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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Simulation Studies of the Effect of Forest Spatial Structure on InSAR Signature

The height of scattering phase retrieved from InSAR data is considered being correlated with the tree height and the spatial structure of the forest stand. Though some researchers have used simple backscattering models to estimate tree height from the height of scattering center, the effect of forest spatial structure on InSAR data is not well understood yet. A three-dimensional coherent radar backscattering model for forest canopies based on realistic three-dimensional scene was used to investigate the effect in this paper. The realistic spatial structure of forest canopies was established either by field measurements (stem map) or through use of forest growth model. Field measurements or a forest growth model parameterized using local environmental parameters provides information of forest species composition and tree sizes in certain growth phases. A fractal tree model (L-system) was used to simulate individual 3- D tree structure of different ages or heights. Trees were positioned in a stand in certain patterns resulting in a 3-D medium of discrete scatterers. The radar coherent backscatter model took the 3-D forest scene as input and simulates the coherent radar backscattering signature. Interferometric SAR images of 3D scenes were simulated and heights of scattering phase centers were estimated from the simulated InSAR data. The effects of tree height, crown cover, crown depth, and the spatial distribution patterns of trees on the scattering phase center were analyzed. The results will be presented in the paper.

Sun, Guoqing↗
DOE OSTI2023

Modeling the effects of carbon payments and forest owner cooperatives on carbon storage and revenue in Pacific Northwest forestlands

The forestlands of the Pacific Northwest store a significant amount of carbon, provide wildlife habitat, generate revenue for forestland owners, and provide for sustainable generation of forest products. Promoting forest carbon storage is increasingly recognized as a key management objective to mitigate climate change. We developed an agent-based model to examine the effectiveness of current land management practices in sustaining forest carbon storage and revenue flows, and the utility of market-based carbon payments in improving carbon storage while also improving revenue generation in the Pacific Northwest. We find that while market based incentives can improve both forest carbon storage and revenue, they are limited in their benefit to small forest owners. Our results highlight the crucial need for additional policy interventions to reduce barriers to market access for small private owners and ensure sustainable forest management in this region. We further show that a forest owners’ cooperative, which aggregates small owners to reduce the costs of participating in a carbon market, can help overcome these barriers and result in both greater carbon storage and a more equitable benefit from carbon payments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Evaluating the E3SM land model version 0 (ELMv0) at a temperate forest site using flux and soil water measurements

Accurate simulations of soil respiration and carbondioxide ( CO 2 ) fluxes are critical to project global biogeochemicalcycles and the magnitude of carbon–climate feedbacks in Earth system models(ESMs). Currently, soil respiration is not represented well in ESMs, and fewstudies have attempted to address this deficiency. In this study, weevaluated the simulation of soil respiration in the Energy Exascale EarthSystem Model (E3SM) land model version 0 (ELMv0) using long-termobservations from the Missouri Ozark AmeriFlux (MOFLUX) forest site in thecentral US. Simulations using the default model parameters underestimatedsoil water potential (SWP) during peak growing seasons and overestimated SWPduring non-growing seasons and consequently underestimated annual soilrespiration and gross primary production (GPP). A site-specific soil waterretention curve greatly improved model simulations of SWP, GPP, and soilrespiration. However, the model continued to underestimate the seasonal andinterannual variabilities and the impact of the extreme drought in 2012.Potential reasons may include inadequate representations of vegetationmortality, the soil moisture function, and the dynamics of microbialorganisms and soil macroinvertebrates. Our results indicate that thesimulations of mean annual GPP and soil respiration can be significantlyimproved by better model representations of the soil water retention curve.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Expansion of high-latitude deciduous forests driven by interactions between climate warming and fire: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic publication. More information in the related references. The dataset is a model (ecosys) output used in a study that examined the potential future shifts in high-latitude vegetation types (PFTs) driven by changes in 21st century climate and fire. The study highlights the importance of mechanistically representing differences in plant functional traits on current and possible 21st century trajectories of Alaskan PFTs. This dataset package includes 13 files in pdf, csv, and nc formats.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Amazon forest response to CO 2 fertilization dependent on plant phosphorus acquisition

