Description: The Land Management Planning Unit (LMPU) feature class displays the plan revision status for FS land management planning units, their boundaries, FS Region, planning phase milestone and associated date, and link to a related planning website. A land management plan provides a framework for integrated resource management and for guiding project and activity decision-making on a nationalforest, grassland, prairie, or other administrative unit. New plan development is required for new NFS units; an existing plan may be amended at any time.
Description: Forest Service national insect and disease risk map total basal area, defined as the total cross-sectional area of all stems in a stand measured at breast height, and expressed as per unit of land area (typically square feet per acre).
Copyright Text: USDA Forest Service FHAAST - NIDRM 2018
Description: This soil burn severity (SBS) dataset is a compilation of all USFS BAER assessment data produced by the U.S. Forest Service in 2017 and 2018. It is a thematic, raster dataset with four burn severity classes: unburned to very low, low, moderate, high. The dataset is created by compiling individual soil burn severity classifications for wildfire incidents. The spatial resolution is 30 meters.
Description: The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ("initial assessments"). Late-season fires, however, may be deferred until the following spring or summer ("extended assessments"). National mosaics of each thematic product are prepared annually and updated at a later date, if needed, to include extended assessments. The associated burned area perimeters are available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php). Assessment type (initial or extended assessment) for each fire is included as an attribute in the perimeter dataset.
Description: Greatest number of consecutive summer (May-Sept) dry days (<1/10 inch of rain) was calculated for each year over the historical (1985-2004) and future (RCP 8.5 2071-2090) time periods; absolute and percent change between these was then calculated. This includes three versions: one based on the 20-year average of summer maxima, one based on the overall maximum, and one based on the 90th percentile value of 20-year maxima.
Description: This raster contains modeled future snow residence time values calculated using a spatial analog model. The model ingests mean winter average temperature and precipitation. For more information about the model, please see Luce et al., [2014] and Lute and Luce [2017]. Absolute changes in snow residence time are in units of days. The temperature and precipitation data are ensemble mean values across 20 global climate models from the CMIP5 experiment [Taylor et al., 2012], downscaled to a 4km grid. For more information on the downscaling method and to access the raw data used to create this dataset, please see Abatzoglou and Brown, [2012] and the Northwest Climate Science Center. We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station, including detailed metadata; these raster data are available for download here.
For more information, refer to metadata: https://www.fs.fed.us/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf
Copyright Text: Lute, A., & Luce, C. H. (2017, April 5). National Forest Climate Change Maps: Your Guide to the Future. Retrieved July 1, 2017, from https://www.fs.fed.us/rm/boise/AWAE/projects/national-forest-climate-change-maps.html
Name: Stream Temperatures in the Western US: 2080s
Display Field: GNIS_NAME
Type: Feature Layer
Geometry Type: esriGeometryPolyline
Description: This layer represents modeled stream temperatures derived from the NorWeST point feature class (NorWest_TemperaturePoints). NorWeST summer stream temperature scenarios were developed for all rivers and streams in the western U.S. from the > 20,000 stream sites in the NorWeST database where mean August stream temperatures were recorded. The resulting dataset includes stream lines (NorWeST_PredictedStreams) and associated mid-points NorWest_TemperaturePoints) representing 1 kilometer intervals along the stream network. Stream lines were derived from the 1:100,000 scale NHDPlus dataset (USEPA and USGS 2010; McKay et al. 2012). Shapefile extents correspond to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs) or in some instances closely correspond to state borders. The line and point shapefiles contain identical modeled stream temperature results. The two feature classes are meant to complement one another for use in different applications. In addition, spatial and temporal covariates used to generate the modeled temperatures are included in the attribute tables at https://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST/ModeledStreamTemperatureScenarioMaps.shtml. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.
Copyright Text: U.S. Forest Service; Rocky Mountain Research Station; Air, Water, and Aquatic Environments Program (AWAE). https://www.fs.fed.us/rm/boise/awae_home.shtml
Name: TCA: Insect and Pathogen Incidence 2014 to 2018
Display Field: Value
Type: Raster Layer
Geometry Type: null
Description: Data are derived from 2014-2018 (0-5 years) aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment & Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during three of the years recorded in the five-year dataset are thought to have significant impacts and likely mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as “very light”.
