Carbon Flux Quantification in the Great Plains Active
Gross primary production (GPP) and ecosystem respiration (Re) are the fundamental environmental characteristics which drive carbon exchanges between terrestrial ecosystems and the atmosphere (Chapin and others, 2009), although other exchanges of carbon, for example, export or direct oxidation (Lovett and others, 2006) can modify net ecosystem production (NEP).
The long-term accumulation of carbon in terrestrial ecosystems results in systems in which carbon contents of soil organic matter (SOM) often exceeds that of biomass (Post and Kwon, 2000). This SOM pool exists as a steady state between GPP and Re in ecosystems unless drivers change or perturbations (for example, climate) occur. As illustrated by Wilhelm and others (2010), conversion of grasslands to agriculture and cultivation practices can result in reduced soil carbon with the release of CO2 to the air by stimulated oxidation, contributing to higher Re. Specific land-use and management practices, therefore, influences NEP with additional reactions caused by irregular climate conditions (Luo, 2007). The isotopic status of the SOM reflects the net inputs by C3 and C4 systems and therefore in native prairies documents climate change (von Fischer and others, 2008) through the Holocene.
The recent concerns and questions being raised over issues such as climate change and alternative energy have driven significant changes in land management practices, especially in the highly agricultural Midwestern U.S. (Wilhelm, and others, 2010). It is important to insure the sustainability of these and other land management practices and to be aware of the potential impacts that such practices can have on NEP or exchanges of carbon with the atmosphere (Anderson-Teixeira and others, 2009). Since the mid-1990s, a highly advanced and growing network of micrometeorological towers has been utilizing eddy covariance methods to measure the exchanges of carbon dioxide, water vapor, and energy between terrestrial ecosystems and the atmosphere. These towers, also known as flux towers, are being strategically placed throughout North America in an effort to effectively represent major ecosystems and to make these data available to the scientific community. Such a dataset offers a unique and valuable resource for use in the study and quantification of carbon exchanges between terrestrial ecosystems and the atmosphere and can ultimately lead to answering the questions raised about resource sustainability.
The focus of this study has been defined as the North American Great Plains, a region primarily consisting of grassland and cultivated cropland (Figure 1). Currently, more than 100 site-years of flux-tower measurements, represented by over 30 individual cropland or grassland sites throughout the Great Plains, have been accumulated and are being analyzed in conjunction with applicable remotely sensed data. Given the terrestrial composition of the focus area, it is essential to account for both grassland and cultivated cropland ecosystems to achieve a comprehensive quantification of NEP. Recent studies have shown that, through the use of complex regression tree modeling, flux tower measurements and remotely sensed data can be utilized to quantify and map NEP in grassland ecosystems across the Great Plains (Zhang and others, 2011) and the dramatic affect that annual climate and land use has on NEP.
Applying similar quantification methods to the cropland ecosystems of the Great Plains will allow for further expansion of NEP quantification and mapping of the region. Such an application first required that major crop types commonly grown in the Great Plains, such as corn, soybeans, and wheat were known with relatively high spatial and temporal resolution. We developed and implemented a crop type classification model, based primarily on weekly time series normalized differential vegetation index (NDVI) data, to account for these major crop types. The models were originally developed for the Greater Platte River Basin, but have the capability to be expanded to cover larger regions, such as the Great Plains. Our efforts are progressing in the area of cropland NEP quantification in the Great Plains and still require additional acquisition and processing of source flux tower data and the development of carbon flux algorithms for the major crop types in the region. Attaining these lingering aspects of carbon fluxes in the Great Plains will greatly increase our ability to comprehensively quantify NEP in the region.
Through all of our research and development in this area, we have also devised an approach that effectively identifies and maps areas within the Great Plains which are poorly represented by the current flux tower distribution. This information could be utilized for future management and planning purposes of the flux tower network.
We integrate our flux quantification with detailed documentation of the carbon isotope status of soil organic matter (SOM) throughout the soil profile and in various particle size fractions. This allows us to quantify C3 and C4 contributions to the SOM, and our analyses across the latitudes of native prairies in North America allow a reconstruction of past systems from which climate information is derived.
