On Small Satellites for Oceanography: A Survey

The recent explosive growth of small satellite operations driven primarily from an academic or pedagogical need, has demonstrated the viability of commercial-off-the-shelf technologies in space. They have also leveraged and shown the need for development of compatible sensors primarily aimed for Earth observation tasks including monitoring terrestrial domains, communications and engineering tests. However, one domain that these platforms have not yet made substantial inroads into, is in the ocean sciences. Remote sensing has long been within the repertoire of tools for oceanographers to study dynamic large scale physical phenomena, such as gyres and fronts, bio-geochemical process transport, primary productivity and process studies in the coastal ocean. We argue that the time has come for micro and nano satellites (with mass smaller than 100 kg and 2 to 3 year development times) designed, built, tested and flown by academic departments, for coordinated observations with robotic assets in situ. We do so primarily by surveying SmallSat missions oriented towards ocean observations in the recent past, and in doing so, we update the current knowledge about what is feasible in the rapidly evolving field of platforms and sensors for this domain. We conclude by proposing a set of candidate ocean observing missions with an emphasis on radar-based observations, with a focus on Synthetic Aperture Radar.Comment: 63 pages, 4 figures, 8 table

Methane Mitigation:Methods to Reduce Emissions, on the Path to the Paris Agreement

The atmospheric methane burden is increasing rapidly, contrary to pathways compatible with the goals of the 2015 United Nations Framework Convention on Climate Change Paris Agreement. Urgent action is required to bring methane back to a pathway more in line with the Paris goals. Emission reduction from “tractable” (easier to mitigate) anthropogenic sources such as the fossil fuel industries and landfills is being much facilitated by technical advances in the past decade, which have radically improved our ability to locate, identify, quantify, and reduce emissions. Measures to reduce emissions from “intractable” (harder to mitigate) anthropogenic sources such as agriculture and biomass burning have received less attention and are also becoming more feasible, including removal from elevated-methane ambient air near to sources. The wider effort to use microbiological and dietary intervention to reduce emissions from cattle (and humans) is not addressed in detail in this essentially geophysical review. Though they cannot replace the need to reach “net-zero” emissions of CO2, significant reductions in the methane burden will ease the timescales needed to reach required CO2 reduction targets for any particular future temperature limit. There is no single magic bullet, but implementation of a wide array of mitigation and emission reduction strategies could substantially cut the global methane burden, at a cost that is relatively low compared to the parallel and necessary measures to reduce CO2, and thereby reduce the atmospheric methane burden back toward pathways consistent with the goals of the Paris Agreement

Adaptive sampling of transient environmental phenomena with autonomous mobile platforms

Submitted in partial fulfillment of the requirements for the degree of Master of Science in Aeronautics and Astronautics at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2019.In the environmental and earth sciences, hypotheses about transient phenomena have been universally investigated by collecting physical sample materials and performing ex situ analysis. Although the gold standard, logistical challenges limit the overall efficacy: the number of samples are limited to what can be stored and transported, human experts must be able to safely access or directly observe the target site, and time in the field and subsequently the laboratory, increases overall campaign expense. As a result, the temporal detail and spatial diversity in the samples may fail to capture insightful structure of the phenomenon of interest. The development of in situ instrumentation allows for near real-time analysis of physical phenomenon through observational strategies (e.g., optical), and in combination with unmanned mobile platforms, has considerably impacted field operations in the sciences. In practice, mobile platforms are either remotely operated or perform guided, supervised autonomous missions specified as navigation between humanselected waypoints. Missions like these are useful for gaining insight about a particular target site, but can be sample-sparse in scientifically valuable regions, particularly in complex or transient distributions. A skilled human expert and pilot can dynamically adjust mission trajectories based on sensor information. Encoding their insight onto a vehicle to enable adaptive sampling behaviors can broadly increase the utility of mobile platforms in the sciences. This thesis presents three field campaigns conducted with a human-piloted marine surface vehicle, the ChemYak, to study the greenhouse gases methane (CH4) and carbon dioxide (CO2) in estuaries, rivers, and the open ocean. These studies illustrate the utility of mobile surface platforms for environmental research, and highlight key challenges of studying transient phenomenon. This thesis then formalizes the maximum seek-and-sample (MSS) adaptive sampling problem, which requires a mobile vehicle to efficiently find and densely sample from the most scientifically valuable region in an a priori unknown, dynamic environment. The PLUMES algorithm — Plume Localization under Uncertainty using Maximum-ValuE information and Search—is subsequently presented, which addresses the MSS problem and overcomes key technical challenges with planning in natural environments. Theoretical performance guarantees are derived for PLUMES, and empirical performance is demonstrated against canonical uniform search and state-of-the-art baselines in simulation and field trials. Ultimately, this thesis examines the challenges of autonomous informative sampling in the environmental and earth sciences. In order to create useful systems that perform diverse scientific objectives in natural environments, approaches from robotics planning, field design, Bayesian optimization, machine learning, and the sciences must be drawn together. PLUMES captures the breadth and depth required to solve a specific objective within adaptive sampling, and this work as a whole highlights the potential for mobile technologies to perform intelligent autonomous science in the future

