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NASA NTRS2024

In-Time Safety Management for Part 139 Airports

Today’s airports are complex multi-faceted ecosystems. Currently, of the 517 certificated airports, 270 are required to use safety management systems (SMSs) to identify and mitigate known hazards and emergent risks and to voluntarily share safety data with commercial operators and tenants. Airports manage a wide variety of hazards. These traffic hubs have direct responsibilities, such as removing foreign object debris from runways and taxiways and configuring runways to help prevent against incursions and tail strikes during takeoff. To ensure safety in the future NAS, the National Academies recommended an In-time Aviation Safety Management System (IASMS). An IASMS will employ services, functions, and capabilities (SFCs) to identify and mitigate hazards that are proactively and predictively managed based on data analytics of detected anomalies, precursors, and trends. SFCs would scale with airport complexity and environmental conditions using increasingly automated systems to respond proactively to hazards and, by using integrated data sources and predictive safety analytical methods, discover new, never before seen risks.

IASMS↗
NASA NTRS2024

Applications of ArcticDEM for measuring volcanic dynamics, landslides, retrogressive thaw slumps, snowdrifts, and vegetation heights

Topographical changes are of fundamental interest to a wide range of Arctic science disciplines faced with the need to anticipate, monitor, and respond to the effects of climate change, including geohazard management, glaciology, hydrology, permafrost, and ecology. This study demonstrates several geomorphological, cryospheric, and biophysical applications of ArcticDEM – a large collection of publicly available, time-dependent digital elevation models (DEMs) of the Arctic. Our study illustrates ArcticDEM's applicability across different disciplines and five orders of magnitude of elevation derivatives, including measuring volcanic lava flows, ice cauldrons, post-failure landslides, retrogressive thaw slumps, snowdrifts, and tundra vegetation heights. We quantified surface elevation changes in different geological settings and conditions using the time series of ArcticDEM. Following the 2014–2015 Bárðarbunga eruption in Iceland, ArcticDEM analysis mapped the lava flow field, and revealed the post-eruptive ice flows and ice cauldron dynamics. The total dense-rock equivalent (DRE) volume of lava flows is estimated to be (1431 ± 2) million m 3 . Then, we present the aftermath of a landslide in Kinnikinnick, Alaska, yielding a total landslide volume of (400 ± 8) × 103 m 3 and a total area of 0.025 km 2 . ArcticDEM is further proven useful for studying retrogressive thaw slumps (RTS). The ArcticDEM-mapped RTS profile is validated by ICESat-2 and drone photogrammetry resulting in a standard deviation of 0.5 m. Volume estimates for lake-side and hillslope RTSs range between 40,000 ± 9000 m 3 and 1,160,000 ± 85,000 m 3 , highlighting applicability across a range of RTS magnitudes. A case study for mapping tundra snow demonstrates ArcticDEM's potential for identifying high-accumulation, late-lying snow areas. The approach proves effective in quantifying relative snow accumulation rather than absolute values (standard deviation of 0.25 m, bias of −0.41 m, and a correlation coefficient of 0.69 with snow depth estimated by unmanned aerial systems photogrammetry). Furthermore, ArcticDEM data show its feasibility for estimating tundra vegetation heights with a standard deviation of 0.3 m (no bias) and a correlation up to 0.8 compared to the light detection and ranging (LiDAR). The demonstrated capabilities of ArcticDEM will pave the way for the broad and pan-Arctic use of this new data source for many disciplines, especially when combined with other imagery products. The wide range of signals embedded in ArcticDEM underscores the potential challenges in deciphering signals in regions affected by various geological processes and environmental influences.

Chunli Dai↗
NASA NTRSDate not supplied

A Grounded Theory of UAS Reported Accidents

Context: The manufacture and operation of sUAS are not as regulated as today’s commercial operation, and their widespread use introduces new risks and hazards to the general public. Aim: Our intent is to understand the various processes that prevent or portend sUAS incidents or accidents and the influence that pilots’ expectations and training have on these processes. Method: We use classic grounded theory on sUAS reported accidents (and incidents). Results: We identified three categories, Control Interference, Reviewing and Reporting, which describe various processes surrounding UAS accidents based on the dataset analyzed. Conclusion: The use of grounded theory can offer a different perspective in the analysis of UAS accidents. In addition, the traceability between data sources and the method results facilitate result validation, and navigation. While the results are preliminary, the concepts can be expanded through the use of other accident datasets or used as basis for UAS accident surveys.

