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NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗

NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗

NASA NTRS2011

Fabrication and Test of an Optical Magnetic Mirror

Traditional mirrors at optical wavelengths use thin metalized or dielectric layers of uniform thickness to approximate a perfect electric field boundary condition. The electron gas in such a mirror configuration oscillates in response to the incident photons and subsequently re-emits fields where the propagation and electric field vectors have been inverted and the phase of the incident magnetic field is preserved. We proposed fabrication of sub-wavelength-scale conductive structures that could be used to interact with light at a nano-scale and enable synthesis of the desired perfect magnetic-field boundary condition. In a magnetic mirror, the interaction of light with the nanowires, dielectric layer and ground plate, inverts the magnetic field vector resulting in a zero degree phase shift upon reflection. Geometries such as split ring resonators and sinusoidal conductive strips were shown to demonstrate magnetic mirror behavior in the microwave and then in the visible. Work to design, fabricate and test a magnetic mirror began in 2007 at the NASA Goddard Space Flight Center (GSFC) under an Internal Research and Development (IRAD) award Our initial nanowire geometry was sinusoidal but orthogonally asymmetric in spatial frequency, which allowed clear indications of its behavior by polarization. We report on the fabrication steps and testing of magnetic mirrors using a phase shifting interferometer and the first far-field imaging of an optical magnetic mirror.

Hagopian, John G.↗

NASA NTRS2018

Star & Planet Formation Studies and Opportunities with SOFIA

Star formation, the most fundamental process in the universe, is linked to planet formation and thus to the origin and evolution of life. We have a general outline of how planets and stars form, yet unraveling the details of the physics and chemistry continues to challenge us. The infrared and submillimeter part of the spectrum hold the most promise for studying the beginnings of star formation. The observational landscape recently shaped by Spitzer, Herschel and ALMA, continues to challenge our current theories. SOFIA, the Stratospheric Observatory for Infrared Astronomy, equipped with state-of-the-art infrared instrumentation to a vantage point at 45,000 feet (13.7 kilometers) flight altitude that is above 99.9 percent of the Earth's water vapor, enables observations in the infrared through terahertz frequencies not possible from the ground. SOFIA is a community observatory, about to start its sixth annual observing cycle. My talk will focus on recent results in advancing star and planet formation processes using SOFIAʼs imaging and polarimetric capabilities, and the upcoming science enabled by the 3rd generation instrument High-Resolution Mid-Infrared Spectrometer (HIRMES) to be commissioned in 2019. I will show how mid-infrared imaging is used to test massive star formation theories, how far-infrared polarimetry on sub-parsec scales is directly testing the role of magnetic fields in molecular clouds, and how velocity-resolved high-resolution spectroscopy will push forward our understanding of proto-planetary disk science. I will also summarize upcoming opportunities with the SOFIA observatory. For the latest news about your flying observatory, see https://sofia.usra.edu/.

Star formation↗

NASA NTRS2018

Field Exploration and Life Detection Sampling for Planetary Analogue Research (FELDSPAR): Variability and Correlation in Biomarker and Mineralogy Measurements from Icelandic Mars Analogues

In situ exploration of planetary environments allows biochemical analysis of sub-centimeter-scale samples; however, landing sites are selected a priori based on measurable meter- to kilometer-scale geological features. Optimizing life detection mission science return requires both understanding the expected biomarker distributions across sample sites at different scales and efficiently using first-stage in situ geochemical instruments to justify later-stage biological or chemical analysis. Icelandic volcanic regions have an extensive history as Mars analogue sites due to desiccation, low nutrient availability, and temperature extremes, in addition to the advantages of geological youth and isolation from anthropogenic contamination. Many Icelandic analogue sites are also rugged and remote enough to create the same type of instrumentation and sampling constraints typically faced by robotic exploration.

Planetary↗

NASA NTRS2018

Two-thirds of Global Cropland Area Impacted by Climate Oscillations

The El Nino Southern Oscillation (ENSO) peaked strongly during the boreal winter 2015-2016, leading to food insecurity in many parts of Africa, Asia and Latin America. Besides ENSO, the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) are known to impact crop yields worldwide. Here we assess for the first time in a unified framework the relationship between ENSO, IOD and NAO and simulated crop productivity at the sub-country scale. Our findings reveal that during 1961-2010, crop productivity is significantly influenced by at least one large-scale climate oscillation in two-thirds of global cropland area. Besides observing new possible links, especially for NAO in Africa and the Middle East, our analyses confirm several known relationships between crop productivity and these oscillations. Our results improve the understanding of climatological crop productivity drivers, which is essential for enhancing food security in many of the most vulnerable places on the planet.

