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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

The NASA Continuous Risk Management Process

As an intern this summer in the GRC Risk Management Office, I have become familiar with the NASA Continuous Risk Management Process. In this process, risk is considered in terms of the probability that an undesired event will occur and the impact of the event, should it occur (ref., NASA-NPG: 7120.5). Risk management belongs in every part of every project and should be ongoing from start to finish. Another key point is that a risk is not a problem until it has happened. With that in mind, there is a six step cycle for continuous risk management that prevents risks from becoming problems. The steps are: identify, analyze, plan, track, control, and communicate & document. Incorporated in the first step are several methods to identify risks such as brainstorming and using lessons learned. Once a risk is identified, a risk statement is made on a risk information sheet consisting of a single condition and one or more consequences. There can also be a context section where the risk is explained in more detail. Additionally there are three main goals of analyzing a risk, which are evaluate, classify, and prioritize. Here is where a value is given to the attributes of a risk &e., probability, impact, and timeframe) based on a multi-level classification system (e.g., low, medium, high). It is important to keep in mind that the definitions of these levels are probably different for each project. Furthermore the risks can be combined into groups. Then, the risks are prioritized to see what risk is necessary to mitigate first. After the risks are analyzed, a plan is made to mitigate as many risks as feasible. Each risk should be assigned to someone in the project with knowledge in the area of the risk. Then the possible approaches to choose from are: research, accept, watch, or mitigate. Next, all risks, mitigated or not, are tracked either individually or in groups. As the plan is executed, risks are re-evaluated, and the attribute values are adjusted as necessary. Metrics are established and monitored as tools for risk tracking. Also a trigger or threshold should be set on the metric data that indicates when an action is needed. Results of this tracking are usually evaluated and reported in a relevant format at weekly or monthly meetings. Choosing controls is the subsequent step, which involves the effects of the tracking. The three basic controls are: close, continue tracking, and re- plan. Finally communicate & document is the last step, but occurs throughout the process. It is vital that main risks, plans, changes, and progress are known by everyone in the project. A good way to keep everyone updated and inform other projects of common issues is by thoroughly documenting project risks. NASA sees value in risk management and believes that projects have greater probability or success by using the NASA Continuous Risk Management Process.

Pokorny, Frank M.↗
NASA NTRS2006

Assessing Hurricane Katrina Vegetation Damage at Stennis Space Center using IKONOS Image Classification Techniques

Hurricane Katrina inflicted widespread damage to vegetation in southwestern coastal Mississippi upon landfall on August 29, 2005. Storm damage to surface vegetation types at the NASA John C. Stennis Space Center (SSC) was mapped and quantified using IKONOS data originally acquired on September 2, 2005, and later obtained via a Department of Defense ClearView contract. NASA SSC management required an assessment of the hurricane s impact to the 125,000-acre buffer zone used to mitigate rocket engine testing noise and vibration impacts and to manage forestry and fire risk. This study employed ERDAS IMAGINE software to apply traditional classification techniques to the IKONOS data. Spectral signatures were collected from multiple ISODATA classifications of subset areas across the entire region and then appended to a master file representative of major targeted cover type conditions. The master file was subsequently used with the IKONOS data and with a maximum likelihood algorithm to produce a supervised classification later refined using GIS-based editing. The final results enabled mapped, quantitative areal estimates of hurricane-induced damage according to general surface cover type. The IKONOS classification accuracy was assessed using higher resolution aerial imagery and field survey data. In-situ data and GIS analysis indicate that the results compare well to FEMA maps of flooding extent. The IKONOS classification also mapped open areas with woody storm debris. The detection of such storm damage categories is potentially useful for government officials responsible for hurricane disaster mitigation.

