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At least 1,009 records · Page 56

UAVSAR Program: Initial Results from New Instrument Capabilities

UAVSAR is an imaging radar instrument suite that serves as NASA's airborne facility instrument to acquire scientific data for Principal Investigators as well as a radar test-bed for new radar observation techniques and radar technology demonstration. Since commencing operational science observations in January 2009, the compact, reconfigurable, pod-based radar has been acquiring L-band fully polarimetric SAR (POLSAR) data with repeat-pass interferometric (RPI) observations underneath NASA Dryden's Gulfstream-III jet to provide measurements for science investigations in solid earth and cryospheric studies, vegetation mapping and land use classification, archaeological research, soil moisture mapping, geology and cold land processes. In the past year, we have made significant upgrades to add new instrument capabilities and new platform options to accommodate the increasing demand for UAVSAR to support scientific campaigns to measure subsurface soil moisture, acquire data in the polar regions, and for algorithm development, verification, and cross-calibration with other airborne/spaceborne instruments.

P-band polarimetry↗

System Performance of an Integrated Airborne Spacing Algorithm with Ground Automation

The National Aeronautics and Space Administration's (NASA's) first Air Traffic Management (ATM) Technology Demonstration (ATD-1) was created to facilitate the transition of mature ATM technologies from the laboratory to operational use. The technologies selected for demonstration are the Traffic Management Advisor with Terminal Metering (TMA-TM), which provides precise time-based scheduling in the Terminal airspace; Controller Managed Spacing (CMS), which provides controllers with decision support tools to enable precise schedule conformance; and Interval Management (IM), which consists of flight deck automation that enables aircraft to achieve or maintain precise spacing behind another aircraft. Recent simulations and IM algorithm development at NASA have focused on trajectory-based IM operations where aircraft equipped with IM avionics are expected to achieve a spacing goal, assigned by air traffic controllers, at the final approach fix. The recently published IM Minimum Operational Performance Standards describe five types of IM operations. This paper discusses the results and conclusions of a human-in-the-loop simulation that investigated three of those IM operations. The results presented in this paper focus on system performance and integration metrics. Overall, the IM operations conducted in this simulation integrated well with ground-based decisions support tools and certain types of IM operational were able to provide improved spacing precision at the final approach fix; however, some issues were identified that should be addressed prior to implementing IM procedures into real-world operations.

Swieringa, Kurt A.↗

SIMPL/AVIRIS-NG Greenland 2015: Flight Report

In August 2015, NASA conducted a two-­aircraft, coordinated campaign based out of Thule Air Base, Greenland, in support of Ice, Cloud, and land Elevation Satellite‐2 (ICESat-2) algorithm development. The survey targeted the Greenland Ice Sheet and sea ice in the Arctic Ocean during the summer melt season. The survey was conducted with a photon-counting laser altimeter in one aircraft and an imaging spectrometer in the second aircraft. Ultimately, the mission, SIMPL/AVIRIS-NG Greenland 2015, conducted nine coordinated science flights, for a total of 37 flight hours over the ice sheet and sea ice.

