Wildfire Risk to Communities Building Cover (Image Service)

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place. National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions. The specific raster datasets included in this publication include: Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel. Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]). Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints. Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel. Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²). Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel. Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²). Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year. Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire. Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero. Additional methodology documentation is provided with the data publication download. (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2). 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.本出版物收录的数据聚焦美国人口聚居区的野火风险组分。上述数据集涵盖美国人口居住点位与野火实地风险——即负面影响发生地点的风险。由美国农业部林务局（USDA Forest Service）落基山研究站（Rocky Mountain Research Station）与Pyrologix有限责任公司生成的年度燃烧概率与火灾强度国家野火风险数据集，构成了社区野火风险（Wildfire Risk to Communities）数据集的核心基础。 研究使用LANDFIRE 2020（版本2.2.0）的植被与荒野燃料数据，作为两套不同但相关的地理空间火灾模拟系统的输入数据。年度燃烧概率通过美国林务局地理空间火灾模拟器（FSim）生成，初始像元分辨率为较粗的270米。为将燃烧概率栅格数据提升至更适用于社区灾害与风险评估的精细分辨率，我们将其上采样至LANDFIRE燃料与植被数据原生的30米分辨率。在此上采样过程中，我们同时将建模得到的燃烧概率值分配至LANDFIRE燃料数据中标记为不可燃的建成区。燃烧概率栅格代表的是2020年末的景观状况。 火灾强度特征以30米分辨率建模完成，流程为先开展覆盖火灾季全部天气相关特征区间的全量FlamMap模拟，再依据各类天气类型的发生概率，将模拟结果整合为多类输出成果。在开展火灾强度建模前，我们已更新LANDFIRE 2020数据，以反映2021与2022年发生的燃料扰动。因此，火灾强度数据集代表的是2022年末的景观状况。 本出版物中代表人口居住点位的数据产品，整合了美国人口普查局（U.S. Census Bureau）2021年的住房单元与人口数量估算数据，以及来自Onegeo与USA Structures的2022年建筑占地面积（building footprint）数据。 本出版物收录的特定栅格数据集包括： 1. 建筑数量（Building Count）：该30米栅格代表每个30米像元内建筑占地面积数据集中的建筑总数量。 2. 建筑密度（Building Density）：该30米栅格代表建筑占地面积数据集中的建筑密度（单位：栋/平方千米[km²]）。 3. 建筑覆盖率（Building Coverage）：该30米栅格描绘了建筑占地面积覆盖的可居住土地面积百分比。 4. 人口数量（Population Count, PopCount）：该30米栅格的像元值代表每个像元内的居住人口总数（单位：人）。 5. 人口密度（Population Density, PopDen）：该30米栅格代表居住人口密度（单位：人/km²）。 6. 住房单元数量（Housing Unit Count, HUCount）：该30米栅格代表每个像元内的住房单元总数量。 7. 住房单元密度（Housing Unit Density, HUDen）：该30米栅格代表住房单元密度（单位：单元/km²）。 8. 住房单元暴露量（Housing Unit Exposure, HUExposure）：该30米栅格代表单个像元内每年潜在受野火影响的住房单元预期数量。该数值为长期年度平均值，并非代表某一特定年份实际受影响的住房单元数量。 9. 住房单元影响度（Housing Unit Impact, HUImpact）：该30米栅格代表若发生火灾时，任意像元内火灾对住房单元的相对潜在影响。该指数结合了火灾强度对住宅的一般影响后果，并利用野火建模得到的火焰长度概率来捕捉火灾的潜在强度。 10. 住房单元风险度（Housing Unit Risk, HURisk）：该30米栅格整合了野火风险的四大核心要素——发生概率、强度、易损性与暴露量——于住房单元密度大于0的像元中。 本数据出版物的下载资源中附带了额外的方法学文档（https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2）。 注意：受Web服务的数据要求限制，本影像服务中的像元值已相较于原始栅格数据集进行了调整。该服务主要用于数据可视化，其相对数值与空间格局得以较大程度保留，但建议用户下载源数据以开展定量分析。