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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Architecture Options for Navigation in Cislunar Space for Human Landing System Vehicles

As part of architecture studies and insight analysis focused on requirements development into Human Landing System lunar architecture designs, multiple studies are underway to understand the sensitivities and options for achieving high precision landing on the lunar surface. The baseline approach utilizes a combination of multiple sensors to capture autonomous state observations of the lander with respect to the lunar surface. These systems are typically constrained in terms of operational altitudes by parameters such as onboard map size, camera focus, or sensor transmitted power (for altimeter observations). While these sensor suites do enable high precision landing, they are typically very complex and expensive. For a human-rated vehicle, fault detection algorithms are needed in addition to redundant sensors drive additional design complexity. Conversely, for these early missions, mass performance is key, so extended analysis is required to identify numbers of sensors, their ideal placement, and integration algorithms. A key part of this analysis is to help identify key sensor suites and options to help alleviate this design tension. An alternate approach is to take advantage and build out in-situ assets to allow for GPS-like navigation within the lunar regime through the use of navigation references or beacons. This can be achieved through the integration of navigation services into potential relays and pre-placed lunar surface assets. This research focuses on the capability of this infrastructure to support navigation in all areas of cislunar space such as: approach to the moon, in orbit around the moon, and ascent/descent operations to the surface. An augmented state linear covariance analysis (LinCov) and navigation state covariance analysis (NavCov) tools were used to assess a variety of navigation reference locations and how they can support vehicle operations through both understanding of state uncertainties and trajectory dispersions. This research helps to supplement existing studies focused on communication link analysis by providing additional insight into specific vehicle operational scenarios that are tied closely to potential Human Landing System scenarios. Key aspect of this analysis focus on the sensitivity to state knowledge of the references, the accuracy of inter-asset measurements, and placement in support of the various scenarios.

Evan J Anzalone↗
NASA NTRSDate not supplied

Mapping National Forest Aboveground Biomass in Mexico by Integrating GEDI, Sentinel‐1 and Sentinel‐2 Data

Accurate mapping of forest aboveground biomass density (AGBD) is required to better understand the role of forests in the global carbon cycle and to support international policies for climate change mitigation and adaptation. Mexico is one of the countries having great potential for the United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation (or UN-REDD program) and there is a growing demand for unbiased Monitoring Reporting Verification systems at a national level. As an effort under NASA’s Carbon Monitoring System (CMS) program, we developed a machine learning model using multi-stream remote sensing measurements as well as topographic data to create a high spatial resolution AGBD map (~100 m) over Mexico (circa 2020). The remote sensing data includes Global Ecosystem Dynamic Investigation (GEDI) lidar, Sentinel 1 Synthetic-Aperture Radar (SAR), and Sentinel-2 multispectral imagery (MSI). GEDI onboard the International Space Station provides unprecedented forest structure and AGBD sampling datasets for model training and validation practices. Our analysis indicates that the developed random forest model can capture 63 % of the spatial variation (RMSE = 33.7 Mg/ha) of AGBD of Mexican forests. We find that shortwave infrared bands of Sentinel-2 MSI and topographical variables from elevation data are the most important variables in the developed AGBD model. Our study highlights methodological opportunities in synergistic uses of multiple sensors for large-scale forest AGBD mapping and shows potential for retrospective analysis and operational monitoring of forest AGBD and its dynamics.

Taejin Park↗
NASA NTRS2024

Narragansett Bay Water Resources: Using Earth Observations to Identify Trends in Harmful Algal Blooms in Narragansett Bay

Narragansett Bay in Rhode Island is known for its quahog, or hard-shell clam, shellfisheries. However, increased levels in harmful algal blooms (HABs) and high phytoplankton biomass events pose threats to quahog populations, creating conditions that limit quahog growth and reproduction. Quahog shortages, along with public health concerns associated with contamination of shellfish from HAB-produced neurotoxins, have contributed to shellfishery closures. NASA DEVELOP partnered with the Environmental Protection Agency’s National Health and Environmental Effects Research Laboratory and Rhode Island’s Department of Environmental Management to use Earth observations and the partners’ in-situ data to visualize phytoplankton bloom events within the bay. We used Sentinel-3 Ocean and Land Color Instrument (OLCI), Landsat 8 and 9 Operational Land Imager (OLI)data to track several proxies of phytoplankton biomass from June 2016 to October 2023. Multiple sensors and in situ datasets were used to assess the feasibility of monitoring phytoplankton biomass accurately in the relatively small sized Narragansett Bay. We determined that Phytoplankton fluorescence line height was the best parameter to monitor phytoplankton biomass in the bay. Although the relatively small size of the bay and the optical complexity of these nearshore waters can pose a challenge, this assessment showed that Earth observations can be a useful complement to the in-situ monitoring of phytoplankton biomass in Narragansett Bay.

