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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Landscape-scale Characterization of Arctic Tundra Vegetation Composition, Structure, and Function with a Multi-sensor Unoccupied Aerial System: Supporting Data.

Airborne remote sensing data collected using the Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform, the Osprey, operated by the Terrestrial Ecosystem Science and Technology (TEST) group (https://www.bnl.gov/testgroup). This package includes data from three flights flown over the NGEE-Arctic Council, Kougarok and Teller sites in July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface 'skin' temperature imagery, as well as surface reflectance at 1 nm intervals in the visible to near-infrared spectral range 350 - 1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model (CHM). In addition, a VNIR surface reflectance file is provided for the trigger locations collected during each flight campaign. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Unprocessed and processed data products are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, *.kml, *hdr, *.enp), tabular (*.dat, *.csv, *.waypoint, ENVI format (no extension)), point cloud (*.laz) and image (*.jpg, *.tif, *png) formats. This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy?s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

PowerAnalytics.jl: User-Centric Power Systems Analysis in Julia

The National Laboratory of the Rockies recently released version 1 of PowerAnalytics.jl, an analysis module for the outputs of its popular open-source electrical power systems modeling platform Sienna. It features an extensible framework - based on the flexible selecting of components, the execution of arbitrary metrics on them, and a familiar DataFrames-based output interface with embedded metadata - to process results in the Sienna style while keeping the interface as simple as possible for non-Julia experts. Here, I describe the package and where it fits into the Sienna ecosystem, how I harnessed user-centered design and Julia features to achieve beginner friendliness without sacrificing performance and expressibility, and what lessons might be drawn from the package's design and implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Concentration-Discharge Relationships in the Six Largest Arctic Rivers, 2003-2019

This dataset provides the results of the analysis of the relationship of dissolved analyte concentrations and river discharges in the six largest Arctic rivers across the global panarctic region (see Figure 1 in documentation file *.pdf). Long-term measurements of dissolved analyte concentrations and river discharge have been collected for each of the Kolyma, Lena, Mackenzie, Ob, Yenisey, and Yukon rivers by the Arctic Great Rivers Observatory (ArcticGRO) project from ~2003-present (Shiklomanov, 2021). The relationship of dissolved analyte concentrations and discharges in each river was characterized by statistical analysis of the slope of the log(concentration) vs log(discharge) (b), the coefficient of variation ratio (CVc/CVq), the 2.5% and 97.5% confidence intervals of b, and assigning a chemostatic, flushing, diluting, or non-systematic behavior category according to Koger (2018). The summary of these analyses for all six rivers is provided in one .csv file. The concentrations of 20 dissolved analytes and discharge measurement data for the individual Kolyma, Lena, Mackenzie, Ob, Yenisey, and Yukon rivers are also provided with this dataset. There are seven *.csv files; one for each river plus the statistical summary. These public ArcticGRO data at "https://www.arcticgreatrivers.org" (Shiklomanov, 2021) were downloaded on Feb 13, 2020, but each river has different measurement dates over the sampling and analysis period. The ArcticGRO metadata document (*.pdf) downloaded on Feb 13, 2020 is also included in this dataset. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices

Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.

Climate models↗
DOE OSTI2021

Stomatal response curves of hybrid poplar, New York, USA, 2020

This data package contains stomatal response to assimilation (m) curves of sunlit canopy leaves of species Populus deltoides Bartr. x Populus nigra L. (hybrid poplar). The trees were grown in pots at Brookhaven National Laboratory, Upton NY, USA, and measurements made using a Licor 6800 portable photosynthesis system. Leaf mass per area (LMA) is included for each measured leaf. The files included in this data package are 2 processed data files (csv) and 6 metadata files (csv), a methods protocol description (pdf) and the complete instrument output (32 xlsx files). File level metadata details are provided in NGT0169.csv in this package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data Visualization: Augmented Reality

In recent years, there has been an increasing interest in developing new technologies for automated characterization and visualization of condition monitoring data. Augmented Reality (AR) is a technology that is being developed to improve such data visualization. Augmented reality has been defined as a technology that merges virtual and physical components in real-time, and in three dimensions. Wearable, commercially-available AR devices allow onsite engineers and technicians to perform inspection tasks with significantly more available information such as comparisons of past and present sensor and imager data, onsite data analysis and result displays, and various forms of metadata including technical drawings, previous inspection reports and maintenance histories, operation manuals, codes and standards, and holograms representing data analysis results superimposed onto the in situ monitored system.

