SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A photogrammetric system uses an array of spaced-apart targets coupled to a structure. Each target exhibits fluorescence when exposed to a broad beam of illumination. A photogrammetric imaging system located remotely with respect to the structure detects and processes the fluorescence (but not the illumination wavelength) to measure the shape of a structure.
The force of Earth's gravity field varies in proportion to the amount of mass near the surface. Spatial and temporal variations in the gravity field can be measured via their effects on the orbits of satellites. The Gravity Recovery and Climate Experiment (GRACE) is the first satellite mission dedicated to monitoring temporal variations in the gravity field. The monthly gravity anomaly maps that have been delivered by GRACE since 2002 are being used to infer changes in terrestrial water storage (the sum of groundwater, soil moisture, surface waters, and snow and ice), which are the primary source of gravity variability on monthly to decadal timescales after atmospheric and oceanic circulation effects have been removed. Other remote sensing techniques are unable to detect water below the first few centimeters of the land surface. Conventional ground based techniques can be used to monitor terrestrial water storage, but groundwater, soil moisture, and snow observation networks are sparse in most of the world, and the countries that do collect such data rarely are willing to share them. Thus GRACE is unique in its ability to provide global data on variations in the availability of fresh water, which is both vital to life on land and vulnerable to climate variability and mismanagement. This chapter describes the unique and challenging aspects of GRACE terrestrial water storage data, examples of how the data have been used for research and applications related to fresh water vulnerability and change, and prospects for continued contributions of satellite gravimetry to water resources science and policy.
Laboratory spectra have shown that CO2 is a powerful diagnostic tool for analyzing infrared data from remote observations, as it has been detected on icy moons in the outer Solar System as well as dust grain surfaces in the interstellar medium (ISM). IR absorption band profiles of CO2 within ice mixtures containing H2O and CH3OH change with respect to temperature and mixture ratios. In this particular study, the CO2 asymmetric stretching mode near 4.3 m (2350 cm (exp-1)), overtone mode near 1.97 m (5080 cm (exp-1)), and the combination bands near 2.7 m (3700 cm (exp-1)), 2.8 m (3600 cm (exp-1)), and 2.02 m (4960 cm (exp -1)), are systematically observed in different mixtures with H2O and CH3OH in temperature ranges from 15K to 150 K. Additionally, some high-temperature deposits (T greater than 50 K) of H2O, CH3OH, and CO2 ice mixtures were performed. These data may then be used to interpret infrared observational data obtained from icy surfaces in the outer Solar System and beyond.
We have studied the utility of high resolution SAR (synthetic aperture radar) for levee monitoring using UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) data collected along the dikes and levees in California's Sacramento-San Joaquin Delta and along the lower Mississippi River. Our study has focused on detecting and tracking changes that are indicative of potential problem spots, namely deformation of the levees, subsidence along the levee toe, and seepage through the levees, making use of polarimetric and interferometric SAR techniques. Here was present some results of those studies, which show that high resolution, low noise SAR imaging could supplement more traditional ground-based monitoring methods by providing early indicators of seepage and deformation.
The threat for aircraft icing in clouds is a significant hazard that routinely impacts aviation operations. Accurate diagnoses and forecasts of aircraft icing conditions requires identifying the location and vertical distribution of clouds with super-cooled liquid water (SLW) droplets, as well as the characteristics of the droplet size distribution. Traditional forecasting methods rely on guidance from numerical models and conventional observations, neither of which currently resolve cloud properties adequately on the optimal scales needed for aviation. Satellite imagers provide measurements over large areas with high spatial resolution that can be interpreted to identify the locations and characteristics of clouds, including features associated with adverse weather and storms. This paper describes new techniques for interpreting cloud products derived from satellite data to infer the flight icing threat to aircraft. For unobscured low clouds, the icing threat is determined using empirical relationships developed from correlations between satellite imager retrievals of liquid water path and droplet size with icing conditions reported by pilots (PIREPS). For deep ice over water cloud systems, ice and liquid water content (IWC and LWC) profiles are derived by using the imager cloud properties to constrain climatological information on cloud vertical structure and water phase obtained apriori from radar and lidar observations, and from cloud model analyses. Retrievals of the SLW content embedded within overlapping clouds are mapped to the icing threat using guidance from an airfoil modeling study. Compared to PIREPS and ground-based icing remote sensing datasets, the satellite icing detection and intensity accuracies are approximately 90% and 70%, respectively, and found to be similar for both low level and deep ice over water cloud systems. The satellite-derived icing boundaries capture the reported altitudes over 90% of the time. Satellite analyses corresponding to the time and location of several recent aviation accidents and with icing PIREPS are also presented that reveal skill in identifying severe icing conditions. These results demonstrate the utility of satellite cloud retrievals for quantitatively diagnosing the potential for icing conditions on temporal and spatial scales that should be useful to the aviation community. Plans are being developed to deliver these new satellite products to the GOES-R Proving Ground in the near future so that they can be evaluated in operational applications.