Sentinel-3↗
NASA NTRSDate not supplied

Firesense: Supporting Operational Partners Through Co-Development

Fire is a natural disturbance and fundamental to many ecosystems, but, across the United States and many areas globally, fires have become more common, larger and more likely to occur at the same time. This has the potential to challenge resource allocation and response and requires coordinated integration of technology and tools at the spatial and temporal scales required by practitioners who make wildland fire management decisions. In the US wildfire is currently managed across state, federal, and tribal agencies who leverage various datasets to guide management decisions. FireSense, a NASA Science Mission Directorate project, aims to develop and deliver trailblazing technology and tools for use before, during, and after wildland fires by working together with land managers and practitioners. FireSense is focused on turning information into solutions by expanding partnerships and collaborations with operational agencies to inform and deliver data and technology to support decisions for wildland fire management. Through implementation across the US and with campaign activities in a variety of landscapes, coordinated research and development work will build upon adaptive and use-inspired approaches that can be put into operation. By leveraging and complementing partner activities through coordinated investment, we support a more comprehensive and cohesive response that can advance fire science to better understand fire behavior and effects through integrated measurement, monitoring and modeling. We present updates from in-progress technology development and field campaigns within the US which include sampling and application across scales with information from multiple sensors collocated with field measurements and highlighting the importance of cross-scale observations during the fire lifecycle. Coordinated investment in new science and technology for the complete fire lifecycle supports a comprehensive and cohesive fire response that help to overcome barriers and anticipate and manage the new reality of extreme fires in a warming world.

Jacquelyn Shuman↗
NASA NTRSDate not supplied

Using NASA Earth Observations to Identify Trends in Harmful Algal Blooms in Narragansett Bay

Narragansett Bay in Rhode Island is known for its quahog, or hard-shell clam, shellfisheries. However, increased levels in harmful algal blooms (HABs) have posed threats to quahog populations and raised public health concerns due to shellfishery closures resulting from contamination with neurotoxins released by phytoplankton. NASA DEVELOP partnered with the Environmental Protection Agency’s National Health and Environmental Effects Research Laboratory and Rhode Island’s Department of Environmental Management to use Earth observations and the partners’ in-situ data to visualize phytoplankton bloom events within the bay. The team used Sentinel-3 Ocean and Land Color Instrument (OLCI), Landsat 8 Operational Land Imager (OLI), and Landsat 9 OLI-2 to track proposed proxies for phytoplankton from June 2016 to October 2023. Multiple sensors and datasets were employed for more accurate results due to the small spatial extent of Narragansett Bay. Chlorophyll-a and total suspended solids were determined to be accurate parameters to monitor HAB events. Although complications could arise due to the relatively small size of the bay and small-scale of the bloom events, which may be difficult to visualize via remote sensing, Earth observations have proven to help strengthen HAB research, monitoring, and prediction and will complement the extensive in-situ data in Narragansett Bay.

Isabella Giordano↗
NASA NTRS1983

Computer simulation study of multiple germanium gamma-ray sensor arrays

Design studies of large high resolution gamma-ray spectrometer systems employing multiple sensors are greatly facilitated by the use of computer simulation techniques. A study has been made by interfacing a detector geometry code with the EGS-3 version of the Stanford Linear Accelerator Center Monte-Carlo Code which has been modified to run on our VAX computer. The detector responses have been simulated for input photons with energies between 0.1 and 10 MeV. Examples are given to illustrate the performance of complex sensor arrays with different configurations of the anticoincidence system. Of particular interest is a design composed of an array of 7 detector elements each consisting of a thin planar Ge (1 cm thick) detector mounted in front of and in tandem with a large coaxial Ge detector.

