The Role of Unmanned Aircraft Systems (UAS) in Disaster Response and Recovery Efforts: Historical, Current and Future

A wide range of legislation has been proposed or put into place that restricts the use of unmanned systems. These actions by legislators and regulators will stifle the growth of this technology and the associated surrounding industry. The largest obstacle to the proliferation of UAS in the U.S. is the FAA. The FAA has designated the location of six test sites that are anticipated to allow for less restrictive and formative research to assess the technologies that the FAA has claimed need to exist in order to integrate UAS into the NAS. Further complicating the adoption of UAS for beneficent causes is the plethora of local and state legislation and regulation. Whilst many state restrictions do have built-in caveats to potentially allow for disaster support utilizing UAS, not all are so explicit. All of these actions make the adoption ofUAS in disaster areas more complex and may sway associated agencies away from purchasing UAS for these uses in the future. This research outlines historical uses of UAS to provide basis for the adoption in disaster relief. Examples of past use of unmanned systems in exigent event response are provided including post-hurricane rescue, wild fire monitoring, and landslide disaster relief. An example of missed opportunities with UAS, the Boston Marathon bombing is also outlined. Current UAS usage in first response is explained including types of platforms and sensors that show promise in such operations. Future considerations for UAS adoption in disaster efforts are outlined

The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

Applications Of An Unmanned Aircraft Vehicle And Remote Cameras For Studying A Sub-Arctic Ecosystem

The midcontinent population of lesser snow geese (Anser caerulescens caerulescens) has increased dramatically since the 1960’s due to changing agricultural practices in their southern wintering areas. The destructive foraging and continued population growth of lesser snow geese has resulted in cascading negative impacts on northern ecosystems. Studying remote sub-Arctic ecosystems is logistically challenging, but the advent of remote sensing technologies (such as drones and remote cameras) may assist ecologists in understanding snow goose ecology. Before these tools can be integrated into snow goose research programs, precursor “proof-of-concept” studies are required to validate tool use. The objectives of this study were to investigate the use of unmanned aircraft systems (hereafter “drones”) and remote cameras for studying various aspects of lesser snow goose ecology within the sub-Arctic ecosystem of the Cape Churchill Peninsula, Manitoba, Canada. We first evaluated impacts of drone surveys on wildlife by measuring drone-induced behavioural responses of nesting lesser snow geese using mini-surveillance cameras. We monitored 25 nests with cameras from 2015-2016, comparing behaviours of birds on days with drone surveys, and on days without surveys. Days with drone surveys resulted in decreased low-vigilance behaviours, and increased high-vigilance behaviours. Similarly, overhead vigilance behaviours increased from a baseline 0.03% of observation time to 0.56% when the drone was overhead, indicating birds were likely observing the drone as it flew overhead. Polar bears (Ursus maritimus) were also monitored via video recording during drone flights in 2016, and they responded in a similar fashion to previously published tourism activity impact estimates (mean vigilance bout lengths during drone surveys = 18.7 ± 2.6 seconds). We estimated goose habitat degradation using photointerpretation of drone imagery and compared estimates to those made with ground-based linear transects. We compared estimates between ground-based transects and those made from unsupervised classification of drone imagery collected at altitudes of 75, 100, and 120 m above ground level (ground sampling distances of 2.4, 3.2, and 3.8 cm respectively). We found large time savings during the data collection step of drone surveys, but these savings were ultimately lost during imagery processing. Based on photointerpretation, overall accuracy of drone imagery was generally high (88.8% to 92.0%) and Kappa coefficients were similar to previously published habitat assessments from drone imagery. Mixed model estimates indicated 75m drone imagery overestimated barren (F2,182 = 100.03, P \u3c 0.0001) and shrub classes (F2,182 = 160.16, P \u3c 0.0001) compared to ground estimates. Inconspicuous graminoid and forb species (non-shrubs) were difficult to detect from drone imagery and were underestimated compared to ground-based transects (F2,182 = 843.77, P \u3c 0.0001). Remote cameras were also used as a remote sensing tool to estimate impacts of Ursid predators on nesting lesser snow geese. From 2013-2018 we deployed 233 remote cameras on goose nests and reviewed images for occurrences of bears and associated avian predators. We recorded the amount of time that female geese spent on and of their nest on days with bears (bear-days), and the day before (control-days). Contrary to predictions, geese spent less total time off-nest on bear-days than control-days (β = -0.32 ± 0.13, P \u3c 0.05). Avian predators were observed more frequently on bear-days (13/18 days) than their paired control-days (2/18 days), and bear presence has a positive effect on avian predator occurrence (β = 3.035 ± 0.916, P \u3c 0.001). We suspect that geese spend more time on-nest in response to bears to defend nests from increased activity of avian predators, and we examined these behaviours using agent-based models. In mixed predator scenarios (bears and avian predators), birds that left their nest early would reduce the probability of nest loss by bears, but had increased risk by avian predators. This work demonstrates that the relationship between nesting geese and bear predators is more complex than commonly depicted, and provides a foundation for future examination of the continued impact of bears on nesting birds. This work demonstrates the value of remote sensing tools for understanding sub-Artic ecosystems and other regions where ecological research is logistically challenging