Carlos Paradis↗
NASA NTRSDate not supplied

A Novel Framework for Multi-Path Data Fusion in Earth Observation and New Observing Strategies: Applications to Predicting Forest Canopy Height

Exponential growth of data from Earth Observation (EO) assets has necessitated the development of sophisticated methods for data interpretation and management. NASA’s New Observing Strategy (NOS) approach aims to coordinate operations among complex heterogenous systems of constellations, requiring advanced Artificial Intelligence and Machine Learning (AI/ML) techniques. Despite significant advancements in AI/ML across various domains, the EO and machine learning for satellite (SatML) fields remain fragmented, often relying on adapted techniques rather than domain-specific solutions. We present a novel end-to-end data fusion framework tailored specifically for EO and SatML, addressing this gap by facilitating rapid development of AI/ML applications. This framework, called, Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), sup- ports the entire AI/ML lifecycle, from dataset manipulation, to model training, evaluation, and logging, and is designed to expedite the development of next-generation NOS deployments and SOTA in EO. We apply our framework to predict canopy height model (CHM) derived from lidar data. We integrate multiple data modalities through a hierarchical, multi-path model architecture, effectively identifying and leveraging the unique strengths of each data source to enhance predictive accuracy. Our experiments demonstrate that the multi-path architecture outperforms traditional single-path models and provides significant advantages in both accuracy and computational efficiency.

Mark Moussa↗
NASA NTRSDate not supplied

Gigatraj: An Atmospheric Trajectory Model

Atmospheric trajectory models have a long history of success in tracking air motions in the lower stratosphere and upper troposphere over periods of up to a few days. Parcels have been traced backwards from observations to identify whatever phenomena (strong convection, volcanic eruptions, rocket launches, etc.) put their signature on them. Parcels have also been initialized at a known event and traced forward to examine their subsequent physical and chemical evolution. We describe a new trajectory model, "gigatraj," that aims to increase exibility by (a) making it straightforward to use new meteorological data sources, including those not based on regular latitude-longitude grids; (b) enabling a run-time choice of vertical coordinate system for kinematic and/or quasi-isentropic calculations; (c) allowing for the output of arbitrary meteorological products, selectable by the user and interpolated to the parcels' locations and times. The model can be run in a serial or parallel processing environment, so that large numbers of parcels can be traced in a reasonable time. Information is presented on model accuracy and performance. The former is demonstrated by runs using both test-pattern winds (comparing expected paths with actual output) and real-world winds (comparing forward and backward runs to characterize how well parcels retrace their paths). Sample cases are also shown, including a reverse domain lling (RDF) calculation illustrating a tropopause fold event. Model output can be displayed using the new Visualization And Lagrangian dynamics Immersive eXtended Reality (VALIXR) system, and an example will be shown. In addition, we describe work to incorporate a version of gigatraj into the Goddard Earth Observing System (GEOS) of NASA's Global Modeling and Assimilation O ce (GMAO) at Goddard Space Flight Center. This enables trajectory calculations to be performed within the running GEOS model at the latter's native time resolution, instead of using the winds from every few hours. It also provides access to all of GEOS's internal variables as they are calculated. This module may be useful, for example, for tracking rapid chemical changes in a Lagrangian framework.

dynamics↗
NASA NTRS1985

Improved mapping of radio sources from VLBI data by least-square fit

A method is described for producing improved mapping of radio sources from Very Long Base Interferometry (VLBI) data. The method described is more direct than existing Fourier methods, is often more accurate, and runs at least as fast. The visibility data is modeled here, as in existing methods, as a function of the unknown brightness distribution and the unknown antenna gains and phases. These unknowns are chosen so that the resulting function values are as near as possible to the observed values. If researchers use the radio mapping source deviation to measure the closeness of this fit to the observed values, they are led to the problem of minimizing a certain function of all the unknown parameters. This minimization problem cannot be solved directly, but it can be attacked by iterative methods which we show converge automatically to the minimum with no user intervention. The resulting brightness distribution will furnish the best fit to the data among all brightness distributions of given resolution.

