1,181 research outputs found

    Investigating the dynamics of Greenland's glacier-fjord systems

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    Over the past two decades, Greenland’s tidewater glaciers have dramatically retreated, thinned and accelerated, contributing significantly to sea level rise. This change in glacier behaviour is thought to have been triggered by increasing atmospheric and ocean temperatures, and mass loss from Greenland’s tidewater glaciers is predicted to continue this century. Substantial research during this period of rapid glacier change has improved our understanding of Greenland’s glacier-fjord systems. However, many of the processes operating in these systems that ultimately control the response of tidewater glaciers to changing atmospheric and oceanic conditions are poorly understood. This thesis combines modelling and remote sensing to investigate two particularly poorly-understood components of glacier-fjord systems, with the ultimate aim of improving understanding of recent glacier behaviour and constraining the stability of the ice sheet in a changing climate. The research presented in this thesis begins with an investigation into the dominant controls on the seasonal dynamics of contrasting tidewater glaciers draining the Greenland Ice Sheet. To do this, high resolution estimates of ice velocity were generated and compared with detailed observations and modelling of the principal controls on seasonal glacier flow, including terminus position, ice mĂ©lange presence or absence, ice sheet surface melting and runoff, and plume presence or absence. These data revealed characteristic seasonal and shorter-term changes in ice velocity at each of the study glaciers in more detail than was available from previous remote sensing studies. Of all the environmental controls examined, seasonal evolution of subglacial hydrology (as inferred from plume observations and modelling) was best able to explain the observed ice flow variations, despite differences in geometry and flow of the study glaciers. The inferred relationships between subglacial hydrology and ice dynamics were furthermore entirely consistent with process-understanding developed at land-terminating sectors of the ice sheet. This investigation provides a more detailed understanding of tidewater glacier subglacial hydrology and its interaction with ice dynamics than was previously available and suggests that interannual variations in meltwater supply may have limited influence on annually averaged ice velocity. The thesis then shifts its attention from the glacier part of the system into the fjords, focusing on the interaction between icebergs, fjord circulation and fjord water properties. This focus on icebergs is motivated by recent research revealing that freshwater produced by iceberg melting constitutes an important component of fjord freshwater budgets, yet the impact of this freshwater on fjords was unknown. To investigate this, a new model for iceberg-ocean interaction is developed and incorporated into an ocean circulation model. This new model is first applied to Sermilik Fjord — a large fjord in east Greenland that hosts Helheim Glacier, one of the largest tidewater glaciers draining the ice sheet — to further constrain iceberg freshwater production and to quantify the influence of iceberg melting on fjord circulation and water properties. These investigations reveal that iceberg freshwater flux increases with ice sheet runoff raised to the power ~0.1 and ranges from ~500-2500 mÂł s⁻Âč during summer, with ~40% of that produced below the pycnocline. It is also shown that icebergs substantially modify the temperature and velocity structure of Sermilik Fjord, causing 1-5°C cooling in the upper ~100 m and invigorating fjord circulation, which in turn causes a 10-40% increase in oceanic heat flux towards Helheim Glacier. This research highlights the important role of icebergs in Greenland’s iceberg congested fjords and therefore the need to include them in future studies examining ice sheet – ocean interaction. Having investigated the effect of icebergs on fjord circulation in a realistic setting, this thesis then characterises the effect of submarine iceberg melting on water properties near the ice sheet – ocean interface by applying the new model to a range of idealised scenarios. This near-glacier region is one which is crucial for constraining ocean-driven retreat of tidewater glaciers, but which is poorly-understood. The simulations show that icebergs are important modifiers of glacier-adjacent water properties, generally acting to reduce vertical variations in water temperature. The iceberg-induced temperature changes will generally increase submarine melt rates at mid-depth and decrease rates at the surface, with less pronounced effects at greater depth. This highlights another mechanism by which iceberg melting can affect ice sheet – ocean interaction and emphasises the need to account for iceberg-ocean interaction when simulating ocean-driven retreat of Greenland’s tidewater glaciers. In summary, this thesis has helped to provide a deeper understanding of two poorly-understood components of Greenland’s tidewater glacier-fjord systems: (i) interactions between subglacial hydrology and ice velocity, and; (ii) iceberg-ocean interaction. This research has enabled more precise interpretations of past glacier behaviour and can be used to inform model development that will help constrain future ice sheet mass loss in response to a changing climate."I must express my gratitude to the University of St Andrews and to the Scottish Alliance for Geoscience, Environment and Society (SAGES) for funding and supporting me as a research student."-- Fundin

