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NASA NTRSDate not supplied

Generation of Continental Scale Percent Tree Cover Product Using Deep-learning and Multi-scale Remote Sensing Data

Spatially explicit percent tree cover (TC) estimation is critical for mapping forest aboveground biomass and its dynamics. While various TC products have been developed, there has not been a generalized framework that can be applied to diverse terrestrial ecosystems due to underlain extreme complexities. Deep learning algorithms can learn a spatial pattern and radiometric characteristics of tree canopy as a robust approximation of physical or empirical models, and thus have emerged as promising and efficient tools for large-scale TC mapping. In this study, we synergistically use very high-resolution aerial imageries (National Agriculture Imagery Program, NAIP) and medium resolution Landsat data to map continental-scale TC (CONUS and Mexico) through a hierarchical deep learning approach (Convolutional Neural Network), i.e., NAIP TC generated from a NAIP model is utilized to train a Landsat model. The produced TC product (hereafter, NEX-TC) is able to capture the spatial pattern of TC distribution and its changes driven by natural disturbance and human land management. We further explore and analyze the reliability and potential uncertainty of the NEX-TC by comparing it to lidar- (lidar-TC), National Land Cover Database (NLCD-TC), and MODIS Vegetation Continuous Field (MODIS-TC). This evaluation practice reveals that TC products based on passive optical sensors tend to underestimate TC across all land cover types while Landsat-based TCs (i.e., NEX-TC & NLCD-TC) perform better than the coarser MODIS TC estimate. Our results show that the NEX-TC is generally comparable to NLCD-TC but it particularly outperforms NLCD-TC and MODIS-TC over the dense forests where lidar-TC indicates >80% TC. These results indicate that our hierarchical deep learning approach and TC product will be effective and useful for characterizing large-scale tree cover and possibly associated carbon dynamics.

Landsat↗

NASA NTRS1991

Description of real-time Ada software implementation of a power system monitor for the Space Station Freedom PMAD DC testbed

The authors describe the Ada language software developed to perform the electrical power system monitoring functions for the NASA Lewis Research Center's Power Management and Distribution (PMAD) DC testbed. The results of the effort to implement this monitor are presented. The PMAD DC testbed is a reduced-scale prototype of the electric power system to be used in Space Station Freedom. The power is controlled by smart switches known as power control components (or switchgear). The power control components are currently coordinated by five Compaq 386/20e computers connected through an 802.4 local area network. The power system monitor algorithm comprises several functions, including periodic data acquisition, data smoothing, system performance analysis, and status reporting. Data are collected from the switchgear sensors every 100 ms, then passed through a 2-Hz digital filter. System performance analysis includes power interruption and overcurrent detection. The system monitor required a hardware timer interrupt to activate the data acquisition function. The execution time of the code was optimized by using an assembly language routine. The routine allows direct vectoring of the processor to Ada language procedures that perform periodic control activities.

Ludwig, Kimberly↗

NASA NTRS1996

A Sundial-Atlas Precursor to the TIMED Mission: A Quick-Response Global Investigation into Coupled Lower Thermospheric, Ionospheric, and Mesospheric Physics

The SUNDIAL-ATLAS effort was a global-scale investigation which responded to the science priorities of the ITM Panel, the Integrated SPD Strategy Implementation Plan as a whole, and the need for potential cost-saving design criteria for the TIMED mission. The investigation focused on coupling processes in the ionospheric-thermospheric system, taking advantage of the timelines of the ATLAS-1 mission (March 1992), and the global-scale ground-based measurement and modeling activities of the SUNDIAL program. The collaborative SUNDIAL-ATLAS activity was the first opportunity for global measurements of the chemistry, kinetics, and electrodynamics which couple the E-, Fl-, and F2-regions into a single interactive system. As such, the program represented an important first step in studying global issues; and accordingly, was an important proof of concept experiment relevant to the strategic mission plans for the ITM community and the upcoming intermediate class satellite program called TIMED. To meet its projected goals, TIMED must perform a number of critical measurements and execute a number of correlations that were to be tried and tested for the first time in the SUNDIAL-ATLAS investigation. This was designed to include global correlations of thermospheric and ionospheric composition during quiet and disturbed conditions and the co-registration of global-scale ground-based measurements with along-track satellite diagnostics. The SUNDIAL component of the current investigation addressed this need by acquiring, reducing, and analyzing a multi-sensor database that complemented and extended that which was generated in the ATLAS mission (Atmospheric Laboratory for Applications and Science). The SUNDIAL data defined the state and condition of the global-scale ionosphere in the altitude range from 100 km to the F2-peak. These data specified the peak heights and densities of the E-, Fl-, and F2-regions, along with the global distributions of intermediate, descending, and sequential layers which play a critical role in the dynamo region of the lower ionospheric-thermospheric domain. The data were collected by the SUNDIAL network of more than 50 ground-based stations utilizing ionosondes, radars, photometers, Fabry-Perot interferometers, and total electron content measurements. The data were acquired during a three-week period centered on the eight-day ATLAS-1 mission, which provided image and photometric sensing of the altitude distributions of the major and minor ions and neutrals in the ITM system. This report focuses on the scientific contributions of the SUNDIAL component of the overall investigation. Specific findings are described in seven papers (attached) published in the Journal of Geophysical Research.

