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NASA NTRS2005

Reconfiguration of Analog Electronics for Extreme Environments: Problem or Solution?

This paper argues in favor of adaptive reconfiguration as a technique to expand the operational envelope of analog electronics for extreme environments (EE). In addition to hardening-by-process and hardening-by-design, "hardening-by-reconfiguration", when applicable, could be used to mitigate drifts, degradation, or damage on electronic devices (chips) in EE, by using re-configurable devices and an adaptive self-reconfiguration of their circuit topology. Conventional circuit design exploits device characteristics within a certain temperature/radiation range; when that is exceeded, the circuit function degrades. On a reconfigurable device, although component parameters change in EE, as long as devices still operate, albeit degraded, a new circuit design, suitable for new parameter values, may be mapped into the reconfigurable structure to recover the initial circuit function. Partly degraded resources are still used, while completely damaged resources are bypassed. Designs suitable for various environmental conditions can be determined prior to operation or can be determined in-situ, by adaptive reconfiguration algorithms running on built-in digital controllers. Laboratory demonstrations of this technique were performed by JPL in several independent experiments in which bulk CMOS reconfigurable devices were exposed to, and degraded by, low temperatures (approx. 196 C), high temperatures (approx.300 C) or radiation (300kRad TID), and then recovered by adaptive reconfiguration using evolutionary search algorithms. Taking this technology from Technology Readiness Level (TRL) 3 to TRL 5 is the target of a current NASA project.

Field Programmable Transistor Array (FPTA)↗

NASA NTRSDate not supplied

Towards Plume Impingement Modeling in Space Environments

After 30 years human presence in low-earth orbit, NASA is returning to the moon and eventually will go to Mars. To this end, NASA is constructing the Lunar Gateway, an ISS-like space station to act as a home base for Lunar exploration. A large space station must be assembled while in orbit, using a “piecemeal” approach. In this context, it means that the different modules will arrive at different times and attach to what is already in service. The ISS provides an excellent example of this approach, and the proposed Lunar Gateway will undergo a similar assembly process. This assembly is achieved via “docking” maneuvers between modules, which are made possible by sequential firings of the onboard reaction control system (RCS) thrusters. They work by firing hot gases to produce adverse thrust and the needed change in velocity to safely finish the docking approach. The issue is that the gas from these thrusters' forms flow structures described as “plumes” and can impinge onto the outer surfaces of the space station, causing unwanted forces and moments, heat loads, sediment deposition, and in extreme cases, even surface erosion. All mechanisms that can damage the space station and must be avoided. Both permanent and visiting modules will have these RCS thruster exhaust impingement problems. Accurately and efficiently modeling these plume is an involved multi-physics calculation but also an important tool when designing the control algorithms of approaching modules. This poster presents progress towards this simulation on two fronts. First is the verification of OpenFOAM for rarefied plume impingement calculations by direct comparison to published DAC cases. Second is the estimation of plume impingement strikes over the time scale of an entire docking event. This is done by using a simple plume source flow model and a prescribed motion visualizer—coded in Python. Together these tools help push NASA's capabilities for simulating these plume impingement effects.

Rarefied Flows↗

NASA NTRS1983

Real-time MST radar signal processing using a microcomputer running under FORTH

Data on power, correlation time, and velocity were obtained at the Urbana radar using microcomputer and a single floppy disk drive. This system includes the following features: (1) measurement of the real and imaginary components of the received signal at 20 altitudes spaced by 1.5 km; (2) coherent integration of these components over a 1/8-s time period; (3) continuous real time display of the height profiles of the two coherently integrated components; (4) real time calculation of the 1 minute averages of the power and autocovariance function up to 6 lags; (5) output of these data to floppy disk once every 2 minutes; (6) display of the 1 minute power profiles while the data are stored to the disk; (7) visual prompting for the operator to change disks when required at the end of each hour of data; and (8) continuous audible indication of the status of the interrupt service routine. Accomplishments were enabled by two developments: the use of a new correlation algorithm and the use of the FORTH language to manage the various low level and high level procedures involved.

