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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....
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...
This data set consists of 19 wideband magnetotelluric (MT) and 23 transient electromagnetic (TEM) soundings collected by the U.S. Geological Survey in June 2020 as part of the Umatilla Indian Reservation Geothermal Resources Assessment: Phase 2 project. Each MT station had 4 components, 2 orthogonal magnetic induction coils and 2 horizontal orthogonal electric dipoles. Data were collected for an average of 18 hours on a repeating schedule of alternating sampling rates of 256 samples/second for 5 hours and 50 minutes and 4096 samples/second for 10 minutes. The schedules were set such that each station was recording the same schedule to allow for remote reference processing. Data were processed with a bounded-influence...
A three-dimensional groundwater flow model (MODFLOW-NWT) of the Columbia Plateau Regional aquifer (CPRAS) in Washington, Oregon, and Idaho was developed to provide an integrated understanding of the hydrologic system to implement effective water-resource management strategies. The U.S. Geological Survey (USGS) Groundwater Resources Program assessed the groundwater availability as part of a national study of regional systems (https://pubs.usgs.gov/circ/1323/). The CPRAS assessment includes the status of groundwater resources, how these resources have changed over time, and development and application of tools to estimate system responses to stresses from future uses and climate variability and change. A major product...
This data set consists of 19 wideband magnetotelluric (MT) and 23 transient electromagnetic (TEM) soundings collected by the U.S. Geological Survey in June 2020 as part of the Umatilla Indian Reservation Geothermal Resources Assessment: Phase 2 project. Each MT station had 4 components, 2 orthogonal magnetic induction coils and 2 horizontal orthogonal electric dipoles. Data were collected for an average of 18 hours on a repeating schedule of alternating sampling rates of 256 samples/second for 5 hours and 50 minutes and 4096 samples/second for 10 minutes. The schedules were set such that each station was recording the same schedule to allow for remote reference processing. Data were processed with a bounded-influence...
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...
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...
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....
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...
This data set consists of 19 wideband magnetotelluric (MT) and 23 transient electromagnetic (TEM) soundings collected by the U.S. Geological Survey in June 2020 as part of the Umatilla Indian Reservation Geothermal Resources Assessment: Phase 2 project. Each MT station had 4 components, 2 orthogonal magnetic induction coils and 2 horizontal orthogonal electric dipoles. Data were collected for an average of 18 hours on a repeating schedule of alternating sampling rates of 256 samples/second for 5 hours and 50 minutes and 4096 samples/second for 10 minutes. The schedules were set such that each station was recording the same schedule to allow for remote reference processing. Data were processed with a bounded-influence...
This data set consists of 19 wideband magnetotelluric (MT) and 23 transient electromagnetic (TEM) soundings collected by the U.S. Geological Survey in June 2020 as part of the Umatilla Indian Reservation Geothermal Resources Assessment: Phase 2 project. Each MT station had 4 components, 2 orthogonal magnetic induction coils and 2 horizontal orthogonal electric dipoles. Data were collected for an average of 18 hours on a repeating schedule of alternating sampling rates of 256 samples/second for 5 hours and 50 minutes and 4096 samples/second for 10 minutes. The schedules were set such that each station was recording the same schedule to allow for remote reference processing. Data were processed with a bounded-influence...
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...
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....
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...
This data set consists of 19 wideband magnetotelluric (MT) and 23 transient electromagnetic (TEM) soundings collected by the U.S. Geological Survey in June 2020 as part of the Umatilla Indian Reservation Geothermal Resources Assessment: Phase 2 project. Each MT station had 4 components, 2 orthogonal magnetic induction coils and 2 horizontal orthogonal electric dipoles. Data were collected for an average of 18 hours on a repeating schedule of alternating sampling rates of 256 samples/second for 5 hours and 50 minutes and 4096 samples/second for 10 minutes. The schedules were set such that each station was recording the same schedule to allow for remote reference processing. Data were processed with a bounded-influence...
This polygon shapefile is the delineation of the upper Umatilla River study area boundary. It is split into two regions, the Blue Mountains and the Lowland Plains, as described in the processing steps. These data were used to analyze precipitation and recharge in the study area as a whole and in the two regions.
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Blue Mountains,
Pendleton,
Umatilla County,
Umatilla River,
inlandWaters
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...
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...
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...
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...
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