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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Correlative Nanoscale Imaging of Strained hBN Spin Defects

Spin defects like the negatively charged boron vacancy color center (V B - ) in hexagonal boron nitride (hBN) may enable new forms of quantum sensing with near-surface defects in layered van der Waals heterostructures. Here, the effect of strain on V B - color centers in hBN is revealed with correlative cathodoluminescence and photoluminescence microscopies. Strong localized enhancement and redshifting of the V B - luminescence is observed at creases, consistent with density functional theory calculations showing V B - migration toward regions with moderate uniaxial compressive strain. The ability to manipulate spin defects with highly localized strain is critical to the development of practical 2D quantum devices and quantum sensors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Seismic Elastic Double-Beam Characterization of Faults and Fractures for CO₂ Storage Site Selection

Site characterization for underground injection and storage of gigatonne-scale CO₂ requires reliable and cost-effective methods to detect and characterize faults and fractures and to assess their stress state and fault activation potential. This is critical, as wastewater injection and disposal have been shown to activate faults and induce earthquakes, and CO₂ leakage remains a key concern for long-term storage. In this project, we developed seismic methods to detect and characterize large-scale sedimentary and crystalline basement faults and associated small-scale fractures below conventional seismic imaging resolution using multicomponent (9C) surface seismic data. Machine learning was used to automatically interpret large-scale faults, providing key information for estimating the maximum magnitude of potential induced earthquakes. High-fidelity imaging was achieved by exploiting redundancy across multiple elastic wave modes, where independent images from different modes and frequencies cross-validate each other. We also used our nonlinear signal comparison (NLSC) method for ground roll removal, improving data quality in complex near-surface conditions. The methods were validated using field data acquired in central Montana. Results show that basement faults extend into the sedimentary section and that small-scale fractures are widespread above the basement. The inferred stress orientation is consistent with regional stress data, and the estimated maximum induced earthquake magnitude is small (Mw ~2.3). The developed workflow provides a practical approach for fault and fracture characterization and for assessing induced seismicity and leakage risk. It is directly applicable to CO₂ storage site selection and to other subsurface systems.

02 PETROLEUM↗
DOE OSTI2022

Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period

The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic and yielded an excellent data set to test the parameterizations of ocean/sea-ice/atmosphere interaction processes in regional climate models (RCMs). In the present paper, near-surface data during MOSAiC are used for the verification of the RCM COnsortium for Small-scale MOdel–Climate Limited area Mode (COSMO-CLM or CCLM). CCLM is used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data with around 6 km resolution, sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead fraction data for the winter period. CCLM simulations show a good agreement with the measurements. Relatively large negative biases for temperature occur for November and December, which are likely associated with a too large ice thickness used by CCLM. The consideration of sea ice leads in the sub-grid parameterization in CCLM yields improved results for the near-surface temperature. ERA5 data show a large warm bias of about 2.5°C and an underestimation of the temperature variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

NETL Plastic Pipes Project (Final Report)

