Mark Miller 2018 raster digital data Flagstaff, AZ U.S. Geological Survey https://doi.org/10.5066/P9QEJGD8 Invasive annual plants such as red brome (Bromus rubens), cheatgrass (B. tectorum), and African mustard (Malcomia africana) can have profound impacts on dryland ecosystems. Potential impacts include the alteration of fuel loads and fire regimes, as well as the competitive displacement of native plant populations. Both of these impacts can significantly degrade habitat quality for wildlife and can have important socioeconomic consequences for human society. To design science-based strategies for mitigating these impacts, managers need information on the relative abundance of invasive plant populations over time and across complex landscapes characterized by multiple management jurisdictions, a range of urban and wildland settings, and diverse biophysical environments. These data consist of areas modeled as containing early season invasives (ESI) such as cheatgrass, red bromem, and African Mustard across Washington County, Utah for 2001 through 2010. Multi-season (spring and summer) satellite imagery (Landsat 5) were used to model the potential extent and distribution of these species. A mapping algorithm toolset, Detection of Early Season Invasives (DESI), which works in conjuction with the ENVI image analysis and processing software, was used throughout the process. Specifically, DESI compares spring and summer Landsat images and models the abundance of ESI as a function in seasonal differences in vegetation greeness. To address an information need, the U.S. Geological Survey (USGS), the Bureau of Land Management (BLM), the National Park Service (NPS Zion National Park and Canyonlands National Park), and the Nature Conservancy collaborated in a project to examine the landscape-level dynamics of invasive annual plants in Washington County, Utah, through the analysis of satellite imagery. Early season invasive annual plant populations were detected and mapped over a sequence of years (e.g., 2001-2010) through application of a mapping algorithm developed in conjunction with cheatgrass studies conducted in the vicinity of Canyonlands National Park (Kokaly 2010a,b). The mapping algorithm compares spring and summer Landsat imagery, and models the abundance of brome grasses and other spring weeds as a function of the seasonal difference in vegetation greenness. A comparison of field data with remotely sensed data in 2006 found the Landsat-based model to be 86 percent accurate in classifying cheatgrass abundance in the Canyonlands area. For the Washington County study area, a comparision of field data collected in summer 2010 with April and July 2010 Landsat images indicated that the mapping algorithm was 90 percent accurate in classifying and mapping the abundance of early season invasive plants. These results indicate that this technique promises to be an effective tool for mapping invasive plant dynamics both on the Colorado Plateau and in the Mojave Desert. A similar multi-temporal image analysis technique has been used to map dynamics of invasive grasses at the scale of the Great Basin (Bradley and Mustard 2006). Information provided by this project will be valuable for management of fuels, fire, sensitive plant and animal populations, and wildland ecosystems. These data were developed to map the extent and distribution of early season invasives (ESI) across Washington County, Utah from 2001 through 2010. They will serve as a means for resource managers, stakeholders, and partners in the ecoregion and at the local level for the management of fuels, fire, sensitive plant and animal populations, wildland ecosystems, and socioeconomic issues. Data users should read the entire metadata record and acquire the manuscripts cited as Cross References to have a complete understanding of how these data were created and developed. The data are specific to the uses identified in this metadata record and any other use of these data would be inappropriate. See 'Distribution liability' statement for more information. 20010101 20101231 observed Complete None planned -115.02176571064 -110.8046244374 38.576467245306 36.335055255516 None Early Season Invasives Invasive Annual Plants Cheatgrass Red Brome African Mustard USGS Metadata Identifier USGS:55d60be0e4b0518e3546a59a None Washington County Utah Southwest United States United States North America None 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 None. none Terry Arundel U.S. Geological Survey Geographer mailing and physical