Bowhill, S. A.↗

NASA NTRS2001

Molecular Simulations in Astrobiology

One of the main goals of astrobiology is to understand the origin of cellular life. In the absence of any record of the earliest ancestors of contemporary cells, protocells, the most direct way to test our understanding of their characteristics is to construct laboratory models of protocells. Such efforts, currently underway in the NASA Astrobiology Program, are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures and developing designs of molecules that are capable of performing protocellular functions. Many of these functions, such as importing nutrients, capturing and storing energy, and responding to changes in the environment, are carried out by proteins bound to membranes. We use computer simulations to address the following, questions about these proteins: (1) How do small proteins (peptides) organize themselves into ordered structures at water-membrane interfaces and insert into membranes? (2) How do peptides aggregate to form membrane-spannin(y structures (e.g., channels)? (3) By what mechanisms do such aggregates perform their functions? The simulations are performed using the molecular dynamics (MD) method. In this method, Newton's equations of motion for each atom in the system are solved iteratively. At each time step, the forces exerted on each atom by the remaining atoms are evaluated by dividing them into two parts. Short-range forces are calculated directly in real space while long-range forces are evaluated in reciprocal space, usually using a particle-mesh algorithm which is of order O(NlnN). Currently, a time step of 2 femtoseconds is typically used, thereby making studies of problems occurring on multi-nanosecond time scales (10(exp 6) - 10(exp 8) time steps) accessible. To address a broader range of problems, simulations need to be extended by three orders of magnitude. Such an extension requires both algorithmic improvements and codes scalable to a large number of parallel processors. Work in this direction is in progress. Two specific series of simulations that demonstrate how peptides self-organize and function in membranes are discussed. In one series of simulations, it was shown that nonpolar peptides, disordered in water, translocate to the nonpolar interior of the membrane and, simultaneously, fold into two different helical structures, which remain in equilibrium. Once in the membrane, the peptides can readily change their orientation, especially in response to local electric fields. This structural and orientational flexibility of peptides with changing conditions may have provided a mechanism of transmitting signals between the environment and the interior of the protocell. In another series of simulations, the mechanism by which a simple protein channel efficiently mediates proton transport across membranes was investigated. This process is a key step in cellular bioenergetics. In the channel under study, proton transport is gated by four histidines that occlude the channel pore. The simulations demonstrate that protons move through the gate by a "shuttle" mechanism, wherein one histidine is protonated on the extracellular side and, subsequently, the proton bound on the opposite side is released.

Pohorille, Andrew↗

NASA NTRS2018

Data Mining for Vortices on the Earth's Magnetosphere - Algorithm Application for Detection and Analysis

Unsteady processes in the solar wind– magnetosphere interaction, such as vortices developed at the magnetopause boundary by the Kelvin–Helmholtz instability, may contribute to the process of mass, momentum and energy transfer into the Earth’s magnetosphere. The research described in this paper validates an algorithm to automatically detect and characterize vortices based on velocity data from simulations. The vortex identification algorithm (VIA) systematically searches the 3-D velocity fields to identify critical points where the magnitude of the velocity vector vanishes. The velocity gradient tensor is computed and its invariants are used to assess vortex structure in the flow field. We use the Community Coordinated Modeling Center (CCMC) Runs on Request capability to create a series of model runs initialized from the conditions observed by the Cluster mission in the Hwang et al. (2011) analysis of Kelvin–Helmholtz vortices observed during southward interplanetary magnetic field (IMF) conditions. We analyze further the properties of the vortices found in the runs, including the velocity changes within their motion across the magnetosheath. We also demonstrate the potential of our tool to identify and characterize other transient features (e.g., flux transfer events, FTEs) with vortical internal structures. We find that the vortices are associated with flows on the magnetosheath side of the magnetopause that reach speeds greater than the solar wind speed at the bow shock.

