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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
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 OSTI2023

Validating a Tethered Balloon System and Optical Technologies for Marine Wildlife Detection and Tracking

The interactions between marine wildlife and marine energy devices are not well understood, leading to regulatory delays for device deployments and testing. Technologies that enable marine wildlife observations can help to fill data gaps and reduce uncertainties about animal–device interactions. A validation test conducted in Galveston Bay near La Porte, Texas, in December 2022 used a technology package consisting of a tethered balloon system and three independent sensor systems, including three-band visible, eight-band multispectral, and single-band thermal to detect three marine-mammal-shaped surrogates. The field campaign aimed to provide an initial step to evaluating the use of the TBS and the effectiveness of the sensor suite for marine wildlife observations and detection. From 2 December to 7 December 2022, 6 flights were conducted under varying altitudes and environmental conditions resulting in the collection of 5454 images. A subset of the images was classified and analyzed with two collection criteria including Beaufort wind force scale and TBS altitude to assess a range of observations of a surrogate from near-shore to offshore based on pixel count. The results of this validation test demonstrate the potential for using TBSs and imaging sensors for marine wildlife observations and offer valuable information for further development and application of this technology for marine energy and other blue economy sectors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Remote Sensing Detection of Growing Season Freeze-Induced Defoliation of Montane Quaking Aspen (Populus tremuloides) in Southern Utah, USA

Growing season freeze events pose a threat to quaking aspen (Populus tremuloides Michx.), leading to canopy defoliation, reduced vigor, and increased mortality, especially for declining montane populations western North America. Detecting the spatial distribution and progression of this damage is challenging due to limited in situ observations in this region. This study represents the first attempt to comprehensively resolve the spatial extent of freeze-induced aspen canopy damage in southern Utah using multispectral remote sensing data. We developed an approach to detect the spatial and temporal dynamics of freeze-damaged aspen stands, focusing on a freeze event from 8–9 June 2020 in southern Utah. By integrating medium- (~250 to 500 m) and high-resolution (~10 m) satellite data, we employed the Normalized Difference Vegetation Index (NDVI) to compare post-freeze conditions with historical norms and pre-freeze conditions. Our analysis revealed NDVI reductions of 0.10 to 0.40 from pre-freeze values and a second flush recovery. We introduced a pixel-based method to evaluate freeze vulnerability, establishing a strong correlation (R values 0.78 to 0.82) between the onset of the first flush (NDVI > 0.50) and the accumulation of 100 growing degree days (GDD). These methods support the potential for retrospective assessments, proactive forest monitoring, and forecasting future risks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

On the Viability of Video Imaging in Leak Rate Quantification: A Theoretical Error Analysis

Optical gas imaging through multispectral cameras is a promising technique for mitigation of methane emissions through localization and quantification of emissions sources. While more advanced cameras developed in recent years have led to lower uncertainties in measuring gas concentrations, a systematic analysis of the uncertainties associated with leak rate estimation have been overlooked. We present a systematic categorization of the involved uncertainties with a focus on a theoretical analysis of projection uncertainties that are inherent to this technique. The projection uncertainties are then quantified using Large Eddy Simulation experiments of a point source release into the atmosphere. Our results show that while projection uncertainties are typically about 5% of the emission rate, low acquisition times and observation of the gas plume at small distances from the emission source (<10 m) can amount to errors of about 20%. Further, we found that acquisition times on the order of tens of seconds are sufficient to significantly reduce (>50%) the projection uncertainties. These findings suggest robust procedures on how to reduce projection uncertainties, however, a balance between other sources of uncertainty due to operational conditions and the employed instrumentation are required to outline more practical guidelines.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Lower-tropospheric CO 2 from near-infrared ACOS-GOSAT observations

We present two new products from near-infrared Greenhouse Gases Observing Satellite (GOSAT) observations: lowermost tropospheric (LMT, from 0 to 2.5 km) and upper tropospheric–stratospheric ( U , above 2.5 km) carbon dioxide partial column mixing ratios. We compare these new products to aircraft profiles and remote surface flask measurements and find that the seasonal and year-to-year variations in the new partial column mixing ratios significantly improve upon the Atmospheric CO 2 Observations from Space (ACOS) and GOSAT (ACOS-GOSAT) initial guess and/or a priori, with distinct patterns in the LMT and U seasonal cycles that match validation data. For land monthly averages, we find errors of 1.9, 0.7, and 0.8 ppm for retrieved GOSAT LMT, U , and X CO 2 ; for ocean monthly averages, we find errors of 0.7, 0.5, and 0.5 ppm for retrieved GOSAT LMT, U , and X CO 2 . In the southern hemispheric biomass burning season, the new partial columns show similar patterns to MODIS fire maps and MOPITT multispectral CO for both vertical levels, despite a flat ACOS-GOSAT prior, and a CO–CO 2 emission factor comparable to published values. The difference of LMT and U , useful for evaluation of model transport error, has also been validated with a monthly average error of 0.8 (1.4) ppm for ocean (land). LMT is more locally influenced than U , meaning that local fluxes can now be better separated from CO 2 transported from far away.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Marine and continental stratocumulus cloud microphysical properties obtained from routine ARM Cimel sunphotometer observations

