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
Contributions to the development of the Western Association of Fish and Wildlife Agencies Sagebrush Conservation Strategy
USGS scientists are contributing to the development of the Western Association of Fish and Wildlife Agencies Sagebrush Conservation Strategy, a strategy intended to provide guidance so that efforts to conserve the iconic greater sage-grouse can be expanded to the entire sagebrush biome to benefit the people and wildlife that depend on it.
Drought Monitoring Datasets Available as OGC Web Map Services (WMS)
Web Services The 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: 21
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2023
These datasets provide early estimates of 2023 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a weekly basis from May to early July. The EAG estimates are developed typically within 7-13 days of the latest satellite observation used for that version. Each weekly release contains four fractional cover maps along with their corresponding confidence m
Predicted exotic annual grass abundance in rangelands of the western United States using various precipitation scenarios for 2022
Invasion of exotic annual grass (EAG), such as cheatgrass (Bromus tectorum), red brome (Bromus rubens), and medusahead (Taeniatherum caput-medusae), could have irreversible degradation impact to arid and semiarid rangeland ecosystems in the western United States. The distribution and abundance of these EAG species are highly influenced by weather variables such as temperature and precipitation. We
Biophysical drivers for predicting the distribution and abundance of invasive yellow sweet clover in the Northern Great Plains
Yellow sweetclover (Melilotus officinalis; YSC), an invasive biennial legume, bloomed throughout the Northern Great Plains (NGP) following greater-than-average precipitation during 2018-2019. YSC can increase nitrogen (N) levels and potentially cause broad changes in the composition of native plant species communities. There is little knowledge of the drivers behind its spatiotemporal variability,
Using seasonal climate scenarios in the ForageAhead annual forage production model for early drought impact assessment
High interannual variability of forage production in semi-arid grasslands leads to uncertainties when livestock producers make decisions such as buying additional feed, relocating animals, or using flexible stocking. Within-season predictions of annual forage production (i.e., yearly production) can provide specific boundaries for producers to make these decisions with more information and possibl
2021 eVIIRS 375-m 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
Exotic annual grass (EAG) phenology estimates in the western U.S. rangelands based on 30-m HLS NDVI: 2017 - 2021
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. The Exotic Annual Grass (EAG) phenology in the western U.S. rangeland based on 30m near seamless Harmonized Landsat and Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) weekly composites between 2016
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 - 2021 (ver. 2.0, December 2022)
These datasets provide historical (2016 - 2021) estimates of fractional cover for exotic annual grass (EAG) species and a native perennial bunch grass. The study area covers arid and semi-arid rangelands of the western U.S. Four fractional cover maps per year comprise this release, along with the corresponding confidence maps, for: 1) a group of 17 species of EAGs; 2) cheatgrass (Bromus tectorum);
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
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
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
Filter Total Items: 20
Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains
ContextYellow sweetclover (Melilotus officinalis; YSC) is an invasive biennial legume that bloomed across the Northern Great Plains in 2018–2019 in response to above-average precipitation. YSC can increase nitrogen (N) levels and potentially cause substantial changes in the composition of native plant species communities. There is little knowledge of the spatiotemporal variability and conditions c
Authors
Sakshi Saraf, Ranjeet John, Reza Goljani Amirkhiz, Venkatesh Kolluru, Khushboo Jain, Matthew B. Rigge, Vincenzo Giannico, Stephen P. Boyte, Jiquan Chen, Geoffrey M. Henebry, Meghann Jarchow, Raffaele Lafortezza
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
Authors
Devendra Dahal, Neal J. Pastick, Stephen P. Boyte, Sujan Parajuli, Michael J. Oimoen, Logan J. Megard
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
Authors
Matt Reeves, Ines Ibanez, Dana Blumenthal, Gang Chen, Qinfeng Guo, Catherine S. Jarnevich, Jennifer Koch, Frank Sapio, Michael D. Schwartz, Ross K. Meentemeyer, Bruce Wylie, Stephen P. Boyte
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
Authors
Neal Pastick, Bruce Wylie, Matthew B. Rigge, Devendra Dahal, Stephen P. Boyte, Matthew O. Jones, Brady W Allred, Sujan Parajuli, Zhuoting Wu
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
Authors
Trenton D Benedict, Jesslyn F. Brown, Stephen P. Boyte, Daniel Howard, Brian Fuchs, Brian D. Wardlow, Tsegaye Tadesse, Kirk Evenson
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
Authors
Neal Pastick, Devendra Dahal, Bruce K. Wylie, Sujan Parajuli, Stephen P. Boyte, Zhuoting Wu
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
Authors
Stephen P. Boyte, Bruce K. Wylie, Yingxin Gu, Donald J. Major
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
Authors
Stephen P. Boyte, Bruce K. Wylie, Donald J. Major
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
Authors
Stephen P. Boyte, Bruce K. Wylie, Matthew B. Rigge, Devendra Dahal
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
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
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
Science and Products
- Science
Contributions to the development of the Western Association of Fish and Wildlife Agencies Sagebrush Conservation Strategy
