Devendra Dahal
Neal J. Pastick
Stephen P. Boyte
Sujan Parajuli
Michael J. Oimoen
20210707
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, July 2021
raster digital data
https://doi.org/10.5066/P9FG6X9Q
Neal J. Pastick
Devendra Dahal
Bruce K. Wylie
Sujan Parajuli
Stephen P. Boyte
Zhouting Wu
20200222
Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony
publication
Remote Sensing
vol. 12, issue 4
n/a
MDPI AG
ppg. 725
https://doi.org/10.3390/rs12040725
These datasets provide early estimates of 2021 exotic annual grasses (EAG) fractional cover predicted on July 1 using satellite observation data available until June 28th. We develop and release one general EAG fractional cover map with cheatgrass (Bromus tectrorum) is dominant species but also includes number of other species, i.e., Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus hordeaceus spp. Hordeaceus, Bromus japonicus, Bromus madritensis L., Bromus madritensis L. ssp. rubens (L.) Duvin, Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitch, and medusahead (Taeniatherum caput-medusae). These datasets were generated leveraging field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring data (AIM) plots; Harmonized Landsat and Sentinel-2 (HLS) based Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI); other relevant environmental, vegetation, remotely sensed, and geophysical drivers; and artificial intelligence/machine learning techniques. A total 17,536 AIM plots from years 2016 - 2019 were used to train an ensemble of five-fold regression models using a cross-validation approach (each observation was used as test data once) that developed EAG fractional cover maps.
The geographic coverage includes arid and semi-arid rangelands in the western U.S.
The purpose for this dataset 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.
This release includes percent cover maps for exotic annual grass, cheatgrass, and medusahead as well as associated confidence maps. The values for percent cover maps range from 0 to 100, However, values for confidence maps range from 0 to 10. A high value on the confidence map denotes high confidence with the mapped pixels.
20210701
publication date
Annually
western United States
-124.9400
-109.0000
49.0000
31.1700
exotic species
annual grass
annual herbaceous
cheatgrass
exotic
Harmonized Landsat Sentinel
Texas brome
medusahead
NDVI
noxious
red brome
sagebrush
rye brome
soft brome
USGS Thesaurus
nonindigenous species
invasive species
remote sensing
Common geographic areas
Arizona
California
Colorado
Great Basin
Idaho
Kansas
Montana
Nebraska
Nevada
New Mexico
North Dakota
North Dakota
Oklahoma
Oregon
South Dakota
Texas
Utah
Washington
Wyoming
United States
western United States
None. Please see 'Distribution Info' for details.
None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations.
Devendra Dahal (CTR)
U.S. Geological Survey, LAND RESOURCES
mailing address
47914 252nd Street
Sioux Falls
SD
57198
US
605-594-2716
ddahal@contractor.usgs.gov
Denali and Tallgrass High Performance Computing (HPC) systems with Linux operating system were used for creating the data.
Open source Geospatial softwares, namely Geospatial Data Abstraction Library (GDAL version 2.4.2, https://gdal.org/download.html ), numpy, pandas, as well as sci-kit learn (https://scikit-learn.org/stable/) with in anaconda Python( version 3.6, https://www.anaconda.com/distribution/) were used to produce these datasets. However, these datasets can be read using proprietary software such as ArcGIS, and ERDAS Imagine.
Files names are:
ExoticAnnualGrass_July2021_PercentCover.img
ExoticAnnualGrass_July2021_Confidence.img
Stephen P. Boyte
2019
Near-real-time Herbaceous Annual Cover in the Sagebrush Ecosystem, USA, July 2019
dataset
https://www.sciencebase.gov
U.S. Geological Survey
https://doi.org/10.5066/P96PVZIF
Stephen P. Boyte
Bruce K. Wylie
2018
Near-real-time Herbaceous Annual Cover in the Sagebrush Ecosystem, USA, July 2018
dataset
https://www.sciencebase.gov
U.S. Geological Survey
https://doi.org/10.5066/P9RIV03D
Devendra Dahal
Bruce K. Wylie
Neal J. Pastick
Sujan Parajuli
2020
Early estimates of Annual Exotic Herbaceous Fractional Cover in the Sagebrush Ecosystem, USA, May 2020
dataset
https://www.sciencebase.gov
U.S. Geological Survey
https://doi.org/10.5066/P9ZZSX5Q
Devendra Dahal
Bruce K. Wylie
Neal J. Pastick
Sujan Parajuli
2020
Early estimates of Annual Exotic Herbaceous Fractional Cover in the Sagebrush Ecosystem, USA, May 2020
dataset
https://www.sciencebase.gov
U.S. Geological Survey
https://doi.org/10.5066/P9ZZSX5Q
Devendra Dahal
Neal J. Pastick
Stephen P. Boyte
Sujan Parajuli
Michael J. Oimoen
2021
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, May 2021, v1
dataset
https://www.sciencebase.gov
U.S. Geological Survey
https://doi.org/10.5066/p9avgrh8
Below is the modelling accuracy for each mapped species :
training test
Name | MAE | RAE | r | MAE | RAE | r
Exotic Annual Grass | 2.06 | 0.17 | 0.98 | 3.13 | 0.46 | 0.82
MAE = median absolute error
RAE = relative absolute error
r= correlation coefficient
No formal logical accuracy tests were conducted.
Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details.
A formal accuracy assessment of the horizontal positional information in the data set has not been conducted.
A formal accuracy assessment of the vertical positional information in the data set has either not been conducted, or is not applicable.
Nathaniel W. Chaney
Eric F. Wood
Alexander B. McBratney
Jonathan W. Hempel
Travis W. Nauman
Colby W. Brungard
Nathan P. Odgers
201607
POLARIS: A 30-meter probabilistic soil series map of the contiguous United States
publication
Geoderma
vol. 274
n/a
Elsevier BV
ppg. 54-67
https://doi.org/10.1016/j.geoderma.2016.03.025
Digital and/or Hardcopy
2016
publication date
POLARIS Soil Data
Input variable to model
D. B. Gesch
G. A. Evans
M. J. Oimoen
S. Arundel
2018
The National Elevation Dataset. American Society for Photogrammetry and Remote Sensing, pp. 83–110
tabular digital data
Digital and/or Hardcopy
2018
publication date
The National Elevation Dataset
Input variable to model
Collin Homer
Jon Dewitz
Suming Jin
George Xian
Catherine Costello
Patrick Danielson
Leila Gass
Michelle Funk
James Wickham
Stephen Stehman
Roger Auch
Kurt Riitters
202004
Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database
publication
ISPRS Journal of Photogrammetry and Remote Sensing
vol. 162
n/a
Elsevier BV
ppg. 184-199
https://doi.org/10.1016/j.isprsjprs.2020.02.019
Digital and/or Hardcopy
2016
publication date
National Land Cover Database (NLCD) 2016 Shrub Component products
Input variable to model
Matthew O. Jones
Brady W. Allred
David E. Naugle
Jeremy D. Maestas
Patrick Donnelly
Loretta J. Metz
Jason Karl
Rob Smith
Brandon Bestelmeyer
Chad Boyd
Jay D. Kerby
James D. McIver
20180919
Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for U.S. rangelands, 1984-2017
publication
Ecosphere
vol. 9, issue 9
n/a
Wiley
ppg. e02430
https://doi.org/10.1002/ecs2.2430
Digital and/or Hardcopy
2012
2019
publication date
Seed source forbs and grasses: Rangeland Analysis Platform (RAP)
Calculated mean absolute error of annual forbs and grasses from years 2012 to 2019 and used as input variable to model
M. M. Thornton
R. Shrestha
Y. Wei
P. E. Thornton
S. Kao
B. E. Wilson
2020
Daymet: Annual Climate Summaries on a 1-km Grid for North America, Version 4
publication
n/a
ORNL Distributed Active Archive Center
https://doi.org/10.3334/ornldaac/1852
Digital and/or Hardcopy
1989
2019
publication date
Climatic variables
Calculated climate normals from 30 years of annual climate data and used as input variable to model
Bruce McCune
Dylan Keon
20020224
Equations for potential annual direct incident radiation and heat load
publication
Journal of Vegetation Science
vol. 13, issue 4
n/a
Wiley
ppg. 603-606
https://doi.org/10.1111/j.1654-1103.2002.tb02087.x
Digital and/or Hardcopy
2002
publication date
potential annual direct incident radiation (PADR)
Input variable to model
G. R.Toevs
J. J. Taylor
C. S. Spurrier
W. C. MacKinnon
M. R. Bobo
2019
Bureau of Land Management Assessment, Inventory, and Monitoring Strategy: For integrated renewable resources management
tabular digital data
Denver, CO.
