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DOE OSTI2025

Degradation and deforestation increase the sensitivity of the Amazon Forest to climate extremes

About 40% of the Brazilian Amazon has been deforested or suffered changes in forest structure through degradation (selective logging, fires, and fragmentation). The impact of forest degradation on the forest’s sensitivity to climate extremes has not been fully explored because of a lack of data and the complex interplay of forest structure and climate drivers. Here, we combined forest structure data from 545 airborne lidar transects (375 ha each) across the Brazilian Amazon with the Ecosystem Demography Model (ED2). We explore the forest’s functional response to near-present (1981–2019) climate extremes under observed forest structure from lidar ( Control ) and two forest structure change scenarios: (1) forest recovery by excluding all future deforestation and degradation ( Recovery ) and (2) expansion of selective logging and deforestation ( Degradation ). Using the Control simulation, we found a close and positive association between local forest aboveground biomass and the predicted gross primary productivity (GPP) and evapotranspiration (ET). Moreover, both GPP and ET respond negatively to extremes in vapor pressure deficit and downwelling shortwave irradiance in degraded forests in Eastern and Southern Amazon, indicating high sensitivity to droughts. Locally high-biomass forest patches showed little or no negative response of GPP and ET to extreme drought conditions whereas low-biomass forest patches in the same locations—typically degraded forest canopies—responded negatively to higher moisture stress. The results from the Recovery scenario showed similar results to simulations with observed structure; however, under the Degradation scenario, low-biomass forest patches became more abundant, resulting in more regions where GPP and ET are negatively impacted by hot drought conditions according to the ED2 model. Our results suggest that local forest structure is a critical determinant of an ecosystem’s response to climate variability, and that the loss of canopy trees in the Amazon through forest degradation could increase and expand forest vulnerability to droughts.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Fine-scale vegetation composition and structure shape spatiotemporal variation in surface albedo across a low Arctic tundra landscape

The unprecedented rate of warming in the Arctic is driving changes in the structure and composition of tundra vegetation. Increases in deciduous tall shrub cover, height, and density are of particular concern, as these changes alter local surface albedo in ways that could amplify effects on the regional surface energy budget (SEB). Despite this importance, significant uncertainties remain in understanding the interplay between fine-scale vegetation patterns and emergent albedo dynamics across space and time. Here, we address these uncertainties by (1) quantifying spatiotemporal variation in surface shortwave albedo and (2) determining the relative influence of fine-scale vegetation composition, structure, and environmental conditions on albedo across a representative low-Arctic tundra landscape on Alaska’s Seward Peninsula. To do this, we synthesized multi-scale, multi-platform remote sensing observations, including a novel Landsat-derived albedo time series, a fine-scale map of Arctic plant functional type (PFT) fractional cover, and airborne LiDAR estimates of canopy height and topography. We show that there are substantial reductions in winter albedo for pixels dominated by tall, woody PFTs (28.13%) relative to pixels dominated by non-woody vegetation, but almost no change in summer albedo (3% increase). Further, we identified a unimodal trend in the relationship between canopy height and the timing of the springtime transition from high (snowy) to low (leafy) albedo (peak at 5.5 m), possibly because of competing ‘snow-fence’ and ‘protrusion’ snow-shrub interactions. To explore the primary drivers of albedo, we constructed a random forest model and found that canopy height and the fractional cover of woody PFTs were as- or more important predictors of winter albedo than topographic features. These findings provide strong evidence for the impacts of local vegetation characteristics on regional surface albedo, highlighting the need for better quantification of snow-shrub interactions to accurately predict the Arctic’s SEB under future environmental change.

Arctic↗

DOE OSTI2023

Methods to Account for CAP-88 PC-Omitted Nuclides in Radioactive Air Emissions From DOE Facilities

Routine research and development activities at US Department of Energy facilities can result in the release of radioactive emissions, potentially exposing the public and the environment. Such emissions are subject to certain Clean Air Act regulations, specifically those outlined in 40 CFR Part 61, Subpart H, which are enforced by the US Environmental Protection Agency. Compliance is determined in part with a dose standard. Doses to members of the public from most US Department of Energy facility airborne emissions are modeled using CAP-88 PC software, the latest version of which contains more than 1,200 radionuclides. Radioactive effluents from US Department of Energy facilities may contain radioisotopes that are not available in CAP-88 PC, and those radioisotopes must still be evaluated to determine whether doses to members of the public are below established limits. CAP-88 PC-omitted radionuclides can be accounted for using alternate methods, including the use of surrogate radionuclides. Here, this paper elucidates the process of accounting for CAP-88 PC-omitted radionuclides by providing criteria and rationale for surrogate selection, a compilation of surrogate radionuclides used in the past by US Department of Energy facilities, a discussion of alternate methods used to account for CAP-88 PC-omitted radionuclides and a comparison of methods and impacts on receptor doses. Ultimately, this paper aims to aid in the process of surrogate selection and consequently to simplify and expedite compliance with Clean Air Act regulations.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2026