Plastic or composite pipelines have been the bane of the utility locating industry because they are neither conductive nor magnetic which are the properties traditionally used to locate buried utilities. Ground penetrating radar (GPR) is an effective geophysical tool for locating plastic/composite pipelines where resistive cover allows for adequate penetration of radar energy. However, GPR has limited applicability in areas where the soil cover is conductive due to significant clay and/or salt content. This study examines complementary near-surface geophysical methods that are potentially useful for locating buried plastic/composite pipelines, either singly or in combination. Specifically, this modeling study used computational numerical methods to forward model the response of GPR, resistivity, seismic, gravity gradiometry, and photoacoustic/thermoacoustic imaging methods to plastic/composite pipelines for various scenarios including: (1) pipe diameters ranging between 2 in. to 12 in.; (2) burial depths ranging between 3 ft. to 4 ft.; (3) various degrees in contrast in physical properties (i.e., electrical permittivity, elasticity, resistivity, density); and (4) various experimental acquisition choices (e.g., GPR radar and seismic source frequencies, electrode spacing). Numerical modeling performed herein reconfirmed that GPR is the preferred method for detecting/locating plastic pipelines. A caveat for GPR detection is that the material covering the plastic pipe (trench fill material and adjacent soil) must be sufficiently resistive to allow the two-way propagation to the required depth of investigation and back to the surface. GPR was the only method modeled in this study that can be used to directly detect plastic pipelines of 2-in.-diameter and larger when buried 3-ft-deep. GPR data processing and imaging also can determine pipe depth, pipe diameter, trench dimensions, and moisture conditions. Seismic modeling results suggest that direct detection of a 12-in.-diameter plastic pipe at 3-ft.-depth may be possible under favorable conditions; however, the associated signature would be weak (e.g., surface- to S-wave, backscattered surface-waves, and/or forward scattered surface-waves to S-wave). Direct pipe detection under field conditions with noise and strong lateral geologic heterogeneity is doubtful. Numerical modeling also suggests that plastic pipelines can be indirectly located by detecting the trench in which they are buried. GPR, direct current (DC) resistivity, and seismic methods have the potential to locate the pipeline trench if there is sufficient contrast between the trench-wall and trench-fill materials for the physical property being measured by each method (i.e., electrical permittivity for GPR; resistivity for DC resistivity; or density, compressional velocity, or shear velocity for seismic). Modeling also indicated that currently available (commercial) gravity gradiometers would be unable to directly detect/locate plastic pipelines ≤ 8-in.-diameter when buried 3-ft.-deep given the typical instrument noise floor for field surveying as well as the expected density variations due to geologic heterogeneity. The numerical modeling performed in this project did not identify a universal geophysical technology that can locate buried plastic pipelines in all parts of the United States (although GPR is suggested for all areas with resistive cover). However, the project results suggest that a towed land streamer simultaneously acquiring multiple geophysical data types including multi-offset GPR, multi-channel DC resistivity, seismic geophone- and/or distributed acoustic sensing (DAS), and potentially photoacoustic/thermoacoustic data would be an appropriate platform for locating buried plastic pipeline. Moreover, the complementary multiphysics data acquired by a towed land streamer would permit the use of joint and/or cooperative inversion frameworks for a more rigorous and consistent data interpretation.

42 ENGINEERING↗
DOE OSTI2024

Dynamical Inference and 3D Imaging of Magnetized Dusty Plasmas

This is the final technical report for this DOE award. The motion of dust particles in a laboratory plasma has been studied for nearly 30 years and has led to the discovery of strongly-coupled crystalline structures and nonequilibrium dynamics governed by gravitational, hydrodynamic, and electrostatic forces. Many of the inter-particle forces are complex; they can be non-reciprocal, and non-additive. Deciphering the mélange of particle interactions has been a challenging task, nevertheless, magnetized dusty plasmas remains an open challenge with limited understanding. With applications ranging from magnetically-confined plasmas for fusion to near-surface planetary environments, this research field is ripe for well-controlled, laboratory experiments and new theoretical tools. In collaboration with the MDPX experiment at Auburn University, this proposal aims to tease apart both the known and unknown forces that drive dusty plasmas in magnetized environments using high-resolution, three-dimensional imaging and particle tracking coupled with modern dynamical inference and machine learning techniques.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

The Ductility of 49Fe-49Co-2V Soft Magnetic Alloy Bar: Surface Effects and Test Methods