This study investigates marine and continental stratocumulus (Sc) cloud properties obtained from an automated implementation of a multispectral photometer retrieval. Photometer methods simultaneously retrieve cloud optical depth (τ) and cloud droplet effective radius (r e ), with estimates for liquid water path (LWP) calculated on the availability of those quantities. These applied methods evaluate retrieved cloud properties for Sc identified during a recent 6 year period over the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program sites in Oklahoma, USA (SGP) and in the Azores, Portugal (ENA). Modest agreement in key quantity retrievals is found between the routine photometer products and multisensor collocated profiling references. Cumulative breakdowns contingent on cloud thickness indicate increases in all retrieved quantities in thicker clouds, with larger discrepancies in the relative performance between the retrievals collected in the presence of drizzle. Under continental cloud conditions, the clouds of a similar thickness and r e to those sampled under marine conditions report a factor of 1.5 larger τ and LWP. An r 2 ≅0.65 is found between photometer τ retrievals and shadowband radiometer measurements, with photometer retrievals reporting a high (relative) bias. The τ intercomparisons indicate that variability between retrievals is a factor of three larger than errors reported from individual retrieval input perturbation tests. Photometer r e retrievals suggest a low r 2 (< 0.1) having a standard deviation ≅ 3 µm when compared to ARM baseline multi-sensor radar/radiometer references (accounting for offsets in the cloud droplet number concentration assumptions of the latter). However, photometer LWP calculations remain relatively unbiased in non-drizzling conditions, with errors O (50 g m −2 ) and r 2 ≅0.5 to collocated radiometer and interferometer references. Additional sensitivity tests for island influences on marine Sc properties suggest that while island-influenced winds may promote larger cloud LWP or thickness, the influence could be within retrieval method uncertainty and/or collocated instrument variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Improving Building Footprint Extraction Using NAIP and 3DEP Lidar Derived Features with Deep Learning

Accurate building footprint extraction is critical for applications ranging from population estimation to disaster management. Although optical imagery provides detailed spectral information, it often struggles with shadows, occlusions, and background clutter in dense urban environments. Lidar data, by contrast, offer precise elevation and structural attributes but face challenges such as variable point density and noise. This study integrates multispectral imagery from the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) with lidar-derived feature height and intensity from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) to improve footprint extraction using a U-Net–based deep learning model. A six-band input stack (RGB, near-infrared, height, intensity) was developed, normalized, and tiled for training and evaluation against Microsoft Global Building Footprints (GBF). Results from the Houston, TX test site show that the six-band model achieved a precision of 0.86, recall of 0.88, F1 score of 0.87, and Intersection-over-Union (IoU) of 0.76, consistently outperforming four-band baselines by reducing false positives while maintaining sensitivity. Predictions on withheld Houston tiles confirmed strong within-region generalization, yielded a precision of 0.78, recall of 0.81, F1 score of 0.79, and IoU of 0.66. Qualitative analysis further revealed limitations stemming from both training label quality and vegetation–building confusion. These findings demonstrate the complementary value of integrating spectral and structural information for robust building footprint extraction and how domain adaptation strategies can be used to enhance cross-regional transferability.

Liu, Jung Kuan [United States Geological Survey (U↗
DOE OSTI2019

Integration and operation of a sUAS-based multi-modal imaging payload equipped with a novel communication system enabling autonomous platform control

Oak Ridge National Laboratory presents a new UAS-mounted multi-modal imaging payload containing five sensors. We have integrated several distinct commercially available sensors onto a large Class-1 autonomous quadcopter aircraft: a LIDAR (Light Detection and Ranging) scanner, a hyperspectral pushbroom sensor, a multispectral camera, a longwave infrared thermal camera, and an RGB camera. The system integrates our proprietary Multi-modal Autonomous Vehicle Network (MAVNet) and communication system, allowing autonomous control via multiple communication networks. Using one common Global Navigation Satellite System (GNSS) and inertial navigation system (INS/GPS), imagery from all sensors are accurately and precisely geolocated and co-registered.