Szuszczewicz, E. P.↗

NASA NTRSDate not supplied

Vision-Based Distributed Sensing at Vertiports for Advanced Air Mobility and Urban Air Mobility Approach and Landing

Advanced Air Mobility (AAM) encompasses a broad vision for air transportation, including Urban Air Mobility (UAM) as a subset. AAM aims to create a more connected and efficient transportation network across various geographical settings. However, navigating AAM aircraft in GPS-denied or degraded environments during approach and landing is challenging. Traditional vision aids like glideslopes and localizers are limited in vertiport environments due to narrow beam constraints and reduced landing angle options. This paper addresses the need for accurate navigation solutions at vertiports by proposing a vision-based distributed sensing (VIDIS) system utilizing cameras with bundle adjustment to assist incoming AAM aircraft during approach and landing while monitoring surface movements to enhance safety and efficiency. Key focus areas for current and future vertiport developers include identifying suitable sensor types and infrastructure standards to support AAM operations and including vertiport markings as vision-based navigation aids. The proposed system offers a novel approach to overcoming navigation challenges in AAM operations, particularly in urban settings where traditional aids may be insufficient. Preliminary simulation results with distributed cameras demonstrate promising outcomes for implementing bundle adjustment techniques to enhance vision-based navigation solutions at vertiports. Generating waypoint-based trajectories via waypoint integration using explicit guidance synthesis (WINGS) creates smooth AAM trajectories for landing at vertiports by using the current waypoint's terminal conditions as the initial conditions for the next waypoint. Combining bundle adjustment's ground-based solution of vertiport features with WINGS, Coplanar Pose from Orthography and Scaling with Iterations (COPOSIT), and an extended Kalman filter (EKF) estimates the state of an incoming aircraft during approach and landing at vertiports. Future work includes testing VIDIS in a high-fidelity simulation and with real-world data.

Distributed sensing↗

NASA NTRSDate not supplied

Vision-Based Distributed Sensing at Vertiports for Advanced Air Mobility and Urban Air Mobility Approach and Landing

Distributed sensing↗

NASA NTRS2002

Enhancing Human Health Using Space Imagery: Summary of Research

The International Space University (ISU) 2002 Summer Session was conducted in Pomona, California, June 29-August 30, 2002. Ninety-nine professionals and students from thirty-one countries attended the Summer Session. More than half of these students participated in the Student Research Design Project entitled, "HI-STAR: Health Improvements through Space Technologies and Resources." ISU's interdisciplinary Student Research Design Projects are intended to have great educational value for the participants and, at the same time, to result in a product that will be useful to the field. The HI-STAR project was a success on both counts. The mission of the ISU students' effort on HI-STAR was to develop and promote a global strategy to help combat malaria using space technology. Like the tiny yet powerful mosquito, HI-STAR is a small program that aspires to make a difference. Timely detection of malaria danger zones is essential to help health authorities and policy makers make decisions about how to manage limited resources for combating malaria. In 2001, the technical support network for prevention and control of malaria epidemics published a study called "Malaria Early Warning Systems: Concepts, Indicators and Partners." This study, funded by Roll Back Malaria, a World Health Organization initiative, offered a framework for a monitoring and early warning system. HI-STAR seeks to build on this proposal and enhance the space elements of the suggested framework. Malaria disease dynamics and distributions are related to environmental variables. From space, environmental conditions that support the growth of mosquito populations can be monitored, Malaria-specific information can be gathered from satellite-borne remote sensing instruments and ground-based sensors. This information can be integrated via geographic information systems (GIS) into a Malaria Information System (MIS) that can provide assessment analyses and risk maps as output. HI-STAR defines and suggests the development of an active MIS as a low-cost tool to help organizations plan their efforts to fight malaria.

