Ecosystem Performance, Productivity and Sustainability Active
Remotely-sensed data forms the backbone of the large-scale maps, models and assessments created at EROS to advance the understanding of Ecosystem Performance, Productivity and Sustainability.
Detailed data from cooperators with site-specific flux towers are integrated to create models of a land cover type - grassland, for example – which can be simulated across a region. Tasks describe exchanges of Gross Primary Productivity (GPP) and Net Ecosystem Exchange (NEE) in the Northern Great Plains and other regions.
Cooperation with the Bureau of Land Management (BLM), universities, and others provides the ground data to develop and validate pixel-specific models of ecosystem performance based on the remote sensing archive, climate, and site potential.
This modeling, EROS’ rich archival resources and the research team’s special ability to combine near real-time, remotely-sensed data with other large spatial data sets, also allows for the evaluation, mapping, and dynamic monitoring of ecosystem performance.
The work is funded through the USGS Land Change Science (LCS) Program, which strives to understand the nation's most pressing environmental, natural resource, and economic challenges by providing the information and tools necessary and identifying possible solutions.
Additional funding comes from USGS National Land Imaging program, BLM, and the U.S. Fish and Wildlife Service (FWS).
EROS’ remote sensing ecology projects include permafrost and biomass mapping in Alaska, the study of invasive grass in the Great Basin, and the identification of lands with the potential for biofuels feedstock. Follow these links for more detailed information on those projects:
- Monitoring Arctic and boreal ecosystems through the assimilation of field-based studies, remote sensing, and modelling
- Cheatgrass Dieoff Time-series Dynamics, 2000-2010
- Identifying Lands Suitable for Biofuel Feedstock Crops by Dynamic Modeling of Ecosystem Performance
- Carbon Flux Quantification in the Great Plains
Below are other science projects associated with this project.
Below are publications associated with this project.
Distribution of near-surface permafrost in Alaska: estimates of present and future conditions
Using satellite vegetation and compound topographic indices to map highly erodible cropland buffers for cellulosic biofuel crop developments in eastern Nebraska, USA
Spatially explicit estimation of aboveground boreal forest biomass in the Yukon River Basin, Alaska
Downscaling 250-m MODIS growing season NDVI based on multiple-date landsat images and data mining approaches
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
Mapping and monitoring cheatgrass dieoff in rangelands of the Northern Great Basin, USA
Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition
Estimating switchgrass productivity in the Great Plains using satellite vegetation index and site environmental variables
Effects of disturbance and climate change on ecosystem performance in the Yukon River Basin boreal forest
Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices
Spatial variability and landscape controls of near-surface permafrost within the Alaskan Yukon River Basin
Annual crop type classification of the U.S. Great Plains for 2000 to 2011
Distribution and landscape controls of organic layer thickness and carbon within the Alaskan Yukon River Basin
- Overview
Remotely-sensed data forms the backbone of the large-scale maps, models and assessments created at EROS to advance the understanding of Ecosystem Performance, Productivity and Sustainability.
Detailed data from cooperators with site-specific flux towers are integrated to create models of a land cover type - grassland, for example – which can be simulated across a region. Tasks describe exchanges of Gross Primary Productivity (GPP) and Net Ecosystem Exchange (NEE) in the Northern Great Plains and other regions.
Cooperation with the Bureau of Land Management (BLM), universities, and others provides the ground data to develop and validate pixel-specific models of ecosystem performance based on the remote sensing archive, climate, and site potential.
This modeling, EROS’ rich archival resources and the research team’s special ability to combine near real-time, remotely-sensed data with other large spatial data sets, also allows for the evaluation, mapping, and dynamic monitoring of ecosystem performance.
The work is funded through the USGS Land Change Science (LCS) Program, which strives to understand the nation's most pressing environmental, natural resource, and economic challenges by providing the information and tools necessary and identifying possible solutions.
Additional funding comes from USGS National Land Imaging program, BLM, and the U.S. Fish and Wildlife Service (FWS).
EROS’ remote sensing ecology projects include permafrost and biomass mapping in Alaska, the study of invasive grass in the Great Basin, and the identification of lands with the potential for biofuels feedstock. Follow these links for more detailed information on those projects:
- Monitoring Arctic and boreal ecosystems through the assimilation of field-based studies, remote sensing, and modelling
- Cheatgrass Dieoff Time-series Dynamics, 2000-2010
- Identifying Lands Suitable for Biofuel Feedstock Crops by Dynamic Modeling of Ecosystem Performance
- Carbon Flux Quantification in the Great Plains
- Science
Below are other science projects associated with this project.
- Publications
Below are publications associated with this project.
