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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

radiative transfer↗
NASA NTRS2005

A Parallel Processing Algorithm for Remote Sensing Classification

A current thread in parallel computation is the use of cluster computers created by networking a few to thousands of commodity general-purpose workstation-level commuters using the Linux operating system. For example on the Medusa cluster at NASA/GSFC, this provides for super computing performance, 130 G(sub flops) (Linpack Benchmark) at moderate cost, $370K. However, to be useful for scientific computing in the area of Earth science, issues of ease of programming, access to existing scientific libraries, and portability of existing code need to be considered. In this paper, I address these issues in the context of tools for rendering earth science remote sensing data into useful products. In particular, I focus on a problem that can be decomposed into a set of independent tasks, which on a serial computer would be performed sequentially, but with a cluster computer can be performed in parallel, giving an obvious speedup. To make the ideas concrete, I consider the problem of classifying hyperspectral imagery where some ground truth is available to train the classifier. In particular I will use the Support Vector Machine (SVM) approach as applied to hyperspectral imagery. The approach will be to introduce notions about parallel computation and then to restrict the development to the SVM problem. Pseudocode (an outline of the computation) will be described and then details specific to the implementation will be given. Then timing results will be reported to show what speedups are possible using parallel computation. The paper will close with a discussion of the results.

Gualtieri, J. Anthony↗
NASA NTRS2007

Air Quality Research and Applications Using AURA OMi Data

The Ozone Monitoring Instrument (OMI) on EOS Aura is a new generation of satellite remote sensing instrument designed to measure trace gas and aerosol absorption at the UV and blue wavelengths. These measurements are made globally at urban scale resolution with no inter-orbital gaps that make them potentially very useful for air quality research, such as the determination of the sources and processes that affect global and regional air quality, and to develop applications such as air quality forecast. However, the use of satellite data for such applications is not as straight forward as satellite data have been for stratospheric research. There is a need for close interaction between the satellite product developers, in-situ measurement programs, and the air quality research community to overcome some of the inherent difficulties in interpreting data from satellite-based remote sensing instruments. In this talk we will discuss the challenges and opportunities in using OMI products for air quality research and applications. A key conclusion of this work is that to realize the full potential of OMI measurements it will be necessary to combine OMI data with data from instruments such as MLS, MODIS, AIRS, and CALIPSO that are currently flying in the "A-train" satellite constellation. In addition similar data taken by satellites crossing the earth at different local times than the A-train (e.g., the recently MetOp satellite) would need to be processed in a consistent manner to study diurnal variability, and to capture the effects on air quality of rapidly changing events such as wild fires.

Bhartia, P.K.↗
NASA NTRS2019

Flood Mapping of Recent Major Hurricane Events with Synthetic Aperture Radar, Commercial Imaging, and Aerial Observations

Floodwater mapping is an important remote sensing process that is used for disaster response, recovery, and damage assessment practices. Developing a system to read in Synthetic Aperture Radar (SAR) data and perform land cover classification will allow for the production of near real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of the situation. SAR is a unique remote sensing tool. Data in this project was obtained by NASA Jet Propulsion Laboratory’s Uninhabited Aerial Vehicle SAR (UAVSAR), an L-band radar mounted to a Gulfstream III jet. Data collected by UAVSAR is similar to what will be available from the NASA-Indian Space Research Organization (NISAR) mission starting in early 2022. Using Python and ArcGIS applications, a model was developed using training samples taken from NOAA post-event aerial photography and UAVSAR data gathered in the aftermath of Hurricane Florence in September 2018.

