Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2022 (ver 6.0, July 2022)
Dates
Publication Date
2022-05-11
Start Date
2022-05
End Date
2022-07
Revision
2022-07-01
Citation
Dahal, D., Boyte, S.P., Parajuli, S., Pastick, N.J., Oimoen, M.J., and Shrestha, D., 2022, Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2022 (ver 6.0, July 2022): U.S. Geological Survey data release, https://doi.org/10.5066/P9FVYOGD.
Summary
These datasets provide early estimates of 2022 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a bi-weekly basis from May to early July. The EAG estimates are developed within one week of the latest satellite observation used for that version. Each bi-weekly release contains four fractional cover maps along with their corresponding confidence maps for: 1) a group of 16 species of EAGs, 2) cheatgrass (Bromus tectorum); 3) medusahead (Taeniatherum caput-medusae); and 4) Sandberg bluegrass (Poa secunda). These datasets were generated leveraging field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) data plots; Harmonized Landsat and Sentinel-2 [...]
Summary
These datasets provide early estimates of 2022 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a bi-weekly basis from May to early July. The EAG estimates are developed within one week of the latest satellite observation used for that version. Each bi-weekly release contains four fractional cover maps along with their corresponding confidence maps for: 1) a group of 16 species of EAGs, 2) cheatgrass (Bromus tectorum); 3) medusahead (Taeniatherum caput-medusae); and 4) Sandberg bluegrass (Poa secunda). These datasets were generated leveraging field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) data plots; Harmonized Landsat and Sentinel-2 (HLS) based Normalized Difference Vegetation Index (NDVI); HLS-based Normalized Difference Water Index (NDWI); other relevant environmental, vegetation, remotely sensed, and geophysical drivers; and artificial intelligence/machine learning techniques. A total of 19,415 AIM plots from years 2016 – 2021 were used to train an ensemble of five-fold regression-tree models using a cross-validation approach (each observation was used as test data once) that developed all the fractional cover maps. The geographic coverage includes arid and semi-arid rangelands in the western U.S at or below 2350-m elevation.
Note:
Maps of May 6th, 2022 were developed using satellite observation data no later than April 29.
Maps of May 18th, 2022 were developed using satellite observation data no later than May 13.
Maps of June 3rd, 2022 were developed using satellite observation data no later than May 27.
Maps of June 15th, 2022 were developed using satellite observation data no later than June 11.
Maps of July 1st, 2022 were developed using satellite observation data no later than June 23.
First release: 2022
Revised: May 17, 2022 (ver. 2.0)
Revised: May 25, 2022 (ver. 3.0)
Revised: June 03, 2022 (ver. 4.0)
Revised: June 15, 2022 (ver. 5.0)
Revised: July 01, 2022 (ver. 6.0)
Click on title to download individual files attached to this item.
Metadata_EAG_July2022_v6.0.xml Original FGDC Metadata
View
39.36 KB
application/fgdc+xml
PercentCover_ArcGIS_ColorRamp.lyr
27 KB
application/x-tika-msoffice
PercentCover_QGIS_ColorRamp.qml
2.24 KB
text/plain
EAG_May2022_PercentCover.png
211.95 KB
image/png
RevisonHistory_v6.txt
1.46 KB
text/plain
Purpose
The purpose for these releases is to provide land managers and researchers with near real time estimates of spatially explicit exotic annual grasses percent cover in the study area. Appropriate use of the data should be defined by the user; however, this data comes with caveats. First, these estimates should be viewed as relative abundances. Second, comparing this dataset to similar datasets with different spatial resolutions or different dates can lead to substantial differences between dataset values.