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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Glide-resolved photoemission spectroscopy: Measuring topological invariants in nonsymmorphic space groups

The two classes of 3D, time-reversal-invariant insulators are known to subdivide into four classes in the presence of glide symmetry. Here, we extend this classification of insulators to include glide-symmetric Weyl metals and find a finer $\mathbb{Z}_4 ⊕\mathbb {Z}$ classification. We further elucidate the smoking-gun experimental signature of each class in the photoemission spectroscopy of surface states. Measuring the $\mathbb{Z}_4$ topological invariant by photoemission relies on identifying the glide representation of the initial Bloch state before photoexcitation—we show how this is accomplished with relativistic selection rules, combined with standard spectroscopic techniques to resolve both momentum and spin. In this work, our method relies on a spin-momentum locking that is characteristic of all glide-symmetric solids (inclusive of insulators and metals in trivial and topological categories). As an orthogonal application, given a glide-symmetric solid with an ideally symmetric surface, we may utilize this spin-momentum locking to generate a source of fully spin-polarized photoelectrons, which have diverse applications in solid-state spectroscopy. Our ab initio calculations predict Ba 2 Pb, stressed Na 3 Bi, and KHgSb to realize all three, nontrivial insulating phases in the $\mathbb{Z}_4$ classification.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Symmetric U(1) and Z 2 spin liquids on the pyrochlore lattice

The geometrically frustrated three-dimensional pyrochlore lattice has been long predicted to host a quantum spin liquid, an intrinsic long-range entangled state with fractionalized excitations. To date, most proposals for pyrochlore materials have focused on quantum spin ice, a U(1) quantum spin liquid whose only low energy excitations are emergent photons of Maxwell type. Here, we explore the possibility of finding pyrochlore quantum spin liquids whose low energy theories go beyond this standard one. We give a complete classification of symmetric U ( 1 ) and Z 2 spin liquids on the pyrochlore lattice within the projective symmetry group framework for fermionic spinons. We find 18 U ( 1 ) spin liquids and 28 Z 2 spin liquids that preserve pyrochlore space-group symmetry while, upon further imposing time-reversal symmetry, the numbers of classes become 16 and 48, respectively. For each class, the most general symmetry-allowed spinon mean-field Hamiltonian is given. Interestingly, we find that several U(1) spin liquid classes possess an unusual gapless multi-nodal-line structure (“nodal star”) in the spinon bands, which is protected by the projective actions of the threefold rotation and screw symmetries of the pyrochlore space group. Through a simple model, we study the effect of gauge fluctuations on such a nodal star spin liquid and propose that the leading terms in the low temperature specific heat have the scaling form C / T ~ T + T / ln T , in contrast to the form C / T ~ T 2 of the standard U(1) pyrochlore spin liquid with gapped spinons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Simple relativistic quark models

A class of phenomenological relativistic models of hadronic systems motivated by quantum chromodynamics that have dual representations as models of mesons and nucleons or quarks and gluons is investigated. These models are designed to provided qualitative insight into the role of sea quarks in hadronic structure and reactions. The model assumption is that the Hamiltonian can be divided into two parts: one that involves degrees of freedom in the same connected local and global color singlet and the remaining interactions that allow the connected local and global color singlets to interact. The first class of interactions results in infinite towers of bare “particles” with hadronic quantum numbers. All but a finite number of these remain stable when the second class of interactions is included. The model interactions are expressed in terms of subhadronic degrees of freedom, which determine the bare hadronic spectrum and the interactions involving the bare hadrons in terms of a small number of subhadronic model parameters. As a first test, this paper considers the simplest case of mesons that interact via a string-breaking interaction. One virtue of this model is that all of the bare meson masses and eigenfunctions can be computed analytically. In addition, the string-breaking interaction leads to production vertices that can also be computed analytically. The relativistic wave functions have a light-front kinematic symmetry. The goal is to find a simple relativistic quantum mechanical model based on subhadronic degrees of freedom that can provide an efficient, qualitatively consistent description of hadronic masses, lifetimes, cross sections, sea quark effects, and electromagnetic properties. The simplicity of the model makes it a potentially useful tool to study the impact of sea quarks on hadronic structure and reactions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Generalized models for inflationary preheating: Oscillations and symmetries

