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142 results for “Neon”
Visual Counts of Tree Reproduction near NEON plots at Harvard Forest since 2020
Mast seeding, a resource pulse that has cascading effects in the environment, is a measure that can provide insight into forest dynamics. When masting data is collected in sequential years it can provide information on how tree populations are responding to climatic and environmental variables, and can also be used to relate to other indices, such as seed-eating animal species. The objective of this study is to quantify the yearly seed production of mast seeding tree species at Harvard Forest which are located near National Ecological Observatory Network (NEON) plots. This project is part of larger NEON-enabled project examining mast seeding on a continental scale at 25 NEON sites in the United States, which uses mast seeding records in conjunction with NEON collected data products like mammal box-trapping, tick drags, and bird point counts. Data collected on mast seeding can be linked to these other indices at regional and continental scales.
Soil Organic Matter Mechanisms of Stabilization (SOMMOS) - enhanced soil characterization data from 40 National Ecological Observatory Network (NEON) sites
Soil organic matter (SOM) is a critical linkage among many ecosystem services that sustain our society and life on Earth. It is the primary energy source for microbes and the principal storehouse of water necessary for plant growth. SOM also stores nutrients for plants and sorbs pollutants that otherwise could contaminate food and water supplies. Soils also help regulate climate by storing carbon that would otherwise be released to the atmosphere and contribute to climate change. The SOMMOS project investigated processes in the soil that protect SOM from being decomposed by microbes, processes that increase its sensitivity to environmental changes, and how changes in climate and land management influence the amount and stability of SOM. The project, which was a collaboration between scientists from the National Ecological Observatory Network (NEON), University of Colorado, University of Michigan, Oregon State University, Virginia Polytechnic Institute and State University, and the USDA-Forest Service, took advantage of soil samples collected across NEON, a major NSF investment in environmental monitoring that covers the entire United States. This continental-scale soil sample set was analyzed for a wide array of physical and chemical properties, well beyond those typically measured on such a large-scale sample set, including radiocarbon, extractable metals, organic matter chemistry by pyrolysis-GCMS, liquid extract fluorescence spectroscopy, and more. In addition to this dataset, archived samples are available from the project for sharing with interested researchers.
Continuous stream CO2 and temperature data and sensor calibration grab samples from five NEON sites (CARI, COMO, KING, MART, WALK), August 2021-April 2024.
This package contains: 1) sensor-based measurements of dissolved CO2 concentration and temperature, and 2) dissolved CO2 concentration from grab samples that were used to calibrate the sensor data, collected at five stream sites in the NEON network (CARI- Caribou Creek, AK; COMO- Como Creek, CO; KING- Kings Creek, KS; MART- Martha Creek, WA; and WALK- Walker Branch, TN) between August 2021 - April 2024. The grab sample dataset contains a combination of samples collected by NEON (DP1.20097.001) and additional samples collected by project personnel. All samples were collected using the headspace equilibration method, and dissolved CO2 concentrations were calculated using the 'neonDissGas' R package (https://github.com/NEONScience/NEON-dissolved-gas). The sensor dataset contains CO2 concentrations measured with an eosGP CO2 gas probe, averaged to 15-minute intervals and corrected to align with grab sample concentrations using a site-specific grab versus sensor regression. Due to inaccuracies in the eosGP temperature data, we instead include the temperature data from NEON that was used to convert CO2 between units of ppmv and umol/L (DP1.20053.001 for CARI, KING, MART, and WALK, and data from the multiparameter sonde for COMO). All NEON data used in this data package references the RELEASE-2025 version of each data product (downloaded February 2025).
Soil and litter microclimate data from NEON and LTER Sites Across Eight U.S. Ecoregions (CliMush Project), 2022–2023
Data include soil and litter measurements for moisture, pH, and carbon-to-nitrogen ratio. Samples were collected from 8 different ecoregions, as determined by NEON, at various NEON/LTER and/or other experimental sites. Soil cores and litter samples were taken in the spring and fall of 2022.