IOD↗

NASA NTRS2016

FluidCam 1 & 2 - UAV-based Fluid Lensing Instruments for High-Resolution 3D Subaqueous Imaging and Automated Remote Biosphere Assessment of Reef Ecosystems

We present NASA ESTO FluidCam 1 & 2, Visible and NIR Fluid-Lensing-enabled imaging payloads for Unmanned Aerial Vehicles (UAVs). Developed as part of a focused 2014 earth science technology grant, FluidCam 1&2 are Fluid-Lensing-based computational optical imagers designed for automated 3D mapping and remote sensing of underwater coastal targets from airborne platforms. Fluid Lensing has been used to map underwater reefs in 3D in American Samoa and Hamelin Pool, Australia from UAV platforms at sub-cm scale, which has proven a valuable tool in modern marine research for marine biosphere assessment and conservation. We share FluidCam 1&2 instrument validation and testing results as well as preliminary processed data from field campaigns. Petabyte-scale aerial survey efforts using Fluid Lensing to image at-risk reefs demonstrate broad applicability to large-scale automated species identification, morphology studies and reef ecosystem characterization for shallow marine environments and terrestrial biospheres, of crucial importance to improving bathymetry data for physical oceanographic models and understanding climate change’s impact on coastal zones, global oxygen production, carbon sequestration.

Chirayath, Ved↗

NASA NTRS2018

NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Neural↗

NASA NTRS2019

WRLES: Wave Resolving Large-Eddy Simulation Code, Theory and Usage

A computational fluid dynamics code has been developed for large-eddy simulations (LES) of turbulent flow. The code uses high-order of accuracy and high-resolution numerical methods to minimize solution error and maximize the resolution of the turbulent structures. Spatial discretization is performed using explicit central differencing. The central differencing schemes in the code include 2nd- to 12th-order standard central difference methods as well as 7-, 9-, 11- and 13-point dispersion relation preserving schemes. Solution filtering and high-order shock capturing are included for stability. Time discretization is performed using multistage Runge-Kutta methods that are up to 4th order accurate. Several options are available to model turbulence including: Baldwin-Lomax and Spalart-Allmaras Reynolds-averaged Navier-Stokes turbulence models, and Smagorinsky, Dynamic Smagorinsky and Vreman sub-grid scale models for LES. This report presents the theory behind the numerical and physical models used in the code and provides a user's manual to the operation of the code.

High-Order↗

NASA NTRS2019

The Space Debris Sensor Experiment

The Space Debris Sensor (SDS) is a NASA Class 1E technology demonstration external payload aboard the International Space Station (ISS). With approximately one square meter of detection area, the SDS is attached to the European Space Agency Columbus module facing the ISS velocity vector with minimal obstruction from ISS hardware. The SDS is the first flight demonstration of the Debris Resistive/Acoustic Grid Orbital NASA-Navy Sensor (DRAGONS) technology developed and matured over 10 years by the NASA Orbital Debris Program Office (ODPO), in concert with the DRAGONS consortium, to provide information on the sub-millimeter scale orbital debris environment. The SDS demonstrated the capacity to read 4 resistive grids at 1 Hz, 40 acoustic sensors at 500 kHz, and record and downlink impact data to the ground. Observable and derived data from the SDS could provide information to models that are critical to understanding risks the small debris environment poses to spacecraft in low Earth orbit. The technology demonstrated by the SDS is a major step forward in monitoring and characterizing the space debris environment. This paper will address the technical performance of the SDS during its operational lifetime and its realization of technical and scientific goals. The SDS was intended to operate for 3 years; however, the payload incurred multiple anomalies during its operational life. Subsequently termed “Anomaly #1,” the first was the symptomatic loss of low data rate 1553 channel command and telemetry. The second, Anomaly #2, was loss of all low- and medium-data rate (Ethernet) telemetry. Anomaly #2 proved to be unrecoverable, leading to loss of the payload after approximately 26 days on-board the ISS. Therefore, this paper also addresses the anomalies that occurred during operation of the SDS, their attribution, and their resolution. Lessons learned are described when relevant to anomaly identification, attribution, and resolution.

