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NASA NTRS2016

Restoration of Apollo Data by the Lunar Data Project/PDS Lunar Data Node: An Update

The Apollo 11, 12, and 14 through 17 missions orbited and landed on the Moon, carrying scientific instruments that returned data from all phases of the missions, included long-lived Apollo Lunar Surface Experiments Packages (ALSEPs) deployed by the astronauts on the lunar surface. Much of these data were never archived, and some of the archived data were on media and in formats that are outmoded, or were deposited with little or no useful documentation to aid outside users. This is particularly true of the ALSEP data returned autonomously for many years after the Apollo missions ended. The purpose of the Lunar Data Project and the Planetary Data System (PDS) Lunar Data Node is to take data collections already archived at the NASA Space Science Data Coordinated Archive (NSSDCA) and prepare them for archiving through PDS, and to locate lunar data that were never archived, bring them into NSSDCA, and then archive them through PDS. Preparing these data for archiving involves reading the data from the original media, be it magnetic tape, microfilm, microfiche, or hard-copy document, converting the outmoded, often binary, formats when necessary, putting them into a standard digital form accepted by PDS, collecting the necessary ancillary data and documentation (metadata) to ensure that the data are usable and well-described, summarizing the metadata in documentation to be included in the data set, adding other information such as references, mission and instrument descriptions, contact information, and related documentation, and packaging the results in a PDS-compliant data set. The data set is then validated and reviewed by a group of external scientists as part of the PDS final archive process. We present a status report on some of the data sets that we are processing.

Lunar↗

NASA NTRS2000

Climate and infectious disease: use of remote sensing for detection of Vibrio cholerae by indirect measurement

It has long been known that cholera outbreaks can be initiated when Vibrio cholerae, the bacterium that causes cholera, is present in drinking water in sufficient numbers to constitute an infective dose, if ingested by humans. Outbreaks associated with drinking or bathing in unpurified river or brackish water may directly or indirectly depend on such conditions as water temperature, nutrient concentration, and plankton production that may be favorable for growth and reproduction of the bacterium. Although these environmental parameters have routinely been measured by using water samples collected aboard research ships, the available data sets are sparse and infrequent. Furthermore, shipboard data acquisition is both expensive and time-consuming. Interpolation to regional scales can also be problematic. Although the bacterium, V. cholerae, cannot be sensed directly, remotely sensed data can be used to infer its presence. In the study reported here, satellite data were used to monitor the timing and spread of cholera. Public domain remote sensing data for the Bay of Bengal were compared directly with cholera case data collected in Bangladesh from 1992-1995. The remote sensing data included sea surface temperature and sea surface height. It was discovered that sea surface temperature shows an annual cycle similar to the cholera case data. Sea surface height may be an indicator of incursion of plankton-laden water inland, e.g., tidal rivers, because it was also found to be correlated with cholera outbreaks. The extensive studies accomplished during the past 25 years, confirming the hypothesis that V. cholerae is autochthonous to the aquatic environment and is a commensal of zooplankton, i.e., copepods, when combined with the findings of the satellite data analyses, provide strong evidence that cholera epidemics are climate-linked.

Cholera/epidemiology↗

NASA NTRS2017

ATD-1 Avionics Phase 2: Post-Flight Data Analysis Report

This report aims to satisfy Air Traffic Management Technology Demonstration - 1 (ATD-1) Statement of Work (SOW) 3.6.19 and serves as the delivery mechanism for the analysis described in Annex C of the Flight Test Plan. The report describes the data collected and derived as well as the analysis methodology and associated results extracted from the data set collected during the ATD-1 Flight Test. All analyses described in the SOW were performed and are covered in this report except for the analysis of Final Approach Speed and its effect on performance. This analysis was de-prioritized and, at the time of this report, is not considered feasible in the schedule and costs remaining.

Scharl, Julien↗

NASA NTRS2023

A Global Land Cover Training Dataset From 1984 to 2020

State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.