Spruce, Joseph P.↗
NASA NTRS2013

NASA Models of Space Radiation Induced Cancer, Circulatory Disease, and Central Nervous System Effects

The risks of late effects from galactic cosmic rays (GCR) and solar particle events (SPE) are potentially a limitation to long-term space travel. The late effects of highest concern have significant lethality including cancer, effects to the central nervous system (CNS), and circulatory diseases (CD). For cancer and CD the use of age and gender specific models with uncertainty assessments based on human epidemiology data for low LET radiation combined with relative biological effectiveness factors (RBEs) and dose- and dose-rate reduction effectiveness factors (DDREF) to extrapolate these results to space radiation exposures is considered the current "state-of-the-art". The revised NASA Space Risk Model (NSRM-2014) is based on recent radio-epidemiology data for cancer and CD, however a key feature of the NSRM-2014 is the formulation of particle fluence and track structure based radiation quality factors for solid cancer and leukemia risk estimates, which are distinct from the ICRP quality factors, and shown to lead to smaller uncertainties in risk estimates. Many persons exposed to radiation on earth as well as astronauts are life-time never-smokers, which is estimated to significantly modify radiation cancer and CD risk estimates. A key feature of the NASA radiation protection model is the classification of radiation workers by smoking history in setting dose limits. Possible qualitative differences between GCR and low LET radiation increase uncertainties and are not included in previous risk estimates. Two important qualitative differences are emerging from research studies. The first is the increased lethality of tumors observed in animal models compared to low LET radiation or background tumors. The second are Non- Targeted Effects (NTE), which include bystander effects and genomic instability, which has been observed in cell and animal models of cancer risks. NTE's could lead to significant changes in RBE and DDREF estimates for GCR particles, and the potential effectiveness of radiation mitigator's. The NSRM- 2014 approaches to model radiation quality dependent lethality and NTE's will be described. CNS effects include both early changes that may occur during long space missions and late effects such as Alzheimer's disease (AD). AD effects 50% of the population above age 80-yr, is a degenerative disease that worsens with time after initial onset leading to death, and has no known cure. AD is difficult to detect at early stages and the small number of low LET epidemiology studies undertaken have not identified an association with low dose radiation. However experimental studies in mice suggest GCR may lead to early onset AD. We discuss modeling approaches to consider mechanisms whereby radiation would lead to earlier onset of occurrence of AD. Biomarkers of AD include amyloid beta (A(Beta)) plaques, and neurofibrillary tangles (NFT) made up of aggregates of the hyperphosphorylated form of the micro-tubule associated, tau protein. Related markers include synaptic degeneration, dentritic spine loss, and neuronal cell loss through apoptosis. Radiation may affect these processes by causing oxidative stress, aberrant signaling following DNA damage, and chronic neuroinflammation. Cell types to be considered in multi-scale models are neurons, astrocytes, and microglia. We developed biochemical and cell kinetics models of DNA damage signaling related to glycogen synthase kinase-3(Beta) (GSK3(Beta)) and neuroinflammation, and considered multi-scale modeling approaches to develop computer simulations of cell interactions and their relationships to A(Beta) plaques and NFTs. Comparison of model results to experimental data for the age specific development of A(Beta) plaques in transgenic mice will be discussed.

Cucinotta, Francis A.↗
NASA NTRS2015

Final NASA Panel Recommendations for Definition of Acceptable Risk of Injury Due to Spaceflight Dynamic Events

A panel of experts was convened in 2010 to help define acceptable injury risk levels for space vehicle launches, landings, and abort scenarios. Classifications of spaceflight-relevant injuries were defined using four categories ranging from minor to severe injury. Limits for each injury category were agreed to, dependent on the expected number of crew exposures in a given vehicle and on whether the flight was considered nominal or off-nominal. Somers et al. captured the findings of this summit in a NASA technical memorandum. This panel was recently re-convened (December 1, 2014) to determine whether the previous recommended injury limits were applicable to newly-designed commercial space flight vehicles. In particular, previous limits were based in part on the number of crew exposures per vehicle and also were sensitive to a definition of nominal and off-nominal vehicle performance. Reconsideration of these aspects led to a new consensus on a definition of injury risk.