Ice Sheet↗

A Comparison of the SOCIT and DebriSat Experiments

This paper explores the differences between, and shares the lessons learned from, two hypervelocity impact experiments critical to the update of orbital debris environment models. The procedures and processes of the fourth Satellite Orbital Debris Characterization Impact Test (SOCIT) were analyzed and related to the ongoing DebriSat experiment. SOCIT was the first hypervelocity impact test designed specifically for satellites in Low Earth Orbit (LEO). It targeted a 1960's U.S. Navy satellite, from which data was obtained to update pre-existing NASA and DOD breakup models. DebriSat is a comprehensive update to these satellite breakup models- necessary since the material composition and design of satellites have evolved from the time of SOCIT. Specifically, DebriSat utilized carbon fiber, a composite not commonly used in satellites during the construction of the US Navy Transit satellite used in SOCIT. Although DebriSat is an ongoing activity, multiple points of difference are drawn between the two projects. Significantly, the hypervelocity tests were conducted with two distinct satellite models and test configurations, including projectile and chamber layout. While both hypervelocity tests utilized soft catch systems to minimize fragment damage to its post-impact shape, SOCIT only covered 65% of the projected area surrounding the satellite, whereas, DebriSat was completely surrounded cross-range and downrange by the foam panels to more completely collect fragments. Furthermore, utilizing lessons learned from SOCIT, DebriSat's post-impact processing varies in methodology (i.e., fragment collection, measurement, and characterization). For example, fragment sizes were manually determined during the SOCIT experiment, while DebriSat utilizes automated imaging systems for measuring fragments, maximizing repeatability while minimizing the potential for human error. In addition to exploring these variations in methodologies and processes, this paper also presents the challenges DebriSat has encountered thus far and how they were addressed. Accomplishing DebriSat's goal of collecting 90% of the debris, which constitutes well over 100,000 fragments, required addressing many challenges stemming from the very large number of fragments. One of these challenges arose in identifying the foam-embedded fragments. DebriSat addressed this by X-raying all of the panels once the loose debris were removed, and applying a detection algorithm developed in-house to automate the embedded fragment identification process. It is easy to see how the amount of data being compiled would be outstanding. Creating an efficient way to catalog each fragment, as well as archiving the data for reproducibility also posed a great challenge for DebriSat. Barcodes to label each fragment were introduced with the foresight that once the characterization process began, the datasheet for each fragment would have to be accessed again quickly and efficiently. The DebriSat experiment has benefited significantly by leveraging lessons learned from the SOCIT experiment along with the technological advancements that have occurred during the time between the experiments. The two experiments represent two ages of satellite technology and, together, demonstrate the continuous efforts to improve the experimental techniques for fragmentation debris characterization.

Ausay, Erick↗

Monitoring O3 and Aerosols with the NASA LaRC Mobile Ozone Lidar System

The NASA's Langley Mobile Ozone Lidar (LMOL) system routinely measures tropospheric ozone and aerosol profiles, and is part of the Tropospheric Lidar Network (TOLNet). Recent upgrades to the system include a new pump laser that has tripled the transmission output power extending measurements up to 8 km in altitude during the day. In addition, software and algorithm developments have improved data output quality and enabled a real-time ozone display capability. In 2016, a number of ozone features were captured by LMOL, including the dynamics of an early-season ozone exceedance that impacted the Hampton Roads region. In this presentation, we will review current LMOL capabilities, recent air quality events observed by the system, and show a comparison of aerosol retrieval through the UV channel and the green line channel.

Ganoe, Rene↗

MPCV Exercise Operational Volume Analysis

In order to minimize the loss of bone and muscle mass during spaceflight, the Multi-purpose Crew Vehicle (MPCV) will include an exercise device and enough free space within the cabin for astronauts to use the device effectively. The NASA Digital Astronaut Project (DAP) has been tasked with using computational modeling to aid in determining whether or not the available operational volume is sufficient for in-flight exercise.Motion capture data was acquired using a 12-camera Smart DX system (BTS Bioengineering, Brooklyn, NY), while exercisers performed 9 resistive exercises without volume restrictions in a 1g environment. Data were collected from two male subjects, one being in the 99th percentile of height and the other in the 50th percentile of height, using between 25 and 60 motion capture markers. Motion capture data was also recorded as a third subject, also near the 50th percentile in height, performed aerobic rowing during a parabolic flight. A motion capture system and algorithms developed previously and presented at last years HRP-IWS were utilized to collect and process the data from the parabolic flight [1]. These motions were applied to a scaled version of a biomechanical model within the biomechanical modeling software OpenSim [2], and the volume sweeps of the motions were visually assessed against an imported CAD model of the operational volume. Further numerical analysis was performed using Matlab (Mathworks, Natick, MA) and the OpenSim API. This analysis determined the location of every marker in space over the duration of the exercise motion, and the distance of each marker to the nearest surface of the volume. Containment of the exercise motions within the operational volume was determined on a per-exercise and per-subject basis. The orientation of the exerciser and the angle of the footplate were two important factors upon which containment was dependent. Regions where the exercise motion exceeds the bounds of the operational volume have been identified by determining which markers from the motion capture exceed the operational volume and by how much. A credibility assessment of this analysis was performed in accordance with NASA-STD-7009 prior to delivery to the MPCV program.