The tensile ductility of 49Fe-49Co-2V (Hiperco® 50A) bar was investigated in both as-received and heat-treated conditions. The as-received/machined specimens exhibit very low ductility compared to samples where heat treatment was the final step prior to testing. Microstructural characterization showed that internal residual strain from bar processing and, most importantly, surface machining damage, cause lower elongation in the as-received material. Because fracture of this intermetallic alloy initiates at the surface, it is particularly susceptible to surface machining damage, i.e., the near-surface region has already exhausted most of its ability to accumulate tensile strain. During heat treatment, the internal residual strain and near-surface machining damage are eliminated and ductility is improved, despite a higher degree of crystallographic ordering in the heat-treated condition (which typically lowers ductility). Furthermore, if machining is again performed after heat treatment, the material again exhibits brittle behavior, even with only light touch-up machining passes. Here, in this work, methods of tensile strain measurement were investigated, namely conventional knife-edge extensometry and noncontact digital image correlation (DIC) on heat-treated material. For clip-on knife-edge extensometry, the range of failure strain was 2.5-5.5% for heat-treated Hiperco. For noncontact methods, ductility up to 7% was observed. The results highlight the tendency for the alloy to fail at surface imperfections, even those produced by application of the extensometer itself. Noncontact laser extensometry is recommended for determining the intrinsic ductility of the alloy. A method of laser surface modification was developed which increased ductility by ~ 100% compared to unmodified samples. The high cooling rates achieved during laser surface processing can bypass the ordering reaction and produce a ductile disordered structure at the surface that exhibits ductile fracture characteristics.

EBSD↗
DOE OSTI2023

Seismic Characterization of the Blue Mountain Geothermal Field

Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant.

58 GEOSCIENCES↗
DOE OSTI2021

Elucidating the Onset of Plasticity in Sliding Contacts Using Differential Computational Orientation Tomography

Abstract Depending on the mechanical and thermal energy introduced to a dry sliding interface, the near-surface regions of the mated bodies may undergo plastic deformation. In this work, we use large-scale molecular dynamics simulations to generate “differential computational orientation tomographs” (dCOT) and thus highlight changes to the microstructure near tribological FCC alloy surfaces, allowing us to detect subtle differences in lattice orientation and small distances in grain boundary migration. The analysis approach compares computationally generated orientation tomographs with their undeformed counterparts via a simple image analysis filter. We use our visualization method to discuss the acting microstructural mechanisms in a load- and time-resolved fashion, focusing on sliding conditions that lead to twinning, partial lattice rotation, and grain boundary-dominated processes. Extracting and laterally averaging the color saturation value of the generated tomographs allows us to produce quantitative time- and depth-resolved maps that give a good overview of the progress and severity of near-surface deformation. Corresponding maps of the lateral standard deviation in the color saturation show evidence of homogenization processes occurring in the tribologically loaded microstructure, frequently leading to the formation of a well-defined separation between deformed and undeformed regions. When integrated into a computational materials engineering framework, our approach could help optimize material design for tribological and other deformation problems. Graphic Abstract .

36 MATERIALS SCIENCE↗
DOE OSTI2026

Combining ToF‐SIMS and Multivariate Analysis to Resolve Active Sites on Ni‐Based HER Catalysts

Unambiguous identification of active sites in heterogeneous catalysis remains a major challenge, particularly for materials with ultrathin, chemically mixed surface layers. Here, we demonstrate a generalizable approach that combines time-of-flight secondary ion mass spectrometry (ToF-SIMS) with multivariate statistical analysis (principal component analysis [PCA] and multivariate curve resolution [MCR]) to resolve catalytically relevant motifs at the nanoscale. Using Ni electrodes as a model system, PCA distinguished hydroxide-enriched domains from oxide- and metal-rich regions, while MCR decomposed depth profiles and 3D images into hydroxide, oxide, and metallic layers with nanometer resolution. A unique secondary-ion fragment, NiO 3 H 3 − (m/z 108.94), emerged as a marker of hydroxide-rich environments and correlated with hydrogen evolution reaction (HER) activity across a series of Ni electrodes. Complementary density functional theory (DFT) calculations revealed that Ni(OH) 2 clusters adjacent to metallic Ni offer the most favorable water dissociation energetics, establishing the structural origin of the marker. While illustrated here for Ni-based HER, this workflow provides a broadly applicable framework to isolate and rank near-surface patterns that govern catalytic activity, thereby extending ToF-SIMS from a qualitative probe to a predictive tool for active site identification.

