Historic and future trends in exotic annual grass (%) cover in the western US (1985 to 2019 and 2025 to 2040)
Dates
Start Date
1985-01-01
End Date
2040-12-31
Publication Date
2021-04-29
Citation
Pastick, N.J., Wylie, B.K., Rigge, M.B., Dahal, D., Boyte, S.P., Jones, M.O., Allred, B.W., Parajuli, S., and Wu, Z., 2021, Historic and future trends in exotic annual grass (%) cover in the western US (1985 to 2019 and 2025 to 2040): U.S. Geological Survey data release, https://doi.org/10.5066/P9Z85VET.
Summary
Exotic annual grasses [EAG] are one of the most damaging biological stressors in western North America. Despite numerous environmental and societal impacts associated with EAG there remains a need to enhance regional monitoring capabilities to better guide management and conservation efforts. Here we provide estimates of historic and potential future trends in EAG abundance that were developed using linear trend analysis and machine learning techniques at a 30-m spatial resolution. Specifically, these data represent historic (1985 to 2019) and potential future (2025-2040) rates of exotic annual grass change as estimated using Theil-Sen regression and a process-constrained, random forest model assuming only changes in climate under [...]
Summary
Exotic annual grasses [EAG] are one of the most damaging biological stressors in western North America. Despite numerous environmental and societal impacts associated with EAG there remains a need to enhance regional monitoring capabilities to better guide management and conservation efforts. Here we provide estimates of historic and potential future trends in EAG abundance that were developed using linear trend analysis and machine learning techniques at a 30-m spatial resolution. Specifically, these data represent historic (1985 to 2019) and potential future (2025-2040) rates of exotic annual grass change as estimated using Theil-Sen regression and a process-constrained, random forest model assuming only changes in climate under Representative Concentration Pathways (RCP 4.5 and 8.5), respectively.
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Related External Resources
Type: Related Primary Publication
Pastick, N.J., Wylie, B.K., Rigge, M.B., Dahal, D., Boyte, S.P., Jones, M.O., Allred, B.W., Parajuli, S., and Wu, Z., 2021, Rapid Monitoring of the Abundance and Spread of Exotic Annual Grasses in the Western United States Using Remote Sensing and Machine Learning: AGU Advances, v. 2, no. 2, https://doi.org/10.1029/2020AV000298.