Global terrestrial models currently predict that the Amazon rainforest will continue to act as a carbon sink in the future, primarily owing to the rising atmospheric carbon dioxide (CO 2 ) concentration. Soil phosphorus impoverishment in parts of the Amazon basin largely controls its functioning, but the role of phosphorus availability has not been considered in global model ensembles—for example, during the Fifth Climate Model Intercomparison Project. Here we simulate the planned free-air CO 2 enrichment experiment AmazonFACE with an ensemble of 14 terrestrial ecosystem models. We show that phosphorus availability reduces the projected CO 2 -induced biomass carbon growth by about 50% to 79 ± 63 g C m -2 yr -1 over 15 years compared to estimates from carbon and carbon–nitrogen models. Our results suggest that the resilience of the region to climate change may be much less than previously assumed. Variation in the biomass carbon response among the phosphorus-enabled models is considerable, ranging from 5 to 140 g C m -2 yr -1 , owing to the contrasting plant phosphorus use and acquisition strategies considered among the models. The Amazon forest response thus depends on the interactions and relative contributions of the phosphorus acquisition and use strategies across individuals, and to what extent these processes can be upregulated under elevated CO 2 .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The Use of Machine Learning Models for Predicting the Dielectric Strength of Gases

Technological advancements in high voltage systems have pushed sulfur hexafluoride (SF6) to its operational limits. Furthermore, this gas has other drawbacks including a high liquefaction temperature and a high global warming potential. Therefore, there has been an urgent need to find alternative gases with high dielectric strength (DS). In this work, density functional theory (DFT) is used to calculate molecular descriptors that are fed into an artificial neural network (ANN) and a random forest (RF). These machine learning (ML) models are then used to predict the DS for hundreds of molecules. A finite element model (FEM) is also used to calculate the electric field profile of multiple simple electrode geometries as the applied voltage to the system is increased. Results indicate that the random forest model has better generalization to unseen data than the neural network. The highest DS value predicted by the RF was 2.16 relative to the experimental DS of SF6. The results also demonstrate how choosing a gas with a higher DS and a geometry with minimal edges and corners can significantly increase the operating voltage of an electrical system. Due to its superior generalization, the RF represents the most promising path toward an accurate DS predictor once sufficient experimental data are available.

Mileski, Matthew [AFIT]↗
DOE OSTI2021

The Significance of Aggregation Methods in Functional Group Modeling

The growth of forests and the feedbacks between forests and environmental changes are central issues in the planetary carbon cycle, global climate change, and basic plant ecology. A challenge to understanding both growth and feedbacks from local to global scales is that many critical metabolic processes vary among species. An innovation in solving this challenge is the recognition that species can be lumped into “functional groups” based on metabolic similarity, and these functional groups can then be studied in computational models that simulate ecosystem function. Despite the vast resources devoted to functional group studies and the progress made by them, an important logical and biological question has not been formally addressed, “How do the groupings alter the results of modeling studies?” To what extent do modeling results depend on the choices made in aggregating taxa into functional groups. Here, we consider the effects of using different aggregation strategies in simulating the carbon dynamics of a deciduous forest. Understanding the impacts that aggregation strategy has on efforts to simulate regional-to-global-scale forest dynamics offers insights into both ecosystem regulation and model function and addresses this central problem in the study of carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Formulation of a Consistent Multi‐Species Canopy Description for Hydrodynamic Models Embedded in Large‐Scale Land‐Surface Representations of Mixed‐Forests

Abstract The plant hydrodynamic approach represents a recent advancement to land surface modeling, in which stomatal conductance responds to water availability in the xylem rather than in the soil. To provide a realistic representation of tree hydrodynamics, hydrodynamic models must resolve processes at the level of a single modeled tree, and then scale the resulting fluxes to the canopy and land surface. While this tree‐to‐canopy scaling is trivial in a homogeneous canopy, mixed‐species canopies require careful representation of the species properties and a scaling approach that results in a realistic description of both the canopy and individual‐tree hydrodynamics, as well as leaf‐level fluxes from the canopy and their forcing. Here, we outline advantages and pitfalls of three commonly used approaches for representing mixed forests in land surface models and present a new framework for scaling vegetation characteristics and fluxes in mixed forests. The new formulation scales fluxes from the tree to canopy level in an energy‐ and mass‐conservative way and allows for a consistent multi‐species/multi‐type canopy description by hydrodynamic models.