Somers, Jeffrey T.↗
NASA NTRS2011

Building a Shared Definitional Model of Long Duration Human Spaceflight

In 1956, on the eve of human space travel Strughold first proposed a simple classification of the present and future stages of manned flight that identified key factors, risks and developmental stages for the evolutionary journey ahead. As we look to optimize the potential of the ISS as a gateway to new destinations, we need a current shared working definitional model of long duration human space flight to help guide our path. Initial search of formal and grey literature augmented by liaison with subject matter experts. Search strategy focused on both the use of term long duration mission and long duration spaceflight, and also broader related current and historical definitions and classification models of spaceflight. The related sea and air travel literature was also subsequently explored with a view to identifying analogous models or classification systems. There are multiple different definitions and classification systems for spaceflight including phase and type of mission, craft and payload and related risk management models. However the frequently used concepts of long duration mission and long duration spaceflight are infrequently operationally defined by authors, and no commonly referenced classical or gold standard definition or model of these terms emerged from the search. The categorization (Cat) system for sailing was found to be of potential analogous utility, with its focus on understanding the need for crew and craft autonomy at various levels of potential adversity and inability to gain outside support or return to a safe location, due to factors of time, distance and location.

Orr, M.↗
NASA NTRS2010

Occupant Protection at NASA

This slide presentation reviews NASA's efforts to arrive at protection of occupants of the ORION space craft on landing. An Abbreviated Injury Scale (AIS) has been developed, it is an anatomically-based, consensus-derived, global severity scoring system that classifies each injury by body region according to its relative importance on a 6-point ordinal scale. It reviews an Operationmally Relevant Injury Scale (ORIS), a classification methodology, and shows charts that detail the results of applying this ORIS to the injury databases. One chart uses NASCAR injury classification. It discusses providing a context for the level of risk inherent in the Orion landings in terms that people understand and have a sense for. For example is the risk of injury during an Orion landing roughly the same, better or worse than: An aircraft carrier landing, a NASCAR crash, or a helicopter crash, etc? The data for NASCAR and Indy Racing league (IRL) racing crash and injury data was reviewed. The risk from the Air Force, Navy, and Army injury data was also reviewed. Past NASA and the Soyuz programs injury risks are also reviewed. The work is an attempt to formulate a recommendation to the Orion Project for an acceptable level of injury risk associated with Nominal and Off-Nominal landing cases. The presentation also discusses the data mining and use of the data to Validate NASA Operationally-Relevant Injury Scale (NORIS) / Military Operationally-Relevant Injury Scale (MORIS), developing injury risk criteria, the types of data that are required, NASCAR modeling techniques and crash data, and comparison with the Brinkley model. The development of injury risk curves for each biodynamic response parameter is discussed. One of the main outcomes of this work is to establish an accurate Automated Test Dummy (ATD) that can be used to measure human tolerances.

Somers, Jeffrey↗
NASA NTRS2019

Towards the Development of Real-Time Normalized Burn Ratio (NBR) and Delta NBR Imagery from GOES-16/17 and S-NPP

Burned landscapes present difficult hydrologic forecasting challenges for National Weather Service Offices. Burned soils and landscapes can be conducive to the development of flash flooding and landslides from heavy precipitation events. The severity of the burn scar can be directly related to the risk for debris flows and flash flooding. Burned Area Reflectance Classification (BARC) map is generated to indicate the degree of burn severity, which is generated initially by high-resolution satellite imagery from sources such as Landsat, and later by labor-intensive efforts conducted at the burn scar by Burned Area Emergency Response (BAER) teams. The challenge for operational meteorologists is that these sources of information are not readily available in near real-time. Landsat imagery, for example, may only be available about once every eight days, and cloudy conditions can obstruct the observation of the burn scar during a single pass. BAER teams cannot conduct assessments until the wildfire has been at least 40 percent contained (up to 80 percent in some regions), and the process itself can take further days to weeks to complete depending on a number of factors. To help remedy this lapse in knowledge, NASA SPoRT has developed the generation of NBR imagery in the Advanced Weather Interactive Processing System (AWIPS) using data from the operational GOES 16 and 17 satellites and S-NPP. This presentation will discuss the development of the GOES- and SNPP-derived NBR and dNBR imagery and their initial evaluation by real-time decision makers.

