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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Comparative Aerial and Ground Based High Throughput Phenotyping for the Genetic Dissection of NDVI as a Proxy for Drought Adaptive Traits in Durum Wheat

Global warming and the increasing frequency and severity of drought events unequivocally underline the urgency to select crops able to sustain growth in rainfed conditions, particularly when grown in Mediterranean countries, where climatic change is expected to exacerbate yield uncertainty (Ortiz et al., 2008; Kelley et al., 2015; Kyratzis et al., 2017). The selection of drought-resistant cultivars increasingly relies on the use of yield-related proxies selected either directly (Reynolds and Tuberosa, 2008) or via marker-assisted selection once the quantitative trait loci (QTLs) underpinning the variability of the relevant trait are identified (Langridge and Reynolds, 2015; Maccaferri et al., 2016; Mason et al., 2018).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Landfill Emissions of Methane Inferred from Unmanned Aerial Vehicle and Mobile Ground Measurements

Municipal solid waste landfills are significant sources of atmospheric methane, the second most important greenhouse gas after carbon dioxide. Large emissions of methane from landfills contribute not only to global climate change, but also to local ozone formation due to the enhancement of radical chain lengths in atmospheric reactions of volatile organic compounds and nitrogen oxides. Several advanced techniques were deployed to measure methane emissions from two landfills in the Southeast Michigan ozone nonattainment area during the Michigan–Ontario Ozone Source Experiment (MOOSE). These techniques included mobile infrared cavity ringdown spectrometry, drone-mounted meteorological sensors and tunable diode laser spectrometry, estimation of total landfill emissions of methane based on flux plane measurements, and Gaussian plume inverse modeling of distributed methane emissions in the presence of complex landfill terrain. The total methane emissions measured at the two landfills were of the order of 500 kg/h, with an uncertainty of around 50%. The results indicate that both landfill active faces and leaking gas collection systems are important sources of methane emissions.

Olaguer, Eduardo P.↗
DOE OSTI2022

Aerial Imagery Can Detect Nitrogen Fertilizer Effects on Biomass and Stand Health of Miscanthus × giganteus

Miscanthus × giganteus (Miscanthus) is a warm-season perennial grass grown for bioenergy feedstock production. Nitrogen (N) fertilizer management is crucial for the sustainability of Miscanthus production. In our two-year study (2018 and 2019), we investigated the role of vegetation indices (VIs) in evaluating N fertilization (0 N, 56 N, 112 N, and 168 N kg ha –1 ) impacts on Miscanthus biomass yield and stand health. The flight campaigns were conducted early, middle, and late during the summer growing season. Among the VIs, mid-summer growing season NDRE provided the best prediction of fresh biomass (R 2 = 0.87 and 0.97) and dry biomass (R 2 = 0.89 and 0.97) in 2018 and 2019, respectively. The VIs generally showed that it was possible to distinguish between 0 N and 168 N treatments, but neither 0 N and 56 N kg ha –1 nor 112 N and 168 N kg ha –1 could be separated. The results from this study highlight the importance of moderate application of N (112 kg N ha –1 ) in improving and maintaining the stand health and biomass yield of Miscanthus over time and suggest that mid-summer growing season VIs, NDRE in particular, can be useful for assessment of Miscanthus stand health and biomass yield.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Deep Convolutional Neural Networks Exploit High-Spatial- and -Temporal-Resolution Aerial Imagery to Phenotype Key Traits in Miscanthus

Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and a low environmental footprint. The increasing interest in this crop requires accelerated selection and the development of new screening techniques. New analytical methods that are more accurate and less labor-intensive are needed to better characterize the effects of genetics and the environment on key traits under field conditions. We used persistent multispectral and photogrammetric UAV time-series imagery collected 10 times over the season, together with ground-truth data for thousands of Miscanthus genotypes, to determine the flowering time, culm length, and biomass yield traits. We compared the performance of convolutional neural network (CNN) architectures that used image data from single dates (2D-spatial) versus the integration of multiple dates by 3D-spatiotemporal architectures. The ability of UAV-based remote sensing to rapidly and non-destructively assess large-scale genetic variation in flowering time, height, and biomass production was improved through the use of 3D-spatiotemporal CNN architectures versus 2D-spatial CNN architectures. The performance gains of the best 3D-spatiotemporal analyses compared to the best 2D-spatial architectures manifested in up to 23% improvements in R2, 17% reductions in RMSE, and 20% reductions in MAE. The integration of photogrammetric and spectral features with 3D architectures was crucial to the improved assessment of all traits. In conclusion, our findings demonstrate that the integration of high-spatiotemporal-resolution UAV imagery with 3D-CNNs enables more accurate monitoring of the dynamics of key phenological and yield-related crop traits. This is especially valuable in highly productive, perennial grass crops such as Miscanthus, where in-field phenotyping is especially challenging and traditionally limits the rate of crop improvement through breeding.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Designing Unmanned Aerial Survey Monitoring Program to Assess Floating Litter Contamination