A Survey of Offline and Online Learning-Based Algorithms for Multirotor UAVs

Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Navigation controllers endowed with different attributes and onboard sensor suites enable multirotor autonomous or semi-autonomous, safe flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the faster-than-exponential increase of available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been, and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environment in which multirotors operate. Learning algorithms combined with model-based control techniques have been proven beneficial when applied to multirotors. This survey summarizes published research since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories, and then, further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey are on online learning algorithms as applied to multirotors with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, and how, and how fast. The outcome of the survey offers a clear understanding of the recent state-of-the-art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real-time.Comment: 26 pages, 6 figures, 4 tables, Survey Pape

Robust, Resilient Networked Communication in Challenged Environments

In challenged environments, digital communication infrastructure may be difficult or even impossible to access. This is especially true in rural and developing regions, as well as in any region during a time of political or environmental crisis. We advance the state of the art in wireless networking and security to design networks and applications that rapidly assess changing networking conditions to restore communication and provide local situational awareness. This dissertation examines new systems for responding to current and emerging needs for wireless networks. This work looks across the wireless ecosystem of widely deployed standards. We develop new tools to improve network assessment and to provide robust and reliable network communication. By incorporating new technological breakthroughs, such as the wide commercial success of Unmanned Aircraft Systems (UAS), we introduce novel methods and systems for existing wireless standards for these challenged networks. We assess how existing technologies and standards function in difficult environments: lacking end-end Internet connectivity, experiencing overload or other resource constraints, and operating in three dimensional space. Through this lens, we demonstrate how to optimize networks to serve marginalized communities outside of first world urban cities and make our networks resilient to natural and political crisis that threaten communication

DRONE AMERICA: THE END OF PRIVACY?

Cutting-edge technological innovations have enabled law enforcement agencies to collect data over a geographical area in relatively short amounts of time. Drones (also known as unmanned aerial vehicles) are becoming increasingly acceptable and employed by state and local law enforcement to become force multipliers. While the Federal Aviation Administration has addressed the integration and safety of flight requirements for law enforcement agencies to utilize drones, federal privacy and data collection regulations are unresolved. This thesis argues that federal regulation is required and attempts to highlight the distinction between surveillance technology and delivery platforms to understand how to approach the regulation of data gathering. In doing so, this thesis uses a political, economic, socio-cultural, and technological (PEST) analysis to examine Title III and relative jurisprudence dealing with both surveillance and aerial platforms. The PEST analysis aims to bring forward the salient points in crafting recommendations and expansion in current legislation that support an increase in citizens’ safety and security, but remain within the bounds of constitutional liberty and the Fourth Amendment.Lieutenant Commander, United States NavyApproved for public release. distribution is unlimite

Naval Research Program 2019 Annual Report

NPS NRP Annual ReportThe Naval Postgraduate School (NPS) Naval Research Program (NRP) is funded by the Chief of Naval Operations and supports research projects for the Navy and Marine Corps. The NPS NRP serves as a launch-point for new initiatives which posture naval forces to meet current and future operational warfighter challenges. NRP research projects are led by individual research teams that conduct research and through which NPS expertise is developed and maintained. The primary mechanism for obtaining NPS NRP support is through participation at NPS Naval Research Working Group (NRWG) meetings that bring together fleet topic sponsors, NPS faculty members, and students to discuss potential research topics and initiatives.Chief of Naval Operations (CNO)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

MOSAiC Implementation Plan

This document is the second version of the Implementation Plan for the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) initiative and lays out a vision of how associated observational, modeling, synthesis, and programmatic objectives can be manifested. The document was drafted during an international workshop in Potsdam in July 2015, and further developed during two additional workshops at AWI Potsdam in December 2015 and February 2016. Support for this planning activity has been provided by the IASC-ICARPIII process, the Alfred Wegener Institute Helmholtz Centre for Polar- and Marine Research, and the University of Colorado/ NOAA-ESRL-PSD. This document provides a framework for planning the logistics of the project, developing scientific observing teams, organizing scientific contributions, coordinating the use of resources, and ensuring MOSAiC’s legacy of data and products. A brief overview and summaries of key science questions are provided in Section 1. Section 2 includes an overview of specific observational requirements, while Section 3 describes the coordination and design of specific field assets. Practical logistics plans are outlined in Section 4. Links with current and future satellite programs and model activities are given in Sections 5 and 6. The MOSAiC data management strategy is given in Section 7. Links to other programs are outlined in Section 8. The appendix (Section 9) lists the parameters to be measured and the participating groups