Berndt, Emily↗
NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗
NASA NTRS2014

Visual Impairment/lntracranial Pressure Risk Clinical Care Data Tools

Prior to 2010, several ISS crewmembers returned from spaceflight with changes to their vision, ranging from a mild hyperopic shift to frank disc edema. As a result, NASA expanded clinical vision testing to include more comprehensive medical imaging, including Optical Coherence Tomography and 3 Tesla Brain and Orbit MRIs. The Space and Clinical Operations (SCO) Division developed a clinical practice guideline that classified individuals based on their symptoms and diagnoses to facilitate clinical care. For the purposes of clinical surveillance, this classification was applied retrospectively to all crewmembers who had sufficient testing for classification. This classification is also a tool that has been leveraged for researchers to identify potential risk factors. In March 2014, driven in part by a more comprehensive understanding of the imaging data and increased imaging capability on orbit, the SCO Division revised their clinical care guidance to outline in‐flight care and increase post‐flight follow up. The new clinical guidance does not include a classification scheme

Van Baalen, Mary↗
NASA NTRS2012

Building a Shared Definitional Model of Long Duration Human Spaceflight

Objective: To establish the need for a shared definitional model of long duration human spaceflight, that would provide a framework and vision to facilitate communication, research and practice In 1956, on the eve of human space travel, Hubertus Strughold first proposed a "simple classification of the present and future stages of manned flight" that identified key factors, risks and developmental stages for the evolutionary journey ahead. As we look to new destinations, we need a current shared working definitional model of long duration human space flight to help guide our path. Here we describe our preliminary findings and outline potential approaches for the future development of a definition and broader classification system

Arias, Diana↗
NASA NTRS2004

Human Health Risk Assessment Simulations in a Distributed Environment for Shuttle Launch

During the launch of a rocket under prevailing weather conditions, commanders at Cape Canaveral Air Force station evaluate the possibility of whether wind blown toxic emissions might reach civilian and military personnel in the near by area. In our model, we focused mainly on Hydrogen chloride (HCL), Nitrogen oxides (NOx) and Nitric acid (HNO3), which are non-carcinogenic chemicals as per United States Environmental Protection Agency (USEPA) classification. We have used the hazard quotient model to estimate the number of people at risk. It is based on the number of people with exposure above a reference exposure level that is unlikely to cause adverse health effects. The risk to the exposed population is calculated by multiplying the individual risk and the number in exposed population. The risk values are compared against the acceptable risk values and GO or NO-go situation is decided based on risk values for the Shuttle launch. The entire model is simulated over the web and different scenaria can be generated which allows management to choose an optimum decision.

Thirumalainambi, Rajkumar↗
NASA NTRS2015

Phase-Angle Dependence of Determinations of Diameter, Albedo, and Taxonomy: A Case Study of NEO 3691 Bede

Parameters important for NEO risk assessment and mitigation include Near-Earth Object diameter and taxonomic classification, which translates to surface composition. Diameters of NEOs are derived from the thermal fluxes measured by WISE, NEOWISE, Spitzer Warm Mission and ground-based telescopes including the IRTF and UKIRT. Diameter and its coupled parameters Albedo and IR beaming parameter (a proxy for thermal inertia and/or surface roughness) are dependent upon the phase angle, which is the Sun-target-observer angle. Orbit geometries of NEOs, however, typically provide for observations at phase angles greater than 20 degrees. At higher phase angles, the observed thermal emission is sampling both the day and night sides of the NEO. We compare thermal models for NEOs that exclude (NEATM) and include (NESTM) night-side emission. We present a case study of NEO 3691 Bede, which is a higher albedo object, X (Ec) or Cgh taxonomy, to highlight the range of H magnitudes for this object (depending on the albedo and phase function slope parameter G), and to examine at different phase angles the taxonomy and thermal model fits for this NEO. Observations of 3691 Bede include our observations with IRTF+SpeX and with the 10 micrometer UKIRT+Michelle instrument, as well as WISE and Spitzer Warm mission data. By examining 3691 Bede as a case study, we highlight the interplay between the derivation of basic physical parameters and observing geometry, and we discuss the uncertainties in H magnitude, taxonomy assignment amongst the X-class (P, M, E), and diameter determinations. Systematic dependencies in the derivation of basic characterization parameters of H-magnitude, diameter, albedo and taxonomy with observing geometry are important to understand. These basic characterization parameters affect the statistical assessments of the NEO population, which in turn, affects the assignment of statistically-assessed basic parameters to discovered but yet-to-be-fully-characterized NEOs.