Radost Stanimirova↗

NASA NTRS1996

Monitoring Tree Moisture Using an Inversion Algorithm Applied to SAR Data from BOREAS

During several field campaigns in spring and summer of '94, the NASA/JPL air-borne sythetic aperature radar (AIRSAR) collected data over the southern and northern study sites of BOREAS. Among the areas over which radar data were collected was the young jack pine tower site, which is generally characterized as have shot but closely space trees with a dense crown layer.

forest canopy moisture↗

NASA NTRS2019

Haystack Ultra-Wideband Satellite Imaging Radar Measurements of the Orbital Debris Environment: 2014-2017

Since the founding of the NASA Orbital Debris Program Office (ODPO) in 1979, the knowledge that orbital debris poses a risk to operational satellites and human spaceflight has been publically available. Services that rely on satellite-based technology such as communications, internet, navigation, and weather forecasting, to name a few, are ubiquitous in modern society. The International Space Station (ISS) has been continuously inhabited by a crew of up to six astronauts since November 2000 and makes, on average, approximately one debris avoidance maneuver per year to avoid objects that are large enough to be tracked by ground-based radars. This places an increased need for understanding the current status of the debris environment (measurements), for the ability to predict the future environment (modeling), and for understanding risk factors for debris creating events (mitigation). For NASA, the measurements, modeling, and mitigation aspects of orbital debris are led by the NASA ODPO at the Johnson Space Center (JSC) in Houston, Texas.This report summarizes radar measurement data from the Haystack Ultra-wideband Satellite Imaging Radar (HUSIR) operated by the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL) and provided to the NASA ODPO. The time period covered by this report includes data collected during the U.S. government fiscal year (FY) 2014 through FY2017. The U.S. government FY begins on 1 October and lasts through 30 September of a given year (i.e., FY2014 lasts from 1 October 1 2013 through 30 September 30 2014). At this report’s release, processed data was unavailable from the Haystack Auxiliary Radar (HAX) due to errors in the calibration data for the radar and limited transmit power; a decision was made by NASA not to collect low-power HAX radar data. This is being resolved by NASA and MIT/LL and data collected during this time period will be released in a separate report.

DRADIS↗

NASA NTRS2003

Perfectly Cold Crystals: What Happens When They Are X-rayed?

For many macromolecular crystals the cryo-preservation of these crystals during X-ray data collection is of crucial importance, particularly at synchrotron facilities where the crystals rapidly receive a high dose of radiation. A practical variable to ensure adequate preservation is the variation of the cryo-protectant present when the crystal is preserved. Our initial approach to study X-ray diffraction data quality as a function of cryo-protectant present when preserving a xylose isomerase crystal shows that the data quality can be tremendously improved by recipe adjustment. Guided by crystal mosaicity estimates, we optimized crystal growth conditions to obtain cryo-preserved xylose isomerase crystals that withstand a very high dose of X-rays, with only the smallest amount of radiation damage at ultra-high resolution (1.2Angstroms). The rate at which damage occurs allowed us to collect a series of complete data sets, which show how the data degradation proceeds over time. We are here presenting data for the xylose isomerase crystallization recipe improvement and our interpretation of the crystal degradation process during X-ray data collection.

vanderWoerd, Mark↗

NASA NTRS2013

Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion-EO-1 Data

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy approx. 70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using approx. 20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was approx. 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or "the curse of high dimensionality") in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI. Index Terms-Hyperion, field reflectance, imaging spectroscopy, HyspIRI, biophysical parameters, hyperspectral vegetation indices, hyperspectral narrowbands, broadbands.