Rodemich, E. R.↗
NASA NTRS2011

Analysis of XMM-Newton Data from Extended Sources and the Diffuse X-Ray Background

Reduction of X-ray data from extended objects and the diffuse background is a complicated process that requires attention to the details of the instrumental response as well as an understanding of the multiple background components. We present methods and software that we have developed to reduce data from XMM-Newton EPIC imaging observations for both the MOS and PN instruments. The software has now been included in the Science Analysis System (SAS) package available through the XMM-Newton Science Operations Center (SOC).

Snowden, Steven↗
NASA NTRS1974

The economic impact of remote sensing data as the source of nonpoint pollution monitoring and control

Nonpoint pollution of streams with sediment as a result of runoff from alternative uses of land has become a socially unacceptable product of economic activity. This report describes a research approach to economically achieve correction of the nonpoint pollution problem. The research approach integrates the economic model with those data which may be obtainable from remotely sensed sources. The economic problem involves measurement of the direct benefits and costs associated with the changes in land management activities necessary to reduce the level of nonpoint pollution. Remotely sensed data from ERTS-1 may provide some of the information required for the economic model which indicates efficient solutions to the nonpoint pollution problem. Three classes of data (i.e., soil categories, vegetative cover, and water turbidity) have the potential to be measured by ERTS-1 systems. There is substantial research which indicates the ability of ERTS-1 to measure these classes of data under selected conditions.

Miller, W. L.↗
NASA NTRS1985

Source energy spectra from demodulation of solar particle data by interplanetary and coronal transport

The data on source energy spectra of solar cosmic rays (SCR), i.e. the data on the spectrum form and on the absolute SCR are of interest for three reasons: (1) the SCR contain the energy comparable to the total energy of electromagnetic flare radiation (less than or equal to 10 to the 32nd power ergs); (2) the source spectrum form indicates a possible acceleration mechanism (or mechanism); and (3) the accelerated particles are efficiently involved in nuclear electromagnetic and plasma processes in the solar atmosphere. Therefore, the data on SCR source spectra are necessary for a theoretical description of the processes mentioned and for the formulation of the consistent flare model. Below it is attempted to sound solar particle sources by means of SCR energy spectrum obtained near the Sun, at the level of the roots of the interplanetary field lines in the upper solar corona. Data from approx. 60 solar proton events (SPE) between 1956-1981. These data were obtained mainly by the interplanetary demodulation of observed fluxes near the Earth. Further, a model of coronal azimuthal transport is used to demodulate those spectra, and to obtain the source energy spectra.

Perez-Peraza, J.↗
NASA NTRS2017

Localization of L-Band RFI Sources from SMAP Data

RFI (Radio-Frequency Interference) in the 1400-1427 MHz band degrades the quality of measurements made by satellite missions such as SMAP (Soil Moisture Active/Passive), Aquarius and SMOS (Soil Moisture and Ocean Salinity). A technique is presented here to estimate the location on the ground of RFI sources using SMAP measurements. The results of this technique have been validated against data derived by other means.

Soldo, Yan↗
NASA NTRS2003

A New Method to Resolve X-Ray Halos around Point Sources with Chandra Data and Its Application to Cygnus X-1

With excellent angular resolution, good energy resolution, and a broad energy band, the Chandra Advanced CCD Imaging Spectrometer (ACIS) is the best instrument for studying the X-ray halos around some Galactic X-ray point sources caused by the dust scattering of X-rays in the interstellar medium. However, the direct images of bright sources obtained with the ACIS usually suffer from severe pileup. Making use of the fact that an isotropic image could be reconstructed from its projection in to any direction, we can reconstruct the images of the X-ray halos from the data obtained with the High Energy Transition Grating Spectrometer (HETGS) and/or in continuos clocking (CC) mode. These data have no or less serious pileup and enable us to take full advantage of the excellent angular resolution of Chandra. With the reconstructed high-resolution images, we can probe the X-ray halos as close as 1" to their associated point sources. Applying this method to Cygnus X-1 observed with the Chandra HETGS in CC mode, we derived an energy-dependent radial halo flux distribution and concluded that in a circular region (2' in radius) centered a the point source: (1) relative to the total intensity, the fractional halo intensity is about 15% at keV and drops to aboout 5% at approximately 6 keV (2) about 50% of the halo photons are within the region of a radius less than 40 inches and (3) the spectrum of the pooint source is slightly distorted by the halo contamination.

