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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Determination of crash test pulses and their application to aircraft seat analysis

Deceleration time histories (crash pulses) from a series of twelve light aircraft crash tests conducted at NASA Langley Research Center (LaRC) were analyzed to provide data for seat and airframe design for crashworthiness. Two vertical drop tests at 12.8 m/s (42 ft/s) and 36 G peak deceleration (simulating one of the vertical light aircraft crash pulses) were made using an energy absorbing light aircraft seat prototype. Vertical pelvis acceleration measured in a 50 percentile dummy in the energy absorbing seat were found to be 45% lower than those obtained from the same dummy in a typical light aircraft seat. A hybrid mathematical seat-occupant model was developed using the DYCAST nonlinear finite element computer code and was used to analyze a vertical drop test of the energy absorbing seat. Seat and occupant accelerations predicted by the DYCAST model compared quite favorably with experimental values.

Alfaro-Bou, E.↗
NASA NTRS1973

Global electromagnetic induction in the moon and planets

Experiments and analyses concerning electromagnetic induction in the moon and other extraterrestrial bodies are summarized. The theory of classical electromagnetic induction in a sphere is first considered, and this treatment is extended to the case of the moon, where poloidal eddy-current response has been found experimentally to dominate other induction modes. Analysis of lunar poloidal induction yields lunar internal electrical conductivity and temperature profiles. Two poloidal-induction analytical techniques are discussed: a transient-response method applied to time-series magnetometer data, and a harmonic-analysis method applied to data numerically Fourier-transformed to the frequency domain, with emphasis on the former technique. Attention is given to complicating effects of the solar wind interaction with both induced poloidal fields and remanent steady fields. The static magnetization field induction mode is described, from which are calculated bulk magnetic permeability profiles. Magnetic field measurements obtained from the moon and from fly-bys of Venus and Mars are studied to determine the feasibility of extending theoretical and experimental induction techniques to other bodies in the solar system.

Dyal, P.↗
NASA NTRS2010

Methods and Results for a Global Precipitation Measurement (GPM) Validation Network Prototype

As one component of a ground validation system to meet requirements for the upcoming Global Precipitation Measurement (GPM) mission, a quasi-operational prototype a system to compare satellite- and ground-based radar measurements has been developed. This prototype, the GPM Validation Network (VN), acquires data from the Precipitation Radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) satellite and from ground radar (GR) networks in the continental U.S. and participating international sites. PR data serve as a surrogate for similar observations from the Dual-frequency Precipitation Radar (DPR) to be present on GPM. Primary goals of the VN prototype are to understand and characterize the variability and bias of precipitation retrievals between the PR and GR in various precipitation regimes at large scales, and to improve precipitation retrieval algorithms for the GPM instruments. The current VN capabilities concentrate on comparisons of the base reflectivity observations between the PR and GR, and include support for rain rate comparisons. The VN algorithm resamples PR and GR reflectivity and other 2-D and 3-D data fields to irregular common volumes defined by the geometric intersection of the instrument observations, and performs statistical comparisons of PR and GR reflectivity and estimated rain rates. Algorithmic biases and uncertainties introduced by traditional data analysis techniques are minimized by not performing interpolation or extrapolation of data to a fixed grid. The core VN dataset consists of WSR-88D GR data and matching PR orbit subset data covering 21 sites in the southeastern U. S., from August, 2006 to the present. On average, about 3.5 overpass events per month for these WSR-88D sites meet VN criteria for significant precipitation, and have matching PR and GR data available. This large statistical sample has allowed the relative calibration accuracy and stability of the individual ground radars, and the quality of the PR reflectivity attenuation correction in convective and stratiform precipitation to be evaluated. We will present results of PR-GR reflectivity and rain rate bias comparisons for each OR site, and for different rain types, for the full data set and as time series. The capabilities of the statistical analysis and vertical cross section tools for display and analysis of individual site overpass event data will be described, and examples of the tools' outputs will be shown.