Forest structural diversity at NEON sites in the continuous USA that experienced recent moderate disturbance
Disturbances can change the structural diversity of forests through time, which can be measured from three-dimensional data provided by LiDAR. Discrete-return LiDAR was used to measure a suite of 19 structural diversity metrics that describe the height, cover and openness, vegetation density, and internal and external heterogeneity of forest vegetation at NEON base plots. Discrete-return LiDAR point clouds from the NEON Aerial Observation Platform (DP1.30003.001) were downloaded September of 2020 and used to estimate the metrics within 40 x 40 m base plots. Metrics were estimated from base plots at 15 NEON forested sites from provisional LiDAR data available from 2014 to 2020. The workflow that produced the data was developed in the program R.
Standardized NEON organismal data (neonDivData)
To standardize NEON organismal data for major taxonomic groups, we first systematically reviewed NEON’s documentations for each taxonomic group. We then discussed as a group and with NEON staff to decide how to wrangle and standardize NEON organismal data. See Li et al. 2022 for more details. All R code to process NEON data products can be obtained through the R package ‘ecocomDP’. Once the data are in ecocomDP format, we further processed them to convert them into long data frames with code on Github (https://github.com/daijiang/neonDivData/tree/master/data-raw), which is also archived here.
NEON soil inorganic nitrogen measurements 2017-2020, derived data and code for Earth's Future manuscript
Nitrogen (N) is a key limiting nutrient in terrestrial ecosystems, but there remain critical gaps in our ability to predict and model controls on soil N cycling. This may be in part due to lack of standardized sampling across broad spatial-temporal scales. In a paper submitted for publication in Earth's future, we introduce a continentally distributed, publicly available dataset collected by the National Ecological Observatory Network (NEON) that can help fill these gaps. To overcome methodological challenges and generate a standardized dataset, we produced a derived data version of soil inorganic N pools and net N transformation rate tables, which accounts for nitrite contamination in blanks. This derived dataset is then used to evaluate sources of variation within the NEON sampling design with mixed effects models, and we also compare measured net N mineralization to simulated fluxes from the Community Earth System Model 2 (CESM2).
Gas exchange velocities (k600), gas exchange rates (K600), and hydraulic geometries for streams and rivers derived from the NEON Reaeration field and lab collection data product (DP1.20190.001)
This dataset contains estimates of gas exchange velocity, gas exchange rate, and hydraulic parameters for streams calculated from tracer-gas experiments and conservative tracer injections collected by the National Ecological Observatory Network (NEON). All input data were collected by NEON and is available on the NEON data portal at https://data.neonscience.org. Specifically, the NEON Reaeration field and lab collection data product (DP1.20190.001) was used to calculate these estimates. Gas exchange was estimated in two ways: first, following an unpooled frequentist approach and second, following a partially pooled Bayesian approach. In addition, a salt-correction was applied to gas exchange estimates for sites where it was possible and necessary. All estimates of gas exchange are included in the file gasExchange_ds.csv. A recommended selection of these estimates is included in the dataset (best_k600_mPerDay and best_K600_mPerDay). The stanfit objects used for the partially pooled Bayesian approach are also included as site-specific model objects for gas exchange velocities and rates. In addition, water velocity was calculated from conservative tracer injections, and mean water depth was calculated from these water velocity estimates and measurements of wetted width and water discharge. All hydraulic parameters are included in the file hydraulics_ds.csv. All processing code is available in the reaRates R package. NEON is sponsored by the National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program.
