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: 19
Multi-species inference of exotic annual and native perennial grasses in rangelands of the western United States using Harmonized Landsat and Sentinel-2 data
The invasion of exotic annual grass (EAG), e.g., cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), into rangeland ecosystems of the western United States is a broad-scale problem that affects wildlife habitats, increases wildfire frequency, and adds to land management costs. However, identifying individual species of EAG abundance from remote sensing, particularly at early
Tools and technologies for quantifying spread and impacts of invasive species
The need for tools and technologies for understanding and quantifying invasive species has never been greater. Rates of infestation vary on the species or organism being examined across the United States, and notable examples can be found. For example, from 2001 to 2003 alone, ash (Fraxinus spp.) mortality progressed at a rate of 12.97 km year −1 (Siegert et al. 2014), and cheatgrass (Bromus tecto
Rapid monitoring of the abundance and spread of exotic annual grasses in the western United States using remote sensing and machine learning
Exotic annual grasses (EAG) are one of the most damaging agents of change in western North America. Despite known socio-environmental effects of EAG there remains a need to enhance monitoring capabilities for better informing conservation and management practices. Here, we integrate field observations, remote sensing and climate data with machine-learning techniques to estimate and assess patterns
Exploring VIIRS continuity with MODIS in an expedited capability for monitoring drought-related vegetation conditions
Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index
Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony
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
Estimating abiotic thresholds for sagebrush condition class in the western United States
Sagebrush ecosystems of the western United States can transition from extended periods of relatively stable conditions to rapid ecological change if acute disturbances occur. Areas dominated by native sagebrush can transition from species-rich native systems to altered states where non-native annual grasses dominate, if resistance to annual grasses is low. The non-native annual grasses provide rel
Validating a time series of annual grass percent cover in the sagebrush ecosystem
We mapped yearly (2000–2016) estimates of annual grass percent cover for much of the sagebrush ecosystem of the western United States using remotely sensed, climate, and geophysical data in regression-tree models. Annual grasses senesce and cure by early summer and then become beds of fine fuel that easily ignite and spread fire through rangeland systems. Our annual maps estimate the extent of the
Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA
Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree model
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
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
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
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
Drought Monitoring Datasets Available as OGC Web Map Services (WMS)
Web ServicesThe Drought Monitoring datasets are available as OGC Web Map Services (WMS). You can access the services using the below links.
Modeling Effects of Climate Change on Cheatgrass Die-Off Areas in the Northern Great Basin
Cheatgrass began invading the Great Basin about 100 years ago, changing large parts of the landscape from a rich, diverse ecosystem to one where a single invasive species dominates. Cheatgrass dominated areas experience more fires that burn more land than in native ecosystems, resulting in economic and resource losses. Therefore, the reduced production, or absence, of cheatgrass in previously inva
Filter Total Items: 15
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2022 (ver 6.0, July 2022)
These datasets provide early estimates of 2022 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a bi-weekly basis from May to early July. The EAG estimates are developed within one week of the latest satellite observation used for that version. Each bi-weekly release contains four fractional cover maps along with their corresponding confidence maps f
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, July 2021, (ver 2.0, January 2022)
These datasets provide early estimates of 2021 exotic annual grasses (EAG) fractional cover predicted on July 1 using satellite observation data available until June 28th. In previous releases, we developed and released one general EAG fractional cover map with an emphasis on cheatgrass (Bromus tectrorum) but also included number of other species, i.e., Bromus arvensis L., Bromus briziformis, Brom
Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species and Sandberg bluegrass in the Sagebrush Biome, USA, 2016 - 2020
These datasets provide historical estimates of fractional cover for some specific exotic annual grasses (EAG) species and a native perennial bunch grass for 2016 - 2020. Four specific fractional cover maps per year along with corresponding confidence maps are included in this release. The maps include; 1) Exotic annual grasses (EAG) fractional cover map that includes cheatgrass (Bromus tectrorum)
C6 Aqua 250-m eMODIS Remote Sensing Phenology Metrics across the conterminous U.S.
