Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2024
Dates
Publication Date
2024-04-19
Start Date
2024-04
End Date
2024-06
Last Revision
2024-06-21
Citation
Dahal, D., Boyte, S.P., Postma, K., Pastick, N.J., and Megard, L., 2024, Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2024 (ver. 10.0, June 2024): U.S. Geological Survey data release, https://doi.org/10.5066/P1Y5TZBM.
Summary
These datasets provide early estimates of 2024 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a weekly basis from April to late June. Typically, the EAG estimates are publicly released within 7-13 days of the latest satellite observation used for that version. Each weekly release contains five fractional cover maps along with their corresponding confidence maps for: 1) a group of 16 species of EAGs, 2) cheatgrass (Bromus tectorum); 3) Field Brome (Bromus arvensis); 4) medusahead (Taeniatherum caput-medusae); and 5) Sandberg bluegrass (Poa secunda). These datasets were generated leveraging field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) [...]
Summary
These datasets provide early estimates of 2024 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a weekly basis from April to late June. Typically, the EAG estimates are publicly released within 7-13 days of the latest satellite observation used for that version. Each weekly release contains five fractional cover maps along with their corresponding confidence maps for: 1) a group of 16 species of EAGs, 2) cheatgrass (Bromus tectorum); 3) Field Brome (Bromus arvensis); 4) medusahead (Taeniatherum caput-medusae); and 5) 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); other relevant environmental, vegetation, remotely sensed, and geophysical drivers; and artificial intelligence/machine learning techniques. A total of 38,242 AIM plots from years 2016–2023 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 and as training data four times) that developed all the fractional cover maps. The geographic coverage includes arid and semi-arid rangelands in the western U.S classified as shrubs or grassland/herbaceous by the 2021 National Land Cover Database at or below 2350-m elevation.
Note:
Maps of April 19th, 2024, were developed using satellite observation data no later than April 12.
Maps of April 29th, 2024, were developed using satellite observation data no later than April 21.
Maps of May 3rd, 2024, were developed using satellite observation data no later than April 26.
Maps of May 10th, 2024, were developed using satellite observation data no later than May 3.
Maps of May 17th, 2024, were developed using satellite observation data no later than May 10.
Maps of May 22nd, 2024, were developed using satellite observation data no later than May 18.
Maps of May 31st, 2024, were developed using satellite observation data no later than May 24.
Maps of June 6th, 2024, were developed using satellite observation data no later than June 1.
Maps of June 14th, 2024, were developed using satellite observation data no later than June 10.
Maps of June 21st, 2024, were developed using satellite observation data no later than June 16.
Releases:
First Release: April 19, 2024 (ver. 1.0)
Revised: April 29, 2024 (ver. 2.0)
Revised: May 03, 2024 (ver. 3.0)
Revised: May 10, 2024 (ver. 4.0)
Revised: May 17, 2024 (ver. 5.0)
Revised: May 22, 2024 (ver. 6.0)
Revised: May 31, 2024 (ver. 7.0)
Revised: June 6, 2024 (ver. 8.0)
Revised: June 14, 2024 (ver. 9.0)
Revised: June 21, 2024 (ver. 10.0)
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Metadata_EAG_June16.2024_v10.0.xml Original FGDC Metadata
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RevisonHistory_v10.txt
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Related External Resources
Type: Related Primary Publication
Dahal, Devendra, Neal J. Pastick, Stephen P. Boyte, Sujan Parajuli, Michael J. Oimoen, and Logan J. Megard. 2022. "Multi-Species Inference of Exotic Annual and Native Perennial Grasses in Rangelands of the Western United States Using Harmonized Landsat and Sentinel-2 Data" Remote Sensing 14, no. 4: 807. https://doi.org/10.3390/rs14040807
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.
