Stephen Boyte
Stephen Boyte is a Research Geographer at USGS EROS in Sioux Falls, SD, USA.
Stephen Boyte is a Research Geographer at USGS EROS in Sioux Falls, SD, USA.
Science and Products
Filter Total Items: 25
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, May 2021, v1
This dataset provides early estimates of 2021 exotic annual grasses (EAG) fractional cover predicted on May 3rd. We develop and release EAG fractional cover map with an emphasis on cheatgrass (Bromus tectrorum) but it also includes number of other species, i.e., Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonic
Using Targeted Training Data to Develop Site Potential for the Upper Colorado River Basin from 2000 - 2018
Defining site potential for an area establishes its possible long-term vegetation growth productivity in a relatively undisturbed state, providing a realistic reference point for ecosystem performance. Modeling and mapping site potential helps to measure and identify naturally occurring variations on the landscape as opposed to variations caused by land management activities or disturbances (Rigge
Early estimates of Annual Exotic Herbaceous Fractional Cover in the Sagebrush Ecosystem, USA, May 2020
The dataset provides an estimate of 2020 herbaceous mostly annual fractional cover predicted on May 1st with an emphasis on annual exotic grasses Historically, similar maps were produced at a spatial resolution of 250m (Boyte et al. 2019 https://doi.org/10.5066/P9ZEK5M1., Boyte et al. 2018 https://doi.org/10.5066/P9KSR9Z4.), but we are now mapping at a 30m resolution (Pastick et al. 2020 doi:10.33
Annual Herbaceous Cover across Rangelands of the Sagebrush Biome
Data is available on https://chohnz.users.earthengine.app/view/wga-product-comparison-means Cheatgrass (Bromus tectorum) and other invasive annual grasses represent one of the single largest threats to the health and resilience of western rangelands. To address this challenge, the Western Governors Association (WGA)-appointed Western Invasive Species Council convened a cheatgrass working group to
Fractional estimates of invasive annual grass cover in dryland ecosystems of western United States (2016 - 2018)
Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid resp
Near-real-time Herbaceous Annual Cover in the Sagebrush Ecosystem, USA, July 2019
This dataset provides a near-real-time estimate of 2019 herbaceous annual cover with an emphasis on annual grass (Boyte and Wylie. 2016. Near-real-time cheatgrass percent cover in the Northern Great Basin, USA, 2015. Rangelands 38:278-284.) This estimate was based on remotely sensed enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) data g
Early Estimates of Herbaceous Annual Cover in the Sagebrush Ecosystem (May 1, 2019)
The dataset provides a spatially explicit estimate of 2019 herbaceous annual percent cover predicted on May 1st with an emphasis on annual grasses. The estimate is based on the mean output of two regression-tree models. For one model, we include, as an independent variable amongst other independent variables, a dataset that is the mean of 17-years of annual herbaceous percent cover (https://doi.or
Near-real-time Herbaceous Annual Cover in the Sagebrush Ecosystem, USA, July 2018
This dataset provides a near-real-time estimate of 2018 herbaceous annual cover with an emphasis on annual grass (Boyte and Wylie. 2016. Near-real-time cheatgrass percent cover in the Northern Great Basin, USA, 2015. Rangelands 38:278-284.) This estimate was based on remotely sensed enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) data g
A Time Series of Herbaceous Annual Cover in the Sagebrush Ecosystem
We integrated 250-m enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) with land cover, biogeophysical (e.g., soils, topography) and climate data into regression-tree software (Cubist). We integrated this data to create a time series of spatially explicit predictions of herbaceous annual vegetation cover in sagebrush ecosystems, with an em
Estimating carbon fluxes using satellite data integrated into regression-tree models in the conterminous United States
Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, enhancing a more complete understanding of broad-scale ecosystem processes. This data release presents maps of estimates of annual gross primary production (GPP) and annual ecosystem respiration (RE) that were derived fr
Near-real-time Estimates of Herbaceous Annual Cover in the Sagebrush Ecosystem (June 19, 2017)
This dataset provides a near-real-time estimate of 2017 herbaceous annual cover with an emphasis on annual grass (Boyte and Wylie. 2016. Near-real-time cheatrass percent cover in the Northern Great Basin, USA, 2015. Rangelands 38:278-284.) This estimate was based on remotely sensed enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) data ga