Distribution of near-surface permafrost in Alaska: estimates of present and future conditions
High-latitude regions are experiencing rapid and extensive changes in ecosystem composition and function as the result of increases in average air temperature. Increasing air temperatures have led to widespread thawing and degradation of permafrost, which in turn has affected ecosystems, socioeconomics, and the carbon cycle of high latitudes. Here we overcome complex interactions among surface andAuthorsNeal J. Pastick, M. Torre Jorgenson, Bruce K. Wylie, Shawn J. Nield, Kristofer D. Johnson, Andrew O. FinleyFilter Total Items: 87Using satellite vegetation and compound topographic indices to map highly erodible cropland buffers for cellulosic biofuel crop developments in eastern Nebraska, USA
Cultivating annual row crops in high topographic relief waterway buffers has negative environmental effects and can be environmentally unsustainable. Growing perennial grasses such as switchgrass (Panicum virgatum L.) for biomass (e.g., cellulosic biofuel feedstocks) instead of annual row crops in these high relief waterway buffers can improve local environmental conditions (e.g., reduce soil erosAuthorsYingxin Gu, Bruce K. WylieSpatially explicit estimation of aboveground boreal forest biomass in the Yukon River Basin, Alaska
Quantification of aboveground biomass (AGB) in Alaska’s boreal forest is essential to the accurate evaluation of terrestrial carbon stocks and dynamics in northern high-latitude ecosystems. Our goal was to map AGB at 30 m resolution for the boreal forest in the Yukon River Basin of Alaska using Landsat data and ground measurements. We acquired Landsat images to generate a 3-year (2008–2010) composAuthorsLei Ji, Bruce K. Wylie, Dana R. N. Brown, Birgit E. Peterson, Heather D. Alexander, Michelle C. Mack, Jennifer R. Rover, Mark P. Waldrop, Jack W. McFarland, Xuexia Chen, Neal J. PastickDownscaling 250-m MODIS growing season NDVI based on multiple-date landsat images and data mining approaches
The satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. The 250-m GSN data estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been used for terrestrial ecosystem modeling and monitoring. High temporal resolution with a wide range of wavelengths make the MODIS lAuthorsYingxin Gu, Bruce K. WylieThe 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 beginnAuthorsStephen P. Boyte, Bruce K. Wylie, Donald J. Major, Jesslyn F. BrownMapping 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 aAuthorsStephen P. Boyte, Bruce K. Wylie, Donald J. MajorApplication-ready expedited MODIS data for operational land surface monitoring of vegetation condition
Monitoring systems benefit from high temporal frequency image data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) system. Because of near-daily global coverage, MODIS data are beneficial to applications that require timely information about vegetation condition related to drought, flooding, or fire danger. Rapid satellite data streams in operational applications have cleaAuthorsJesslyn F. Brown, Daniel M. Howard, Bruce K. Wylie, Aaron M. Friesz, Lei Ji, Carolyn GackeEstimating switchgrass productivity in the Great Plains using satellite vegetation index and site environmental variables
Switchgrass is being evaluated as a potential feedstock source for cellulosic biofuels and is being cultivated in several regions of the United States. The recent availability of switchgrass land cover maps derived from the National Agricultural Statistics Service cropland data layer for the conterminous United States provides an opportunity to assess the environmental conditions of switchgrass ovAuthorsYingxin Gu, Bruce K. Wylie, Daniel M. HowardEffects of disturbance and climate change on ecosystem performance in the Yukon River Basin boreal forest
A warming climate influences boreal forest productivity, dynamics, and disturbance regimes. We used ecosystem models and 250 m satellite Normalized Difference Vegetation Index (NDVI) data averaged over the growing season (GSN) to model current, and estimate future, ecosystem performance. We modeled Expected Ecosystem Performance (EEP), or anticipated productivity, in undisturbed stands over the 20AuthorsBruce K. Wylie, Matthew B. Rigge, Brian Brisco, Kevin Mrnaghan, Jennifer R. Rover, Jordan LongGeostatistical estimation of signal-to-noise ratios for spectral vegetation indices
In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation iAuthorsLei Ji, Li Zhang, Jennifer R. Rover, Bruce K. Wylie, Xuexia ChenSpatial variability and landscape controls of near-surface permafrost within the Alaskan Yukon River Basin
The distribution of permafrost is important to understand because of permafrost's influence on high-latitude ecosystem structure and functions. Moreover, near-surface (defined here as within 1 m of the Earth's surface) permafrost is particularly susceptible to a warming climate and is generally poorly mapped at regional scales. Subsequently, our objectives were to (1) develop the first-known binarAuthorsNeal J. Pastick, M. Torre Jorgenson, Bruce K. Wylie, Joshua R. Rose, Matthew Rigge, Michelle Ann WalvoordAnnual crop type classification of the U.S. Great Plains for 2000 to 2011
The purpose of this study was to increase the spatial and temporal availability of crop classification data. In this study, nearly 16.2 million crop observation points were used in the training of the US Great Plains classification tree crop type model (CTM). Each observation point was further defined by weekly Normalized Difference Vegetation Index, annual climate, and a number of other biogeophyAuthorsDaniel M. Howard, Bruce K. WylieDistribution and landscape controls of organic layer thickness and carbon within the Alaskan Yukon River Basin
Understanding of the organic layer thickness (OLT) and organic layer carbon (OLC) stocks in subarctic ecosystems is critical due to their importance in the global carbon cycle. Moreover, post-fire OLT provides an indicator of long-term successional trajectories and permafrost susceptibility to thaw. To these ends, we 1) mapped OLT and associated uncertainty at 30 m resolution in the Yukon River BaAuthorsNeal J. Pastick, Matthew B. Rigge, Bruce K. Wylie, M. Torre Jorgenson, Joshua R. Rose, Kristofer D. Johnson, Lei Ji