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Keyword: landscape management

Wildfire Hazard Potential (WHP) for the conterminous United States (270-m GRID), version 2018 continuous (2nd Edition)

Datasets Posted on: December 30, 2020
Federal wildfire managers often want to know, over large landscapes, where wildfires are likely to occur and how intense they may be. To meet this need we developed a map that we call wildfire hazard potential (WHP) – a raster geospatial product that can help to inform evaluations of wildfire risk or prioritization of fuels management needs across very large spatial scales (millions of acres).

Wildfire Hazard Potential (WHP) for the conterminous United States (270-m GRID), version 2018 classified (2nd Edition)

Datasets Posted on: December 30, 2020
Federal wildfire managers often want to know, over large landscapes, where wildfires are likely to occur and how intense they may be. To meet this need we developed a map that we call wildfire hazard potential (WHP) – a raster geospatial product that can help to inform evaluations of wildfire risk or prioritization of fuels management needs across very large spatial scales (millions of acres).

Species occurrence data from the aquatic eDNAtlas database

Datasets Posted on: December 30, 2020
The eDNA samples in the eDNAtlas database describe species occurrence locations and were collected by the U.S. Forest Service and numerous agencies that have partnered with the National Genomics Center for Wildlife and Fish Conservation (NGC) throughout the United States. This project began in 2015, but updates will include legacy data that were collected using the same protocol. The eDNAtlas database consists of three feature classes.

Digital surface, terrain, and canopy height models for Moscow Mountain in 2009

Datasets Posted on: December 30, 2020
This data publication contains three digital rasters with a spatial resolution of one meter: 1) a digital surface model (DSM), which represents the highest lidar return in each grid cell; 2) a digital terrain model (DTM), a representation of the ground surface with vegetation and other non-ground returns removed; and 3) a canopy height model (CHM), a representation of the height of vegetation above the ground surface.

Digital surface, terrain, and canopy height models for Boise Basin Experimental Forest in 2018

Datasets Posted on: December 30, 2020
This data publication contains three digital rasters with a spatial resolution of one meter: 1) a digital surface model (DSM), which represents the highest lidar return in each grid cell; 2) a digital terrain model (DTM), a representation of the ground surface with vegetation and other non-ground returns removed; and 3) a canopy height model (CHM), a representation of the height of vegetation above the ground surface.

Digital surface, terrain, and canopy height models for Priest River Experimental Forest in 2011

Datasets Posted on: December 30, 2020
This data publication contains three digital rasters with a spatial resolution of one meter: 1) a digital surface model (DSM), which represents the highest lidar return in each grid cell; 2) a digital terrain model (DTM), a representation of the ground surface with vegetation and other non-ground returns removed; and 3) a canopy height model (CHM), a representation of the height of vegetation above the ground surface.

Digital surface, terrain, and canopy height models for Deception Creek Experimental Forest in 2011

Datasets Posted on: December 30, 2020
This data publication contains three digital rasters with a spatial resolution of one meter: 1) a digital surface model (DSM), which represents the highest lidar return in each grid cell; 2) a digital terrain model (DTM), a representation of the ground surface with vegetation and other non-ground returns removed; and 3) a canopy height model (CHM), a representation of the height of vegetation above the ground surface.

Digital surface, terrain, and canopy height models for Priest River Experimental Forest in 2002 (2nd Edition)

Datasets Posted on: December 30, 2020
The data publication contains 1 meter raster data sets for three different digital elevation models (DEM) for the Priest River Experimental Forest in north Idaho in 2002. The first is a digital terrain model (DTM), which is the ground surface with all vegetation and human-made structures removed. The second is a digital surface model (DSM), which includes all vegetation and human-made structures.

Soil and vegetation responses to 1967-1968 disturbances on the Miller Creek Demonstration Forest: thirty year data

Datasets Posted on: December 30, 2020
This data publication includes vegetation and soil data acquired 30-years after an experiment was initiated (1967-1968) on the Miller Creek Demonstration Forest in western Montana, USA, to determine the effects of harvest in combination with different prescribed fire severities on conifer regeneration. A wildfire in August 1967 burned or reburned initial plots.

Wildfire Hazard Potential for the United States (270-m), version 2020 (3rd Edition)

Datasets Posted on: December 30, 2020
This dataset is the 2020 version of wildfire hazard potential (WHP) for the United States. The files included in this data publication represent an update to any previous versions of WHP or wildland fire potential (WFP) published by the USDA Forest Service. WHP is an index that quantifies the relative potential for wildfire that may be difficult to control, used as a measure to help prioritize where fuel treatments may be needed.

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