Duncan, Andrew↗
DOE OSTI2016

XDV: A proposal for an X-ray Doppler Velocimetry diagnostic

Presentation describes proposal to arrange several bent crystals, each tuned to a different center wavelength within the motional Doppler-broadened profile of a suitable x-ray emission line, to produce a multispectral image that maps wavelength shift to line-of-sight velocity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection

One of the fundamental assumptions of compressive sensing (CS) is that a signal can be reconstructed from a small number of samples by solving an optimization problem with the appropriate regularization term. Two standard regularization terms are the L1 norm and the total variation (TV) norm. We present a comparison of CS reconstruction results based on these two approaches in the context of chemical detection, and we demonstrate that optimization based on the L1 norm outperforms optimization based on the TV norm. Our comparison is driven by CS sampling, reconstruction, and chemical detection in two real-world datasets: the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset and the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Both datasets contain the release of a chemical simulant such as glacial acetic acid, triethyl phosphate, and sulfur hexafluoride. For chemical detection we use the adaptive coherence estimator (ACE) and bulk coherence, and we propose algorithmic ACE thresholds to define the presence or absence of a chemical of interest in both un-compressed data cubes and reconstructed data cubes. The un-compressed data cubes provide an approximate ground truth. We demonstrate that optimization based on either the L1 norm or TV norm results in successful chemical detection at a compression rate of 90%, but we show that L1 optimization is preferable. We present quantitative comparisons of chemical detection on reconstructions from the two methods, with an emphasis on the number of pixels with an ACE value above the threshold.

Hyperspectral imagery, L1-norm, total variation, A↗
DOE OSTI2020

Window obscuration sensors for mobile gas and chemical imaging cameras

An infrared (IR) imaging system for determining a concentration of a target species in an object is disclosed. The imaging system can include an optical system including a focal plane array (FPA) unit behind an optical window. The optical system can have components defining at least two optical channels thereof, said at least two optical channels being spatially and spectrally different from one another. Each of the at least two optical channels can be positioned to transfer IR radiation incident on the optical system towards the optical FPA. The system can include a processing unit containing a processor that can be configured to acquire multispectral optical data representing said target species from the IR radiation received at the optical FPA. One or more of the optical channels may be used in detecting objects on or near the optical window, to avoid false detections of said target species.

Mallery, Ryan↗
DOE OSTI2022

Window obscuration sensors for mobile gas and chemical imaging cameras

An infrared (IR) imaging system for determining a concentration of a target species in an object is disclosed. The imaging system can include an optical system including a focal plane array (FPA) unit behind an optical window. The optical system can have components defining at least two optical channels thereof, said at least two optical channels being spatially and spectrally different from one another. Each of the at least two optical channels can be positioned to transfer IR radiation incident on the optical system towards the optical FPA. The system can include a processing unit containing a processor that can be configured to acquire multispectral optical data representing said target species from the IR radiation received at the optical FPA. One or more of the optical channels may be used in detecting objects on or near the optical window, to avoid false detections of said target species.

Mallery, Ryan↗
DOE OSTI2023

Window obscuration sensors for mobile gas and chemical imaging cameras

An infrared (IR) imaging system for determining a concentration of a target species in an object is disclosed. The imaging system can include an optical system including a focal plane array (FPA) unit behind an optical window. The optical system can have components defining at least two optical channels thereof, said at least two optical channels being spatially and spectrally different from one another. Each of the at least two optical channels can be positioned to transfer IR radiation incident on the optical system towards the optical FPA. The system can include a processing unit containing a processor that can be configured to acquire multispectral optical data representing said target species from the IR radiation received at the optical FPA. One or more of the optical channels may be used in detecting objects on or near the optical window, to avoid false detections of said target species.

Mallery, Ryan↗
DOE OSTI2023

Window obscuration sensors for mobile gas and chemical imaging cameras

An infrared (IR) imaging system for determining a concentration of a target species in an object is disclosed. The imaging system can include an optical system including a focal plane array (FPA) unit behind an optical window. The optical system can have components defining at least two optical channels thereof, said at least two optical channels being spatially and spectrally different from one another. Each of the at least two optical channels can be positioned to transfer IR radiation incident on the optical system towards the optical FPA. The system can include a processing unit containing a processor that can be configured to acquire multispectral optical data representing said target species from the IR radiation received at the optical FPA. One or more of the optical channels may be used in detecting objects on or near the optical window, to avoid false detections of said target species.

