<div>The National Aeronautics and Space Administration (NASA) has a vision for Advanced Air Mobility (AAM) based on safely introducing aviation services to missions that were previously not served or under-served. Many potential AAM missions lie in metropolitan areas that are beset by various types of uncertainty and potential constraints. Radio interference from other electronic devices can render unreliable communication between flying vehicles to ground operators. Buildings have irregular surfaces that degrade GPS localization performance. Skyscrapers can induce spontaneous turbulence that degrades vehicles' navigational accuracy. However, the potential market demands for aerial passenger-carrying and package delivery services have attracted investments. For example, Google WingX, Amazon Prime Air, and Joby Aviation are well-known companies developing AAM systems and services. If the market visions are realized, how will safety be assessed and maintained with high-density AAM operations?</div><div><br></div><div>While there are multiple technology candidates for realizing high-density AAM operations in urban environments, the means to accomplish the requisite first step of assessing the airspace safety of an integrated AAM eco-system from the candidate technologies is crucial but as yet unclear. This dissertation proposes an entropy-based framework for assessing the airspace safety level for low-altitude airspace in an AAM setting. The framework includes a conceptual model for depicting the information flows between air vehicles and an air traffic authority (ATA) and the use of a probability distribution to represent the traffic state. Subsequently, the framework embeds three airspace-level metrics for assessing airspace safety and uncertainty levels. The traffic safety severity metric quantifies the traffic safety level. The traffic entropy quantifies the uncertainty level of the traffic state distribution. Finally, the temperature is the ratio of the traffic safety severity to the traffic entropy. The temperature is similar to the traffic safety severity but gives a higher weight to the instance with a safe traffic state. </div><div><br></div><div>Simulation studies show that the combined use of the three metrics can evaluate relative airspace safety levels even if the unsafe conditions do not occur. The use cases include using the metrics for real-time airspace safety level monitoring and comparing the design of airspace systems and operational strategies. Additionally, this study demonstrates using a heat map to visualize vehicle-level metrics and assess designs of UAM airspace structures. The contribution of this study includes two parts. First, the temperature metric can heuristically assess a probability function. Based on the definition of the cost function, the temperature metric gives a higher weighting to the instance of the probability function with a lower cost value. This study constructs several triggers for predicting if a near-miss event would happen in the airspace. The temperature-based trigger has a better prediction accuracy than the cost-function-based trigger. Secondly, the temperature can visualize the safety level of an airspace structure with the considerations of the environmental and vehicle state measurement uncertainty. The locations with high-temperature values indicate that the regions are more likely to have endangered vehicles. Although this framework does not provide any means of resolving the unsafe conditions, it can be powerful in the comparison of different airspace design concepts and identify the weaknesses of either airspace design or operational strategies. </div>

Safety is the top priority for the aviation industry and a safe airport environment is essential to aviation safety. However, due to the increasing prevalence of UAS in recent years, UAS sightings have become a potential threat to airports. When UAS appear in the vicinity of airports, they bring safety concerns and result in negative operational and economic impacts on airports. Since the FAA’s mission is to provide the safest and most efficient aerospace system in the world, further research regarding the threat of UAS sightings to airports is needed. The purpose of this study is to investigate the threat of UAS to airports and in the national airspace system (NAS). This study includes three primary components: the analysis of 6,551 Federal Aviation Administration (FAA) UAS sighting reports, a case study of the impacts of the UAS sighting at Newark Liberty International Airport (EWR) on January 22, 2019, and a synthesis of airport operator perspectives based on interviews with airport personnel at five airports. The analysis of UAS sighting reports shows the characteristics of UAS sightings, the case study on EWR UAS illustrates the impact of the UAS sighting at the airport, and interview results illustrate the current perspective of airport operators regarding the risk of UAS. Along with the results, the scientific methods of identifying and analyzing the characteristics of UAS sightings in controlled airspace close to airports could be used by researchers to study UAS sightings in the future. Findings from this study may be beneficial to multiple stakeholders, including airport personnel, regulators, entrepreneurs, and vendors in the aviation industry. <br>

Various remote sensing methods are being utilized to assess fundamental properties of forest stands, such as the height of trees, already for decades. Nevertheless, the use of Unmanned Aerial Vehicles in the combination with Structure from Motion software for these purposes, experiences its boom right now and thus not many pertinent studies exist yet. Such system producing image-based point clouds was utilized in this work to gain the canopy elevation data. To identify individual trees and to extract their height from these remote sensing data, a unique software tool called 'UAV Forest Explorer' was developed. Twenty forest study plots was established to acquire the field measurements ground truth data about almost 1 500 trees to thoroughly test the tool and calculate demanded statistics. The research proved, that the tool is fully applicable on all types of forest stands.