Below are publications associated with this project.
Rapid crop cover mapping for the conterminous United States
Geospatial data mining for digital raster mapping
Rapid crop cover mapping for the conterminous United States
Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems
Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA
Productivity and CO2 exchange of Great Plains ecoregions. I. Shortgrass steppe: Flux tower estimates
Estimating carbon and showing impacts of drought using satellite data in regression-tree models
The interacting roles of climate, soils, and plant production on soil microbial communities at a continental scale
Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier
Grassland and cropland net ecosystem production of the U.S. Great Plains: Regression tree model development and comparative analysis
An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data
Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition
Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices
- Overview
Gross primary production (GPP) and ecosystem respiration (Re) are the fundamental environmental characteristics which drive carbon exchanges between terrestrial ecosystems and the atmosphere (Chapin and others, 2009), although other exchanges of carbon, for example, export or direct oxidation (Lovett and others, 2006) can modify net ecosystem production (NEP).
The long-term accumulation of carbon in terrestrial ecosystems results in systems in which carbon contents of soil organic matter (SOM) often exceeds that of biomass (Post and Kwon, 2000). This SOM pool exists as a steady state between GPP and Re in ecosystems unless drivers change or perturbations (for example, climate) occur. As illustrated by Wilhelm and others (2010), conversion of grasslands to agriculture and cultivation practices can result in reduced soil carbon with the release of CO2 to the air by stimulated oxidation, contributing to higher Re. Specific land-use and management practices, therefore, influences NEP with additional reactions caused by irregular climate conditions (Luo, 2007). The isotopic status of the SOM reflects the net inputs by C3 and C4 systems and therefore in native prairies documents climate change (von Fischer and others, 2008) through the Holocene.
The recent concerns and questions being raised over issues such as climate change and alternative energy have driven significant changes in land management practices, especially in the highly agricultural Midwestern U.S. (Wilhelm, and others, 2010). It is important to insure the sustainability of these and other land management practices and to be aware of the potential impacts that such practices can have on NEP or exchanges of carbon with the atmosphere (Anderson-Teixeira and others, 2009). Since the mid-1990s, a highly advanced and growing network of micrometeorological towers has been utilizing eddy covariance methods to measure the exchanges of carbon dioxide, water vapor, and energy between terrestrial ecosystems and the atmosphere. These towers, also known as flux towers, are being strategically placed throughout North America in an effort to effectively represent major ecosystems and to make these data available to the scientific community. Such a dataset offers a unique and valuable resource for use in the study and quantification of carbon exchanges between terrestrial ecosystems and the atmosphere and can ultimately lead to answering the questions raised about resource sustainability.
The focus of this study has been defined as the North American Great Plains, a region primarily consisting of grassland and cultivated cropland (Figure 1). Currently, more than 100 site-years of flux-tower measurements, represented by over 30 individual cropland or grassland sites throughout the Great Plains, have been accumulated and are being analyzed in conjunction with applicable remotely sensed data. Given the terrestrial composition of the focus area, it is essential to account for both grassland and cultivated cropland ecosystems to achieve a comprehensive quantification of NEP. Recent studies have shown that, through the use of complex regression tree modeling, flux tower measurements and remotely sensed data can be utilized to quantify and map NEP in grassland ecosystems across the Great Plains (Zhang and others, 2011) and the dramatic affect that annual climate and land use has on NEP.
Applying similar quantification methods to the cropland ecosystems of the Great Plains will allow for further expansion of NEP quantification and mapping of the region. Such an application first required that major crop types commonly grown in the Great Plains, such as corn, soybeans, and wheat were known with relatively high spatial and temporal resolution. We developed and implemented a crop type classification model, based primarily on weekly time series normalized differential vegetation index (NDVI) data, to account for these major crop types. The models were originally developed for the Greater Platte River Basin, but have the capability to be expanded to cover larger regions, such as the Great Plains. Our efforts are progressing in the area of cropland NEP quantification in the Great Plains and still require additional acquisition and processing of source flux tower data and the development of carbon flux algorithms for the major crop types in the region. Attaining these lingering aspects of carbon fluxes in the Great Plains will greatly increase our ability to comprehensively quantify NEP in the region.
