We integrated 250-m enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) with land cover, biogeophysical (e.g., soils, topography) and climate data into regression-tree software (Cubist®). We integrated this data to create a time series of spatially explicit predictions of herbaceous annual vegetation cover in sagebrush ecosystems, with an emphasis on annual grasses. Annual grass cover in sagebrush ecosystems is highly variable year-to-year because it is strongly dependent on highly variable weather patterns, particularly precipitation timing and totals. Annual grass cover also reflects past disturbances and management decisions. We produced 17 consecutive years (2000 – 2016) of herbaceous annual vegetation cover data in 1% cover increments, which allows extensive analyses of annual grass dynamics. Annual grasses, especially cheatgrass (Bromus tectorum), result in grass-fire cycles that endanger human-built structures, reduce air quality, and compromise hunting resources by destroying wildlife habitat. These sagebrush ecosystems, whose character and composition are dramatically altered, are critical for water quality and the survival of sagebrush-dependent wildlife. The geographic coverage of the study area includes the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas. We applied a mask to areas above 2250 m elevation because annual grasses are unlikely to exist at substantial cover at or above this threshold (Boyte et al. 2016. doi: 10.1016/j.rama.2016.03.002). To target likely sagebrush ecosystems, the same mask also covered pixels classified as something other than shrub or grassland/herbaceous by the National Land Cover Database(NLCD). For the study area, the annual herbaceous cover mean for the 17-year period equaled 7.31% with a standard deviation of 7.03%. The study area's mean pixel value for the study period ranged from 0-96%, although individual years' pixel values reached 100%. Training data (n = 33,746) were harvested from 30-meter spatially explicit 2001 cheatgrass (Peterson, E.B. 2005. doi:10.1080/01431160500127815), 2006 annual grass (Peterson, E. B. 2007. A map of annual grasses in the Owyhee Uplands, Spring 2006, derived from multitemporal Landsat 5 TM imagery. Report for the U.S.D.I. Bureau of Land Management, Nevada State Office, Reno, by the Nevada Natural Heritage Program, Carson City, Nevada), and 2013 - 2015 herbaceous annual (data acquired from the USGS sagebrush ecosysem team at the Center for Earth Resources Observation and Science) datasets. These datasets were spatially averaged to 250 meters to match the resolution of the remotely sensed data. References: Boyte, S.P, B.K. Wylie, and D.J. Major. 2016. Cheatgrass percent cover change: Comparing recent estimates to climate change-driven predictions in the northern Great Basin. Rangeland Ecology and Management 69:265-279. https://dx.doi.org/10.1016/j.rama.2016.03.002. Gu, Y. B.K. Wylie, S.P. Boyte, J. Picotte, D.M. Howard, K. Smith, K.J. Nelson. 2016. An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely sensed data. Remote Sensing. https://dx.doi.org/10.3390/rs8110943. Peterson, E.B. 2005. Estimating cover of an invasive grass (Bromus tectorum) using tobit regression and phenology derived from two dates of Landsat ETM+ data. International Journal of Remote Sensing 26:2491-2507. http://dx.doi.org/10.1080/01431160500127815. Peterson, E.B. 2007. 2007. A map of annual grasses in the Owyhee Uplands, Spring 2006, derived from multitemporal Landsat 5 TM inagery. Report prepared for: USDOI, Bureau of Land Management, Nevada State Office, Reno, NV. Nevada Natural Heritage program website -- http://heritage.nv.gov/gis; last accessed October 12, 2017.