The paradigm of the inflationary universe provides a possible explanation for several observed cosmological properties. In order for such solutions to be successful, the universe must convert the energy stored in the inflaton potential into standard model particles through a process known as reheating. In this paper, we reconsider the reheating process for the case where the inflaton potential respects an approximate (but spontaneously broken) conformal symmetry during the reheating epoch. After reviewing the Effective Field Theory of Reheating, we present solutions for the nonlinear oscillations of the inflaton field, derive the corresponding Hill’s equation for the coupled reheating field, and determine the stability diagram for parametric resonance. For this class of models —the simplest realization being a scalar field with a quartic term—the expansion of the universe drives the coupled field toward a more unstable part of parameter space, in contrast to the standard case. We also generalize this class of models to include quadratic breaking terms in the potential during the reheating epoch and address the process of stability in that universality class of models.

Barrowes, Leia [University of Michigan, Ann Arbor]↗
DOE OSTI2024

Feedback-based quantum algorithms for ground state preparation

The ground state properties of quantum many-body systems are a subject of interest across chemistry, materials science, and physics. Thus, algorithms for finding ground states can have broad impacts. Variational quantum algorithms are one class of ground state algorithms that has received significant attention in recent years. These algorithms utilize a hybrid quantum-classical computing framework to prepare ground states on quantum computers. However, this requires solving a classical optimization problem that can become prohibitively expensive in high dimensions. Here, we develop formulations of feedback-based quantum algorithms for ground state preparation that can be used to address this challenge for two broad classes of Hamiltonians: Fermi-Hubbard Hamiltonians, and molecular Hamiltonians represented in second quantization. Feedback-based quantum algorithms are optimization-free; in place of classical optimization, quantum circuit parameters are set according to a deterministic feedback law derived from quantum Lyapunov control principles. This feedback law guarantees a monotonic improvement in solution quality with respect to the depth of the quantum circuit. A variety of numerical illustrations are provided that analyze the convergence and robustness of feedback-based quantum algorithms for these problem classes. Published by the American Physical Society 2024

Larsen, James B. (ORCID:000000020777440X)↗
DOE OSTI2025

Strong CP and flavor in multi-Higgs theories

We introduce a class of multi-Higgs doublet extensions of the Standard Model that solve the strong problem with profound consequences for the flavor sector. The Yukawa matrices are constrained to have many zero entries by a “Higgs-flavor” symmetry, , that acts on Higgs and quark fields. The violation of both and occurs in the Higgs mass matrix so that, for certain choices of charges, the strong parameter is zero at tree level. Radiative corrections to are computed in this class of theories. They vanish in realistic two-Higgs doublet models with . We also construct realistic three-Higgs models with , where the one-loop results for are model-dependent. Requiring has important implications for the flavor problem by constraining the Yukawa coupling and Higgs mass matrices. Contributions to from higher-dimension operators are computed at one loop and can also be sufficiently small, although the hierarchy problem of this class of theories is worse than in the Standard Model.

Hall, Lawrence↗
DOE OSTI2026

Feature Engineering and Ensemble Methods for Imbalanced ICS Intrusion Detection: Pipeline Audit and Constrained Evaluation

Industries are becoming increasingly connected and are more vulnerable to cyberattacks due to the widened attack surface. Industrial Control Systems (ICS) are among the most critical sectors that malicious actors can target, as such attacks can cause significant operational disruption and physical damage. It is imperative to detect such attacks as early as possible. This paper evaluates constraint-conditioned optimistic performance estimates for traditional ML models in ICS intrusion detection (i.e., estimates obtained under contiguous, non-shuffled temporal evaluation without test-set alteration, but with pre-split feature engineering that may introduce temporal leakage, due to dataset constraints). Our findings are threefold. First, we quantify how iterative feature engineering affects tree-based ensemble performance and examine how pipeline decisions (split strategy, sampling scope, and cleaning policy) can inflate or reduce reported IDS results under constraint-bound evaluation. Second, we compare intrinsic class-imbalance handling across ensemble models. Third, under our current pipeline constraints (including pre-split feature engineering), CatBoost achieves the best performance on Water Storage Tank (accuracy: 0.9831, class-1 F1: 0.9682), while Light- GBM achieves the best performance on Gas Pipeline (accuracy: 0.9618, class-1 F1: 0.9086).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