HER active sites↗
DOE OSTI2025

Dimorphos’s Material Properties and Estimates of Crater Size from the DART Impact

On 2022 September 26, the Double Asteroid Redirection Test (DART) spacecraft intentionally collided with Dimorphos, the moon of the binary asteroid system 65803 Didymos. This collision provided the first full-scale test of a kinetic impactor for planetary defense. Images from DART’s DRACO camera revealed Dimorphos to be an oblate spheroid covered in boulders of varying sizes and shapes. Very little was known about Dimorphos prior to DART’s impact, including its shape, structure, and material properties. Approach observations and those following the DART impact have provided crucial knowledge that narrows the parameter space relevant to modeling the impact into Dimorphos. Here we present the results of a suite of hydrocode simulations of the DART impact on Dimorphos. Despite remaining uncertainties, initial models of DART’s kinetic impact provide important information about the results of DART (e.g., potential crater size and morphology, ejecta mass) and the properties of Dimorphos. Simulations here suggest that Dimorphos has near-surface strength ranging from a few Pascals to tens of kPa, which corresponds to crater sizes of ~40–60 m. Simulated crater sizes provide a crucial comparison metric for the European Space Agency Hera mission when it arrives at the Didymos system. Hera’s measurement of crater size in combination with measurement of Dimorphos’s mass will allow us to assess our simulations and provide the information needed to make the DART impact experiment both the first test of a planetary defense mitigation mission and the first full-scale planetary defense simulation validation exercise.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-Informed and Data-Driven Prediction of Residual Stress in Three-Dimensional Machining

Efficient and reliable prediction of machining-induced residual stress (RS) is a key requirement for truly integrated computational materials engineering (ICME). Currently available process modeling approaches, including empirical, analytical, and numerical methodologies lack predictive power and require substantial calibration and validation data. Moreover, most model-based approaches consider only two-dimensional (2D) (i.e., orthogonal), cutting processes. Meanwhile, industrial processes such as milling, turning, and drilling are inherently three-dimensional (3D). The present work attempts to bridge the gap between 2D and 3D through careful consideration of the process physics, including geometric, kinematic, and size-effect constraints to realize robust prediction of how RS develops in 3D machining. Using a novel in-situ experimental technique and digital image correlation (DIC) to determine equivalent Hertzian contact widths, contact pressures, and friction coefficients, the proposed methodology leverages a discretized conversion algorithm that includes multi-pass shakedown effects. This paper presents a semi-analytical model to predict machining-induced RS in 3D turning operations, which are used representatively for 3D processes more generally. Rather than follow a ‘brute force’ 3D FEM approach or conduct countless experiments to train a purely data-driven machine learning algorithm, the proposed approach builds on previous 2D modeling work. Through careful consideration of the process physics, including complex geometry/kinematic considerations of 3D turning, the authors demonstrated an experimentally calibrated approach, as well as validation based on published RS data. Model predictions and previously published measurement data of RS depth profiles for turning of Inconel 718 were compared for a range of process parameters. Correlation between the proposed 3D model and validation data was found to be within the margin of experimental error for most conditions. The proposed model appears to capture the overall behavior of 3D RS depth profiles with acceptable accuracy, particularly the key metrics of near-surface stress, peak stress magnitude and location, as well as overall stress profile depth. This report presents a physics-informed, data-driven approach for efficient calibration of a 2D model for machining-induced RS through DIC analysis of in-situ characterized subsurface displacement fields.