Anz-Meador, P.↗

NASA NTRS2016

Onboard Data Analysis for the Planetary Instrument for X-Ray Lithography

Imaging spectrometers are invaluable instruments for robotic science exploration, enabling quantitative maps of physical and chemical properties at high spatial resolution. This is particularly valuable in remote missions to other planetary bodies like Mars. The PIXL instrument on the Mars 2020 rover will deploy an arm-mounted X-Ray fluorescence spectrometer to map chemical composition at sub-millimeter scales. Its high resolution places dramatic new demands on instrument placement accuracy and measurement time. We address these challenges using novel on board data analysis strategies inspired by FRC science autonomy research.

Thompson, David R.↗

NASA NTRS2020

Investigating Relationships Between Geochemistry and Physical Grain Characteristics Along a Glacio-Fluvial-Aeolian Sediment Transport Pathway Using µXRF

Iceland’s basaltic volcanic rocks and glacial, fluvial, and aeolian landscapes resemble those studied on Mars, which makes it an ideal location to study the evolution of basaltic landscapes, the weathering and alteration of basaltic sediments in cold and wet environments, and the generation of a basaltic sedimentary record. The SAND-E: Semi-Autonomous Navigation for Detrital Environments project examines physical and chemical changes in sediments transported through basaltic fluvial and aeolian environments, and tests operational scenarios, which include a drone and a robotic rover instrumented with autonomous terrain analysis software. As part of the SAND-E project, we examined a glacial outwash plain at Skjaldbreiðauhraun, in SW Iceland. This study uses micro X-ray fluorescence (μXRF) to examine the chemical and physical properties of unconsolidated sediment-size fractions from 710 μm to < 63 μm along a downstream transect from a glacial sourced watershed. μXRF is ideally suited for this task because it maps elemental distributions at sub-grain scales thereby allowing a direct correlation between grainsize, grain shape, and chemistry. It is also a good analog technique for the Mars 2020 mission equipped with the PIXL (Planetary Instrument for X-ray Lithochemistry) that will be deployed at Jezero Crater, Mars.

Champion, E.↗

NASA NTRSDate not supplied

Near real-time air quality forecasts using the NASA GEOS model

NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.

Air Quality Forecast↗

NASA NTRS2017

Precision Projector Laboratory: Detector Characterization with an Astronomical Emulation Testbed

As astronomical observations benefit from improved sensitivity, the effectiveness of scientific programs is becoming limited by systematics that often originate in poorly understood image sensor behavior. Traditional, bottom-up detector characterization methods provide one way to model underlying detector physics, and generate ever more faithful numerical simulations, but this approach is vulnerable to preconceptions and over-simplification. The alternative top-down approach is laboratory emulation, which enables observation, calibration, and analysis scenarios to be tested without relying on a complete understanding of the underlying physics. This complements detector characterization and simulation efforts by testing their validity. We describe a laboratory facility and experimental testbed that supports the emulation of a wide range of mission concepts such as gravitational weak lensing measurements by WFIRST and high precision spectrophotometry of transiting exoplanets by JWST. An Offner relay projects readily customizable "scenes" (e.g. stars, galaxies, spectra) with very low optical aberration over the full area of a typical optical or near infrared image sensor. f/8 and slower focal ratios may be selected, spanning those of most proposed space missions and approximating the point spread function (PSF) size of seeing limited ground based surveys. Diffraction limited PSFs are projected over a wide field of view and wavelength range to deliver highly predictable image patterns down to sub-pixel scales with stable intensity and fine motion control. The testbed enables realistic validation of detector performance on science-like images, which aids mission design and survey strategy, as well as targeted investigations of various detector effects.

Seshadri, Suresh↗

NASA NTRS2019

The operational environment and rotational acceleration of asteroid (101955) Bennu from OSIRIS-REx observations

During its approach to asteroid (101955) Bennu, NASA's Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) spacecraft surveyed Bennu's immediate environment, photometric properties, and rotation state. Discovery of a dusty environment, a natural satellite, or unexpected asteroid characteristics would have had consequences for the mission's safety and observation strategy. Here we show that spacecraft observations during this period were highly sensitive to satellites (sub-meter scale) but reveal none, although later navigational images indicate that further investigation is needed. We constrain average dust production in September 2018 from Bennu's surface to an upper limit of 150g s −1 averaged over 34 min. Bennu's disk-integrated photometric phase function validates measurements from the pre-encounter astronomical campaign. We demonstrate that Bennu's rotation rate is accelerating continuously at 3.63+- 0.52 x10 −6 degrees A day -2 , likely due to the Yarkovsky-O'Keefe-Radzievskii-Paddack (YORP) effect, with evolutionary implications.