1 kV 150 A Bidirectional Isolated DC/DC Converter With Full Range ZVS For Charger Application

This paper focusses on the development of a bidirectional DC/DC converter based on dual active bridge (DAB) converter for 1 kV class fast charger applications. A novel modulation technique is proposed to achieve zero voltage switching across the entire operating range of a vehicle battery system. The topology includes a tap changer to support multiple class of vehicles. The full range ZVS operation will allow high efficiency operation even at light load, reduced dv/dt to improve transformer insulation lifetime and mitigate EMI impact. A 1 kV class, 150 A prototype was developed to validate the proposed concepts.

Kandula, Prasad↗
DOE OSTI2022

C6 Hydroxymethyl-Substituted Carbapenem MA-1-206 Inhibits the Major Acinetobacter baumannii Carbapenemase OXA-23 by Impeding Deacylation

Acinetobacter baumannii has become a major nosocomial pathogen, as it is often multidrug-resistant, which results in infections characterized by high mortality rates. The bacterium achieves high levels of resistance to β-lactam antibiotics by producing β-lactamases, enzymes which destroy these valuable agents. Historically, the carbapenem family of b-lactam antibiotics have been the drugs of choice for treating A. baumannii infections. However, their effectiveness has been significantly diminished due to the pathogen’s production of carbapenem-hydrolyzing class D β-lactamases (CHDLs); thus, new antibiotics and inhibitors of these enzymes are urgently needed. Here, we describe a new carbapenem antibiotic, MA-1-206, in which the canonical C6 hydroxyethyl group has been replaced with hydroxymethyl. The antimicrobial susceptibility studies presented here demonstrated that this compound is more potent than meropenem and imipenem against A. baumannii producing OXA-23, the most prevalent CHDL of this pathogen, and also against strains producing the CHDL OXA-24/40 and the class B metallo-β-lactamase VIM-2. Our kinetic and mass spectrometry studies revealed that this drug is a reversible inhibitor of OXA-23, where inhibition takes place through a branched pathway. X-ray crystallographic studies, molecular docking, and molecular dynamics simulations of the OXA-23-MA-1-206 complex show that the C6 hydroxymethyl group forms a hydrogen bond with the carboxylated catalytic lysine of OXA-23, effectively preventing deacylation. These results provide a promising strategy for designing a new generation of CHDL-resistant carbapenems to restore their efficacy against deadly A. baumannii infections. Carbapenem antibiotics are the drugs of choice for treatment of deadly infections caused by Gram-negative bacteria. However, their efficacy is severely compromised by the wide spread of carbapenem-hydrolyzing class D β-lactamases (CHDLs). The importance of this research is the discovery that substitution of the canonical hydroxyethyl group of carbapenems by a hydroxymethyl significantly enhances stability against inactivation by the major CHDL of Acinetobacter baumannii, OXA-23. These results provide a novel strategy for designing next-generation, carbapenemase-stable carbapenems to fight multidrug-resistant infections caused by Gram-negative pathogens

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The Transcription Factor Roc1 Is a Key Regulator of Cellulose Degradation in the Wood-Decaying Mushroom Schizophyllum commune

Wood-decaying fungi of the class Agaricomycetes (phylum Basidiomycota) are saprotrophs that break down lignocellulose and play an important role in nutrient recycling. They secrete a wide range of extracellular plant cell wall degrading enzymes that break down cellulose, hemicellulose, and lignin, the main building blocks of plant biomass. Although the production of these enzymes is regulated mainly at the transcriptional level, no activating regulators have been identified in any wood-decaying fungus in the class Agaricomycetes. We studied the regulation of cellulase expression in the wood-decaying fungus Schizophyllum commune. Comparative genomics and transcriptomics on two wild isolates revealed a Zn 2 Cys 6 -type transcription factor gene (roc1) that was highly upregulated during growth on cellulose, compared to glucose. It is only conserved in the class Agaricomycetes. A roc1 knockout strain showed an inability to grow on medium with cellulose as sole carbon source, and growth on cellobiose and xylan (other components of wood) was inhibited. Growth on non-wood-related carbon sources was not inhibited. Cellulase gene expression and enzyme activity were reduced in the Δroc1 strain. ChIP-Seq identified 1474 binding sites of the Roc1 transcription factor. Promoters of genes involved in lignocellulose degradation were enriched with these binding sites, especially those of LPMO (lytic polysaccharide monooxygenase) CAZymes, indicating that Roc1 directly regulates these genes. A conserved motif was identified as the binding site of Roc1, which was confirmed by a functional promoter analysis. Together, Roc1 is a key regulator of cellulose degradation and the first identified in wood-decaying fungi in the phylum Basidiomycota.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Multiplicity and rapidity dependence of $\textrm{K}^{*}(\mathrm {{\textbf {892}}})^{0}$ and $\mathrm {\phi ({\textbf {1020}})}$ production in p–Pb collisions at $\sqrt{s_{\textrm{NN}}} =$ 5.02 TeV

