Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. Here, we integrated in situ observations, weekly composites of harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables (e.g. soils and topography) and machine learning techniques to develop fractional estimates of exotic annual grass cover at a 30-m spatial resolution from 2016 to 2018. Comparisons with Bureau of Land Management Assessment, Inventory, and Monitoring (AIM) field data (2016 and 2017) indicate good agreement between observed and mapped values (n = 1700; r = 0.83; mean absolute error [MAE] = 11), as constructed from an ensemble of regression tree models, with slightly lower agreement between mapped values and independent field observations (n = 112; r = 0.65; MAE =14). Geographic coverage of the study area includes portions of Oregon, California, Idaho, and Nevada.

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.

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.

Invasive annual grasses (IAGs) present a persistent challenge for the ecological management of rangelands, particularly the imperiled sagebrush biome in western North America. Cheatgrass (Bromus tectorum), medusahead (Taeniatherum caput-medusae), and Ventenata spp. are spreading across sagebrush rangelands and already occupy at least 200,000 kilometers squared (km sq.) of the intermountain west. The loss and degradation of native plant communities caused by IAGs threatens the persistence of sagebrush obligate species such as the Greater Sage-grouse (Centrocercus urophasianus) and pygmy rabbit (Brachylagus idahoensis). IAGs convert sagebrush landscapes to monocultures of non-native grasslands that substantially increase the risk of wildfire and degrade important ecosystem services including forage production and quality, soil stability, and carbon sequestration. As a result, the economic consequences of IAGs are substantial. Successful management of IAG invasions depends on extensive and accurate geospatial data that is accessible and interpretable by those charged with managing landscapes across the sagebrush biome. The past decade has seen a rapid growth in these products, yet researchers and managers both report a persistent research-implementation gap between the availability of products and their application. To address this problem, we first conducted a systematic literature review to inventory spatial products released over the past decade that map cheatgrass, medusahead, and Ventenata within the western U.S. at regional and national scales. We then developed a series of informational data resources to guide land managers in understanding and selecting the best available spatial data for their management needs. This Excel-readable .xlsx file version database product represents a searchable, filterable, and sortable collection of summary information for each IAG spatial data product, published from January 2010 to February 2021, we summarized as part of our review. An additional, machine-readable .csv file version of the database is also available for users.

Invasive annual grasses (IAGs) present a persistent challenge for the ecological management of rangelands, particularly the imperiled sagebrush biome in western North America. Cheatgrass (Bromus tectorum), medusahead (Taeniatherum caput-medusae), and Ventenata spp. are spreading across sagebrush rangelands and already occupy at least 200,000 kilometers squared (km sq.) of the intermountain west. The loss and degradation of native plant communities caused by IAGs threatens the persistence of sagebrush obligate species such as the Greater Sage-grouse (Centrocercus urophasianus) and pygmy rabbit (Brachylagus idahoensis). IAGs convert sagebrush landscapes to monocultures of non-native grasslands that substantially increase the risk of wildfire and degrade important ecosystem services including forage production and quality, soil stability, and carbon sequestration. As a result, the economic consequences of IAGs are substantial. Successful management of IAG invasions depends on extensive and accurate geospatial data that is accessible and interpretable by those charged with managing landscapes across the sagebrush biome. The past decade has seen a rapid growth in these products, yet researchers and managers both report a persistent research-implementation gap between the availability of products and their application. To address this problem, we first conducted a systematic literature review to inventory spatial products released over the past decade that map cheatgrass, medusahead, and Ventenata within the western U.S. at regional and national scales. We then developed a series of informational data resources to guide land managers in understanding and selecting the best available spatial data for their management needs. This Excel-readable .xlsx file version database product represents a searchable, filterable, and sortable collection of summary information for each IAG spatial data product, published from January 2010 to February 2021, we summarized as part of our review. An additional, machine-readable .csv file version of the database is also available for users.