Melancon, Alexander M.↗
DOE OSTI2022

Developing a New Criticality Safety Hands-On Training Utilizing ZPPR Plates

Nuclear criticality safety is an extremely important part of the work at Los Alamos National Laboratory (LANL). As part of the work LANL performs to continue to keep criticality safety a top priority, LANL has developed and regularly teaches nuclear criticality safety training classes for both the United States Department of Energy Nuclear Criticality Safety Program as well as internal trainings for LANL employees. A portion of the training classes is comprised of hands-on demonstrations, where students get the opportunity to handle special nuclear material at the National Criticality Experiments Research Center (NCERC). One hands-on demonstration uses the “Class foils,” thin HEU foils which are stacked with lucite moderator plates. A hand-stack is performed until the multiplication reaches the “three-quarters rule,” where the demonstration is continued remotely on a vertical lift assembly up until the system is critical. This hands-on demonstration eventually achieves a critical configuration and follows the ANS-1 guidelines on an approach to critical. Another hands-on demonstration involves handling clad plutonium and neptunium spheres, and follows procedures using criticality safety evaluations to ensure that the hands-on demonstrations remain subcritical.This hands-on demonstration also involves the use of polyethylene shells around the plutonium sphere to demonstrate how additional reflector increases the criticality of a system. This paper is focused on developing a new hands-on demonstration using Zero Power Physics Reactor (ZPPR) plates. This new hands-on demonstration will follow the ANS-8 standards as it is not desired to achieve criticality with the ZPPR plates during the hands-on demonstration. A hands-on demonstration using multiple plutonium parts will likely be more applicable to personnel who handle plutonium on a daily basis, such as LANL glovebox operators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning

Cover cropping between cash crop growing seasons is a multifunctional conservation practice. Timely and accurate monitoring of cover crop traits, notably aboveground biomass and nutrient content, is beneficial to agricultural stakeholders to improve management and understand outcomes. Currently, there is a scarcity of spatially and temporally resolved information for assessing cover crop growth. Remote sensing has a high potential to fill this need, but conventional empirical regression operated with coarse-resolution multispectral data has large uncertainties. Therefore, this study utilized airborne hyperspectral imaging techniques and developed new process-guided machine learning approaches (PGML) for cover crop monitoring. Specifically, we deployed an airborne hyperspectral system covering visible to shortwave-infrared wavelengths (400–2400 nm) to acquire high spatial (0.5 m) and spectral (3–5 nm) resolution reflectance over 23 cover crop fields across Central Illinois in March and April of 2021. Airborne hyperspectral surface reflectance with high spectral and spatial resolution can be well matched with field data to quantify cover crop traits. Furthermore, the PGML models were pre-trained by synthetic data from soil-vegetation radiative transfer modeling (one million records), and then fine-tuned with field data of cover crop biomass and nutrient content. Results show that airborne hyperspectral data with PGML can achieve high accuracy to predict cover crop aboveground biomass (R 2 = 0.72, relative RMSE = 15.16%) and nitrogen content (R 2 = 0.69, relative RMSE = 16.59%) through leave-one-field-out cross-validation. Unlike the pure data-driven approach (e.g., partial least-squares regression), PGML incorporated radiative transfer knowledge and obtained higher predictive performance with fewer field data. Meanwhile, with field data for model fine-tuning, PGML predicted biomass more accurately than the inversion of radiative transfer models. Here we also found that the red edge has a high contribution in quantifying aboveground biomass and nitrogen content, followed by green and shortwave spectra. This study demonstrated the first attempt of utilizing hyperspectral remote sensing to accurately quantify cover crop traits. We highlight the strength of PGML in exploiting sensing data to quantify ecosystem variables to advance agroecosystem monitoring for sustainable agricultural management.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Tropical African wildfire aerosols trigger teleconnections over mid-to-high latitudes of Northern Hemisphere in January

This study investigates the impacts of African wildfire aerosols (primary organic carbon, black carbon and sulfate) on the Northern Hemispheric in January. We found that wildfire aerosols emitted from equatorial Africa result in two mid-to-high latitudes atmospheric Rossby wave trains. One is from subtropical Atlantic propagating northeastward across Europe to Siberia, and the other one propagates eastward from Middle East across Asia to Pacific Northwest. The maximum positive geopotential height anomaly locates in Europe, concurrent with a greater-than-2 K land surface warming. These Rossby wave trains are excited by the atmospheric heating that caused by the wildfire aerosols in equatorial Africa and propagate into extratropics with the help of the westerly jet. Based on the diabatic heat budget analysis, the Rossby wave source is primarily from the solar absorption of black carbon of African wildfire. The present study emphasizes that wildfire aerosols, especial the absorbing aerosols, would have profound climate effects on remote regions and thus need more attentions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