The transverse-momentum (p T ) spectra of K*(892) 0 and Φ(1020) measured with the ALICE detector up to p T = 16 GeV/c in the rapidity range -1.2 < y < 0.3, in p–Pb collisions at the center-of-mass energy per nucleon–nucleon collision $\sqrt{s_{\textrm{NN}}}$ = 5.02 TeV are presented as a function of charged particle multiplicity and rapidity. The measured p T distributions show a dependence on both multiplicity and rapidity at low p T whereas no significant dependence is observed at high p T . A rapidity dependence is observed in the p T -integrated yield (dN/dy), whereas the mean transverse momentum ($\langle$p T $\rangle$) shows a flat behavior as a function of rapidity. The rapidity asymmetry (Y asym ) at low p T (< 5 GeV/c) is more significant for higher multiplicity classes. At high p T , no significant rapidity asymmetry is observed in any of the multiplicity classes. Both K*(892) 0 and Φ(1020) show similar Y asym . The nuclear modification factor (Q CP ) as a function of p T shows a Cronin-like enhancement at intermediate p T , which is more prominent at higher rapidities (Pb-going direction) and in higher multiplicity classes. At high p T (> 5 GeV/c), the Q CP values are greater than unity and no significant rapidity dependence is observed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

QuantumNAT: quantum noise-aware training with noise injection, quantization and normalization

Parameterized Quantum Circuits (PQC) are promising towards quantum advantage on near-term quantum hardware. However, due to the large quantum noises (errors), the performance of PQC models has a severe degradation on real quantum devices. Take Quantum Neural Network (QNN) as an example, the accuracy gap between noise-free simulation and noisy results on IBMQ-Yorktown for MNIST-4 classification is over 60%. Existing noise mitigation methods are general ones without leveraging unique characteristics of PQC; on the other hand, existing PQC work does not consider noise effect. To this end, we present QuantumNAT, a PQC-specific framework to perform noise-aware optimizations in both training and inference stages to improve robustness. We experimentally observe that the effect of quantum noise to PQC measurement outcome is a linear map from noise-free outcome with a scaling and a shift factor. Motivated by that, we propose post-measurement normalization to mitigate the feature distribution differences between noise-free and noisy scenarios. Furthermore, to improve the robustness against noise, we propose noise injection to the training process by inserting quantum error gates to PQC according to realistic noise models of quantum hardware. Lastly, post-measurement quantization is introduced to quantize the measurement outcomes to discrete values, achieving the denoising effect. Extensive experiments on 8 classification tasks using 6 quantum devices demonstrate that QuantumNAT improves accuracy by up to 43%, and achieves over 94% 2-class, 80% 4-class, and 34% 10-class classification accuracy measured on real quantum computers. The code for construction and noise-aware training of PQC is available in the TorchQuantum library.