42 ENGINEERING↗
DOE OSTI2022

A Catalog of Candidate Double and Lensed Quasars from Gaia and WISE Data

Making use of strong correlations between closely separated multiple or double sources and photometric and astrometric metadata in Gaia Early Data Release 3 (EDR3), we generate a catalog of candidate double- and multiply imaged lensed quasars and active galactic nuclei (AGNs), comprising 3140 systems. It includes two partially overlapping parts: a sample of distant (redshifts mostly greater than 1) sources with perturbed data; and systems that have been resolved into separate components by Gaia at separations less than 2''. For the first part, which is roughly one-third of the published catalog, we synthesized 0.617 million redshifts using multiple machine-learning prediction and classification methods, using independent photometric and astrometric data from Gaia EDR3 and the Wide-field Infrared Survey Explorer, with accurate spectroscopic redshifts from the Sloan Digital Sky Survey (SDSS) as a training set. Using these synthetic redshifts, we estimate a 4.9% rate of interlopers with spectroscopic redshifts below 1 in this part of the catalog. Unresolved candidate double and dual AGNs and quasars are selected as sources with a marginally high BP/RP excess factor (phot_bp_rp_excess_factor), which is sensitive to source extent, limiting our search to high-redshift quasars. For the second part of the catalog, additional filters on measured parallax and near-neighbor statistics are applied to diminish the propagation of the remaining stellar contaminants. The estimated rate of the positives (double or multiple sources) is 98%, and the estimated rate of dual (physically related) quasars is greater than 54%. A few dozen serendipitously found objects of interest are discussed in more detail, including known and new lensed images, planetary nebulae, young IR stars of peculiar morphology, and quasars with catastrophic redshift errors in SDSS.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

RITE Gen5 KHPS Performance - Period A

Includes Kinetic Hydropower System (KHPS) Turbine performance data from the RITE Gen5 KHPS turbine at the 5 meter size. This data was collected over Period A which was a 39 day span ending on 11/12/2021. This data was collected under the European Marine Energy Centre (EMEC) power performance assessment. Along with performance data, the data includes setup data, project metadata, and characteristic data for RITE Gen5 KHPS (5m) turbine.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Vegetation Warming Experiment: Leaf mass area, leaf carbon and nitrogen content, Utqiagvik, Alaska, 2021-2022

Leaf mass per area (LMA), and leaf carbon and nitrogen content of an Arctic graminoid, Carex aquatilis, from within warming chambers and paired control plots. The plants were sampled in July 2021 and July 2022 as part of the Zero Power Warming (ZPW) vegetation warming experiment conducted on the Barrow Environmental Observatory (BEO), Utqiagvik (formerly Barrow), Alaska. Samples include leaves used for gas exchange measurements and bulk harvests from each warming chamber and paired ambient plot. The files included in this data package are in .csv format, and include 3 data files and 4 metadata files. See related datasets for plant physiology, phenology, thaw depth and environmental conditions of the plots and warming chambers. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths: Supporting Data

The extent and duration of snow cover is predicted to be altered as the climate changes. Developing high-resolution estimates of snow cover change is crucial for estimating changes in snow cover and the effects of these changes on watershed and ecosystems processes. Remote sensing tools have been a common method for rapidly mapping snow covered area (SCA) across a landscape. The most common remote sensing method for estimating SCA uses satellite-based calculations of the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI is effective at catchment- to regional-scale estimates of SCA, but due to spatial resolution limitations of SWIR measurements, NDSI cannot be used to assess fine-scale SCA. In this work, we develop a new algorithm, called the Blue Snow Threshold (BST) algorithm, that maps high-resolution SCA by calculating a threshold on the blue wavelengths from high-resolution satellite imagery. This data package includes Orthorectified IKONOS-2 imagery (IkonosTestImage.tif) from August 14, 2004 at 1.00 meters Ground Sample Distance for Cook Inlet, Alaska (59.966414 , -152.982975). The Blue Snow Threshold algorithm (BST.py) was then used to produce a snow cover estimate (IkonosTestImage_BST.tif) for this study area. Additional imagery metadata is included in the ImageInfo.txt file. See Thaler et al., 2023 (https://doi.org/10.1016/j.rse.2022.113403) for more information about the BST algorithm.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

NGEE Arctic Integrated Modeling (IM2): Improved subgrid hillslope hydrologic connectivity

