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At least 73 records · Page 4

Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0

Abstract. Fire is a fundamental part of the Earth system, with impacts on vegetation structure, biomass, and community composition, the latter mediated in part via key fire-tolerance traits, such as bark thickness. Due to anthropogenic climate change and land use pressure, fire regimes are changing across the world, and fire risk has already increased across much of the tropics. Projecting the impacts of these changes at global scales requires that we capture the selective force of fire on vegetation distribution through vegetation functional traits and size structure. We have adapted the fire behavior and effects module, SPITFIRE (SPread and InTensity of FIRE), for use with the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a size-structured vegetation demographic model. We test how climate, fire regime, and fire-tolerance plant traits interact to determine the biogeography of tropical forests and grasslands. We assign different fire-tolerance strategies based on crown, leaf, and bark characteristics, which are key observed fire-tolerance traits across woody plants. For these simulations, three types of vegetation compete for resources: a fire-vulnerable tree with thin bark, a vulnerable deep crown, and fire-intolerant foliage; a fire-tolerant tree with thick bark, a thin crown, and fire-tolerant foliage; and a fire-promoting C4 grass. We explore the model sensitivity to a critical parameter governing fuel moisture and show that drier fuels promote increased burning, an expansion of area for grass and fire-tolerant trees, and a reduction of area for fire-vulnerable trees. This conversion to lower biomass or grass areas with increased fuel drying results in increased fire-burned area and its effects, which could feed back to local climate variables. Simulated size-based fire mortality for trees less than 20 cm in diameter and those with fire-vulnerable traits is higher than that for larger and/or fire-tolerant trees, in agreement with observations. Fire-disturbed forests demonstrate reasonable productivity and capture observed patterns of aboveground biomass in areas dominated by natural vegetation for the recent historical period but have a large bias in less disturbed areas. Though the model predicts a greater extent of burned fraction than observed in areas with grass dominance, the resulting biogeography of fire-tolerant, thick-bark trees and fire-vulnerable, thin-bark trees corresponds to observations across the tropics. In areas with more than 2500 mm of precipitation, simulated fire frequency and burned area are low, with fire intensities below 150 kW m−1, consistent with observed understory fire behavior across the Amazon. Areas drier than this demonstrate fire intensities consistent with those measured in savannas and grasslands, with high values up to 4000 kW m−1. The results support a positive grass–fire feedback across the region and suggest that forests which have existed without frequent burning may be vulnerable at higher fire intensities, which is of greater concern under intensifying climate and land use pressures. The ability of FATES to capture the connection between fire disturbance and plant fire-tolerance strategies in determining biogeography provides a useful tool for assessing the vulnerability and resilience of these critical carbon storage areas under changing conditions across the tropics.

54 ENVIRONMENTAL SCIENCES↗

ICME for Creep of Ni-Base Superalloys in Advanced Ultra-Supercritical Steam Turbines

The goal of this project contains three major aspects: (1) critical assessment of existing experimental data, (2) critical assessment of existing modeling capabilities and (3) development of new modeling capabilities that are critical but currently missing in predicting long-term creep behavior of Ni-base superalloys used in advanced ultra- supercritical (A-USC) steam turbines for coal-fired power plants. The project uses advanced materials informatics to datamine the experimental data, uncertainty quantification tools to rigorously assess existing creep-life models, and integration of phase-field and FFT-based crystal plasticity to develop a multi-scale, physics-based, microstructure-sensitive creep model.

20 FOSSIL-FUELED POWER PLANTS↗

Wildfire Segmentation From Remotely Sensed Data Using Quantum-Compatible Conditional Vector Quantized-Variational Autoencoders

Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.

quantum machine learning↗

PETN Exploding Bridgewire (EBW) Detonators: A Review

Exploding bridgewire (EBW) detonators have been used in weapon systems since the 1940s but there is huge debate surrounding how energy is transferred throughout the EBW firing system and the mechanism by which the exploding wire leads explosive detonation. This report summarizes the underpinning technologies and physical processes that are currently understood and reviews the various efforts to quantify the mechanism by which the PETN is initiated. The behavior of the firing system is very well understood and predictable. The energy delivered to the wire has been empirically modelled but further investigation is required to understand the role of material heterogeneities and their effect on initiation. The energy delivered by the exploding wire has been quantified in many studies but it is not possible to correlate these to a particular design or firing regime and thus definitive conclusions are impossible. The energy absorbed by the PETN is also not well understood. Therefore, the initiation mechanism within the PETN EBWs has not been determined for EBW detonators. There is strong evidence that a shock-to-detonation (SDT) mechanism is not the sole cause of initiation. There is insufficient understanding of how electrical sparks transmit energy to PETN to make any judgement regarding the role gas ionization plays. Deflagration-to-detonation (DDT) seems like the most likely candidate for initiation but there is still a great lack of evidence for the presence of this mechanism.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Fire Detection Tradeoffs as a Function of Vehicle Parameters