We integrated 250-m enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) with land cover, biogeophysical (e.g., soils, topography) and climate data into regression-tree software (Cubist®). We integrated this data to create a time series of spatially explicit predictions of herbaceous annual vegetation cover in sagebrush ecosystems, with an emphasis on annual grasses. Annual grass cover in sagebrush ecosystems is highly variable year-to-year because it is strongly dependent on highly variable weather patterns, particularly precipitation timing and totals. Annual grass cover also reflects past disturbances and management decisions. We produced 17 consecutive years (2000 – 2016) of herbaceous annual vegetation cover data in 1% cover increments, which allows extensive analyses of annual grass dynamics. Annual grasses, especially cheatgrass (Bromus tectorum), result in grass-fire cycles that endanger human-built structures, reduce air quality, and compromise hunting resources by destroying wildlife habitat. These sagebrush ecosystems, whose character and composition are dramatically altered, are critical for water quality and the survival of sagebrush-dependent wildlife. The geographic coverage of the study area includes the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas. We applied a mask to areas above 2250 m elevation because annual grasses are unlikely to exist at substantial cover at or above this threshold (Boyte et al. 2016. doi: 10.1016/j.rama.2016.03.002). To target likely sagebrush ecosystems, the same mask also covered pixels classified as something other than shrub or grassland/herbaceous by the National Land Cover Database(NLCD). For the study area, the annual herbaceous cover mean for the 17-year period equaled 7.31% with a standard deviation of 7.03%. The study area's mean pixel value for the study period ranged from 0-96%, although individual years' pixel values reached 100%. Training data (n = 33,746) were harvested from 30-meter spatially explicit 2001 cheatgrass (Peterson, E.B. 2005. doi:10.1080/01431160500127815), 2006 annual grass (Peterson, E. B. 2007. A map of annual grasses in the Owyhee Uplands, Spring 2006, derived from multitemporal Landsat 5 TM imagery. Report for the U.S.D.I. Bureau of Land Management, Nevada State Office, Reno, by the Nevada Natural Heritage Program, Carson City, Nevada), and 2013 - 2015 herbaceous annual (data acquired from the USGS sagebrush ecosysem team at the Center for Earth Resources Observation and Science) datasets. These datasets were spatially averaged to 250 meters to match the resolution of the remotely sensed data. References: Boyte, S.P, B.K. Wylie, and D.J. Major. 2016. Cheatgrass percent cover change: Comparing recent estimates to climate change-driven predictions in the northern Great Basin. Rangeland Ecology and Management 69:265-279. https://dx.doi.org/10.1016/j.rama.2016.03.002. Gu, Y. B.K. Wylie, S.P. Boyte, J. Picotte, D.M. Howard, K. Smith, K.J. Nelson. 2016. An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely sensed data. Remote Sensing. https://dx.doi.org/10.3390/rs8110943. Peterson, E.B. 2005. Estimating cover of an invasive grass (Bromus tectorum) using tobit regression and phenology derived from two dates of Landsat ETM+ data. International Journal of Remote Sensing 26:2491-2507. http://dx.doi.org/10.1080/01431160500127815. Peterson, E.B. 2007. 2007. A map of annual grasses in the Owyhee Uplands, Spring 2006, derived from multitemporal Landsat 5 TM inagery. Report prepared for: USDOI, Bureau of Land Management, Nevada State Office, Reno, NV. Nevada Natural Heritage program website -- http://heritage.nv.gov/gis; last accessed October 12, 2017.

The dataset provides an estimate of 2020 herbaceous mostly annual fractional cover predicted on May 1st with an emphasis on annual exotic grasses Historically, similar maps were produced at a spatial resolution of 250m (Boyte et al. 2019 https://doi.org/10.5066/P9ZEK5M1., Boyte et al. 2018 https://doi.org/10.5066/P9KSR9Z4.), but we are now mapping at a 30m resolution (Pastick et al. 2020 doi:10.3390/rs12040725). 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 (https://hls.gsfc.nasa.gov/); relevant environmental, vegetation, remotely sensed, and geophysical drivers. These data were integrating into regression tree (RT) models for prediction of weekly cloud free Normalized Difference Vegetation Index (NDVI). A total 11,002 AIM plots from years 2016 - 2019 were used to train an ensemble of five-fold RT models using a cross-validation approach (each observation was used as test data once). Cheatgrass (Bromus tectrorum) is the most common species, however, number of other species were included in this study: Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus madritensis L., Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., and Taeniatherum caput-medusae. The geographic coverage includes rangelands in the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas. We applied a mask to areas above 2700-m elevation to target areas of unlikely substantial annual grass cover. To target likely sagebrush ecosystems, the mask also removed pixels classified as something other than shrub or grassland/herbaceous by the 2016 National Land Cover Dataset (NLCD).

The dataset provides an estimate of 2020 herbaceous mostly annual fractional cover predicted on May 1st with an emphasis on annual exotic grasses Historically, similar maps were produced at a spatial resolution of 250m (Boyte et al. 2019 https://doi.org/10.5066/P9ZEK5M1., Boyte et al. 2018 https://doi.org/10.5066/P9KSR9Z4.), but we are now mapping at a 30m resolution (Pastick et al. 2020 doi:10.3390/rs12040725). 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 (https://hls.gsfc.nasa.gov/); relevant environmental, vegetation, remotely sensed, and geophysical drivers. These data were integrating into regression tree (RT) models for prediction of weekly cloud free Normalized Difference Vegetation Index (NDVI). A total 11,002 AIM plots from years 2016 - 2019 were used to train an ensemble of five-fold RT models using a cross-validation approach (each observation was used as test data once). Cheatgrass (Bromus tectrorum) is the most common species, however, number of other species were included in this study: Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonicus, Bromus madritensis L., Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc., and Taeniatherum caput-medusae. The geographic coverage includes rangelands in the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas. We applied a mask to areas above 2700-m elevation to target areas of unlikely substantial annual grass cover. To target likely sagebrush ecosystems, the mask also removed pixels classified as something other than shrub or grassland/herbaceous by the 2016 National Land Cover Dataset (NLCD).

