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This presentation will be presented at FY 2020 SDRD Annual (Webex) Meeting scheduled for September 23–24, 2020. A Spectral Evaluation of the Application of Super-Resolution to Commercial Satellite Imagery, STL-010-18, Year 3
This presentation contains photographs of the integration of the Gunsmoke-J Flight Unit 4 satellite at the VOX Space, Inc., facility located in Long Beach California. The photographs were taken on April 29, 2021. VOX Space has granted their approval for release if LANL and the DoD sponsor also concur.
Understanding and quantifying the performance loss rate (PLR) of PV plants is crucial for predicting energy production, improving future designs and construction practices and optimizing maintenance activities. One difficulty in PLR analysis is ensuring irradiance sensor accuracy, since unaccounted for sensor drift can cause a perceived shift in performance similar in magnitude to typical PLR values. This study compares the calculated PLR values for a set of 24 large-scale plants (calculated for each of the 1132 inverters) using ground-based, satellite-based, and clear sky irradiance to examine the impact.
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.
Many regions within Georgia, South Carolina, and North Carolina experienced record-breaking rainfall and catastrophic winds due to Hurricane Helene. Helene made landfall on Florida’s big bend on September 26 th , 2024, as a category 4 hurricane, and tracked northward through Georgia and the southern Appalachian Mountains before dissipating on September 29 th , 2024. One significant impact of the hurricane was severe damage to tree canopies across the southeastern United States. This study utilizes satellite-based remotely sensed data provided by the National Aeronautical and Space Administration (NASA) to examine the resilience of these tree canopies following the hurricane. Specifically, the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Cover Type, Soil Moisture Active Passive (SMAP), and the Global Precipitation Measurement (GPM) mission datasets were employed to study the tree canopy’s response to such events in the Central Savannah River Area (CSRA). A significant increase was observed in the LAI in November of 2024. This increase in LAI was likely influenced by warmer-than-average temperatures throughout October of 2024, along with a second record-breaking rainfall event in the CSRA on November 6th, 2024. The LAI increase was then broken down into land cover type to understand which areas contributed most. The complex nature and resilience of trees became evident, offering opportunities for further exploration and application in urban planning, emergency response, and environmental management.
High-resolution population modeling is important for supporting effective decision-making across diverse sectors. LandScan Mosaic generates population estimates at the level of individual buildings and aggregates them to 3 arc-second grids, and this approach performs well in regions where building footprint data are comprehensive and reliable. However, large portions of the globe still suffer from incomplete, sparse, or entirely missing building stock datasets, creating a structural limitation for strictly building-based population models. To address this research gap, this study proposes a computer vision-based framework that employs Google Earth Engine satellite embeddings and UNet, which allows us to directly impute grid-level population estimates in building-data-deficient areas. Applied to Taiwan as a case study, the framework achieved strong predictive performance with R$^{2}$ of 0.89, RMSE of 18.70, and MAE of 8.41, outperforming traditional machine learning approaches. Notably, the proposed framework effectively addressed building false-positive errors inherent in Global Human Settlement Layer (GHSL) data, correctly identifying uninhabited areas that were erroneously classified as populated. The framework also offers significant advantages for global population mapping, particularly in terms of scalability and temporal consistency, thereby extending the coverage and accuracy of high-resolution population products in data-scarce regions worldwide. Urban planners, decision makers, and related stakeholders can obtain granular population distributions to support more accurate and targeted infrastructure investment, service delivery, resource allocation, and risk assessment decisions.
The objective of this program is to create basin-specific methane emissions inventories of the Haynesville and Anadarko (Woodford shale) basins, based on truly basin-scale data collection and analysis. Emissions of natural gas, predominantly composed of methane, present a significant financial loss to the oil and gas sector and reduce the competitiveness of American energy sources. While large emissions, above 10 kg/h, occur at only a small percentage of oil and gas sites, they account for a disproportionately large volume of emissions. To address this, we executed a concurrent campaign combining cutting-edge bottom-up modeling with top-down aerial and satellite surveys to capture the heavy-tailed distribution of these large emissions to accurately characterize total emissions throughout both regions.
The Geostationary Operational Environmental Satellite (GOES) records sky brightness temperatures and irradiances at several wavelengths. From these the cloud microphysical and optical properties can be routinely retrieved. These properties are derived using the technique proposed by Minnis et al. 2020, and is similar to the technique used for deriving cloud properties from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The derived GOES properties for the duration of the second Wind Forecasting Improvement Project (WFIP2) within the domain are reported here. Nominally the retrieved dataset has a 30- minute temporal and a 9 km spatial resolution.
Data from the SENTINEL-1 satellite constellation (SENTINEL-1a and SENTINEL-1b), from PNNL.
Satellite imagery data set containing labels for soalr installation mounting configuration.
