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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

NASA Lewis Helps Develop Advanced Saw Blades for the Lumber Industry

NASA Lewis Research Center's Structures and Material Divisions are centers of excellence in high-temperature alloys for aerospace applications such as advanced aircraft and rocket engines. Lewis' expertise in these fields was enlisted in the development of a new generation of circular sawblades for the lumber industry to use in cutting logs into boards. The U.S. Department of Agriculture's (USDA) Forest Products Laboratory and their supplier had succeeded in developing a thinner sawblade by using a nickel-based alloy, but they needed to reduce excessive warping due to residual stresses. They requested assistance from Lewis' experts, who successfully eliminated the residual stress problem and increased blade strength by over 12 percent. They achieved this by developing an innovative heat treatment based on their knowledge of nickel-based superalloys used in aeropropulsion applications.

Source record↗
NASA NTRS1987

The factor of scale in remote sensing

A method that measures the spatial structure of images as a function of spatial resolution is presented for selecting the appropriate scale for remote sensing. Graphs are obtained by imaging the scene at fine resolution and then collapsing the image to successively coarser resolutions while calculating the local variance. For the spatial resolution of SPOT and TM imagery, local image variance is relatively high for forested and urban/suburban environments, indicating that information-extraction techniques using texture, context, and mixture modeling are appropriate for these sensor systems. For agricultural environments where local variance is low, more traditional classifiers are appropriate.

Woodcock, Curtis E.↗
DOE OSTI2020

Fungal community structure and function shifts with atmospheric nitrogen deposition

Fungal decomposition of soil organic matter depends on soil nitrogen (N) availability. This ecosystem process is being jeopardized by changes in N inputs that have resulted from a tripling of atmospheric N deposition in the last century. Soil fungi are impacted by atmospheric N deposition due to higher N availability, as soils are acidified, or as micronutrients become increasingly limiting. Fungal communities that persist with chronic N deposition may be enriched with traits that enable them to tolerate environmental stress, which may trade-off with traits enabling organic matter decomposition. We hypothesized that fungal communities would respond to N deposition by shifting community composition and functional gene abundances toward those that tolerate stress but are weak decomposers. We sampled soils at seven eastern US hardwood forests where ambient N deposition varied from 3.2 to 12.6 kg N ha -1 year -1 , five of which also have experimental plots where atmospheric N deposition was simulated through fertilizer application treatments (25–50 kg N ha -1 year -1 ). Fungal community and functional responses to fertilizer varied across the ambient N deposition gradient. Fungal biomass and richness increased with simulated N deposition at sites with low ambient deposition and decreased at sites with high ambient deposition. Fungal functional genes involved in hydrolysis of organic matter increased with ambient N deposition while genes involved in oxidation of organic matter decreased. One of four genes involved in generalized abiotic stress tolerance increased with ambient N deposition. In summary, we found that the divergent response to simulated N deposition depended on ambient N deposition levels. Fungal biomass, richness, and oxidative enzyme potential were reduced by N deposition where ambient N deposition was high suggesting fungal communities were pushed beyond an environmental stress threshold. Fungal community structure and function responses to N enrichment depended on ambient N deposition at a regional scale.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Dark photon dark matter in the presence of inhomogeneous structure

Dark photon dark matter will resonantly convert into visible photons when the dark photon mass is equal to the plasma frequency of the ambient medium. In cosmological contexts, this transition leads to an extremely efficient, albeit short-lived, heating of the surrounding gas. Existing work in this field has been predominantly focused on understanding the implications of these resonant transitions in the limit that the plasma frequency of the Universe can be treated as being perfectly homogeneous, \ie neglecting inhomogeneities in the electron number density. In this work we focus on the implications of heating from dark photon dark matter in the presence of inhomogeneous structure (which is particularly relevant for dark photons with masses in the range $10^{-15} \, {\rm eV} \, \lesssim m_{A^\prime} \lesssim 10^{-12}$ eV), emphasizing both the importance of inhomogeneous energy injection, as well as the sensitivity of cosmological observations to the inhomogeneities themselves. More specifically, we derive modified constraints on dark photon dark matter from the Ly-$\alpha$ forest, and show that the presence of inhomogeneities allows one to extend constraints to masses outside of the range that would be obtainable in the homogeneous limit, while only slightly relaxing their strength. We then project sensitivity for near-future cosmological surveys that are hoping to measure the 21cm transition in neutral hydrogen prior to reionization, and demonstrate that these experiments will be extremely useful in improving sensitivity to masses near $\sim 10^{-14}$ eV, potentially by several orders of magnitude. Finally, we discuss implications for reionization, early star formation, and late-time $y$-type spectral distortions, and show that probes which are inherently sensitive to the inhomogeneous state of the Universe could resolve signatures unique to the light dark photon dark matter scenario, and thus offer a fantastic potential for a positive detection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Seasonal and long-term variations in leaf area of Congolese rainforest

