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At least 1,423 records · Page 79
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NASA NTRS2010

Spacecraft Internal Acoustic Environment Modeling

Acoustic modeling can be used to identify key noise sources, determine/analyze sub-allocated requirements, keep track of the accumulation of minor noise sources, and to predict vehicle noise levels at various stages in vehicle development, first with estimates of noise sources, later with experimental data. This paper describes the implementation of acoustic modeling for design purposes by incrementally increasing model fidelity and validating the accuracy of the model while predicting the noise of sources under various conditions. During FY 07, a simple-geometry Statistical Energy Analysis (SEA) model was developed and validated using a physical mockup and acoustic measurements. A process for modeling the effects of absorptive wall treatments and the resulting reverberation environment were developed. During FY 08, a model with more complex and representative geometry of the Orion Crew Module (CM) interior was built, and noise predictions based on input noise sources were made. A corresponding physical mockup was also built. Measurements were made inside this mockup, and comparisons were made with the model and showed excellent agreement. During FY 09, the fidelity of the mockup and corresponding model were increased incrementally by including a simple ventilation system. The airborne noise contribution of the fans was measured using a sound intensity technique, since the sound power levels were not known beforehand. This is opposed to earlier studies where Reference Sound Sources (RSS) with known sound power level were used. Comparisons of the modeling result with the measurements in the mockup showed excellent results. During FY 10, the fidelity of the mockup and the model were further increased by including an ECLSS (Environmental Control and Life Support System) wall, associated closeout panels, and the gap between ECLSS wall and mockup wall. The effect of sealing the gap and adding sound absorptive treatment to ECLSS wall were also modeled and validated.

Chu, Shao-Sheng R.↗
NASA NTRS2013

Application of Statistically Derived CPAS Parachute Parameters

The Capsule Parachute Assembly System (CPAS) Analysis Team is responsible for determining parachute inflation parameters and dispersions that are ultimately used in verifying system requirements. A model memo is internally released semi-annually documenting parachute inflation and other key parameters reconstructed from flight test data. Dispersion probability distributions published in previous versions of the model memo were uniform because insufficient data were available for determination of statistical based distributions. Uniform distributions do not accurately represent the expected distributions since extreme parameter values are just as likely to occur as the nominal value. CPAS has taken incremental steps to move away from uniform distributions. Model Memo version 9 (MMv9) made the first use of non-uniform dispersions, but only for the reefing cutter timing, for which a large number of sample was available. In order to maximize the utility of the available flight test data, clusters of parachutes were reconstructed individually starting with Model Memo version 10. This allowed for statistical assessment for steady-state drag area (CDS) and parachute inflation parameters such as the canopy fill distance (n), profile shape exponent (expopen), over-inflation factor (C(sub k)), and ramp-down time (t(sub k)) distributions. Built-in MATLAB distributions were applied to the histograms, and parameters such as scale (sigma) and location (mu) were output. Engineering judgment was used to determine the "best fit" distribution based on the test data. Results include normal, log normal, and uniform (where available data remains insufficient) fits of nominal and failure (loss of parachute and skipped stage) cases for all CPAS parachutes. This paper discusses the uniform methodology that was previously used, the process and result of the statistical assessment, how the dispersions were incorporated into Monte Carlo analyses, and the application of the distributions in trajectory benchmark testing assessments with parachute inflation parameters, drag area, and reefing cutter timing used by CPAS.