A comprehensive framework to assess elemental mercury in the Department of Energy: A descriptive analysis

Objective: This study investigated the relationship between personal breathing zone air samples and associated biological monitoring urine samples collected in the U.S. Department of Energy Operations. Methods: We performed standard descriptive analyses of the air sample and BEI monitoring data. We also provide a list of direct-reading instruments used in mercury field assessments. Results: A total of 2,330 air samples and 265 BEI data were analyzed. These data were grouped into 16 job titles, excluding categories with fewer than 10 samples, which resulted in 11 job titles. Conclusions: We conclude that industrial hygiene airborne sample data alone may not provide a complete assessment in exposure determination. We suggested the necessity of incorporating biological monitoring to determine the exposure.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Comprehensive study of radon progeny attachment to surfaces

Low energy, low rate experiments, such as searches for neutrinoless double β decay and dark matter, require unprecedentedly low levels of background in order to deliver their full science potential. 210 Po driven, neutron induced background, caused by nuclear (α, n) reactions on low-Z materials, direct background contributions of the 210 Po α radiation and desorption of the 210 Pb progeny 210 Bi from surfaces into the detector medium are of particular of concern. These backgrounds depend on details of the components’ exposure to radon-loaded laboratory air and, thus, their handling history. The attachment rates of airborne radon progeny to surfaces, needed for the estimation of these background rates, are poorly understood. This article reports the results of a campaign comprising of more than 1200 attachment measurements, performed for nine different materials. Correlations of the attachment with environmental parameters such as air exchange rate, electrical surface potential, temperature, atmospheric pressure, and relative humidity have been studied and found to be significant only in case of the first two. Attachment modeling, using the Jacobi model, is compared to data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

DENSECL: Haze Mitigation Using Dense Blocks and Contrastive Loss Regularization

Haze, which occurs as a result of the scattering of light in the atmosphere by small particles, diminishes the visibility of scene objects, inflicting important image applications such as object detection. To address the problem, this paper introduces a new physics-based end-to-end deep learning approach to haze mitigation in outdoor scenes, including those in airborne images. The proposed model named DenseCL is designed with dense blocks and adopts a contrastive loss function as an additional regularization. The model also maintains the cycle consistency by remapping the dehazed outputs into a hazy image using the physics-based light scattering function. DenseCL has been trained with publicly available outdoor images and demonstrates outstanding performance on outdoor, indoor, and remotely sensed nonhomogeneous haze satellite images.

Mitra, Somosmita↗

DOE OSTI2024

Estimation of Forest Aboveground Biomass from Derivatives of Vegetation-Structure Profiles

Several studies have found that the vertical Fourier transform of lidar, interferometric Synthetic Aperture Radar (SAR), and stereo photogrammetric profiles at empirically-determined spatial frequencies enables high-performance forest aboveground biomass (AGB) estimation. Linear combinations of real and imaginary parts of Fourier transforms of Tomographic (multi-baseline) SAR (TomoSAR) profiles, from Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne data, generate ~20%-precision estimates of AGB in the Saskatchewan area of Canada. We found that this 20% precision can be improved to ~15%, a factor of 30% improvement in root mean square error (RMSE) if, in addition to using Fourier transforms of the profile itself, we use Fourier transforms of the spatial, vertical derivative of the profile. The formulation of this "derivative" algorithm is the subject of this paper.

