Background/Question/Methods The National Ecological Observatory Network (NEON) is a continental-s... more Background/Question/Methods The National Ecological Observatory Network (NEON) is a continental-scale research platform with a projected lifetime of 30 years. NEON’s purpose is to provide high-quality open source data that informs ecological research on the drivers of environmental change and enables forecasting its responses. To accomplish this, NEON is currently establishing 60 environmental research sites. At these research sites, the terrestrial observing system (TOS) surveys in-situ biomass, insect populations etc. These inventories can be viewed as the boundary conditions or drivers for biophysical processes on much shorter time scales. On the other hand, NEON’s terrestrial infrastructure system (TIS) performs sensor-based measurements of the biophysical responses of an ecosystem, such as evapotranspiration. To establish valid relationships between these drivers and responses, two contradicting requirements must be fulfilled: Both types of observations shall be representative ...
The objective of this study is to assess the feasibility and quality of eddy-covariance flux meas... more The objective of this study is to assess the feasibility and quality of eddy-covariance flux measurements from a weight-shift microlight aircraft (WSMA). Firstly, we investigate the precision of the wind measurement ( σu,v ≤ 0.09 m s−1, σw = 0.04 m s −1), the lynchpin of flux calculations from aircraft. From here, the smallest resolvable changes in friction velocity (0.02 m s−1), and sensible- (5 W m−2) and latent (3 W m−2) heat flux are estimated. Secondly, a seven-day flight campaign was performed near Lindenberg (Germany). Here we compare measurements of wind, temperature, humidity and respective fluxes between a tall tower and the WSMA. The maximum likelihood functional relationship (MLFR) between tower and WSMA measurements considers the random error in the data, and shows very good agreement of the scalar averages. The MLFRs for standard deviations (SDs, 2–34%) and fluxes (17–21%) indicate higher estimates of the airborne measurements compared to the tower. Considering the 99....
Background/Question/Methods: Eddy covariance (EC) observations of ecologically relevant trace gas... more Background/Question/Methods: Eddy covariance (EC) observations of ecologically relevant trace gas and energy fluxes are too sparse spatially for direct assimilation into gridded earth system models (ESMs) or for comparison with large-scale observations. The spatial coverage of a tower EC measurement may represent less than 1% of the domains typically covered by ESMs and remote sensing data. It is hence desirable to improve the spatial representativeness and temporal consistency of EC measurements for improving ecological inference. The objectives of this study are (i) to provide consistent flux time-series for target regions, rather than for a spatio-temporally variable patch of surface close to tower sites, and (ii) to test the applicability of the presented procedure across eco-climatological gradients covering some of the complexity of NEON sites. Based on the environmental response function approach (ERF, Metzger et al., 2013), we developed a procedure to produce spatio-temporal...