Morris, Kenneth R.↗
DOE OSTI2023

Faster-than-real-time Simulation with Demonstration for Resilient DER Integration

The US electric grid is facing operational, stability, and security challenges. Transmission system operators need some measure of visibility into distribution system renewable generation. Distribution system generation needs to support transmission system voltage. The grid is experiencing an expansion in measurement systems. How to take full advantage of this expansion and defend against attacks, both cyber and physical, poses additional challenges. The Faster-than-real-time Simulation with demonstration for Resilient DER Integration project set out to do the following: a. Flatten the voltage profile through the feeders and system for cost saving and voltage stabilization needs. b. Increase the amount of intermittent distributed energy resources (IDERs) that could be deployed on a utility feeder and provide 100% or more energy needed for the demands on that feeder, and c. based on an accurate model (Digital Twin) of the utilities system, be able to detect any abnormalities on the utilities distribution system. To manage the voltage and increase IDER penetration (a,b), Graph Trace Analysis is employed in a time-series, optimal power flow to coordinate the time-varying feedback control setpoints of a distribution feeder’s utility control devices. Under the coordinated control are a Load Tap Changing Transformer, a voltage regulator, and five switched capacitor banks. The feeder serves over 2000 customers, the feeder secondaries are modeled, and the feeder has 2.3 MW of PV generation, corresponding to a 17.4% penetration of PV generation. The feeder model has over 12,000 components, where every customer load bus and PV generator are modeled. The accuracy of the power flow solution is compared against historical meter voltage measurements, the improvement in conservation voltage reduction energy savings as a function of the coordinated control desired voltage profile is investigated, and the increase in PV penetration of the coordinated control over the existing control is presented. To achieve improved control performance while observing system operation constraints, bellwether Advanced Metering Infrastructure (AMI) voltage measurements are used to adjust the desired voltage profile used by the optimal power flow analysis. To detect and alleviate or negate attacks or failures on the distribution and transmission utility grids (c) the grid needs to be resilient and self-healing. In this project software was designed to do just that. At the center of the software is an Integrated System Model (ISM) that spans from transmission to secondary distribution. The ISM is employed in real-time abnormality detection, voltage stability forecasting, and multi-mode control. Testing results are presented for: 1—attacks on utility infrastructure; 2—energy savings from optimal control; 3—distribution system control response during a low voltage transmission system event; 4—cyber-attacks on PV inverters, where physical inverters are used in hard-ware-in-the-simulation-loop studies. Contributions of this work include real-time analysis that spans from three-phase transmission through secondary distribution; an approach for detecting abnormalities that employs measurements from three independent measurement systems; and a multi-mode distribution system control that responds to cyber-attacks, physical attacks, equipment failures, and transmission system needs.

Integrated System Model, Graph Trace Analysis, Adv↗
DOE OSTI2023

Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics

Abstract A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the library’s ability to be useful outside the lab by detecting malathion in the environment. The engineered host cell, a living malathion sensor, can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2019

A Segmentation Algorithm for Characterizing Rise and Fall Segments in Seasonal Cycles: An Application to XCO2 to Estimate Benchmarks and Assess Model Bias

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

Calle, Leonardo↗
NASA NTRS2019

A Segmentation Algorithm for Characterizing Rise and Fall Segments in Seasonal Cycles: an Application to Xco2 to Estimate Benchmarks and Assess Model Bias

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

segmentation algorithm↗
DOE OSTI2026

SPRUCE FT-ICR MS, Bulk Chemistry, and Mass Loss from Litter Decomposition Study in Experimental Plots, Marcell Experimental Forest, Minnesota, 2015-2017

This dataset contains molecular, bulk chemical, and mass loss measurements from a litter decomposition study at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. This site is in a Sphagnum spp. ombrotrophic bog forest. Litterbags were deployed into the peat in September 2015 across three warming levels (+0, +4.5, and +9°C) under ambient and elevated carbon dioxide (CO₂ - +500 ppm) and retrieved after roughly 0.5, 1, and 2 years of field incubation (2015-09-23 to 2017-08-02). Litterbags containing six peatland litter types: black spruce needles (Picea mariana - SPL), spruce fine roots (SPR), Sphagnum angustifolium (ANG), Sphagnum magellanicum (MAG), Labrador tea leaves (Rhododendron groenlandicum - LTL), and Labrador tea roots (LTR). Molecular composition of water-soluble organic matter extracts was characterized using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) at 9.4 Tesla, operated in negative ion mode with electrospray ionization, providing molecular formula assignments and compound-class distributions across the decomposition time series. Bulk chemical characterization included elemental analysis (percent carbon, nitrogen, and phosphorus) and Fourier Transform Infrared Spectroscopy (FTIR) to quantify functional group composition. Litter mass loss was tracked gravimetrically at each retrieval interval, expressed as percent mass remaining relative to initial dry mass for each litter type and treatment combination. These data are valuable for understanding how vegetation shifts driven by increased atmospheric CO2 and temperature in peatlands alter litter inputs and organic matter stabilization trajectories, with implications for projecting and modeling peatland carbon cycling. This dataset contains two data files in comma-separated value (.csv) format. Additional metadata are provided: two data dictionaries and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

decomposition↗
DOE OSTI2022

Factors Controlling a Synthetic Aperture Radar (SAR) Derived Root-Zone Soil Moisture Product over The Seward Peninsula of Alaska