Fire survivability depends on the detection of and response to a fire before it has produced an unacceptable environment in the vehicle. This detection time is the result of interplay between the fire burning and growth rates; the vehicle size; the detection system design; the transport time to the detector (controlled by the level of mixing in the vehicle); and the rate at which the life support system filters the atmosphere, potentially removing the detected species or particles. Given the large differences in critical vehicle parameters (volume, mixing rate and filtration rate) the detection approach that works for a large vehicle (e.g. the ISS) may not be the best choice for a smaller crew capsule. This paper examines the impact of vehicle size and environmental control and life support system parameters on the detectability of fires in comparison to the hazard they present. A lumped element model was developed that considers smoke, heat, and toxic product release rates in comparison to mixing and filtration rates in the vehicle. Recent work has quantified the production rate of smoke and several hazardous species from overheated spacecraft polymers. These results are used as the input data set in the lumped element model in combination with the transport behavior of major toxic products released by overheating spacecraft materials to evaluate the necessary alarm thresholds to enable appropriate response to the fire hazard.

microgravity↗

Fire Detection Tradeoffs as a Function of Vehicle Parameters

Fire survivability depends on the detection of and response to a fire before it has produced an unacceptable environment in the vehicle. This detection time is the result of interplay between the fire burning and growth rates; the vehicle size; the detection system design; the transport time to the detector (controlled by the level of mixing in the vehicle); and the rate at which the life support system filters the atmosphere, potentially removing the detected species or particles. Given the large differences in critical vehicle parameters (volume, mixing rate and filtration rate) the detection approach that works for a large vehicle (e.g. the ISS) may not be the best choice for a smaller crew capsule. This paper examines the impact of vehicle size and environmental control and life support system parameters on the detectability of fires in comparison to the hazard they present. A lumped element model was developed that considers smoke, heat, and toxic product release rates in comparison to mixing and filtration rates in the vehicle. Recent work has quantified the production rate of smoke and several hazardous species from overheated spacecraft polymers. These results are used as the input data set in the lumped element model in combination with the transport behavior of major toxic products released by overheating spacecraft materials to evaluate the necessary alarm thresholds to enable appropriate response to the fire hazard.

microgravity↗

Quantifying and Optimizing the Energy Benefits of Mass Timber Construction

The International Mass Timber Alliance (IMTA) is a global organization of industry leaders, engineers, scientists, and associations dedicated to advancing mass timber construction. Its mission is to generate and disseminate scientific data supporting the development of standardized construction and energy efficient practices that promote the adoption of mass timber worldwide. IMTA collaborated with Oak Ridge National Laboratory (ORNL) to leverage ORNL’s expertise in building envelope modeling and testing to evaluate how mass timber construction can reduce peak heating and cooling demand, lower overall energy use, and improve resilience during power outages. A previous study of 80 mass timber buildings in Finland found measured energy use up to 50% lower than predicted by simulation. This project aimed to validate and extend those findings for U.S. buildings through analytical modeling, laboratory testing, and full-scale building evaluations. The research focused on the thermal performance of low-embodied-energy wall assemblies, such as cross-laminated timber (CLT) panels and log walls, with particular attention to the effects of thermal inertia on indoor comfort and energy performance. While mass timber’s structural and fire-resistance properties are well documented, its whole-building thermal behavior has received limited attention. Field data, simulation results, and resilience testing from this study will inform future modeling practices, design guidelines, and construction practices by quantifying the unique thermal and demand-flexibility benefits of mass timber construction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Evaporation Induced Entrainment of Contaminants from Evaporating and Burning Liquids.