Carl William Hergenrother↗

NASA NTRS2019

The operational environment and rotational acceleration of asteroid (101955) Bennu from OSIRIS-REx observations

During its approach to asteroid (101955) Bennu, NASA’s Origins, Spectral Interpretation, Resource Identiﬁcation, and Security-Regolith Explorer (OSIRIS-REx) spacecraft surveyed Bennu’s immediate environment, photometric properties, and rotation state. Discovery of a dusty environment, a natural satellite, or unexpected asteroid characteristics would have had consequences for the mission’s safety and observation strategy. Here we show that spacecraft observations during this period were highly sensitive to satellites (sub-meter scale) but reveal none, although later navigational images indicate that further investigation is needed. We constrain average dust production in September 2018 from Bennu’s surface to an upper limit of 150 g/s averaged over 34 min. Bennu’s disk-integrated photometric phase function validates measurements from the pre-encounter astronomical campaign. We demonstrate that Bennu’s rotation rate is accelerating continuously at 3.63 ± 0.52 × 10^(–6) degrees/sq. day, likely due to the Yarkovsky–O’Keefe–Radzievskii–Paddack (YORP) effect, with evolutionary implications.

C. W. Hergenrother↗

NASA NTRS2021

The high energy Universe at ultra-high resolution: the power and promise of X-ray interferometry

We propose the development of X-ray interferometry (XRI), to reveal the Universe at high energies with ultra-high spatial resolution. With baselines which can be accommodated on a single spacecraft, XRI can reach 100 μ as resolution at 10 Å (1.2 keV) and 20 μ as at 2 Å (6 keV), enabling imaging and imaging-spectroscopy of (for example) X-ray coronae of nearby accreting supermassive black holes (SMBH) and the SMBH ‘shadow’; SMBH accretion flows and outflows; X-ray binary winds and orbits; stellar coronae within ∼100 pc and many exoplanets which transit across them. For sufficiently luminous sources XRI will resolve sub-pc scales across the entire observable Universe, revealing accreting binary SMBHs and enabling trigonometric measurements of the Hubble constant with X-ray light echoes from quasars or explosive transients. A multi-spacecraft ‘constellation’ interferometer would resolve well below 1 μ as, enabling SMBH event horizons to be resolved in many active galaxies and the detailed study of the effects of strong field gravity on the dynamics and emission from accreting gas close to the black hole.

Phil Uttley↗

NASA NTRS2020

Investigation Of Prandtl-Ishlinskii Hysteresis Compensation for Deep Space Optical Communications Pointing Control

This paper studies the simulated performance of the Deep Space Optical Communication (DSOC) laser downlink pointing control system that has a pointing requirement at the sub-microradian scale. To achieve the required pointing precision, the DSOC Flight Laser Transceiver (FLT) point-ahead mirror (PAM) employs piezoelectric actuators, which are widely used in precision applications due to their high motion resolution, rapid response, and high output forces. However, a piezoelectric actuator's performance is often limited by nonlinearities in their response, most significantly hysteresis. To study the effect of hysteresis on our simulated pointing performance, an invertible hysteresis model is developed using the Classical Prantdl-Ishlinskii (CPI) method. The simulation includes models of DSOC hardware and electronic system components based on key parameters (bias, frequency response, noise), with the hysteresis modeled in the PAM piezo actuator dynamics. A Proportional-Integral controller provides feedback with measurements from two potential sources: strain gauge sensors or a photon-counting camera. To compensate for hysteresis behavior, a feedforward inverse CPI hysteresis model is implemented in the control system. Two CPI models are built using different sampling methods (uniform sampling and cosine spacing) and controller performance is compared. For the maximum command range of ±400 μrad at 1 mHz, results show that the feedforward combined with feedback control reduces output tracking error by 50% or 58% (to 0.20 or 0.38 μrad) compared to feedback control alone (from 0.40 or 0.90 μrad), corresponding to the uniform-sampled and cosine-sampled CPI models, respectively.

Marinello, Lindsey A.↗