Root-zone soil moisture exerts a fundamental control on vegetation, energy balance, and the carbon cycle in Arctic ecosystems, but it is still not well understood in vast, remote, and understudied regions of discontinuous permafrost. The root-zone soil moisture product (30 m resolution) used in this analysis was retrieved from a time-series P-Band (420–440 MHz) synthetic aperture radar (SAR) backscatter observations (August 2017 & October 2017). While similar approaches have been taken to retrieve surface (0 cm to 5 cm) soil moisture from L-Band (1.2 GHz) SAR backscatter, this is one of the first known attempts at reaching the root-zone in permafrost regions. Here, we analyze secondary factors (excluding primary factors, such as precipitation) controlling summer (August) soil moisture at depths of 6 cm, 12 cm, and 20 cm over a 4500 km2 area on the Seward Peninsula of Alaska. Using a random forest model, we quantify the impact of topography, vegetation, and meteorological factors on soil moisture distributions. In developing the random forest model, we explore a variety of feature scales (30 m, 60 m, 90 m, 120 m, 180 m, and 240 m), tune hyperparameters (the structure of individual decision trees making up the ensemble including the number and depth of trees), and perform the final feature selection using cross-validated recursive feature elimination. Results suggest that root-zone soil moisture on the Seward Peninsula is primarily controlled by vegetation at 6 cm, but deeper in the soil column topography and meteorological factors, such as predominant winter wind direction and summer insolation, play a larger role. The random forest model accounts for 40% to 60% of the variation observed (R2 = 0.44 at 6 cm, R2 = 0.52 at 12 cm, R2 = 0.58 at 20 cm). These results indicate that vegetation is the dominant control on soil moisture shallow in the soil column, but the impact of vegetation does not extend to deeper layers retrieved from P-Band SAR backscatter.

Dann, Julian↗
NASA NTRS1987

Analysis models for the estimation of oceanic fields

A general model for statistically optimal estimates is presented for dealing with scalar, vector and multivariate datasets. The method deals with anisotropic fields and treats space and time dependence equivalently. Problems addressed include the analysis, or the production of synoptic time series of regularly gridded fields from irregular and gappy datasets, and the estimate of fields by compositing observations from several different instruments and sampling schemes. Technical issues are discussed, including the convergence of statistical estimates, the choice of representation of the correlations, the influential domain of an observation, and the efficiency of numerical computations.

Carter, E. F.↗
NASA NTRS1988

Statistical error analysis in CCD time-resolved photometry with applications to variable stars and quasars

Differential photometric time series obtained from CCD frames are tested for intrinsic variability using a newly developed analysis of variance technique. In general, the objects used for differential photometry will not all be of equal magnitude, so the techniques derived here explicitly correct for differences in the measured variances due to photon statistics. Other random-noise terms are also considered. The technique tests for the presence of intrinsic variability without regard to its random or periodic nature. It is then applied to observations of the variable stars ZZ Ceti and US 943 and the active extragalactic objects OQ 530, US 211, US 844, LB 9743, and OJ 287.

Howell, Steve B.↗
NASA NTRS1995

Global tropospheric experiment at the Hong Kong Atmosphere Chemistry Measurement Station

The major activities of the Global Tropospheric Experiment at the Hong Kong Atmospheric Chemistry Measurement Station are presented for the period 1 January - 31 December 1995. Activities included data analysis, reduction, and archiving of atmospheric measurements and sampling. Sampling included O3, CO, SO2, NO, TSP, RSP, and ozone column density. A data archive was created for the surface meteorological data. Exploratory data analysis was performed, including examination of time series, frequency distributions, diurnal variations and correlation. The major results have been or will be published in scientific journals as well as presented at conferences/workshops. Abstracts are attached.

