This thesis investigates the origin, sedimentological characteristics, and paleoglacial significance of large latero-frontal moraines and moraine-dammed glacial lakes and their potential to generate glacial lake outburst flood (GLOF) events in the Cordillera Blanca, Perú and Iceland. This topic is particularly important as the potential for GLOF events in high altitude regions is increasing as ongoing global climate warming causes rapid glacier recession and the growth of lakes impounded by unstable moraines. The first chapter of this thesis introduces the characteristics of moraine dammed lakes and GLOFs and provides details of the study areas in Perú and Iceland that were selected for this work (Chapter 1). Chapter 2 investigates the glacial history of the Cordillera Blanca, Perú through the compilation, mapping, and analysis of dated moraines in the region. The formation of moraines by different glaciers in the same region at approximately the same time is interpreted to indicate a period of regional climate conditions that were favourable for glacier expansion and/or equilibrium. Six stages of glacial activity are identified from this analysis, ranging in age from older than 35 thousand years (Stage 1) to modern (Stage 6). The third chapter of this thesis identifies the geomorphic and sedimentologic characteristics of a moraine-dammed supraglacial lake (Llaca Lake) in the Cordillera Blanca, Perú. The combined use of imagery collected with an uncrewed-aerial vehicle (UAV), field sedimentological observations and geomorphological mapping allowed the creation of a landsystem model that summarizes the current geomorphic and sedimentologic environment of Llaca Lake (Chapter 3). This is the first study to describe the landform-sediment assemblages in a tropical moraine-dammed supraglacial lake and provides a framework for further landsystem analysis of growing supraglacial lakes that are at risk of GLOF events. The fourth chapter of this thesis describes the sedimentary architecture of the eastern lateral moraine of Gígjökull in southern Iceland. An uncrewed-aerial vehicle was used to acquire high resolution photographs of an exposure through the lateral moraine that allowed the identification of seven lithofacies types and three lithofacies associations. Documentation of the sedimentary architecture of the eastern lateral moraine of Gígjökull enhances understanding of moraine development and the identification of areas of hydrogeological weakness that can reduce the structural integrity of the moraine. The research findings presented in this thesis utilize a glacial sedimentological and geomorphological approach to investigating the relationship between current and past glacial processes in the study areas, and the role that these processes play in determining the characteristics and stability of large ice marginal moraines that impound glacial lakes. This work also furthers our understanding of the dynamic surface processes at work in high altitude regions such as the Cordillera Blanca. Identifying and determining the relationships between current and past processes, sediments and landforms will enhance understanding of the role of large moraines damming glacial lakes in other high-altitude regions such as the Himalayas, British Columbia, Patagonia, and New Zealand and the associated risk of GLOF events. / Thesis / Doctor of Science (PhD)

Lakes and oceans are threatened by harmful algal blooms (HABs), caused mostly by toxic cyanobacteria. When people or animals drink the toxic water, it can be damaging to their health, potentially leading to hospitalization or even death. In some cases, these toxins are not just limited to the water, but can become airborne through wave breaking, bubble bursting, and spume droplet formation. New information is needed regarding the transport and fate of HAB-associated aerosols. The overall goal of this research was to monitor particle concentrations and measure meteorological conditions near HAB sites to determine the conditions that may lead to increased exposure to HAB cells and toxins in the atmosphere. By creating predictions of which conditions and locations will be experiencing higher aerosol levels at any given time, models could be used to inform the public and policy makers to ensure that appropriate responses and safety measures can be taken. The research also includes experiments to study plumes of colored smoke, as a proxy for the transport of biological particles such as HAB cells, pollen, and pathogens. The first objective of this research was to explore associations between measured weather conditions and particle concentrations measured above active HABs and HAB sites using drone-based sensor packages. The second objective was to monitor wind and particle concentrations near freshwater and marine HABs using ground-based sensor packages. The third objective was to model HAB aerosol behavior at a beach level to predict respiratory irritation. The fourth objective was to use aerial and ground-based sensors and images of colored smoke to predict particle concentrations at different distances and intensity levels downwind from the source(s). / Doctor of Philosophy / Certain environmental conditions in lakes and oceans can favor unhealthy amounts of algal growth. Overgrowth can lead to harmful algal blooms (HABs). This occurs when algae produce toxins that make the water unsafe for humans and animals to drink. Sometimes these toxins don't stay in the water and can become toxic airborne particles. We need more information to understand what happens to create and transport these airborne toxins produced by HABs. This research aims to monitor particle concentrations and weather to find what conditions lead to more aerosolized toxins. Accurate predictions of high levels of HAB toxins in the air could be used to alert the public. This work utilized colored smoke released outdoors as a visual indicator of particle movement in the air. The specific objectives of this research were to (1) find associations between weather and particle concentrations at lake HAB sites using drone-mounted samplers at a height above 30 feet from the ground, (2) find associations between weather and particle concentrations at lake and ocean HAB sites using samplers at a height of 5 feet from the ground, (3) predict human throat irritation levels from airborne toxins at the beach level, and (4) use a drone sensor, a ground sensor, and simultaneous video footage of a controlled smoke release to visually track airborne particles.