Cheatgrass (Bromus tectorum) and other invasive annual grasses represent one of the single largest threats to the health and resilience of western rangelands. To address this challenge, the Western Governors Association (WGA)-appointed Western Invasive Species Council convened a cheatgrass working group to develop a new regional vision for invasive annual grass management across the West. Foundational to implementing this new vision is the creation of a common spatial map to guide strategic actions. The WGA cheatgrass working group sought to develop a 30-m base map of annual herbaceous cover to support a common spatial strategy for tackling invasive annual grasses across the western U.S. Here, we leverage three large-scale datasets to provide land managers with a product estimating the recent extent (2016-2018) of annuals across western rangelands. Input annual herbaceous datasets include Rangeland Analysis Platform (Jones et al. 2018), US Geological Survey (USGS) Harmonized Landsat and Sentinel (Pastick et al. 2020, Pastick et al. in prep) and USGS National Land Cover Database (NLCD) (Rigge et al. 2020). These three datasets are combined using a weighted mean approach to generate the final annual herbaceous mean cover product across the sagebrush biome (Jeffries and Finn 2019). References: Jeffries, M.I., and Finn, S.P. 2019. The Sagebrush Biome Range Extent, as Derived from Classified Landsat Imagery: U.S. Geological Survey data release, https://doi.org/10.5066/P950H8HS. Jones, M.O., Allred, B.W., Naugle, D.E., Maestas, J.D., Donnelly, P., Metz, L.J., Karl, J., Smith, R., Bestelmeyer, B., Boyd, C., Kerby, J.D., McIver, J.D. 2018. Innovation in rangeland monitoring: annual, 30m, plant functional type percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9, e02430. https://doi.org/10.1002/ecs2.2430. Pastick, N.J., Dahal, D., Wylie, B.K., Parajuli, S., Boyte, S.P., Wu, Z. 2020. Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony. Remote Sens. 12, 725. Pastick, N.J., Dahal, D., Wylie, B.K., Rigge, M.B., Jones, M.O, Allred, B.W., Boyte, S.P., Parajuli, S., and Wu, Z. In prep. Rapid monitoring of the occurrence and spread of exotic annual grasses in the western United States using remote sensing and machine learning. Global Change Biology. Reeves, M., and Mitchell, J. 2011. Extent of Coterminous US Rangelands: Quantifying Implications of Differing Agency Perspectives. Rangeland Ecology and Management 64: 585-597. Rigge, M., Shi, H., Homer, C., Danielson, P., Granneman, B. 2019. Long-term trajectories of fractional component change in the Northern Great Basin, USA. Ecosphere: e02762. Rigge, M., Homer, C., Cleeves, L., Meyer, D., Bunde, B., Shi, H., Xian, G., Bobo, M. 2020. Quantifying Western U.S. Rangelands as Fractional Components with Landsat. Remote Sensing. 12: 412.

Cheatgrass (Bromus tectorum) and other invasive annual grasses represent one of the single largest threats to the health and resilience of western rangelands. To address this challenge, the Western Governors Association (WGA)-appointed Western Invasive Species Council convened a cheatgrass working group to develop a new regional vision for invasive annual grass management across the West. Foundational to implementing this new vision is the creation of a common spatial map to guide strategic actions. The WGA cheatgrass working group sought to develop a 30-m base map of annual herbaceous cover to support a common spatial strategy for tackling invasive annual grasses across the western U.S. Here, we leverage three large-scale datasets to provide land managers with a product estimating the recent extent (2016-2018) of annuals across western rangelands. Input annual herbaceous datasets include Rangeland Analysis Platform (Jones et al. 2018), US Geological Survey (USGS) Harmonized Landsat and Sentinel (Pastick et al. 2020, Pastick et al. in prep) and USGS National Land Cover Database (NLCD) (Rigge et al. 2020). These three datasets are combined using a weighted mean approach to generate the final annual herbaceous mean cover product across the sagebrush biome (Jeffries and Finn 2019). References: Jeffries, M.I., and Finn, S.P. 2019. The Sagebrush Biome Range Extent, as Derived from Classified Landsat Imagery: U.S. Geological Survey data release, https://doi.org/10.5066/P950H8HS. Jones, M.O., Allred, B.W., Naugle, D.E., Maestas, J.D., Donnelly, P., Metz, L.J., Karl, J., Smith, R., Bestelmeyer, B., Boyd, C., Kerby, J.D., McIver, J.D. 2018. Innovation in rangeland monitoring: annual, 30m, plant functional type percent cover maps for U.S. rangelands, 1984-2017. Ecosphere 9, e02430. https://doi.org/10.1002/ecs2.2430. Pastick, N.J., Dahal, D., Wylie, B.K., Parajuli, S., Boyte, S.P., Wu, Z. 2020. Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony. Remote Sens. 12, 725. Pastick, N.J., Dahal, D., Wylie, B.K., Rigge, M.B., Jones, M.O, Allred, B.W., Boyte, S.P., Parajuli, S., and Wu, Z. In prep. Rapid monitoring of the occurrence and spread of exotic annual grasses in the western United States using remote sensing and machine learning. Global Change Biology. Reeves, M., and Mitchell, J. 2011. Extent of Coterminous US Rangelands: Quantifying Implications of Differing Agency Perspectives. Rangeland Ecology and Management 64: 585-597. Rigge, M., Shi, H., Homer, C., Danielson, P., Granneman, B. 2019. Long-term trajectories of fractional component change in the Northern Great Basin, USA. Ecosphere: e02762. Rigge, M., Homer, C., Cleeves, L., Meyer, D., Bunde, B., Shi, H., Xian, G., Bobo, M. 2020. Quantifying Western U.S. Rangelands as Fractional Components with Landsat. Remote Sensing. 12: 412.