Yao, Yangsen↗
NASA NTRSDate not supplied

(ODIN): An Open Source, Low-Latency Data Integration & Visualization Framework for the NASA System Wide Safety Project's Disaster Response Safety Demonstration Series

The Open Data Integration Framework (ODIN) is an open source, low latency data integration and visualization framework (https://github.com/NASARace/race-odin) developed under NASA’s System WideSafety Program to demonstrate new safety capabilities designed to improve US airspace operations. Safety demonstrations are a set of increasingly complex (from public safety perspective) disaster response scenarios under which air systems must operate with increased capacity and include: 1) Wildland fire response, 2) Hurricane relief and recovery, 4) Emergency medical delivery via UAS and 4) Urban disaster relief. To accommodate disaster response, ODIN is field deployable and can scale on one or more multi-core, commodity laptops operating with full to limited or intermittent internet connectivity, conditions likely encountered during operations. ODIN runs as webserver with local, persistent data storage to serve either public or a secured, ad hoc network (e.g., an incident command post). The current released ODIN, ODIN-Fire is tailored for wildland fire management incorporating information on satellite overpasses with links to the near real-time data and imagery from the respective agencies. Included are winds data, an important variable for emergency responders and airspace operations, and high-resolution wind forecasts generated by super-computing resources and ingested into ODIN. As an open-source project, ODIN has attracted interest from multiple entities. We will show how 1) a commercial field instrument and data provider uses ODIN to help users visualize, publish and integrate their in-situ sensor network data and 2) ODIN’s capabilities to ingest, integrate and display near-real time satellite data with air traffic and a USFS winds forecast model used in fire response and post-fire assessment. Within NASA ODIN demonstrated novel, near terminal airspace safety capabilities for a project close-out event and previously it monitored the national airspace in real-time to meet an agency milestone. ODIN is presently under development for the anticipated hurricane relief and response demonstration notionally scheduled for the 2025-27 time frame and is available from NASA's github at the above link.

Aeronautics↗
NASA NTRS2010

The HST/ACS Coma Cluster Survey. II. Data Description and Source Catalogs

The Coma cluster, Abell 1656, was the target of a HST-ACS Treasury program designed for deep imaging in the F475W and F814W passbands. Although our survey was interrupted by the ACS instrument failure in early 2007, the partially-completed survey still covers approximately 50% of the core high density region in Coma. Observations were performed for twenty-five fields with a total coverage area of 274 aremin(sup 2), and extend over a wide range of cluster-centric radii (approximately 1.75 Mpe or 1 deg). The majority of the fields are located near the core region of Coma (19/25 pointings) with six additional fields in the south-west region of the cluster. In this paper we present SEXTRACTOR source catalogs generated from the processed images, including a detailed description of the methodology used for object detection and photometry, the subtraction of bright galaxies to measure faint underlying objects, and the use of simulations to assess the photometric accuracy and completeness of our catalogs. We also use simulations to perform aperture corrections for the SEXTRACTOR Kron magnitudes based only on the measured source flux and its half-light radius. We have performed photometry for 76,000 objects that consist of roughly equal numbers of extended galaxies and unresolved objects. Approximately two-thirds of all detections are brighter than F814W=26.5 mag (AB), which corresponds to the 10sigma, point-source detection limit. We estimate that Coma members are 5-10% of the source detections, including a large population of compact objects (primarily GCs, but also cEs and UCDs), and a wide variety of extended galaxies from cD galaxies to dwarf low surface brightness galaxies. The initial data release for the HST-ACS Coma Treasury program was made available to the public in August 2008. The images and catalogs described in this study relate to our second data release.

Hammer, Derek↗
NASA NTRS2016

The Kepler Science Data Processing Pipeline Source Code Road Map

We give an overview of the operational concepts and architecture of the Kepler Science Processing Pipeline. Designed, developed, operated, and maintained by the Kepler Science Operations Center (SOC) at NASA Ames Research Center, the Science Processing Pipeline is a central element of the Kepler Ground Data System. The SOC consists of an office at Ames Research Center, software development and operations departments, and a data center which hosts the computers required to perform data analysis. The SOC's charter is to analyze stellar photometric data from the Kepler spacecraft and report results to the Kepler Science Office for further analysis. We describe how this is accomplished via the Kepler Science Processing Pipeline, including, the software algorithms. We present the high-performance, parallel computing software modules of the pipeline that perform transit photometry, pixel-level calibration, systematic error correction, attitude determination, stellar target management, and instrument characterization.