RAPID STRUCTURE DETECTION IN SUPPORT OF DISASTER RESPONSE : A CASE STUDY OF THE 2018 KILAUEA VOLCANO ERUPTION

Disaster response requires timely damage assessment to prioritize rescue and restoration resources. However, providing critical and actionable knowledge after a natural disaster can be challenging due to the scale and the type of damages. This paper describes how remote sensing and machine learning techniques can be used to support rapid structure detection in the wake of a disaster. We use high resolution satellite imagery to identify structures on Hawaii’s Big Island to support the Federal Emergency Management Agency’s response efforts during the 2018 K¯ilauea lava flow incident. This framework specifically showcases the generalizability of CNN models with no need to collect additional training samples to quickly map structures in pre- and post-event imagery and provide timely information to assist government agencies evaluating the extent and potential loss of disaster. With this case study, we further point out future directions to benefit similar larger scale efforts based on the lessons learned.

Laverdiere, Melanie↗
NASA NTRS2009

Winter QPF Sensitivities to Snow Parameterizations and Comparisons to NASA CloudSat Observations

Steady increases in computing power have allowed for numerical weather prediction models to be initialized and run at high spatial resolution, permitting a transition from larger scale parameterizations of the effects of clouds and precipitation to the simulation of specific microphysical processes and hydrometeor size distributions. Although still relatively coarse in comparison to true cloud resolving models, these high resolution forecasts (on the order of 4 km or less) have demonstrated value in the prediction of severe storm mode and evolution and are being explored for use in winter weather events . Several single-moment bulk water microphysics schemes are available within the latest release of the Weather Research and Forecast (WRF) model suite, including the NASA Goddard Cumulus Ensemble, which incorporate some assumptions in the size distribution of a small number of hydrometeor classes in order to predict their evolution, advection and precipitation within the forecast domain. Although many of these schemes produce similar forecasts of events on the synoptic scale, there are often significant details regarding precipitation and cloud cover, as well as the distribution of water mass among the constituent hydrometeor classes. Unfortunately, validating data for cloud resolving model simulations are sparse. Field campaigns require in-cloud measurements of hydrometeors from aircraft in coordination with extensive and coincident ground based measurements. Radar remote sensing is utilized to detect the spatial coverage and structure of precipitation. Here, two radar systems characterize the structure of winter precipitation for comparison to equivalent features within a forecast model: a 3 GHz, Weather Surveillance Radar-1988 Doppler (WSR-88D) based in Omaha, Nebraska, and the 94 GHz NASA CloudSat Cloud Profiling Radar, a spaceborne instrument and member of the afternoon or "A-Train" of polar orbiting satellites tasked with cataloguing global cloud characteristics. Each system provides a unique perspective. The WSR-88D operates in a surveillance mode, sampling cloud volumes of Rayleigh scatterers where reflectivity is proportional to the sixth moment of the size distribution of equivalent spheres. The CloudSat radar provides enhanced sensitivity to smaller cloud ice crystals aloft, as well as consistent vertical profiles along each orbit. However, CloudSat reflectivity signatures are complicated somewhat by resonant Mie scattering effects and significant attenuation in the presence of cloud or rain water. Here, both radar systems are applied to a case of light to moderate snowfall within the warm frontal zone of a cold season, synoptic scale storm. Radars allow for an evaluation of the accuracy of a single-moment scheme in replicating precipitation structures, based on the bulk statistical properties of precipitation as suggested by reflectivity signatures.