Collado-Vega, Yaireska M.↗

NASA NTRS2015

Benefits Assessment for Single-Airport Tactical Runway Configuration Management Tool (TRCM)

The System-Oriented Runway Management (SORM) concept was developed as part of the Airspace Systems Program (ASP) Concepts and Technology Development (CTD) Project, and is composed of two basic capabilities: Runway Configuration Management (RCM), and Combined Arrival/Departure Runway Scheduling (CADRS). RCM is the process of designating active runways, monitoring the active runway configuration for suitability given existing factors, and predicting future configuration changes; CADRS is the process of distributing arrivals and departures across active runways based on local airport and National Airspace System (NAS) goals. The central component in the SORM concept is a tool for taking into account all the various factors and producing a recommendation for what would be the optimal runway configuration, runway use strategy, and aircraft sequence, considering as many of the relevant factors required in making this type of decision, and user preferences, if feasible. Three separate tools were initially envisioned for this research area, corresponding to the time scale in which they would operate: Strategic RCM (SRCM), with a planning horizon on the order of several hours, Tactical RCM (TRCM), with a planning horizon on the order of 90 minutes, and CADRS, with a planning horizon on the order of 15-30 minutes[1]. Algorithm development was initiated in all three of these areas, but the most fully developed to date is the TRCM algorithm. Earlier studies took a high-level approach to benefits, estimating aggregate benefits across most of the major airports in the National Airspace Systems (NAS), for both RCM and CADRS [2]. Other studies estimated the benefit of RCM and CADRS using various methods of re-sequencing arrivals to reduce delays3,4, or better balancing of arrival fixes5,6. Additional studies looked at different methods for performing the optimization involved in selecting the best Runway Configuration Plan (RCP) to use7-10. Most of these previous studies were high-level or generic in nature (not focusing on specific airports), and benefits were aggregated for the entire NAS, with relatively low fidelity simulation of SORM functions and aircraft trajectories. For SORM research, a more detailed benefits assessment of RCM and CADRS for specific airports or metroplexes is needed.

Oseguera-Lohr, Rosa↗

NASA NTRS2015

The Suomi National Polar-Orbiting Partnership (SNPP): Continuing NASA Research and Applications

The Suomi National Polar-orbiting Partnership (SNPP) satellite was successfully launched into a polar orbit on October 28, 2011 carrying 5 remote sensing instruments designed to provide data to improve weather forecasts and to increase understanding of long-term climate change. SNPP provides operational continuity of satellite-based observations for NOAA's Polar-orbiting Operational Environmental Satellites (POES) and continues the long-term record of climate quality observations established by NASA's Earth Observing System (EOS) satellites. In the 2003 to 2011 pre-launch timeframe, NASA's SNPP Science Team assessed the adequacy of the operational Raw Data Records (RDRs), Sensor Data Records (SDRs), and Environmental Data Records (EDRs) from the SNPP instruments for use in NASA Earth Science research, examined the operational algorithms used to produce those data records, and proposed a path forward for the production of climate quality products from SNPP. In order to perform these tasks, a distributed data system, the NASA Science Data Segment (SDS), ingested RDRs, SDRs, and EDRs from the NOAA Archive and Distribution and Interface Data Processing Segments, ADS and IDPS, respectively. The SDS also obtained operational algorithms for evaluation purposes from the NOAA Government Resource for Algorithm Verification, Independent Testing and Evaluation (GRAVITE). Within the NASA SDS, five Product Evaluation and Test Elements (PEATEs) received, ingested, and stored data and performed NASA's data processing, evaluation, and analysis activities. The distributed nature of this data distribution system was established by physically housing each PEATE within one of five Climate Analysis Research Systems (CARS) located at either at a NASA or a university institution. The CARS were organized around 5 key EDRs directly in support of the following NASA Earth Science focus areas: atmospheric sounding, ocean, land, ozone, and atmospheric composition products. The PEATES provided the system level interface with members of the NASA SNPP Science Team and other science investigators within each CARS. A sixth Earth Radiation Budget CARS was established at NASA Langley Research Center (NASA LaRC) to support instrument performance, data evaluation, and analysis for the SNPP Clouds and the Earth's Radiant Budget Energy System (CERES) instrument. Following the 2011 launch of SNPP, spacecraft commissioning, and instrument activation, the NASA SNPP Science Team evaluated the operational RDRs, SDRs, and EDRs produced by the NOAA ADS and IDPS. A key part in that evaluation was the NASA Science Team's independent processing of operational RDRs and SDRs to EDRs using the latest NASA science algorithms. The NASA science evaluation was completed in the December 2012 to April 2014 timeframe with the release of a series of NASA Science Team Discipline Reports. In summary, these reports indicated that the RDRs produced by the SNPP instruments were of sufficiently high quality to be used to create data products suitable for NASA Earth System science and applications. However, the quality of the SDRs and EDRs were found to vary greatly when considering suitability for NASA science. The need for improvements in operational algorithms, adoption of different algorithmic approaches, greater monitoring of on-orbit instrument calibration, greater attention to data product validation, and data reprocessing were prominent findings in the reports. In response to these findings, NASA, in late 2013, directed the NASA SNPP Science Team to use SNPP instrument data to develop data products of sufficiently high quality to enable the continuation of EOS time series data records and to develop innovative, practical applications of SNPP data. This direction necessitated a transition of the SDS data system from its pre-launch assessment mode to one of full data processing and production. To do this, the PEATES, which served as NASA's data product testing environment during the prelaunch and early on-orbit periods, were transitioned to Science Investigator-led Processing Systems (SIPS). The distributed data architecture was maintained in this new system by locating the SIPS at the same institutions at which the CARS and PEATES were located. The SIPS acquire raw SNPP instrument Level 0 (i.e. RDR) data over the full SNPP mission from the NOAA ADS and IDPS through the NASA SDS Data Distribution and Depository Element (SD3E). The SIPS process those data into NASA Level 1, Level 2, and global, gridded Level 3 standard products using peer-reviewed algorithms provided by members of the NASA Science Team. The SIPS work with the NASA SNPP Science Team in obtaining enhanced, refined, or alternate real-time algorithms to support the capabilities of the Direct Readout Laboratory (DRL). All data products, algorithm source codes, coefficients, and auxiliary data used in product generation are archived in an assigned NASA Distributed Active Archive Center (DAAC).