    Robust and Flexible Persistent Scatterer Interferometry for Long-Term and Large-Scale Displacement Monitoring

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    Die Persistent Scatterer Interferometrie (PSI) ist eine Methode zur Überwachung von Verschiebungen der ErdoberflĂ€che aus dem Weltraum. Sie basiert auf der Identifizierung und Analyse von stabilen Punktstreuern (sog. Persistent Scatterer, PS) durch die Anwendung von AnsĂ€tzen der Zeitreihenanalyse auf Stapel von SAR-Interferogrammen. PS Punkte dominieren die RĂŒckstreuung der Auflösungszellen, in denen sie sich befinden, und werden durch geringfĂŒgige Dekorrelation charakterisiert. Verschiebungen solcher PS Punkte können mit einer potenziellen Submillimetergenauigkeit ĂŒberwacht werden, wenn Störquellen effektiv minimiert werden. Im Laufe der Zeit hat sich die PSI in bestimmten Anwendungen zu einer operationellen Technologie entwickelt. Es gibt jedoch immer noch herausfordernde Anwendungen fĂŒr die Methode. Physische VerĂ€nderungen der LandoberflĂ€che und Änderungen in der Aufnahmegeometrie können dazu fĂŒhren, dass PS Punkte im Laufe der Zeit erscheinen oder verschwinden. Die Anzahl der kontinuierlich kohĂ€renten PS Punkte nimmt mit zunehmender LĂ€nge der Zeitreihen ab, wĂ€hrend die Anzahl der TPS Punkte zunimmt, die nur wĂ€hrend eines oder mehrerer getrennter Segmente der analysierten Zeitreihe kohĂ€rent sind. Daher ist es wĂŒnschenswert, die Analyse solcher TPS Punkte in die PSI zu integrieren, um ein flexibles PSI-System zu entwickeln, das in der Lage ist mit dynamischen VerĂ€nderungen der LandoberflĂ€che umzugehen und somit ein kontinuierliches Verschiebungsmonitoring ermöglicht. Eine weitere Herausforderung der PSI besteht darin, großflĂ€chiges Monitoring in Regionen mit komplexen atmosphĂ€rischen Bedingungen durchzufĂŒhren. Letztere fĂŒhren zu hoher Unsicherheit in den Verschiebungszeitreihen bei großen AbstĂ€nden zur rĂ€umlichen Referenz. Diese Arbeit befasst sich mit Modifikationen und Erweiterungen, die auf der Grund lage eines bestehenden PSI-Algorithmus realisiert wurden, um einen robusten und flexiblen PSI-Ansatz zu entwickeln, der mit den oben genannten Herausforderungen umgehen kann. Als erster Hauptbeitrag wird eine Methode prĂ€sentiert, die TPS Punkte vollstĂ€ndig in die PSI integriert. In Evaluierungsstudien mit echten SAR Daten wird gezeigt, dass die Integration von TPS Punkten tatsĂ€chlich die BewĂ€ltigung dynamischer VerĂ€nderungen der LandoberflĂ€che ermöglicht und mit zunehmender ZeitreihenlĂ€nge zunehmende Relevanz fĂŒr PSI-basierte Beobachtungsnetzwerke hat. Der zweite Hauptbeitrag ist die Vorstellung einer Methode zur kovarianzbasierten Referenzintegration in großflĂ€chige PSI-Anwendungen zur SchĂ€tzung von rĂ€umlich korreliertem Rauschen. Die Methode basiert auf der Abtastung des Rauschens an Referenzpixeln mit bekannten Verschiebungszeitreihen und anschließender Interpolation auf die restlichen PS Pixel unter BerĂŒcksichtigung der rĂ€umlichen Statistik des Rauschens. Es wird in einer Simulationsstudie sowie einer Studie mit realen Daten gezeigt, dass die Methode ĂŒberlegene Leistung im Vergleich zu alternativen Methoden zur Reduktion von rĂ€umlich korreliertem Rauschen in Interferogrammen mittels Referenzintegration zeigt. Die entwickelte PSI-Methode wird schließlich zur Untersuchung von Landsenkung im Vietnamesischen Teil des Mekong Deltas eingesetzt, das seit einigen Jahrzehnten von Landsenkung und verschiedenen anderen Umweltproblemen betroffen ist. Die geschĂ€tzten Landsenkungsraten zeigen eine hohe VariabilitĂ€t auf kurzen sowie großen rĂ€umlichen Skalen. Die höchsten Senkungsraten von bis zu 6 cm pro Jahr treten hauptsĂ€chlich in stĂ€dtischen Gebieten auf. Es kann gezeigt werden, dass der grĂ¶ĂŸte Teil der Landsenkung ihren Ursprung im oberflĂ€chennahen Untergrund hat. Die prĂ€sentierte Methode zur Reduzierung von rĂ€umlich korreliertem Rauschen verbessert die Ergebnisse signifikant, wenn eine angemessene rĂ€umliche Verteilung von Referenzgebieten verfĂŒgbar ist. In diesem Fall wird das Rauschen effektiv reduziert und unabhĂ€ngige Ergebnisse von zwei Interferogrammstapeln, die aus unterschiedlichen Orbits aufgenommen wurden, zeigen große Übereinstimmung. Die Integration von TPS Punkten fĂŒhrt fĂŒr die analysierte Zeitreihe von sechs Jahren zu einer deutlich grĂ¶ĂŸeren Anzahl an identifizierten TPS als PS Punkten im gesamten Untersuchungsgebiet und verbessert damit das Beobachtungsnetzwerk erheblich. Ein spezieller Anwendungsfall der TPS Integration wird vorgestellt, der auf der Clusterung von TPS Punkten basiert, die innerhalb der analysierten Zeitreihe erschienen, um neue Konstruktionen systematisch zu identifizieren und ihre anfĂ€ngliche Bewegungszeitreihen zu analysieren