Albedo and taxonomy↗
NASA NTRS1993

Developing interpretable models with optimized set reduction for identifying high risk software components

Applying equal testing and verification effort to all parts of a software system is not very efficient, especially when resources are limited and scheduling is tight. Therefore, one needs to be able to differentiate low/high fault frequency components so that testing/verification effort can be concentrated where needed. Such a strategy is expected to detect more faults and thus improve the resulting reliability of the overall system. This paper presents the Optimized Set Reduction approach for constructing such models, intended to fulfill specific software engineering needs. Our approach to classification is to measure the software system and build multivariate stochastic models for predicting high risk system components. We present experimental results obtained by classifying Ada components into two classes: is or is not likely to generate faults during system and acceptance test. Also, we evaluate the accuracy of the model and the insights it provides into the error making process.

Briand, Lionel C.↗
NASA NTRS2006

Understanding Cataract Risk in Aerospace Flight Crew And Review of Mechanisms of Cataract Formation

Induction of cataracts by occupational exposure in flight crew has been an important topic of interest in aerospace medicine in the past five years, in association with numerous reports of flight-associated disease incidences. Due to numerous confounding variables, it has been difficult to determine if there is increased cataract risk directly caused by interaction with the flight environment, specifically associated with added radiation exposure during flight. Military aviator records from the United States Air Force (USAF) and Navy (USN) and US astronauts at the National Aeronautics and Space Administration (NASA)/Lyndon B. Johnson Space Center (JSC) were evaluated for the presence, location and age of diagnosis of cataracts. Military aviators were found to have a statistically significant younger average age of onset of their cataracts compared with astronauts, however the incidence density of cataracts was found to be statistically higher in astronauts than in military aviators. USAF and USN aviator s cataracts were most commonly located in the posterior subcapsular region of the lens while astronauts cataracts were most likely to originate generally in the cortical zone. A prospective clinical trial which controls for confounding variables in examination technique, cataract classification, diet, exposure, and pharmacological intervention is needed to determine what percentage of the risk for cataracts are due to radiation, and how to best develop countermeasures to protect flight crews from radiation bioeffects in the future.

Jones, Jeffrey A.↗
NASA NTRS2007

Surveillance and Control of Malaria Transmission in Thailand using Remotely Sensed Meteorological and Environmental Parameters

These slides address the use of remote sensing in a public health application. Specifically, this discussion focuses on the of remote sensing to detect larval habitats to predict current and future endemicity and identify key factors that sustain or promote transmission of malaria in a targeted geographic area (Thailand). In the Malaria Modeling and Surveillance Project, which is part of the NASA Applied Sciences Public Health Applications Program, we have been developing techniques to enhance public health's decision capability for malaria risk assessments and controls. The main objectives are: 1) identification of the potential breeding sites for major vector species; 2) implementation of a risk algorithm to predict the occurrence of malaria and its transmission intensity; 3) implementation of a dynamic transmission model to identify the key factors that sustain or intensify malaria transmission. The potential benefits are: 1) increased warning time for public health organizations to respond to malaria outbreaks; 2) optimized utilization of pesticide and chemoprophylaxis; 3) reduced likelihood of pesticide and drug resistance; and 4) reduced damage to environment. !> Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. The NASA Earth science data sets that have been used for malaria surveillance and risk assessment include AVHRR Pathfinder, TRMM, MODIS, NSIPP, and SIESIP. Textural-contextual classifications are used to identify small larval habitats. Neural network methods are used to model malaria cases as a function of the remotely sensed parameters. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Discrete event simulations are used for modeling the detailed interactions among the vector life cycle, sporogonic cycle and human infection cycle, under the explicit influences of selected extrinsic and intrinsic factors. The output of the model includes the individual infection status and the quantities normally observed in field studies, such as mosquito biting rates, sporozoite infection rates, gametocyte prevalence and incidence. Results are in good agreement with mosquito vector and human malaria data acquired by Coleman et al. over 4.5 years in Kong Mong Tha, a remote village in western Thailand. Application of our models is not restricted to the Greater Mekong Subregion. Our models have been applied to malaria in Indonesia, Korea, and other regions in the world with similar success.