Through all of our research and development in this area, we have also devised an approach that effectively identifies and maps areas within the Great Plains which are poorly represented by the current flux tower distribution. This information could be utilized for future management and planning purposes of the flux tower network.
We integrate our flux quantification with detailed documentation of the carbon isotope status of soil organic matter (SOM) throughout the soil profile and in various particle size fractions. This allows us to quantify C3 and C4 contributions to the SOM, and our analyses across the latitudes of native prairies in North America allow a reconstruction of past systems from which climate information is derived. - Publications
Below are publications associated with this project.
Rapid crop cover mapping for the conterminous United States
Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing seasonAuthorsDevendra Dahal, Bruce K. Wylie, Daniel HowardFilter Total Items: 47Geospatial data mining for digital raster mapping
We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural nAuthorsBruce K. Wylie, Neal J. Pastick, Joshua J. Picotte, Carol DeeringRapid crop cover mapping for the conterminous United States
Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing seasonAuthorsDevendra Dahal, Bruce K. Wylie, Daniel HowardSpatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems
Drylands are the habitat and source of livelihood for about two fifths of the world’s population and are highly susceptible to climate and anthropogenic change. To understand the vulnerability of drylands to changing environmental conditions, land managers need to effectively monitor rates of past change and remote sensing offers a cost-effective means to assess and manage these vast landscapes. HAuthorsNeal J. Pastick, Bruce K. Wylie, Zhuoting WuFusing 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 DahalProductivity and CO2 exchange of Great Plains ecoregions. I. Shortgrass steppe: Flux tower estimates
The shortgrass steppe (SGS) occupies the southwestern part of the Great Plains. Half of the land is cultivated, but significant areas remain under natural vegetation. Despite previous studies of the SGS carbon cycle, not all aspects have been completely addressed, including gross productivity, ecosystem respiration, and ecophysiological parameters. Our analysis of 1998 − 2007 flux tower measuremenAuthorsTagir G. Gilmanov, Jack A. Morgan, Niall P. Hanan, Bruce K. Wylie, Nithya Rajan, David P. Smith, Daniel M. HowardEstimating 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 interacting roles of climate, soils, and plant production on soil microbial communities at a continental scale
Soil microbial communities control critical ecosystem processes such as decomposition, nutrient cycling, and soil organic matter formation. Continental scale patterns in the composition and functioning of microbial communities are related to climatic, biotic, and edaphic factors such as temperature and precipitation, plant community composition, and soil carbon, nitrogen, and pH. Although these reAuthorsMark P. Waldrop, JoAnn M. Holloway, David B. Smith, Martin B. Goldhaber, R. E. Drenovsky, K. M. Scow, R. Dick, Daniel M. Howard, Bruce K. Wylie, James B. GraceByEcosystems Mission Area, Energy and Minerals Mission Area, Climate Research and Development Program, Energy Resources Program, Land Change Science Program, Mineral Resources Program, National Laboratories Program, Science and Decisions Center, Earth Resources Observation and Science (EROS) Center , Geology, Minerals, Energy, and Geophysics Science Center, Wetland and Aquatic Research CenterTemporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier
Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008 to 2013. In this investigation, we sought to contrAuthorsAaron M. Friesz, Bruce K. Wylie, Daniel M. HowardGrassland and cropland net ecosystem production of the U.S. Great Plains: Regression tree model development and comparative analysis
This paper presents the methodology and results of two ecological-based net ecosystem production (NEP) regression tree models capable of up scaling measurements made at various flux tower sites throughout the U.S. Great Plains. Separate grassland and cropland NEP regression tree models were trained using various remote sensing data and other biogeophysical data, along with 15 flux towers contributAuthorsBruce K. Wylie, Daniel Howard, Devendra Dahal, Tagir Gilmanov, Lei Ji, Li Zhang, Kelcy SmithAn 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 NelsonApplication-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 GackeGeostatistical 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 Chen