remote sensing↗

NASA NTRS2017

An Unobtrusive System to Measure, Assess, and Predict Cognitive Workload in Real-World Environments

Across many careers, individuals face alternating periods of high and low attention and cognitive workload, which can result in impaired cognitive functioning and can be detrimental to job performance. For example, some professions (e.g., fire fighters, emergency medical personnel, doctors and nurses working in an emergency room, pilots) require long periods of low workload (boredom), followed by sudden, high-tempo operations during which they may be required to respond to an emergency and perform at peak cognitive levels. Conversely, other professions (e.g., air traffic controllers, market investors in financial industries, analysts) require long periods of high workload and multitasking during which the addition of just one more task results in cognitive overload resulting in mistakes. An unobtrusive system to measure, assess, and predict cognitive workload could warn individuals, their teammates, or their supervisors when steps should be taken to augment cognitive readiness. In this talk I will describe an approach to this problem that we have found to be successful across work domains including: (1) a suite of unobtrusive, field-ready neurophysiological, physiological, and behavioral sensors that are chosen to best suit the target environment; (2) custom algorithms and statistical techniques to process and time-align raw data originating from the sensor suite; (3) probabilistic and statistical models designed to interpret the data into the human state of interest (e.g., cognitive workload, attention, fatigue); (4) and machine-learning techniques to predict upcoming performance based on the current pattern of events, and (5) display of each piece of information depending on the needs of the target user who may or may not want to drill down into the functioning of the system to determine how conclusions about human state and performance are determined. I will then focus in on our experimental results from our custom functional near-infrared spectroscopy sensor, designed to operate in real-world environments to be worn comfortably (e.g., positioned into a baseball cap or a surgeons cap) to measure changes in brain blood oxygenation without adding burden to the individual being assessed.