Romero, Leah M.↗
NASA NTRS2014

NASA's Newest Orbital Debris Ground-based Telescope Assets: MCAT and UKIRT

NASA's Orbital Debris Program Office (ODPO) will break ground on Ascension Island in 2014 to build the newest optical (0.30 - 1.06 micrometers) ground-based telescope asset dedicated to the study of orbital debris. The Meter Class Autonomous Telescope (MCAT) is a 1.3m optical telescope designed to track objects in orbits ranging from Low Earth Orbit (LEO) to Geosynchronous Earth Orbit (GEO). Ascension Island is located in the South Atlantic Ocean, offering longitudinal sky coverage not afforded by the Ground-based Electro-Optical Deep Space Surveillance (GEODSS) network. With a fast-tracking dome, a suite of visible wide-band filters, and a time-delay integration (TDI) capable camera, MCAT is capable of multiple observing modes ranging from tracking cataloged debris targets to surveying the overall debris environment. Access to the United Kingdom Infrared Telescope (UKIRT) will extend our spectral coverage into the near- (0.8-5 micrometers) and mid- to far-infrared (8-25 micrometers) regime. UKIRT is a 3.8m telescope located on Mauna Kea on the Big Island of Hawaii. At nearly 14,000-feet and above the atmospheric inversion layer, this is one of the premier astronomical sites in the world and is an ideal setting for an infrared telescope. An unprecedented one-third of this telescope's time has been allocated to collect orbital debris data for NASA's ODPO over a 2-year period. UKIRT has several instruments available to obtain low-resolution spectroscopy in both the near-IR and the mid/far-IR. Infrared spectroscopy is ideal for constraining the material types, albedos and sizes of debris targets, and potentially gaining insight into reddening effects caused by space weathering. In addition, UKIRT will be used to acquire broadband photometric imaging at GEO with the Wide Field Camera (WFCAM) for studying known objects of interest as well as collecting data in survey-mode to discover new targets. Results from the first stage of the debris campaign will be presented. The combination of these ground-based telescope assets will yield spectral coverage ranging from 0.3 - 25 micrmeters, allowing orbital debris to be studied in depth across a wider wavelength range in the visible and IR than ever previously studied by ODPO. Located on opposite sides of the world and in opposite hemispheres, they offer access to nearly the entire GEO belt on any given night, allowing immediate coverage of nearly any time-critical break-up event. By expanding the methods for surveying, detecting, and characterizing orbital debris, we can better model the debris environment and ultimately gain insight into how to mitigate potential collisions for future missions.

Lederer, S. M.↗
NASA NTRS2008

Mission and Navigation Design for the 2009 Mars Science Laboratory Mission

NASA s Mars Science Laboratory mission will launch the next mobile science laboratory to Mars in the fall of 2009 with arrival at Mars occurring in the summer of 2010. A heat shield, parachute, and rocket-powered descent stage, including a sky crane, will be used to land the rover safely on the surface of Mars. The direction of the atmospheric entry vehicle lift vector will be controlled by a hypersonic entry guidance algorithm to compensate for entry trajectory errors and counteract atmospheric and aerodynamic dispersions. The key challenges for mission design are (1) develop a launch/arrival strategy that provides communications coverage during the Entry, Descent, and Landing phase either from an X-band direct-to-Earth link or from a Ultra High Frequency link to the Mars Reconnaissance Orbiter for landing latitudes between 30 deg North and 30 deg South, while satisfying mission constraints on Earth departure energy and Mars atmospheric entry speed, and (2) generate Earth-departure targets for the Atlas V-541 launch vehicle for the specified launch/arrival strategy. The launch/arrival strategy employs a 30-day baseline launch period and a 27-day extended launch period with varying arrival dates at Mars. The key challenges for navigation design are (1) deliver the spacecraft to the atmospheric entry interface point (Mars radius of 3522.2 km) with an inertial entry flight path angle error of +/- 0.20 deg (3 sigma), (2) provide knowledge of the entry state vector accurate to +/- 2.8 km (3 sigma) in position and +/- 2.0 m/s (3 sigma) in velocity for initializing the entry guidance algorithm, and (3) ensure a 99% probability of successful delivery at Mars with respect to available cruise stage propellant. Orbit determination is accomplished via ground processing of multiple complimentary radiometric data types: Doppler, range, and Delta-Differential One-way Ranging (a Very Long Baseline Interferometry measurement). The navigation strategy makes use of up to five interplanetary trajectory correction maneuvers to achieve entry targeting requirements. The requirements for cruise propellant usage and atmospheric entry targeting and knowledge are met with ample margins.