Carroll, Mary Ann↗
NASA NTRS1999

"Novel Techniques in Non-Stationary Analysis of Rotorcraft Vibration Signitures"

This research effort produced new methods to analyze the performance of linear predictors that track non-stationary processes. Specifically, prediction methods have been applied to the vibration pattern of rotorcraft drivetrains. This analysis is part or a larger rotorcraft Health and Usage Monitoring System (HUMS) that can diagnose immediate failures of the subsystems, as indicated by abrupt change in the vibration signature, and prognosticate future health, by examining the vibration patterns against long-term trends. This problem is described by a earlier joint paper co-authored by members of the funding agency and the recipient institutions prior to this grant effort. Specific accomplishments under this grant include the following: (1) Definition of a framework for analysis of non-stationary time-series estimation using the coefficients of an adaptive filter. (2) Description of a novel method of combining short-term predictor error and long-term regression error to analyze the performance of a non-stationary predictor. (3) Formulation of a multi-variate probability density function that quantifies the performance of a adaptive predictor by using the short- and long-term error variables in a Gamma function distribution. and (4) Validation of the mathematical formulations with empirical data from NASA flight tests and simulated data to illustrate the utility beyond the domain of vibrating machinery.

Meng, Teresa↗
NASA NTRS1994

(abstract) Mount Rainier: New Remote Sensing Observations of a Decade Volcano

Mount Rainier was selected as a Decade Volcano by the International Association of Volcanology and Chemistry of the Earth's Interior. The purpose of this selection is to focus scientific and public attention on Mount Rainier during the current decade, the United Nations-designated International Decade of Natural Hazard Reduction. The Mount Rainier science plan calls for remote sensing surveys to monitor the volcano. To date, we have conducted airborne surveys with visible and near-infrared, thermal infrared, and interferometric radar instruments. Our preliminary analysis of some night-time time-series thermal infrared survey data sets of the summit suggests that, aside from seasonal variations in snow cover, there have been no qualitative changes in the size or pattern of the summit hot spots. Day-time airborne surveys were done to record the current surface appearance of the volcano and map hydrothermal alteration in the summit region. An interferometric radar survey yielded a high-resolution digital elevation model (DEM) which serves as a base for the registration of the other remote sensing data sets. More importantly, the DEM documents the current topography of glaciers and valleys. Planned biannual radar survey of mount rainier will produce a data set from which seasonal changes in glacier and valley topography can be characterized. Such characterization is essential if we are to recognize geothermally induced changes in snow and ice cover.

infrared radar interferometry glaciers seasonal va↗
NASA NTRS2012

Seasonal Variability of Saturn's Atmosphere

The seasonal variability of Saturn's clouds and weather layer, currently displaying a variety of phenomena (convective storms, planetary waves, giant storms and lightning-induced events, etc.) is not yet fully understood. Variations of Saturn's radiance at 5.2 microns, a spectral region dominated by thermal emission in an atmospheric window containing weak gaseous absorption, contain a strong axisymmetric component as well as large discrete features at low and mid-latitudes that are several degrees colder than the planetary average and uncorrelated with features at shorter wavelengths that are dominated by reflected sunlight (Yanamandra-Fisher et al., 2001. Icarus, Vol. 150). The characterization of several fundamental atmospheric properties and processes, however, remains incomplete, namely: How do seasons affect (a) the global distribution of gaseous constituents and aerosols; and (b) temperatures and the stability against convection and large scale-atmospheric transport? Do 5-micron clouds have counterparts at other altitude levels? What changes occur during the emergence of Great White Storms? Data acquired at the NASA/IRTF and NAOJ/Subaru from 1995 - 2011; since 2004, high-resolution multi-spectral and high-spatial imaging data acquired by the NASA/ESA Cassini mission, represents half a Saturnian year or two seasons. With the addition of detailed multi-spectral data sets acquired by amateur observers, we study these dramatic phenomena to better understand the timeline of the evolution of these events. Seasonal (or temporal) trends in the observables such as albedo of the clouds, thermal fields of the atmosphere as function of altitude, development of clouds, hazes and global abundances of various hydrocarbons in the atmosphere can now be modeled. We will present results of our ongoing investigation for the search and characterization of periodicities over half a Saturnian year, based on a non-biased a priori approach and time series techniques (such as Principal Component Analysis, PCA and Lomb-Scargle periodograms, LSP).