Downscaled 30 m weekly MODIS NDVI for the Central Great Basin
To help characterize the groundwater system at Homestake Mining Company Superfund Site near Milan, New Mexico, the U.S. Geological Survey collected borehole geophysical and groundwater-quality data in cooperation with the U.S. Environmental Protection Agency during JulyOctober 2016. The following borehole geophysical data were collected from wells at or near the Homestake Mining Company Superfund
Filter Total Items: 23
Estimating carbon and showing impacts of drought using satellite data in regression-tree models
Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, allowing a better understanding of broad-scale ecosystem processes. The current study presents annual gross primary production (GPP) and annual ecosystem respiration (RE) for 2000–2013 in several short-statured vegetatio
Authors
Stephen P. Boyte, Bruce K. Wylie, Danny Howard, Devendra Dahal, Tagir G. Gilmanov
An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data
Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve
Authors
Yingxin Gu, Bruce K. Wylie, Stephen P. Boyte, Joshua J. Picotte, Danny Howard, Kelcy Smith, Kurtis Nelson
Near-real-time cheatgrass percent cover in the Northern Great Basin, USA, 2015
Cheatgrass (Bromus tectorum L.) dramatically changes shrub steppe ecosystems in the Northern Great Basin, United States.Current-season cheatgrass location and percent cover are difficult to estimate rapidly.We explain the development of a near-real-time cheatgrass percent cover dataset and map in the Northern Great Basin for the current year (2015), display the current year’s map, provide analysis
Authors
Stephen P. Boyte, Bruce K. Wylie
Cheatgrass percent cover change: Comparing recent estimates to climate change − Driven predictions in the Northern Great Basin
Cheatgrass (Bromus tectorum L.) is a highly invasive species in the Northern Great Basin that helps decrease fire return intervals. Fire fragments the shrub steppe and reduces its capacity to provide forage for livestock and wildlife and habitat critical to sagebrush obligates. Of particular interest is the greater sage grouse (Centrocercus urophasianus), an obligate whose populations have decline
Authors
Stephen P. Boyte, Bruce K. Wylie, Donald J. Major
The integrated rangeland fire management strategy actionable science plan
The Integrated Rangeland Fire Management Strategy (hereafter Strategy, DOI 2015) outlined the need for coordinated, science-based adaptive management to achieve long-term protection, conservation, and restoration of the sagebrush (Artemisia spp.) ecosystem. A key component of this management approach is the identification of knowledge gaps that limit implementation of effective strategies to meet
Authors
Cameron L. Aldridge, Ken Berg, Chad S. Boyd, Stephen P. Boyte, John B. Bradford, Ed Brunson, John H. Cissel, Courtney J. Conway, Anna D. Chalfoun, Jeanne C. Chambers, Patrick Clark, Peter S. Coates, Michele R. Crist, Dawn M. Davis, Nicole DeCrappeo, Patricia A. Deibert, Kevin E. Doherty, Louisa B. Evers, Deborah M. Finch, Sean P. Finn, Matthew J. Germino, Nancy F. Glenn, Corey Gucker, John A. Hall, Steven E. Hanser, Douglas W. Havlina, Julie A. Heinrichs, Matt Heller, Collin G. Homer, Molly E. Hunter, Ruth W. Jacobs, Jason W. Karl, Richard Kearney, Susan K Kemp, Francis F. Kilkenny, Steven T. Knick, Karen Launchbaugh, Daniel J. Manier, Kenneth E. Mayer, Susan E. Meyer, Adrian P. Monroe, Eugénie MontBlanc, Beth A. Newingham, Michael L. Pellant, Susan L. Phillips, David S. Pilliod, Mark A. Ricca, Bryce A. Richardson, Jeffrey A. Rose, Nancy Shaw, Roger L. Sheley, Douglas J. Shinneman, Lief A. Wiechman, Bruce K. Wylie
The integration of geophysical and enhanced Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index data into a rule-based, piecewise regression-tree model to estimate cheatgrass beginning of spring growth
Cheatgrass exhibits spatial and temporal phenological variability across the Great Basin as described by ecological models formed using remote sensing and other spatial data-sets. We developed a rule-based, piecewise regression-tree model trained on 99 points that used three data-sets – latitude, elevation, and start of season time based on remote sensing input data – to estimate cheatgrass beginn
Authors
Stephen P. Boyte, Bruce K. Wylie, Donald J. Major, Jesslyn F. Brown
Mapping and monitoring cheatgrass dieoff in rangelands of the Northern Great Basin, USA
Understanding cheatgrass (Bromus tectorum) dynamics in the Northern Great Basin rangelands, USA, is necessary to effectively manage the region’s lands. This study’s goal was to map and monitor cheatgrass performance to identify where and when cheatgrass dieoff occurred in the Northern Great Basin and to discover how this phenomenon was affected by climatic, topographic, and edaphic variables. We a