Bohrer, G.↗
DOE OSTI2021

The response of coarse root biomass to long-term CO 2 enrichment and nitrogen application in a maturing Pinus taeda stand with a large broadleaved component

Elevated atmospheric CO 2 (eCO 2 ) typically increases aboveground growth in both growth chamber and free-air carbon enrichment (FACE) studies. We report on the impacts of eCO 2 and nitrogen amendment on coarse root biomass and net primary productivity (NPP) at the Duke FACE study, where half of the eight plots in a 30-year-old loblolly pine (Pinus taeda, L.) plantation, including competing naturally regenerated broadleaved species, were subjected to eCO 2 (ambient, aCO 2 plus 200 ppm) for 15–17 years, combined with annual nitrogen amendments (11.2 g N m -2 ) for 6 years. Allometric equations were developed following harvest to estimate coarse root (>2 mm diameter) biomass. Pine root biomass under eCO 2 increased 32%, 1.80 kg m -2 above the 5.66 kg m -2 observed in aCO 2 , largely accumulating in the top 30 cm of soil. In contrast, eCO2 increased broadleaved root biomass more than twofold (aCO 2 : 0.81, eCO 2 : 2.07 kg m -2 ), primarily accumulating in the 30–60 cm soil depth. Combined, pine and broadleaved root biomass increased 3.08 kg m -2 over aCO 2 of 6.46 kg m -2 , a 48% increase. Elevated CO 2 did not increase pine root:shoot ratio (average 0.24) but increased the ratio from 0.57 to 1.12 in broadleaved species. Averaged over the study (1997–2010), eCO 2 increased pine, broadleaved and total coarse root NPP by 49%, 373% and 86% respectively. Nitrogen amendment had smaller effects on any component, singly or interacting with eCO 2 . A sustained increase in root NPP under eCO 2 over the study period indicates that soil nutrients were sufficient to maintain root growth response to eCO 2 . These responses must be considered in computing coarse root carbon sequestration of the extensive southern pine and similar forests, and in modelling the responses of coarse root biomass of pine–broadleaved forests to CO 2 concentration over a range of soil N availability.

9 allometry↗
DOE OSTI2017

Modeling the impacts of wood pellet demand on forest dynamics in southeastern United States

Abstract The export of wood pellets from the southeastern United States (USA) has grown significantly in recent years, following rising demand from Europe. Increased wood pellet demand could lead to spatially variable changes in timberland management and area in the USA. This study presents an assessment of the impacts of increasing wood pellet demand (an additional 11.6 Mt by 2030) on land‐use dynamics, taking into account developments in other wood product markets as well as expected changes in other land uses. An economic model for the forest sector of the southeastern USA (SRTS) was linked to a land‐use change model (PLUC) to identify potential locations of land‐use change following scenarios of demand for pellets and other wood products. Projections show that in the absence of additional demand for wood pellets, natural timberland area is projected to decline by 450–15 000 km 2 by 2030, mainly through urbanization and pine plantation establishment. Under the high wood pellet demand scenario, more (2000–7500 km 2 ) natural timberland area is retained and more (8000–20 000 km 2 ) pine plantation is established. Shifts from natural timberland to pine plantation occur predominantly in the Atlantic coastal region. Future work will assess the impact of projected transitions in natural timberland and pine plantations on biodiversity and carbon storage. This modeling framework can be applied for multiple scenarios and land‐use projections to identify locations of timberland area changes for the whole southeastern USA, thereby informing the debate about potential impacts of wood pellet demand on land‐use dynamics and environmental services. © 2017 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Chemical Industry and John Wiley & Sons, Ltd.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

The Green Ocean Amazon Experiment (GoAmazon2014/5) Observes Pollution Affecting Gases, Aerosols, Clouds, and Rainfall over the Rain Forest