Power allocation algorithms that determine how much power should be allocated to pilot and data symbols play an important role in addressing the trade-off between accurate channel estimation and high high spectral efficiency for data symbols in the presence of time-varying fading channels. Dealing with this trade-off is highly non-trivial when the channel changes or ages rapidly in time. Specifically, channel aging renders the often used assumption that the channel parameters can be regarded constant between channel estimation instances invalid. Previous works have addressed the problem of the pilot spacing problem for Rayleigh fading channels. In this work, a power control algorithm is developed for both Rayleigh fading and Rician fading channels to deal with the above trade-off. Specifically, in this report, the uplink channel of a multi-user multiple input multiple output system is investigated. The fading channel is estimated by a suitable auto-regressive model using the associated auto-correlation function. Then the signal-to-interference-plus-noise ratio and spectral efficiency are calculated as a function of the power allocation ratio and other parameters of the communication network. The proposed power control algorithm is designed to find the upper bound of the spectral efficiency. As application examples, two uncrewed aerial vehicle networks are also modeled, in which the performance of the proposed power control algorithm is simulated to find how the parameters of the network will influence the algorithm results. Our investigation shows that the proposed power control algorithm performs well in the presence of fading communication channels and outperforms the benchmark case of equal power allocation between pilot and data symbols. / Effektallokeringsalgoritmen som bestämmer hur mycket effekt som ska allokeras till pilotsymboler och datasymboler är mycket viktig för att fånga avvägningen mellan korrekt kanaluppskattning och ett högt signal till störnings plus brusförhållande för en tidsvarierande fädning kanal. Tidigare arbete har löst problemet med pilotavstånds-problemet för Rayleigh fädning kanaler. I detta arbete genereras effektstyrnings-algoritmen för både Rayleigh fading och Rician fädning kanaler för att hantera avvägningen. I denna rapport genereras först en upplänkskanal för ett fleranvändarsystem med flera ingångar med flera utdata. Fädningskanalen uppskattas av den autoregressiva modellen med hjälp av autokorrelations funktionen. Sedan beräknas signal till interferens plus brusförhållandet och spektral effektivitet som en funktion av effekttilldelnings förhållandet och andra parametrar för kommunikationsnätverket. Effektstyrnings algoritmen är att hitta den övre gränsen för den spektrala effektiviteten. I detta arbete modelleras också två obemannade flygfordonsnätverk och prestanda för effektstyrningsalgoritmen simuleras också på dessa två modeller för att hitta hur nätverkets parametrar kommer att påverka algoritmresultaten.

The use of uncrewed aerial vehicle to map the environment increased significantly in the last decade enabling a finer assessment of the land cover. However, creating accurate maps of the environment is still a complex and costly task. Deep learning (DL) is a new generation of artificial neural network research that, combined with remote sensing techniques, allows a refined understanding of our environment and can help to solve challenging land cover mapping issues. This research focuses on the vegetation segmentation of kettle holes. Kettle holes are small, pond-like, depressional wetlands. Quantifying the vegetation present in this environment is essential to assess the biodiversity and the health of the ecosystem. A machine learning workflow has been developed, integrating a superpixel segmentation algorithm to build a robust dataset, which is followed by a set of DL architectures to classify 10 plant classes present in kettle holes. The best architecture for this task was Xception, which achieved an average F1-score of 85% in the segmentation of the species. The application of solely 318 samples per class enabled a successful mapping in the complex wetland environment, indicating an important direction for future health assessments in such landscapes.