It is important to understand temporal and spatial variations in the structure and photosynthetic capacity of tropical rainforests in a world of changing climate, increased disturbances and human appropriation. The equatorial rainforests of Central Africa are the second largest and least disturbed of the biodiversly-rich and highly productive rainforests on Earth. Currently, there is a dearth of knowledge about the phenological behavior and long-term changes that these forests are experiencing. Here, this study reports on leaf area seasonality and its time trend over the past two decades as assessed from multiple remotely sensed datasets. Seasonal variations of leaf area in Congolese forests derived from MODIS data co-vary with the bimodal precipitation pattern in this region, with higher values during the wet season. Independent observational evidence derived from MISR and EPIC sensors in the form of angular reflectance signatures further corroborate this seasonal behavior of leaf area. The bimodal patterns vary latitudinally within this large region. Two sub-seasonal cycles, each consisting of a dry and wet season, could be discerned clearly. These exhibit different sensitivities to changes in precipitation. Contrary to a previous published report, no widespread decline in leaf area was detected across the entire extent of the Congolese rainforests over the past two decades with the latest MODIS Collection 6 dataset. Long-term precipitation decline did occur in some localized areas, but these had minimal impacts on leaf area, as inferred from MODIS and MISR multi-angle observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Comparison of total emitted solar-induced chlorophyll fluorescence (SIF) and top-of-canopy (TOC) SIF in estimating photosynthesis

We report many studies have shown that solar-induced fluorescence (SIF) has a good potential to predict gross primary production (GPP) of vegetation. What we measured by remote sensing or near-surface platforms is top-of-canopy (TOC) SIF (SIF toc ), which is not necessarily equal or proportional to total emitted SIF (SIF tot ) from the entire canopy due to the (re)absorption and scattering effects. However, photosynthesis, the process that plants use to fix carbon from the atmosphere, occurs at the entire vertical canopy. Here, by using the recollision theory, we calculated SIF tot at 760 nm from the measured SIF toc , hyperspectral reflectance (R), canopy interception (i) and leaf albedo (ω l ). Among them, both SIF toc and R can be obtained from concurrent TOC spectral measurements; i and ω l in the near-infrared region can be estimated from the open access datasets with a good accuracy. Our result confirms that the measured SIF toc only accounts for a small fraction of SIF tot : the above-the-canopy sensor can only “see” on average 22.9% of SIF tot at Harvard Forest. SIF tot has the following advantages over SIF toc in estimating GPP: (1) SIF tot improves the diurnal estimate of canopy GPP, especially capable to capture the midday depression of photosynthesis which may cause the large discrepancies between SIF toc and GPP on a diurnal basis, (2) SIF tot produces a stronger correlation with GPP from plants with complex canopy structure or under sky conditions with more diffuse irradiance, and (3) the SIF tot -GPP relationship shows a stronger resilience to environmental stresses. The fluorescence escape ratio (f esc ), the ratio between SIF toc multiplied by π and SIF tot , is mostly determined by the sun-canopy-sensor geometry and leaf inclination distribution. The effect of LAI and the leaf chlorophyll concentration on f esc is marginal at the 760 nm wavelength. Our results suggest that converting SIF toc into SIF tot provides a better tool to understand and estimate GPP.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Application of Skylab imagery to some geological and environmental problems in Italy

Four topics are considered: regional geology of Sicily, volcanic surveillance in southern Italy, hydrogeology (with special regard given to the discovery and mapping of paleoriverbeds), and crop investigation. The discovery of unknown lineaments and structures in Sicily contributes to the geological knowledge of this region and in particular to the mechanical phenomena involving the upper part of the crust. An attempt was made to relate the status of vegetation surrounding Etna volcano to the magmatic gas escapes filtering through the soil. False-color Skylab images were used to analyze the vigor of the Etnean forestal belt vegetation canopy in order to map possible gas-vent ways as well as the 'active' microfractures. In northern Italy, buried channels were mapped in the Venetian Plain, and a tentative cost-benefit evaluation was done in the field of vegetational studies, both disease detection and species inventory were performed in the Po River Delta and in northwestern Italy.