Mallery, Ryan↗
DOE OSTI2017

Effects of Rangeland Evapotranspiration on Groundwater Recharge, Discharge, and Flow at the Tuba City, Arizona, Disposal Site - 17159

The US Department of Energy Office of Legacy Management is modeling groundwater flow and contaminant transport at a former uranium mill site near Tuba City, Arizona. A goal is to project groundwater travel times and flow volumes between the former mill site and Moenkopi Wash, a down-gradient stream. Aquifer recharge and discharge are sensitive model parameters; however, assigning representative rates is inexact and involves approximations. We applied a remote sensing algorithm to determine spatially variable evapotranspiration (ET), precipitation recharge, and groundwater discharge rates for use in a large scale groundwater flow model for the site. ET is the combination of evaporation and plant transpiration from soil and groundwater. We estimated landscape-scale ET over a 13-year period (2000 to 2012) for distinct plant communities within a 3531-hectare groundwater model domain (GMD), and then evaluated effects of ET on groundwater recharge and discharge within the GMD. Our empirical algorithm was derived from ground ET measurements, multispectral satellite imagery, and temperature data. Groundwater recharge or discharge rates were calculated for each plant community (or ET Zone) as the difference between precipitation (PPT) and ET rates. Recharge occurred in plant communities where PPT exceeded ET; discharge occurred where ET exceeded PPT. Estimates of groundwater recharge rates for upland plant communities that survive on meteoric water ranged from 0 to 88 mm yr{sup -1}. Estimates of groundwater discharge for phreatophyte plant communities ranged from 23 mm yr{sup -1} to 150 mm yr{sup -1}. Phreatophytes are plants that survive by extracting groundwater. Zero recharge occurred in plant communities where ET rates equaled annual average precipitation. An increase in groundwater recharge (PPT > ET) was associated with past land disturbances and heavy livestock grazing in upland areas. Groundwater discharge (ET > PPT) was highest in riparian phreatophyte communities, but lower than optimal in upland phreatophyte communities impacted by heavy grazing. Protecting phreatophyte communities from grazing at Tuba City could potentially increase ET from 153 mm yr{sup -1} to at least 500 mm yr{sup -1}. The validity of using the ET algorithm to estimate recharge and discharge was evaluated by comparing the net volumetric outflow rate for the model domain with base flow measurements collected from Moenkopi Wash. Modeled outflow approximated measured flow gains in the Wash, increasing our confidence in the ET algorithm and in the model. Results suggest that rangeland management practices that reduce groundwater recharge rates and increase groundwater discharge rates should be evaluated as part of an overall groundwater remediation strategy. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Window obscuration sensors for mobile gas and chemical imaging cameras

An infrared (IR) imaging system for determining a concentration of a target species in an object is disclosed. The imaging system can include an optical system including a focal plane array (FPA) unit behind an optical window. The optical system can have components defining at least two optical channels thereof, said at least two optical channels being spatially and spectrally different from one another. Each of the at least two optical channels can be positioned to transfer IR radiation incident on the optical system towards the optical FPA. The system can include a processing unit containing a processor that can be configured to acquire multispectral optical data representing said target species from the IR radiation received at the optical FPA. One or more of the optical channels may be used in detecting objects on or near the optical window, to avoid false detections of said target species.

Mallery, Ryan↗
DOE OSTI2024

Window obscuration sensors for mobile gas and chemical imaging cameras

An infrared (IR) imaging system for determining a concentration of a target species in an object is disclosed. The imaging system can include an optical system including a focal plane array (FPA) unit behind an optical window. The optical system can have components defining at least two optical channels thereof, said at least two optical channels being spatially and spectrally different from one another. Each of the at least two optical channels can be positioned to transfer IR radiation incident on the optical system towards the optical FPA. The system can include a processing unit containing a processor that can be configured to acquire multispectral optical data representing said target species from the IR radiation received at the optical FPA. One or more of the optical channels may be used in detecting objects on or near the optical window, to avoid false detections of said target species.

Mallery, Ryan↗
DOE OSTI2023

BlueHat

"Bluehat" is a collection of python routines to facilitate the analysis of spectral (multispectral and hyperspectral) remote sensing imagery, with an emphasis on various kind of target detection. The package provides collections of utilities that developers can use in producing algorithms for specific target detection scenarios. Although "bluehat" is mostly expected to be used for remote sensing imagery (which often comes from satellite sensors), and although early versions of "bluehat" were used to design algorithms that were then rewritten for onboard processing; "bluehat" itself is not not specially designed for space and/or satellite application.

Theiler, James↗