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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 “base” map as an intermediary....
Tags: AZ, Arizona, Arizona Plateau, Black Hills, Blue Mountains, All tags...
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Climate change over the past century has altered vegetation community composition and species distributions across rangelands in the western United States. The scale and magnitude of climatic influences are largely unknown. We used fractional component cover data for rangeland functional groups and weather data from the 1985 to 2023 reference period in conjunction with soils and topography data to develop empirical models describing the spatio-temporal variation in component cover. To investigate the ramifications of future change across the western US, we extended models based on historical relationships over the reference period to model landscape effects based on future weather conditions from two emissions scenarios...
Tags: AB, AZ, Alberta, Arizona, Arizona Plateau, All tags...
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This dataset provides spatial predictions of habitat suitability for current (1950 – 2000 yr) and mid-Holocene (8.3 ka – 4.2 ka) intervals using hindcasting, and three separate paleo-distributions calibrated on the packrat midden archive: those without bias correction (naïve), those created with a standard method (standard), and those created with a novel alternative (modeled) incorporating a three-stage model of bias. The raster layers contained here accompany the manuscript Inman et al. 2018 and were used to evaluate utility of a novel bias correction method (modeled) over classic methods. Spatial predictions of habitat suitability were created using MaxEnt version 3.4.0 (Phillips et al., 2006), a widely-used...
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These data were compiled for the Altar Valley Conservation Alliance and the U. S. Fish and Wildlife Service to identify and quantify the spatial distribution of fine fuels in relation to wildfire management across jurisdictional boundaries. Objective(s) of our study were to map the 2021 annual distribution of the biomass (kg/ha) of fine fuels (grasses, shrubs, and forbs) for the whole of the Altar Valley, AZ, including the Buenos Aires National Wildlife Refuge. These data represent estimated biomass of fine fuels (kg/ha) at a 10-m resolution. These data were collected/created in September through October 2021 for the Altar Valley, located in Pima County, AZ, USA. These data were collected/created by the U.S. Geological...
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Predictions of raven occurrence in the absence of anthropogenic environmental effects. Raven point counts were related to landscape covariates using Bayesian hierarchical occupancy models and the means of the posterior distributions for relevant effects were used to generate the predictions.
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
Tags: AB, AZ, Alberta, Arizona, Arizona Plateau, All tags...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
Tags: AB, AZ, Alberta, Arizona, Arizona Plateau, All tags...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 “base” map as an intermediary....
Tags: AZ, Arizona, Arizona Plateau, Black Hills, Blue Mountains, All tags...
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The need to monitor change in sagebrush steppe is urgent due to the increasing impacts of climate change, shifting fire regimes, and management practices on ecosystem health. Remote sensing provides a cost-effective and reliable method for monitoring change through time and attributing changes to drivers. We report an automated method of mapping rangeland fractional component cover over a large portion of the Northern Great Basin, USA, from 1986 to 2016 using a dense Landsat imagery time series. 2012 was excluded from the time-series due to a lack of quality imagery. Our method improved upon the traditional change vector method by considering the legacy of change at each pixel. We evaluate cover trends stratified...
Tags: AZ, Arizona, Arizona Plateau, Black Hills, Blue Mountains, All tags...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
Tags: AB, AZ, Alberta, Arizona, Arizona Plateau, All tags...
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The USGS RCMAP (Rangeland Condition Monitoring Assessment and Projection) project has worked with BLM scientists and land managers to develop actionable remote-sensing based vegetation classifications. RCMAP quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2024. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, shrub height, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. The mapping area included eight regions which were subsequently...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013-2018 from Assessment, Inventory, and Monitoring (AIM) instead of using the 2016 “base” map as an intermediary....
Tags: AZ, Arizona, Arizona Plateau, Black Hills, Blue Mountains, All tags...
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The need to monitor change in sagebrush steppe is urgent due to the increasing impacts of climate change, shifting fire regimes, and management practices on ecosystem health. Remote sensing provides a cost-effective and reliable method for monitoring change through time and attributing changes to drivers. We report an automated method of mapping rangeland fractional component cover over a large portion of the Northern Great Basin, USA, from 1986 to 2016 using a dense Landsat imagery time series. 2012 was excluded from the time-series due to a lack of quality imagery. Our method improved upon the traditional change vector method by considering the legacy of change at each pixel. We evaluate cover trends stratified...
Tags: AZ, Arizona, Arizona Plateau, Black Hills, Blue Mountains, All tags...
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As part of a 2018 Northwest Climate Adaptation and Science Center project, USGS researchers are releasing a series of spatially-explicit land-cover projections for the period 2018-2050 covering part of the northern Great Basin (Beaty Butte Herd Management Area, Hart Mountain National Antelope Refuge, and Sheldon National Refuge). The dataset contains an empirically-based business-as-usual (BAU) and an RCP8.5 climate change scenario executed for shrub, herbaceous, and bare cover types. Each scenario is executed 30 times (i.e. Monte Carlo simulations) to account for variability across historical change estimates derived from annual fractional cover maps generated by the National Land Cover Database. The map dates...
