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NASA NTRS2020

Cambridge Urban Development - Quantifying Changes in Urban Albedo with NASA Earth Observations to Reduce Urban Heat Island Effect in Cambridge, Massachusetts

The urban heat island (UHI) effect occurs when urban areas have temperatures that are warmer on average than the surrounding suburban and rural regions. Low albedo surfaces traditionally found in urban landscapes, such as dark asphalt and rooftops, absorb solar irradiance and reemit heat, which contributes to the UHI effect. Cambridge,Massachusetts,part of the Boston metropolitan area,expects severe impacts on human health,infrastructure, and local environmental features due to increasing urban heat. The Massachusetts – Boston NASA DEVELOP team partnered with the City of Cambridge Community Development Department and the American Geophysical Union’s Thriving Earth Exchange to inform ongoing planning and heat mitigation efforts. High Resolution Orthoimagery (HRO) and National Agriculture Imagery Program (NAIP) scenes were analyzed in conjunction with ancillary datasets to assess changes in rooftop albedo between 2008 and 2018. Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to determine nighttime land surface temperature (LST) between 2003 and 2019. Lastly, temperature anomalies were calculated using seasonally averaged nighttime LST values obtained from Aqua MODIS to display ‘hot spots’ for summers between 2004 and 2019. On average, albedo increased by 0.12 across the city, with the majority of positive change in commercial areas. Analysis of mean summer nighttime LST between 2003 and 2019 revealed a non-significant increase in temperature, although temperature anomaly maps demonstrated that Cambridge was warmer on average than rural areas. These results were incorporated in the interactive Cambridge Urban Heat Dashboard, which allows users to explore temporal trends in albedo.

Nicole Ramberg-Pihl↗
NASA NTRS2020

Ellicott City Disasters II - Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗
NASA NTRS2020

Cambridge Urban Development: Quantifying Changes in Urban Albedo with NASA Earth Observations to Reduce Urban Heat Island Effect in Cambridge, Massachusetts

The urban heat island (UHI) effect occurs when urban areas have temperatures that are warmer on average than the surrounding suburban and rural regions. Low albedo surfaces traditionally found in urban landscapes, such as dark asphalt and rooftops, absorb solar irradiance and reemit heat, which contributes to the UHI effect. Cambridge, Massachusetts, part of the Boston metropolitan area, expects severe impacts on human health, infrastructure, and local environmental features due to increasing urban heat. The Massachusetts – Boston NASA DEVELOP team partnered with the City of Cambridge Community Development Department and the American Geophysical Union’s Thriving Earth Exchange to inform ongoing planning and heat mitigation efforts. High Resolution Orthoimagery (HRO) and National Agriculture Imagery Program (NAIP) scenes were analyzed in conjunction with ancillary datasets to assess changes in rooftop albedo between 2008 and 2018. Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to determine nighttime land surface temperature (LST) between 2003 and 2019. Lastly, temperature anomalies were calculated using seasonally averaged nighttime LST values obtained from Aqua MODIS to display ‘hot spots’ for summers between 2004 and 2019. On average, albedo increased by 0.12 across the city, with the majority of positive change in commercial areas. Analysis of mean summer nighttime LST between 2003 and 2019 revealed a non-significant increase in temperature, although temperature anomaly maps demonstrated that Cambridge was warmer on average than rural areas. These results were incorporated in the interactive Cambridge Urban Heat Dashboard, which allows users to explore temporal trends in albedo.

Nicole Ramberg-Pihl↗
NASA NTRS2020

Ellicott City Disasters III: Building a Real-Time Statistical Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗
NASA NTRSDate not supplied

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗
NASA NTRS2018

Rapid, Comprehensive, Mission Architecting at the Jet Propolusion Laboratory

One of the first multi-disciplinary optimization challenges a mission concept faces is finding an initial system level architecture that simultaneously satisfies the constraints of cost, the requirements of science, and the capabilities of engineering. Compounding this challenge, especially in the early formulation of an architecture, is communicating amongst all key stakeholders, in this multidimensional space of constraints and requirements, where the current architecture is not yet adequately defined, or if it is defined, where it is broken. Recently, a factor of two improvement in the speed of development of the engineering architecture, while also comprehensively considering scientific performance and cost, has been achieved through a single screen visualization dashboard (“S-Chart”), a cost allocation tool, segment level analogy databases and parametric relationships for segment technical capabilities and their technical (Size, Weight, Power, and Data) and financial (Cost) capabilities and/or accommodation requirements.