workload collection↗

NASA NTRSDate not supplied

Modeling Atmospheric Science Knowledge from Research Publications

NASA Earth Science Data Centers contain enormous amounts of remote sensing digital data. It is often a significant challenge for users to find data suitable for their research topic in these vast archives. One of the approaches is the usage-driven dataset discovery, where users seek publications on projects similar to their intended study. For this approach to be effective, users need a clear connection between the underlying data in the publications and the study objectives; this is not often apparent to non-expert users. Tools and methodologies that can help facilitate and organize these connections are therefore valuable for creating improved knowledge mappings, which can be further used by search engines to suggest data or publications best tailored to a user’s specific research goal. As an illustration of these challenges, in this work we focus on the atmospheric chemistry processes related to Earth environmental impacts such as ozone depletion, aerosols, smog formation, acid rain, and radiative forcing. We further limit our study to publications that use data from the Microwave Limb Sounder (MLS) instrument flown on the Aura Earth Observing System. To create knowledge representations of science carried out in these publications, we use existing ontologies such as the Global Change Master Directory (GCMD) and Semantic Web for Earth and Environmental Terminology (SWEET). These ontologies together encompass term dictionaries that include measured variables, names of molecules or radicals, mission and instrument names, locations, action words, among many others. Based on these terms acknowledge graph database was populated with the terms retrieved from scientific publications that study atmospheric chemistry. These databases can be used to further enhance the automation of knowledge discovery and facilitate machine learning and artificial intelligence algorithms or applications. These tools and methods can also be extended to apply to content from other related Earth science domains.

Irina Gerasimov↗

NASA NTRS1991

NASA SETI microwave observing project: Sky Survey element

The SETI Sky Survey Observing Program is one of two complimentary strategies that NASA plans to use in its microwave Search for Extraterrestrial Intelligence (SETI). The primary objective of the sky survey is to search the entire sky over the frequency range of 1.0 to 10.0 GHz for evidence of narrow band signals of extraterrestrial intelligent origin. Frequency resolutions of 30 Hz or narrower will be used across the entire band. Spectrum analyzers with upwards of ten million channels are required to keep the survey time approximately 6 years. Data rates in excess of 10 megabits per second will be generated in the data taking process. Sophisticated data processing techniques will be required to determine the ever changing receiver baselines, and to detect and archive potential SETI signals. Existing radio telescopes, including several of NASA's Deep Space Network (DSN) 34 meter antennas located at Goldstone, CA and Tidbinbilla, Australia will be used for the observations. The JPL has the primary responsibility to develop and carry out the sky survey. In order to lay the foundation for the full scale SETI Sky Survey, a prototype system is being developed at the JPL. The system will be installed at the new 34-m high efficiency antenna at the Deep Space Station (DSS) 13 research and development station, Goldstone, CA, where it will be used to initiate the observational phase of the NASA SETI Sky Survey. It is anticipated that the early observations will be useful to test signal detection algorithms, scan strategies, and radio frequency interference rejection schemes. The SETI specific elements of the prototype system are: (1) the Wide Band Spectrum Analyzer (WBSA); a 2-million channel fast Fourier transformation (FFT) spectrum analyzer which covers an instantaneous bandpass of 40 MHz; (2) the signal detection processor; and (3) the SETI Sky Survey Manager, a network-based C-language environment that provides observatory control, performs data acquisition and analysis algorithms. A high level description of the prototype hardware and software systems will be given and the current status of the system development will be reported.

Klein, M. J.↗

NASA NTRS2011

Performance of the GLAS Space Lidar Receiver Through Its Seven-Year Space Mission

NASA s Ice, Cloud, and land Elevation Satellite (ICESat) mission [1,2] carrying the Geoscience Laser Altimeter System (GLAS) Instrument, was launched on January 12, 2003. The three lasers on ICESat have made a total of 1.98 billion laser shot measurements of the Earth s surface and atmosphere during its 17 science data collection campaigns over its seven year operating lifetime. ICESat completed its science mission after the last laser stopped operating in October 2009. The spacecraft was de-orbited on August 30, 2010. The GLAS instrument carried 3 diode-pumped Q-switched Nd:YAG lasers, which emitted 6-nsec wide pulses at 1064 and 532 nm at a 40-Hz rate. There are three lidar receiver channels, a 1064 nm surface altimetry channel, a 1064 nm cloud backscattering lidar channel, and a 532 nm cloud and aerosol backscattering lidar channel. The altimetry and cloud backscatter channels used Si avalanche photodiode (APD) operated in analog mode as in the Mars Global Surveyor s Mars Orbital Laser Altimeter [3,4]. GLAS also utilized a number of new technologies and techniques for space lidar, including passively Q-switched diode-pumped Nd:YAG lasers, a 1-m diameter telescope, a temperature tuned etalon optical bandpass filter, Si APD single photon counting detectors, 1 Gsample/sec waveform digitizers, ultra stable clock oscillators, and digital signal processing and detection algorithms [5]. A global position system (GPS) receiver was used to provide the spacecraft position and epoch times. The ICESat mission provided a unique opportunity to monitor the lidar component performance in the space environment over a multi-year time period. We performed a number of engineering tests periodically to monitor the lidar receiver performance, including receiver sensitivity, timing precision, detector dark noise, etc. A series of engineering tests were also performed after the end of the science mission to evaluate the performance of the spare detector, oscillator, waveform digitizer, and GPS receiver. An experiment was conducted which pointed GLAS to Venus to test the receiver sensitivity to star light and to verify GLAS bore sight with respect to the spacecraft coordinate system. These tests provided unique data to assess the degradation and the rate of change of these key lidar components due to space radiation and aging. They also helped to validate new techniques to operate and calibrate future space lidars.