Vegetation↗

NASA NTRSDate not supplied

Dissertation Defense: Three Papers on Organizational Decision-Making and Change at NASA Goddard Space Flight Center

This is a three-paper dissertation that responds to a call for empirical research on stakeholder influence in the implementation of evaluation recommendations. The literature is consistent but mostly theoretical: stakeholder participation should positively influence and increase the probability of implementation (see e.g., Christie, 2011; Cousins & Earl, 1992; Fetterman, 2004). In response, I use two Goddard Space Flight Center engineering technical training and development programs to build a causal argument supporting my proposition that stakeholder influence in the logic modeling process impacts organizational decision-making and change. I first introduce the logic modeling process as a tool to involve stakeholders in program and evaluation design (paper 1); then use the data collection process to implement the collaboratively constructed logic model outcomes (paper 2); and, finally, I triangulate the data using the Toal (2009) Evaluation Involvement Scale (paper 3). I do this to “increase the validity of the findings” (Noble & Heale, 2019, p. 67) and to garner potential mechanisms. I found that stakeholder participation in the logic modeling process positively influenced the use of data to inform recommendations which in turn positively influenced implementation of evaluation recommendations. I identified the process itself as the mechanism and the key decision points as links or influencers. Potential factors included power and interest (measured by role) and time-on-job.

organizational decision-making↗

NASA NTRS1989

The use of aircraft and satellite remote sensing of phytoplankton chlorophyll concentrations in case 2 estuarine waters of the Chesapeake Bay

Two projects using remote sensing of phytoplankton chlorophyll concentrations in the Chesapeake Bay estuary were proposed. The first project used aircraft remote sensing with a compact radiometer system developed at NASA's Goddard Space Flight Center (GSFC), the Ocean Data Acquisition System (ODAS). ODAS includes three radiometers at 460, 490, and 520 nm, an infrared temperature sensor (PRT-5), Loran-C for navigation, and a data acquisition system using a PC and mass storage device. This instrument package can be flown in light aircraft at relatively low expense, permitting regular and frequent flights. Sixteen flights with ODAS were completed using the Virginia Institute of Marine Science's De Havilland 'Beaver'. The goal was to increase spatial and temporal resolution in assaying phytoplankton pigment concentrations in the Chesapeake. At present, analysis is underway of flight data collected between March and July 1989. The second project focused on satellite data gathered with the Nimbus-7 Coastal Zone Color Scanner (CZSC) between late 1978 and mid 1986. The problem in using CZSC data for the Chesapeake Bay is that the optical characteristics of this (and many) coastal and estuarine waters are distinct from those of the open ocean for which algorithms for computing pigment concentrations were developed. The successful use of CZCS data for the estuary requires development of site-specific algorithms and analytical approaches. Of principal importance in developing site-specific procedures is the availability of in-situ data on pigment concentrations. A significant data set was acquired from EPA's Chesapeake Bay Program in Annapolis, Maryland, and clear satellite scenes are being analyzed for which same-day sea truth measurements of pigment were obtained. Both the University of Miami and GSFC Seapak systems are being used in this effort. The main finding to date is an expected one, i.e., the algorithms developed for oceanic waters are inadequate to compute pigment concentrations for the Case 2 waters of the Chesapeake Bay. One reason is the overestimation of aerosol radiances by assuming that water-leaving radiance in Band 4 of CZCS (670 nm) is zero, an assumption that is invalid for the Bay. This prompted any attempts to iterative procedures for estimating the proportion of the Band 4 radiance that is actually attributable to aerosol by estimating the water-leaving component using optical data. A cruise on the Chesapeake the week of 7 August 1989 was conducted to collect additional optical data necessary to this task.

