Data represent predicted cheatgrass (Bromus tectorum) cover from a quantile regression model. We used quantile regression to model cheatgrass abundance as a function of climate, weather, and disturbance, treating outputs as low to high invasion scenarios.The model was developed using cheatgrass cover data collected by the Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) program, paired with covariates representing climate, weather, fire history, and disturbance. Quantile regression estimates different coefficients for each predictor variable at each quantile of interest, allowing a given environmental variable to be more or less important at the high end of the response distribution. The predictions at each statistical quantile of interest can be interpreted as invasion scenarios, as they correspond to low, medium, and high cheatgrass cover for a given set of environmental conditions. This metadata file describes three raster files, which share a geographic extent and resolution and which represent predictions from different quantiles of the same quantile regression model.

Data represent predicted cheatgrass (Bromus tectorum) cover from a quantile regression model. We used quantile regression to model cheatgrass abundance as a function of climate, weather, and disturbance, treating outputs as low to high invasion scenarios.The model was developed using cheatgrass cover data collected by the Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) program, paired with covariates representing climate, weather, fire history, and disturbance. Quantile regression estimates different coefficients for each predictor variable at each quantile of interest, allowing a given environmental variable to be more or less important at the high end of the response distribution. The predictions at each statistical quantile of interest can be interpreted as invasion scenarios, as they correspond to low, medium, and high cheatgrass cover for a given set of environmental conditions. This metadata file describes three raster files, which share a geographic extent and resolution and which represent predictions from different quantiles of the same quantile regression model.

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

This dataset provides an estimate of 2015 cheatgrass percent cover in the northern Great Basin at 250 meter spatial resolution. The dataset was generated by integrating eMODIS NDVI satellite data with independent variables that influence cheatgrass germination and growth into a regression-tree model. Individual pixel values range from 0 to 100 with an overall mean value of 9.85 and a standard deviation of 12.78. A mask covers areas not classified as shrub/scrub or grass/herbaceous by the 2001 National Land Cover Database. The mask also covers areas higher than 2000 meters in elevation because cheatgrass is unlikely to exist at more than 2% cover above this threshold. Cheatgrass is an invasive grass that has invaded much of the Great Basin. It grows from seed, usually early in spring, and rapidly matures, produces seed, and dies. Its presence can deplete early-season moisture reserves and put native vegetation at a competitive disadvantage. In addition, it contributes fine fuels that facilitate fire ignition and fire spread. Rangeland fires are often stand replacing events in sagebrush communities, commuities which historically dominated much of the northern Great Basin. Increasing fire return intervals, increasing fire intensities, land management practices, and development have all contributed to the fragmentation of sagebrush ecosystems, which are critical for greater sagegrouse survival.

Probability map of Cheatgrass occurrence in relation to vegetation, abiotic and anthropogenic features.

Probability map of Cheatgrass occurrence in relation to vegetation, abiotic, and anthropogenic features.

Probability map of Cheatgrass occurrence in relation to vegetation, abiotic, and anthropogenic features.

Probability map of Cheatgrass occurrence in relation to vegetation, abiotic and anthropogenic features.

Probability map of Cheatgrass occurrence in relation to vegetation, abiotic and anthropogenic features.

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.