Mission design↗
NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
DOE OSTI2024

Assessing the Viability of Geothermal Microgrid Deployment: A Geospatial Analysis Across the United States

Geothermal microgrids hold a potential of supplying clean and dependable power to communities throughout the United States (US), all while sidestepping the expenses associated with connecting to strained or isolated power grids. Nonetheless, their implementation is still in its early stages in the country. The objective of this analysis is to leverage available data to pinpoint regions across the US that exhibit favorable conditions for the development of geothermal microgrids. Drawing from a variety of sources, including estimates of geothermal resources, the costs associated with geothermal energy generation and electricity transmission, existing microgrid locations, and subsidy programs, we aim to identify promising areas for further exploration. By mapping out the contiguous US, Alaska, and Hawaii, we delineate regions with high relative favorability for geothermal microgrid deployment. Our findings reveal the presence of highly favorable regions across the Western states of the contiguous US, as well as isolated areas in Alaska and Hawaii. Furthermore, we delve into a discussion on state policies and incentive programs, considering their role in fostering favorable conditions or posing barriers to geothermal microgrid development.

Alaska↗
NASA NTRS2013

Fall, Recovery and Characterization of the Novato L6 Chondrite Breccia

The Novato L6 chondrite fragmental breccia fell in California on 17 October 2012, and was recovered after the Cameras for Allsky Meteor Surveillance (CAMS) project determined the meteor's trajectory between 95 and 45 km altitude. The final fragmentation at 33 1 km altitude was exceptionally well documented by digital photographs. The first sample was recovered before rain hit the area. First results from a consortium study of the meteorite's characterization, cosmogenic and radiogenic nuclides, origin and conditions of the fall are presented. Some meteorites did not retain fusion crust and show evidence of spallation. Before entry, the meteoroid was 35+/-5 cm in diameter (mass 80+/-35 kg) with a cosmic ray exposure age of 9+/-1 Ma, if it had a one-stage exposure history. However, based on the cosmogenic nuclide inventory, a two-stage exposure history is more likely, with lower shielding in the last few Ma. Thermoluminescence data suggest a collision event within the last approx. 0.1 Ma. Novato likely belonged to the class of shocked L chondrites that have a common shock age of 470 Ma, based on the U,Th-He age of 460+/-220 Ma. The measured orbits of Novato, Jesenice and Innisfree are consistent with a proposed origin of these shocked L chondrites in the Gefion asteroid family, but leave open the possibility that they came to us directly from the 5:2 mean motion resonance with Jupiter. Novato experienced a stronger compaction than did other L6 chondrites of shock-stage S4. Despite this, a freshly broken surface shows a wide range of organic compounds.

meteorites↗
NASA NTRS2013

Memory-Efficient Onboard Rock Segmentation

Rockster-MER is an autonomous perception capability that was uploaded to the Mars Exploration Rover Opportunity in December 2009. This software provides the vision front end for a larger software system known as AEGIS (Autonomous Exploration for Gathering Increased Science), which was recently named 2011 NASA Software of the Year. As the first step in AEGIS, Rockster-MER analyzes an image captured by the rover, and detects and automatically identifies the boundary contours of rocks and regions of outcrop present in the scene. This initial segmentation step reduces the data volume from millions of pixels into hundreds (or fewer) of rock contours. Subsequent stages of AEGIS then prioritize the best rocks according to scientist- defined preferences and take high-resolution, follow-up observations. Rockster-MER has performed robustly from the outset on the Mars surface under challenging conditions. Rockster-MER is a specially adapted, embedded version of the original Rockster algorithm ("Rock Segmentation Through Edge Regrouping," (NPO- 44417) Software Tech Briefs, September 2008, p. 25). Although the new version performs the same basic task as the original code, the software has been (1) significantly upgraded to overcome the severe onboard re source limitations (CPU, memory, power, time) and (2) "bulletproofed" through code reviews and extensive testing and profiling to avoid the occurrence of faults. Because of the limited computational power of the RAD6000 flight processor on Opportunity (roughly two orders of magnitude slower than a modern workstation), the algorithm was heavily tuned to improve its speed. Several functional elements of the original algorithm were removed as a result of an extensive cost/benefit analysis conducted on a large set of archived rover images. The algorithm was also required to operate below a stringent 4MB high-water memory ceiling; hence, numerous tricks and strategies were introduced to reduce the memory footprint. Local filtering operations were re-coded to operate on horizontal data stripes across the image. Data types were reduced to smaller sizes where possible. Binary- valued intermediate results were squeezed into a more compact, one-bit-per-pixel representation through bit packing and bit manipulation macros. An estimated 16-fold reduction in memory footprint relative to the original Rockster algorithm was achieved. The resulting memory footprint is less than four times the base image size. Also, memory allocation calls were modified to draw from a static pool and consolidated to reduce memory management overhead and fragmentation. Rockster-MER has now been run onboard Opportunity numerous times as part of AEGIS with exceptional performance. Sample results are available on the AEGIS website at http://aegis.jpl.nasa.gov.