Phenological dynamics of terrestrial ecosystems reflect the response of the Earth's vegetation canopy to changes in climate and hydrology and are thus important to monitor operationally. Researchers at the U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center have developed methods for documenting the seasonal dynamics of vegetation in an operational fashion from sat
Conterminous United States Remote Sensing Phenology Metrics Database
Phenological dynamics of terrestrial ecosystems reflect the response of the Earth's vegetation canopy to changes in climate and hydrology and are thus important to monitor operationally. Researchers at the U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center have developed methods for documenting the seasonal dynamics of vegetation in an operational fashion from sat
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
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
Science and Products
- Publications
Filter Total Items: 19
Multi-species inference of exotic annual and native perennial grasses in rangelands of the western United States using Harmonized Landsat and Sentinel-2 data
The invasion of exotic annual grass (EAG), e.g., cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), into rangeland ecosystems of the western United States is a broad-scale problem that affects wildlife habitats, increases wildfire frequency, and adds to land management costs. However, identifying individual species of EAG abundance from remote sensing, particularly at earlyTools and technologies for quantifying spread and impacts of invasive species
The need for tools and technologies for understanding and quantifying invasive species has never been greater. Rates of infestation vary on the species or organism being examined across the United States, and notable examples can be found. For example, from 2001 to 2003 alone, ash (Fraxinus spp.) mortality progressed at a rate of 12.97 km year −1 (Siegert et al. 2014), and cheatgrass (Bromus tectoRapid monitoring of the abundance and spread of exotic annual grasses in the western United States using remote sensing and machine learning
Exotic annual grasses (EAG) are one of the most damaging agents of change in western North America. Despite known socio-environmental effects of EAG there remains a need to enhance monitoring capabilities for better informing conservation and management practices. Here, we integrate field observations, remote sensing and climate data with machine-learning techniques to estimate and assess patternsExploring VIIRS continuity with MODIS in an expedited capability for monitoring drought-related vegetation conditions
Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation IndexCharacterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony
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 respEstimating abiotic thresholds for sagebrush condition class in the western United States
Sagebrush ecosystems of the western United States can transition from extended periods of relatively stable conditions to rapid ecological change if acute disturbances occur. Areas dominated by native sagebrush can transition from species-rich native systems to altered states where non-native annual grasses dominate, if resistance to annual grasses is low. The non-native annual grasses provide relValidating a time series of annual grass percent cover in the sagebrush ecosystem
We mapped yearly (2000–2016) estimates of annual grass percent cover for much of the sagebrush ecosystem of the western United States using remotely sensed, climate, and geophysical data in regression-tree models. Annual grasses senesce and cure by early summer and then become beds of fine fuel that easily ignite and spread fire through rangeland systems. Our annual maps estimate the extent of theFusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA
Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree modelEstimating 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 vegetatioThe 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 meetAn 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 improveNear-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 - Science
Drought Monitoring Datasets Available as OGC Web Map Services (WMS)
Web ServicesThe Drought Monitoring datasets are available as OGC Web Map Services (WMS). You can access the services using the below links.Modeling Effects of Climate Change on Cheatgrass Die-Off Areas in the Northern Great Basin
Cheatgrass began invading the Great Basin about 100 years ago, changing large parts of the landscape from a rich, diverse ecosystem to one where a single invasive species dominates. Cheatgrass dominated areas experience more fires that burn more land than in native ecosystems, resulting in economic and resource losses. Therefore, the reduced production, or absence, of cheatgrass in previously inva - Data
Filter Total Items: 15
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2022 (ver 6.0, July 2022)
These datasets provide early estimates of 2022 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a bi-weekly basis from May to early July. The EAG estimates are developed within one week of the latest satellite observation used for that version. Each bi-weekly release contains four fractional cover maps along with their corresponding confidence maps fEarly Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, July 2021, (ver 2.0, January 2022)
These datasets provide early estimates of 2021 exotic annual grasses (EAG) fractional cover predicted on July 1 using satellite observation data available until June 28th. In previous releases, we developed and released one general EAG fractional cover map with an emphasis on cheatgrass (Bromus tectrorum) but also included number of other species, i.e., Bromus arvensis L., Bromus briziformis, BromFractional Estimates of Multiple Exotic Annual Grass (EAG) Species and Sandberg bluegrass in the Sagebrush Biome, USA, 2016 - 2020
These datasets provide historical estimates of fractional cover for some specific exotic annual grasses (EAG) species and a native perennial bunch grass for 2016 - 2020. Four specific fractional cover maps per year along with corresponding confidence maps are included in this release. The maps include; 1) Exotic annual grasses (EAG) fractional cover map that includes cheatgrass (Bromus tectrorum)C6 Aqua 250-m eMODIS Remote Sensing Phenology Metrics across the conterminous U.S.
Phenological dynamics of terrestrial ecosystems reflect the response of the Earth's vegetation canopy to changes in climate and hydrology and are thus important to monitor operationally. Researchers at the U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center have developed methods for documenting the seasonal dynamics of vegetation in an operational fashion from satConterminous United States Remote Sensing Phenology Metrics Database
Phenological dynamics of terrestrial ecosystems reflect the response of the Earth's vegetation canopy to changes in climate and hydrology and are thus important to monitor operationally. Researchers at the U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center have developed methods for documenting the seasonal dynamics of vegetation in an operational fashion from satEarly 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 japonicUsing 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 (RiggeEarly 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:10.33Annual 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 toNear-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 gEarly 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.orNear-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 - News