Species-level estimated abundances and zero counts of nighttime collected female mosquitoes 2014 - 2022 (Derived from NEON Mosquitoes sampled from CO2 traps (DP1.10043.001, RELEASE-2024))
This Level 2 data package contains species level estimated abundances, including zero counts, and estimated mean number of female mosquitoes per trap derived from the NEON Mosquitoes sampled from CO2 traps (DP1.10043.001), RELEASE-2024 Level 0 data (https://doi.org/10.48443/3cyq-6v47). The data set includes mosquito records of traps collecting mosquito samples at night, for up to 24 trap hours, across a total of 20 terrestrial core and 27 terrestrial gradient sites from 2014 to 2022. To ensure high confidence in abundance estimates, records were only included when at least 90% of collected individuals were identified to sex, and 90% of female specimens were identified to species. Information across multiple QC/QA fields within the NEON mosquito data was evaluated to identify and exclude records where confidence in estimated abundances may have been compromised. Species level zero counts were added for all species collected at least once within the sampling year and trap location. Additionally, species level zero counts were included for trap events where only male mosquitoes had been collected or where QC/QA remarks indicated traps were inactive due to cold temperatures. The data set provides an analysis ready time series of estimated abundances across NEON sites and plots. An R Markdown file that contains descriptions of the QC/QA and data filtering steps along with annotated code, as well as data tables used to filter active and inactive trap events based on QC/QA fields, are published with the data package. Any questions about this data package should be directed to Amely Bauer listed under contacts.
Crown Traits of Broadleaf Deciduous Trees at NEON Forest Sites (2018-2022)
Using NEON Airborne Observation Platform (AOP) measurements collected in 2018-2022 from nine broadleaf deciduous NEON forest sites, we quantified a broad suite of structural metrics and spectral reflectance indices for 305 tree crowns that were delineated in the field by NEON and met our data quality criteria. For each tree crown, we used 1-m^3 voxelated AOP LiDAR data to compute structural metrics, including plant area index (PAI), leaf area index (LAI), top rugosity, maximum canopy height (MAXCH), mean outer canopy height (MOCH), rumple, accumulative plant area density and accumulative LiDAR intensity at multiple tree heights. We used AOP imaging spectrometer to compute several spectral indices, including NDVI, NIRv, EVI, NDWI and chlorophyll index of red edge/green. The data are suitable for ecophysiological studies at tree crown and/or species level. The broad spatial extent allows for the exploration of variability in structure and function of common north American tree species across wide environmental gradients.
NEON Provisional Continuous and Field Discharge - Water Year 2024 (2023-10-01 - 2024-09-30), United States
As of the date of this publication in EDI, NEON publishes one-minute continuous discharge data that have been corrected and gap-filled in the Continuous Discharge (DP4.00130.001, https://data.neonscience.org/data-products/DP4.00130.001) data product. As part of a recent manuscript describing the data quality improvements to NEON's continuous discharge data brought on my implementation of corrections and gap-filling, a mixture of released and provisional NEON data were downloaded. To ensure the reproducibility of the data set used to conduct the analysis presented in the manuscript, the downloaded provisional data, which is subject to change, is saved as a static data set in this EDI publication. The same is done for the Discharge Field Collection (DP1.20048.001, https://data.neonscience.org/data-products/DP1.20048.001) data product, which is used in the same analysis.
Dissolved CO2, CH4, and ions in groundwater from five sites in the NEON network (CARI, COMO, KING, MART, WALK), U.S., 2021-2024.
This package contains groundwater chemistry measurements collected from groundwater wells between June 2021 – May 2024 at five sites in the U.S. National Ecological Observatory Network (NEON): CARI- Caribou Creek, AK; COMO- Como Creek, CO; KING- Kings Creek, KS; MART- Martha Creek, WA; and WALK- Walker Branch, TN. The dataset includes concentrations of dissolved gases (CO2 and CH4), ions (F, Cl, NO2, Br, NO3, PO4, SO4, Na, NH4, K, Mg, Ca), silica (Si), and nutrients measured by colorimetric methods (NO3, NH4, PO4). When available, we also report measurements of water temperature, specific conductivity (SpC), pH, dissolved O2, and barometric pressure. Sample collection and analysis was conducted across three labs with additional assistance from the NEON Research Support Services program. Whenever possible, we matched our field sampling methods to NEON’s protocols for groundwater sampling to ensure samples would be comparable to preexisting data from these sites. Any deviations from these protocols are described in the methods.
