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NASA NTRSDate not supplied

Overview of the NASA Earth Action Strategies Wildland Fire Initiative

As part of NASA’s new Earth Action strategy, the Wildland Fire initiative was established, which includes both the NASA Wildland Fire Program (WFP) and the FireSense project. NASA has over 50 years of experience generating data and technology to enhance fire science and operational management. The WFP’s mission is threefold: 1) assemble communities of practice through collaborative efforts with government, academia, and the private sector; 2) co-develop knowledge and applications with relevant partners and stakeholders in the wildfire community; and 3) improve wildland fire management through the transitioning of NASA data, technology, tools, and science to stakeholder organizations. The WFP is focusing on supporting proactive fire management, including situational awareness, preparedness, and risk mitigation. This will be accomplished through selected projects that identify management challenges, relevant to partners and end users, and the NASA data that will be utilized to deliver innovative solutions to enhance the management of wildland fires. Examples include: i) investigation of evaporative stress from OpenET to help predict the risk of wildfire occurrence in watersheds; ii) incorporation of space based LiDAR for the generation of 3-dimensional forest fuel metrics, used to improve wildfire risk and behavior models; iii) integration of global, multi-platform geostationary active-fire data in near-real-time into NASA’s Fire Information for Resource Management System (FIRMS); and iv) identification of post-fire ecohydrological conditions using thermal, multispectral, synthetic aperture radar (SAR), and hyperspectral remotely-sensed data to improve flood hazard forecast models. The FireSense project is a US-focused 5-year project that will focus on delivering NASA’s unique Earth science and technological capabilities to operational agencies, striving towards enhancing fire fighting and air quality management. The project will include airborne campaigns and new technology that will likely have global implications. Initial stakeholder engagement led FireSense to focus on four use-cases focused on the characterization and measurement of: (i) pre-fire fuels conditions, (ii) active fire-dynamics; (iii) post-fire impact and threats; and iv) air quality impacts and forecasting, each-developed with identified stakeholders.

Wildland Fire program↗

NASA NTRS2001

The RadarSAT-MAMM automated mission planner

The RadarSAT Modified Antarctic Mapping Mission (MAMM) ran from September to November 2000. It consisted of over 2400 synthetic aperture radar (SAR) data takes over Antarctica that had to satisfy coverage and other scientific criteria while obeying tight resource and operational constraints. Developing these plans is a time and knowledge intensive effort. It required over a work-year to manually develop a comparable plan for AMM-1, the precursor mission to MAMM. This paper describes the automated mission planning system for MAMM, which dramatically reduced mission-planning costs to just a few workweeks, and enabled rapid generation of 'what-if' scenarios for evaluating mission-design trades. This latter capability informed several critical design decisions and was instrumental in accurately costing the mission.

RadarSAT Modified Antarctic Mapping Mission (MAMM)↗

NASA NTRS1981

Aspects of the mechanisms of smoke generation by burning materials

An investigation of smoke generation during the burning of natural and synthetic solid materials (relevant to fire safety problems), under simulated fire conditions, is presented. Smoke formation mechanisms, including flaming and nonflaming combustion, are reviewed, and the complex physical, chemical, and electrical processes, important in smoke particulate production, are identified. With reference to the smoke formation mechanisms, measured experimental data are discussed, and include effects of ventilation gas temperature, dependence on material composition, and chemical analysis of smoke particulates. Significant differences in smoke characteristics are observed between flaming and nonflaming conditions, which is attributed to specific differences in controlling mechanisms and resultant ways leading to particulate formation. The effects of polymer substrate properties and effects of additives for a given substrate on smoke properties are also discussed in terms of basic processes. It is shown that many of the measured trends can be interpreted by considering postulated mechanisms of particulate formation.

Bankston, C. P.↗

NASA NTRS1992

Radar Altimetry For Topographical Mapping

Two altimeter concepts based on spaceborne synthetic-aperture-radar (SAR) proposed to gather global, uniformly sampled, high-resolution, topographical data on land and ice. Wide swath and high pulse rate of SAR makes possible to acquire spatially contiguous data over large areas in reasonable amount of time, and time needed for altimetric processing of SAR data considerably less than that needed to generate topographical maps from stereoscopic photographs. Radar less vulnerable to cloud cover and other weather conditions than optical techniques.

Im, Eastwood↗

NASA NTRS1985

Topographic mapping from interferometric synthetic aperture radar observations

Interferometric SAR observations are a basis for the generation of high resolution topographic maps of a region through the interpretation of interference fringes. The combination of height, along-track, and slant range measurements is sufficient for rectification of the radar imagery for cartographic applications, in addition to furnishing an accurate determination of the scene topography. Topographic maps have been derived on the basis of data recorded by both aircraft and Seasat-A satellite radars. A comparison of the maps obtained with U.S. Geological Survey Contour maps indicates a high degree of correlation between two sets of altitude data.