This interim report details model development, theory, and a literature review focusing on the evaporation induced entrainment (sub-boiling) of contaminated liquids. Entrainment from a variety of sources is the topic of DOE Handbook 3010, and this report deals more broadly with fire related airborne sources of contaminants in hazardous operations. Relatively few studies have examined sub-boiling behavior in the past, however, it can be a phenomenon that presents a fire related risk under hazardous operations. Molecular dynamics simulations are used to infer the gaseous evolution of coordinated complexes, and a model for a water/plutonium/nitrate system is deduced from the simulation results by evaluating the statistical trends of the results. Questions remain as to the chemical reactivity and longevity of entrained species. A generalized computer model capability and simple analytical model assumptions are developed for predicting the results of these and other (boiling and solid entrainment) scenarios. Verification related predictions using these models are illustrated.

36 MATERIALS SCIENCE↗

Spacecraft Fire Safety Technology Development Plan For Exploration Missions

To date, NASA’s spaceflight operations in the past 5 decades have been limited to a narrow range of conditions from a fire safety perspective. The currently anticipated missions outside of low earth orbit will substantially expand this parameter space to include, extended durations, dormancy intervals, increased oxygen concentrations, partial gravity conditions and the presence of surface dust. All of these changes can have significant impacts on fire safety system design and operations. The overall state of understanding is discussed in this paper along with the identification of the needs for spacecraft fire safety technology development. These needs have been assembled into a roadmap maintained by the Environmental Control and Life Support System Capability Leadership Team that has evolved as the exploration mission concepts have changed. This roadmap continues to communicate the spacecraft fire safety needs for exploration and guide technology development efforts. This paper summarizes the major recent developments in our understanding of spacecraft fire behavior and mitigation. A review of the major technology development needs and discussion of their objectives, status, and future plans is presented. The plan for transitioning knowledge, hardware, and modeling capability resulting from these development efforts to specific exploration vehicle programs and missions is also discussed.

Fire safety↗

Charge-flow structures as polymeric early-warning fire alarm devices

The charge-flow transistor (CFT) and its applications for fire detection and gas sensing were investigated. The utility of various thin film polymers as possible sensing materials was determined. One polymer, PAPA, showed promise as a relative humidity sensor; two others, PFI and PSB, were found to be particularly suitable for fire detection. The behavior of the charge-flow capacitor, which is basically a parallel-plate capacitor with a polymer-filled gap in the metallic tip electrode, was successfully modeled as an RC transmission line. Prototype charge-flow transistors were fabricated and tested. The effective threshold voltage of this metal oxide semiconductor was found to be dependent on whether surface or bulk conduction in the thin film was dominant. Fire tests with a PFI-coated CFT indicate good sensitivity to smouldering fires.

Sechen, C. M.↗

Modeling and Analysis of Realistic Fire Scenarios in Spacecraft

An accidental fire inside a spacecraft is an unlikely, but very real emergency situation that can easily have dire consequences. While much has been learned over the past 25+ years of dedicated research on flame behavior in microgravity, a quantitative understanding of the initiation, spread, detection and extinguishment of a realistic fire aboard a spacecraft is lacking. Virtually all combustion experiments in microgravity have been small-scale, by necessity (hardware limitations in ground-based facilities and safety concerns in space-based facilities). Large-scale, realistic fire experiments are unlikely for the foreseeable future (unlike in terrestrial situations). Therefore, NASA will have to rely on scale modeling, extrapolation of small-scale experiments and detailed numerical modeling to provide the data necessary for vehicle and safety system design. This paper presents the results of parallel efforts to better model the initiation, spread, detection and extinguishment of fires aboard spacecraft. The first is a detailed numerical model using the freely available Fire Dynamics Simulator (FDS). FDS is a CFD code that numerically solves a large eddy simulation form of the Navier-Stokes equations. FDS provides a detailed treatment of the smoke and energy transport from a fire. The simulations provide a wealth of information, but are computationally intensive and not suitable for parametric studies where the detailed treatment of the mass and energy transport are unnecessary. The second path extends a model previously documented at ICES meetings that attempted to predict maximum survivable fires aboard space-craft. This one-dimensional model implies the heat and mass transfer as well as toxic species production from a fire. These simplifications result in a code that is faster and more suitable for parametric studies (having already been used to help in the hatch design of the Multi-Purpose Crew Vehicle, MPCV).

life support↗

Determination of cloud optical depth with the multiple field of view sun photometer

An approach to determining cloud optical depth is presented which utilizes a multiple-field-of-view sun photometer, along with modeled behavior of transmittance ratios as a function of the field of view, to infer the optical depth. The data used in the analysis were collected during the FIRE Stratocumulus IFO, which took place from July 15 to 17, 1987. Values which were taken within an hour of local solar noon are presented and compared with the values computed for mu sub 0 = 1.0. The zenith angle for the measurements was between 11 and 18 deg. It is found that this method of inferring the optical depth indicates most clouds with optical depths within these limits. The measured curves have a slightly different shape than do the modeled ratios.

