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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

NDVI and Multispectral Images, UAS Flights, Council Road Site, Seward Peninsula, Alaska, June 2022

The dataset contains Normalized Difference Vegetation Index (NDVI) and multispectral images collected on June 18, 2022 using an UAS (Unmanned Aerial System) Yuneec H520, Mapir Survey 3, and Parrot Sequoia cameras. This data was created to accompany the following datasets:- NGEE Arctic CO2, CH4 and Energy Eddy-Covariance (EC) Flux Tower Auxiliary Measurements, Council Road Mile Marker 71, Seward Peninsula, Alaska (https://doi.org/10.5440/1526749)- Sun-induced chlorophyll fluorescence/eddy covariance measurements at NGEE Arctic Council site, Seward Peninsula, Alaska, 2022 (https://doi.org/10.15485/2466175)- NGEE Arctic Meteorological Data from Instrumented PCDC Stations, Council Site, Seward Peninsula, Alaska, 2018 - 2023 (https://doi.org/10.5440/1529604)Survey 3 is RGN (Red, Greeen, Near Infra Red) camera with 87° Horizontal Field of View (HFOV) (19mm) f/2.8 Aperture, 550nm/660nm/850nm (Red+Green+NIR) filter transmission and produced geo referenced jpg and raw format images. Parrot Sequoia is a camera with 16 megapixel RGB (Red, Green, Blue) sensor and 1.2 megapixel monochrome sensors in discrete spectral bands: Green (550nm - 40Nm Bandwidth), Red (660nm - 40nm Bandwidth), Red Edge (735nm - 10nm Bandwidth) and Near Infrared (790nm - 40nm Bandwidth).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 OSTI2022

Multispectral and Thermal Imager Onboard Aerial Platforms Instrument Handbook

The MicaSense Altum imager is an off-the-shelf, synchronized multispectral and thermal camera, combined with a Global Positioning System (GPS) unit and downwelling light sensor (DLS). The Altum takes images of the land surface within its field of view (FOV) across five visible bands and one longwave infrared thermal band. The sensor records the radiance and converts it to digital numbers. Imagery is radiometrically corrected, taking into account the sensor calibration, lens distortions, vignette effects, sun angle, and atmospheric effects (scattering and absorption). The photogrammetry software Agisoft PhotoScan v 1.4 is used to align and stitch the images into a larger composite image using the technique of structure from motion image capture to construct a dense cloud and 3D model of the surface, which is used to produce a digital elevation model of the terrain surveyed and orthomosaic imagery. Land surface leaf area index, albedo, and surface skin temperature are provided for the user. The user can perform raster calculations on the imagery to produce various other vegetative indices, which are used to indicate plant health and land surface characteristics, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Water Index (NDWI), surface temperature, and Enhanced_vegetation_index (EVI). This code is provided with the readme file for each data set. The images have a resolution of 20-60 cm/pixel, which are subsampled to 100 cm/pixel.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Predicting Switchgrass Biomass Yields Using a Spectral Vegetation Index Derived from Multispectral Satellite Imagery