Zebker, H. A.↗

NASA NTRS2007

Fusion of Synthetic and Enhanced Vision for All-Weather Commercial Aviation Operations

NASA is developing revolutionary crew-vehicle interface technologies that strive to proactively overcome aircraft safety barriers that would otherwise constrain the full realization of the next-generation air transportation system. A piloted simulation experiment was conducted to evaluate the complementary use of Synthetic and Enhanced Vision technologies. Specific focus was placed on new techniques for integration and/or fusion of Enhanced and Synthetic Vision and its impact within a two-crew flight deck during low visibility approach and landing operations. Overall, the experimental data showed that significant improvements in situation awareness, without concomitant increases in workload and display clutter, could be provided by the integration and/or fusion of synthetic and enhanced vision technologies for the pilot-flying and the pilot-not-flying. During non-normal operations, the ability of the crew to handle substantial navigational errors and runway incursions were not adversely impacted by the display concepts although the addition of Enhanced Vision did not, unto itself, provide an improvement in runway incursion detection.

Bailey, Randall E.↗

NASA NTRSDate not supplied

SWIPE: Spectral Water Inversion Processor and Emulator

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will be discussing the progress made developing SWIPE: Spectral Water Inversion Processor and Emulator. SWIPE is a platform for advanced modeling of coastal and inland aquatic habitats. The goal is create a comprehensive and cohesive system to leverage recent advancements in computation and machine learning to develop a synthetic training ground for sensitivity studies and algorithm development. The four principal facets of SWIPE include: 1. Advanced two-layer coated sphere bio-optical modeling and GPU radiative transfer modeling, 2. Big Data involving massive synthetic spectral libraries of optical properties of various global aquatic particles, surface reflectance, and top-of-atmosphere reflectance, all at hyperspectral resolution leveraging high-end computing systems at NASA Ames Research Center, 3. Deep Learning for algorithm development for water quality inversion of concentrations of common biogeophysical variables as well as optics, full uncertainty characterization by water type, and forward emulation, and lastly, 4. Image Processing for application of developed retrieval algorithms for both hyperspectral and multispectral sensors with experimental corrections for global adjacency, noise, sunglint, and benthic reflectance. This presentation will demonstrate the Equivalent Algal Populations (EAP) two-layer coated sphere scattering model which has been used develop spectral libraries of hyperspectral inherent optical properties of roughly 80 species of phytoplankton, covering 15 different classes and nine taxonomic functional types. The EAP model was also used to derive spectral properties of 10 different non-algal particle functional types. Examples of how the SMART-G (Speed-up Monte-carlo Advanced Radiative Transfer using GPU) radiative transfer code is used to model optically complex aquatic signals will be presented and discussed in the context of creating a massive synthetic database which can leverage the full power of next generation machine learning techniques and high end computing for water quality inversion. We will discuss our active investigation in things like appropriate model architectures, dimensionality reduction techniques such as PCA and autoencoders, uncertainty quantification and abstaining, and which variables actually benefit most from hyperspectral information versus multispectral resolution. We are also curious about questions relating to cost/benefit analysis in terms of computation resources, neural network complexity, and data volumes. Answers to these questions will hopefully elaborate on cost efficiency for potential future sensor design considerations.

SWIPE↗

NASA NTRS2011

Techniques and Tools for Estimating Ionospheric Effects in Interferometric and Polarimetric SAR Data

The InSAR Scientific Computing Environment (ISCE) is a flexible, extensible software tool designed for the end-to-end processing and analysis of synthetic aperture radar data. ISCE inherits the core of the ROI_PAC interferometric tool, but contains improvements at all levels of the radar processing chain, including a modular and extensible architecture, new focusing approach, better geocoding of the data, handling of multi-polarization data, radiometric calibration, and estimation and correction of ionospheric effects. In this paper we describe the characteristics of ISCE with emphasis on the ionospheric modules. To detect ionospheric anomalies, ISCE implements the Faraday rotation method using quadpolarimetric images, and the split-spectrum technique using interferometric single-, dual- and quad-polarimetric images. The ability to generate co-registered time series of quad-polarimetric images makes ISCE also an ideal tool to be used for polarimetric-interferometric radar applications.

Ionosphere↗

NASA NTRS1981

Further SEASAT SAR coastal ocean wave analysis

Analysis techniques used to exploit SEASAT synthetic aperture radar (SAR) data of gravity waves are discussed and the SEASAT SAR's ability to monitor large scale variations in gravity wave fields in both deep and shallow water is evaluated. The SAR analysis techniques investigated included motion compensation adjustments and the semicausal model for spectral analysis of SAR wave data. It was determined that spectra generated from fast Fourier transform analysis (FFT) of SAR wave data were not significantly altered when either range telerotation adjustments or azimuth focus shifts were used during processing of the SAR signal histories, indicating that SEASAT imagery of gravity waves is not significantly improved or degraded by motion compensation adjustments. Evaluation of the semicausal (SC) model using SEASAT SAR data from Rev. 974 indicates that the SC spectral estimates were not significantly better than the FFT results.