Finarelli, Margaret G.↗

NASA NTRS2014

Method and Apparatus of Multiplexing and Acquiring Data from Multiple Optical Fibers Using a Single Data Channel of an Optical Frequency-Domain Reflectometry (OFDR) System

A method and system for multiplexing a network of parallel fiber Bragg grating (FBG) sensor-fibers to a single acquisition channel of a closed Michelson interferometer system via a fiber splitter by distinguishing each branch of fiber sensors in the spatial domain. On each branch of the splitter, the fibers have a specific pre-determined length, effectively separating each branch of fiber sensors spatially. In the spatial domain the fiber branches are seen as part of one acquisition channel on the interrogation system. However, the FBG-reference arm beat frequency information for each fiber is retained. Since the beat frequency is generated between the reference arm, the effective fiber length of each successive branch includes the entire length of the preceding branch. The multiple branches are seen as one fiber having three segments where the segments can be resolved. This greatly simplifies optical, electronic and computational complexity, and is especially suited for use in multiplexed or branched OFS networks for SHM of large and/or distributed structures which need a lot of measurement points.

Parker, Jr., Allen R↗

NASA NTRS2003

Radiation Climatology of the Greenland Ice Sheet Derived from Greenland Climate Network Data

The magnitude of shortwave and longwave dative fluxes are critical to surface energy balance variations over the Greenland ice sheet, affecting many aspects of its climate, including melt rates, the nature of low-level temperature inversions, the katabatic wind regime and buoyant stability of the atmosphere. Nevertheless, reliable measurements of the radiative fluxes over the ice sheet are few in number, and have been of limited duration and areal distribution (e.g. Ambach, 1960; 1963, Konzelmann et al., 1994, Harding et al., 1995, Van den Broeke, 1996). Hourly GC-Net radiation flux measurements spanning 1995-2001 period have been used to produce a monthly dataset of surface radiation balance components. The measurements are distributed widely across Greenland and incorporate multiple sensors

Steffen, Konrad↗

NASA NTRSDate not supplied

Machine Learning Algorithms for Aerosol and Cloud Detection Using CATS on the ISS

Clouds and aerosols are one of the largest uncertainties in understanding and forecasting the Earth’s changing climate system. The type and height of aerosols are important factors in determining the top-of-atmosphere (TOA) radiation budget, either direct reflection of solar radiation back to space and/or absorption of solar radiation. In addition to their impact on the Earth’s climate system, aerosols near the surface from wildfires, man-made pollution events, and dust storms are hazardous to human health. The phase and height of clouds also play a critical role in determining the role of clouds in the Earth’s climate system. Cirrus clouds in the upper troposphere can induce a significant daytime TOA warming effect, while liquid water clouds near the surface cause a large corresponding cooling effect. Lidar measurements provide accurate vertically resolved information about clouds and aerosols, including complex multi-layer scenes where passive sensors are challenged and at night, when passive sensors are unable to measure cloud and aerosol properties. The Cloud-Aerosol Transport System (CATS) is a lidar instrument that operated for 33 months on the International Space Station (ISS) at the 1064 nm wavelength to measure attenuated total backscatter and depolarization ratio. These fundamental measurements are used to derive “vertical feature mask” cloud and aerosol products, including layer top/base heights, layer geometrical thickness, aerosol type, and cloud phase. While space-based lidar systems like CATS provide cloud and aerosol vertical distributions that improve our understanding of the climate system, averaging of the daytime data from these sensors is required, at the expense of spatial resolution, to improve the daytime signal-to noise (SNR) and thus atmospheric layer detection. This presentation shows results from machine learning (ML) techniques that, when applied to CATS data: 1. improve the 1064 nm SNR 2. enable detection of atmospheric features during daytime with a horizontal resolution of 350 m or 5 km (compared to the 60 km required for standard CATS data products) 3. increase the number of atmospheric layers detected in the CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination, cloud phase, and aerosol typing compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime. The ML tools described in this paper can facilitate the development of smaller, low-cost lidar systems in the future and enable real-time accessibility of lidar data products from future lidar systems for monitoring and forecasting of hazardous events.