ABSTRACT Background/Question/Methods The National Ecological Observatory Network (NEON) is a cont... more ABSTRACT Background/Question/Methods The National Ecological Observatory Network (NEON) is a continental-scale research platform currently in development to assess the causes of ecological change across a projected 30-year timeframe. A suite of standardized sensor-based measurements (Terrestrial Instrument System (TIS) measurements) and in-situ field sampling and observations (Terrestrial Observation System (TOS) activities) will be conducted across 20 ecoclimatic domains (60 terrestrial sites) in the U.S. NEON’s TIS measurements and TOS activities are designed to observe the temporal and spatial dynamics of key drivers and ecological processes and responses to change within each of the 60 terrestrial research sites. To establish valid relationships between these drivers and site-specific responses, two contradicting requirements must be fulfilled: (i) both types of observations shall be representative of the same ecosystem, and (ii) they shall not significantly influence one another. We outline the theoretical background and algorithmic process for determining areas of mutual representativeness and exclusion around TIS measurements and develop a procedure which quantitatively optimizes this trade-off through: (i) quantifying the source area distributions of TIS measurements, (ii) determining the ratio of user-defined impact threshold to effective impact area for different TOS activities, and (iii) determining the range of feasible distances between TIS locations and TOS activities. Results/Conclusions For a given TOS activity, the upwind distance from the TIS location required to stay below the same impact threshold differs among sites. These differences arise from site-specific environmental properties and corresponding differences in the TIS setups. As an example, we present results for three NEON sites (Disney, Harvard, Sterling). For below- and above-ground biomass sampling, as well as insect traps the minimum distances vary among sites in the range of 30 m–70 m, 35 m–105 m, and 180 m–245 m, respectively. Analogously, the maximum distance for mutual representativeness (90% cumulative flux footprint) varies between 290 m–610 m. This approach provides an evidence-based and repeatable method for combining sensor-based measurements and field sampling and observations at predefined levels of disturbance and spatial representativeness. This approach provides an evidence-based and repeatable method for combining sensor-based measurements and field sampling and observations at predefined levels of disturbance and spatial representativeness. Such a framework is an essential prerequisite to warrant establishing reliable relationships between ecosystem drivers and responses that are captured by different observation methods. Ultimately, the ability to improve our understanding of continental-scale ecology depends on the comparability of these relationships among research sites.
The ability to evaluate the validity of data is essential to any investigation, and manual "... more The ability to evaluate the validity of data is essential to any investigation, and manual "eyes on" assessments of data quality have dominated in the past. Yet, as the size of collected data continues to increase, so does the effort required to assess their quality. This challenge is of particular concern for networks that automate their data collection, and has resulted in the automation of many quality assurance and quality control analyses. Unfortunately, the interpretation of the resulting data quality flags can become quite challenging with large data sets. We have developed a framework to summarize data quality information and facilitate interpretation by the user. Our framework consists of first compiling data quality information and then presenting it through 2 separate mechanisms; a quality report and a quality summary. The quality report presents the results of specific quality analyses as they relate to individual observations, while the quality summary takes a...
Background/Question/Methods The National Ecological Observatory Network (NEON) is a continental-s... more Background/Question/Methods The National Ecological Observatory Network (NEON) is a continental-scale research platform with a projected lifetime of 30 years. NEON’s purpose is to provide high-quality open source data that informs ecological research on the drivers of environmental change and enables forecasting its responses. To accomplish this, NEON is currently establishing 60 environmental research sites. At these research sites, the terrestrial observing system (TOS) surveys in-situ biomass, insect populations etc. These inventories can be viewed as the boundary conditions or drivers for biophysical processes on much shorter time scales. On the other hand, NEON’s terrestrial infrastructure system (TIS) performs sensor-based measurements of the biophysical responses of an ecosystem, such as evapotranspiration. To establish valid relationships between these drivers and responses, two contradicting requirements must be fulfilled: Both types of observations shall be representative ...
The objective of this study is to assess the feasibility and quality of eddy-covariance flux meas... more The objective of this study is to assess the feasibility and quality of eddy-covariance flux measurements from a weight-shift microlight aircraft (WSMA). Firstly, we investigate the precision of the wind measurement ( σu,v ≤ 0.09 m s−1, σw = 0.04 m s −1), the lynchpin of flux calculations from aircraft. From here, the smallest resolvable changes in friction velocity (0.02 m s−1), and sensible- (5 W m−2) and latent (3 W m−2) heat flux are estimated. Secondly, a seven-day flight campaign was performed near Lindenberg (Germany). Here we compare measurements of wind, temperature, humidity and respective fluxes between a tall tower and the WSMA. The maximum likelihood functional relationship (MLFR) between tower and WSMA measurements considers the random error in the data, and shows very good agreement of the scalar averages. The MLFRs for standard deviations (SDs, 2–34%) and fluxes (17–21%) indicate higher estimates of the airborne measurements compared to the tower. Considering the 99....