Nakano, G. H.↗
NASA NTRS2016

Assessing the Transferability of Statistical Predictive Models for Leaf Area Index Between Two Airborne Discrete Return LiDAR Sensor Designs Within Multiple Intensely Managed Loblolly Pine Forest Locations in the South-Eastern USA

Leaf area is an important forest structural variable which serves as the primary means of mass and energy exchange within vegetated ecosystems. The objective of the current study was to determine if leaf area index (LAI) could be estimated accurately and consistently in five intensively managed pine plantation forests using two multiple-return airborne LiDAR datasets. Field measurements of LAI were made using the LiCOR LAI2000 and LAI2200 instruments within 116 plots were established of varying size and within a variety of stand conditions (i.e. stand age, nutrient regime and stem density) in North Carolina and Virginia in 2008 and 2013. A number of common LiDAR return height and intensity distribution metrics were calculated (e.g. average return height), in addition to ten indices, with two additional variants, utilized in the surrounding literature which have been used to estimate LAI and fractional cover, were calculated from return heights and intensity, for each plot extent. Each of the indices was assessed for correlation with each other, and was used as independent variables in linear regression analysis with field LAI as the dependent variable. All LiDAR derived metrics were also entered into a forward stepwise linear regression. The results from each of the indices varied from an R2 of 0.33 (S.E. 0.87) to 0.89 (S.E. 0.36). Those indices calculated using ratios of all returns produced the strongest correlations, such as the Above and Below Ratio Index (ABRI) and Laser Penetration Index 1 (LPI1). The regression model produced from a combination of three metrics did not improve correlations greatly (R2 0.90; S.E. 0.35). The results indicate that LAI can be predicted over a range of intensively managed pine plantation forest environments accurately when using different LiDAR sensor designs. Those indices which incorporated counts of specific return numbers (e.g. first returns) or return intensity correlated poorly with field measurements. There were disparities between the number of different types of returns and intensity values when comparing the results from two LiDAR sensors, indicating that predictive models developed using such metrics are not transferable between datasets with different acquisition parameters. Each of the indices were significantly correlated with one another, with one exception (LAI proxy), in particular those indices calculated from all returns, which indicates similarities in information content for those indices. It can then be argued that LiDAR indices have reached a similar stage in development to those calculated from optical-spectral sensors, but which offer a number of advantages, such as the reduction or removal of saturation issues in areas of high biomass.

Sumnall, Matthew↗
NASA NTRS1982

Concept for a multiple resolution pushbroom sensor

A general purpose pushbroom sensor will have parameters determined by the needs of the majority of potential users. These parameters may not satisfy the needs of certain users: Agriculture requires a very short return visit interval; cartography requires a very small pixel size; land use and geology would be satisfied with moderate resolution and seasonal return times. The aggregate solution of these needs would produce a sensor with extremely high data rates. A sensor conept is proposed which may meet the combined needs without the extreme data rate.

Billingsley, F. C.↗
NASA NTRS1982

Concept for a multiple resolution pushbroom sensor

A general purpose pushbroom sensor will have parameters determined by the needs of the majority of potential users. These parameters may not satisfy the needs of certain users: Agriculture requires a very short return visit interval; cartography requires a very small pixel size; land use and geology would be satisfied with moderate resolution and seasonal return times. The aggregate solution of these needs would produce a sensor with extremely high data rates. A sensor concept is proposed which may meet the combined needs without the extreme data rate. Previously announced in STAR as N82-28735

Billingsley, F. C.↗
NASA NTRS1989

Multiple-Cantilever Torque Sensor

Sensitivity to spurious loads small. High stiffness, high resolution, and ease of fabrication among features of specially designed torque sensor. Device flexible and sensitive to torque about its cylindrical axis and stiff enough to be insensitive to bending about any perpendicular axis. Measures and transmits torque between driving and driven plates.