Autonomous Aerial Water Sampling

Obtaining spatially separated, high frequency water samples from rivers and lakes is critical to enhance our understanding and effective management of fresh water resources. In this thesis we present an aerial water sampler and verify the system in field experiments. The aerial water sampler has the potential to vastly increase the speed and range at which scientists obtain water samples while reducing cost and effort. The water sampling system includes: 1) a mechanism to capture three 20 ml samples per mission; 2) sensors and algorithms for safe navigation and altitude approximation over water; and 3) software components that integrate and analyze sensor data, control the vehicle, and drive the sampling mechanism. In this thesis we validate the system in the lab, characterize key sensors, and present results of outdoor experiments. We compare water samples from local lakes obtained by our system to samples obtained by traditional sampling techniques. We find that nearly all water properties are consistent between the two techniques. These experiments show that despite the challenges associated with flying precisely over water, it is possible to quickly obtain water samples with an Unmanned Aerial Vehicle (UAV). Advisers: Carrick Detweiler and Matthew B. Dwye

Autonomous Aerial Water Sampling

Obtaining spatially separated, high frequency water samples from rivers and lakes is critical to enhance our understanding and effective management of fresh water resources. In this thesis we present an aerial water sampler and verify the system in field experiments. The aerial water sampler has the potential to vastly increase the speed and range at which scientists obtain water samples while reducing cost and effort. The water sampling system includes: 1) a mechanism to capture three 20 ml samples per mission; 2) sensors and algorithms for safe navigation and altitude approximation over water; and 3) software components that integrate and analyze sensor data, control the vehicle, and drive the sampling mechanism. In this thesis we validate the system in the lab, characterize key sensors, and present results of outdoor experiments. We compare water samples from local lakes obtained by our system to samples obtained by traditional sampling techniques. We find that nearly all water properties are consistent between the two techniques. These experiments show that despite the challenges associated with flying precisely over water, it is possible to quickly obtain water samples with an Unmanned Aerial Vehicle (UAV). Advisers: Carrick Detweiler and Matthew B. Dwye

Advances in the development of innovative sensor platforms for field analysis

none8noSustainable growth, environmental preservation, and improvement of life quality are strategic fields of worldwide interest and cornerstones of international policies. Humanity health and prosperity are closely related to our present choices on sustainable development. The main sources of pollution concern industry, including mining, chemical companies, and refineries, wastewater treatment; and consumers themselves. In order to guide and evaluate the eects of environmental policies, diuse monitoring campaigns and detailed (big) data analyses are needed. In this respect, the development and availability of innovative sensor platforms for field analysis and remote sensing are of crucial relevance. In this review, we provide an overview of the area, analyzing the major needs, available technologies, novel approaches, and perspectives. Among environmental pollutants that threaten the biosphere, we focus on inorganic and organic contaminants, which aect air and water quality. We describe the technologies for their assessment in the environment and then draw some conclusions and mention future perspectives opened by the integration of sensing technologies with robotics and the Internet of Things. Without the ambition to be exhaustive in such a rapidly growing field, this review is intended as a support for researchers and stakeholders looking for current, state-of-the-art, and key enabling technologies for environmental monitoring.openRizzato S.; Leo A.; Monteduro Anna Grazia; Chiriaco Maria Serena; Primiceri E.; Sirsi F.; Milone A.; Maruccio G.Rizzato, S.; Leo, A.; Monteduro, ANNA GRAZIA; Chiriaco', MARIA SERENA; Primiceri, E.; Sirsi, F.; Milone, A.; Maruccio, G

A Study of the Feasibility of Autonomous Surface Vehicles

As climate change is continuing to negatively impact our environment, it is important for society to take a closer look at our oceans. Oceans are a significant part of our environment and contain telltale information about the systems that allow society to function. However, there are many difficulties and limitations in current ocean observation systems. In this report, we examine the use of autonomous surface vehicles to collect data and perform tasks in a variety of environmental areas

A Study of the Feasibility of Autonomous Surface Vehicles

As climate change is continuing to negatively impact our environment, it is important for society to take a closer look at our oceans. Oceans are a significant part of our environment and contain telltale information about the systems that allow society to function. However, there are many difficulties and limitations in current ocean observation systems. In this report, we examine the use of autonomous surface vehicles to collect data and perform tasks in a variety of environmental areas