Harding, Lawrence W., Jr.↗

NASA NTRS1995

Using dark current data to estimate AVIRIS noise covariance and improve spectral analyses

Starting in 1994, all AVIRIS data distributions include a new product useful for quantification and modeling of the noise in the reported radiance data. The 'postcal' file contains approximately 100 lines of dark current data collected at the end of each data acquisition run. In essence this is a regular spectral-image cube, with 614 samples, 100 lines and 224 channels, collected with a closed shutter. Since there is no incident radiance signal, the recorded DN measure only the DC signal level and the noise in the system. Similar dark current measurements, made at the end of each line are used, with a 100 line moving average, to remove the DC signal offset. Therefore, the pixel-by-pixel fluctuations about the mean of this dark current image provide an excellent model for the additive noise that is present in AVIRIS reported radiance data. The 61,400 dark current spectra can be used to calculate the noise levels in each channel and the noise covariance matrix. Both of these noise parameters should be used to improve spectral processing techniques. Some processing techniques, such as spectral curve fitting, will benefit from a robust estimate of the channel-dependent noise levels. Other techniques, such as automated unmixing and classification, will be improved by the stable and scene-independence noise covariance estimate. Future imaging spectrometry systems should have a similar ability to record dark current data, permitting this noise characterization and modeling.

Boardman, Joseph W.↗

NASA NTRSDate not supplied

Constraints on the Origin of Mercury’s Large Core from Core-Mantle Differentiation Models

Mercury’s core is notoriously large when compared to other planets of our solar system. The origin of this large core is still uncertain. Available data on the surface composition and internal structure of Mercury from the past MESSENGER mission and future data collected by BepiColombo will continue to provide clues to Mercury’s formation. Here, we present results combining experimental data on elemental distribution between core and mantle with spacecraft data, to estimate bulk Mercury composition. We applied this strategy to major elements (Fe, Si, Mg, Al and O), as well as minor elements (Cr and Ti), and compared derived compositions to chondritic data and the chemical compositions of the other terrestrial planets. Our results show that Mercury has a chemical composition significantly different from all known materials of the solar system. In addition, numerous scenarios were proposed to explain Mercury’s structure, including “chaotic models” such as giant impacts and “orderly models” such as aerodynamic sorting. Here, we tested whether Mercury’s composition can be explained by mantle stripping by impacts. We will show how such a scenario reconciles several features of Mercury’s geochemistry with chondritic data. We will also discuss the outlook of additional constraints from supplementary data potentially collected by BepiColombo.

Mercury↗

NASA NTRS1987

Performability modeling based on real data: A casestudy

Described is a measurement-based performability model based on error and resource usage data collected on a multiprocessor system. A method for identifying the model structure is introduced and the resulting model is validated against real data. Model development from the collection of raw data to the estimation of the expected reward is described. Both normal and error behavior of the system are characterized. The measured data show that the holding times in key operational and error states are not simple exponentials and that a semi-Markov process is necessary to model the system behavior. A reward function, based on the service rate and the error rate in each state, is then defined in order to estimate the performability of the system and to depict the cost of different types of errors.

Hsueh, M. C.↗

NASA NTRS1988

Performability modeling based on real data: A case study

Described is a measurement-based performability model based on error and resource usage data collected on a multiprocessor system. A method for identifying the model structure is introduced and the resulting model is validated against real data. Model development from the collection of raw data to the estimation of the expected reward is described. Both normal and error behavior of the system are characterized. The measured data show that the holding times in key operational and error states are not simple exponentials and that a semi-Markov process is necessary to model system behavior. A reward function, based on the service rate and the error rate in each state, is then defined in order to estimate the performability of the system and to depict the cost of apparent types of errors.