Burl, Michael C.↗
NASA NTRS2021

Mapping Global Forest Age from Forest Inventories, Biomass and Climate Data

Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. However, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. This study provides a new global distribution of forest age circa 2010, estimated using a machine learning approach trained with more than 40 000 plots using forest inventory, biomass and climate data. First, an evaluation against the plot-level measurements of forest age reveals that the data-driven method has a relatively good predictive capacity of classifying old-growth vs. non-old-growth (precision = 0.81 and 0.99 for old-growth and non-old-growth, respectively) forests and estimating corresponding forest age estimates (NSE = 0.6 – Nash–Sutcliffe efficiency – and RMSE = 50 years – root-mean-square error). However, there are systematic biases of overestimation in young- and underestimation in old-forest stands, respectively. Globally, we find a large variability in forest age with the old-growth forests in the tropical regions of Amazon and Congo, young forests in China, and intermediate stands in Europe. Furthermore, we find that the regions with high rates of deforestation or forest degradation (e.g. the arc of deforestation in the Amazon) are composed mainly of younger stands. Assessment of forest age in the climate space shows that the old forests are either in cold and dry regions or warm and wet regions, while young–intermediate forests span a large climatic gradient. Finally, comparing the presented forest age estimates with a series of regional products reveals differences rooted in different approaches and different in situ observations and global-scale products. Despite showing robustness in cross-validation results, additional methodological insights on further developments should as much as possible harmonize data across the different approaches. The forest age dataset presented here provides additional insights into the global distribution of forest age to better understand the global dynamics in the forest water and carbon cycles. The forest age datasets are openly available at https://doi.org/10.17871/ForestAgeBGI.2021 (Besnard et al., 2021).

Simon Besnard↗
NASA NTRS1972

Space shuttle abort separation pressure investigation. Volume 1, Part A: Booster data at Mach 5

Pressure data obtained from a joint Langley Research Center (LaRC)/Marshall Space Flight Center (MSFC) Space Shuttle about stage separation wind tunnel test are presented. The .00556 scale models of the McDonnell-Douglas orbiter and booster configurations were tested in proximity in Tunnel A of the Von Karman Facility (VKF), Arnold Engineering Development Center (AEDC). Mach numbers were 5.0, 3.0, and 2.0 and nominal Reynolds numbers were 1.09, 1.60, and 1.74 million per foot, respectively. Pressure data were obtained for the booster upper surface and orbiter lower surface at angles of attack of -10 deg, -5, 0, 5, and 10 deg for zero degrees sideslip. The models were tested at incidence angles of 0 and 5 deg for several separation distances and power conditions. Plug nozzles utilizing air were used to simulate booster and orbiter plumes at various altitudes along a nominal ascent trajectory. Powered conditions were 100, 50, and 0 percent of full power for the orbiter and 100, 50 and 0 percent of full power for the booster. Data were also obtained with the booster canard off in close proximity.