Nash, Alfred E.↗
NASA NTRS2021

Prediction of Safety Incidents

Crystal Ball is an application being developed that accesses multiple safety databases as a means to improve prediction of safety incidents. Year 1 was data integration, year 2 was predictive modeling, and then year 3(FY20), was the merging of those two prior year efforts into the final application, Crystal Ball (ssc.crystalball.insight.nasa.gov). Crystal Ball sits on the Insight platform (Insight is a NASA platform used to process, manage, integrate, analyze and visualize data at scale, insight.nasa.gov). InFY20, the project focus concentrated on the larger vision of Prediction of Safety Incidents using Crystal Ball as the data source. The Insight platform developer incorporated the predictive modeled data sets, and included a graphical user interface, resulting in a Dashboard for the Crystal Ball application; This application is a one-stop-shop for SMA employees working across data sets and provides a snapshot of current relative risk in different types of locations across the center. The ultimate goal is to have a tool that management can use to aid in decisions that are based on data already being collected. Ideally, the tool would highlight areas of increased risk for any given day. SMA will be conducting case studies to further refine the process of identifying higher areas of risk and potentially strategically direct resources where needed more. Our partners who leveraged funds for this project may consider use at other NASA organizations.

Kamili Shaw↗
NASA NTRS2021

LMI Automated Air Cargo Operations Market Research and Forecast

Air cargo companies and aircraft manufacturers are making significant investments to enable the movement of cargo via various levels of automated aircraft, such as aircraft with simplified operations requiring a pilot, remotely monitored or piloted aircraft, and fully autonomous aircraft. These investments will enable greater utilization of aircraft while unlocking new air markets traditionally served by ground transportation only. Many cargo companies and aerospace experts envision an operating environment where a single pilot can remotely pilot numerous aircraft for significant increases in aircraft utilization. The future operating environment is also expected to include air cargo companies flying smaller aircraft from airports and distribution centers outside of major U.S. cities directly to city centers, avoiding congested roads and increasing the velocity of cargo shipments, particularly those that are high-value, time-sensitive, and security sensitive (e.g., pharmaceuticals). These are just a few benefits and use cases cargo companies and aerospace experts see with the advancement of automated aircraft. To better understand industry’s direction, NASA asked the LMI team to research the forecasted market, timeline, risks, and opportunities for integrating unmanned air cargo vehicles into the National Airspace System (NAS) for the development and prioritization of the NASA Air Traffic Management Exploration’s research portfolio. To begin the market assessment, we gathered data via numerous interviews with key stakeholders and subject matter experts and literature reviews. We then incorporated the data into a custom-developed systems dynamics model and visualization dashboard. The systems dynamics model classifies the size of the market (e.g., overall fleet size of automated aircraft) for four distinct use cases over the next 20 years. The model projects the year in which various types of automated aircraft will enter the commercial cargo market based on our team’s collective research on when the aircraft will become viable due to manufacturing and certification timelines and the lifespan of current, traditional aircraft in service, to name a few factors. While this report defines our team’s estimated timeline of entry and growth, the model is dynamic—it enables NASA users to change variables based on future-year events. If the necessary technology does not mature in accordance with our assumptions, then NASA can change the entry of service point to a future year to evaluate the changes in market size in the out years. This flexibility will be key to deciding when and how NASA should invest in various areas.

air traffic management↗
NASA NTRS2021

Southern California Health & Air Quality: Using Remote Sensing to Detect the Frequency and Drivers of Red Tide Blooms in California to Assist in the Management of Human and Marine Exposure to Algal Toxins

In 2020, the dinoflagellate species Lingulodinium polyedra was measured at unprecedented levels off the southern California coast, raising concern for local communities. At high levels, L. polyedra can cause marine life mortality, food-borne illness, and respiratory-related health risks in humans. In partnership with the California Office of Environmental Health Hazard Assessment, the National Oceanic and Atmospheric Administration Southwest Fisheries Science Center, the California Department of Public Health, and the University of California San Diego’s Scripps Institution of Oceanography, this project utilized satellite imagery to visualize and analyze spatiotemporal trends of historical red tide events associated with L. polyedra. Using the Suomi National Polar-orbiting Partnership’s (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), Aqua’s Moderate Resolution Imaging Spectroradiometer (MODIS), and Global Change Observation Mission – Climate (GCOM-C) Second Generation Global Imager (SGLI), the team assessed the validity of using multiple sensors in detecting chlorophyll-a as a proxy for dinoflagellate dominated-algal blooms. The results suggest that VIIRS imagery processed using the Color Index algorithm from Hu et al. (2013), amongst all other algorithms and Earth observations assessed, shows the most promise in identifying L. polyedra blooms. The end products included an ArcGIS Dashboard and Google Earth Engine tool that when combined, provided users with spatial and temporal trends, interactive interfaces to analyze the effectiveness of various sensors and algorithms, and an overall contribution to aid in the management of human health and the economy impacted by harmful algal blooms.