Kepler pipeline software↗
NASA NTRS1986

Identifying deforestation in Brazil using multiresolution satellite data

The use of multiresolution satellite data to monitor deforestation on a continental/subcontinental scale is examined. MSS, local area coverage (LAC), global area coverage (GAC), and GOES data were applied to the study of deforestation in Rondonia, Brazil; the characteristics of these sensors are described. The probability thresholding and vegetation-index thresholding procedures used to process the data in order to differentiate between forest from nonforest are analyzed. The LAC, GAC, and GOES data are compared to MSS data. It is observed that GOES data is not useful for monitoring colonization projects due to excessive noise in the data; the GAC data is only applicable in large areas of contiguous forest clearing; the MSS data when available is applicable as a ground data reference source for differentiating cleared areas from primary; the LAC data are capable of delineating colonization clearings; and the probability thresholding procedure differentiates forest from nonforest more accurately than the vegetation-index procedure. The data reveal that the LAC data combined with the probability threshold procedure provide the best data-source/classification-procedure combination.

Nelson, R.↗
NASA NTRS1980

Cataloged infrared sources in NIPSS data. I - The RSO 1 catalog

A small number of selected near-infrared and visual photographic pairs from the Steward Observatory Near Infrared Photographic Sky Survey have been examined for content of stars more red than (V-I) of about 2.5 magnitudes. A simple manual extraction of these objects was carried out as a part of a preliminary evaluation of survey data and techniques for reducing it; the resulting list has been compiled as the first installment of a Catalog of Red Stellar Objects (Craine et al. 1979). Results of a cross correlation of this catalog with the IRC, AFGL, and EIC infrared catalogs are here presented. The results indicate that these photographs may be particularly useful for purposes of optical identification of short-wavelength infrared sources to limits much fainter than represented by presently existing infrared catalogs.

Horner, V. M.↗
NASA NTRS2003

Moving NASA Remote Sensing Data to the GIS Environment for Health Studies

There has been an increasing demand by the health community for improved data on many different environmental factors relevant to the links between the environment and disease occurrence and transmission. These data are important for GIS-based monitoring, risk mapping, and surveillance of epidemiological parameters on a large number of different spatial, temporal, and spectral resolutions. Accordingly, NASA is developing new approaches to data collection and distribution in order to improve access to multiple sources of data streams to increase spatial and temporal coverage. Methods are being developed to incorporate different, scalable capabilities to handle multiple data sources by adding, deleting and replacing components as required as well as associated tools for their management. An approach has been to search for innovative solutions focused on the creation, use and manipulation of data stored in many different archives. These include data transformation and combination as well as data and information tools that can assist the public health and science community to use existing and anticipated products in new and flexible ways. This presentation will provide an inventory of geophysical parameters derived from satellite remote sensing sensors that are useful for GIS-based public health studies. The presentation will also discuss the physical and scientific limitations of access to and use of these data for health applications such as resolution and format differences, lack of software interoperability, data access problems. Finally, there will be a summary of the recent steps the NASA program has taken to bring NASA-generated satellite products to a wider range of users in the GIS community.

Vicente, Gilberto A.↗
NASA NTRS2003

Adjoint-Based Methods for Estimating CO2 Sources and Sinks from Atmospheric Concentration Data

Work to develop adjoint-based methods for estimating CO2 sources and sinks from atmospheric concentration data was initiated in preparation for last year's summer institute on Carbon Data Assimilation (CDAS) at the National Center for Atmospheric Research in Boulder, CO. The workshop exercises used the GSFC Parameterized Chemistry and Transport Model and its adjoint. Since the workshop, a number of simulations have been run to evaluate the performance of the model adjoint. Results from these simulations will be presented, along with an outline of challenges associated with incorporating a variety of disparate data sources, from sparse, but highly precise, surface in situ observations to less accurate, global future satellite observations.

Andrews, Arlyn E.↗