This study utilizes satellite data to detect undocumented oil and gas wells, which pose significant environmental concerns, including greenhouse gas emissions. Three key findings emerge from the study. Firstly, the problem of imbalanced data is addressed by recommending oversampling techniques like Rotation–GaussianBlur–Solarization data augmentation (RGS), the Synthetic Minority Over-Sampling Technique (SMOTE), or ADASYN (an extension of SMOTE) over undersampling techniques. The performance of borderline SMOTE is less effective than that of the rest of the oversampling techniques, as its performance relies heavily on the quality and distribution of data near the decision boundary. Secondly, incorporating pre-trained models trained on large-scale datasets enhances the models’ generalization ability, with models trained on one county’s dataset demonstrating high overall accuracy, recall, and F1 scores that can be extended to other areas. This transferability of models allows for wider application. Lastly, including persistent homology (PH) as an additional input improves performance for in-distribution testing but may affect the model’s generalization for out-of-distribution testing. A careful consideration of PH’s impact on overall performance and generalizability is recommended. Overall, this study provides a robust approach to identifying undocumented oil and gas wells, contributing to the acceleration of a net-zero economy and supporting environmental sustainability efforts.
The Cape Canaveral Barrier Island, home to the National Aeronautics and Space Administration (NASA)’s Kennedy Space Center and the United States (U.S.) Space Force’s Cape Canaveral Space Force Station, is situated in a unique ecological transition zone that supports diverse wildlife. This study evaluates the recent changes in vegetation cover (2016–2023) and dune elevation (2007–2017) within the Cape Canaveral Barrier Island using high-resolution optical satellite and light detection and ranging (LiDAR) data. The study period was chosen to depict the time period of a recent increase in rocket launches. The study objectives include assessing changes in vegetation communities, identifying detectable impacts of liquid propellant launches on nearby vegetation, and evaluating dune elevation and tide level shifts near launchpads. The results indicate vegetation cover changes, including mangrove expansion in wetland areas and the conversion of coastal strands to denser scrubs and hardwood forests, which were likely influenced by mild winters and fire management. While detectable impacts of rocket launches on nearby vegetation were observed, they were less severe than those caused by solid rocket motors. Compounding challenges, such as rising tide levels, beach erosion, and wetland loss, potentially threaten the resilience of launch operations and the surrounding habitats. The volume and scale of launches continue to increase, and a balance between space exploration and ecological conservation is required in this biodiverse region. This study focuses on the assessment of barrier islands’ shorelines.
Stellar and black hole feedback heat and disperse surrounding cold gas clouds, launching gas flows off circumnuclear and galactic disks, producing a dynamic interstellar medium. On large scales bordering the cosmic web, feedback drives enriched gas out of galaxies and groups, seeding the intergalactic medium with heavy elements. In this way, feedback shapes galaxy evolution by shutting down star formation and ultimately curtailing the growth of structure after the peak at redshift 2–3. To understand the complex interplay between gravity and feedback, we must resolve both the key physics within galaxies and map the impact of these processes over large scales, out into the cosmic web. The Advanced X-ray Imaging Satellite (AXIS) is a proposed X-ray probe mission for the 2030s with arcsecond spatial resolution, large effective area, and low background. AXIS will untangle the interactions of winds, radiation, jets, and supernovae with the surrounding interstellar medium across the wide range of mass scales and large volumes driving galaxy evolution and trace the establishment of feedback back to the main event at cosmic noon. This white paper is part of a series commissioned for the AXIS Probe mission concept; additional AXIS white papers can be found at the AXIS website.
We present the discovery of two new 10 day period giant planets from the Transiting Exoplanet Survey Satellite mission, whose masses were precisely determined using a wide diversity of ground-based facilities. TOI-481 b and TOI-892 b have similar radii (0.99 ± 0.01 R{sub J} and 1.07 ± 0.02 R{sub J}, respectively), and orbital periods (10.3311 days and 10.6266 days, respectively), but significantly different masses (1.53 ± 0.03 M{sub J} versus 0.95 ± 0.07 M{sub J}, respectively). Both planets orbit metal-rich stars ([Fe/H] = +0.26±0.05 dex and [Fe/H] = +0.24±0.05 for TOI-481 and TOI-892, respectively) but at different evolutionary stages. TOI-481 is a M{sub ⋆} = 1.14 ± 0.02 M{sub ⊙}, R{sub ⋆} = 1.66 ± 0.02 R{sub ⊙} G-type star (T{sub eff} = 5735±72 K), that with an age of 6.7 Gyr, is in the turn-off point of the main sequence. TOI-892 on the other hand, is a F-type dwarf star (T{sub eff} = 6261±80 K), which has a mass of M{sub ⋆} = 1.28 ± 0.03 M{sub ⊙} and a radius of R{sub ⋆} = 1.39 ± 0.02 R{sub ⊙}. TOI-481 b and TOI-892 b join the scarcely populated region of transiting gas giants with orbital periods longer than 10 days, which is important to constrain theories of the formation and structure of hot Jupiters.