Background/Question/Methods: Eddy covariance (EC) observations of ecologically relevant trace gas... more Background/Question/Methods: Eddy covariance (EC) observations of ecologically relevant trace gas and energy fluxes are too sparse spatially for direct assimilation into gridded earth system models (ESMs) or for comparison with large-scale observations. The spatial coverage of a tower EC measurement may represent less than 1% of the domains typically covered by ESMs and remote sensing data. It is hence desirable to improve the spatial representativeness and temporal consistency of EC measurements for improving ecological inference. The objectives of this study are (i) to provide consistent flux time-series for target regions, rather than for a spatio-temporally variable patch of surface close to tower sites, and (ii) to test the applicability of the presented procedure across eco-climatological gradients covering some of the complexity of NEON sites. Based on the environmental response function approach (ERF, Metzger et al., 2013), we developed a procedure to produce spatio-temporal...
ABSTRACT Background/Question/Methods The National Ecological Observatory Network (NEON) is a cont... more ABSTRACT Background/Question/Methods The National Ecological Observatory Network (NEON) is a continental-scale research platform currently in development to assess the causes of ecological change across a projected 30-year timeframe. A suite of standardized sensor-based measurements (Terrestrial Instrument System (TIS) measurements) and in-situ field sampling and observations (Terrestrial Observation System (TOS) activities) will be conducted across 20 ecoclimatic domains (60 terrestrial sites) in the U.S. NEON’s TIS measurements and TOS activities are designed to observe the temporal and spatial dynamics of key drivers and ecological processes and responses to change within each of the 60 terrestrial research sites. To establish valid relationships between these drivers and site-specific responses, two contradicting requirements must be fulfilled: (i) both types of observations shall be representative of the same ecosystem, and (ii) they shall not significantly influence one another. We outline the theoretical background and algorithmic process for determining areas of mutual representativeness and exclusion around TIS measurements and develop a procedure which quantitatively optimizes this trade-off through: (i) quantifying the source area distributions of TIS measurements, (ii) determining the ratio of user-defined impact threshold to effective impact area for different TOS activities, and (iii) determining the range of feasible distances between TIS locations and TOS activities. Results/Conclusions For a given TOS activity, the upwind distance from the TIS location required to stay below the same impact threshold differs among sites. These differences arise from site-specific environmental properties and corresponding differences in the TIS setups. As an example, we present results for three NEON sites (Disney, Harvard, Sterling). For below- and above-ground biomass sampling, as well as insect traps the minimum distances vary among sites in the range of 30 m–70 m, 35 m–105 m, and 180 m–245 m, respectively. Analogously, the maximum distance for mutual representativeness (90% cumulative flux footprint) varies between 290 m–610 m. This approach provides an evidence-based and repeatable method for combining sensor-based measurements and field sampling and observations at predefined levels of disturbance and spatial representativeness. This approach provides an evidence-based and repeatable method for combining sensor-based measurements and field sampling and observations at predefined levels of disturbance and spatial representativeness. Such a framework is an essential prerequisite to warrant establishing reliable relationships between ecosystem drivers and responses that are captured by different observation methods. Ultimately, the ability to improve our understanding of continental-scale ecology depends on the comparability of these relationships among research sites.
The ability to evaluate the validity of data is essential to any investigation, and manual "... more The ability to evaluate the validity of data is essential to any investigation, and manual "eyes on" assessments of data quality have dominated in the past. Yet, as the size of collected data continues to increase, so does the effort required to assess their quality. This challenge is of particular concern for networks that automate their data collection, and has resulted in the automation of many quality assurance and quality control analyses. Unfortunately, the interpretation of the resulting data quality flags can become quite challenging with large data sets. We have developed a framework to summarize data quality information and facilitate interpretation by the user. Our framework consists of first compiling data quality information and then presenting it through 2 separate mechanisms; a quality report and a quality summary. The quality report presents the results of specific quality analyses as they relate to individual observations, while the quality summary takes a...
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Papers by Stefan Metzger