42 ENGINEERING↗
DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2024

Identification of Defects and the Origins of Surface Noise on Hydrogen–Terminated (100) Diamond

Near-surface nitrogen vacancy centres are critical to many diamond-based quantum technologies such as information processors and nanosensors. Surface defects play an important role in the design and performance of these devices. The targeted creation of defects is central to proposed bottom-up approaches to nanofabrication of quantum diamond processors, and uncontrolled surface defects may generate noise and charge trapping which degrade shallow NV device performance. Surface preparation protocols may be able to control the production of desired defects and eliminate unwanted defects, but only if their atomic structure can first be conclusively identified. This work uses a combination of scanning tunnelling microscopy (STM) imaging and first-principles simulations to identify several surface defects on H:C(100)—2 × 1 surfaces prepared using chemical vapour deposition (CVD). The atomic structure of these defects is elucidated, from which the microscopic origins of magnetic noise and charge trapping are determined based on the modeling of their paramagnetic properties and acceptor states. Rudimentary control of these deleterious properties is demonstrated through STM tip-induced manipulation of the defect structure. Furthermore, the results validate accepted models for CVD diamond growth by identifying key adsorbates responsible for the nucleation of new layers.

36 MATERIALS SCIENCE↗
DOE OSTI2026

On the formation of swelling and related flaws in laser powder bed fusion

Process monitoring in laser powder bed fusion additive manufacturing can provide insights into stochastic anomalies, melt pool and plume dynamics, and part quality. Swelling, a build anomaly where overbuilt material protrudes through the powder layer after recoating, is readily detectable in post-recoat visible light images of the powder bed. Here, this work identifies several of the underlying mechanisms driving swelling formation by analyzing the influence of processing parameters, laser scan paths, and build plate locations on the presence of swelling detected in situ. Swelling near the edge of the part and swelling in the internal region of the part are shown to correlate with different process conditions. Edge and internal swelling may be driven by different phenomena, with edge swelling predominately occurring on the edge of a part facing the laser module and correlated to clusters of near-surface voids (detected with X-ray computed tomography). A larger spot size, higher laser power, and lower scan velocity also increased the presence of edge swelling. Laser spot size and scan path influenced internal swelling, which occurred preferentially with a larger spot size and in regions with large melt pools, caused by localized heat accumulation due to non-optimal processing parameters or scan path strategies. For coupons processed with a slicer-defined maximum scan vector length, swelling seldom occurred at internal vector-stripe boundaries. These results provide a mechanistic understanding of how swelling can be linked to material flaws, insight into how some instances of swelling can be avoided, and evidence supporting the use of swelling as an in situ indicator for quality assurance and part qualification.

Anomaly↗
DOE OSTI2022

Validation of seismic velocity models in southern California with full-waveform simulations

SUMMARY Crustal seismic velocity models provide essential information for many applications including earthquake source properties, simulations of ground motion and related derivative products. We present a systematic workflow for assessing the accuracy of velocity models with full-waveform simulations. The framework is applied to four regional seismic velocity models for southern California: CVM-H15.11, CVM-S4.26, CVM-S4.26.M01 that includes a shallow geotechnical layer, and the model of Berg et al. For each model, we perform 3-D viscoelastic wave propagation simulations for 48 virtual seismic noise sources (down to 2 s) and 44 moderate-magnitude earthquakes (down to 2 s generally and 0.5 s for some cases) assuming a minimum shear wave velocity of 200 m s–1. The synthetic waveforms are compared with observations associated with both earthquake records and noise cross-correlation data sets. We measure, at multiple period bands for well-isolated seismic phases, traveltime delays and normalized zero-lag cross-correlation coefficients between the synthetic and observed data. The obtained measurements are summarized using the mean absolute derivation of time delay and the mean correlation coefficient. These two metrics provide reliable statistical representations of model quality with consistent results in all data sets. In addition to assessing the overall (average) performance of different models in the entire study area, we examine spatial variations of the models’ quality. All examined models show good phase and waveform agreements for surface waves at periods longer than 5 s, and discrepancies at shorter periods reflecting small-scale heterogeneities and near-surface structures. The model performing best overall is CVM-S4.26.M01. The largest misfits for both body and surface waves are in basin structures and around large fault zones. Inaccuracies generated in these areas may affect tomography and model simulation results at other regions. The seismic velocity models for southern California can be improved by adding better resolved structural representations of the shallow crust and volumes around the main faults.