Authors
Stephen P. Boyte, Bruce K. Wylie, Donald J. Major
Projecting future grassland productivity to assess thesustainability of potential biofuel feedstock areas in theGreater Platte River Basin
This study projects future (e.g., 2050 and 2099) grassland productivities in the Greater Platte River Basin (GPRB) using ecosystem performance (EP, a surrogate for measuring ecosystem productivity) models and future climate projections. The EP models developed from a previous study were based on the satellite vegetation index, site geophysical and biophysical features, and weather and climate driv
Authors
Yingxin Gu, Bruce K. Wylie, Stephen P. Boyte, Khem P. Phuyal
Influence of management and precipitation on carbon fluxes in greatplains grasslands
Suitable management and sufficient precipitation on grasslands can provide carbon sinks. The net carbon accumulation of a site from the atmosphere, modeled as the Net Ecosystem Productivity (NEP), is a useful means to gauge carbon balance. Previous research has developed methods to integrate flux tower data with satellite biophysical datasets to estimate NEP across large regions. A related method
Authors
Matthew B. Rigge, Bruce K. Wylie, Li Zhang, Stephen P. Boyte
Ecosystem performance monitoring of rangelands by integrating modeling and remote sensing
Monitoring rangeland ecosystem dynamics, production, and performance is valuable for researchers and land managers. However, ecosystem monitoring studies can be difficult to interpret and apply appropriately if management decisions and disturbances are inseparable from the ecosystem's climate signal. This study separates seasonal weather influences from influences caused by disturbances and manage
Authors
Bruce K. Wylie, Stephen P. Boyte, Donald J. Major
Identifying grasslands suitable for cellulosic feedstock crops in the Greater Platte River Basin: dynamic modeling of ecosystem performance with 250 m eMODIS
This study dynamically monitors ecosystem performance (EP) to identify grasslands potentially suitable for cellulosic feedstock crops (e.g., switchgrass) within the Greater Platte River Basin (GPRB). We computed grassland site potential and EP anomalies using 9-year (2000–2008) time series of 250 m expedited moderate resolution imaging spectroradiometer Normalized Difference Vegetation Index data,
Authors
Yingxin Gu, Stephen P. Boyte, Bruce K. Wylie, Larry L. Tieszen
Science and Products
Filter Total Items: 25
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, May 2021, v1
This dataset provides early estimates of 2021 exotic annual grasses (EAG) fractional cover predicted on May 3rd. We develop and release EAG fractional cover map with an emphasis on cheatgrass (Bromus tectrorum) but it also includes number of other species, i.e., Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus japonic
Using Targeted Training Data to Develop Site Potential for the Upper Colorado River Basin from 2000 - 2018
Defining site potential for an area establishes its possible long-term vegetation growth productivity in a relatively undisturbed state, providing a realistic reference point for ecosystem performance. Modeling and mapping site potential helps to measure and identify naturally occurring variations on the landscape as opposed to variations caused by land management activities or disturbances (Rigge
Early estimates of Annual Exotic Herbaceous Fractional Cover in the Sagebrush Ecosystem, USA, May 2020
The dataset provides an estimate of 2020 herbaceous mostly annual fractional cover predicted on May 1st with an emphasis on annual exotic grasses Historically, similar maps were produced at a spatial resolution of 250m (Boyte et al. 2019 https://doi.org/10.5066/P9ZEK5M1., Boyte et al. 2018 https://doi.org/10.5066/P9KSR9Z4.), but we are now mapping at a 30m resolution (Pastick et al. 2020 doi:10.33
Annual Herbaceous Cover across Rangelands of the Sagebrush Biome
Data is available on https://chohnz.users.earthengine.app/view/wga-product-comparison-means Cheatgrass (Bromus tectorum) and other invasive annual grasses represent one of the single largest threats to the health and resilience of western rangelands. To address this challenge, the Western Governors Association (WGA)-appointed Western Invasive Species Council convened a cheatgrass working group to
Fractional estimates of invasive annual grass cover in dryland ecosystems of western United States (2016 - 2018)
Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid resp
Near-real-time Herbaceous Annual Cover in the Sagebrush Ecosystem, USA, July 2019