Molthan, Andrew↗
NASA NTRS2010

Operating and Managing a Backup Control Center

Due to the criticality of continuous mission operations, some control centers must plan for alternate locations in the event an emergency shuts down the primary control center. Johnson Space Center (JSC) in Houston, Texas is the Mission Control Center (MCC) for the International Space Station (ISS). Due to Houston s proximity to the Gulf of Mexico, JSC is prone to threats from hurricanes which could cause flooding, wind damage, and electrical outages to the buildings supporting the MCC. Marshall Space Flight Center (MSFC) has the capability to be the Backup Control Center for the ISS if the situation is needed. While the MSFC Huntsville Operations Support Center (HOSC) does house the BCC, the prime customer and operator of the ISS is still the JSC flight operations team. To satisfy the customer and maintain continuous mission operations, the BCC has critical infrastructure that hosts ISS ground systems and flight operations equipment that mirrors the prime mission control facility. However, a complete duplicate of Mission Control Center in another remote location is very expensive to recreate. The HOSC has infrastructure and services that MCC utilized for its backup control center to reduce the costs of a somewhat redundant service. While labor talents are equivalent, experiences are not. Certain operations are maintained in a redundant mode, while others are simply maintained as single string with adequate sparing levels of equipment. Personnel at the BCC facility must be trained and certified to an adequate level on primary MCC systems. Negotiations with the customer were done to match requirements with existing capabilities, and to prioritize resources for appropriate level of service. Because some of these systems are shared, an activation of the backup control center will cause a suspension of scheduled HOSC activities that may share resources needed by the BCC. For example, the MCC is monitoring a hurricane in the Gulf of Mexico. As the threat to MCC increases, HOSC must begin a phased activation of the BCC, while working resource conflicts with normal HOSC activities. In a long duration outage to the MCC, this could cause serious impacts to the BCC host facility s primary mission support activities. This management of a BCC is worked based on customer expectations and negotiations done before emergencies occur. I.

Marsh, Angela L.↗
NASA NTRS1997

STRIPE: Remote Driving Using Limited Image Data

Driving a vehicle, either directly or remotely, is an inherently visual task. When heavy fog limits visibility, we reduce our car's speed to a slow crawl, even along very familiar roads. In teleoperation systems, an operator's view is limited to images provided by one or more cameras mounted on the remote vehicle. Traditional methods of vehicle teleoperation require that a real time stream of images is transmitted from the vehicle camera to the operator control station, and the operator steers the vehicle accordingly. For this type of teleoperation, the transmission link between the vehicle and operator workstation must be very high bandwidth (because of the high volume of images required) and very low latency (because delayed images can cause operators to steer incorrectly). In many situations, such a high-bandwidth, low-latency communication link is unavailable or even technically impossible to provide. Supervised TeleRobotics using Incremental Polyhedral Earth geometry, or STRIPE, is a teleoperation system for a robot vehicle that allows a human operator to accurately control the remote vehicle across very low bandwidth communication links, and communication links with large delays. In STRIPE, a single image from a camera mounted on the vehicle is transmitted to the operator workstation. The operator uses a mouse to pick a series of 'waypoints' in the image that define a path that the vehicle should follow. These 2D waypoints are then transmitted back to the vehicle, where they are used to compute the appropriate steering commands while the next image is being transmitted. STRIPE requires no advance knowledge of the terrain to be traversed, and can be used by novice operators with only minimal training. STRIPE is a unique combination of computer and human control. The computer must determine the 3D world path designated by the 2D waypoints and then accurately control the vehicle over rugged terrain. The human issues involve accurate path selection, and the prevention of disorientation, a common problem across all types of teleoperation systems. STRIPE is the only semi-autonomous teleoperation system that can accurately follow paths designated in monocular images on varying terrain. The thesis describes the STRIPE algorithm for tracking points using the incremental geometry model, insight into the design and redesign of the interface, an analysis of the effects of potential errors, details of the user studies, and hints on how to improve both the algorithm and interface for future designs.