mathematical models↗

GPS/Optical/Inertial Integration for 3D Navigation Using Multi-Copter Platforms

In concert with the continued advancement of a UAS traffic management system (UTM), the proposed uses of autonomous unmanned aerial systems (UAS) have become more prevalent in both the public and private sectors. To facilitate this anticipated growth, a reliable three-dimensional (3D) positioning, navigation, and mapping (PNM) capability will be required to enable operation of these platforms in challenging environments where global navigation satellite systems (GNSS) may not be available continuously. Especially, when the platform's mission requires maneuvering through different and difficult environments like outdoor opensky, outdoor under foliage, outdoor-urban and indoor, and may include transitions between these environments. There may not be a single method to solve the PNM problem for all environments. The research presented in this paper is a subset of a broader research effort, described in [1]. The research is focused on combining data from dissimilar sensor technologies to create an integrated navigation and mapping method that can enable reliable operation in both an outdoor and structured indoor environment. The integrated navigation and mapping design is utilizes a Global Positioning System (GPS) receiver, an Inertial Measurement Unit (IMU), a monocular digital camera, and three short to medium range laser scanners. This paper describes specifically the techniques necessary to effectively integrate the monocular camera data within the established mechanization. To evaluate the developed algorithms a hexacopter was built, equipped with the discussed sensors, and both hand-carried and flown through representative environments. This paper highlights the effect that the monocular camera has on the aforementioned sensor integration scheme's reliability, accuracy and availability.

Dill, Evan T.↗

Validation and Scaling of Soil Moisture in a Semi-Arid Environment: SMAP Validation Experiment 2015 (SMAPVEX15)

The NASA SMAP (Soil Moisture Active Passive) mission conducted the SMAP Validation Experiment 2015 (SMAPVEX15) in order to support the calibration and validation activities of SMAP soil moisture data products. The main goals of the experiment were to address issues regarding the spatial disaggregation methodologies for improvement of soil moisture products and validation of the in situ measurement upscaling techniques. To support these objectives high-resolution soil moisture maps were acquired with the airborne PALS (Passive Active L-band Sensor) instrument over an area in southeast Arizona that includes the Walnut Gulch Experimental Watershed (WGEW), and intensive ground sampling was carried out to augment the permanent in situ instrumentation. The objective of the paper was to establish the correspondence and relationship between the highly heterogeneous spatial distribution of soil moisture on the ground and the coarse resolution radiometer-based soil moisture retrievals of SMAP. The high-resolution mapping conducted with PALS provided the required connection between the in situ measurements and SMAP retrievals. The in situ measurements were used to validate the PALS soil moisture acquired at 1-km resolution. Based on the information from a dense network of rain gauges in the study area, the in situ soil moisture measurements did not capture all the precipitation events accurately. That is, the PALS and SMAP soil moisture estimates responded to precipitation events detected by rain gauges, which were in some cases not detected by the in situ soil moisture sensors. It was also concluded that the spatial distribution of the soil moisture resulted from the relatively small spatial extents of the typical convective storms in this region was not completely captured with the in situ stations. After removing those cases (approximately10 of the observations) the following metrics were obtained: RMSD (root mean square difference) of0.016m3m3 and correlation of 0.83. The PALS soil moisture was also compared to SMAP and in situ soil moisture at the 36-km scale, which is the SMAP grid size for the standard product. PALS and SMAP soil moistures were found to be very similar owing to the close match of the brightness temperature measurements and the use of a common soil moisture retrieval algorithm. Spatial heterogeneity, which was identified using the high-resolution PALS soil moisture and the intensive ground sampling, also contributed to differences between the soil moisture estimates. In general, discrepancies found between the L-band soil moisture estimates and the 5-cm depth in situ measurements require methodologies to mitigate the impact on their interpretations in soil moisture validation and algorithm development. Specifically, the metrics computed for the SMAP radiometer-based soil moisture product over WGEW will include errors resulting from rainfall, particularly during the monsoon season when the spatial distribution of soil moisture is especially heterogeneous.

SMAPVEX15↗

Reducing Multisensor Satellite Monthly Mean Aerosol Optical Depth Uncertainty: 1. Objective Assessment of Current AERONET Locations