Cassinis, R.↗
NASA NTRS1992

Supervised fully polarimetric classification of the Black Forest test site: From MAESTROI to MAC Europe

A study on the performance of a supervised fully polarimetric maximum likelihood classifier for synthetic aperture radar (SAR) data when applied to a specific classification context: forest classification based on age classes and in the presence of a sloping terrain is presented. For the experimental part, the polarimetric AIRSAR data at P, L, and C-band, acquired over the German Black Forest near Freiburg in the frame of the 1989 MAESTRO-1 campaign and the 1991 MAC Europe campaign was used, MAESTRO-1 with an ESA/JRC sponsored campaign, and MAC Europe (Multi-sensor Aircraft Campaign); in both cases the multi-frequency polarimetric JPL Airborne Synthetic Aperture Radar (AIRSAR) radar was flown over a number of European test sites. The study is structured as follows. At first, the general characteristics of the classifier and the dependencies from some parameters, like frequency bands, feature vector, calibration, using test areas lying on a flat terrain are investigated. Once it is determined the optimal conditions for the classifier performance, we then move on to the study of the slope effect. The bulk of this work is performed using the Maestrol data set. Next the classifier performance with the MAC Europe data is considered. The study is divided into two stages: first some of the tests done on the Maestro data are repeated, to highlight the improvements due to the new processing scheme that delivers 16 look data. Second we experiment with multi images classification with two goals: to assess the possibility of using a training set measured from one image to classify areas in different images; and to classify areas on critical slopes using different viewing angles. The main points of the study are listed and some of the results obtained so far are highlighted.

Degrandi, G.↗
NASA NTRS2013

Characterization of ASTER GDEM Elevation Data over Vegetated Area Compared with Lidar Data

Current researches based on areal or spaceborne stereo images with very high resolutions (less than 1 meter) have demonstrated that it is possible to derive vegetation height from stereo images. The second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is a state-of-the-art global elevation data-set developed by stereo images. However, the resolution of ASTER stereo images (15 meters) is much coarser than areal stereo images, and the ASTER GDEM is compiled products from stereo images acquired over 10 years. The forest disturbances as well as forest growth are inevitable in 10 years time span. In this study, the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar data. The factors possibly affecting the extraction of vegetation canopy height considered include (1) co-registration of DEMs; (2) spatial resolution of digital elevation models (DEMs); (3) spatial vegetation structure; and (4) terrain slope. The results show that accurate co-registration between ASTER GDEM and the National Elevation Dataset (NED) is necessary over mountainous areas. The correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.43 by degrading resolutions from 1 arc-second to 5 arc-seconds and further improved to 0.6 if only homogenous vegetated areas were considered.

vegetation canopy↗
DOE OSTI2021

A review of imputation strategies for isobaric labeling-based shotgun proteomics

The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is uniquely different than unlabeled studies. In this review, we compare the efficacy of nine imputation methods on a CPTAC proteomics iTRAQ dataset. Imputation methods were evaluated with regard to accuracy, variability, statistical hypothesis test inference and run time over datasets consisting of varying number of iTRAQ plexes and percentages of missing data. In general, expectation maximization and random forest imputation methods yielded the best performances, and constant-based methods performed poorly consistently across all dataset sizes and percentages of missing values. For datasets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. Based on the findings in this review, there are core imputation methods that perform higher for isobaric-labeled proteomics data, but great care and consideration as to whether imputation should be used should be given for datasets comprised of a small number of samples, as well as to factors such as computational time and reproducibility of imputation values.

Bramer, Lisa M.↗
DOE OSTI2023

The eco-evolutionary role of fire in shaping terrestrial ecosystems

Fire is an inherently evolutionary process, even though much more emphasis has been given to ecological responses of plants and their associated communities to fire. Here, in this work, we synthesize contributions to a Special Feature entitled ‘Fire as a dynamic ecological and evolutionary force’ and place them in a broader context of fire research. Topics covered in this Special Feature include a perspective on the impacts of novel fire regimes on differential forest mortality, discussions on new approaches to investigate vegetation-fire feedbacks and resulting plant syndromes, synthesis of fire impacts on plant–fungal interactions, and a meta-analysis of arthropod community responses to fire. We conclude by suggesting pathways forward to better understand the ecological and evolutionary consequences of fire. These include developing ecological and evolutionary databases for fire ecology, integrating hierarchical genetic structure or phylogenetic structure, and developing new experimental frameworks that limit context-dependent outcomes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

