Fractional estimates of exotic annual grass cover in dryland ecosystems of western United States (2016 – 2019)
Dates
Publication Date
2021-04-22
Start Date
2016
End Date
2019
Citation
Devendra, D., Pastick, N.J., Parajuli, S., and Wylie, B.K., 2021, Fractional estimates of exotic annual grass cover in dryland ecosystems of western United States (2016 – 2019): U.S. Geological Survey data release, https://doi.org/10.5066/P9XT1BV2.
Summary
The dryland ecosystems of the western United States have been invaded by exotic annual grasses, such as cheatgrass (Bromus tectorum L.), that has promoted increased fire activity and reduced biodiversity detrimental to socio-environmental systems. The use of remote sensing tools to monitor exotic annual grass cover and dynamics over large areas can support early detection and rapid response initiatives. This dataset was generated using in situ observations from Bureau of Land Management's (BLM) Assessment, Inventory, and Monitoring data (AIM) plots, weekly composites of harmonized Landsat and Sentinel-2 (HLS) data, relevant environmental, vegetation, remotely sensed, and geophysical factors and machine learning techniques to develop [...]
Summary
The dryland ecosystems of the western United States have been invaded by exotic annual grasses, such as cheatgrass (Bromus tectorum L.), that has promoted increased fire activity and reduced biodiversity detrimental to socio-environmental systems. The use of remote sensing tools to monitor exotic annual grass cover and dynamics over large areas can support early detection and rapid response initiatives. This dataset was generated using in situ observations from Bureau of Land Management's (BLM) Assessment, Inventory, and Monitoring data (AIM) plots, weekly composites of harmonized Landsat and Sentinel-2 (HLS) data, relevant environmental, vegetation, remotely sensed, and geophysical factors and machine learning techniques to develop fractional estimates of exotic annual grass cover at a 30-m spatial resolution for 2016 to 2019. A total of 10,906 AIM plots from years 2016 - 2019 were used to train an ensemble of regression tree models (n=5). Besides cheatgrass (Bromus tectorum), other species such as Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus mardritensis L.,Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., Taeniatherum caput-medusae were included in the study. The geographic coverage includes rangelands in the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas.
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Fractional estimates of exotic annual grass cover in dryland ecosystems of western United States 2016-2019.xml Original FGDC Metadata
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Confidence map of grass cover in the western US 2018.zip
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Confidence map of grass cover in the western US 2019.zip
255.96 MB
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Confidence map of grass cover in the western US 2016.zip
251.85 MB
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Confidence map of grass cover in the western US 2017.zip
254.57 MB
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Annual estimate of grass cover in the western US 2018.zip
448.85 MB
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Annual estimate of grass cover in the western US 2019.zip
465.55 MB
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Annual estimate of grass cover in the western US 2016.zip
475.11 MB
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Annual estimate of grass cover in the western US 2017.zip
483.48 MB
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Related External Resources
Type: Related Primary Publication
Pastick, N.J., Dahal, D., Wylie, B.K., Parajuli, S., Boyte, S.P., and Wu, Z., 2020, Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony: Remote Sensing, v. 12, no. 4, p. 725, https://doi.org/10.3390/rs12040725.
The goal of this research is to develop an exotic annual grass mapping system that leverages in situ observations, remotely sensed datasets, and data mining techniques for mapping exotic annual grass (%) cover (30-m spatial resolution) throughout drylands ecosystems in the western United States. 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 mapped values.
Data release was made private hours after going public. Author will need to update some information on abstract and metadata info before the release can go public.