Various space-based sensors have been designed and corresponding algorithms developed to retrieve aerosol optical depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite data sets. Surface-based observations aim to provide ground truth for validating satellite data; hence, their deployment locations should preferably contain as much spatial information as possible, i.e., high spatial representativeness. Using a novel Ensemble Kalman Filter (EnKF)- based approach, we objectively evaluate the spatial representativeness of current Aerosol Robotic Network (AERONET) sites. Multisensor monthly mean AOD data sets from Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, Sea-viewing Wide Field-of-view Sensor, Ozone Monitoring Instrument, and Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar are combined into a 605-member ensemble, and AERONET data are considered as the observations to be assimilated into this ensemble using the EnKF. The assessment is made by comparing the analysis error variance (that has been constrained by ground-based measurements), with the background error variance (based on satellite data alone). Results show that the total uncertainty is reduced by approximately 27% on average and could reach above 50% over certain places. The uncertainty reduction pattern also has distinct seasonal patterns, corresponding to the spatial distribution of seasonally varying aerosol types, such as dust in the spring for Northern Hemisphere and biomass burning in the fall for Southern Hemisphere. Dust and biomass burning sites have the highest spatial representativeness, rural and oceanic sites can also represent moderate spatial information, whereas the representativeness of urban sites is relatively localized. A spatial score ranging from 1 to 3 is assigned to each AERONET site based on the uncertainty reduction, indicating its representativeness level.

aerosol optical depth↗

MABEL Iceland 2012 Flight Report

In March and April 2012, NASA conducted an airborne lidar campaign based out of Keflavik, Iceland, in support of Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) algorithm development. The survey targeted the Greenland Ice Sheet, Iceland ice caps, and sea ice in the Arctic Ocean during the winter season. Ultimately, the mission, MABEL Iceland 2012, including checkout and transit flights, conducted 14 science flights, for a total of over 80 flight hours over glaciers, icefields, and sea ice.

MABEL↗

Water-Leaving Contribution to Polarized Radiation Field Over Ocean

The top-of-atmosphere (TOA) radiation field from a coupled atmosphere-ocean system (CAOS) includes contributions from the atmosphere, surface, and water body. Atmo-spheric correction of ocean color imagery is to retrieve water-leaving radiance from the TOA measurement, from which ocean bio-optical properties can be obtained. Knowledge of the ab-solute and relative magnitudes of water-leaving signal in the TOA radiation field is important for designing new atmospheric correction algorithms and developing retrieval algorithms for new ocean biogeochemical parameters. In this paper we present a systematic sensitivity study of water-leaving contribution to the TOA radiation field, from 340 nm to 865 nm, with polarization included. Ocean water inherent optical properties are derived from bio-optical models for two kinds of waters, one dominated by phytoplankton (PDW) and the other by non-algae particles (NDW). In addition to elastic scattering, Raman scattering and fluorescence from dissolved organic matter in ocean waters are included. Our sensitivity study shows that the polarized reflectance is minimized for both CAOS and ocean signals in the backscattering half plane, which leads to numerical instability when calculating water leaving relative contribution, the ratio between polarized water leaving and CAOS signals. If the backscattering plane is excluded, the water-leaving polarized signal contributes less than 9% to the TOA polarized reflectance for PDW in the whole spectra. For NDW, the polarized water leaving contribution can be as much as 20% in the wavelength range from 470 to 670 nm. For wavelengths shorter than 452 nm or longer than 865 nm, the water leaving contribution to the TOA polarized reflectance is in general smaller than 5% for NDW. For the TOA total reflectance, the water-leaving contribution has maximum values ranging from 7% to 16% at variable wavelengths from 400 nm to 550 nm from PDW. The water leaving contribution to the TOA total reflectance can be as large as 35%for NDW, which is in general peaked at 550 nm. Both the total and polarized reflectances from water-leaving contributions approach zero in the ultraviolet and near infrared bands. These facts can be used as constraints or guidelines when estimating the water leaving contribution to the TOA reflectance for new atmospheric correction algorithms for ocean color imagery.

inelastic scatterin↗

Blowing Snow Sublimation and Transport over Antarctica from 11 Years of CALIPSO Observations