The Observations and Modeling of the Green Ocean Amazon 2014–2015 (GoAmazon2014/5) experiment took place around the urban region of Manaus in central Amazonia across 2 years. The urban pollution plume was used to study the susceptibility of gases, aerosols, clouds, and rainfall to human activities in a tropical environment. Many aspects of air quality, weather, terrestrial ecosystems, and climate work differently in the tropics than in the more thoroughly studied temperate regions of Earth. GoAmazon2014/5, a cooperative project of Brazil, Germany, and the United States, employed an unparalleled suite of measurements at nine ground sites and on board two aircraft to investigate the flow of background air into Manaus, the emissions into the air over the city, and the advection of the pollution downwind of the city. Here in this paper, to visualize this train of processes and its effects, observations aboard a low-flying aircraft are presented. Comparative measurements within and adjacent to the plume followed the emissions of biogenic volatile organic carbon compounds (BVOCs) from the tropical forest, their transformations by the atmospheric oxidant cycle, alterations of this cycle by the influence of the pollutants, transformations of the chemical products into aerosol particles, the relationship of these particles to cloud condensation nuclei (CCN) activity, and the differences in cloud properties and rainfall for background compared to polluted conditions. The observations of the GoAmazon2014/5 experiment illustrate how the hydrologic cycle, radiation balance, and carbon recycling may be affected by present-day as well as future economic development and pollution over the Amazonian tropical forest.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Estimating forest productivity with Thematic Mapper and biogeographical data

Spectral data from the Landsat Thematic Mapper (TM) on three forest exosystems (the southern Illinois, the Great Smoky Mountains regions in Tennessee and North Carolina, and the central Adirondack Mountains in New York) were used in conjunction with ground-collected measures of forest productivity and such information as the area's slope, aspect, elevation, and soil and vegetation types, to develop models of regional forest productivity. It is shown that the models developed may be used to estimate the productivity of a region with a high degree of confidence, but that the reliability of single-pixel estimates is poor. The characteristics of a given ecosystem determine which spectral values are most closely related to forest productivity. Thus, mid-IR, NIR, and visible bands are most significant in Illinois and New York, while the thermal band is relatively more important in the Smokies.

Cook, Elizabeth A.↗
NASA NTRS1994

Retrieval of Forest Canopy Parameters for OTTER Using an Optimized Technique

An inversion algorithm based on a nonlinear optimization technique is used to retrieve a subset of forest canopy parameters that can be used in ecosystem modeling. The inversion algorithm uses a parametric model derived from a discrete component forest scattering model which includes all major scattering contributions to radar backscatter. The parametric model, however, is derived for cases where only one mechanism dominates the scattering process, in this case, branch layer volume scattering.The inversion algorithm is shown to be a useful tool in the quantative monitoring of canopy moisture status, as well as a building block of its future versions which will include a more comprehensive set of forest scattering parameters as unknowns.

forest↗
NASA NTRS1977

Remote moisture-content balance

Automatic balance monitors wetness of wood samples to determine forest fire hazards. Fuel model consists of four wooden dowels that will absorb precipitation moisture and humidity at rate related to moisture absorption by forest wood. Model wetness is determined from weight changes as monitored by electronic balance.

Blomseth, R. A.↗
NASA NTRS2011

Applications of the BIOPHYS Algorithm for Physically-Based Retrieval of Biophysical, Structural and Forest Disturbance Information

Canopy reflectance model inversion using look-up table approaches provides powerful and flexible options for deriving improved forest biophysical structural information (BSI) compared with traditional statistical empirical methods. The BIOPHYS algorithm is an improved, physically-based inversion approach for deriving BSI for independent use and validation and for monitoring, inventory and quantifying forest disturbance as well as input to ecosystem, climate and carbon models. Based on the multiple-forward mode (MFM) inversion approach, BIOPHYS results were summarized from different studies (Minnesota/NASA COVER; Virginia/LEDAPS; Saskatchewan/BOREAS), sensors (airborne MMR; Landsat; MODIS) and models (GeoSail; GOMS). Applications output included forest density, height, crown dimension, branch and green leaf area, canopy cover, disturbance estimates based on multi-temporal chronosequences, and structural change following recovery from forest fires over the last century. Good correspondences with validation field data were obtained. Integrated analyses of multiple solar and view angle imagery further improved retrievals compared with single pass data. Quantifying ecosystem dynamics such as the area and percent of forest disturbance, early regrowth and succession provide essential inputs to process-driven models of carbon flux. BIOPHYS is well suited for large-area, multi-temporal applications involving multiple image sets and mosaics for assessing vegetation disturbance and quantifying biophysical structural dynamics and change. It is also suitable for integration with forest inventory, monitoring, updating, and other programs.