John Yorks↗

NASA NTRS2022

Neurosymbolic Hybrid Approach to Driver Collision Warning

There are two main algorithmic approaches to autonomous driving systems: (1) An end-to-end system in which a single deep neural network learns to map sensory input directly into appropriate warning and driving responses. (2) A mediated hybrid recognition system in which a system is created by combining independent modules that detect each semantic feature. While some researchers believe that deep learning can solve any problem, others believe that a more engineered and symbolic approach is needed to cope with complex environments with less data. Deep learning alone has achieved state-of-the-art results in many areas, from complex gameplay to predicting protein structures. In particular, in image classification and recognition, deep learning models have achieved accuracies as high as humans. But sometimes it can be very difficult to debug if the deep learning model doesn't work. Deep learning models can be vulnerable and are very sensitive to changes in data distribution. Generalization can be problematic. It's usually hard to prove why it works or doesn't. Deep learning models can also be vulnerable to adversarial attacks. Here, we combine deep learning-based object recognition and tracking with an adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System (NARS), that can adapt to its environment by building concepts based on perceptual sequences. We achieved an improved intersection-over-union (IOU) object recognition performance of 0.65 in the adaptive retraining model compared to IOU 0.31 in the COCO data pre-trained model. We improved the object detection limits using RADAR sensors in a simulated environment, and demonstrated the weaving car detection capability by combining deep learning-based object detection and tracking with a neurosymbolic model.

Wang, Pei↗

NASA NTRS2004

Clouds and Aerosols in the Climate System

Remote sensing of cloud and aerosol properties from space is reviewed for present and planned international satellite sensors. Techniques that are being used to enhance our ability to characterize the global distribution of cloud and aerosol properties include well-calibrated multispectral radiometers that rely on visible, near-infrared, and thermal infrared channels, sometimes including polarization as well. The availability of thermal channels to aid in cloud screening for aerosol properties is an important additional piece of information that has not always been incorporated into sensor designs. In this paper, we describe the radiative properties of clouds as currently determined from satellites (cloud fraction, optical thickness, cloud top pressure, and cloud effective radius), and highlight the global and regional cloud microphysical properties currently available for assessing climate variability and forcing. In addition, we will frustrate the radiative and microphysical properties of aerosol particles that are currently available from space-based observations, as well as enhancements to aerosol remote sensing over bright-reflecting surfaces that is anticipated in the next couple of years. Finally, we will describe the aerosol optical and radiative properties available from the globally distributed AERONET network of ground-based sun-sky radiometers.

King, Michael D.↗

NASA NTRS2012

Experimental Evaluation of the "Polished Panel Optical Receiver" Concept on the Deep Space Network's 34 Meter Antenna

The potential development of large aperture ground-based "photon bucket" optical receivers for deep space communications has received considerable attention recently. One approach currently under investigation proposes to polish the aluminum reflector panels of 34-meter microwave antennas to high reflectance, and accept the relatively large spotsize generated by even state-of-the-art polished aluminum panels. Here we describe the experimental effort currently underway at the Deep Space Network (DSN) Goldstone Communications Complex in California, to test and verify these concepts in a realistic operational environment. A custom designed aluminum panel has been mounted on the 34 meter research antenna at Deep-Space Station 13 (DSS-13), and a remotely controlled CCD camera with a large CCD sensor in a weather-proof container has been installed next to the subreflector, pointed directly at the custom polished panel. Using the planet Jupiter as the optical point-source, the point-spread function (PSF) generated by the polished panel has been characterized, the array data processed to determine the center of the intensity distribution, and expected communications performance of the proposed polished panel optical receiver has been evaluated.

photon counting detection↗

NASA NTRSDate not supplied

Considerations for Optimal Sensor Placement for Higher Accuracy Object Localization for Urban Air Mobility

Previous research into object localization has shown that sensor placement and alignment plays an important role in achieving higher accuracy levels of the estimated location of a tracked Urban Air Mobility Vehicle. In general, a near-orthogonal intersection between the ground node observation vectors results in the highest accuracy due to a smaller overlapping uncertainty region between both. This applies to triangulation by means of ground node camera angle observations as well as trilateration by means of ground node distance measurements. However, this simple concept is not easily fulfilled with a network of a limited number of static ground nodes and a moving object to be localized. This case study performs sensitivity analyses and explores practical ways on how to achieve higher estimate accuracy levels in this context.

sensor placement↗

NASA NTRS2025

Considerations for Optimal Sensor Placement for Higher Accuracy Object Localization for Urban Air Mobility

sensor placement↗

NASA NTRS2013

Distributive, Non-destructive Real-time System and Method for Snowpack Monitoring

A ground-based system that provides quasi real-time measurement and collection of snow-water equivalent (SWE) data in remote settings is provided. The disclosed invention is significantly less expensive and easier to deploy than current methods and less susceptible to terrain and snow bridging effects. Embodiments of the invention include remote data recovery solutions. Compared to current infrastructure using existing SWE technology, the disclosed invention allows more SWE sites to be installed for similar cost and effort, in a greater variety of terrain; thus, enabling data collection at improved spatial resolutions. The invention integrates a novel computational architecture with new sensor technologies. The invention's computational architecture is based on wireless sensor networks, comprised of programmable, low-cost, low-powered nodes capable of sophisticated sensor control and remote data communication. The invention also includes measuring attenuation of electromagnetic radiation, an approach that is immune to snow bridging and significantly reduces sensor footprints.