Treuhaft, Robert↗

DOE OSTI2024

Aboveground Biomass Estimation Using NISAR Simulated ALOS-2 Time Series Data

Aboveground biomass (AGB) is a critical parameter to better understand the global carbon cycle and to develop sustainable forest management. However, a large uncertainty prevails. L-band SAR data have demonstrated strong potential to accurately retrieve AGB over low-biomass regions (<100 Mg ha-1). The upcoming NASA-ISRO Synthetic Aperture Radar mission will collect data at L- and S-band over earth’s landmass with a repeat period of 12 days, allowing us to have ample data for monitoring biomass and its dynamics. One of the key science requirements of the mission is to produce annual AGB maps at 1-ha resolution with RMS accuracy of 20 Mg/ha for 80 percentage of area over low-biomass regions in Calibration/Validation sites. The NISAR biomass algorithm will generate AGB maps based on the parameterization of semi-empirical model along with NISAR time-series dual pol data (HH and HV). To calibrate and validate the model for mission requirements, the mission will use reference estimates of AGB produced from ground inventory plots and airborne LiDAR data collected over selected sites distributed across different global ecoregions. This paper presents the initial results of the calibration/validation of the NISAR AGB retrieval algorithm over the Lenoir Landing (LENO), Alabama, USA site using NISAR simulated ALOS-2 time series data. Five multi-temporal dual-pol HH and HV NISAR Simulated ALOS 2 data collections were used as input to assess the performance of the model. The model AGB retrieval results shows that the NISAR model was able to achieve RMS accuracy within 20 Mg/ha.

Ramachandran, Naveen [Jet Propulsion Laboratory, C↗

DOE OSTI2025

Improved Gas Plume Identification Using Nearest Neighbor Methods for Background Estimation

Longwave infrared (LWIR) hyperspectral imaging (HSI) can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Identification is used after detection to increase confidence in weakly detected plumes, reduce false positives from detection, and distinguish between similar and confounding material signatures. Background estimation is an important step used to reveal the unique spectral characteristics of the detected gas, allowing the identification model to determine what the gas is specifically. The importance of proper background estimation increases when dealing with weak signals, large libraries of gases of interest, and uncommon or heterogeneous backgrounds. In this article, we propose two methods for background estimation: a novel k-nearest segments (KNS) algorithm and the standard k-nearest neighbors (KNN) algorithm. We test our methods and three existing background estimation methods for comparison against global background estimation to determine which performs best at estimating the true background radiance under a plume and for increasing identification confidence using a neural network classification model. We compare the different methods using 640 simulated weak plumes in an urban environment. For identification, our KNS algorithm improves median neural network identification confidence by 53.2%. For background radiance estimation, the KNN algorithm provides a median of 49 times less RMSE than global background estimation. Furthermore, KNN is the easiest method to tune for different plumes, making it an excellent “out of the box” background estimator.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Modeling Aerial Gamma-Ray Backgrounds Using Non-negative Matrix Factorization

Airborne gamma-ray surveys are useful for many applications, ranging from geology and mining to public health and nuclear security. In all these contexts, the ability to decompose a measured spectrum into a linear combination of background source terms can provide useful insights into the data and lead to improvements in the techniques that use spectral energy windows. Multiple methods for the linear decomposition of spectra exist but are subject to various drawbacks, such as allowing negative photon fluxes or requiring detailed Monte Carlo modeling. In this work, we propose using non-negative matrix factorization (NMF) as a data-driven approach to spectral decomposition. Using aerial surveys that include flights over water, we demonstrate that the mathematical approach of NMF finds physically relevant structure in the aerial gamma-ray background, namely, that measured spectra can be expressed as the sum of nearby terrestrial emission, distant terrestrial emission, and radon and cosmic emission. Furthermore, these NMF background components are compared with the background components obtained by noise-adjusted singular value decomposition (NASVD), which contain negative photon fluxes and, thus, do not represent the emission spectra in as straightforward a way. Finally, we comment on the potential areas of research that are enabled by NMF decompositions, such as new approaches to spectral anomaly detection and data fusion.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Remote sensing of cytotype and its consequences for canopy damage in quaking aspen

Abstract Mapping geographic mosaics of genetic variation and their consequences via genotype x environment interactions at large extents and high resolution has been limited by the scalability of DNA sequencing. Here, we address this challenge for cytotype (chromosome copy number) variation in quaking aspen, a drought‐impacted foundation tree species. We integrate airborne imaging spectroscopy data with ground‐based DNA sequencing data and canopy damage data in 391 km 2 of southwestern Colorado. We show that (1) aspen cover and cytotype can be remotely sensed at 1 m spatial resolution, (2) the geographic mosaic of cytotypes is heterogeneous and interdigitated, (3) triploids have higher leaf nitrogen, canopy water content, and carbon isotope shifts (δ 13 C) than diploids, and (4) canopy damage varies among cytotypes and depends on interactions with topography, canopy height, and trait variables. Triploids are at higher risk in hotter and drier conditions.