Dissolved greenhouse gas concentrations derived from the NEON dissolved gases in surface water data product (DP1.20097.001)
This dataset contains partial pressure and molar concentration of dissolved carbon dioxide, methane, and nitrous oxide in 34 streams, rivers, and lakes calculated from headspace equilibration samples collected by the National Ecological Observatory Network (NEON). All input data were collected by NEON and is available on the NEON data portal at https://data.neonscience.org. Specifically, in situ dissolved gas concentrations were calculated from the air and headspace mixing ratios provided by the NEON Dissolved gases in surface water data product (DP1.20097.001), adjusted for sample and water temperature (DP1.20097.001, DP1.20264.001, DP1.20053.001), barometric pressure (DP1.20097.001, DP1.00004.001), and alkalinity (DP1.20093.001). The final set of inputs is found in the file, input_file, and the processing scripts are available at https://github.com/kellyaho/NEON-GHG-processing. The file, output_file, contains the raw outputs from running the input_file through the processing scripts. There are three outputs for each gas for each sample, one for each of three different pre-equilibration headspace mixing ratios (paired atmospheric samples, loess smoothing of atmospheric samples, and site-specific median). The file, GHG_final, contains the final dataset. This GHG_final uses the outputs from output_file calculated with paired atmospheric samples, and substitutes 0.01 μatm, 0.001 μM, 0.001 μatm, and 0.001 μM for any negative instances of pCH4, [CH4], pN2O, and [N2O], respectively. See methods for more detail. Please cite the NEON data inputs (listed below), in addition to this dataset, when using the data. NEON is sponsored by the National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program.
NEON distributed initial soil characterization dataset (DP1.10047.001) modified for statistical analysis of organic carbon and extractable metals in Hall and Thompson (2021)
We compiled National Ecological Observatory Network (NEON) datasets related to the initial distributed soil sampling effort and subsetted them (removed samples with missing values for certain variables, and several samples with extreme values) for use in statistical analyses to describe relationships between soil organic carbon (SOC) and metals measured in several soil chemical extractions. The NEON provisional data products we used were DP1.10047.001 and DP1.10008.001, which were subsequently combined by NEON as a single data product DP1.10047.001, “Soil physical and chemical properties, distributed initial characterization”. These datasets were used for the analyses reported in a manuscript by Hall and Thompson (2021) in the Soil Science Society of America Journal.
AOP01 Correspondence between plant traits and NEON Airborne Observatory Platform (AOP) data at Konza Prairie (2017)
Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, allowing high-resolution trait mapping. However, the reliability of these data depend on establishing rigorous links with in-situ field measurements. We tested the accuracy of NEON’s readily available AOP derived data products – Leaf Area Index, Total biomass, Ecosystem structure (Canopy height model; CHM), and Canopy Nitrogen by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The weakest relationships were between AOP Canopy Nitrogen and ground-based measures of Nitrogen, as well as the CHM and ground-based canopy height measurements. We also examined how well the full reflectance spectra (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had an R2 of more than 0.25. For all vegetation traits, R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 14-64%. Our results suggest that currently available AOP derived data products are unreliable, at least at this grassland site, and should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although additional assessment of varying spatial scales of field and AOP data, as well as corrections and data pre-processing to improve data quality, are recommended. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area,
NEON rivers Level 0 multisonde temperature data - Jan 2019 to Jul 2022
The National Ecological Observatory Network (NEON; https://www.neonscience.org) collects water temperature measurements from its three river sites using thermistors measuring at various depths throughout the water column. This data is published as part of the water temperature at specific depth in surface water data product (DP1.20264.001). At various times, gaps may occur in this data. The multisonde used to measure water quality (DP1.20288.001) also collects water temperature measurements that can potentially be used to fill these gaps. This data is not published by NEON as part of the Level 1 water quality data product because it is not as accurate. The calibration of the multisonde temperature sensor is also factory set and cannot be adjusted. This data package contains the Level 0 multisonde water temperature data for NEON river sites from Jan 1 2019 to Jul 31 2022. This is raw data that has not been QA/QC'ed. NEON makes no guarantees about the accuracy of this data.