Wang, Hanrui↗
DOE OSTI2026

Scalability Analysis of Quantum Models for Stress and Emotion Detection

Stress and emotion detection from high-dimensional physiological signals is a challenging task, particularly when aiming for accurate classification across diverse behavioral states. Quantum machine learning (QML) is promising for modeling such high-dimensional data, but scalability is limited by qubit resources and the exponential cost of classical statevector simulation. This work studies the scalability of quantum support vector machines (QSVMs) for binary stress detection and three-class emotion recognition (Negative/Neutral/Positive) under varying qubit counts and angle-encoding strategies. We also present a comparison study with one-feature-per-qubit (1:1) and two-features-per-qubit (2:1) mappings. Experiments are executed on HPC infrastructure using NVIDIA CUDA-Q to evaluate performance, variance, and class-dependent separability at higher-qubit setups. Results show that larger Hilbert spaces can improve peak accuracy but may increase instability. At the same time, dense 2:1 encoding yields more consistent stress detection performance. For emotion recognition, scaling improves discrimination for classes like Negative and Positive more than Neutral. We find that effective QML scaling is task-dependent and benefits more from encoding design than simply increasing qubit count.

Onim, Md. Saif Hassan [University of Tennessee, Kn↗
DOE OSTI2022

PyChargeModel (Oriented Programming Based Electric Vehicle and Electric Vehicle Supply Equipment Charging Model in Python) [SWR-22-39]

The PyChargeModel creates two classes called "ElectricVehicles" and "evse_class", which can be used to create multiple instances of electric vehicles (EVs) and electric vehicle supply equipment (EVSE or charging ports) and simulate charging behavior. These objects can be instantiated with several properties such as battery chemistries, battery pack sizes, cell sizes, charging port power, dc or ac chargers etc. The objects can communicate with each other by calling different methods built within the classes. Through these methods, each EV object can be assigned to an EVSE, charged either using a default protocol or using setpoint values communicated from a site controller via the EVSE object.

Mishra, Partha↗
DOE OSTI2021

TListSpectrum

TListSpectrum is a C++ class developed inside the CERN high-energy physics analysis C++ framework ROOT. This class structure was developed to assist in the processing, visualization, and analysis of list-mode or time-stamped radiation spectroscopy data. The class structure currently contains parsing and functionality to synthesize list-mode data from CAEN and Mirion Lynx radiation spectroscopy digital acquisition systems along with feature functionality to post-process data sets and build coincident data sets from the instrument.

Pierson, Bruce↗
DOE OSTI2025

Seismic Contingency Auto Generator

This code takes in premade earthquake scenario XML files from USGS, power grid data, and converts them into a contingency file (.con file) that can be used by power grid solvers. Within the .con file are a number (Specified by the user) of contingencies that have randomly failed power transformers based on their likelihood of failure and peak ground acceleration (PGA) value around the transformer. The transformers' likelihood of failure was calculated based on a variety of finite element modeling on various transformer designed for specific transformer voltage classes. Parameters from these FEM were used to create generic fragility curves for transformers within a specific voltage class, which correspond with earthquake PGA values to produced a probability of failure for a given earthquake scenario. More refined versions of this process, such as specifying specific transformer design categories within a voltage class, could also be applied in future iterations of the software.