Kasischke, E. S.↗

NASA NTRS1993

(abstract) Monitoring Seasonal State and Mapping Species in Alaskan Taiga Using Imaging Radar as Input to CO(sub 2) Flux Models

Changes in the seasonal CO(sub 2) flux of the boreal forests may result from increased atmospheric CO(sub 2) concentrations and associated atmospheric warming. To monitor this potential change, a combination of remote sensing information and ecophysiological models are required. In this paper we address the use of synthetic aperture radar (SAR) data to provide some of the input to the ecophysiological models: forest type, freeze/thaw state which limits the growing season for conifers, and leaf on/off state which limits the growing season for deciduous species. AIRSAR data collected in March 1988 during an early thaw event and May 1991 during spring breakup are used to generate species maps and to determine the sensitivity of SAR to canopy freeze/thaw transitions. These data are also used to validate a microwave scattering model which is then used to determine the sensitivity of SAR to leaf on/off and soil freeze/thaw transitions. Finally, a CO(sub 2) flux algorithm which utilizes SAR data and an ecophysiological model to estimate CO(sub 2) flux is presented. CO(sub 2) flux maps are generated from which areal estimates of CO(sub 2) flux are derived.

leaf cover boreal forests radar backscatter↗

NASA NTRS2009

Evaluation of Fused Synthetic and Enhanced Vision Display Concepts for Low-Visibility Approach and Landing

NASA is developing revolutionary crew-vehicle interface technologies that strive to proactively overcome aircraft safety barriers that would otherwise constrain the full realization of the next generation air transportation system. A piloted simulation experiment was conducted to evaluate the complementary use of Synthetic and Enhanced Vision technologies. Specific focus was placed on new techniques for integration and/or fusion of Enhanced and Synthetic Vision and its impact within a two-crew flight deck during low-visibility approach and landing operations. Overall, the experimental data showed that significant improvements in situation awareness, without concomitant increases in workload and display clutter, could be provided by the integration and/or fusion of synthetic and enhanced vision technologies for the pilot-flying and the pilot-not-flying. Improvements in lateral path control performance were realized when the Head-Up Display concepts included a tunnel, independent of the imagery (enhanced vision or fusion of enhanced and synthetic vision) presented with it. During non-normal operations, the ability of the crew to handle substantial navigational errors and runway incursions were neither improved nor adversely impacted by the display concepts. The addition of Enhanced Vision may not, of itself, provide an improvement in runway incursion detection without being specifically tailored for this application.

Bailey, Randall E.↗

NASA NTRS2017

Effects of Inventory Bias on Landslide Susceptibility Calculations

Many landslide inventories are known to be biased, especially inventories for large regions such as Oregon's SLIDO or NASA's Global Landslide Catalog. These biases must affect the results of empirically derived susceptibility models to some degree. We evaluated the strength of the susceptibility model distortion from postulated biases by truncating an unbiased inventory. We generated a synthetic inventory from an existing landslide susceptibility map of Oregon, then removed landslides from this inventory to simulate the effects of reporting biases likely to affect inventories in this region, namely population and infrastructure effects. Logistic regression models were fitted to the modified inventories. Then the process of biasing a susceptibility model was repeated with SLIDO data. We evaluated each susceptibility model with qualitative and quantitative methods. Results suggest that the effects of landslide inventory bias on empirical models should not be ignored, even if those models are, in some cases, useful. We suggest fitting models in well-documented areas and extrapolating across the study region as a possible approach to modeling landslide susceptibility with heavily biased inventories.

Oregon↗

NASA NTRS2017

Effects of Inventory Bias on Landslide Susceptibility Calculations

Many landslide inventories are known to be biased, especially inventories for large regions such as Oregons SLIDO or NASAs Global Landslide Catalog. These biases must affect the results of empirically derived susceptibility models to some degree. We evaluated the strength of the susceptibility model distortion from postulated biases by truncating an unbiased inventory. We generated a synthetic inventory from an existing landslide susceptibility map of Oregon, then removed landslides from this inventory to simulate the effects of reporting biases likely to affect inventories in this region, namely population and infrastructure effects. Logistic regression models were fitted to the modified inventories. Then the process of biasing a susceptibility model was repeated with SLIDO data. We evaluated each susceptibility model with qualitative and quantitative methods. Results suggest that the effects of landslide inventory bias on empirical models should not be ignored, even if those models are, in some cases, useful. We suggest fitting models in well-documented areas and extrapolating across the study region as a possible approach to modelling landslide susceptibility with heavily biased inventories.

landslide↗

NASA NTRS2020

Aircraft Engine Run-To-Failure Data Set Under Real Flight Conditions

The generation of data-driven prognostics models requires the availability of datasets with run-to-failure trajectories. In order to contribute to the development of these methods, the dataset provides a new realistic dataset of run-to-failure trajectories for a small fleet of aircraft engines under realistic flight conditions. The damage propagation modelling used for the generation of this synthetic dataset builds on the modelling strategy from previous work [1] and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet [2]. Secondly, it extends the degradation modelling by relating the degradation process to the operation history. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model [3]. More details about the generation process can be found in [4].

