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, 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.

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.

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.

Phenological dynamics of terrestrial ecosystems reflect the response of the Earth's vegetation canopy to changes in climate and hydrology and are thus important to monitor operationally. The Exotic Annual Grass (EAG) phenology in the western U.S. rangeland based on 30m near seamless Harmonized Landsat and Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) weekly composites between 2016 and 2021 (Dahal et al., 2022) were processed using these 3 methods: (1) NDVI threshold-based method, (2) manual phenological metrics, and (3) modeling and mapping. The EAG phenology model produced eight metrics identifying the sustainable growth characteristics of 16 EAG species throughout level III Commission for Environmental Cooperation ecoregions, which cover over 190 million hectares of western U.S. potential rangeland for 2017 to 2021. The current suites of 30-m spatial resolution phenological metrics are Start of Season Time (SOST); Start of Season NDVI (SOSN); End of Season Time (EOST); End of Season NDVI (EOSN); Maximum Time (MAXT); Maximum NDVI (MAXN); Duration (DUR); and Amplitude (AMP). Datasets 2017 to 2020 were developed using manually interpreted training data from their respective year, but 2021 was developed from unseen NDVI datasets to test robustness of the phenology model. References: Dahal, D.; Pastick, N.J.; Boyte, S.P.; Parajuli, S.; Oimoen, M.J.; Megard, L.J. 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 2022, 14, doi:10.3390/rs14040807.

Phenological dynamics of terrestrial ecosystems reflect the response of the Earth's vegetation canopy to changes in climate and hydrology and are thus important to monitor operationally. The Exotic Annual Grass (EAG) phenology in the western U.S. rangeland based on 30m near seamless Harmonized Landsat and Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) weekly composites between 2016 and 2021 (Dahal et al., 2022) were processed using these 3 methods: (1) NDVI threshold-based method, (2) manual phenological metrics, and (3) modeling and mapping. The EAG phenology model produced eight metrics identifying the sustainable growth characteristics of 16 EAG species throughout level III Commission for Environmental Cooperation ecoregions, which cover over 190 million hectares of western U.S. potential rangeland for 2017 to 2021. The current suites of 30-m spatial resolution phenological metrics are Start of Season Time (SOST); Start of Season NDVI (SOSN); End of Season Time (EOST); End of Season NDVI (EOSN); Maximum Time (MAXT); Maximum NDVI (MAXN); Duration (DUR); and Amplitude (AMP). Datasets 2017 to 2020 were developed using manually interpreted training data from their respective year, but 2021 was developed from unseen NDVI datasets to test robustness of the phenology model. References: Dahal, D.; Pastick, N.J.; Boyte, S.P.; Parajuli, S.; Oimoen, M.J.; Megard, L.J. 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 2022, 14, doi:10.3390/rs14040807.

Phenological dynamics reflect the vegetation response to changes in weather, vegetation composition, plant life stages pertinent to both agricultural and fire management and are thus important to monitor operationally. The Exotic Annual Grass (EAG) phenology in the western U.S. rangeland based on 30-m Harmonized Landsat and Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) weekly composites between 2016 and 2024 (Dahal et al., 2022) were processed using these 3 methods: (1) NDVI threshold-based method, (2) manual phenological metrics, and (3) modeling and mapping. The EAG phenology model produced two metrics (Start of Season Time [SOST] and End of Season Time [EOST]) and calculated six metrics for identifying the sustained growth characteristics of 15 EAG species throughout 190 million hectares of western U.S. rangeland for 2017 to 2024. The current suite of phenological metrics are SOST; Start of Season NDVI (SOSN); EOST; End of Season NDVI (EOSN); Maximum Time (MAXT); Maximum NDVI (MAXN); Duration (DUR); and Amplitude (AMP). Datasets from 2017 to 2021 were developed using manually interpreted training data specific to each year, while datasets from 2022 to 2024 were produced using the same training set supplemented with additional automated datasets. References: Dahal, D.; Pastick, N.J.; Boyte, S.P.; Parajuli, S.; Oimoen, M.J.; Megard, L.J. 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 2022, 14, doi:10.3390/rs14040807.

Phenological dynamics reflect the vegetation response to changes in weather, vegetation composition, plant life stages pertinent to both agricultural and fire management and are thus important to monitor operationally. The Exotic Annual Grass (EAG) phenology in the western U.S. rangeland based on 30-m Harmonized Landsat and Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) weekly composites between 2016 and 2024 (Dahal et al., 2022) were processed using these 3 methods: (1) NDVI threshold-based method, (2) manual phenological metrics, and (3) modeling and mapping. The EAG phenology model produced two metrics (Start of Season Time [SOST] and End of Season Time [EOST]) and calculated six metrics for identifying the sustained growth characteristics of 15 EAG species throughout 190 million hectares of western U.S. rangeland for 2017 to 2024. The current suite of phenological metrics are SOST; Start of Season NDVI (SOSN); EOST; End of Season NDVI (EOSN); Maximum Time (MAXT); Maximum NDVI (MAXN); Duration (DUR); and Amplitude (AMP). Datasets from 2017 to 2021 were developed using manually interpreted training data specific to each year, while datasets from 2022 to 2024 were produced using the same training set supplemented with additional automated datasets. References: Dahal, D.; Pastick, N.J.; Boyte, S.P.; Parajuli, S.; Oimoen, M.J.; Megard, L.J. 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 2022, 14, doi:10.3390/rs14040807.

Phenological dynamics of terrestrial ecosystems reflect the response of the Earth's vegetation canopy to changes in climate and hydrology and are thus important to monitor operationally. The Exotic Annual Grass (EAG) phenology in the western U.S. rangeland based on 30m near seamless Harmonized Landsat and Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) weekly composites between 2016 and 2023 (Dahal et al., 2022) were processed using these 3 methods: (1) NDVI threshold-based method, (2) manual phenological metrics, and (3) modeling and mapping. The EAG phenology model produced two metrics (Start of Season Time (SOST) and End of Season Time (EOST)) and calculated six metrics for identifying the sustained growth characteristics of 16 EAG species throughout level III Commission for Environmental Cooperation ecoregions, which cover over 190 million hectares of western U.S. potential rangeland for 2017 to 2021. The current suites of 30-m spatial resolution phenological metrics are SOST; Start of Season NDVI (SOSN); EOST; End of Season NDVI (EOSN); Maximum Time (MAXT); Maximum NDVI (MAXN); Duration (DUR); and Amplitude (AMP). Datasets 2017 to 2021 were developed using manually interpreted training data from their respective year, but 2022 and 2023 was developed from unseen NDVI datasets to test robustness of the phenology model. References: Dahal, D.; Pastick, N.J.; Boyte, S.P.; Parajuli, S.; Oimoen, M.J.; Megard, L.J. 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 2022, 14, doi:10.3390/rs14040807.