https://www.usgs.gov/centers/eros
Location
Mundt Federal Building
47914 252nd Street
Sioux Falls
, SD
57198-9801
USA
|
The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 “base” map as an intermediary....
Tags: AZ,
Arizona,
Arizona Plateau,
Black Hills,
Blue Mountains, All tags...
CA,
CO,
California,
Chihuahuan,
Chihuahuan Desert,
Colorado,
Colorado Plateau,
Columbia Plateau,
Desert,
Grand Canyon,
Great Basin,
Gunnison,
ID,
Idaho,
MT,
Mediterranean California,
Middle Rockies,
Mojave,
Montana,
ND,
NE,
NM,
NV,
Nebraska,
Nevada,
New Mexico,
North Dakota,
North Plains,
Northern Great Plains,
Northern Great Salt Lake Desert,
Northern Mountainous,
Northern Rocky Mountains,
OR,
Oregon,
Plains,
Plateau,
Rocky Mountains,
SD,
Sierra Nevada,
Sonoran,
Sonoran Desert,
South Dakota,
Southern Great Salt Lake Desert,
Southern Rocky Mountains,
Southwest Tablelands,
TX,
Texas,
The Rockies,
Three Forks,
UT,
United States,
Utah,
WA,
WY,
Wasatch,
Washington,
Western US,
Wyoming,
Wyoming Basin,
Yellowstone,
annual herbaceous,
back-in-time,
bare ground,
big sagebrush,
biota,
climate change,
environment,
geoscientificInformation,
grass,
grassland change,
herbaceous,
imageryBaseMapsEarthCover,
litter,
mts,
rangeland,
rangeland management,
sagebrush,
shrub,
shrubland,
shrubland change,
shrubland ecosystems,
shrublands,
terrestrial ecosystems,
time series,
trends,
vegetation,
vegetation change, Fewer tags
|
LANDFIRE's (LF) 2022 update (LF 2022) Existing Vegetation Cover (EVC) represents the vertically projected percent cover of the live canopy for a 30-m cell. EVC is produced separately for tree, shrub, and herbaceous lifeforms. Training data depicting percentages of canopy cover are obtained from plot-level ground-based visual assessments and lidar observations. These are combined with Landsat imagery (from multiple seasons), to inform models built independently for each lifeform. Tree, shrub, and herbaceous lifeforms each have a potential range from 10% to 100% (cover values less than 10% are binned into the 10% value). The three independent lifeform datasets are merged into a single product based on the dominant...
Tags: AK,
Alaska,
EVC,
Existing Vegetation Cover,
LANDFIRE 2022, All tags...
LF 2022,
OCONUS,
U.S. Forest Service (USFS),
U.S. Geological Survey (USGS),
US,
United States,
biota,
fires,
geographic information systems,
geospatial datasets,
hazard preparedness,
image collections,
imageryBaseMapsEarthCover,
raster digital data,
remote sensing, Fewer tags
|
LANDFIRE (LF) disturbance products are developed to provide temporal and spatial information related to landscape change. Historical Disturbance (HDist) is developed from the base annual LF disturbance products, and attribute code system, to represent the history of disturbance for a 10-year span. Each year's disturbance scenarios are checked against time relevant LF vegetation products to check for logical inconsistencies. Errant codes are flagged and updated to a discard code with the remaining disturbance types cross-walked/aggregated to Fuel Disturbance (FDist) types. HDist includes the year of disturbance that is recorded for that pixel. In LF 2022, the time since disturbance code is the same for both HDist...
Tags: HDist,
HI,
Hawaii,
Historical Disturbance,
LANDFIRE 2022, All tags...
LF 2022,
OCONUS,
U.S. Forest Service (USFS),
U.S. Geological Survey (USGS),
US,
United States,
biota,
disturbance,
fires,
geographic information systems,
geospatial datasets,
hazard preparedness,
image collections,
imageryBaseMapsEarthCover,
raster digital data,
remote sensing, Fewer tags
|
LANDFIRE (LF) 2022 Fuel Vegetation Type (FVT) represents the LF Existing Vegetation Type Ecological Systems (EVT) product, modified to represent pre-disturbance EVT in areas where disturbances have occurred over the past 10 years. Due to shifting EVT codes and labels throughout the years, the FVT codes are based on an early version of EVT codes translated from the current version. FVT is an input for fuel transitions related to disturbance. Fuel products in LF 2022 were created with LF 2016 Remap vegetation in non-disturbed areas. To designate disturbed areas where FVT is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance (FDist) product are used. All existing disturbances...
Tags: EVT,
Existing Vegetation Type,
FVT,
Fuel Vegetation Type,
IA, All tags...
Insular Areas,
LANDFIRE 2022,
LF 2022,
OCONUS,
Puerto Rico,
U.S. Forest Service (USFS),
U.S. Geological Survey (USGS),
US,
US Virgin Islands,
United States,
biota,
fires,
geographic information systems,
geospatial datasets,
hazard preparedness,
image collections,
imageryBaseMapsEarthCover,
raster digital data,
remote sensing, Fewer tags
|
LANDFIRE's (LF) 2022 Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand. CC is a vertical projection of the tree canopy cover onto an imaginary horizontal plane. CC supplies information for fire behavior models to determine the probability of crown fire initiation, provide input in the spotting model, calculate wind reductions, and to calculate fuel moisture conditioning. To create this product, plot level CC values are calculated using the canopy fuel estimation software, Forest Vegetation Simulator (FVS). Pre-disturbance CC and Canopy Height (CH) are used as predictors of disturbed CC using a linear regression equation per Fuel Vegetation Type (FVT), disturbance type/severity, and...
Tags: CC,
CONUS,
Canopy Cover,
Conterminous United States,
Continental U.S., All tags...
LANDFIRE 2022,
LF 2022,
U.S. Forest Service (USFS),
U.S. Geological Survey (USGS),
US,
United States,
biota,
fires,
geographic information systems,
geospatial datasets,
hazard preparedness,
image collections,
imageryBaseMapsEarthCover,
raster digital data,
remote sensing, Fewer tags
|
View more...
|
