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A Time Series of Herbaceous Annual Cover in the Sagebrush Ecosystem

Dates

Publication Date
Start Date
2000-01-01
End Date
2016-12-31

Citation

Boyte, S.P., and Wylie, B.K., 2017, A Time Series of Herbaceous Annual Cover in the Sagebrush Ecosystem: U.S. Geological Survey data release, https://doi.org/10.5066/F71J98QK.

Summary

We integrated 250-m enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) with land cover, biogeophysical (e.g., soils, topography) and climate data into regression-tree software (Cubist®). We integrated this data to create a time series of spatially explicit predictions of herbaceous annual vegetation cover in sagebrush ecosystems, with an emphasis on annual grasses. Annual grass cover in sagebrush ecosystems is highly variable year-to-year because it is strongly dependent on highly variable weather patterns, particularly precipitation timing and totals. Annual grass cover also reflects past disturbances and management decisions. We produced 17 consecutive years (2000 – 2016) [...]

Contacts

Point of Contact :
Stephen P Boyte
Originator :
Stephen P Boyte, Bruce K Wylie
Metadata Contact :
Stephen P Boyte
Distributor :
U.S. Geological Survey - ScienceBase
USGS Mission Area :
Land Resources
SDC Data Owner :
Earth Resources Observation and Science (EROS) Center

Attached Files

Click on title to download individual files attached to this item.

AnnGrassCover.7z
“Annual Grass Cover”
181.26 MB application/x-7z-compressed
ColorRamp.tif
“Color Ramp”
thumbnail 57.17 KB image/geotiff

Purpose

We developed these data so that a long-term record of spatially explicit herbaceous annual cover is available for researchers to conduct analyses of dynamic sagebrush ecosystems that are imperiled. These analyses should be helpful to land managers and policy makers as they strive to better understand the landscapes that they manage and set policy to conserve. Appropriate use of the data should be defined by the user; however, these data comes with caveats: 1) these estimates are relative abundances, not exact values (average model error = 5.8% when comparing all training data values to corresponding estimated values); 2) each pixel represent 250 meters and can include an horizontal error of up to 125 meters; 3) comparing this dataset to datasets with different spatial resolutions can lead to substantial differences in pixel values, especially in heterogenous areas where pixels mixed with multiple cover types are common.

Additional Information

Identifiers

Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/F71J98QK

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