Sun, Xiaoli↗

NASA NTRS2013

Mapping Impervious Surfaces Globally at 30m Resolution Using Global Land Survey Data

Impervious surfaces, mainly artificial structures and roads, cover less than 1% of the world's land surface (1.3% over USA). Regardless of the relatively small coverage, impervious surfaces have a significant impact on the environment. They are the main source of the urban heat island effect, and affect not only the energy balance, but also hydrology and carbon cycling, and both land and aquatic ecosystem services. In the last several decades, the pace of converting natural land surface to impervious surfaces has increased. Quantitatively monitoring the growth of impervious surface expansion and associated urbanization has become a priority topic across both the physical and social sciences. The recent availability of consistent, global scale data sets at 30m resolution such as the Global Land Survey from the Landsat satellites provides an unprecedented opportunity to map global impervious cover and urbanization at this resolution for the first time, with unprecedented detail and accuracy. Moreover, the spatial resolution of Landsat is absolutely essential to accurately resolve urban targets such a buildings, roads and parking lots. With long term GLS data now available for the 1975, 1990, 2000, 2005 and 2010 time periods, the land cover/use changes due to urbanization can now be quantified at this spatial scale as well. In the Global Land Survey - Imperviousness Mapping Project (GLS-IMP), we are producing the first global 30 m spatial resolution impervious cover data set. We have processed the GLS 2010 data set to surface reflectance (8500+ TM and ETM+ scenes) and are using a supervised classification method using a regression tree to produce continental scale impervious cover data sets. A very large set of accurate training samples is the key to the supervised classifications and is being derived through the interpretation of high spatial resolution (approx. 2 m or less) commercial satellite data (Quickbird and Worldview2) available to us through the unclassified archive of the National Geospatial Intelligence Agency (NGA). For each continental area several million training pixels are derived by analysts using image segmentation algorithms and tools and then aggregated to the 30m resolution of Landsat. Here we will discuss the production/testing of this massive data set for Europe, North and South America and Africa, including assessments of the 2010 surface reflectance data. This type of analysis is only possible because of the availability of long term 30m data sets from GLS and shows much promise for integration of Landsat 8 data in the future.

global land survey↗

NASA NTRS2012

Remote Sensing of Tropical Ecosystems: Atmospheric Correction and Cloud Masking Matter