Blowing snow processes commonly occur over the earth's ice sheets when the 10 mile wind speed exceeds a threshold value. These processes play a key role in the sublimation and redistribution of snow thereby influencing the surface mass balance. Prior field studies and modeling results have shown the importance of blowing snow sublimation and transport on the surface mass budget and hydrological cycle of high-latitude regions. For the first time, we present continent-wide estimates of blowing snow sublimation and transport over Antarctica for the period 2006-2016 based on direct observation of blowing snow events. We use an improved version of the blowing snow detection algorithm developed for previous work that uses atmospheric backscatter measurements obtained from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite. The blowing snow events identified by CALIPSO and meteorological fields from MERRA-2 are used to compute the blowing snow sublimation and transport rates. Our results show that maximum sublimation occurs along and slightly inland of the coastline. This is contrary to the observed maximum blowing snow frequency which occurs over the interior. The associated temperature and moisture reanalysis fields likely contribute to the spatial distribution of the maximum sublimation values. However, the spatial pattern of the sublimation rate over Antarctica is consistent with modeling studies and precipitation estimates. Overall, our results show that the 2006-2016 Antarctica average integrated blowing snow sublimation is about 393 +/- 196 Gt yr(exp -1), which is considerably larger than previous model-derived estimates. We find maximum blowing snow transport amount of 5 Mt km-1 yr(exp -1) over parts of East Antarctica and estimate that the average snow transport from continent to ocean is about 3.7 Gt yr(exp -1). These continent-wide estimates are the first of their kind and can be used to help model and constrain the surface mass budget over Antarctica.

Antarctica Sublimation Ice-sheet↗

MABEL Alaska 2014 Flight Report

In July and August 2014, NASA conducted an airborne lidar campaign based out of Fort Wainwright,Fairbanks, Alaska, in support of Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) algorithm development. The survey targeted Alaskan glaciers and icefields and sea ice in the Arctic Ocean during the summer melt season. Ultimately, the mission, MABEL Alaska 2014, including checkout and transit flights, conducted 11 science flights, for a total of over 50 flight hours over glaciers, icefields, and sea ice.

Alaska↗

Development of New Research-Quality Low-Resource Magnetometers for Small Satellites

Researchers from the University of Michigan (UM) and NASA Goddard Spaceflight Center (GSFC) are partnering to develop new types of magnetometers for use on future small satellites. These new instruments not only fulfill stringent requirements for low-amplitude and high-precision measurements, they are also enabling the team to develop a new approach to achieve high-quality magnetic measurements from space, without the need for a boom. Typically, space-based magnetometers are deployed on a boom that extends from the space vehicle to reduce exposure of magnetic noise emanating from the spacecraft, which could potentially contaminate measurements. The UMNASA team has developed algorithms to identify and eliminate spacecraft magnetic noise, which will allow placement of these economical, science-grade instrument magnetometers on and inside the satellite bus, instead of on a boom.

Small Spacecraft↗

Transforming Satellite Data into Weather Forecasts

A NASA project spans the gap between research and operations, introducing new composites of satellite imagery to weather forecasters to prepare for the next generation of satellites. Satellite imagery has been an immense benefit to weather forecasters. With it, they can assess aviation hazards such as low clouds, monitor thunderstorms, and track the evolution of dust plumes. Satellite sensors are continually evolving to provide ever greater imaging capabilities, and researchers continue to develop advanced techniques to identify hazards in satellite imagery. However, barriers can prevent experimental products from reaching forecasters in the operational environment. A NASA project has developed an interactive process whereby weather forecasters learn to interpret the latest satellite imagery and apply it to their operations. Forecasters then provide feedback to the researchers who are developing algorithms and products to further improve future products. This effort has taken on greater importance with the anticipated launch of a new series of satellites that will provide substantially greater amounts of data than are currently available.

Emily Berndt↗

Seasonal and Interannual Assessment of Cloud Cover and Atmospheric Constituents Across the Amazon (2000 - 2015): Insights for Remote Sensing and Climate Analysis