Yanamandra-Fisher, Padma A.↗
NASA NTRS2018

Northern Hemisphere Surface Freeze-Thaw Product from Aquarius L-Band Radiometers

In the Northern Hemisphere, seasonal changes in surface freeze–thaw (FT) cycles are an important component of surface energy, hydrological and eco-biogeochemical processes that must be accurately monitored. This paper presents the weekly polar-gridded Aquarius passive L-band surface freeze–thaw product (FT-AP) distributed on the Equal-Area Scalable Earth Grid version 2.0, above the parallel 50 N, with a spatial resolution of 36 km36 km. The FT-AP classification algorithm is based on a seasonal threshold approach using the normalized polarization ratio, references for frozen and thawed conditions and optimized thresholds. To evaluate the uncertainties of the product, we compared it with another satellite FT product also derived from passive microwave observations but at higher frequency: the resampled 37 GHz FT Earth Science Data Record (FTESDR). The assessment was carried out during the overlapping period between 2011 and 2014. Results show that 77.1% of their common grid cells have an agreement better than 80 %. Their differences vary with land cover type (tundra, forest and open land) and freezing and thawing periods. The best agreement is obtained during the thawing transition and over forest areas, with differences between product mean freeze or thaw onsets of under 0.4 weeks. Over tundra, FT-AP tends to detect freeze onset 2–5 weeks earlier than FT-ESDR, likely due to FT sensitivity to the different frequencies used. Analysis with mean surface air temperature time series from six in situ meteorological stations shows that the main discrepancies between FT-AP and FT-ESDR are related to false frozen retrievals in summer for some regions with FT-AP. The Aquarius product is distributed by the U.S. National Snow and Ice Data Center (NSIDC) at https://nsidc.org/data/aq3_ft/versions/5 with the DOI https://doi.org/10.5067/OV4R18NL3BQR.

Prince, Michael↗
NASA NTRS2022

Okefenokee Water Resources: Using Earth Observations to Assess Hydrologic Changes and Wildfire Risk in the Okefenokee Swamp

The Okefenokee Swamp is a vital ecosystem known for its protection of biodiversity, peatland carbon sinks, and recreational and economic opportunities for local residents. The swamp has experienced several wildfires since the 1990s, and new development along the borders of Okefenokee National Wildlife Refuge (ONWR) threatens to alter hydrologic activity and increase fire frequency. NASA DEVELOP partnered with staff at the ONWR to determine the feasibility of using satellite imagery to assess wildfire risk and map changes in vegetation cover. Using data from NASA satellites Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI), Soil Moisture Active Passive (SMAP) data from the USDA’s Crop Condition and Soil Moisture Analytics Tool (Crop-CASMA), European Space Agency (ESA) Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI), the DEVELOP team assessed the relationship between hydrologic change, vegetation cover, and wildfire risk in the swamp. Results showed that the southern portion of ONWR has been burned the most since 1990 and has greater water stability than other areas of the refuge. The team also found that the largest pockets of mature forests remain in the northernmost regions. Soil moisture anomaly readings may serve as an indicator of fire conditions. The team used these results to create a vegetation map, a swamp water visibility time series map, a historical wildfire correlation analysis, and a methodology tutorial. These products will assist the ONWR in making informed management decisions about the future of the Okefenokee Swamp.

Brianne Kendall↗
NASA NTRS2022

Along-Orbit Analysis of GRACE Follow-On Inter-Satellite Laser Ranging Measurements for Sub-Monthly Surface Mass Variations

We examined the sensitivity of GRACE Follow-On (GRACE-FO) laser ranging interferometer (LRI) measurements to sub-monthly time-variable gravity (TVG) signals caused by transient, high-frequency mass changes in the Earth system. GRACE-FO LRI provides complementary inter-satellite ranging measurements with higher precision over a wider range of frequencies than the baseline K-band microwave ranging system. The common approach for studying mass variation relies on the inverted TVG or mascon solutions over a period of, for example, one month or 10 days which are adversely affected by temporal aliasing and/or smoothing. In this article, we present the alternative along-orbit analysis methodology in terms of line-of-sight gravity difference (LGD) to fully exploit the higher precision LRI measurements for examination of sub-monthly mass changes. The discrepancy between “instantaneous” LGD LRI observations and monthly mean LGD (from Level-2 data) at satellite altitude indicates the sub-monthly gravitational variability not captured by monthly-mean solutions. In conjunction with the satellite ocean altimetry observations, high frequency non-tidal atmosphere and ocean models, and hydrology models, we show that the LGD LRI observations detect the high-frequency oceanic mass variability in the Argentine Basin and the Gulf of Carpentaria, and sub-monthly variations in surface (river) water in the Amazon Basin. We demonstrate the benefits gained from repeat ground track analysis of GRACE-FO LRI data in the case of the Amazon surface water flow. The along-orbit analysis methodology based on LGD LRI time series presented here is especially suitable for quantifying temporal and spatial evolution of extreme, rapidly changing mass variations.

Khosro Ghobadi-Far↗