REMOTE CONTROL↗
NASA NTRS1997

Remote Sensing of Ocean Color in the High Arctic

With four years of NASA SeaWiFS funding I established a completely new capability and expertise for in-water optical measurements nearly from scratch and with very little optical background. My first-year budget included only capital for a profiling spectral radiometer. Over the next 30 months we conducted six cruises and collected almost 300 optical profiles in challenging environments; many were collected from 21' launches. I also changed institutions during this period: it is very disruptive to move, set up a new lab, and hire and train new people, etc. We also did not have access to NASA funds for almost a year during the move because of difficulties in subcontracting and/or transferring funds. Nevertheless, we delivered data sets from six bio-optical cruises from three high latitude regions, although only two or three cruises from two areas were promised for our SeaWiFS research. The three Canadian Arctic field programs comprise the most comprehensive high latitude bio-optical and biogeochemical data sets in existence. Optical and pigment data from all six cruises have been submitted to NASA and are being included in the algorithm development test set. Additional data are still being submitted.

Cota, G. F.↗
NASA NTRS1981

Recent developments with the ORSER system

Additions to the ORSER remote sensing data processing package are described. The ORSER package consists of about 35 individual programs that are grouped into preprocessing, data analysis, and display subsystems. Additional data formats and data management, data transformation, and geometric correlation programs were supplemented to the preprocessing subsystem. Enhancements to the data analysis techniques include a maximum likelihood classifier (MAXCLASS) and a new version of the STATS program which makes delineation of training areas easier and allows for detection of outlier points. Ongoing developments are also described.

Baumer, G. M.↗
NASA NTRS2009

An Operational Perspective of Total Lightning Information

The close and productive collaborations between the NWS Warning and Forecast Office, the Short Term Prediction and Research Transition Center at NASA Marshall Space Flight Center and the University of Alabama in Huntsville have provided a unique opportunity for science sharing and technology transfer. One significant technology transfer that has provided immediate benefits to NWS forecast and warning operations is the use of data from the North Alabama Lightning Mapping Array. This network consists of ten VHF receivers deployed across northern Alabama and a base station located at the National Space Science and Technology Center. Preliminary investigations done at WFO Huntsville, along with other similar total lightning networks across the country, have shown distinct correlations between the time rate-of-change of total lightning and trends in intensity/severity of the parent convective cell. Since May 2003 when WFO HUN began receiving these data - in conjunction with other more traditional remotely sensed data (radar, satellite, and surface observations) -- have improved the situational awareness of the WFO staff. The use of total lightning information, either from current ground based systems or future space borne instrumentation, may substantially contribute to the NWS mission, by enhancing severe weather warning and decision-making processes. Operational use of the data has been maximized at WFO Huntsville through a process that includes forecaster training, product implementation, and post event analysis and assessments. Since receiving these data, over 50 surveys have been completed highlighting the use of total lightning information during significant events across the Tennessee Valley. In addition, around 150 specific cases of interest have been archived for collaborative post storm analysis. From these datasets, detailed trending information from radar and total lightning can be compared to corresponding damage reports. This presentation will emphasize the effective use of total lightning information in warning decision making along with best practices for implementation of new technologies into operations.

Nadler, David J.↗
NASA NTRSDate not supplied

Neural Network Atmospheric Correction of Remote Sensing Imagery Over Water Using a Synthetic Dataset

Remote sensing atmospheric correction methods have primarily focused on imagery over land. However, accurate correction over water is important for monitoring and research of aquatic environments. More research in this area is ongoing, though one of the biggest challenges is enough quality data to develop and validate correction methods. This is especially true for neural network (NN) -based models which have shown promise in this area given enough quality data. To address this deficiency of data, we are leveraging a synthetic dataset produced by a model called SWIPE that uses radiative transfer modeling to simulate the atmospheric effects on water-leaving (WL) reflectance to estimate top-of-atmosphere (TOA) reflectance. This allows us to produce almost unlimited pairs of WL reflectance and corresponding TOA reflectance for model training across a variety of atmospheric conditions. We use two approaches for our atmospheric correction model. One uses a conditional variational autoencoder (VAE) to estimate a single WL reflectance value from a single TOA reflectance value. The second is based on a UNET architecture and estimates an array of WL reflectance values from an array of TOA reflectance values. The goal of the second method is to capture atmospheric effects that occur spatially between values within the array as compared to the first method.

