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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 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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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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Western U.S. rangelands have been quantified as six fractional cover (0-100%) components over the Landsat archive (1985-2018) at 30-m resolution, termed the “Back-in-Time” (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. We leverage field data observed concurrently with HRS imagery over multiple years and locations in the Western U.S. to dramatically expand the spatial extent and sample size of validation analysis relative to a direct comparison to field observations and to previous work. We compare HRS and BIT data in the corresponding space and time. Our objectives were to evaluate the temporal and spatio-temporal relationships between HRS and BIT data, and to compare...
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Predictions of an anthropogenic influence on raven occurrence index intersected with sage-grouse concentration areas. The anthropogenic influence index indicates where resource subsidies are contributing the most to raven occurrence.
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Raven point counts were related to landscape covariates using Bayesian hierarchical occupancy models and the mean of the predicted posterior distribution for raven occurrence was used to visualize results.
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An index of anthropogenic influences on raven populations. 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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FY2014Avoiding cheatgrass dominance following tree-reduction treatments on woodland-encroached sagebrush communities is a priority for managers in the Great Basin. Perennial herbaceous and weedy annual cover have been related to site resilience after treatment and associated with soil climate regimes and site physical characteristics. Additional investigation of site characteristics associated with vegetation response will allow us to better decide which sites to treat and whether seeding is needed or not in conjunction with tree reduction treatments. Site-level planning also requires an understanding of how climate change may influence vegetation response to treatments. We propose to associate site-measured soil...
    map background search result map search result map Using Soil Climate and Geospatial Environmental Characteristics to Determine Plant Community Resilience to Fire and Fire Surrogate Treatments Raven study site locations in the Great Basin, derived from survey locations 2007 - 2016 Predicted probability of raven occurrence across the Great Basin, USA, 2007 – 2016 (Fig. 3) 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) Anthropogenic influence index for raven populations in the Great Basin, 2007-2016 (Fig. 4C) Prediction of raven occurrence intersected with high impact areas for sage-grouse populations in the Great Basin, 2007-2016 (Fig. 5A) Anthropogenic influence on raven occurrence index within sage-grouse concentration areas in the Great Basin, 2007-2016 (Fig. 5B) Temporal and Spatio-Temporal High-Resolution Satellite Data for the Validation of a Landsat Time-Series of Fractional Component Cover Across Western United States (U.S.) Rangelands Spatially-explicit land-cover scenarios of federal lands in the northern Great Basin: 2018-2050 Rangeland Condition Monitoring Assessment and Projection (RCMAP) 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 Temporal and Spatio-Temporal High-Resolution Satellite Data for the Validation of a Landsat Time-Series of Fractional Component Cover Across Western United States (U.S.) Rangelands Prediction of raven occurrence intersected with high impact areas for sage-grouse populations in the Great Basin, 2007-2016 (Fig. 5A) Anthropogenic influence on raven occurrence index within sage-grouse concentration areas in the Great Basin, 2007-2016 (Fig. 5B) Using Soil Climate and Geospatial Environmental Characteristics to Determine Plant Community Resilience to Fire and Fire Surrogate Treatments Predicted probability of raven occurrence across the Great Basin, USA, 2007 – 2016 (Fig. 3) 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) Anthropogenic influence index for raven populations in the Great Basin, 2007-2016 (Fig. 4C) Rangeland Condition Monitoring Assessment and Projection (RCMAP)