: Minimal Exposure, Indirect Exposure, Direct Exposure, WTZ-Tree, WTZ-Shrub, WTZ-Grass, WTZ-Agriculture, WTZ-Non-Vegetated, Outlying Wildlands, and Water (which represent 'Value' = 0-9, respectively). Value4 = Cell value for 4-class attributes (0-4), see 'Zone4' for description of each class Zone4 = Wildfire Transmission Zones (WTZ) broken into 4 classes: Minimal Exposure, Indirect Exposure, Direct Exposure, Wildfire Transmission Zone, and NA = not applicable (which represent 'Value' = 0-4, respectively). SUPPLEMENTAL FILES (8) 1. \Supplements\WRC_V2_CWiRRZ_GISDataSymbology.pdf: Portable Document Format (PDF) file containing suggested class definitions and colors for displaying the CWiRRZ raster datasets in GIS software. 2. \Supplements\WRC_V2_Methods_Community_WildfireRiskReductionZones.pdf: PDF file containing detailed descriptions of the data products included in this publication and the methods used to create them. Summary Data (provided as both CSV and XLSX) 3. \Supplements\Summaries_CSV\WRC_V2_Community_Summary.csv: Comma-separated values (CSV) file containing tabular summaries of the spatial data included in this publication for each of the 31,895 U.S. Census Places considered as communities for this analysis. Detailed descriptions of all columns in the summary table are provided in \Supplements\WRC_V2_SummaryTableFieldDescriptions.csv. 4. \Supplements\Summaries_CSV\WRC_V2_County_Summary.csv: CSV file containing tabular summaries of the spatial data included in this publication for Counties covering the 50 U.S. states. Detailed descriptions of all columns in the summary table are provided in \Supplements\WRC_V2_SummaryTableFieldDescriptions.csv. 5. \Supplements\Summaries_CSV\WRC_V2_State_Summary.csv: CSV file containing tabular summaries of the spatial data included in this publication for each of the 50 U.S. States and the District of Columbia. Detailed descriptions of all columns in the summary table are provided in \Supplements\WRC_V2_SummaryTableFieldDescriptions.csv. 6. \Supplements\Summaries_CSV\WRC_V2_TribalArea_Summary.csv: CSV file containing tabular summaries of the spatial data included in this publication for each of the 736 U.S. Tribal Areas. Detailed descriptions of all columns in the summary table are provided in \Supplements\WRC_V2_SummaryTableFieldDescriptions.csv. 7. \Supplements\Summaries_CSV\WRC_V2_SummaryTableFieldDescriptions.csv: CSV file describing fields included in the state, county, tribal area, and community CSV summary tables. 8. \Supplements\Summaries_XLSX\WRC_V2_AllJursidictions_Summary.xlsx: XLSX file containing tabular summaries of the spatial data included in this publication for each of the jurisdictions in this analysis (states, counties, tribal areas, and places). Detailed descriptions of all columns in the summary table are provided in the Field Descriptions tab. (This same content is also available as individual CSV files in \Supplements\Summaries_CSV.) Scott, Joe H.; Dillon, Gregory K.; Jaffe, Melissa R.; Vogler, Kevin C.; Olszewski, Julia H.; Callahan, Michael N.; Karau, Eva C.; Lazarz, Mitchell T.; Short, Karen C.; Riley, Karin L.; Finney, Mark A.; Grenfell, Isaac C. 2024. Wildfire Risk to Communities: Spatial datasets of landscape-wide wildfire risk components for the United States. 2nd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2020-0016-2 Jaffe, Melissa R.; Scott, Joe H.; Callahan, Michael N.; Dillon, Gregory K.; Karau, Eva C.; Lazarz, Mitchell T. 2024. Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States. 2nd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2020-0060-2 EPSG 6.18.3(9.3.1.2) Band_1 4.000000 0.000000 16 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