Hein, Paul F.↗

Organization of brain tissue - Is the brain a noisy processor.

This paper presents some thoughts on functional organization in cerebral tissue. 'Spontaneous' wave and unit firing are considered as essential phenomena in the handling of information. Various models are discussed which have been suggested to describe the pseudorandom behavior of brain cells, leading to a view of the brain as an information processor and its role in learning, memory, remembering and forgetting.

Adey, W. R.↗

Terrain-Influenced Winds and Fire-Fire Interactions in Wildland Fire Simulations [Dissertation]

Ensemble-based approaches to prescribed fire planning cannot be supported by computational fluid dynamics based models like FIRETEC and the Wildland-Urban Interface Fire Dynamics Simulator (WFDS) because they are too computationally expensive and cannot leverage large eddy simulation approaches like CAWFE and WRF-SFIRE because they have too coarse of resolution. QUIC-Fire was developed to fill this gap but it cannot currently address complex terrain, that is typical for instance in the Western United States. This dissertation describes a variety of improvements made to QUIC-Fire and its various incorporated algorithms in an effort to make it a viable tool in simulating wildland and prescribed fires on terrain. The modifications made to QUIC-Fire are described in three chapters. The first chapter describes the extension of the diagnostic wind model QUIC-URB, the wind engine of QUIC-Fire, to a terrain-following coordinate system. The terraininfluenced winds it generates are analyzed and compared. In particular, this chapter presents the mathematical derivation of the wind solver leading to a linear system of equations that are solved through the successive over-relaxation method. The model is validated against a standard test used in previous works (the Askervein Hill) and against a new dataset from measurements in the Socorro Mountains, New Mexico. The terrain-following implementation captures the correct phenomenology for the isolated Askervein Hill, with a wind speed up at the top of the hill. The model agrees well with measurements on the upwind side of the peak, but overestimates speed-up on the downwind side of the hill. This is due to the inability of the model to generate flow separation and wake-eddy dynamics. On a common laptop, the divergence-free wind field is obtained in 6 s, making the solver appealing for coupled fire-atmosphere simulations. The Socorro Mountain is highly complex, with many cliff faces, peaks, and valleys. Although the model captures the magnitude and direction of inlet and outlet areas of the domain, it performs rather poorly in the valley region and in the regions near the steep cliffs. Hence, the model shows good agreement with data in areas of open sloped terrain but lacks in areas where flow separation and thermally driven effects may be present (neither effect is addressed in this work). In the second chapter the implementation of the terrain-following version of QUIC-URB into QUIC-Fire, and the necessary changes needed to include terrain are described. No changes to the underlying fire spread algorithm are made other than what is required to correctly account for the inclusion of terrain. Previously published FIRETEC results that use five different topographies that share the same centerline profile are compared to simulation results from the modified QUIC-Fire that use the same topographies and fuels. QUIC-Fire results show overall similar behaviors in terms of how the topographies affect fire shapes and trends in spread rates. Due to the terrain-following version of QUIC-URB being unable to generate flow separations at the crest of hills, fire spread rates in these regions across all topographies are over-predicted when compared to FIRETEC. Lateral fire growth shows similar trends with FIRETEC between topographies but does not capture the increase in spread due to a diagonal interface between grassland and forested fuel region of the domain. These results suggest that there are three algorithms within QUIC-Fire that could use improvement: how flame tilt angle is accounted for, the incorporation of non-local drag effects, and the inclusion of the wake-eddy parameterizations that are used in QUIC-URB. Lastly, the third chapter describes a modification to the initial guess used for the calculation of the QUIC-URB mass-conserved wind solution during fire simulations. The modification is aimed at improving fire-fire interactions in QUIC-Fire simulations. The modification consists of using the solution from the previous timestep as the starting point for the calculation of the solution for the next timestep. Fire-fire interactions is greatly improved by the change but a new source of error is introduced. Due to how plumes are modelled in QUIC-Fire the new solution contains errors where gaps in the plume structure are present. However, these errors are mostly limited to the upper atmosphere, where they do not affect fire behavior at the surface, and their magnitude isn’t significant enough to discount the amount of new fire phenomenology now captured in QUIC-Fire with the change.