Development of techniques to simplify the process of investigation and estimate of natural resources in remote and relatively unexplored areas, Venezuela

The author has identified the following significant results. Based upon the results obtained by both interpretations, radar and satellite, highly fractured zones were outlined and checked against geologic rock samples. Most of the rock samples were collected along the Ventuari River that crosses most of the project area from NE to SE. The structural orientation and fracture patterns were taken into consideration during the planning of flight lines, also the magnetic declination of the area. Most of the flight lines were oriented N 30 deg W across known structural trends; the altitude above ground was 450 feet, and the distance between lines was 3000 feet. The ERTS image permits the obtention of a panoramic vision of the area, and facilitates the exact delineation of major drainages. Topographic changes are easily observed. Rocky outcrops are easily distinguishable on band 5 and 7. Shrub vegetation zones are distinguishable on both bands, without possible separation of forest types, this is possible only when the physiographic conditions of small textural differences are taken into account.

Romero, A. C.↗
DOE OSTI2020

Conserved white-rot enzymatic mechanism for wood decay in the Basidiomycota genus Pycnoporus

White-rot (WR) fungi are pivotal decomposers of dead organic matter in forest ecosystems and typically use a large array of hydrolytic and oxidative enzymes to deconstruct lignocellulose. However, the extent of lignin and cellulose degradation may vary between species and wood type. Here, we combined comparative genomics, transcriptomics and secretome proteomics to identify conserved enzymatic signatures at the onset of wood-decaying activity within the Basidiomycota genus Pycnoporus. We observed a strong conservation in the genome structures and the repertoires of protein-coding genes across the four Pycnoporus species described to date, despite the species having distinct geographic distributions. We further analysed the early response of P. cinnabarinus, P. coccineus and P. sanguineus to diverse (ligno)-cellulosic substrates. We identified a conserved set of enzymes mobilized by the three species for breaking down cellulose, hemicellulose and pectin. The co-occurrence in the exo-proteomes of H 2 O 2 -producing enzymes with H 2 O 2 -consuming enzymes was a common feature of the three species, although each enzymatic partner displayed independent transcriptional regulation. Finally, cellobiose dehydrogenase-coding genes were systematically co-regulated with at least one AA9 lytic polysaccharide monooxygenase gene, indicative of enzymatic synergy in vivo. This study highlights a conserved core white-rot fungal enzymatic mechanism behind the wood-decaying process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu

Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Predicting oxidation damage in ultra high-temperature borides: A machine learning approach

Ultra-high temperature (UHT) borides are ceramics materials with melting points above 3000 °C for structural applications in extreme environments. However, at temperatures exceeding 1600 °C and under oxidizing conditions, the material suffers from detrimental degradation. Optimized design and performance of diboride materials under such extreme conditions requires filling the missing composition-microstructure-oxidation gap. This study proposes a computational data-driven framework to connect the processing and microstructure of Ultra-high temperature borides with the oxidation damage. Random Forest Regressor (RFR) model is adopted to forecast the oxide scale thickness developed after oxidation testing based on processing variables and microstructural features. The model trained on a dataset consisting of 107 samples of experimental data extracted from the literature aims to predict oxidation damage. With proper data manipulation and fine model tuning, the predictor could forecast the oxide scale thickness of UHT diborides with a Mean Absolute Error of 37.45 μm and an R-square of 0.83. This model could be used as a high-throughput scheme to design and test new UHT diborides materials computationally. Furthermore, a model with larger composition capabilities could also be developed in the future as more experimental data become available.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Soil Metabolomics Predict Microbial Taxa as Biomarkers of Moisture Status in Soils from a Tidal Wetland

We present observations from a laboratory-controlled study on the impacts of extreme wetting and drying on a wetland soil microbiome. Our approach was to experimentally challenge the soil microbiome to understand impacts on anaerobic carbon cycling processes as the system transitions from dryness to saturation and vice-versa. Specifically, we tested for impacts on stress responses related to shifts from wet to drought conditions. We used a combination of high-resolution data for small organic chemical compounds (metabolites) and biological (community structure based on 16S rRNA gene sequencing) features. Using a robust correlation-independent data approach, we further tested the predictive power of soil metabolites for the presence or absence of taxa. Here, we demonstrate that taking an untargeted, multidimensional data approach to the interpretation of metabolomics has the potential to indicate the causative pathways selecting for the observed bacterial community structure in soils.