The structure of the outskirts of galaxies provides valuable information about their past and evolution. Due to their projected orientation, edge-on isolated galaxies effectively serve as test labs in which to study the three-dimensional structures of galaxies, including warps and flares, and to explore the possible sources of such distortions. We analyzed the structure of the apparently isolated edge-on ultra-thin galaxy UGC 11859 to look for the presence of distortions. The deep optical imaging observations (${\mu }_{\mathrm{lim}}=30.6$ and 30.0 $\mathrm{mag}\ {\mathrm{arcsec}}^{-2}$ in the g- and r-bands, respectively) we acquired with the 10.4 m Gran Telescopio Canarias are used to derive the radial and vertical surface brightness profiles and g - r color radial profile. We find that UGC 11859's disk displays a significant gravitational distortion. A warp is clearly detected on one side of the disk, and the galactic plane on both sides of the center shows increasing scale height with an increasing galactocentric radius, indicating the presence of a flare in the stellar distribution. The surface brightness profile of the disk shows a sharp break at 24 kpc galactocentric radius, and a steep decline to larger radii, an "edge-on truncation," which we associate with the presence of the flare. The present study is the first observational support for a connection between truncations and flares. Just beyond the warped side of the disk, a faint galaxy is observed within a small angular distance, identified as a potential interacting companion. Based on ultradeep g and r photometry we estimate that if the potential companion is at the same distance as UGC 11859, the stellar mass of the satellite galaxy is log 10 (M ⊙ ) = ${6.33}_{-0.02}^{+0.02}$ .
We report results from a systematic wide-area search for faint dwarf galaxies at heliocentric distances from 0.3 to 2 Mpc using the full 6 yr of data from the Dark Energy Survey (DES). Unlike previous searches over the DES data, this search specifically targeted a field population of faint galaxies located beyond the Milky Way virial radius. We derive our detection efficiency for faint, resolved dwarf galaxies in the Local Volume with a set of synthetic galaxies and expect our search to be complete to M$_{V}$ ∼ (−7, −10) mag for galaxies at D = (0.3, 2.0) Mpc. We find no new field dwarfs in the DES footprint, but we report the discovery of one high-significance candidate dwarf galaxy at a distance of , a potential satellite of the Local Volume galaxy NGC 55, separated by 47′ (physical separation as small as 30 kpc). We estimate this dwarf galaxy to have an absolute V-band magnitude of and an azimuthally averaged physical half-light radius of , making this one of the lowest surface brightness galaxies ever found with . This is the largest, most diffuse galaxy known at this luminosity, suggesting possible tidal interactions with its host.
Abstract Space scientists often face the question of whether data collected by different instruments are measurements of the same source population. This paper proposes a statistical validation method for evaluating the agreement between such related data sets. It offers a detailed case study focused on validating a new data set from the Interstellar Boundary Explorer (IBEX) mission, which serves as a practical how-to guide for similar analyses. Since 2008, the IBEX satellite has been gathering data on heliospheric energetic neutral atoms (ENAs) while being exposed to various sources of background noise, such as cosmic rays and solar energetic particles. The IBEX mission initially released only a qualified triple-coincidence (qABC) data product, which was designed to provide observations of ENAs free of background contamination. Further measurements revealed that the qABC data were in fact susceptible to contamination, having relatively low ENA counts and high background rates. To mitigate this issue, the mission team recently considered releasing a certain qualified double-coincidence (qBC) data product, which has roughly twice the detection rate of the qABC data product. This paper presents a simulation-based validation of the new qBC data product against the already-released qABC data product. The results show that the qBCs can plausibly be said to be measuring the same source population as the qABCs up to an average absolute deviation of 3.6%. Visual diagnostics provide additional confirmation of source rate coherence across data products. The framework introduced here is general and can be applied to other validation problems both within and outside the field of space physics.
We use the latest measurements of the Milky Way satellite population from the Dark Energy Survey and Pan-STARRS1 to infer the most stringent astrophysical bound to date on velocity-dependent interactions between dark matter particles and protons. We model the momentum-transfer cross section as a power law of the relative particle velocity v with a free normalizing amplitude, σ MT = σ 0 v n , to broadly capture the interactions arising within the nonrelativistic effective theory of dark matter–proton scattering. The scattering leads to a momentum and heat transfer between the baryon and dark matter fluids in the early universe, ultimately erasing structure on small physical scales and reducing the abundance of low-mass halos that host dwarf galaxies today. From the consistency of observations with the cold collisionless dark matter paradigm, using a new method that relies on the most robust predictions of the linear perturbation theory, we infer an upper limit on σ0 of 1.4 × 10 -23 , 2.1 × 10 -19 , and 1.0 × 10 -12 cm 2 , for interaction models with n = 2, 4, and 6, respectively, for a dark matter particle mass of 10 MeV. These results improve observational limits on dark matter–proton scattering by orders of magnitude and thus provide an important guide for viable sub-GeV dark matter candidates.