This dataset provides a near-real-time estimate of 2019 herbaceous annual cover with an emphasis on annual grass (Boyte and Wylie. 2016. Near-real-time cheatgrass percent cover in the Northern Great Basin, USA, 2015. Rangelands 38:278-284.) This estimate was based on remotely sensed enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) data g
Early Estimates of Herbaceous Annual Cover in the Sagebrush Ecosystem (May 1, 2019)
The dataset provides a spatially explicit estimate of 2019 herbaceous annual percent cover predicted on May 1st with an emphasis on annual grasses. The estimate is based on the mean output of two regression-tree models. For one model, we include, as an independent variable amongst other independent variables, a dataset that is the mean of 17-years of annual herbaceous percent cover (https://doi.or
Near-real-time Herbaceous Annual Cover in the Sagebrush Ecosystem, USA, July 2018
This dataset provides a near-real-time estimate of 2018 herbaceous annual cover with an emphasis on annual grass (Boyte and Wylie. 2016. Near-real-time cheatgrass percent cover in the Northern Great Basin, USA, 2015. Rangelands 38:278-284.) This estimate was based on remotely sensed enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) data g
A Time Series of Herbaceous Annual Cover in the Sagebrush Ecosystem
We integrated 250-m enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) with land cover, biogeophysical (e.g., soils, topography) and climate data into regression-tree software (Cubist). We integrated this data to create a time series of spatially explicit predictions of herbaceous annual vegetation cover in sagebrush ecosystems, with an em
Estimating carbon fluxes using satellite data integrated into regression-tree models in the conterminous United States
Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, enhancing a more complete understanding of broad-scale ecosystem processes. This data release presents maps of estimates of annual gross primary production (GPP) and annual ecosystem respiration (RE) that were derived fr
Near-real-time Estimates of Herbaceous Annual Cover in the Sagebrush Ecosystem (June 19, 2017)
This dataset provides a near-real-time estimate of 2017 herbaceous annual cover with an emphasis on annual grass (Boyte and Wylie. 2016. Near-real-time cheatrass percent cover in the Northern Great Basin, USA, 2015. Rangelands 38:278-284.) This estimate was based on remotely sensed enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) data ga
Downscaled 30 m weekly MODIS NDVI for the Central Great Basin
To help characterize the groundwater system at Homestake Mining Company Superfund Site near Milan, New Mexico, the U.S. Geological Survey collected borehole geophysical and groundwater-quality data in cooperation with the U.S. Environmental Protection Agency during JulyOctober 2016. The following borehole geophysical data were collected from wells at or near the Homestake Mining Company Superfund
Filter Total Items: 23
Estimating carbon and showing impacts of drought using satellite data in regression-tree models
Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, allowing a better understanding of broad-scale ecosystem processes. The current study presents annual gross primary production (GPP) and annual ecosystem respiration (RE) for 2000–2013 in several short-statured vegetatio
Authors
Stephen P. Boyte, Bruce K. Wylie, Danny Howard, Devendra Dahal, Tagir G. Gilmanov
An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data
Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve
Authors
Yingxin Gu, Bruce K. Wylie, Stephen P. Boyte, Joshua J. Picotte, Danny Howard, Kelcy Smith, Kurtis Nelson
Near-real-time cheatgrass percent cover in the Northern Great Basin, USA, 2015
Cheatgrass (Bromus tectorum L.) dramatically changes shrub steppe ecosystems in the Northern Great Basin, United States.Current-season cheatgrass location and percent cover are difficult to estimate rapidly.We explain the development of a near-real-time cheatgrass percent cover dataset and map in the Northern Great Basin for the current year (2015), display the current year’s map, provide analysis
Authors
Stephen P. Boyte, Bruce K. Wylie
Cheatgrass percent cover change: Comparing recent estimates to climate change − Driven predictions in the Northern Great Basin
Cheatgrass (Bromus tectorum L.) is a highly invasive species in the Northern Great Basin that helps decrease fire return intervals. Fire fragments the shrub steppe and reduces its capacity to provide forage for livestock and wildlife and habitat critical to sagebrush obligates. Of particular interest is the greater sage grouse (Centrocercus urophasianus), an obligate whose populations have decline
Authors
Stephen P. Boyte, Bruce K. Wylie, Donald J. Major
The integrated rangeland fire management strategy actionable science plan