Trimmer, L. L.↗
DOE OSTI2022

Submersible Multistage Centrifugal Pump for Versatile Test Reactor Cartridge Test Loop

Submersible multistage centrifugal pumps are ideal for pumping in narrow confined spaces and achieving necessary head pressures and flow rates. Once a diameter is determined then manipulation of the number of stages and motor speed are all that are required to meet desired flow conditions. The Versatile Test Reactor (VTR) closed loop cartridge systems will need forced convection cooling independent of the main reactor. A multistage centrifugal pump can meet the necessary flow rates and pumping pressures while minimizing space taken. The pump considered for this work was based off a deep well submersible pump, a variation of a multistage centrifugal pump. We experimented with two pump sizes, 5 cm (2 inch) and 7.5 cm (3 inch) diameters. These diameters were chosen to fit into the inner diameter of standard 5 and 7.5 (2 and 3 inch) Schedule 40 pipe, respectively. This made the design for the test loop both simpler and less expensive as the need for an engineered pump housing was eliminated. Initial test cartridge planning indicated space for only a 5 cm (2 inch) diameter pump, though early testing of this size showed the need for an abnormally high-speed and high-power motor. Fine tuning of the cartridge design allowed a pump size increase to 7.5 cm (3 inches), which was the pump size most extensively tested in this work. The test loop is composed of various sizes of PVC and aluminum piping components in a loop configuration. The pump is driven by a Pittman 250 W (1/3 horsepower) electric motor with maximum speed of 3,450 RPM. Testing consisted of running the pump at a constant motor speed while varying a control valve to restrict flow through the loop, with differential pressure and flow rate recorded. This was done for one and two stage configurations for the 5 cm (2 inch) diameter impeller design and one, two, and three stage configurations for the 7.5 cm (3 inch diameter) impeller design, respectively. Due to pumping power requirements, two and three stage 7.5 cm (3 inch) diameter impeller testing at higher flowrates lowered the motor speed substantially. In regions where motor speed could not be maintained constant, the data were discarded. The test loop was also reconfigured to allow for the pump to be tested for pressure drop in a stalled or inoperable (0 RPM) flow condition. Demonstration of adequate natural convection cooling of the test cartridge fuel type is necessary under accident conditions, and this will depend upon the flow resistance through the impeller assembly when the pump is not operating. Thus, accurate knowledge of the effective impeller assembly loss coefficient is important for safety evaluations. The test loop was modified to provide water inlet and outlets on either side of the pump impeller stack, and a metered flow of lab water was provided in order to measure the pressure drop across the cartridges as a function of flowrate. Data from the pump head curve testing developed as part of this work and supported by analysis using pump head affinity laws indicates that a three stage 7.5 cm (3 inch) pump impeller design will meet target requirements for coolant flow within the VTR cartridge sodium cartridge at full power conditions [1] with margin; this corresponds to a flowrate of 45 l/min (12 gpm) at a pressure drop of 6.1 m (20 feet) of water head. The results of the pressure loss measurements across the impeller assembly when the pump is stationary (i.e., at 0 RPM) indicate that the pressure loss coefficient is 0.921 for a two impeller stack configuration; this value is calculated based on the flow velocity through the minimum available flow area within a single stage of the impeller which corresponds to 1.4 cm2.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2009

International Space Station Solar Alpha Rotary Joint Failure Analysis: The Materials and Processes Perspective

This paper summarizes the on-orbit structural dynamic data and the related modal analysis, model validation and correlation performed for the ISS configurations spanning ISS Stage 12A. The objective of this analysis is to validate and correlate analytical models used to verify the ISS critical interface dynamic loads and improve its fatigue life prediction. On-Orbit dynamic responses were measured during the ISS configurations throughout ISS Stage 12A by the two main ISS instrumentation systems; Internal Wireless Instrumentation System (IWIS) and the Structural Dynamic Measurement System (SDMS). These nominal on-orbit events include Russian vehicle docking and undockings. Also, the ISS photogrammetric system recorded the movements of the 2A and 4A solar arrays during a modified ISS maneuver. Modal analyses were performed on the measured data to extract modal parameters including frequency, damping and mode shapes. Correlation and comparisons between the test and analytical frequencies and mode shapes were performed to assess the accuracy of the analytical models for the ISS configurations under consideration.