Harmful algal bloom↗
NASA NTRSDate not supplied

What (and How) MERRA-2 Reanalysis Data are Used in Applied Sciences

The Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2) is the global atmospheric data reanalysis for the satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO), using the Goddard Earth Observing System Model (GEOS)version 5.12.4. The data are officially distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). MERRA-2 data have been widely used by the Earth sciences and application community. Since MERRA-2 data were released in early 2016, the number of registered data users has grown steadily from 1,252 in 2016 to 6477 in 2020. By the end of October 2021, ~16 petabytes (over 360 million files) of data have been distributed to more than 18,900 users. Searching in Google Scholar (https://scholar.google.com/), we have found over 7,000 articles, published between January 2017 and May 2021, involving the use ofMERRA-2 data. The figure shows the numbers for various application areas in which theMERRA-2 data have been used, covering almost all of the application areas defined in NASA Applied Sciences (http://appliedsciences.nasa.gov). The largest number of articles are found in disaster research, with the subcategories ordered in flood, wildfires, hurricanes and cyclones, and other forms of severe weather. In this presentation, we will discuss the preliminary findings from a review of the selected literature that uses MERRA-2 data in applied sciences. The current analytic and interoperable data services at GES DISC are listed, such as the on-the-fly subset and analysis service, NASA Giovanni; THREDDS Data Server(TDS); and Python Jupyter notebooks. In addition, we will introduce two new services for supporting the open sciences: My Dashboard and Related Publications.

data management↗
NASA NTRS2022

Phoenix Climate: Employing NASA Earth Observations to Conduct Site Suitability Analyses on Residential Tree Planting Initiatives in Phoenix, AZ

Phoenix, Arizona is the hottest city in the United States, with daytime summer temperatures consistently reaching upwards of 100°F. As these daytime temperatures continue to climb, heat-related illnesses and morbidity also increase. The City of Phoenix hopes to secure funding to implement the American Rescue Plan Act (ARPA) residential tree equity accelerator program. This funding will be used for targeted investments in underserved neighborhoods to increase tree canopy cover, engaging 5,000 households across selected neighborhoods. By partnering with the City of Phoenix, the Arizona Office of Heat Mitigation, and Arizona State University’s Urban Climate Research Center, our team identified residential neighborhoods, block groups within qualified census tracts, and parcels to be prioritized in the ARPA program. We conducted an analysis using NASA Earth observations, movement and heat exposure data, sociodemographic data, and tree canopy data. For Earth observations, we acquired daytime land surface temperature from the Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) and land cover classification from the United States Geological Survey (USGS) National Land Cover Database (NLCD). The project will support the prioritization of city resources and tree plantings based on community vulnerability, as well as help initiate public engagement efforts and literacy with an interactive dashboard and GIS layers that contribute to the city’s property information portal.