This data product represents the integration of new code capability for arctic tundra hillslope hydrologic processes into the Energy Exascale Earth System Model (E3SM), through the E3SM Land Model (ELM) component. This code integration is the result of collaborative effort between the NGEE Arctic project and the E3SM project. The current ELM represents water movement primarily through vertical processes, such as precipitation, canopy interception, evaporation, infiltration, and soil water movement. Lateral water movement—such as surface runoff, subsurface flow, and river transport—plays a significant role in the hydrological cycle, especially in regions with varied topography. While E3SM includes a runoff routing component representing water transport in the river network, the lateral transport of water at the subgrid scale within the land model has previously not been taken into account. With the recent development of topographic units within the ELM subgrid data structure, there is an opportunity to simulate hillslope hydrologic connectivity by introducing water transport along topographic gradients. We expect that more realistic representation of hillslope hydrologic processes will lead to improved predictions of both soil water content and river network flows. Lateral transport of water at and near the surface is represented as a sub-grid process in this new code development. Water is tracked as it moves from higher to lower elevations within a gridcell. This capability uses the nested hierarchical sub-grid scheme within ELM to connect water fluxes from sub-grid elements with higher elevation to those with lower elevation. This data record consists of a single document (pdf format) that describes the theoretical basis for the hillslope hydrology processes added to ELM, and describes the modifications made to the ELM code. The Methods section of this metadata record includes a link to the public E3SM code repository where the exact code modifications as integrated in E3SM can be accessed. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

Thornton, Peter E [ORNL] (ORCID:0000000247595158)↗
DOE OSTI2025

OEDI—Solar Grid Integration Data and Analytics Library

As a part of the Open Energy Data Initiative, this effort aims to develop and demonstrate novel distribution state estimation, control optimization, and transient analysis as well as provide access to data, data integration, and mapping information. More specifically, the focus of the effort will be on physics-based distribution system state estimation, hybrid (physics-based and machine learning) distribution optimal power flow, and event detection/analysis for solar integration and analytics. This work will enable reproducible, robust, replicable, and generalizable R&D in simulation and emulation of solar system integration. These test models and datasets will provide an integrated library for developing and testing power system operation technologies. To make the library user-friendly, this project will provide data curation tools such as data translators, mapping scripts and APIs, database schemas and metadata, interfaces and user dashboard, source code for the reference algorithms, description of the use-cases/scenarios, and comprehensive information on all the assumptions.

14 SOLAR ENERGY↗
DOE OSTI2024

Vegetation Warming Experiment: Chamber and ambient plot digital camera imagery for vegetation phenology, Utqiagvik (Barrow), Alaska, 2017

Time lapse photography (*.jpg) of experimental plots within five warming chambers (ZPWs) and paired control plots located on the Barrow Environmental Observatory (BEO), Barrow (now Utqiagvik), Alaska. Images were recorded from 21 June to 18 September, 2017 to capture vegetation dynamics during the growing season. Vegetation phenology, including green up and senescence were captured. Images were recorded daily at 30 minute intervals, from 11:00-14:30 Alaska daylight time (AKDT, UTC-8), using Wingscapes TimelapseCam cameras. The target species was Petasites frigidus. Also included multiple metadata files including reporting formats as *.pdf and *.csv files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Waste Compliance and Tracking System (WCATS) Version 3 Requirements Document

This document describes the end-user requirements for the Waste Compliance and Tracking System (WCATS) project in accordance with the WCATS Software Quality Management Plan, EPC-WMP-WCATSPLAN-001. The WCATS application shall support the generation, characterization, processing, and shipment of LANL radioactive, hazardous, and industrial waste. Regulatory drivers include RCRA hazardous waste, DOT shipping, NNSA nuclear material control and accountability, DOE nuclear safety, TSDF permit, and transuranic waste certification requirements. The system will utilize a task-based architecture that supports the spectrum of treatment, storage, disposal, administrative, and characterization based unit operations necessary to manage waste from cradle to grave. The application design shall readily accommodate new facilities, processes, workflow, signature requirements, and so forth, via end-user established metadata. WCATS will provide support for representing waste storage and disposal facilities, buildings, rooms, and grid layouts (x, y, z) to support waste and radioactive material inventory management. Nuclear material at risk (MAR), DOE hazard rating (e.g., Category II facility) compliance per DOE-STD-1027, and permit inventory requirements will be configurable for any storage or disposal facility, or waste operation, and the system will automatically evaluate and enforce those requirements. In addition, the application will support the characterization and management of the entire range of hazardous and radioactive wastes (TRU, MTRU, LLW, MLLW, hazardous waste, etc.) that might be colocated or processed at a permitted facility. Some capabilities not found in traditional systems include user-defined tank systems for liquid waste, user-defined work paths (i.e., sequence of operations), and an equipment subsystem for tracking the calibration, maintenance, and inspection of tools used to process waste, such as torque wrenches, scales, pH probes, etc. The application incorporates a desktop and mobile user interface as shown in Figure 1. The mobile interface supports field operations, such as waste item characterization, intra-facility transfers, internal and external audits, and shipment preparation and receipt.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