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Flagstaff AZ 86001 United States 928-556-7174 tarundel@usgs.gov Funding and the results of data collection and image processing for this work were provided by Bureau of Land Management. Environment as of Metadata Creation: Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.3.1 (Build 4959) Service Pack N/A (Build N/A) Kokaly, R.F. 2011 Reston, VA U.S. Geological Survey https://pubs.usgs.gov/of/2010/1302/ Kokaly, R.F. 2011 Reston, VA U.S. Geological Survey https://pubs.usgs.gov/of/2010/1327/ No formal attribute accuracy tests were conducted No formal logical accuracy tests were conducted Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. No formal positional accuracy tests were conducted No formal positional accuracy tests were conducted U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) 2010 Raster Digital Data (Aerial Imagery) Sioux Falls, SD U.S. Geological Survey http://earthexplorer.usgs.gov/ Digital and/or Hardcopy Resources 20010101 20101231 publication date Source Input 1 Two Landsat 5 Thematic Mapper images from the spring and summer of 2001-2010 were used to generate early season invasives maps. Step 1 - Acquire Landsat imagery from the U.S. Geological Survey Website: http://earthexplorer.usgs.gov/ 2010 Step 2 - Convert Raw Digital Number Landsat imagery to Radiance. 2010 Step 3 - Determine path radiance estimate 2010 Step 4 - Convert the Radiance image to Reflectance 2010 Step 5 - Generate and assess masks for snow\cloud, shadow, water, and areas of recent burn. 2010 Step 6 - Check the geographic agreement between spring and summer images. 2010 Step 7 - Calculate Normalized Difference Vegetation Index 2010 Step 8 - Calculate Difference Normalized Difference Vegetation Index 2010 Step 9 - Generate early season invasive map 2010 Raster Pixel 25464 28451 Universal Transverse Mercator 12N 0.9996 -111.0 0.0 500000.0 0.0 coordinate pair 2.220024164500956E-9 2.220024164500956E-9 METERS D WGS 1984 WGS 1984 6378137.0 298.257223563 Cheatgrass[Year]_[DateSpring (mmdd)]_[DateSummer (mmdd)]_filtered_masked Raster data and classification values from detecting and mapping early invasive annual plants using Landsat 5 scenes for the years 2001-2010 Producer defined Color Index Value assigned to pixels by DESI based on spectral and spatial characteristics. Kokaly, R.F., 2011, Detecting cheatgrass on the Colorado Plateau using Landsat data: A tutorial for the DESI software: U.S. Geological Survey Open-File Report 2010-1327, 88p. 0 No Data - Pixels with no color index value Kokaly, R.F., 2011, Detecting cheatgrass on the Colorado Plateau using Landsat data: A tutorial for the DESI software: U.S. Geological Survey Open-File Report 2010-1327, 88p. 1 Not Classified - Pixels in which cheatgrass was not detected or in which cheatgrass was detected but none of the 8 neighboring pixels contains cheatgrass. Kokaly, R.F., 2011, Detecting cheatgrass on the Colorado Plateau using Landsat data: A tutorial for the DESI software: U.S. Geological Survey Open-File Report 2010-1327, 88p. 2 Lower Probability Spectral and Spatial - Pixels in which have a dNdvi above the low probability threshold but below the high probability threshold and only 1 of the 8 neighboring pixels contains cheatgrass. Kokaly, R.F., 2011, Detecting cheatgrass on the Colorado Plateau using Landsat data: A tutorial for the DESI software: U.S. Geological Survey Open-File Report 2010-1327, 88p. 3 Lower Probability Spatial - Pixels which have a dNDVI above the high probability threshold and only 1 of the 8 neighboring pixels contains cheatgrass. Kokaly, R.F., 2011, Detecting cheatgrass on the Colorado Plateau using Landsat data: A tutorial for the DESI software: U.S. Geological Survey Open-File Report 2010-1327, 88p. 4 Lower Probability Spectral - Pixels which have dNDVI above the low probability threshold but below the high probability threshold and more than 1 of the 8 neighboring pixels contains cheatgrass. Kokaly, R.F., 2011, Detecting cheatgrass on the Colorado Plateau using Landsat data: A tutorial for the DESI software: U.S. Geological Survey Open-File Report 2010-1327, 88p. 5 High Probability - Pixels which have a dNDVI above the high probability threshold and more than 1 of the 8 neighboring pixels contains cheatgrass. Kokaly, R.F., 2011, Detecting cheatgrass on the Colorado Plateau using Landsat data: A tutorial for the DESI software: U.S. Geological Survey Open-File Report 2010-1327, 88p. 6 - 255 No Data - Pixels with no color index value Kokaly, R.F., 2011, Detecting cheatgrass on the Colorado Plateau using Landsat data: A tutorial for the DESI software: U.S. Geological Survey Open-File Report 2010-1327, 88p. U.S. Geological Survey - ScienceBase U.S. Geological Survey mailing and physical

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Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. This zip file contains data available in GeoTIFF (tif), Text (txt), and XML (xml) formats. The user must have software capable of uncompressing the Zip file and displaying the raster, text, and xml data. 20200827 Terry Arundel U.S. Geological Survey Geographer mailing and physical

2255 North Gemini Drive

Flagstaff AZ 86001 United States 928-556-7174 928-556-7092 tarundel@usgs.gov Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 None These data were compiled for government use and represent the results of data collection/processing for a specific Bureau of Land Management activity/project. The Bureau of Land Management and the U.S. Geological Survey make no representation as to the suitability or accuracy of these data for any other purpose and disclaims any liability for errors that the data may contain. As such, it is only valid for its intended use, content, time, and accuracy specifications. While there are no explicit constraints on the use of these data, please exercise appropriate and professional judgment in the use and interpretation of these data. Acknowledgement of the originating agencies would be appreciated in products derived from these data.