Description: A national roads vector dataset has been acquired from the USFS INFRA database ‘II_Road_Core_ATM’ table and vector features via the EDW and on 03/07/2019. INFRA is the official corporate transportation dataset for the USFS. Not all roads in the ‘II_Road_Core_ATM’ INFRA table have an associated vector feature and therefore are not used in the TCA analysis. The data were reattributed using the logic found in the table below to represent four new classes: (1) Unimproved, (2) Paved, (3) Light Duty, (4) Other, and Decommissioned roads.
Copyright Text: Source: USFS INFRA and EDW (March 07, 2019)
Description: A national roads vector dataset has been acquired from the USFS INFRA database ‘II_Road_Core_ATM’ table and vector features via the EDW and on 03/07/2019. INFRA is the official corporate transportation dataset for the USFS. Not all roads in the ‘II_Road_Core_ATM’ INFRA table have an associated vector feature and therefore are not used in the TCA analysis. The data were reattributed using the logic found in the table below to represent four new classes: (1) Unimproved, (2) Paved, (3) Light Duty, (4) Other, and Decommissioned roads.
Copyright Text: Source: USFS INFRA and EDW (March 07, 2019)
Description: A national roads vector dataset has been acquired from the USFS INFRA database ‘II_Road_Core_ATM’ table and vector features via the EDW and on 03/07/2019. INFRA is the official corporate transportation dataset for the USFS. Not all roads in the ‘II_Road_Core_ATM’ INFRA table have an associated vector feature and therefore are not used in the TCA analysis. The data were reattributed using the logic found in the table below to represent four new classes: (1) Unimproved, (2) Paved, (3) Light Duty, (4) Other, and Decommissioned roads.
Copyright Text: Source: USFS INFRA and EDW (March 07, 2019)
Description: A national roads vector dataset has been acquired from the USFS INFRA database ‘II_Road_Core_ATM’ table and vector features via the EDW and on 03/07/2019. INFRA is the official corporate transportation dataset for the USFS. Not all roads in the ‘II_Road_Core_ATM’ INFRA table have an associated vector feature and therefore are not used in the TCA analysis. The data were reattributed using the logic found in the table below to represent four new classes: (1) Unimproved, (2) Paved, (3) Light Duty, (4) Other, and Decommissioned roads.
Copyright Text: Source: USFS INFRA and EDW (March 07, 2019)
Description: A national roads vector dataset has been acquired from the USFS INFRA database ‘II_Road_Core_ATM’ table and vector features via the EDW and on 03/07/2019. INFRA is the official corporate transportation dataset for the USFS. Not all roads in the ‘II_Road_Core_ATM’ INFRA table have an associated vector feature and therefore are not used in the TCA analysis. The data were reattributed using the logic found in the table below to represent four new classes: (1) Unimproved, (2) Paved, (3) Light Duty, (4) Other, and Decommissioned roads.
Copyright Text: Source: USFS INFRA and EDW (March 07, 2019)
Description: A national roads vector dataset has been acquired from the USFS INFRA database ‘II_Road_Core_ATM’ table and vector features via the EDW and on 03/07/2019. INFRA is the official corporate transportation dataset for the USFS. Not all roads in the ‘II_Road_Core_ATM’ INFRA table have an associated vector feature and therefore are not used in the TCA analysis. The data were reattributed using the logic found in the table below to represent four new classes: (1) Unimproved, (2) Paved, (3) Light Duty, (4) Other, and Decommissioned roads.
Copyright Text: Source: USFS INFRA and EDW (March 07, 2019)
Description: This indicator uses LANDFIRE’s Vegetation Departure (VDEP) vegetation departure raster (US_140VDEP_20180620). The layer was developed using a similarity index to compare modeled reference conditions (vegetation types and succession classes) to the current abundance of seral stages within vegetation types.