Lurie, Boris J.↗
NASA NTRS1988

Sensor calibration for multiple direction reflectance observations

Spectral and radiometric calibrations of a pointable airborne spectroradiometer called ASAS are discussed. A laboratory integrating hemisphere is used to characterize the radiometric respones of ASAS detectors. Radiometric responses are linear except for an initial build-up lag in response to low levels of radiance. Assuming radiometric stability in flight, raw ASAS digital counts can be transformed to absolute spectral radiance values with an uncertainty of 5.5 percent attributable to the laboratory calibration. The calibrations are being applied to radiometrically correct ASAS data acquired from multiple view directions over a tall grass prairie during the 1987 growing season.

Irons, James R.↗
NASA NTRS2011

Integrating Multiple Space Ground Sensors to Track Volcanic Activity

Volcanic activity can occur with little or no warning. Increasing numbers of space borne assets can enable coordinated measurements of volcanic events to enhance both scientific study and hazard response. We describe the use of space and ground measurements to target further measurements as part of a worldwide volcano monitoring system. We utilize a number of alert systems including the MODVOLC, GOESVOLC, US Air Force Weather Advisory, and Volcanic Ash Advisory Center (VAAC) alert systems. Additionally we use in-situ data from ground instrumentation at a number of volcanic sites, including Iceland.

volcanoes↗
NASA NTRS2011

Multi-Sensor Aerosol Products Sampling System

Global and local properties of atmospheric aerosols have been extensively observed and measured using both spaceborne and ground-based instruments, especially during the last decade. Unique properties retrieved by the different instruments contribute to an unprecedented availability of the most complete set of complimentary aerosol measurements ever acquired. However, some of these measurements remain underutilized, largely due to the complexities involved in analyzing them synergistically. To characterize the inconsistencies and bridge the gap that exists between the sensors, we have established a Multi-sensor Aerosol Products Sampling System (MAPSS), which consistently samples and generates the spatial statistics (mean, standard deviation, direction and rate of spatial variation, and spatial correlation coefficient) of aerosol products from multiple spacebome sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS. Samples of satellite aerosol products are extracted over Aerosol Robotic Network (AERONET) locations as well as over other locations of interest such as those with available ground-based aerosol observations. In this way, MAPSS enables a direct cross-characterization and data integration between Level-2 aerosol observations from multiple sensors. In addition, the available well-characterized co-located ground-based data provides the basis for the integrated validation of these products. This paper explains the sampling methodology and concepts used in MAPSS, and demonstrates specific examples of using MAPSS for an integrated analysis of multiple aerosol products.

Petrenko, M.↗
NASA NTRS1992

Sensor fusion V; Proceedings of the Meeting, Boston, MA, Nov. 15-17, 1992

Topics addressed include 3D object perception, human-machine interface in multisensor systems, sensor fusion architecture, fusion of multiple and distributed sensors, interface and decision models for sensor fusion, computational networks, simple sensing for complex action, multisensor-based control, and metrology and calibration of multisensor systems. Particular attention is given to controlling 3D objects by sketching 2D views, the graphical simulation and animation environment for flexible structure robots, designing robotic systems from sensorimotor modules, cylindrical object reconstruction from a sequence of images, an accurate estimation of surface properties by integrating information using Bayesian networks, an adaptive fusion model for a distributed detection system, multiple concurrent object descriptions in support of autonomous navigation, robot control with multiple sensors and heuristic knowledge, and optical array detectors for image sensors calibration. (No individual items are abstracted in this volume)

Schenker, Paul S.↗
NASA NTRSDate not supplied

Generating Essential Climate Variables from Multiple Satellite Hyperspectral Remote Sensors