Successful scaling of perennial bioenergy crop production requires a landscape design that optimizes the benefits of finite lands for people, communities, and environments. Utilizing marginal areas is the key to sustainable bioenergy crop production (Ssegane et al., 2015, 2016). Marginal areas are often small-sized lands and unevenly distributed across the agricultural landscape (Ssegane et al., 2016); thus, a systematic, semi-automated remote sensing method is needed as an effective means of estimating bioenergy crop yields across landscapes. Argonne National Laboratory (Argonne) is currently developing a tool, Scaling Up Perennial Bioenergy Economics and Ecosystem Services Tool (SUPERBEEST), to identify marginal agricultural lands and quantify environmental and economic effects of perennial bioenergy crop production systems. The tool aims to provide users a path to foster the sustainable and productive integration of bioenergy crops in the Midwestern agricultural landscape. Reliable, cost-effective, and timely estimation of bioenergy crop yields using remote sensing would help calculate and track the success of integrated bioenergy crops in the landscape for those communities. Argonne previously conducted feasibility studies for estimating biomass yields for bioenergy feedstock, corn and perennial grass using spectral vegetation indices (SVIs)1 derived from optical imagery (Hamada et al., 2015, 2021). In both studies, SVIs, more specifically those sensitive to plant chlorophyll or nitrogen contents, showed potential for estimating or predicting biomass yields with a correlation of determination (R2) ranging from 0.54 to 0.96, indicating a value for further investigation as a viable means of quantifying bioenergy feedstock production across large landscapes. Thus, the goal of this study is to evaluate the feasibility of use of SVIs as a means of estimating or predicting switchgrass biomass yields at harvest using publicly available multispectral satellite imagery. The feasibility analysis was performed using four study areas of mature switchgrass located in Virginia. Objectives are to (1) examine the SVIs and establish their relationships with switchgrass biomass yields at harvest, (2) develop a parsimonious image processing model for predicting at-harvest yields by applying the relationships with the most promising spectral index and (3) map switchgrass yields predicted by the image processing model across the study sites. The calibration to field data will rely on switchgrass biomass yields determined by the baling method, representing a potential challenge to the analysis but an important practical aspect for future applications. With this research design, the study aimed to gain insights into enabling remote sensing-based estimation of bioenergy crop yields in a reliable, cost- effective, and timely manner across large, heterogeneous landscapes.

09 BIOMASS FUELS↗
DOE OSTI2023

Multispectral and thermal surface imagery and surface elevation mosaics - SGP July 2022

This data set contains high-resolution image products (orthomosaics) acquired from midsized uncrewed aerial systems that have been processed for value-added quality. The instrument itself, a multispectral imager, the Altum by Micasense, captures six spectral bands (red, green blue, NIR, red edge, and LWIR/thermal1) as radiance, which is converted to reflectance via custom code. The code used to develop these images first uses tools from the Micasense Python library2 to apply dark level corrections, row gradient corrections, and radiometric corrections. Next, it uses the processing API from Agisoft Metashape software to align and mosaic the processed imagery, following the processes developed by the USGS' structure from motion workflow documentation.3 Captures at different altitudes (recorded in MSL) produce an orthomosaic, a tif image containing information related to the six spectral bands, and a digital elevation model (DEM), a tif image containing information related to the elevation of the surveyed terraine. Metadata included in every image can be used to extract lat, lon, and reflectance values. 1 https://www.arm.gov/publications/tech_reports/handbooks/doe-sc-arm-tr-281.pdf 2 https://micasense.github.io/imageprocessing/MicaSense%20Image%20Processing%20Setup.html 3 https://pubs.usgs.gov/of/2021/1039/ofr20211039.pdf

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multispectral and thermal surface imagery and surface elevation mosaics - Pendleton Feb 2023

This data set contains high-resolution image products (orthomosaics) acquired from midsized uncrewed aerial systems that have been processed for value-added quality. The instrument Altum multispectral imager by Micasense, captures in six bands (blue - 475nm, green - 560nm, red - 668nm, red edge - 717nm, near-infrared - 840 and LWIR/thermal - 11000nm. The optical bands are converted to reflectance via custom code using the instantaneous band horizontal irradiance ratio to the radiance of the pixel. The code used to develop these images first uses tools from the Micasense Python library to apply dark level corrections, row gradient corrections, and radiometric corrections. Next, it uses the processing API from Agisoft Metashape software to align and mosaic the processed imagery, following the processes developed by the USGS' structure from motion workflow documentation. Captures from different altitudes are used to produce an orthomosaic at each height. A tif image containing information related to the six spectral bands, and a digital elevation model (DEM), a tif image containing information related to the elevation of the surveyed terrain. 1 https://www.arm.gov/publications/tech_reports/handbooks/doe-sc-arm-tr-281.pdf 2 https://micasense.github.io/imageprocessing/MicaSense%20Image%20Processing%20Setup.html 3 https://pubs.usgs.gov/of/2021/1039/ofr20211039.pdf

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multispectral impedance determination under dynamic load conditions