Space-based astrophysical and remote sensing observations often require the detection and measurement of light originating from distant and relatively faint objects. These observations are highly susceptible to scattered light which may introduce imaging artifacts, obscure object details, and increase measurement noise. This paper describes the initial work of characterizing representative black materials used in coronagraph instruments and other spaceborne instruments. Measurements of “blackness” and the achieved reflectance of black silicon are provided in the spectral range from 400nm to 2500nm using 8o directional hemispherical measurements. The bidirectional reflectance of black silicon was also measured at discrete wavelengths, 633nm, and 1064nm, using the optical scatterometer located at NASA Goddard Space Flight Center’s Diffuser Calibration Laboratory (DCL). A 100mm diameter black silicon sample was fabricated and optically characterized. The BRDF of other well-known black materials such as Z306 and Fractal Black are also presented and discussed.
Space-based astrophysical and remote sensing observations often require the detection and measurement of light originating from distant and relatively faint objects. These observations are highly susceptible to scattered light which may introduce imaging artifacts, obscure object details, and increase measurement noise. This paper describes the initial work of characterizing representative black materials used in coronagraph instruments and other spaceborne instruments. Measurements of “blackness” and the achieved reflectance of black silicon are provided in the spectral range from 400nm to 2500nm using 8o directional hemispherical measurements. The bidirectional reflectance of black silicon was also measured at discrete wavelengths, 633nm, and 1064nm, using the optical scatterometer located at NASA Goddard Space Flight Center’s Diffuser Calibration Laboratory (DCL). A 100mm diameter black silicon sample was fabricated and optically characterized. The BRDF of other well-known black materials such as Z306 and Fractal Black are also presented and discussed.
In preparation for the release of the Orbital Debris Engineering Model (ORDEM) version 3.1, the NASA Orbital Debris Program Office (ODPO) revisited how orbiting debris populations of characteristic sizes smaller than 1 cm were modeled. The primary contributor to the population of sub-centimeter debris particles is the surface deterioration or erosion of spacecraft materials exposed to the outer-space environment. Because small particulates are not directly trackable by remote sensing, the primary means of detection is via historical counts of small impact features on flown radiator and window surfaces of the U.S. Space Transportation System (STS, also known as the Space Shuttle) from 1995-2011. Historic NASA studies of high-velocity impact tests have related impact-feature size to particle mass and velocity for certain STS surfaces, so that a corresponding particle size may be inferred from each small-impact feature observed. Micro-debris populations are then estimated by modeling the path and orientation of an STS mission through a simulated debris environment, and the densities of this simulated environment are rescaled to approximate the number of observed STS impact features. Monte-Carlo methods are further employed to gauge the estimation uncertainty of the rescaled environment. A description of the chosen methodologies for estimating and adjusting the micro-debris population model, and the results, are presented.
Coastal upwelling regions are one of the most dynamic areas of the world’s oceans. The California and Baja California Coasts are impacted by both coastal upwelling and the California Current, leading to frontal activity that is captured by gradients in both Sea Surface Temperature (SST) and Sea Surface Salinity (SSS). Satellite data are a great source of spatial data to study fronts. However, biases near coastal areas and coarse resolutions can impair its usefulness in upwelling areas. In this work gradients in SST from NASA Multi-Scale Ultra-High Resolution (MUR) and in two SSS products derived from the Soil Moisture Active Passive (SMAP) NASA mission are compared directly with gradients derived from the Saildrone uncrewed vehicles to validate the gradients as well as to assess their ability to detect known frontal features. The three remotely sensed data sets (MURSST/JPL, SMAP SSS/RSS, SMAP SSS) were co-located with the Saildrone data prior to the calculation of the gradients. Wavelet analysis is used to determine how well the satellite derived SST and SSS products are reproducing the Saildrone derived gradients. Overall results indicate the remote sensing products are reproducing features of known areas of coastal upwelling. Differences between the SST and SSS gradients are mainly associated with the limitations of the microwave derived SSS coverage near land and its reduced spatial resolution. The results are promising for using remote sensing data sets to monitor frontal structure along the California Coast and the application to long term changes in coastal upwelling and dynamics.