Blonder, Benjamin↗

DOE OSTI2024

Coinfection of Influenza A and B and Human OC43 Coronavirus in Normal Human Bronchial Epithelial Cells

Influenza viruses and seasonal coronaviruses are pathogens transmitted via an airborne route that can cause respiratory diseases in humans that have similar symptoms such as fever, cough, and pneumonia. These two viruses can infect similar human tissues, such as the respiratory tract and nasal, bronchial, and alveolar epithelial cells. Influenza virus and seasonal coronavirus coinfections are poorly understood. Here, we coinfected normal human bronchial epithelial (NHBE) cells with influenza A/California/04/09 (IAV) or B/Victoria/504/2000 (IBV) strains and the seasonal human beta-coronavirus OC43 and evaluated viral replication capacities. We also examined changes in the expression of various cytokines/chemokines by qPCR and Luminex assay. We observed that the replication of IAV and IBV was not affected by coinfection with OC43. However, coinfection reduced OC43 titers (~3-fold) compared with infection with OC43 alone. Select cytokine/chemokine expression was increased in coinfected cells compared with all single infections with greater differences seen between coinfected cells and cells infected with OC43 alone compared with IAV- or IBV-infected cells. In addition, IL-8 and IL-1RA showed the highest expression among a panel of 22 cytokines by Luminex. As the rate of influenza and seasonal coronavirus coinfection continue to increase, our findings may help set guidelines for the treatments of the individuals coinfected with both viruses.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Foliar functional traits from imaging spectroscopy across biomes in eastern North America

Summary Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes. With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America. Model validation accuracy varied among traits (normalized root mean squared error, 9.1–19.4%; coefficient of determination, 0.28–0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28–81% provided high confidence for multiple traits concurrently. Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental‐scale functional diversity.

Wang, Zhihui↗

DOE OSTI2025

Proximal remote sensing: an essential tool for bridging the gap between high‐resolution ecosystem monitoring and global ecology

Summary A new proliferation of optical instruments that can be attached to towers over or within ecosystems, or ‘proximal’ remote sensing, enables a comprehensive characterization of terrestrial ecosystem structure, function, and fluxes of energy, water, and carbon. Proximal remote sensing can bridge the gap between individual plants, site‐level eddy‐covariance fluxes, and airborne and spaceborne remote sensing by providing continuous data at a high‐spatiotemporal resolution. Here, we review recent advances in proximal remote sensing for improving our mechanistic understanding of plant and ecosystem processes, model development, and validation of current and upcoming satellite missions. We provide current best practices for data availability and metadata for proximal remote sensing: spectral reflectance, solar‐induced fluorescence, thermal infrared radiation, microwave backscatter, and LiDAR. Our paper outlines the steps necessary for making these data streams more widespread, accessible, interoperable, and information‐rich, enabling us to address key ecological questions unanswerable from space‐based observations alone and, ultimately, to demonstrate the feasibility of these technologies to address critical questions in local and global ecology.

Plant Sciences↗

DOE OSTI2022

Pareto Optimal and Dual-Objective Geometric and Structural Design of an Underwater Kite for Closed-Loop Flight Performance

This paper presents the formulation and results for a control-aware optimization of the combined geometric and structural design of an energy-harvesting underwater kite. Because kite-based energy-harvesting systems, both airborne and underwater, possess strong coupling between closed-loop flight control, geometric design, and structural design, consideration of all three facets of the design within a single codesign framework is highly desirable. However, while prior literature has addressed one or two attributes of the design at a time, this work constitutes the first comprehensive effort aimed at addressing all three. In particular, focusing on the goals of power maximization and mass minimization, we present a codesign formulation that fuses a geometric optimization tool, structural optimization tool, and closed-loop flight efficiency map. The resulting integrated codesign tool is used to address two mathematical optimization formulations that exhibit subtle differences: a Pareto optimal formulation and a dual-objective formulation that focuses on a weighted power-to-mass ratio as the techno-economic metric of merit. Further, based on the resulting geometric and structural designs, using a mediumfidelity closed-loop simulation tool, the proposed formulation is shown to achieve more than three times the powerto-mass ratio of a previously published, unoptimized benchmark design.