16S rRNA gene↗
NASA NTRS2021

Discrete anisotropic radiative transfer modelling of solar-induced chlorophyll fluorescence: Structural impacts in geometrically explicit vegetation canopies

Solar-induced fluorescence (SIF) is a subtle but informative optical signal of vegetation photosynthesis. Remotely sensed SIF integrates environmental, physiological and structural changes that alter photosynthesis at leaf, plant and canopy scales. Radiative transfer models are ideally suited to investigate the complex sources of variability in the SIF signal to guide the interpretation of SIF retrievals from airborne and space-borne platforms. Here, we coupled the Fluspect-Cx model of leaf optical properties and chlorophyll-a fluorescence with the Discrete Anisotropic Radiative Transfer (DART) model to upscale SIF from individual leaves to three-dimensional (3D) structurally explicit canopies. For one-dimensional homogeneous (turbid-like) canopies, DART-SIF was nearly identical to SIF simulated in two existing models, SCOPE and mSCOPE (RMSE <0.221 W./sq.m.μm.sr). DART simulations in geometrically explicit 3D canopies offered four important insights regarding the influence of vegetation structure on the multi-angular top-of-canopy SIF signal. First, changes in the 3D canopy architecture of maize crops, represented by leaf density (leaf area index), and plant clumping (canopy closure) had a larger impact on SIF than the modelled photosynthetic efficiency distinction between sun-adapted and shade-adapted foliage. Second, clumping of leaves at the crop and stand levels was identified as one of the key driving factors of multi-angular anisotropy of red and far-red SIF (686 and 740 nm) for both maize and eucalyptus canopies. Third, non-photosynthetic woody material had a significant impact on top-of-canopy SIF in modelled 3D forest stands. Wood shadowing decreased the photosynthetically active radiation absorbed by green leaves, and consequently the SIF emissions, by 10% in sparse and 17% in dense eucalyptus stands. The wood obstruction (blocking) effect, quantified as a relative difference of SIF escape probabilities from canopies with and without wood in the nadir viewing direction, decreased far-red SIF by 4–6% but it had a smaller and sometimes positive influence (by less than 2%) on red SIF. Fourth, DART 3D radiative budget profiles revealed that the majority of the SIF signal from a dense eucalyptus stand originated from the top 25% of the simulated canopy. Interestingly, the introduction of bark-covered woody elements did not alter the simulated balance and omnidirectional escape factor of red SIF in this upper canopy part but did raise significantly both of them in case of far-red SIF. These results demonstrate the importance of 3D radiative transfer and radiative budget simulations for investigating SIF interactions in structurally complex plant canopies and for a better understanding of spatiotemporal and multi-angular remote sensing SIF observations.

Zbyněk Malenovský↗
DOE OSTI2023

Rapidly Changing Range Limits in a Warming World: Critical Data Limitations and Knowledge Gaps for Advancing Understanding of Mangrove Range Dynamics in the Southeastern USA

Climate change is altering species’ range limits and transforming ecosystems. For example, warming temperatures are leading to the range expansion of tropical, cold-sensitive species at the expense of their cold-tolerant counterparts. In some temperate and subtropical coastal wetlands, warming winters are enabling mangrove forest encroachment into salt marsh, which is a major regime shift that has significant ecological and societal ramifications. Here, we synthesized existing data and expert knowledge to assess the distribution of mangroves near rapidly changing range limits in the southeastern USA. We used expert elicitation to identify data limitations and highlight knowledge gaps for advancing understanding of past, current, and future range dynamics. Mangroves near poleward range limits are often shorter, wider, and more shrublike compared to their tropical counterparts that grow as tall forests in freeze-free, resource-rich environments. The northern range limits of mangroves in the southeastern USA are particularly dynamic and climate sensitive due to abundance of suitable coastal wetland habitat and the exposure of mangroves to winter temperature extremes that are much colder than comparable range limits on other continents. Thus, there is need for methodological refinements and improved spatiotemporal data regarding changes in mangrove structure and abundance near northern range limits in the southeastern USA. Advancing understanding of rapidly changing range limits is critical for foundation plant species such as mangroves, as it provides a basis for anticipating and preparing for the cascading effects of climate-induced species redistribution on ecosystems and the human communities that depend on their ecosystem services.

54 ENVIRONMENTAL SCIENCES↗