DOI: 10.5281/zenodo.7876393
title: Mean Annual Herbaceous Cover for the Sagebrush Biome, USA (2020 - 2022)

description: <strong>Abstract: </strong>Cheatgrass (Bromus tectorum) and other invasive annual grasses are the single largest threat to sagebrush rangeland health and resilience (Doherty et al. 2022). To address this challenge, NRCS’ Working Lands for Wildlife, the Western Governors Association (WGA), and diverse partners are helping implement a new proactive spatial plan to tackle invasive annuals known as “Defend the Core” (Maestas et al. 2022). Foundational to implementing this new approach is the creation of a common spatial map of invasion severity to guide strategic actions. In 2020, a WGA-led cheatgrass working group an annual herbaceous cover map that summarized the extent of annuals using three remotely-sensed data products for the years 2016 - 2018 (Maestas et al. 2020). This updated product reports annual herbaceous cover for the years 2020 - 2022 using only cover data from the Rangeland Analysis Platform. Data coverage includes all rangelands within the U.S. sagebrush biome. <strong>Purpose: </strong>The goal of the annual herbaceous cover map is to support a common spatial strategy for tackling invasive annual grasses across the western U.S. As with all remote sensing-based products, the map presented here is best used alongside local knowledge and data. The map is intended to facilitate cross-boundary regional planning, and it is anticipated that state and local partners will further refine priority areas for management using additional information. <strong>Methodology: </strong>This product used the Rangeland Analysis Platform V3 cover product from years 2020, 2021, and 2022. A mean composite was generated from the yearly raster data using the ‘annual herbaceous functional type’ (AFG) layer, representing percent cover of annual forb and grasses. The methodology for producing the cover product is described in Allred et al. 2021. Cover error for AFG in RAP Cover V3 was 7.0% (MAE) and 11.0% (RMSE). More information can be found at https://rangelands.app/products/. The data is clipped to the extent of the sagebrush biome using the data from Jeffries and Finn (2019). Some important considerations must be kept in mind when using this product. First, the data layer depicts cover for all annual herbaceous species, not just invasive annual grasses. However, annual herbaceous cover is a useful surrogate for invasive annuals on arid rangelands in the sagebrush biome where native annuals typically represent a small proportion of vegetation cover most years. Second, the product reflects modeled predictions, so error must also be considered. This data product is best suited to highlight patterns of invasive annuals where they are known to be widely distributed and cannot be used in isolation to confirm the absence of invasive species. <strong>Time Period of Data:</strong> Start Date: 2020-01-01 End Date: 2022-12-31 <strong>Coordinate Reference System</strong>: Data are in WGS84 Geographic Coordinate System (EPSG:4326); spatial resolution is approximately 30m. <strong>Data format: </strong>Cloud Optimized GeoTiff <strong>Data Value: </strong>Percent (%) annual herbaceous cover <strong>Data type: </strong>Byte <strong>Nodata value: </strong>255 <strong>Bounding Coordinates:</strong> West: -122.116081408 East: -102.260259357 North: 49.0016614443 South: 34.2918384983 <strong>Keywords:</strong> Terrestrial ecosystems Vegetation Invasive species Grassland ecosystems Remote sensing Grasslands Cheatgrass Great Basin Biota Geoscientific information <strong>Access Constraints: </strong>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ Data are provided "as is" without warranty of any kind, express or implied. <strong>Use Constraints:</strong> None. <strong>Previous Version(s): </strong>Maestas et al. 2020 <strong>Data Credit: </strong>University of Montana, USDA-NRCS <strong>Data Attribution: </strong>Allred et al. 2021
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