USGS scientists are contributing to the development of the Western Association of Fish and Wildlife Agencies Sagebrush Conservation Strategy, a strategy intended to provide guidance so that efforts to conserve the iconic greater sage-grouse can be expanded to the entire sagebrush biome to benefit the people and wildlife that depend on it.Drought Monitoring Datasets Available as OGC Web Map Services (WMS)
Web Services The 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: 21
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2023
These datasets provide early estimates of 2023 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a weekly basis from May to early July. The EAG estimates are developed typically within 7-13 days of the latest satellite observation used for that version. Each weekly release contains four fractional cover maps along with their corresponding confidence mPredicted exotic annual grass abundance in rangelands of the western United States using various precipitation scenarios for 2022
Invasion of exotic annual grass (EAG), such as cheatgrass (Bromus tectorum), red brome (Bromus rubens), and medusahead (Taeniatherum caput-medusae), could have irreversible degradation impact to arid and semiarid rangeland ecosystems in the western United States. The distribution and abundance of these EAG species are highly influenced by weather variables such as temperature and precipitation. WeBiophysical drivers for predicting the distribution and abundance of invasive yellow sweet clover in the Northern Great Plains
Yellow sweetclover (Melilotus officinalis; YSC), an invasive biennial legume, bloomed throughout the Northern Great Plains (NGP) following greater-than-average precipitation during 2018-2019. YSC can increase nitrogen (N) levels and potentially cause broad changes in the composition of native plant species communities. There is little knowledge of the drivers behind its spatiotemporal variability,Using seasonal climate scenarios in the ForageAhead annual forage production model for early drought impact assessment
High interannual variability of forage production in semi-arid grasslands leads to uncertainties when livestock producers make decisions such as buying additional feed, relocating animals, or using flexible stocking. Within-season predictions of annual forage production (i.e., yearly production) can provide specific boundaries for producers to make these decisions with more information and possibl2021 eVIIRS 375-m 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 satExotic annual grass (EAG) phenology estimates in the western U.S. rangelands based on 30-m HLS NDVI: 2017 - 2021
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. The Exotic Annual Grass (EAG) phenology in the western U.S. rangeland based on 30m near seamless Harmonized Landsat and Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) weekly composites between 2016Early 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 - 2021 (ver. 2.0, December 2022)
These datasets provide historical (2016 - 2021) estimates of fractional cover for exotic annual grass (EAG) species and a native perennial bunch grass. The study area covers arid and semi-arid rangelands of the western U.S. Four fractional cover maps per year comprise this release, along with the corresponding confidence maps, for: 1) a group of 17 species of EAGs; 2) cheatgrass (Bromus tectorum);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 satC6 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 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 japonic - Publications
Filter Total Items: 20
Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains
ContextYellow sweetclover (Melilotus officinalis; YSC) is an invasive biennial legume that bloomed across the Northern Great Plains in 2018–2019 in response to above-average precipitation. YSC can increase nitrogen (N) levels and potentially cause substantial changes in the composition of native plant species communities. There is little knowledge of the spatiotemporal variability and conditions cAuthorsSakshi Saraf, Ranjeet John, Reza Goljani Amirkhiz, Venkatesh Kolluru, Khushboo Jain, Matthew B. Rigge, Vincenzo Giannico, Stephen P. Boyte, Jiquan Chen, Geoffrey M. Henebry, Meghann Jarchow, Raffaele LafortezzaMulti-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 earlyAuthorsDevendra Dahal, Neal J. Pastick, Stephen P. Boyte, Sujan Parajuli, Michael J. Oimoen, Logan J. MegardTools 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 tectoAuthorsMatt Reeves, Ines Ibanez, Dana Blumenthal, Gang Chen, Qinfeng Guo, Catherine S. Jarnevich, Jennifer Koch, Frank Sapio, Michael D. Schwartz, Ross K. Meentemeyer, Bruce Wylie, Stephen P. BoyteRapid 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 patternsAuthorsNeal Pastick, Bruce Wylie, Matthew B. Rigge, Devendra Dahal, Stephen P. Boyte, Matthew O. Jones, Brady W Allred, Sujan Parajuli, Zhuoting WuExploring 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 IndexAuthorsTrenton D Benedict, Jesslyn F. Brown, Stephen P. Boyte, Daniel Howard, Brian Fuchs, Brian D. Wardlow, Tsegaye Tadesse, Kirk EvensonCharacterizing 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 respAuthorsNeal Pastick, Devendra Dahal, Bruce K. Wylie, Sujan Parajuli, Stephen P. Boyte, Zhuoting WuEstimating 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 relAuthorsStephen P. Boyte, Bruce K. Wylie, Yingxin Gu, Donald J. MajorValidating 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 theAuthorsStephen P. Boyte, Bruce K. Wylie, Donald J. MajorFusing 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 modelAuthorsStephen P. Boyte, Bruce K. Wylie, Matthew B. Rigge, Devendra DahalEstimating 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 vegetatioAuthorsStephen P. Boyte, Bruce K. Wylie, Danny Howard, Devendra Dahal, Tagir G. GilmanovThe 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 meetAuthorsCameron 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. WylieAn 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 improveAuthorsYingxin Gu, Bruce K. Wylie, Stephen P. Boyte, Joshua J. Picotte, Danny Howard, Kelcy Smith, Kurtis Nelson - News