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Predictions of raven occurrence in the absence of natural environmental effects. Raven point counts were related to landscape covariates using Bayesian hierarchical occupancy models and the means of the posterior distributions for relevant effects were used to generate the predictions.
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
Tags: AB, AZ, Alberta, Arizona, Arizona Plateau, All tags...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, high-resolution training was revised using an improved neural-net classifier and modelling approach. These data serve as foundation to the RCMAP approach. The training database was...
Tags: AB, AZ, Alberta, Arizona, Arizona Plateau, All tags...
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The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across the western U.S. using Landsat imagery from 1985-2020. The RCMAP product suite consists of eight fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub and rule-based error maps including the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. We used an updated version of the 2016 base training data, with a more aggressive forest mask and reduced shrub and sagebrush cover bias in pinyon-juniper woodlands. We pooled training data in areas...
Tags: AZ, Arizona, Arizona Plateau, Black Hills, Blue Mountains, All tags...


map background search result map search result map Raven study site locations in the Great Basin, derived from survey locations 2007 - 2016 Predictions of raven occurrence in the absence of natural environmental effects in the Great Basin, 2007-2016 (Fig. 4A) Predictions of raven occurrence in the absence of anthropogenic environmental effects in the Great Basin, 2007-2016 (Fig. 4B) Prediction of raven occurrence intersected with high impact areas for sage-grouse populations in the Great Basin, 2007-2016 (Fig. 5A) Spatial predictions of habitat suitability for present-day (1950 – 2000 yr) and mid-Holocene (8.3 ka – 4.2 ka) time intervals Spatially-explicit land-cover scenarios of federal lands in the northern Great Basin: 2018-2050 Remote Sensing Shrub/Grass National Land Cover Database (NLCD) Back-in-Time (BIT) Annual Herbaceous Products for the Western U.S., 1985 - 2018 Remote Sensing Shrub/Grass National Land Cover Database (NLCD) Back-in-Time (BIT) Litter Products for the Western U.S., 1985 - 2018 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across the Western U.S. 1985-2020 - Perennial Herbaceous Predicted biomass of fine fuel for Altar Valley, Arizona, 2021 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across the Western U.S. 1985-2021 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Rangeland Condition Monitoring Assessment and Projection (RCMAP) Non Sagebrush Shrub Fractional Component Time-Series Across the Western U.S. 1985-2021 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Perennial Herbaceous Fractional Component Time-Series Across the Western U.S. 1985-2021 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Annual Herbaceous Fractional Component Time-Series Across Western North America from 1985-2023 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Non Sagebrush Shrub Fractional Component Time-Series Across Western North America from 1985-2023 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Perennial Herbaceous Fractional Component Time-Series Across Western North America from 1985-2023 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Sagebrush Fractional Component Time-Series Across Western North America from 1985-2023 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Shrub Fractional Component Time-Series Across Western North America from 1985-2023 Projections of Rangeland Fractional Component Cover Across Western Northern American Rangelands for Representative Concentration Pathways (RCP) 4.5 and 8.5 Scenarios for the 2020s, 2050s, and 2080s Time-Periods Predicted biomass of fine fuel for Altar Valley, Arizona, 2021 Spatially-explicit land-cover scenarios of federal lands in the northern Great Basin: 2018-2050 Raven study site locations in the Great Basin, derived from survey locations 2007 - 2016 Prediction of raven occurrence intersected with high impact areas for sage-grouse populations in the Great Basin, 2007-2016 (Fig. 5A) Predictions of raven occurrence in the absence of natural environmental effects in the Great Basin, 2007-2016 (Fig. 4A) Predictions of raven occurrence in the absence of anthropogenic environmental effects in the Great Basin, 2007-2016 (Fig. 4B) Rangeland Condition Monitoring Assessment and Projection (RCMAP) Annual Herbaceous Fractional Component Time-Series Across Western North America from 1985-2023 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Non Sagebrush Shrub Fractional Component Time-Series Across Western North America from 1985-2023 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Perennial Herbaceous Fractional Component Time-Series Across Western North America from 1985-2023 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Sagebrush Fractional Component Time-Series Across Western North America from 1985-2023 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Shrub Fractional Component Time-Series Across Western North America from 1985-2023 Projections of Rangeland Fractional Component Cover Across Western Northern American Rangelands for Representative Concentration Pathways (RCP) 4.5 and 8.5 Scenarios for the 2020s, 2050s, and 2080s Time-Periods Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across the Western U.S. 1985-2021 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Non Sagebrush Shrub Fractional Component Time-Series Across the Western U.S. 1985-2021 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Perennial Herbaceous Fractional Component Time-Series Across the Western U.S. 1985-2021 Spatial predictions of habitat suitability for present-day (1950 – 2000 yr) and mid-Holocene (8.3 ka – 4.2 ka) time intervals Remote Sensing Shrub/Grass National Land Cover Database (NLCD) Back-in-Time (BIT) Annual Herbaceous Products for the Western U.S., 1985 - 2018 Remote Sensing Shrub/Grass National Land Cover Database (NLCD) Back-in-Time (BIT) Litter Products for the Western U.S., 1985 - 2018 Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across the Western U.S. 1985-2020 - Perennial Herbaceous Rangeland Condition Monitoring Assessment and Projection (RCMAP)