58 GEOSCIENCES↗

Using Satellite‐Derived Fire Arrival Times for Coupled Wildfire‐Air Quality Simulations at Regional Scales of the 2020 California Wildfire Season

Abstract Wildfire frequency has increased in the Western US over recent decades, driven by climate change and a legacy of forest management practices. Consequently, human structures, health, and life are increasingly at risk due to wildfires. Furthermore, wildfire smoke presents a growing hazard for regional and national air quality. In response, many scientific tools have been developed to study and forecast wildfire behavior, or test interventions that may mitigate risk. In this study, we present a retrospective analysis of 1 month of the 2020 Northern California wildfire season, when many wildfires with varying environments and behavior impacted regional air quality. We simulated this period using a coupled numerical weather prediction model with online atmospheric chemistry, and compare two approaches to representing smoke emissions: an online fire spread model driven by remotely sensed fire arrival times and a biomass burning emissions inventory. First, we quantify the differences in smoke emissions and timing of fire activity, and characterize the subsequent impact on estimates of smoke emissions. Next, we compare the simulated smoke to surface observations and remotely sensed smoke; we find that despite differences in the simulated smoke surface concentrations, the two models achieve similar levels of accuracy. We present a detailed comparison between the performance and relative strengths of both approaches, and discuss potential refinements that could further improve future simulations of wildfire smoke. Finally, we characterize the interactions between smoke and meteorology during this event, and discuss the implications that increases in regional smoke may have on future meteorological conditions.

54 ENVIRONMENTAL SCIENCES↗

Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. Here, in this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed.

54 ENVIRONMENTAL SCIENCES↗

Deep-learning-based canopy height model generation from sub-meter resolution panchromatic satellite imagery

Canopy height models (CHMs) with sufficient resolution to distinguish individual trees are useful for a variety of applications. However, standard techniques to acquire such data, such as airborne lidar surveying, are often prohibitively expensive. Deep learning techniques for generating CHMs from high-resolution imagery are an attractive option to reduce costs. To date, success with these methods has been demonstrated using multichannel aerial photography and specialized satellite data products derived from multiple sensors, neither of which is commonly available at temporal resolutions finer than one year. Here we demonstrate a method to generate sub-meter resolution CHMs in three forests in California using a more abundant data source: sub-meter resolution, panchromatic satellite imagery from a single sensor. We show that phenology and species composition play important roles in model transferability; when trained using imagery from a single conifer forest in autumn, the model performs well on autumn imagery from a second conifer forest several hundred kilometers distant with no re-training. With modest additions to the training dataset, the same model generates minimally biased estimates of canopy height in both conifer and deciduous forests during multiple seasons. Because the model operates on satellite data with global coverage and a relatively short return interval, we propose its suitability to extrapolate tree-level canopy height data to remote regions and conduct high-temporal resolution monitoring of forest structure. We furthermore demonstrate the workflow’s applicability to fire modeling by conducting simulations in forests populated by trees measured using both this approach and airborne lidar surveying. We find minimal differences in fire behavior relative to a baseline case in which only statistical distributions of tree height and crown area are known. This result underscores the value of forest structural information derived from our workflow for improving the fidelity of wildland fire simulations, among other ecological applications.

54 ENVIRONMENTAL SCIENCES↗

Collective neural network behavior in a dynamically driven disordered system of superconducting loops

Collective properties of complex systems composed of many interacting components such as neurons in our brain can be modeled by artificial networks based on disordered systems. We show that a disordered neural network of superconducting loops with Josephson junctions can exhibit computational properties like categorization and associative memory in the time evolution of its state in response to information from external excitations. Superconducting loops can trap multiples of fluxons in many discrete memory configurations defined by the local free energy minima in the configuration space of all possible states. A memory state can be updated by exciting the Josephson junctions to fire or allow the movement of fluxons through the network as the current through them surpasses their critical current thresholds. Simulations performed with a lumped element circuit model of a 4-loop network show that information written through excitations is translated into stable states of trapped flux and their time evolution. Experimental implementation on a high-Tc superconductor YBCO-based 4-loop network shows dynamically stable flux flow in each pathway characterized by the correlations between junction firing statistics. Neural network behavior is observed as energy barriers separating state categories in simulations in response to multiple excitations, and experimentally as junction responses characterizing different flux flow patterns in the network. The state categories that produce these patterns have different temporal stabilities relative to each other and the excitations. This provides strong evidence for time-dependent (short-to-long-term) memories, that are dependent on the geometrical and junction parameters of the loops, as described with a network model.

Josephson junctions↗