Impedance testing devices, circuits, systems, and related methods are disclosed. A Device Under Test (DUT) is excited with a multispectral excitation signal for an excitation time period while the DUT is under a load condition from a load operably coupled to the DUT. A response of the DUT is sampled over a sample time period. The sample time period is configured such that it includes an in-band interval during the excitation time period and one or more out-of-band intervals outside of the in-band interval. A response of the DUT to the load condition during the in-band interval is estimated by analyzing samples of the response from the one or more out-of-band intervals. Adjusted samples are computed by subtracting the estimated load response during the in-band interval from the samples from the in-band interval. An impedance of the DUT is estimated by analyzing the adjusted samples.

Christophersen, Jon P.↗
DOE OSTI2023

Voltage‐Modulated Untwist Deformations and Multispectral Optical Effects from Ion Intercalation into Chiral Ceramic Nanoparticles

Abstract Reconfiguration of chiral ceramic nanostructures after ion intercalation should favor specific nanoscale twists leading to strong chiroptical effects. In this work, V 2 O 3 nanoparticles are shown to have “built‐in” chiral distortions caused by binding of tartaric acid enantiomers to the nanoparticle surface. As evidenced by spectroscopy/microscopy techniques and calculations of nanoscale chirality measures, the intercalation of Zn 2+ ions into the V 2 O 3 lattice results in particle expansion, untwist deformations, and chirality reduction. Coherent deformations in the particle ensemble manifest as changes in sign and positions of circular polarization bands at ultraviolet, visible, mid‐infrared (IR), near‐IR (NIR), and IR wavelengths. The g‐ factors observed for IR and NIR spectral diapasons are ≈100–400 times higher than those for previously reported dielectric, semiconductor, and plasmonic nanoparticles. Nanocomposite films layer‐by‐layer assembled (LBL) from V 2 O 3 nanoparticles reveal cyclic‐voltage‐driven modulation of optical activity. Device prototypes for IR and NIR range problematic for liquid crystals and other organic materials are demonstrated. High optical activity, synthetic simplicity, sustainable processability, and environmental robustness of the chiral LBL nanocomposites provide a versatile platform for photonic devices. Similar reconfigurations of particle shapes are expected for multiple chiral ceramic nanostructures, leading to unique optical, electrical, and magnetic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs

Abstract Graph learning, when used as a semi-supervised learning (SSL) method, performs well for classification tasks with a low label rate. We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi- or hyperspectral image segmentation. Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification. This work builds on recent advances in the design of novel active learning acquisition functions (e.g., the Model Change approach in arXiv:2110.07739) while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods. In addition to improvements in the accuracy, our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Isolating the Vibrational Spectra of the Red Chlorophylls in Photosystem I with Multispectral Two-Dimensional Spectroscopy

Photosystem I (PSI) uses an antenna of chlorophyll (Chl) molecules to create a charge separated state with high quantum efficiency. Understanding the charge separation mechanism is currently hindered by spectral overlap between the antenna and reaction center (RC) Chls and the fact that energy transfer and electron transfer occur with similar time scales. Here, we characterize the antenna excited states by applying two-dimensional electronic (2DES) and two-dimensional electronic-vibrational (2DEV) spectroscopy to PSI complexes with closed RCs. Comparison of the 2DES and 2DEV spectra, which evolve with the same kinetics, enabled characterization of the vibrational modes of the antenna during energy equilibration between spectrally distinct Chls. Through global analysis, we learn how energy transfer between the Bulk and Red Chls presents in the 2DEV spectra and we definitively identify vibrations of the cationic components of the mixed exciton and intermolecular charge transfer states associated with the Red Chls. This work enables future studies of the initial charge separation mechanism of PSI by 2DEV spectroscopy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer

Curbing methane emissions is among the most effective actions that can be taken to slow down global warming. However, monitoring emissions remains challenging, as detection methods have a limited quantification completeness due to trade-offs that have to be made between coverage, resolution and detection accuracy. Here we show that deep learning can overcome the trade-off in terms of spectral resolution that comes with multi-spectral satellite data, resulting in a methane detection tool with global coverage and high temporal and spatial resolution. We compare our detections with airborne methane measurement campaigns, which suggests that our method can detect methane point sources in Sentinel-2 data down to plumes of 0.01 km 2 , corresponding to 200 to 300 kg CH 4 /h -1 sources. Our model shows an order of magnitude improvement over the state-of-the-art, providing a significant step towards the automated, high resolution detection of methane emissions at a global scale, every few days.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Thermal, RGB, and Multispectral Unexploded Ordnance Data Collection

As of May 1, 2026 Landmine contamination affects 58 countries and many hundreds of thousands of km² of land. For example, the National Mine Action Program Demining Ukraine reporting that up to 144,000 km² of territory are potentially contaminated and require survey and clearance (National Mine Action Program “Demining Ukraine,” n.d.) alone. This contamination includes mines and other explosive remnants of war and continues to constrain civilian access, agricultural use, infrastructure recovery, and broader socioeconomic activity (International Campaign to Ban Landmines–Cluster Munition Coalition [ICBL-CMC], 2024; Mine Action Review, 2024). Current response activities rely on established mine-action approaches including non-technical survey, technical survey, clearance, and explosive ordnance disposal, consistent with international mine-action terminology and operational practice (United Nations Mine Action Service [UNMAS], 2024; Geneva International Centre for Humanitarian Demining [GICHD], 2023). In this context, UAV-based sensing, including UAV-mounted thermal imaging, may provide a useful supplementary capability by supporting faster, safer detection and mapping of suspect hazards prior to ground intervention (Smiljanic, 2022).

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Temporal Analysis and Scene Change Detection in Multispectral Overhead Imagery

Scene change detection can be a tedious and time consuming process especially when concerning large geographical areas, and the process can be even more cumbersome when analyzing changes in an area over large spans of time. Developing a useful way to help analysts recognize at what points in time significant changes to a scene have occurred can allow them to better focus their efforts in characterizing events. Applications include: Facility monitoring, Construction chronology, Monitoring of vehicle/aircraft activity, Characterization of larger sequences of events. In large areas exceeding hundreds to thousands of square kilometers in size, it can be difficult localizing when scene changes have occurred. Analysts can spend hours going through imagery to try to identify new construction, monitor facility activities, monitor vehicle movement, etc. where the object of interest may only be a few square meters. Our goal is to help cut down this time by giving analysts change maps with hot spots of change, allowing them to focus on regions that have experienced actual change in time frames they're interested in. Additionally, by combining these change maps into layers within a data cube, analysts can examine the change maps from a temporal perspective, allowing events to be characterized over spans of time. By opening the data cube in an imaging software capable of separating the layers, we can analyze the change maps sequentially, allowing us to examine scene changes occurring over time. As an example, we examined overhead imagery from Planet Labs of what appears to be a parking lot on Fort Irwin over the course of a year using ENVI, a geospatial satellite imagery analysis software. Using ENVI, we generate a graph of changes over time, and notice a particular segment near the end of our analysis window where no changes are detected. Examination of the actual satellite imagery reveals that during this time span, the parking lot was empty. This could be due to facility shutdown for maintenance or upgrades, or possibly even total workforce/vehicle fleet movement. Information like this could help analysts better characterize events, as well as to help create clearer timelines in larger sequences of events. Workflow steps: - Collect multiple maps of the same AOI (Area of Interest) during a time span of interest; - Generate change maps from AOI maps; - Generate data cube from change maps. An analyst can use the data cube to help inspect an AOI for activities within a time span of interest. If an event of interest is discovered, the analyst can then refer to the maps corresponding to the appropriate dates and times in the data cube to see precisely what is transpiring. The biggest objective being worked on is improving the change detection methodology employed. We currently use PCA-EM (Principal Component Analysis with Expectation Maximization), but we are currently focusing on implementing IR-MAD (Iteratively Reweighted Multivariate Alteration Detection) to be used in conjunction with PCA-EM in an effort to decrease false positivity and noise in the change maps we generate.

42 ENGINEERING↗