Wildfire Risk to Communities Population Count (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. Metadata and Downloads: (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专门针对美国人口聚居区域，展示野火风险相关要素。这些数据集涵盖了美国人口居住分布情况，以及野火的实地风险——即灾害发生地点处的风险。由美国农业部（USDA）林务局落基山研究站与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，是“社区野火风险”数据集的核心基础。 研究采用LANDFIRE 2020（版本2.2.0）的植被与野外可燃物数据，作为两套不同但相关的地理空间火灾模拟系统的输入参数。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于评估社区灾害与风险的更高分辨率，我们将其重采样至LANDFIRE可燃物与植被数据原生的30米分辨率。在此重采样过程中，我们同时将模拟得到的燃烧概率值拓展至LANDFIRE可燃物数据中标记为不可燃的建成区域。燃烧概率栅格数据反映的是2020年末的景观状况。 火灾强度特征以30米分辨率进行建模，流程为先开展覆盖火灾季全部天气相关特征范围的全量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年发生的可燃物扰动。因此，火灾强度数据集反映的是2022年末的景观状况。 本出版物中代表人口居住分布的数据产品，整合了美国人口普查局2021年的住房单元与人口数量估算数据，以及Onegeo与USA Structures提供的2022年建筑基底数据。 本出版物收录的具体栅格数据集包括： 1. 建筑数量（Building Count）：该30米分辨率栅格代表每个30米像元内建筑基底数据集中的建筑总数量。 2. 建筑密度（Building Density）：该30米分辨率栅格代表建筑基底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米分辨率栅格展示建筑基底覆盖的可居住土地面积百分比。 4. 人口数量（Population Count, PopCount）：该30米分辨率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米分辨率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米分辨率栅格代表每个像元内的住房单元总数。 7. 住房单元密度（Housing Unit Density, HUDen）：该30米分辨率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米分辨率栅格代表单个像元内每年可能遭受野火影响的预期住房单元数量。该数值为长期年度平均值，不可用于代表特定年份实际暴露的住房单元数量。 9. 住房单元影响度（Housing Unit Impact, HUImpact）：该30米分辨率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。该指数综合考虑火灾强度对住宅的一般影响，并利用野火模拟得到的火焰长度概率，捕捉火灾的潜在强度。 10. 住房单元风险度（Housing Unit Risk, HURisk）：该30米分辨率栅格整合了住房单元密度大于0的像元处野火风险的四大核心要素——发生概率、强度、易感性与暴露量。 本数据出版物的下载包中附带了详细的方法学文档。 元数据与下载地址：(https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2) 注意：由于Web服务的数据要求，本影像服务中的像元值已与原始栅格数据集有所调整。本服务主要用于数据可视化，其相对值与空间格局已基本保留，但建议用户下载原始数据用于定量分析。