Hyperspectral observations from satellite-based sensors provide high information content for the Earth’s atmospheric and surface properties. Traditionally, long-term climate products are derived by performing spatial and temporal averaging of level-2 satellite products. There are two shortcomings of this approach. First, it is a time-consuming process to generate level-2 data products since modern hyperspectral satellite sensors have millions of observations each day with thousands of spectral channels for each observation. Secondly, differences in level-2 retrieval algorithms can lead to errors in the fused multi-satellite data. We have developed a radiometrically consistent spectral fingerprinting method, which overcomes the above-mentioned shortcomings, to derive climate change signals from multiple satellite sensors using spatiotemporally averaged level-1 data. We have applied this method to data collected from Atmospheric Infrared Sounder (AIRS) on Aqua satellite and Cross-track Infrared Sounder (CrIS) on SNPP and NOAA20 and generated decade-long climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. A key component to this work is a set of observational-based radiative kernels produced from CrIS level-1 data using a single field of view (SFOV) optimal estimation retrieval algorithm. Only limited CrIS level-1 data (e.g., 1-2 years of data) are needed to the derive radiative kernels. Our Principal Component-based Radiative Model (PCRTM) enables us to perform SFOV retrievals under all sky conditions and provides radiative kernels (including those for clouds) needed by the spectral fingerprinting method. In this presentation, we will describe the basic methodology, the details of the algorithm, and results from NASA Aqua AIRS and Suomi-NPP CrIS data. The method can be applied to study future hyperspectral remote sensors such as CLARREO (Climate Absolute Radiance and Refractivity Observatory) Pathfinder (CPF), Tropospheric Emissions: Monitoring of Pollution (TEMPO), Surface Biology and Geology (SBG), Atmosphere Observing System (AOS).

Xu Liu↗
NASA NTRS2017

Damage Detection Sensor System for Aerospace and Multiple Applications

NASA has identified structural health monitoring and damage detection and verification as critical needs in multiple technology roadmaps. The sensor systems can be customized for detecting location, damage size, and depth, with velocity options and can be designed for particular environments for monitoring of impact or physical damage to a structure. The damage detection system has been successfully demonstrated in a harsh environment and remote integration tested over 1000 miles apart. Multiple applications includes: Spacecraft and Aircraft; Inflatable, Deployable and Expandable Structures; Space Debris Monitoring; Space Habitats; Military Shelters; Solar Arrays, Smart Garments and Wearables, Extravehicular activity (EVA) suits; Critical Hardware Enclosures; Embedded Composite Structures; and Flexible Hybrid Printed Electronics and Systems. For better implementation and infusion into more flexible architectures, important and improved designs in advancing embedded software and GUI interface, and increasing flexibility, modularity, and configurable capabilities of the system are currently being carried out.

Williams, Martha↗
NASA NTRS2004

Multisensor Arrays for Greater Reliability and Accuracy

Arrays of multiple, nominally identical sensors with sensor-output-processing electronic hardware and software are being developed in order to obtain accuracy, reliability, and lifetime greater than those of single sensors. The conceptual basis of this development lies in the statistical behavior of multiple sensors and a multisensor-array (MSA) algorithm that exploits that behavior. In addition, advances in microelectromechanical systems (MEMS) and integrated circuits are exploited. A typical sensor unit according to this concept includes multiple MEMS sensors and sensor-readout circuitry fabricated together on a single chip and packaged compactly with a microprocessor that performs several functions, including execution of the MSA algorithm. In the MSA algorithm, the readings from all the sensors in an array at a given instant of time are compared and the reliability of each sensor is quantified. This comparison of readings and quantification of reliabilities involves the calculation of the ratio between every sensor reading and every other sensor reading, plus calculation of the sum of all such ratios. Then one output reading for the given instant of time is computed as a weighted average of the readings of all the sensors. In this computation, the weight for each sensor is the aforementioned value used to quantify its reliability. In an optional variant of the MSA algorithm that can be implemented easily, a running sum of the reliability value for each sensor at previous time steps as well as at the present time step is used as the weight of the sensor in calculating the weighted average at the present time step. In this variant, the weight of a sensor that continually fails gradually decreases, so that eventually, its influence over the output reading becomes minimal: In effect, the sensor system "learns" which sensors to trust and which not to trust. The MSA algorithm incorporates a criterion for deciding whether there remain enough sensor readings that approximate each other sufficiently closely to constitute a majority for the purpose of quantifying reliability. This criterion is, simply, that if there do not exist at least three sensors having weights greater than a prescribed minimum acceptable value, then the array as a whole is deemed to have failed.

Immer, Christopher↗
NASA NTRS1994

Capacitive Sensors And Targets Would Measure Alignments

Multiple capacitive sensors and active targets used to measure distance between, and relative orientation of, two objects. Sensed target signals processed and used by control systems to align objects to be joined. Shapes, sizes, and layouts of sensors and targets optimized for specific application. Particular layout of targets and sensors enables determination of relative position and orientation of two objects in all six degrees of freedom.

Jenstrom, Del T.↗