The linear polarization of sunlight reflected by ocean surfaces in the shortwave infrared (SWIR), at geometries where specular reflection dominates the signal, is a direct function of the refractive index of the surface microlayer(SML). This simple physical concept is at the base of a novel technique presented in this study. We invert observations obtained by the airborne Research Scanning Polarimeter (RSP) in the sun glint region, where each pixel's radiance is dominated by the signal originating from the wave slopes oriented precisely to cause specular reflection. The SWIR wavelength ensures minimization of aerosol interference when radiance travels through the atmosphere; strong absorption by the water body then limits the penetration depth to the first micrometer or so, effectively probing the SML. The resulting Degree of Linear Polarization (DoLP) is then governed by the refractive index via the Fresnel law for the specfic pixel geometry, independently of the wind speed. The selected dataset concerns several field deployments from both low- and high-altitude aircraft, including total reflectance measurements with the sole purpose of accounting for the residual aerosol effect.Stable retrievals from transects above pure seawater yielded values of refractive index that match the values published in the literature within an accuracy of 5 x 10 [superscript 4]. Flying over the oil spill caused by the explosion of the Deepwater Horizon platform, detected variations were found compatible with the presence of an oil slick. The robustness of the results, guaranteedby the high RSP polarimetric accuracy (less than or equal to 0:2%), opens the possibility for remote-sensing detection of other entities that similarly affect the refractive index including whitecaps, microplastics, biological gels, seaweed and grass mats.
A survey is reported of methods for sounding the atmospheric temperature profile by remote measurements. The emphasis for this period was placed on sounding in the microwave region of the spectrum, sounding in cloudy atmosphere, and measuring sea temperatures remotely. Summaries of the research in the following areas are included: orbital detection of stratospheric aerosols, monthly precipitation charts for the world, determining planetary cloud structure by remote polarization measurement, analysis of Mariner 6 and 7 multicolor photometric photographs of Mars, and techniques for photometric detection of extrasolar planets.
Cultural resource management consists of research to identify, evaluate, document and assess cultural resources, planning to assist in decision-making, and stewardship to implement the preservation, protection and interpretation of these decisions and plans. One technique that may be useful in cultural resource management archaeology is remote sensing. It is the acquisition of data and derivative information about objects or materials (targets) located on the Earth's surface or in its atmosphere by using sensor mounted on platforms located at a distance from the targets to make measurements on interactions between the targets and electromagnetic radiation. Included in this definition are systems that acquire imagery by photographic methods and digital multispectral sensors. Data collected by digital multispectral sensors on aircraft and satellite platforms play a prominent role in many earth science applications, including land cover mapping, geology, soil science, agriculture, forestry, water resource management, urban and regional planning, and environmental assessments. Inherent in the analysis of remotely sensed data is the use of computer-based image processing techniques. Geographical information systems (GIS), designed for collecting, managing, and analyzing spatial information, are also useful in the analysis of remotely sensed data. A GIS can be used to integrate diverse types of spatially referenced digital data, including remotely sensed and map data. In archaeology, these tools have been used in various ways to aid in cultural resource projects. For example, they have been used to predict the presence of archaeological resources using modern environmental indicators. Remote sensing techniques have also been used to directly detect the presence of unknown sites based on the impact of past occupation on the Earth's surface. Additionally, remote sensing has been used as a mapping tool aimed at delineating the boundaries of a site or mapping previously unknown features. All of these applications are pertinent to the goals of site discovery and assessment in cultural resource management.
The utility of remote sensing techniques to urban data acquisition problems in several distinct areas was identified. This endeavor included a comparison of remote sensing systems for urban data collection, the extraction of housing quality data from aerial photography, utilization of photographic sensors in urban transportation studies, urban change detection, space photography utilization, and an application of remote sensing techniques to the acquisition of data concerning intra-urban commercial centers. The systematic evaluation of variable extraction for urban modeling and planning at several different scales, and the model derivation for identifying and predicting economic growth and change within a regional system of cities are also studied.
General-purpose, water-quality sampling process detects microorganisms and integrates sampling, sample preservation, and sample analysis. System overcomes difficulties in correlation of surface measurements with remote measurements and minimizes human error in handling of samples and instrumentation.
Monitoring forest cover and its changes are a major application for optical remote sensing. In this paper, we present an approach to integrate Landsat, ASTER and MODIS data for forest change detection. Moderate resolution (10-100m) images (e.g. Landsat and ASTER) acquired from different seasons and times are normalized to one "standard" date using MODIS data products as reference. The normalized data are then used to compute forest disturbance index for forest change detection. Comparing to the results from original data, forest disturbance index from the normalized images is more consistent spatially and temporally. This work demonstrates an effective approach for mapping forest change over a large area from multiple moderate resolution sensors on various acquisition dates.
The study of vegetation community and structural change has been central to ecology for over a century, yet the ways in which disturbances reshape the physical structure of forest canopies remain relatively unknown. Moderate severity disturbances affect different canopy strata and plant species, resulting in variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits (CSTs) with known links to ecosystem function. We used lidar-derived CSTs within a machine learning framework to detect and describe the structural changes that result from various disturbance agents, including moderate severity fire, ice storm damage, age-related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, senescence, pathogen, and insect infestation altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, counter to common assumptions regarding many moderate severity disturbances. While findings are limited in their generalizability due to lack of replication among disturbances, they do suggest that the current limitations of standard disturbance detection methods—such as optical-based remote sensing platforms, which are often above-canopy perspectives—limit our ability to understand the full ecological and structural impacts of disturbance, and to evaluate the consistency of structural patterns within and among disturbance agents. A more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structural change may potentially improve the modeling, detection, and prediction of functional implications of moderate severity disturbance as well as broaden our understanding of the ecological impacts of disturbance.