Alison Bautista↗
NASA NTRSDate not supplied

New Hayabusa2 and OSIRIS REx Curation Facilities at Nasa Johnson Space Center

Asteroids are made up of rocky material that is left over from the formation of our solar system [1]. This material holds the key to unlocking information about the history of the planets and our sun. Hayabusa2 and OSIRIS-REx are two sample return missions that have collected rocky material from carbonaceous asteroids which are rich in water, carbon, and organic compounds [1]. Hayabusa2 is a spacecraft that collected samples from asteroid Ryugu launched by the Japan Aerospace Exploration Agency (JAXA). Samples were returned to Earth in December 2020 and 10 percent of the collected material is currently housed in a state-of-the-art laboratory at NASA Johnson Space Center (JSC) dedicated to Ryugu material [1]. OSIRIS-REx is a spacecraft launched by NASA that collected samples from asteroid Bennu. The samples are on their journey to Earth and will arrive in September 2023. They will be stored in a separate cleanroom dedicated to Bennu material. From these two missions, scientists will examine dust particles and rocky material to research clues on how the early solar system formed and how life began [2]. Because these samples are so precious and rare, it is important to keep them free from Earth contamination. In September of 2020, a massive construction project began to provide curation facilities to meet the needs of two incoming asteroid collections, Hayabusa2 and OSIRIS-REx. Despite numerous setbacks caused by the coronavirus pandemic, these facilities have been completed in the fall of 2021. Some of these setbacks included strict PPE requirements, keeping safe working distances as well as access to the facilities as NASA was operating under a Stage 3 condition in response to the pandemic. These facilities will provide curation laboratories to safely house the precious asteroid samples as well as facilities to manipulate samples, do sample preparation and process samples into and out of the labs for future studies by the scientific community. The lab suite consists of several different rooms including an initial anteroom (ISO 7), a staging area (ISO 7), two gowning areas (one for OSIRIS-REx and one for Hayabusa2; both ISO 6)), the Haybusa2 Curation Laboratory (ISO 5), the OSIRIS-REx Curation Laboratory (ISO 5), an ultramicrotomy laboratory (ISO 7) and a thin section laboratory (not a cleanroom). To maintain cleanliness levels necessary for these lab spaces quarterly to monthly Balazs organic and inorganic sampling has been conducted on a regular basis before, during and after the construction project to monitor the cleanliness levels in the new labs that will house the asteroid samples. These new facilities feature a Ruuvi (Grafana) monitoring system that measures temperature, humidity, and pressure of the lab suite. This is a small puck shaped sensor that sends signals to the Grafana cloud where the data can be plotted and set up on dashboards within the lab suite. These sensors are easy to set up and install and they run off a cell battery that is good for 3-5 years before needing replacement. This system can deliver text messages and emails to the appropriate personnel when values fall outside of set limits. In addition to the monitoring system, this new lab suite features viewing windows from several different locations and sliding motion censored doors. We have obtained some state-of-the-art equipment to outfit the lab spaces (desiccators, microscopes, ultramicrotome) and are in the process of outfitting the suite with additional items such as: nitrogen gloveboxes, additional desiccators, micromanipulators, etc. The Hayabusa2 samples provided to NASA by JAXA included 23 millimeter sized grains and 4 containers with finer material. OSIRIS-REx is expected to return as much as 400 grams of asteroid Bennu. Samples will be stored in a nitrogen glove box and separated into nitrogen sealed transfer containers. Small Bennu and Ryugu particles will be handled and prepared (e.g., ultra-thin sections for scanning electron microscopy (SEM) or transmission electron microscopy (TEM)) using an Axis Pro micromanipulator and Leica EM UC7 ultramicrotome. Analyzing samples at the atomic scale will provide greater insight to the origins of the solar system and life formation on Earth. All the curation preparation for these sample collections is essential to ongoing research and efforts to understand our solar system, both nowand for future generations.

Curation↗
NASA NTRSDate not supplied

Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)

The scientific community is faced with a need for greatly improved data sharing, analysis, visualization and advanced collaboration based firmly on open science principles. Recent and upcoming launches of new satellite missions with more complex and voluminous data, as well as the ever more urgent need to better understand the global carbon budget and related ecological processes, provided the immediate rational for the ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP). This highly collaborative joint project of ESA and NASA established a framework between ESA and NASA to share data, science algorithms and compute resources in order to foster and accelerate scientific research conducted by ESA and NASA EO data users. Presented to the public in October 2021, the current version of MAAP provides a common cloud-based platform with computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of global above-ground biomass. Data from the Global Ecosystem Dynamics Investigation (GEDI) mission on the International Space Station and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) have been instrumental in the first products of MAAP including the first comprehensive map of Boreal above-ground Biomass and a current Global Biomass Harmonization Activity, but the platform is also being specifically designed to support the forthcoming ESA Biomass mission and incorporate data from the upcoming NASA-ISRO SAR (NISAR) mission. While these missions and the corresponding research which includes airborne, field, and calibration/validation data collection and analyses, provide a wealth of data and information relating to global biomass estimation, they also present data storing, processing and sharing challenges. The NISAR mission alone will produce about 80TB/day. These large data volumes present a challenge that would otherwise place accessibility limits on the scientific community and impact scientific progress. Other challenges being addressed by MAAP include: 1) Enabling researchers to easily discover, process, visualize and analyze large volumes of data from both agencies; 2) Providing a wide variety of data in the same coordinate reference frame to enable comparison, analysis, data evaluation, and data generation; 3) Providing a version-controlled science algorithm development environment that supports tools, co-located data and processing resources; and 4) Addressing intellectual property and sharing challenges related to collaborative algorithm development and sharing of data and algorithms. MAAP products can be explored on the MAAP Dashboard at https://earthdata.nasa.gov/maap-biomass or the joint platform entrance at scimaap.net. MAAP also can be accessed through individual NASA (https://maap-project.org) and ESA (https://esa-maap.org/) landing pages.