LiDAR Point Cloud Data from the 2018 NGEE Arctic UAS Campaign at the Kougarok 64 Field Site, Seward Peninsula, Alaska

Airborne remote sensing data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) hexacopter platform operated by NGEE Arctic scientists from the EES-14 group at Los Alamos National Laboratory. These data were collected in July 2018 at a field site near mile marker 64 along the Kougarok road (Nome-Taylor Highway) between Nome, Alaska and Taylor, Alaska. A DJI Matrice 600 Pro Airframe and Routescene UAV LiDARSystem was used to collect LiDAR data. The LiDAR data has undergone basic post-processing using Routescene LidarViewer Pro software to create point cloud data (.laz files). This data package contains point clouds (.laz), processing metadata files (json.lvp), and post-processed kinematic files (.csv). Ancillary aircraft data, flight mission parameters, weather conditions, raw LiDAR data, and RGB imagery can be found in NGA298.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Pan-Arctic Vegetation Cover (PAVC) database v1.1

The Pan-Arctic Vegetation Cover (PAVC) database contains synthesized field-data observations of vegetation cover from 978 Arctic Alaska plots with observations from 2010 to 2021. The cover datasets contain plot data at both the plant functional type (PFT) and species-level resolution, with standardized PFT definitions and species names. We synthesized publicly available point-intercept and visual estimate plots from the Arctic Vegetation Archive of Alaska, the Alaska Vegetation Plots Database, the North Slope Science Catalog, and the National Ecological Observatory Network; as well as previously unpublished data from the Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic).Users will find four synthesized datasets, 4 associated data descriptor (dd) files, and 1 metadata file in the PAVC database:synthesized_species_fcover.csv contains fractional cover (fcover) for unique accepted species names, where names include vegetation identified at the family, genus, species, subspecies, and variety levels, as well as general functional types across all 5 data sources. The synthesized_species_fcover_dd.csv accompanies this dataset with header information.synthesized_pft_fcover.csv contains fcover for the following PFTs: non-vascular plants with lichen and bryophyte subcategories, trees with deciduous and evergreen subcategories, shrubs with deciduous and evergreen subcategories, graminoids (grasses), and forbs (herbaceous flowering plants) measured as total cover. Litter and “other” cover are also included as total cover. Additional “types” include water and bare ground, which were measured as top cover. The synthesized_pft_fcover_dd.csv accompanies this dataset with header information.species_pft_checklist.csv is a lookup table containing the translation from a dataset species name to an accepted species name and to a PFT. This table can be used to clarify our species to PFT adjudications, and to aid users in assigning their own PFTs. Any issues found in this checklist should be reported in the Issues tab of our github.survey_unit_information.csv contains auxiliary information about the plots synthesized in this database. It contains useful information for filtering plots of interest based on temporal, geospatial, and contextual information about the plot surveys.flmd.csv contains metadata information about each file in the database.This research was performed as a part of the NGEE Arctic project. The NGEE Arctic project was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Full spectrum (350-2500 nm) canopy spectral reflectance, Seward Peninsula, Alaska, 2018

Full-range (350-2500 nm) canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including canopy-scale species endmember spectra, plot-scale spectra, and transect spectra as well as non-vegetated surface (NVS) spectra. The datasets were collected at the three core NGEE-Arctic watersheds, Kougarok, Teller, and Council, as well as the supplemental Kougarok Mile 80 site within the larger Seward Peninsula, Alaska region. The data were collected in July 2018 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see associated dataset). Species included in the spectra and functional trait dataset include Alnus viridis, Betula nana, Betula glandulosa, Arctous alpina, and Salix pulchra. This data package includes 35 files in a variety of formats including processed canopy spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA208_flmd.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

UAS remote sensing (DJI Phantom 4 RTK platform): RGB orthomosaic, digital surface and canopy height models, plant functional type map, Seward Peninsula, Alaska, 2019

Airborne remote sensing data collected using a DJI Phantom 4 RTK unoccupied aerial system (UAS) ? operated by the University of Maine Wheatland Geospatial Lab (https://wheatlandlab.org/). This package includes data from 17 flights flown over the NGEE-Arctic Council, Kougarok, Kougarok Mile80, Teller, and Teller Mile32 sites in July 2019. The Phantom 4 RTK is a drone platform that collects very high spatial resolution optical red/green/blue (RGB) imagery. Derived image products include point cloud, ortho-mosaiced RGB, a digital surface model (DSM) using the structure from motion (SfM) technique, and a canopy height model (CHM). Unprocessed and processed data products (1,000+ files) are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, *hdr,), ENVI image file (*.dat), point cloud (*.laz) and image (*.jpg, *.tif, *png) formats. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