We used Rangeland Condition Monitoring Assessment and Projection (RCMAP) maps of annual herbaceous fractional components (mostly invasive annual grasses) to calculate mean rate of change in invasive annual grass cover over five-year time periods. We also created a map that identifies zones of the sagebrush biome that could be prioritized for different management goals. The invasion of annual grasses has altered fire regimes and has contributed to the decline of sagebrush ecosystems. The vast expanse of annual grass invasions has required land managers to prioritize treatments in locations where they expect to be able to make a meaningful impact on invasion outcomes. Maps of invasive annual grass cover are useful in that they show the extent and severity of the invasion, but on their own, cover maps do not illustrate context such as how invasive cover is changing over time (i.e., increasing, stable, decreasing). The rate of change in invasive annual grass cover describes the trajectory of invasion. This information can be used by land managers to fine-tune priority locations and strategies for invasive species treatments in addition to other data sources (e.g., invasive annual grass cover maps).

We used Rangeland Condition Monitoring Assessment and Projection (RCMAP) maps of annual herbaceous fractional components (mostly invasive annual grasses) to calculate mean rate of change in invasive annual grass cover over five-year time periods. We also created a map that identifies zones of the sagebrush biome that could be prioritized for different management goals. The invasion of annual grasses has altered fire regimes and has contributed to the decline of sagebrush ecosystems. The vast expanse of annual grass invasions has required land managers to prioritize treatments in locations where they expect to be able to make a meaningful impact on invasion outcomes. Maps of invasive annual grass cover are useful in that they show the extent and severity of the invasion, but on their own, cover maps do not illustrate context such as how invasive cover is changing over time (i.e., increasing, stable, decreasing). The rate of change in invasive annual grass cover describes the trajectory of invasion. This information can be used by land managers to fine-tune priority locations and strategies for invasive species treatments in addition to other data sources (e.g., invasive annual grass cover maps).

Cheatgrass (Bromus tectorum) cover data were derived from the Bureau of Land Management’s Assessment Inventory, and Monitoring data and paired with geospatial data representing climate, weather, and disturbances. We derived covariates to capture both the climatic averages (1981-2010) that underlie long-term suitability, hereafter referred to as climate, and conditions during the year of observation, hereafter referred to as weather, that can drive annual variation in invasive grass cover (e.g., fall germination conditions were matched to cheatgrass cover sampled the following spring). Custom variables reflected cheatgrass natural history. Covariates describing geological context (e.g., aspect, elevation, soils), plant communities based on geophysical conditions and natural disturbance regimes, fire history (binary burned or unburned), human disturbance and infrastructure, and management history were used to represent processes that may limit or facilitate cheatgrass invasion