Dasgupta, Rajib↗
NASA NTRS1994

The early bolometric evolution of Nova Cygni 1992

We report on combined ultraviolet and optical observations of early stages of the outburst of Nova Cygni 1992. We show that the initial optical rise and decline were produced by the dramatic increase in UV line opacity and its gradual subsequent lifting as the ejecta expand. The source of the M(sub v) - t(sub 3) relation is easily understood in light of these low-resolution UV spectra. The multiwavelength data confirm the theoretical prediction that a nova evolves at a constant bolometric luminosity in the early stages of outburst. We verify the prediction for this nova for at least the first month of the outburst. The detection of far-UV radiation with the Voyager Ultraviolet Spectrometer and the detection of an increasing X-ray flux with Roentgen Satellite (ROSAT) imply that this phase lasted for more than 6 months.

Shore, Steven N.↗
NASA NTRS1973

Identification of phenological stages and vegetative types for land use classification

The author has identified the following significant results. Classification of digital data for mapping Alaskan vegetation has been compared to ground truth data and found to have accuracies as high as 90%. These classifications are broad scale types as are currently being used on the Major Ecosystems of Alaska map prepared by the Joint Federal-State Land Use Planning Commission for Alaska. Cost estimates for several options using the ERTS-1 digital data to map the Alaskan land mass at the 1:250,000 scale ranged between $2.17 to $1.49 per square mile.

Mckendrick, J. D.↗
DOE OSTI2023

From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II—Harnessing data

Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Additionally, biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in “data desert” regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Mapping Inundation from Hurricane Florence (2018) with L-Band Synthetic Aperture Radar, Commercial Imagery, and Ancillary Data via Machine Learning Classification

During and after flooding events, mapping the extent of floodwaters aids in the distribution of resources, recovery efforts, and damage assessment practices. Development of a land cover classification system focused on mapping inundation after major hurricane events using synthetic aperture radar (SAR) data could allow for the production of near-real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of a developing situation. Complimentary optical and SAR images from domestic and foreign entities are brought together through activations of the International Charter: Space and Major Disasters to support response efforts, from true-color, near-infrared, and thermal remote sensing data obtained by NASA, NOAA, and international satellites to the collection of high-resolution true color aerial photography by NOAA and the National Geodetic Survey. In response to Hurricane Florence of 2018, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. The resulting fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. In this study, true-color NOAA aerial and commercial satellite imagery are used in conjunction with four UAVSAR data swaths centered on the Lumberton and Cape Fear River basins in southeastern North Carolina to develop a Random Forest classification model focused on mapping open water and floodwater otherwise obscured by vegetation or lingering cloud cover. Ancillary building footprint, transportation route, and population data will also be incorporated into the classification scheme to estimate the societal impacts of flooding based on the proximity of features to detected inundation. Preliminary results from the Hurricane Florence case study will be discussed in addition to the limitations of available validation data for assessment of the classifier’s accuracy.

Alexander M Melancon↗
NASA NTRS1981

Monitoring of heavy flooding by orbital remote sensing: The example of the Doce river valley

The application of temporal LANDSAT data to study floods was verified, and the natural features responsible for this phenomenon were surveyed using the Doce river valley as a test site, because of the catastrophic (1978-1979) flood. Data from LANDSAT images and CCT's were used. Geomorphical mapping evaluated morphostructural features. Seven and nine classes of water surfaces for dry and rainy seasons were analyzed. The magnitude of the changes from preflood to postflood stage are estimated. The single Pixel program was applied to correlate the drainage basin characteristics to the grey level of LANDSAT data.

Dejesusparada, N.↗