The Integrated Rangeland Fire Management Strategy (hereafter Strategy, DOI 2015) outlined the need for coordinated, science-based adaptive management to achieve long-term protection, conservation, and restoration of the sagebrush (Artemisia spp.) ecosystem. A key component of this management approach is the identification of knowledge gaps that limit implementation of effective strategies to meet
Authors
Cameron L. Aldridge, Ken Berg, Chad S. Boyd, Stephen P. Boyte, John B. Bradford, Ed Brunson, John H. Cissel, Courtney J. Conway, Anna D. Chalfoun, Jeanne C. Chambers, Patrick Clark, Peter S. Coates, Michele R. Crist, Dawn M. Davis, Nicole DeCrappeo, Patricia A. Deibert, Kevin E. Doherty, Louisa B. Evers, Deborah M. Finch, Sean P. Finn, Matthew J. Germino, Nancy F. Glenn, Corey Gucker, John A. Hall, Steven E. Hanser, Douglas W. Havlina, Julie A. Heinrichs, Matt Heller, Collin G. Homer, Molly E. Hunter, Ruth W. Jacobs, Jason W. Karl, Richard Kearney, Susan K Kemp, Francis F. Kilkenny, Steven T. Knick, Karen Launchbaugh, Daniel J. Manier, Kenneth E. Mayer, Susan E. Meyer, Adrian P. Monroe, Eugénie MontBlanc, Beth A. Newingham, Michael L. Pellant, Susan L. Phillips, David S. Pilliod, Mark A. Ricca, Bryce A. Richardson, Jeffrey A. Rose, Nancy Shaw, Roger L. Sheley, Douglas J. Shinneman, Lief A. Wiechman, Bruce K. Wylie
The integration of geophysical and enhanced Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index data into a rule-based, piecewise regression-tree model to estimate cheatgrass beginning of spring growth
Cheatgrass exhibits spatial and temporal phenological variability across the Great Basin as described by ecological models formed using remote sensing and other spatial data-sets. We developed a rule-based, piecewise regression-tree model trained on 99 points that used three data-sets – latitude, elevation, and start of season time based on remote sensing input data – to estimate cheatgrass beginn
Authors
Stephen P. Boyte, Bruce K. Wylie, Donald J. Major, Jesslyn F. Brown
Mapping and monitoring cheatgrass dieoff in rangelands of the Northern Great Basin, USA
Understanding cheatgrass (Bromus tectorum) dynamics in the Northern Great Basin rangelands, USA, is necessary to effectively manage the region’s lands. This study’s goal was to map and monitor cheatgrass performance to identify where and when cheatgrass dieoff occurred in the Northern Great Basin and to discover how this phenomenon was affected by climatic, topographic, and edaphic variables. We a
Authors
Stephen P. Boyte, Bruce K. Wylie, Donald J. Major
Projecting future grassland productivity to assess thesustainability of potential biofuel feedstock areas in theGreater Platte River Basin
This study projects future (e.g., 2050 and 2099) grassland productivities in the Greater Platte River Basin (GPRB) using ecosystem performance (EP, a surrogate for measuring ecosystem productivity) models and future climate projections. The EP models developed from a previous study were based on the satellite vegetation index, site geophysical and biophysical features, and weather and climate driv
Authors
Yingxin Gu, Bruce K. Wylie, Stephen P. Boyte, Khem P. Phuyal
Influence of management and precipitation on carbon fluxes in greatplains grasslands
Suitable management and sufficient precipitation on grasslands can provide carbon sinks. The net carbon accumulation of a site from the atmosphere, modeled as the Net Ecosystem Productivity (NEP), is a useful means to gauge carbon balance. Previous research has developed methods to integrate flux tower data with satellite biophysical datasets to estimate NEP across large regions. A related method
Authors
Matthew B. Rigge, Bruce K. Wylie, Li Zhang, Stephen P. Boyte
Ecosystem performance monitoring of rangelands by integrating modeling and remote sensing
Monitoring rangeland ecosystem dynamics, production, and performance is valuable for researchers and land managers. However, ecosystem monitoring studies can be difficult to interpret and apply appropriately if management decisions and disturbances are inseparable from the ecosystem's climate signal. This study separates seasonal weather influences from influences caused by disturbances and manage
Authors
Bruce K. Wylie, Stephen P. Boyte, Donald J. Major
Identifying grasslands suitable for cellulosic feedstock crops in the Greater Platte River Basin: dynamic modeling of ecosystem performance with 250 m eMODIS
This study dynamically monitors ecosystem performance (EP) to identify grasslands potentially suitable for cellulosic feedstock crops (e.g., switchgrass) within the Greater Platte River Basin (GPRB). We computed grassland site potential and EP anomalies using 9-year (2000–2008) time series of 250 m expedited moderate resolution imaging spectroradiometer Normalized Difference Vegetation Index data,
Authors
Yingxin Gu, Stephen P. Boyte, Bruce K. Wylie, Larry L. Tieszen