Peddle, Derek R.↗
DOE OSTI2025

Assessing Simulations of Forest Hurricane Disturbance and Recovery in Puerto Rico by ELM-FATES Using Field Measurements

In the past three decades, Puerto Rico (PR) experienced five hurricanes that met or exceeded category three, and they caused severe forest structural damage and elevated tree mortality. To improve our mechanistic understanding of hurricane impacts on tropical forests and assess hurricane-affected forest dynamics in Earth system models, we use in situ forest measurements at the Bisley Experimental Watersheds in Northeast PR to evaluate the Functionally Assembled Terrestrial Ecosystem Simulator coupled with the Energy Exascale Earth System Model Land Model (ELM-FATES). The observations show that before Hurricane Hugo, 77.3% of the aboveground biomass (AGB) is from the shade-tolerant plant function type (PFT). The Hugo-induced mortality rates are over ~50%, and they induce a ~39% AGB reduction, which recovers to a level like the pre-Hugo condition in 2014, following a second, lower intensity hurricane, Georges. We perform numerical experiments that simulate damage from Hugo and Georges on the forests, including defoliation, sapwood and structural biomass damage, and hurricane-induced mortality. ELM-FATES can reasonably represent coexistence between the two PFTs–light-demanding and shade-tolerant–for both the pre-Hugo and post-Hugo conditions. The model represents a reasonable size distribution of mid-and large-sized trees although it underestimates AGB, likely due to the overestimated nonhurricane mortality. ELM-FATES temporarily stimulated leaf biomass and diameter increment after Georges, an effect that should be tested with observations of future hurricane defoliation events. This research indicates that addressing model-data mismatches in tree mortality and understory dynamics are essential to simulation of more extreme hurricane effects under climate change.

58 GEOSCIENCES↗
DOE OSTI2024

Towards a liana plant functional type for vegetation models

Lianas (woody climbers) are crucial components of tropical forests and they have been increasingly recognized to have profound effects on tropical forest carbon dynamics. Despite their importance, lianas' representation in vegetation models remains limited, partly due to the complexity of liana-tree dynamics and the diversity in liana life history strategies. This paper provides a comprehensive review of advances and challenges for mechanistically representing lianas in forest ecosystem models and a proposed path towards effectively representing lianas in these models. Defining a liana plant functional type is a significant challenge because of the high morphological and physiological diversity amongst liana species, and because of their structural association with trees. Here, we identify critical liana traits that likely should contribute to establishing a liana plant functional type, along with key processes to properly represent lianas in ecosystem models. Subsequently, we discuss a variety of possible liana implementation strategies with their associated strengths, limitations, computational costs and data requirements. A fundamental redesign of the tree-centric demographic vegetation models seems appropriate to accommodate the unique growth and competition strategies of lianas. We illustrate the potential of such models with a single-site case study where we disentangle putative mechanisms of liana increasing abundance. Furthermore, we underscore the critical need for comprehensive liana demographic and functional data (including long-term, physiological, and pantropical observations) for the qualitative implementation and evaluation in the proposed modeling efforts. Currently, there is a scarcity of liana data and the data that do exist have a neotropical bias. We finally introduce a new liana functional trait database that can centralize existing liana trait data, incentivize improved data gathering and thus facilitate model development and scientific analyses.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Investigation to develop a multistage forest sampling inventory system using ERTS-1 imagery

The author has identified the following significant results. The annotation system produced a RMSE of about 200 m ground distance in the MSS data system with the control data used. All the analytical MSS interpretation models tried were highly significant. However, the gains in forest sampling efficiency that can be achieved by using the models vary from zero to over 50 percent depending on the area to which they are applied and the sampling method used. Among the sampling methods tried, regression sampling yielded substantial and the most consistent gains. The single most significant variable in the interpretation model was the difference between bands 5 and 7. The contrast variable, computed by the Hadamard transform was significant but did not contribute much to the interpretation model. Forest areas containing very large timber volumes because of large tree sizes were not separable from areas of similar crown cover but containing smaller trees using ERTS image interpretation only. All correlations between space derived timber volume predictions and estimates obtained from aerial and ground sampling were relatively low but significant and stable. There was a much stronger relationship between variables derived from MSS and U2 data than between U2 and ground data.

Langley, P. G.↗