Vaagensmith, Bjorn [Idaho National Laboratory (INL↗
DOE OSTI2026

matsim-agents v1.0

matsim-agents is a multi-agent AI framework for atomistic materials simulation and discovery. It orchestrates large language models (LLMs), machine-learned interatomic potentials (MLIPs), and DFT codes into a single agentic loop running on laptops and DOE leadership-class supercomputers. MULTI-AGENT ORCHESTRATION A LangGraph state machine with three nodes: a Planner that converts a natural-language research objective into structured tasks; an Executor that dispatches atomistic tools and loops until the queue is empty; and an Analyst that summarizes results into a human-readable report. State is checkpointed after every step and human-in-the-loop gates can be inserted at any edge. HYPOTHESIS-DRIVEN DISCOVERY CHAT An interactive REPL (matsim-agents chat) that couples LLM dialogue with atomistic simulation. Chemical formulas are automatically detected in conversation turns and trigger a full crystal-phase exploration: structure generation → relaxation → stability scoring → result injection back into the conversation, creating a closed hypothesis-refinement loop. CRYSTAL PHASE ENUMERATION Given a composition, the phase explorer enumerates prototypes by stoichiometry: elemental (fcc/bcc/hcp/sc/diamond), binary 1:1 (rocksalt/CsCl/zincblende/ wurtzite/fluorite/rutile), ternary 1:1:3 (cubic perovskite), ternary 1:2:4 (perovskite + spinel), quaternary 1:1:2:6 (Fm-3m double perovskite). 2-D prototypes (graphene, h-BN, MoS2 2H/1T) and multilayer stacking are also supported via --include-2d and --num-layers. SUPERCELL GENERATION AND SITE DECORATION Auto-tiling to a minimum atom count (--min-atoms), explicit NxNxN tiling (--supercell), symmetry-distinct site decorations (--n-orderings), and isotropic lattice-scale sweeps (--lattice-scales) for volume bracketing. MLFF RELAXATION AND STABILITY SCORING HydraGNN (multi-headed GNN) drives structure relaxation via ASE with FIRE, BFGS, or BFGSLineSearch. Stability output: delta-E/atom ranking across phases and a max-residual-force dynamical-stability proxy. Other MLIPs (MACE, NequIP, Orb) can be plugged in through the same interface. DFT BACKENDS Quantum ESPRESSO pw.x and VASP 6.6 are first-class labellers. Both have validated GPU builds and SLURM/PBS launchers for three DOE platforms: Frontier (AMD MI250X, ROCm), Aurora (Intel PVC, oneAPI), Perlmutter (NVIDIA A100, CUDA). QE produces ~100 binaries (pw.x, ph.x, epw.x, ...). VASP supports scf, relax, vc-relax, and vc-relax-shape run types. ACTIVE-LEARNING LOOP matsim-agents al run CONFIG.yaml drives an iterative HydraGNN-DFT loop: MD generates candidates → ensemble/MC-dropout uncertainty selects the most informative → DFT labels them in parallel inside one allocation → dataset grows → HydraGNN retrains → repeat. DFT backend is a single YAML toggle (dft.backend: vasp | qe). LLM-generated seed structures are supported (no curated POSCAR library needed). Config uses ${VAR}, ${VAR:-default}, ${VAR:?msg} shell-style substitution for cross-user/cross-site portability. LLM BACKENDS Ollama (local, default), vLLM (HPC multi-GPU serving), OpenAI, Anthropic, HuggingFace Transformers+Accelerate. Selected at runtime via flag or env var with no code changes. HPC PORTABILITY Same Python entry points run on Frontier (ROCm 7.2), Aurora (oneAPI), and Perlmutter (CUDA 12). DFT and ML stacks are never co-loaded in the same shell; they couple through the scheduler and filesystem. Advanced multi-node launchers (serve, discovery-chat, single-relaxation, active-learning, QE warm-start) are provided for all three platforms. CODABENCH COMPETITION BUNDLE A self-contained benchmark: 159 atomistic test structures across 11 material classes, 5 tasks (formation energy, forces, ML relaxation, AI-DFT relaxation, phase stability ranking), public/private leaderboard split (30/70), and four ready-to-run baselines: MACE-MP-0, HydraGNN, UMA, AllScAIP.

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
DOE OSTI2023

Effects of forest degradation classification on the uncertainty of aboveground carbon estimates in the Amazon

Tropical forests are critical for the global carbon budget, yet they have been threatened by deforestation and forest degradation by fire, selective logging, and fragmentation. Existing uncertainties on land cover classification and in biomass estimates hinder accurate attribution of carbon emissions to specific forest classes. In this study, we used textural metrics derived from PlanetScope images to implement a probabilistic classification framework to identify intact, logged and burned forests in three Amazonian sites. We also estimated biomass for these forest classes using airborne lidar and compared biomass uncertainties using the lidar-derived estimates only to biomass uncertainties considering the forest degradation classification as well. Our classification approach reached overall accuracy of 0.86, with accuracy at individual sites varying from 0.69 to 0.93. Logged forests showed variable biomass changes, while burned forests showed an average carbon loss of 35%. We found that including uncertainty in forest degradation classification significantly increased uncertainty and decreased estimates of mean carbon density in two of the three test sites. Our findings indicate that the attribution of biomass changes to forest degradation classes needs to account for the uncertainty in forest degradation classification. By combining very high-resolution images with lidar data, we could attribute carbon stock changes to specific pathways of forest degradation. This approach also allows quantifying uncertainties of carbon emissions associated with forest degradation through logging and fire. Both the attribution and uncertainty quantification provide critical information for national greenhouse gas inventories.

54 ENVIRONMENTAL SCIENCES↗