deep learning↗
DOE OSTI2025

Effect of Rocky Mountains and Tibetan Plateau 1998 Spring Land Temperature on N. American and East Asian Summer Precipitation Anomalies

This work follows up on the GEWEX/LS4P Phase I (LS4P-I) experiments, a community effort highlighting the spring land surface temperature anomalies in the Tibetan Plateau (TP) as a useful source for subseasonal to seasonal (S2S) prediction of summer precipitation in global hot spot regions, particularly in East Asia and North America. This paper extends the investigation to both the US Rocky Mountain (RM) region and the TP, considering the 1998 summer drought/flood event in North America/East Asia, respectively, as a case study. A previously developed initialization method for land surface temperature/subsurface temperature (LST/SUBT) is used in the NCEP Global Forecast System, coupled with a land model, SSiB2 (GFS/SSiB2), to produce observed RM cold May temperature anomaly. Forward simulation yields June precipitation anomalies at five remote locations. Likewise, the TP warm May temperature anomaly also produces June precipitation anomalies at these five locations. The effects of RM (cold) and TP (warm) temperature anomalies are consistent in the US South Coastal regions and the south Yangtze River Basin, yielding 49% (42%) of observed drought and 34% (44%) of observed flood, respectively. These LST/SUBT effects in RM and TP induce a global large-scale wave train linking North America with the TP, affecting the subtropical westerly jet and thereby modulating summer precipitation. Global SST effect is examined for comparison but does not yield statistically significant June precipitation anomalies in GFS/SSiB2. Furthermore, this study adds to evidence that high-mountain LST effects in the RM and TP are first-order sources of S2S precipitation predictability in summer months.

Nayak, Hara Prasad [University of California, Los ↗
DOE OSTI2021

Deep Learning Systems for Increased Safeguards Surveillance Review Productivity

Nuclear safeguards inspectors expend significant time and maintain intense focus in reviewing video surveillance for safeguards relevant events. To increase efficiency and reduce the time burden of safeguards inspectors performing surveillance data review, this paper presents a novel deep learning (DL) systems concept to integrate generalized DL models into the safeguards surveillance review workflow. The Agency is investigating several DL algorithms for object recognition, localization, tracking, and flagging relevant activities. The project team is working closely with nuclear safeguards inspectors to identify review use cases (based on specific safeguards objectives) and collect their associated requirements. We focused on CANDU and LWR Nuclear Power Plants (NPPs) and their associated dry storage areas as these present a particularly heavy burden on the inspector surveillance review process due to the number of these facilities under safeguards worldwide. Initial DL algorithm results on safeguards data are promising. Using a convolutional neural network, the team attained a mean average precision (mAP) of 92.9% identifying and localizing spent fuel (SF) casks from a 475 surveillance image dataset. Further, the team had initial success in training a recurrent neural network to identify reactor area activities in video clips, successfully indicating when SF casks enter or exit a pool. We discuss how such DL algorithms would be integrated into the Next Generation Surveillance Review (NGSR) software application. Another issue impacting review productivity is the long time inspectors may have to wait when running these algorithms in NGSR. We present a concept to pre-process remotely collected surveillance data with DL models as the data arrives to IAEA headquarters so that results are already available when starting a new review in NGSR. The proposed DL system concept shows a pathway and workflow for increasing an inspector’s surveillance review productivity by quickly and accurately identifying declared and undeclared safeguards relevant objects and activities in large quantities of surveillance imagery data.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS2003

Integrated Human Centered Systems Analysis for Aircraft Separation from Icing Conditions

This document contains five papers on coping with ice formation during the operation of aircraft. The first paper uses a pilot survey to identify the desired attributes of future information systems for aircraft icing. The second paper investigates the influence of potential remote ice-detection system features on pilot decision making. The third paper investigates the effectiveness of aviation weather forecasting along aircraft trajectories. The fourth paper applied a human-centered systems analysis to the adverse aircraft weather encounter problem in order to identify desirable functions of weather and icing information. The objective of the fifth paper, a viewgraph presentation, is to propose means to improve aviation weather information, training, and procedures based on a human-centered systems approach.

Hansman, Robert John↗