Frolik, Jeff↗

NASA NTRS2010

SPOT Program

A Spacecraft Position Optimal Tracking (SPOT) program was developed to process Global Positioning System (GPS) data, sent via telemetry from a spacecraft, to generate accurate navigation estimates of the vehicle position and velocity (state vector) using a Kalman filter. This program uses the GPS onboard receiver measurements to sequentially calculate the vehicle state vectors and provide this information to ground flight controllers. It is the first real-time ground-based shuttle navigation application using onboard sensors. The program is compact, portable, self-contained, and can run on a variety of UNIX or Linux computers. The program has a modular objec-toriented design that supports application-specific plugins such as data corruption remediation pre-processing and remote graphics display. The Kalman filter is extensible to additional sensor types or force models. The Kalman filter design is also strong against data dropouts because it uses physical models from state and covariance propagation in the absence of data. The design of this program separates the functionalities of SPOT into six different executable processes. This allows for the individual processes to be connected in an a la carte manner, making the feature set and executable complexity of SPOT adaptable to the needs of the user. Also, these processes need not be executed on the same workstation. This allows for communications between SPOT processes executing on the same Local Area Network (LAN). Thus, SPOT can be executed in a distributed sense with the capability for a team of flight controllers to efficiently share the same trajectory information currently being computed by the program. SPOT is used in the Mission Control Center (MCC) for Space Shuttle Program (SSP) and International Space Station Program (ISSP) operations, and can also be used as a post -flight analysis tool. It is primarily used for situational awareness, and for contingency situations.

Smith, Jason T.↗

NASA NTRS1993

Model reduction for the dynamics and control of large structural systems via neutral network processing direct numerical optimization

Three neural network processing approaches in a direct numerical optimization model reduction scheme are proposed and investigated. Large structural systems, such as large space structures, offer new challenges to both structural dynamicists and control engineers. One such challenge is that of dimensionality. Indeed these distributed parameter systems can be modeled either by infinite dimensional mathematical models (typically partial differential equations) or by high dimensional discrete models (typically finite element models) often exhibiting thousands of vibrational modes usually closely spaced and with little, if any, damping. Clearly, some form of model reduction is in order, especially for the control engineer who can actively control but a few of the modes using system identification based on a limited number of sensors. Inasmuch as the amount of 'control spillover' (in which the control inputs excite the neglected dynamics) and/or 'observation spillover' (where neglected dynamics affect system identification) is to a large extent determined by the choice of particular reduced model (RM), the way in which this model reduction is carried out is often critical.

Becus, Georges A.↗

NASA NTRS1992

Activities relating to understanding the initiation, organization and structure of moist convection in the Southeast environment

In the spring and summer of 1986, NASA/Marshall Space Flight Center (MSFC) will sponsor the Satellite Precipitation And Cloud Experiment (SPACE) to be conducted in the Central Tennessee, Northern Alabama, and Northeastern Mississippi area. The field program will incorporate high altitude flight experiments associated with meteorological remote sensor development for future space flight, and an investigation of precipitation processes associated with mesoscale and small convective systems. In addition to SPACE, the MIcroburst and Severe Thunderstorm (MIST) program, sponsored by the National Science Foundation (NSF), and the FAA-Lincoln Laboratory Operational Weather Study (FLOWS), sponsored by the Federal Aviation Administration (FAA), will take place concurrently within the SPACE experiment area. All three programs (under the joint acronym COHMEX (COoperative Huntsville Meteorological EXperiment)) will provide a data base for detailed analysis of mesoscale convective systems while providing ground truth comparisons for remote sensor evaluation. The purpose of this document is to outline the experiment design criteria for SPACE, and describe the special observing facilities and data sets that will be available under the COHMEX joint program. In addition to the planning of SPACE-COHMEX, this document covers three other parts of the program. The field program observations' main activity was the operation of an upper air rawinsonde network to provide ground truth for aircraft and spacecraft observations. Another part of the COHMEX program involved using boundary layer mesoscale models to study and simulate the initiation and organization of moist convection due to mesoscale thermal and mechanical circulations. The last part of the program was the collection, archival and distribution of the resulting COHMEX-SPACE data sets.

Mcnider, Richard T.↗