42 ENGINEERING↗

DOE OSTI2024

A Phenomenological Thermal Spray Wall Interaction Modeling Framework Applied to a High Temperature Ignition Assistant Device

Airborne compression ignition engines must operate with reliable ignition systems to achieve proper ignition at every cycle, particularly at high altitudes. Glow-plug-based ignition-assistant (IA) devices can provide the necessary energy to preheat the fuel and ensure ignitability of the fuel-air mixture. Ignitability of liquid sprays can be facilitated via direct impingement onto the hot IA surface, however this comes with adverse effects on the IA durability. Therefore, optimizing an IA’s design requires detailed understanding of the physics of fuel spray impingement of superheated surfaces. While spray impingement on relatively low wall temperatures has been extensively studied and appropriate numerical models have been proposed through the years, fundamental understanding of high-speed liquid spray impingement on superheated walls is still elusive. This work aims to formulate a phenomenological thermal spray-wall interaction framework for modeling the film-boiling-induced heat transfer, atomization, and dispersion of fuel spray droplets impinging onto a superheated IA device. A qualitative comparison of the new phenomenological model is performed against optical experiments from the literature of an F-24 fuel spray injected onto an IA device located 12 mm away from the injector tip. The temperature of the IA was set at 1400 K. The fuel injection pressure was 400 bar, while the ambient gas pressure and temperature were 30 bar and 800 K, respectively. The performance of the phenomenological model is evaluated in comparison with two other state-of-art models from the literature. A qualitative analysis of the different spray and fuel-air mixture characteristics is performed to outline the differences in the predictions offered by the new phenomenological model and the two state-of-art spray-wall interaction models.

droplet dispersion↗

DOE OSTI2026

Spectrograph stabilization using a single-delay interferometer on the Hale Telescope

We describe a technique for spectrograph stabilization useful when conventional mitigation techniques of vacuum tanks, thermal insulation, and laser frequency comb may be impractical, expensive, heavy, or bulky. This includes spectrographs on airborne platforms or mounted on telescopes where they suffer a changing gravity vector or other drifts. Placing a fixed-delay interferometer in series with a spectrograph forms an externally dispersed interferometer (EDI). This produces a uniform sinusoidal comb multiplying input spectrum, creating (through heterodyning) beats (moiré patterns). In Fourier space for low frequencies up to the comb frequency, the moiré generated signal counter-rotates to ordinary spectra under an unknown disperser wavenumber drift Δx. This generates a large negative feedback signal useful in a conceptual control loop, to converge rapidly to a stable spectrum and yield Δx. A modified EDI data analysis algorithm (“crossfading”) combines frequency-weighted moiré with conventional spectrum to cancel net output spectrum reaction to Δx. Needing only a single-delay, this is a practical improvement over prior crossfading analyses requiring multiple delays. We test crossfading on ThAr data near 4850 cm−1 taken on Hale telescope in an earlier project. In a single pass, we reduce drift 20 times. Using seven iterations, we reduce 0.5 cm−1 (31 km/s Doppler equivalent) drift to 4×10−7 cm−1 (2.5 cm/s). The interferometer delay can wander, because linearity of phase versus wavenumber interpolates science features between bracketing calibrating spectral references. Second, mathematically reversing the heterodyning effect doubles effective spectral resolution without changing disperser slit.

Erskine, David J [Lawrence Livermore National Labo↗

DOE OSTI2020

Activities and risk factors associated with fall-related injuries among US Army soldiers

Introduction Falls/near falls are the second leading cause of hospitalisation and outpatient visits among US Army soldiers. While numerous studies have evaluated fall-related or near fall-related injuries among elderly adults, few have evaluated this association among young adults. The objective of this study is to describe the characteristics and risk factors associated with fall-related or near fall-related injuries among male US Army soldiers. Methods This is a cross-sectional study of male US Army Airborne Division soldiers (n=5187). Electronic surveys captured demographic, lifestyle, physical training (PT), fitness and injury data during spring/summer of 2016. Multiple logistic regression was used to identify independent risk factors of fall-related or near fall-related injuries, adjusting for potential confounders. Results Primary findings indicated that activities and risk factors associated with fall-related or near fall-related injuries among soldiers included younger age (≤35 years), holding a job that required minimal lifting activities, slower 2-mile run times and not running during personal PT. Conclusions The findings from this study suggest that male US Army soldiers and other physically active men may benefit from (1) obtaining and/or maintaining higher aerobic endurance and muscular strength, and (2) training focused on preventing fall-related injuries during PT, road marching and sports/recreational activities. Moreover, prevention strategies and education should further target younger soldiers (≤35 years old), as younger age is not modifiable.

General & Internal Medicine↗