Monitoring marine contamination by floating litter can be particularly challenging since debris are continuously moving over a large spatial extent pushed by currents, waves, and winds. Floating litter contamination have mostly relied on opportunistic surveys from vessels, modeling and, more recently, remote sensing with spectral analysis. This study explores how a low-cost commercial unmanned aircraft system equipped with a high-resolution RGB camera can be used as an alternative to conduct floating litter surveys in coastal waters or from vessels. The study compares different processing and analytical strategies and discusses operational constraints. Collected UAS images were analyzed using three different approaches: (i) manual counting (MC), using visual inspection and image annotation with object counts as a baseline; (ii) pixel-based detection, an automated color analysis process to assess overall contamination; and (iii) machine learning (ML), automated object detection and identification using state-of-the-art convolutional neural network (CNNs). Our findings illustrate that MC still remains the most precise method for classifying different floating objects. ML still has a heterogeneous performance in correctly identifying different classes of floating litter; however, it demonstrates promising results in detecting floating items, which can be leveraged to scale up monitoring efforts and be used in automated analysis of large sets of imagery to assess relative floating litter contamination.

Almeida, Sílvia (ORCID:0000000320636826)↗
DOE OSTI2024

Observing low-altitude features in ozone concentrations in a shoreline environment via uncrewed aerial systems

Abstract. Ozone is a pollutant formed in the atmosphere by photochemical processes involving nitrogen oxides (NOx) and volatile organic compounds (VOCs) when exposed to sunlight. Tropospheric boundary layer ozone is regularly measured at ground stations and sampled infrequently through balloon, lidar, and crewed aircraft platforms, which have demonstrated characteristic patterns with altitude. Here, to better resolve vertical profiles of ozone within the atmospheric boundary layer, we developed and evaluated an uncrewed aircraft system (UAS) platform for measuring ozone and meteorological parameters of temperature, pressure, and humidity. To evaluate this approach, a UAS was flown with a portable ozone monitor and a meteorological temperature and humidity sensor to compare to tall tower measurements in northern Wisconsin. In June 2020, as a part of the WiscoDISCO20 campaign, a DJI M600 hexacopter UAS was flown with the same sensors to measure Lake Michigan shoreline ozone concentrations. This latter UAS experiment revealed a low-altitude structure in ozone concentrations in a shoreline environment showing the highest ozone at altitudes from 20–100 m a.g.l. These first such measurements of low-altitude ozone via a UAS in the Great Lakes region revealed a very shallow layer of ozone-rich air lying above the surface.

Meteorology & Atmospheric Sciences↗
DOE OSTI2017

ARM Aerial Facility (AAF) - Unmanned Aircraft Systems, Fine Condensation Particle Counter via CVI Inlet, Quality Controlled

The Fine Condensation Particle Counter (or CPCF) measures sub-micron aerosol particle number concentration by condensing fluid onto particles and causing them to grow to sizes that are easily detectable by optical scattering. Counts particles with sizes that range from 10 to 3000 nanometers. Quality controlled and merged with AAF Nav data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ARM Aerial Facility (AAF) - Unmanned Aircraft Systems, Cloud Droplet Probe

The Cloud Droplet Probe (CDP) is designed to measure cloud droplet size distribution from 2 µm to 50 µm. The CDP and an appropriate data system can also calculate various other parameters including particle concentrations, effective diameter (ED), Median Volume Diameter (MVD), and Liquid Water Content (LWC).

54 ENVIRONMENTAL SCIENCES↗