Bureau of Land Management, National Operations Center, Denver
https://doimspp.sharepoint.com/sites/blm-oc/drs/SitePages/BLM Terrestrial AIM Data (TerrADat and LMF).aspx
Digital and/or Hardcopy
2016
2019
observed
BLM AIM
Used for training models
Field observations of EAG fractional cover including individual species information (2016 – 2019) were quantified by the BLM AIM, https://aim.landscapetoolbox.org/). We compiled first hit (FH) Terrestrial AIM Database (TerrADat) and Landscape Monitoring Framework (LMF) databases of BLM AIM to aggregate exotic annual species with the annual grass cover. The AIM plots used in this study were divided into five random subsets.
20210703
Biophysical (i.e. soil texture fraction, available water capacity and organic matter content in the first 30 cm of the soil from POLARIS Soil Data [Chaney et al., 2016]); National Elevation Dataset [Gesch et al. 2018]; National Land Cover Database (NLCD) 2016 Shrub Component products [Homer et al. 2020]; seed source forbs and grasses (from Rangeland Analysis Platform (RAP) [Jones et al., 2018]); climatic variables (annual precipitation normal, annual mean temperature normal, summer precipitation normal, summer maximum temperature normal, winter precipitation normal, and winter mean temperature normal of 1985 – 2019 from Daymet [Thornton et al. 2020]); potential annual direct incident radiation (PADR) [McCune and Dylan 2002]; and phenocurves (HLS based cloud free weekly (NDVI and NDWI) composites [Pastick et al. 2020]), as well as other phenocurves derivatives (Maximum value of a season and time of the maximum value) were extracted at each field site. Spectral data was extracted coincident with the year of the field observations because annual grass cover can vary substantially from year to year based on local weather and site conditions.
20210703
An ensemble of five regression models were developed and optimized withholding one of the unique randomized subsets as test each time. The ensemble of models was constructed using scikit-learn xgboost machine learning algorithm with GridsearchCV hyperparameters optimization, and multioutput variable wrapper
20210703
The optimized models were then used to predict multiple grass cover maps (EAG, cheatgrass, and medusahead) for each permutation. The final annual grass cover maps were the median of the five predicted maps for the species and the median absolute error (MAE) of these five maps serves as an associated confidence map. The final percent cover maps are comparable to first hit values of AIM plots.
20210703
We applied a mask to areas above 2250m elevation because the AIM data used to train the models did not include enough points beyond this elevation to effectively model EAG. To target likely rangeland ecosystems, the mask also covered pixels classified by the 2016 National Land Cover Dataset (NLCD) as something other than shrub or grassland/herbaceous.
20210705
Raster
Grid Cell
80862
65613
1
Albers Conical Equal Area
29.5
45.5
-96.0
23.0
0.0
0.0
row and column
30.0
30.0
meters
North_American_Datum_1983
GRS 1980
6378137.0
298.257222101
Exotic Annual Grasses Percent Cover
Raster geospatial data file.
Producer Defined
OID
Internal object identifier.
Producer Defined
Sequential unique whole numbers that are automatically generated.
Value
Per pixel percent cover of exotic annual grasses
Producer Defined
255
Areas that are not grass or shrub defined by NLCD2016 and elevation above 2250 meters
Producer defined
0
100
Count
Number of raster cells with this value.
Producer Defined
3.0
3191319766.0
Exotic Annual Grass Confidence
Raster geospatial data file.
Producer Defined
OID
Internal object identifier.
Producer Defined
Sequential unique whole numbers that are automatically generated.
Value
Confidence on predicted exotic annual grasses percent cover
Producer Defined
255
Areas that are not grass or shrub defined by NLCD2016 and elevation above 2250 meters.
Producer defined
0
10
unitless
Count
Number of raster cells with this value.
Producer Defined
10.0
3191319765.0
This metadata represents datasets for all listed species, i.e. exotic annual grasses, cheatgrass, and medusahead. The datasets provide near real time estimation of 2021 annual percent cover for above mentioned species and the prediction was made as of July 1, 2021.
https://doi.org/10.5066/P9FG6X9Q
GS ScienceBase
U.S. Geological Survey
mailing address
Denver Federal Center, Building 810, Mail Stop 302
Denver
CO
80225
United States
1-888-275-8747
sciencebase@usgs.gov
Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty
Digital Data
https://doi.org/10.5066/P9FG6X9Q
None
20210707
Devendra Dahal (CTR)
U.S. Geological Survey, LAND RESOURCES
mailing address
47914 252nd Street
Sioux Falls
SD
57198
US
605-594-2716
ddahal@contractor.usgs.gov
FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata
FGDC-STD-001.1-1999