Great Basin bristlecone pine (Pinus longaeva D.K. Bailey) is a keystone species of the subalpine forest in the Great Basin and western Colorado Plateau ecoregions in Utah, Nevada, and California. Bristlecone pine is also the world's longest-lived non-clonal organism, with individuals occasionally reaching ages up to 5,000 years old. Because of its longevity, bristlecone pine contains an important proxy record of climate data in its growth rings. Despite its ecological and scientific importance, bristlecone pine's distribution and associated environmental drivers are poorly understood. Geospatial technologies, including unmanned aircraft systems (UAS), remote sensing, geographic information systems (GIS), and spatial modeling techniques can be used to quantify and characterize biotic and abiotic factors that constrain the fundamental and realized niches of bristlecone pine and other subalpine forest species. In Chapter 1, we describe workflows and important technical and logistical considerations for collecting aerial imagery in mountainous areas using small UAS, enabling high-quality remotely sensed datasets to be assembled to study the ecology of subalpine forests. In Chapter 2, we discuss a unique outlier population of bristlecone pine found in the Stansbury Mountains, Utah. We used GIS to delineate boundaries for five small stands of bristlecone pine and examined two competing hypotheses that could explain the species' presence in the range: 1) that the current population is a relict from the Pleistocene, or 2) that long-distance dispersal mechanisms led to bristlecone pine's migration from other mountain ranges during or after the warming period of the Pleistocene/Holocene transition. Potential migration routes and barriers to migration were considered in our effort to understand the dynamics behind the presence of this unique disjunct population of bristlecone pine. Chapter 3 describes a comprehensive mapping effort for bristlecone pine across its entire distribution. Using data from historic maps, vegetation surveys, herbarium records, and an online ecological database, we compiled nearly 500 individual map polygons in a public-facing online GIS database representing locations where bristlecone pine occurs. Using these occurrence data, we modeled the suitable habitat of the species with Maximum Entropy (MaxEnt), examining the relative importance of 60 environmental variables in constraining the species distribution. A probability map was generated for bristlecone pine, and the environmental variables were ranked in order of their predictive power in explaining the species distribution. We found that January mean dewpoint temperature and February precipitation explained over 80% of the species distribution according to the MaxEnt model, suggesting that the species favors drier air conditions and increased snowfall during winter months. These three studies demonstrate that geospatial tools can be effectively used to quantify and characterize the habitat of bristlecone pine, leading to improved management and conservation of the species in the face of multiple threats, including mountain pine beetle (MPB), white pine blister rust (WPBR), and possible habitat constriction due to climate change.

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In the advent of increasing the number of operable unmanned aerial systems (UAS) over the next years, a challenge exists in regard to the noise signature that these machines may generate. In this work, we perform advanced computational simulations to study the flow around an airfoil and the associated noise radiating to the near- and farield. The airfoil size and the freestream velocity are representative of a typical UAS. The study is aimed at investigating the characteristics of the aerodynamic noise radiating from an airfoil at various angles of attack, Reynolds number and Mach number. The numerical tool is a high-order compressible Navier-Stokes solver, using Runge-Kutta explicit time integration and dispersion-relation-preserving spatial discretization. Various results in terms of velocity and pressure distribution around the airfoil, and sound pressure level spectra calculated from different probe points located in the near- and farield are compared to each other and discussed.

Modern prosumer small unmanned aircraft systems (sUAS) have eliminated many historical barriers to aerial remote sensing and photogrammetric survey data generation. The relatively low cost and operational ease of these platforms has driven their adoption for numerous geospatial applications including professional surveying and mapping. However, significant debate exists among geospatial professionals and academics regarding prosumer sUAS ability to achieve “survey-grade” geospatial accuracy ≤ 0.164 ft. in their derivative survey data. To address this debate, a controlled accuracy test experiment was conducted in accordance with federal standards whereby prosumer sUAS geospatial accuracies were reported between 15.367 ft. – 0.09 ft. horizontally and 496.734 ft. – 0.330 ft. vertically at the 95% confidence level. These results suggest prosumer sUAS derived survey data fall short of “survey-grade” accuracy in this experiment. Therefore, traditional surveying instruments and methods should not be relinquished in favor of prosumer sUAS for complex applications requiring “survey-grade” accuracy at this time.