58 GEOSCIENCES↗
DOE OSTI2023

Observations and Machine-Learned Models of Near-Surface Permafrost along the Koyukuk River, Alaska, USA

This dataset contains GeoTIFs (raster) and GeoPackages (vector) that map observations of near-surface permafrost and not-permafrost from a field campaign conducted near the village of Huslia, AK along the Koyukuk River and its floodplain in July 2018. These data were collected as part of a campaign to understand if and how permafrost impacts riverbank erosion. This problem cannot be assessed without knowing where permafrost exists. Permafrost was observed via frost probing (to a maximum depth of one meter), coring (to a maximum depth of two meters) and bank/bar excavations. An additional boat survey was performed wherein expert (Joel Rowland) judgment assessed the presence or absence of distinctive permafrost features (e.g., overhanging tundra mats, thermoerosional niching, ice wedges, active drainage of ice melt from soils). This dataset also contains the input features and results of two machine learning models (random forest and convolutional neural network) that extrapolate the observations to the full floodplain that may be useful for building, testing, or validating other machine-learned permafrost models. Permafrost data are provided as georasters of the same shape and geovectors (polylines/polygons) and are all projected into EPSG:32605. All data can be visualized with a GIS (QGIS, ArcGIS, etc.).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

UAS remote sensing (AltaX platform): Red-green-blue (RGB) imagery, thermal infrared (TIR) imagery, and canopy reflectance (Level 0 data), Seward Peninsula, Alaska, 2023

Airborne remote sensing data collected using Brookhaven National Laboratory’s (BNL) heavy-lift unoccupied aerial system (UAS) AltaX platform on the Seward Peninsula, Alaska, USA. This package includes raw (L0) data from 11 flights flown over the NGEE-Arctic Kougarok mile marker 64 (MM64) and Teller MM 27 sites in July, 2023. The AltaX 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 near-surface surface reflectance at 1 nm intervals in the visible to near-infrared spectral range from ~350 – 1000 nm. This package provides the Level 0 (raw, unprocessed) data collected by the platform and sensors. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Data and metadata are provided as text (*.txt, *.json), binary (*.dat, *.ulg), tabular (*.csv), geospatial (*.kml. *kmz), image (*.jpg, *.png, *.tif) and pdf formats. The metadata documents contain flight campaign, platform, sensor, flight and file metadata, along with a description of the L0 data and the file naming scheme.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 OSTI2024

UAS remote sensing (AltaX platform): Red-green-blue (RGB) imagery, thermal infrared (TIR) imagery, and canopy reflectance (Level 0 data), Seward Peninsula, Alaska, 2022

Airborne remote sensing data collected using Brookhaven National Laboratory’s (BNL) heavy-lift unoccupied aerial system (UAS) AltaX platform on the Seward Peninsula, Alaska, USA. This package includes raw (L0) data from 10 flights flown over the NGEE-Arctic Kougarok mile marker 64 (MM64) and Teller MM 27 sites in July, 2022. The AltaX 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 near-surface surface reflectance at 1 nm intervals in the visible to near-infrared spectral range from ~350 – 1000 nm. This package provides the Level 0 (raw, unprocessed) data collected by the platform and sensors. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Data and metadata are provided as text (*.txt, *.json), binary (*.dat, *.ulg), tabular (*.csv), geospatial (*.kml. *kmz), image (*.jpg, *.png, *.tif) and pdf formats. The metadata documents contain flight campaign, platform, sensor, flight and file metadata, along with a description of the L0 data and the file naming scheme.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↗