Tropical rainforests are significant contributors to the global cycles of energy, water and carbon. As a result, monitoring of the vegetation status over regions such as Amazonia has been a long standing interest of Earth scientists trying to determine the effect of climate change and anthropogenic disturbance on the tropical ecosystems and its feedback on the Earth's climate. Satellite-based remote sensing is the only practical approach for observing the vegetation dynamics of regions like the Amazon over useful spatial and temporal scales, but recent years have seen much controversy over satellite-derived vegetation states in Amazônia, with studies predicting opposite feedbacks depending on data processing technique and interpretation. Recent results suggest that some of this uncertainty could stem from a lack of quality in atmospheric correction and cloud screening. In this paper, we assess these uncertainties by comparing the current standard surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4 and MYD13A2 - Vegetation Index) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to results obtained from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. MAIAC uses a new cloud screening technique, and novel aerosol retrieval and atmospheric correction procedures which are based on time-series and spatial analyses. Our results show considerable improvements of MAIAC processed surface reflectance compared to MYD09/MYD13 with noise levels reduced by a factor of up to 10. Uncertainties in the current MODIS surface reflectance product were mainly due to residual cloud and aerosol contamination which affected the Normalized Difference Vegetation Index (NDVI): During the wet season, with cloud cover ranging between 90 percent and 99 percent, conventionally processed NDVI was significantly depressed due to undetected clouds. A smaller reduction in NDVI due to increased aerosol levels was observed during the dry season, with an inverse dependence of NDVI on aerosol optical thickness (AOT). NDVI observations processed with MAIAC showed highly reproducible and stable inter-annual patterns with little or no dependence on cloud cover, and no significant dependence on AOT (p less than 0.05). In addition to a better detection of cloudy pixels, MAIAC obtained about 20-80 percent more cloud free pixels, depending on season, a considerable amount for land analysis given the very high cloud cover (75-99 percent) observed at any given time in the area. We conclude that a new generation of atmospheric correction algorithms, such as MAIAC, can help to dramatically improve vegetation estimates over tropical rain forest, ultimately leading to reduced uncertainties in satellite-derived vegetation products globally.

tropical ecosystems↗

NASA NTRS1974

Automated thematic mapping and change detection of ERTS-A images

The author has identified the following significant results. A diffraction pattern analysis of MSS images led to the development of spatial signatures for farm land, urban areas and mountains. Four spatial features are employed to describe the spatial characteristics of image cells in the digital data. Three spectral features are combined with the spatial features to form a seven dimensional vector describing each cell. Then, the classification of the feature vectors is accomplished by using the maximum likelihood criterion. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month, but vary substantially between seasons. Three ERTS-1 images from the Phoenix, Arizona area were processed, and recognition rates between 85% and 100% were obtained for the terrain classes of desert, farms, mountains, and urban areas. To eliminate the need for training data, a new clustering algorithm has been developed. Seven ERTS-1 images from four test sites have been processed through the clustering algorithm, and high recognition rates have been achieved for all terrain classes.

Gramenopoulos, N.↗

NASA NTRS2015

The Process of Bringing Dark Data to Light: The Rescue of the Early Nimbus Satellite Data

Myriad environmental satellite missions are currently orbiting the earth. The comprehensive monitoring by these sensors provide scientists, policymakers, and the public critical information on the earths weather and climate system. The state of the art technology of our satellite monitoring system is the legacy of the first environment satellites, the Nimbus systems launched by NASA in the mid-1960s. Such early data can extend our climate record and provide important context in longer-term climate changes. However, the data was stowed away and, over the years, largely forgotten. It was nearly lost before its value was recognized and attempts to recover the data were undertaken. This paper covers what it took the authors to recover, navigate and reprocess the data into modern formats so that it could be used as a part of the satellite climate record. The procedures to recover the Nimbus data, from both film and tape, could be used by other data rescue projects, however the algorithms presented will tend to be Nimbus specific. Data rescue projects are often both difficult and time consuming but the data they bring back to the science community makes these efforts worthwhile.

dark↗

NASA NTRS1995

Follow-the-leader algorithm for the payload inspection and processing system

This report summarizes the author's summer 1995 work at NASA Kennedy Space Center in the Advanced System Division. The assignment was path planning for the Payload Inspection and Processing System (PIPS). PIPS is an automated system, programmed off-line for inspection of Space Shuttle payloads after integration and prior to launch. PIPS features a hyper-redundant 18-dof serpentine truss manipulator capable of snake-like motions to avoid obstacles. The path planning problem was divided into two segments: (1) determining an obstacle-free trajectory for the inspection camera at the manipulator tip to follow; and (2) development of a follow-the-leader (FTL) algorithm which ensures whole-arm collision avoidance by forcing ensuing links to follow the same tip trajectory. The summer 1995 work focused on the FTL algorithm. This report summarizes development, implementation, testing, and graphical demonstration of the FTL algorithm for prototype PIPS hardware. The method and code was developed in a modular manner so the final PIPS hardware may use them with minimal changes. The FTL algorithm was implemented using MATLAB software and demonstrated with a high-fidelity IGRIP model. The author also supported implementation of the algorithm in C++ for hardware control. The FTL algorithm proved to be successful and robust in graphical simulation. The author intends to return to the project in summer 1996 to implement path planning for PIPS.