cloud computing↗
NASA NTRSDate not supplied

Low Latency Flux and Concentration Datasets in Support of Greenhouse Gas Monitoring Based on NASA's GEOS Modeling and Data Assimilation System

We present efforts to develop space-based greenhouse gas monitoring systems that can provide low latency information and traceability to independent observations. Through support from its Carbon Monitoring System program, NASA has developed the capability to assimilate XCO2 retrievals from the Orbiting Carbon Observatory, 2 (OCO-2) into the Goddard Earth Observing System (GEOS) Constituent Data Assimilation System (CoDAS) to create gap-filled, three-dimensional (3D) estimates of CO2 mixing ratio. When OCO-2 data are not available, concentration fields are further informed by a bottom-up flux package based on remotely sensed fire radiative power, nighttime lights, and vegetation reflectance combined with estimates of atmospheric growth rate based on surface in situ data. The 3D nature of this dataset supports evaluation with independent aircraft data, helping to ensure transparency of remotely sensed data products. These quasi-operational data are currently produced 2-3 months behind real time and are distributed via NASA and international dashboard services to a variety of end users. In this presentation, we provide an overview of the system as well as remaining data gaps and modeling challenges. We also highlight the application of this dataset for detecting emissions anomalies associated with COVID-19 and comparing against independent emissions estimates. Finally, we highlight a new NASA initiative called the Earth Information System (EIS), which aims to support open science and applications by leveraging emerging cloud computing capabilities to increase access to NASA’s greenhouse gas datasets, opportunities for co-development, and transparency in methods for analysis and flux attribution.

Lesley Ott↗
NASA NTRSDate not supplied

Developing and Testing Two Interfaces for Supplemental Data Service Provider (SDSP) Tools to Support UAS Traffic Management (UTM)

Researchers conducted a usability study using two graphical user interfaces (GUIs) to explore how individuals interpret and interact with different preflight information displays, and to inform the development of Uncrewed Aircraft System (UAS) preflight planning predictive support tools to assess and mitigate flight hazards and risks. A series of preflight risk-assessment tasks were developed to evaluate participant performance using the Supplemental Data Service Provider-Consolidated Dashboard (SDSP-CD) and the Human Automation Team Interface System (HATIS) GUIs. Participants were trained to use both interfaces and their performance was evaluated. These evaluations focused on participants’ preflight planning activities. Objective data on performance tasks across different scenarios involving multi-UASs, as well as self-reports of interactions and subjective experiences using the GUIs were collected. Scores on the system usability scale (SUS) and on a simple task set were examined, as well as user feedback on open-ended questions, to inform development and identify potential improvements to the interfaces.

sUAAV interfaces↗
NASA NTRSDate not supplied

Developing and Testing Two Interfaces for Supplemental Data Service Provider (SDSP) Tools to Support UAS Traffic Management (UTM)

Researchers conducted a usability study using two graphical user interfaces (GUIs) to explore how individuals interpret and interact with different preflight information displays, and to inform the development of Uncrewed Aircraft System (UAS) preflight planning predictive support tools to assess and mitigate flight hazards and risks. A series of preflight risk-assessment tasks were developed to evaluate participant performance using the Supplemental Data Service Provider-Consolidated Dashboard (SDSP-CD) and the Human Automation Team Interface System (HATIS) GUIs. Participants were trained to use both interfaces and their performance was evaluated. These evaluations focused on participants’ preflight planning activities. Objective data on performance tasks across different scenarios involving multi-UASs, as well as self-reports of interactions and subjective experiences using the GUIs were collected. Scores on the system usability scale (SUS) and on a simple task set were examined, as well as user feedback on open-ended questions, to inform development and identify potential improvements to the interfaces.

sUAAV interfaces↗
NASA NTRSDate not supplied

VEDA: Visualization, Exploration, & Data Analysis

NASA's Visualization, Exploration, and Data Analysis (VEDA) project is an open-source science cyberinfrastructure for data processing, visualization, exploration, and geographic information systems (GIS) capabilities. Developed collaboratively and mostly reusing existing open-source components, VEDA consolidates GIS delivery mechanisms, processing platforms, analysis services, and visualization tools and provides an ecosystem of open tools for addressing Earth science research and application needs through the public-facing VEDA Dashboard. In this presentation, Dr. Freitag will provide an overview of VEDA and how it can potentially serve the AOS community.

Brian Freitag↗