SSWP Non-Native Invasive Plants
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The SSWP Non-Native Invasive Plants Study is one of many relicensing documents for the South SWP (SSWP) Hydropower Project Number 2426. The California Department of Water Resources and the Los Angeles Department of Water and Power applied to the Federal Energy Regulatory Commission for a new license of the SSWP Project located in Los Angeles County, California along the West Branch of the State Water Project (SWP). The SWP provides southern California with many benefits, including an affordable water supply, reliable regional clean energy, opportunities to integrate green energy, accessible public recreation opportunities, and environmental benefits.
National Land Cover Database (NLCD) 2016 Shrubland Fractional Components for the Western U.S. (ver. 2.0, October 2019)
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Quantifying Western U.S. shrublands as a series of fractional components with remote sensing provides a new way to understand these changing ecosystems. The USGS NLCD team in collaboration with the BLM has produced the most comprehensive remote sensing-based quantification of Western U.S. shrublands to date. Nine shrubland ecosystem components, including percent shrub, sagebrush (Artemisia spp.), big sagebrush, herbaceous, annual herbaceous, litter, and bare ground cover, along with sagebrush and shrub heights, were quantified at 30-m resolution by mapping region. Each region required extensive ground measurement for model training and validation, two scales of remote sensing data from commercial high-resolution satellites and Landsat 8, and regression tree modeling to create component predictions. In the mapped portion (1,946,100 km²) of the total study area (2,557,556 km²), bare ground averaged 46.8%, shrub 14.4%, sagebrush 4.4%, big sagebrush 3.1%, herbaceous 22.8%, annual herbaceous 4.3% and litter 15.6%. Shrub height averaged 39.8 cm and sagebrush height 10.5 cm. Component accuracies using independent validation averaged R² values of 0.46, RMSE of 10.37 and nRMSE of 0.12, and cross validation averaged R² values of 0.72, RMSE of 5.09 and nRMSE of 0.062. Component composition strongly diverges by level III ecoregions, where 13 of 22 ecoregions are bare ground dominant, 8 are herbaceous dominant, and one is shrub dominant. Sagebrush physically covers 86,219 km², or 4.4%, of our study area, but it is present in 835,507 km², or 42.9%, of the non-masked area of our study area, underscoring its widespread distribution. This version contains some confusion between pinyon-juniper tree cover and shrubs. In a subsequent version, we have applied a more aggressive masking of tree canopy cover to each rangeland component. Specifically, we lowered the tree canopy cover threshold for exclusion from 40 to 25%. For pixels with 1-25% tree canopy cover we ensured that our primary components (shrub, herbaceous, litter, and bare ground) cover summed to 100% when added with the tree canopy. And, for the secondary components (sagebrush, big sagebrush, sagebrush height and shrub height) we reconciled to the primary component (shrub), excluding any pinyon-juniper woodlands. For the updated version with these changes applied, see https://doi.org/10.5066/P9MJVQSQ. This version of data were used as training for the Back-in-Time (BIT) fractional cover time series available at https://doi.org/10.5066/P9C9O66W. Component products can also be downloaded from www.mrlc.gov.
National Land Cover Database (NLCD) 2016 Shrubland Fractional Components for the Western U.S. (ver. 2.0, October 2019)
공공데이터포털
Quantifying Western U.S. shrublands as a series of fractional components with remote sensing provides a new way to understand these changing ecosystems. The USGS NLCD team in collaboration with the BLM has produced the most comprehensive remote sensing-based quantification of Western U.S. shrublands to date. Nine shrubland ecosystem components, including percent shrub, sagebrush (Artemisia spp.), big sagebrush, herbaceous, annual herbaceous, litter, and bare ground cover, along with sagebrush and shrub heights, were quantified at 30-m resolution by mapping region. Each region required extensive ground measurement for model training and validation, two scales of remote sensing data from commercial high-resolution satellites and Landsat 8, and regression tree modeling to create component predictions. In the mapped portion (1,946,100 km²) of the total study area (2,557,556 km²), bare ground averaged 46.8%, shrub 14.4%, sagebrush 4.4%, big sagebrush 3.1%, herbaceous 22.8%, annual herbaceous 4.3% and litter 15.6%. Shrub height averaged 39.8 cm and sagebrush height 10.5 cm. Component accuracies using independent validation averaged R² values of 0.46, RMSE of 10.37 and nRMSE of 0.12, and cross validation averaged R² values of 0.72, RMSE of 5.09 and nRMSE of 0.062. Component composition strongly diverges by level III ecoregions, where 13 of 22 ecoregions are bare ground dominant, 8 are herbaceous dominant, and one is shrub dominant. Sagebrush physically covers 86,219 km², or 4.4%, of our study area, but it is present in 835,507 km², or 42.9%, of the non-masked area of our study area, underscoring its widespread distribution. This version contains some confusion between pinyon-juniper tree cover and shrubs. In a subsequent version, we have applied a more aggressive masking of tree canopy cover to each rangeland component. Specifically, we lowered the tree canopy cover threshold for exclusion from 40 to 25%. For pixels with 1-25% tree canopy cover we ensured that our primary components (shrub, herbaceous, litter, and bare ground) cover summed to 100% when added with the tree canopy. And, for the secondary components (sagebrush, big sagebrush, sagebrush height and shrub height) we reconciled to the primary component (shrub), excluding any pinyon-juniper woodlands. For the updated version with these changes applied, see https://doi.org/10.5066/P9MJVQSQ. This version of data were used as training for the Back-in-Time (BIT) fractional cover time series available at https://doi.org/10.5066/P9C9O66W. Component products can also be downloaded from www.mrlc.gov.
Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species in the Sagebrush Biome, USA - 2018
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This dataset release provides historical (2016 - 2023) estimates of fractional cover for Exotic Annual Grass (EAG) species and a native perennial bunch grass in the arid and semi-arid rangelands of the western United States. The dataset includes four (five for 2023) fractional cover maps per year, accompanied by corresponding confidence maps, for a group of 16 species of EAGs, Cheatgrass (Bromus tectorum); Medusahead (Taeniatherum caput-medusae); and Sandberg Bluegrass (Poa secunda). Field Brome (Bromus arvensis) is added as individual map species in 2023. The data were generated using a combination of field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) plots; remotely sensed data from the Harmonized Landsat and Sentinel-2 (HLS) product (specifically Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and various environmental, vegetation, remotely sensed, and geophysical drivers. Additionally, artificial intelligence and machine learning techniques were employed in the data generation process. It should be noted that the training of regression-tree models and the development of historical maps (2016-2020) utilized a total of 17,536 AIM plots from years 2016 – 2019. For the creation of 2021 maps, 19,415 AIM plots from years 2016 - 2021 were employed, and 2022 maps, 28,901 AIM plots from 2016-2022 were used. In the case of 2016 – 2020 maps, areas above 2250-m elevation and pixels classified other than grassland/herbaceous or shrub (likely rangelands) were masked based on the 2016 National Land Cover Database (NLCD). 2021 onward maps, areas above 2350-m elevation and pixels classified as other than grassland/herbaceous by the 2019 NLCD for 2021 and 2022 and 2021 NLCD for 2023 were masked. The seed source variable from the Rangeland Analysis Platform (RAP) [Jones et al., 2018]) was used as one of the drivers for modeling of 2016 – 2020 maps but was not utilized for modeling of 2021 and later maps. Additionally, HLS NDWI were not used after 2021 maps. All other predictor variables are identical for all sets of maps. For details, please check data quality information section.