Williams, Robert L., II↗

NASA NTRS2012

Generating Ground Reference Data for a Global Impervious Surface Survey

We are engaged in a project to produce a 30m impervious cover data set of the entire Earth for the years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. The GLS data from Landsat provide an unprecedented opportunity to map global urbanization at this resolution for the first time, with unprecedented detail and accuracy. Moreover, the spatial resolution of Landsat is absolutely essential to accurately resolve urban targets such as buildings, roads and parking lots. Finally, with GLS data available for the 1975, 1990, 2000, and 2005 time periods, and soon for the 2010 period, the land cover/use changes due to urbanization can now be quantified at this spatial scale as well. Our approach works across spatial scales using very high spatial resolution commercial satellite data to both produce and evaluate continental scale products at the 30m spatial resolution of Landsat data. We are developing continental scale training data at 1m or so resolution and aggregating these to 30m for training a regression tree algorithm. Because the quality of the input training data are critical, we have developed an interactive software tool, called HSegLearn, to facilitate the photo-interpretation of high resolution imagery data, such as Quickbird or Ikonos data, into an impervious versus non-impervious map. Previous work has shown that photo-interpretation of high resolution data at 1 meter resolution will generate an accurate 30m resolution ground reference when coarsened to that resolution. Since this process can be very time consuming when using standard clustering classification algorithms, we are looking at image segmentation as a potential avenue to not only improve the training process but also provide a semi-automated approach for generating the ground reference data. HSegLearn takes as its input a hierarchical set of image segmentations produced by the HSeg image segmentation program [1, 2]. HSegLearn lets an analyst specify pixel locations as being either positive or negative examples, and displays a classification of the study area based on these examples. For our study, the positive examples are examples of impervious surfaces and negative examples are examples of non-impervious surfaces. HSegLearn searches the hierarchical segmentation from HSeg for the coarsest level of segmentation at which selected positive example locations do not conflict with negative example locations and labels the image accordingly. The negative example regions are always defined at the finest level of segmentation detail. The resulting classification map can be then further edited at a region object level using the previously developed HSegViewer tool [3]. After providing an overview of the HSeg image segmentation program, we provide a detailed description of the HSegLearn software tool. We then give examples of using HSegLearn to generate ground reference data and conclude with comments on the effectiveness of the HSegLearn tool.

Tilton, James C.↗

NASA NTRS2021

Determining Planetary Boundary Layer Height by Micro-pulse Lidar with Validation by UAV Measurements

Planetary boundary layer height (PBLH) is often used to characterize the structure of the lower atmosphere. Aerosol lidar, a ground-based remote sensing method, provides the vertical distribution of aerosol at a high temporal resolution observation data, from which, the PBL structure and the position of the PBL top can be comprehensively studied. PBLH determination with lidar data depends primarily on the characteristic turbulent motions in the atmosphere and the geophysical location. However, lidar determination of PBLH over densely populated subtropical locations has rarely been discussed; thus, developing retrieval techniques suitable to these areas is necessary. In this study, four PBLH determination methods (Gradient, δ–threshold, Haar wavelet transform, and hybrid image processing) are applied to estimate the PBLH from lidar observations over an urban area in East Asia, and one—the Gradient method—relied on potential temperature measurements from an unmanned aerial vehicle (UAV) flights to validate our results. Our results indicate that a combination of the gradient method and δ-threshold method can provide better results, in terms of diurnal pattern, than using either method individually. Furthermore, the Haar wavelet and the Hybrid image processing can detect the PBL development comparably well, but both methods are dependent on their initial conditions and optimized algorithm settings. In addition, the accompanying UAV observations are conclusively shown to have a high degree of efficacy for validating the lidar data. This research highlights that a combination of PBLH determination methods can better describe the PBLH evolution throughout a day in some cases, while in others less common determination methods are proving useful, and a suite of retrieval methods should still be explored for precisely mapping the PBL in densely populated subtropical areas.

Yueh-Chen Wang↗