Kiang, Richard K.↗
NASA NTRS2019

A Risk Informed Approach to Reliability Requirements Tailoring

Every institution has their “recipe” for success. Cell phones and personal computers seem like they are designed to be obsolete in 2-3 years. Automobiles seem like they are designed to have a power train failure within 1000 miles of the extended warranty expiration; at least that’s been my experience. The Jet Propulsion Laboratory (JPL) is not any different. JPL has a tried and proven recipe for success because in space things can’t fail. Or else. However, in today’s competitive environment and funding limitations, that recipe for success is being challenged and the resultant increased risk accepted. This paper will describe JPL’s Risk Informed Decision Making (RIDM) approach to tailoring reliability requirements based on mission classification and other project characteristics.

Klohoker, John↗
NASA NTRS2021

Mapping the Extent of Mangrove Ecosystem Degradation by Integrating an Ecological Conceptual Model with Satellite Data

Anthropogenic and natural disturbances can cause degradation of ecosystems, reducing their capacity to sustain biodiversity and provide ecosystem services. Understanding the extent of ecosystem degradation is critical for estimating risks to ecosystems, yet there are few existing methods to map degradation at the ecosystem scale and none using freely available satellite data for mangrove ecosystems. In this study, we developed a quantitative classification model of mangrove ecosystem degradation using freely available earth observation data. Crucially, a conceptual model of mangrove ecosystem degradation was established to identify suitable remote sensing variables that support the quantitative classification model, bridging the gap between satellite-derived variables and ecosystem degradation with explicit ecological links. We applied our degradation model to two case-studies, the mangroves of Rakhine State, Myanmar, which are severely threatened by anthropogenic disturbances, and Shark River within the Everglades National Park, USA, which is periodically disturbed by severe tropical storms. Our model suggested that 40% (597 km2) of the extent of mangroves in Rakhine showed evidence of degradation. In the Everglades, the model suggested that the extent of degraded mangrove forest increased from 5.1% to 97.4% following the Category 4 Hurricane Irma in 2017. Quantitative accuracy assessments indicated the model achieved overall accuracies of 77.6% and 79.1% for the Rakhine and the Everglades, respectively. We highlight that using an ecological conceptual model as the basis for building quantitative classification models to estimate the extent of ecosystem degradation ensures the ecological relevance of the classification models. Our developed method enables researchers to move beyond only mapping ecosystem distribution to condition and degradation as well. These results can help support ecosystem risk assessments, natural capital accounting, and restoration planning and provide quantitative estimates of ecosystem degradation for new global biodiversity targets.

Calvin K. F. Lee↗
NASA NTRS2013

Evidence Report: Risk of Inadequate Human-Computer Interaction

Human-computer interaction (HCI) encompasses all the methods by which humans and computer-based systems communicate, share information, and accomplish tasks. When HCI is poorly designed, crews have difficulty entering, navigating, accessing, and understanding information. HCI has rarely been studied in an operational spaceflight context, and detailed performance data that would support evaluation of HCI have not been collected; thus, we draw much of our evidence from post-spaceflight crew comments, and from other safety-critical domains like ground-based power plants, and aviation. Additionally, there is a concern that any potential or real issues to date may have been masked by the fact that crews have near constant access to ground controllers, who monitor for errors, correct mistakes, and provide additional information needed to complete tasks. We do not know what types of HCI issues might arise without this "safety net". Exploration missions will test this concern, as crews may be operating autonomously due to communication delays and blackouts. Crew survival will be heavily dependent on available electronic information for just-in-time training, procedure execution, and vehicle or system maintenance; hence, the criticality of the Risk of Inadequate HCI. Future work must focus on identifying the most important contributing risk factors, evaluating their contribution to the overall risk, and developing appropriate mitigations. The Risk of Inadequate HCI includes eight core contributing factors based on the Human Factors Analysis and Classification System (HFACS): (1) Requirements, policies, and design processes, (2) Information resources and support, (3) Allocation of attention, (4) Cognitive overload, (5) Environmentally induced perceptual changes, (6) Misperception and misinterpretation of displayed information, (7) Spatial disorientation, and (8) Displays and controls.

Kritina Holden↗