Lignin, litter, and soil carbon decomposition from soil samples collected from 20 National Ecological Observatory Network (NEON) sites in 2019
These data support the findings of a manuscript by Huang et al. (2023) published in Nature Communications (doi pending). We used incubations of soil and stable isotope measurements to measure lignin, litter, and SOC decomposition over an 18-month lab incubation and assessed their relationships with geochemical, microbial, N-related and climatic factors across 156 mineral soils collected from 20 National Ecological Observatory Network (NEON) sites, which span broad biophysical gradients (climate, soil, and vegetation type) across North America. The soils were collected in 2019. Lignin decomposition and biogeochemical variables were also measured in an approximately 12-month field incubation.
GOES-R Land Surface Products at AmeriFlux and NEON Eddy Covariance Tower Locations
The terrestrial carbon cycle varies dynamically over short periods that can be difficult to observe. Geostationary (“weather”) satellites like the Geostationary Operational Environmental Satellite - R Series (GOES-R) deliver near-hemispheric imagery at a ten-minute cadence, and its Advanced Baseline Imager (ABI) measures visible and near-infrared spectral bands that can be used to estimate land surface properties and carbon dioxide flux. GOES-R data are designed for real-time dissemination and are difficult to link with eddy covariance time series of land-atmosphere carbon dioxide exchange. We compiled three-year time series of GOES-R land surface attributes including visible and near-infrared reflectances, land surface temperature, and downwelling shortwave radiation (DSR) at 318 ABI fixed grid pixels containing eddy covariance towers for years 2020-2022. We demonstrate how to best combine satellite and in-situ datasets, and show how ABI attributes useful for carbon cycle science vary across space and time. By connecting observation networks that infer rapid changes to the carbon cycle, we can gain a richer understanding of the processes that control it.
Structural Diversity from the NEON Discrete-Return LiDAR Point Cloud in 2013-2022
Structural diversity, characterizing the volumetric capacity and physical arrangement of biotic components in an ecosystem, controls critical ecosystem functions like light interception, hydrology, and microclimate. This product generates structural diversity metrics for the NEON sites, sourced from the Discrete-Return LiDAR Point Cloud from the NEON Aerial Observation Platform (DP1.30003.001; collected in March 2023). Using R programming, we computed the metrics detailing height, heterogeneity, and density at 30 m, aligned to the Landsat grids, for 243 site years in 57 NEON sites from 2013 to 2022.
WPE01 Assessing the value added of NEON for using machine learning to quantify vegetation mosaics and woody plant encroachment at Konza Prairie
Woody encroachment, or invasion of woody plants, is rapidly shifting tallgrass prairie into shrub and evergreen dominated ecosystems, mainly due to exclusion of fire. Tracking the pace and extent of woody encroachment is difficult because shrubs and small trees are much smaller than the coarse resolution (>10m2) of common remote sensed images. However, the US government has been investing in finer resolution (<2m2) remote sensing through USDA NAIP and the National Ecological Observatory Network (NEON), both of which cost multi-million dollars each year and contain different remote sensed products. We compared two methods of classification (random forests and support vector machines) with these two freely available remotely sensed aerial images to determine if and how much NEON adds to classification accuracy and determine which method of machine learning was more accurate. All models have very high overall classification accuracy (>91%), with the NEON image a few percent more accurate than NAIP. The NEON image significantly relies on canopy height (LiDAR) to make classifications, but the importance of bands is more evenly distributed during NAIP classification. Lastly, accuracy for Eastern Red Cedar specifically is high with NEON (78-84%), compared to the relatively low classification accuracy using NAIP imagery (55-61%).
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OpenNeuro
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