The quantitative assessment of cloud cover and atmospheric constituents improves our ability to exploit the climate feedback into the Amazon basin. In the 21st century, three droughts have already occurred in the Amazonia (e.g. 2005, 2010, 2015), inducing regional changes in the seasonal patterns of atmospheric constituents. In addition to climate, the atmospheric dynamic and attenuation properties are long-term challenges for satellite-based remote sensing of this ecosystem: high cloudiness, abundant water vapor content and biomass burning season. Therefore, while climatology analysis supports the understanding of atmospheric variability and trends, it also offers valuable insights for remote sensing applications. In this study, we evaluate the seasonal and interannual variability of cloud cover and atmospheric constituents (aerosol loading, water vapor and ozone content) over the Amazon basin, with focus on both climate analysis and remote sensing implications. We take the advantage of new atmosphere daily products at 1 km resolution derived from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data. An intercomparison of Aerosol Robotic Network (AERONET) and MAIAC aerosol optical depth (AOD) and columnar water vapor (CWV) showed quantitative information with a correlation coefficient higher than 0.81. Our results show distinct regional patterns of cloud cover across the Amazon basin: northwestern region presets a persistent cloud cover (> 80%) throughout the year, while low cloud cover (0-20%) occurs in the southern Amazon during the dry season. The cloud-free period in the southern Amazon is followed by an increase in the atmospheric burden due to fire emissions. Our results reveal that AOD records are changing in terms of area and intensity. During the 2005 and 2010 droughts, the positive AOD anomalies (δ > 0.1) occurred over 39.03% (240.3 million ha) and 27.14% (165.99 million ha) of total basin in the SON season, respectively. In contrast, the recent 2015 drought occurred towards the end of year (October through December) and these anomalies were observed over 23.72% (145 million ha) affecting areas in the central and eastern Amazon unlike previous droughts. The water vapor presents high concentration values (4.0-5.0 g/sq.cm) in the wet season (DJF), while we observed a strong spatial gradient from northwestern to southeastern of the basin during the dry season. In addition, we also found a positive trend of water vapor content ( ∼0.3 g/sq.cm) between 2000 and 2015. The total ozone typically varies between 220 and 270 DU, and it has a seasonal change of ∼25-35 DU from wet season to dry season caused by large emissions of ozone precursors and long-range transport. Finally, while this study contributes to climatological analysis of atmospheric constituents, the remote sensing users can also understand the regional constraints caused by atmospheric attenuation, such as high aerosol loading and cloud obstacles for surface observations.

ozone↗

NASA's SnowEx Campaign and Measuring Global Snow from Space

Snow blankets 30% of Earth's land surface (60% of northern hemisphere land) in midwinter, dramatically changing our planet's land surface and affecting our weather for months. Seasonal snow is critically important to society for the management of water resources, natural hazards, water security, and in many economic sectors. The only practical way to estimate the quantity of snow on a global scale is through satellites. Despite 4 decades of satellite observations, the highly variable nature of snow still presents significant challenges toward achieving this goal. For example, current space-based techniques underestimate snow water equivalent (SWE) by as much as 50%, and model-based estimates can differ greatly versus estimates based on remotely-sensed observations. Snow community consensus is that a multi-sensor approach is needed to adequately address global snow, combined with modeling and data assimilation to fill the gaps in space and time. What remains, then, is how best to combine and use the various sensors under different types of snow conditions and confounding factors. NASA's multi-year SnowEx airborne campaign is designed to collect measurements needed to enable algorithm development and to guide those mission trade studies. Year 1 (2017) focused on the distribution of snow-water equivalent (SWE) and the snow energy balance in a forested environment. This paper will discuss the various remote sensing options for snow, the challenging factors, and describe the recently-completed first year of SnowEx in Colorado, USA. Ground-based remote sensing and in situ data collection involved nearly 100 participants over three weeks. The airborne campaign included nine sensors on five aircraft. We will conclude by discussing options for a future snow satellite mission.

campaign↗

Simulation Evaluations of an Autonomous Urban Air Mobility Network Management and Separation Service

This paper presents an initial implementation of an autonomous Urban Air Mobility network management and aircraft separation service for urban airspace that does 1) departure and arrival scheduling across the network, 2) continuous trajectory management to ensure safe separation between aircraft, and 3) seamless integration with traditional operations. The highly-autonomous AutoResolver algorithm developed for traditional aviation was extended to provide these capabilities. An evaluation of this initial implementation was conducted in fast-time simulations using a dense, two-hour traffic scenario with Urban Air Mobility aircraft flying between a network of 20 vertiports in the Dallas-Fort Worth metroplex. When the spatial separation was reduced from 0:3nmi to 0:1nmi, the total de- lay decreased by 7:3%; when the temporal separation was reduced from 60s to 45s, the total delay decreased by 28:4%. The total number of conflict resolutions decreased by 26% and 17%, respectively. Furthermore, when a scheduling horizon greater than the duration of UAM flights was used (50min), most conflicts were resolved pre-departure producing ground delay. By comparison, when a shorter scheduling horizon was used (8min), most conflicts were resolved post-departure generating airborne delay. For all scheduling and separation constraints tested, AutoResolver prevented loss of separation from occurring. Urban Air Mobility operations have the ability to revolutionize how people and goods are transported and this paper presents initial research focusing on the high levels of autonomy required for an airspace system capable of scaling to handle significantly higher densities of aircraft.

Bosson, Christabelle S.↗