CMAPSS↗

NASA NTRS2014

Evolution of a Reconfigurable Processing Platform for a Next Generation Space Software Defined Radio

The National Aeronautics and Space Administration (NASA)Harris Ka-Band Software Defined Radio (SDR) is the first, fully reprogrammable space-qualified SDR operating in the Ka-Band frequency range. Providing exceptionally higher data communication rates than previously possible, this SDR offers in-orbit reconfiguration, multi-waveform operation, and fast deployment due to its highly modular hardware and software architecture. Currently in operation on the International Space Station (ISS), this new paradigm of reconfigurable technology is enabling experimenters to investigate navigation and networking in the space environment.The modular SDR and the NASA developed Space Telecommunications Radio System (STRS) architecture standard are the basis for Harris reusable, digital signal processing space platform trademarked as AppSTAR. As a result, two new space radio products are a synthetic aperture radar payload and an Automatic Detection Surveillance Broadcast (ADS-B) receiver. In addition, Harris is currently developing many new products similar to the Ka-Band software defined radio for other applications. For NASAs next generation flight Ka-Band radio development, leveraging these advancements could lead to a more robust and more capable software defined radio.The space environment has special considerations different from terrestrial applications that must be considered for any system operated in space. Each space mission has unique requirements that can make these systems unique. These unique requirements can make products that are expensive and limited in reuse. Space systems put a premium on size, weight and power. A key trade is the amount of reconfigurability in a space system. The more reconfigurable the hardware platform, the easier it is to adapt to the platform to the next mission, and this reduces the amount of non-recurring engineering costs. However, the more reconfigurable platforms often use more spacecraft resources. Software has similar considerations to hardware. Having an architecture standard promotes reuse of software and firmware. Space platforms have limited processor capability, which makes the trade on the amount of amount of flexibility paramount.

Telecommunication↗

NASA NTRS1974

The scientific justification for obtaining ocean wave data of various kinds with Seasat-A

Synthetic aperture radar images of sea surface in deep water as well as two candidate instruments that essentially scan along a line are considered for obtaining spectral wave information with SEASAT-A. An altimeter provides data on wave height for every 50 or 100 kilometers along subsatellite track routine. A combination of these instruments is proposed to obtain wind generated ocean waves and swells.

Pierson, W. J.↗

NASA NTRS1995

Distributed and collaborative synthetic environments

Fast graphics workstations and increased computing power, together with improved interface technologies, have created new and diverse possibilities for developing and interacting with synthetic environments. A synthetic environment system is generally characterized by input/output devices that constitute the interface between the human senses and the synthetic environment generated by the computer; and a computation system running a real-time simulation of the environment. A basic need of a synthetic environment system is that of giving the user a plausible reproduction of the visual aspect of the objects with which he is interacting. The goal of our Shastra research project is to provide a substrate of geometric data structures and algorithms which allow the distributed construction and modification of the environment, efficient querying of objects attributes, collaborative interaction with the environment, fast computation of collision detection and visibility information for efficient dynamic simulation and real-time scene display. In particular, we address the following issues: (1) A geometric framework for modeling and visualizing synthetic environments and interacting with them. We highlight the functions required for the geometric engine of a synthetic environment system. (2) A distribution and collaboration substrate that supports construction, modification, and interaction with synthetic environments on networked desktop machines.

Bajaj, Chandrajit L.↗

NASA NTRS2010

DBSAR's First Multimode Flight Campaign

The Digital Beamforming SAR (DBSAR) is an airborne imaging radar system that combines phased array technology, reconfigurable on-board processing and waveform generation, and advances in signal processing to enable techniques not possible with conventional SARs. The system exploits the versatility inherently in phased-array technology with a state-of-the-art data acquisition and real-time processor in order to implement multi-mode measurement techniques in a single radar system. Operational modes include scatterometry over multiple antenna beams, Synthetic Aperture Radar (SAR) over several antenna beams, or Altimetry. The radar was flight tested in October 2008 on board of the NASA P3 aircraft over the Delmarva Peninsula, MD. The results from the DBSAR system performance is presented.

Rincon, Rafael F.↗