Hsueh, M. C.↗

NASA NTRS1999

Measuring Tropospheric Winds from Space Using a Coherent Doppler Lidar Technique

The global measurement of tropospheric wind profiles has been cited by the operational meteorological community as the most important missing element in the present and planned observing system. The most practical and economical method for obtaining this measurement is from low earth orbit, utilizing a Doppler lidar (laser radar) technique. Specifically, this paper will describe the coherent Doppler wind lidar (CDWL) technique, the design and progress of a current space flight project to fly such a system on the Space Shuttle, and plans for future flights of similar instruments. The SPARCLE (SPAce Readiness Coherent Lidar Experiment) is a Shuttle-based instrument whose flight is targeted for March, 2001. The objectives of SPARCLE are three-fold: Confirm that the coherent Doppler lidar technique can measure line-of-sight winds to within 1-2 m/s accuracy; Collect data to permit validation and improvement of instrument performance models to enable better design of future missions; and Collect wind and backscatter data for future mission optimization and for atmospheric studies. These objectives reflect the nature of the experiment and its program sponsor, NASA's New Millennium Program. The experiment is a technology validation mission whose primary purpose is to provide a space flight validation of this particular technology. (It should be noted that the CDWL technique has successfully been implemented from ground-based and aircraft-based platforms for a number of years.) Since the conduct of the SPARCLE mission is tied to future decisions on the choice of technology for free-flying, operational missions, the collection of data is intrinsically tied to the validation and improvement of instrument performance models that predict the sensitivity and accuracy of any particular present or future instrument system. The challenges unique to space flight for an instrument such as SPARCLE and follow-ons include: Obtaining the required lidar sensitivity from the long distance of orbit height to the lower atmosphere; Maintaining optical alignments after launch to orbit, and during operations in "microgravity"; Obtaining pointing knowledge of sufficient accuracy to remove the speed of the spacecraft (and the rotating Earth) from the measurements; Providing sufficient power (not a problem on the Shuttle) and cooling to the instrument. The paper will describe the status and challenges of the SPARCLE project, the value of obtaining wind data from orbit, and will present a roadmap to future instruments for scientific research and operational meteorology.

Miller, Timothy L.↗

NASA NTRS2000

VHF Radar Measurements of Tropical Forests in Panama: Results from the BioSAR Deployment in Central America

A synthetic aperture radar sensor operating in 5 bands between 80 and 120 MHz was flown over forested areas in the canal zone of the Republic of Panama in an experiment to measure biomass in heavy tropical forests. The sensor is a pulse coherent SAR flown on a small aircraft and oriented straight down. The doppler history is processed to collect data on the ground in rectangular cells of varying size over a range of incidence angles fore and aft of nadir (+45 to - 45 degrees). Sensor data consists of 5 frequency bands with 20 incidence angles per band. Sensor data for over 12+ sites were collected with forest stands having biomass densities ranging from 50 to 300 tons/ha dry above ground biomass. Results are shown exploring the biomass saturation thresholds using these frequencies, the system design is explained, and preliminary attempts at data visualization using this unique sensor design are described.

Imhoff, Marc↗

NASA NTRS2000

Low Frequency Radar Sensor Observations of Tropical Forests in the Panama Canal Area

Imhoff, M. L.↗

NASA NTRS1988

Strain accumulation in the Santa Barbara Channel, 1971-1987

Geophysical evidence suggests a significant amount of north-south convergence occurs across the Santa Barbara Channel. Tectonic studies indicate a discrepancy between observed fault slip in California and the North American-Pacific plate motion. Newer plate motion models (NUVEL-1) yield a lower rate of convergence. Global Positioning System (GPS) data collected in the Santa Barbara Channel in 1987, when combined with 1971 trilateration measurements, should be sufficient to resolve the present-day convergence rate. In early 1987. from January 3 to 7, GPS data were collected at 14 sites in California and at 5 additional stations throughout North America. The data can be used to estimate the rate of crustal deformation (convergence) ocurring across the Santa Barbara Channel. The GPS baselines were computed with the Bernese 2nd generation software. A comparison was made between baseline lengths obtained with the Burnese and MIT softwares. Baseline changes from 1971 to January, 1987 (GPS-Bernese) across the Santa Barbara Channel were computed. A uniform strain model was calculated from the baseline changes. The present-day rate of convergence across the Santa Barbara Channel was determined to be 8 to 10 mm/yr. This conclusion is obtained from changes in the baseline length measured with a 1971 trilateration survey and a January, 1987, GPS survey. The rapid convergence rate, in addition to the history of large seismic events, suggests this region is a prime target for future geodetic and geophysical studies.

Larsen, Shawn↗