A light-detection system for an optical radar, or lidar, unit to be used for remote temperature and composition measurements was designed, built, and bench tested. This detection system processes three return signal wavelengths: two Raman wavelengths, and the Rayleigh-Mie wavelength at 694.3 nanometers. Means of coping with photomultiplier tube instabilities and limitations are discussed. Circuits for gain control, ranging, and digitizing are included. The phototube gains can be switched fully on in 80 meters (450 nsec) or off in 30 meters (200 nsec) of range. The range circuit processes signals from 0.1 to 2 kilometers, with an estimated range resolution of less than 5 meters.
National Aeronautics and Space Administration’s (NASA) Goddard Space Flight Center (GSFC) operates a constellation of ten geosynchronous Tracking and Data Relay Satellites (TDRS). The TDRS constellation consists of multiple geosynchronous communication relay satellites located around the equator so they can provide continual coverage of any mission in low earth orbit. The TDRS are located primarily in three oceanic regions around the earth. NASA’s White Sands Complex provides the ground communication support for TDRS located over the Atlantic and Pacific Oceans. Another TDRS ground station in Guam supports the TDRS over the Indian Ocean. With these satellites the TDRS network can provide continuous coverage of satellites in low-earth orbit. The NASA Space Network (SN) project office at GSFC manages the constellation of spacecraft. Major customers of the TDRS constellation include, but are not limited to, the International Space Station and the Hubble Space Telescope. The TDRS constellation has three generations of satellites and has been active for over 30 years providing reliable communication links between customer satellites and corresponding ground stations. However, one of the major concerns for TDRS, and in any space mission, is to ensure the health and safety of the spacecraft. Generally, engineers use telemetry data to monitor and analyze the performance and state of health of the spacecraft. Telemetry data contains hundreds of parameters that monitor each important component in the spacecraft, which can be utilized to recognize and characterize the behavior of the spacecraft. Each parameter contains considerable information to represent time-dependent properties of each spacecraft subsystem and component. During the entire life of a TDRS spacecraft, thousands of gigabytes of telemetry data are transmitted in real-time from the spacecraft to the ground station at the White Sands Complex in Las Cruces, New Mexico, and recorded as historical data sets for engineers to process and analyze the events that occurred on-orbit. These parameters contain the function of multiple spacecraft subsystems, such as the attitude control system (ACS), Thermal, Electrical Power Subsystem (EPS), etc. . The first and second generations have exceeded their required lifetime and NASA is keen to manage these spacecrafts carefully in order to maximize the remaining life using the spacecraft telemetry. The challenge is to know when the risk of losing a spacecraft in geosynchronous orbit exceeds the benefit of continued operations for customer support. In the TDRS fleet, the EPS is the most critical subsystem related to spacecraft operations. Failure of the EPS would strand a spacecraft in geosynchronous orbit. Since EPS provides power to the spacecraft, component failures ultimately lead to the inability to support the spacecraft loads and the communications payload. For instance, TDRS-8 has several anomalies in EPS including the Bus Voltage Limiter (BVL) shunt current, solar array loss of circuits, and failed battery cells. Any of these anomalies can cause critical issues to the spacecraft. Therefore, developing a system to analyze and perform early detection of a potential anomaly is an important issue in telemetry data analysis. In recent years, Telemetry Mining (TM) has been proposed to process telemetry data by using Data Mining (DM) techniques such as classification, clustering, regression and anomaly detection. Anomaly detection, also known as outlier detection, has been widely used in many data mining areas such as remote sensing, medical data processing and digital image processing. The goal of anomaly detection is to detect abnormal data, which contains a relatively low probability of occurrence among the entire data set. Early detection of anomalies is one of the most significant issues in managing the spacecraft configuration. If anomalies can be detected early enough, then the redundant resources can be used to extend the life of the operational spacecraft. We present an unsupervised anomaly detection method to process the EPS data extracted from TDRS-8. This is different from traditional analytical methods, which use telemetry data to illustrate behavior and physical meaning of each spacecraft component. TM connects multiple parameters as a vector and then conducts data analysis on this high dimension telemetry vector. This method is looking at the properties of a high dimensional vector that is able to consider the relationship between different parameters in the anomaly detection problem. This kind of method performs much better than the traditional limit checking method. In addition, we propose a new approach of real-time anomaly detection to process telemetry data in real-time, which can then be applied to spacecraft monitoring with high reliability, low cost and high accuracy.