This dataset provides a near-real-time estimate of 2017 herbaceous annual cover with an emphasis on annual grass (Boyte and Wylie. 2016. Near-real-time cheatrass percent cover in the Northern Great Basin, USA, 2015. Rangelands 38:278-284.) This estimate was based on remotely sensed enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) data gathered through June 19, 2017. This is the second iteration of an early estimate of herbaceous annual cover for 2017 over the same geographic area. The previous dataset used eMODIS NDVI data gathered through May 1 (https://doi.org/10.5066/F7445JZ9). The pixel values for this most recent estimate ranged from 0 to100% with an overall mean value of 7.02% and a standard deviation of +/-9.08. The model's test mean error rate (n = 1664), based on nine different randomizations, equaled 5.2% with a standard deviation of +/- 0.09. Overall statistics between the May and June datasets were similar. However, some individual pixel differences can be considerable and are attributed to changing conditions on the ground that are reflected in the satellite data. These changes can influence how the models relate the dependent variable to the independent variables. Both datasets were generated by integrating ground-truth measurements of annual herbaceous percent cover with 250-m spatial resolution eMODIS NDVI satellite derived data and geophysical variables into regression-tree software. The geographic coverage includes the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas. We applied a mask to areas above 2250-m elevation because annual grasses are unlikely to exist at substantial cover above this threshold. To target likely sagebrush ecosystems, the mask also covered pixels classified as something other than shrub or grassland/herbaceous by the 2011 National Land Cover Dataset (NLCD). The model was not trained on any masked pixels. Cheatgrass (Bromus tectorum) is the most common annual grass in the study area. It grows from seed, usually in spring, matures quickly, produces seed, and dies. After dying, cheatgrass contributes fine fuels that facilitate fire ignition and spread throughout sagebrush ecosystems. These fires remove sagebrush stands. Increasing fire frequencies, land management practices, and development have all contributed to the fragmentation of the once expansive sagebrush ecosystems. These ecosystems are critical for water quality, reduced fire threats, and the survival of sagebrush-dependent wildlife.

This dataset provides a near-real-time estimate of 2017 herbaceous annual cover with an emphasis on annual grass (Boyte and Wylie. 2016. Near-real-time cheatrass percent cover in the Northern Great Basin, USA, 2015. Rangelands 38:278-284.) This estimate was based on remotely sensed enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) data gathered through June 19, 2017. This is the second iteration of an early estimate of herbaceous annual cover for 2017 over the same geographic area. The previous dataset used eMODIS NDVI data gathered through May 1 (https://doi.org/10.5066/F7445JZ9). The pixel values for this most recent estimate ranged from 0 to100% with an overall mean value of 7.02% and a standard deviation of +/-9.08. The model's test mean error rate (n = 1664), based on nine different randomizations, equaled 5.2% with a standard deviation of +/- 0.09. Overall statistics between the May and June datasets were similar. However, some individual pixel differences can be considerable and are attributed to changing conditions on the ground that are reflected in the satellite data. These changes can influence how the models relate the dependent variable to the independent variables. Both datasets were generated by integrating ground-truth measurements of annual herbaceous percent cover with 250-m spatial resolution eMODIS NDVI satellite derived data and geophysical variables into regression-tree software. The geographic coverage includes the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas. We applied a mask to areas above 2250-m elevation because annual grasses are unlikely to exist at substantial cover above this threshold. To target likely sagebrush ecosystems, the mask also covered pixels classified as something other than shrub or grassland/herbaceous by the 2011 National Land Cover Dataset (NLCD). The model was not trained on any masked pixels. Cheatgrass (Bromus tectorum) is the most common annual grass in the study area. It grows from seed, usually in spring, matures quickly, produces seed, and dies. After dying, cheatgrass contributes fine fuels that facilitate fire ignition and spread throughout sagebrush ecosystems. These fires remove sagebrush stands. Increasing fire frequencies, land management practices, and development have all contributed to the fragmentation of the once expansive sagebrush ecosystems. These ecosystems are critical for water quality, reduced fire threats, and the survival of sagebrush-dependent wildlife.