    An adaptive scalloping suppression method for ScanSAR images based on the Kalman filter

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    Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause

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    Astronomical imaging technologies are basic tools for the exploration of the universe, providing basic data for the research of astronomy and space physics. The Soft X-ray Imager (SXI) carried by the Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) aims to capture two-dimensional (2-D) images of the Earth’s magnetosheath by using soft X-ray imaging. However, the observed 2-D images are affected by many noise factors, destroying the contained information, which is not conducive to the subsequent reconstruction of the three-dimensional (3-D) structure of the magnetopause. The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models. This makes it difficult to establish the mapping relationship between SXI-simulated observation images and target images by using mathematical models. We propose an image restoration algorithm for SXI-simulated observation images that can recover large-scale structure information on the magnetosphere. The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image, whose mapping relationship with the target image is established by the patch estimator. The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator. Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task, according to the peak signal-to-noise ratio and structural similarity. The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques, significantly improving the reconstruction results. Hence, the proposed technology may be feasible for processing SXI-simulated observation images

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    ICEBEAR-3D: An Advanced Low Elevation Angle Auroral E region Imaging Radar

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    The Ionospheric Continuous-wave E region Bistatic Experimental Auroral Radar (ICEBEAR) is an auroral E~region radar which has operated from 7 December 2017 until the September 2019. During the first two years of operation, ICEBEAR was only capable of spatially locating E~region scatter and meteor trail targets in range and azimuth. Elevation angles were not determinable due to its East-West uniform linear receiving antenna array. Measuring elevation angles of targets when viewing from low elevation angles with radar interferometers has been a long standing problem. Past high latitude radars have attempted to obtain elevation angles of E~region targets using North-South baselines, but have always resulted in erroneous elevation angles being measured in the low elevation regime (0° to ≈30° above the horizon), leaving interesting scientific questions about scatter altitudes in the auroral E~region unanswered. The work entailed in this thesis encompasses the design of the ICEBEAR-3D system for the acquisition of these important elevation angles. The receiver antenna array was redesigned using a custom phase error minimization and stochastic antenna location perturbation technique, which produces phase tolerant receiver antenna arrays. The resulting 45-baseline sparse non-uniform coplanar T-shaped array was designed for aperture synthesis radar imaging. Conventional aperture synthesis radar imaging techniques assume point-like incoherent targets and image using a Cartesian basis over a narrow field of view. These methods are incompatible with horizon pointing E~region radars such as ICEBEAR. Instead, radar targets were imaged using the Suppressed Spherical Wave Harmonic Transform (Suppressed-SWHT) technique. This imaging method uses precalculated spherical harmonic coefficient matrices to transform the visibilities to brightness maps by direct matrix multiplication. The under sampled image domain artefacts (dirty beam) were suppressed by the products of differing harmonic order brightness maps. From the images, elevation and azimuth angles of arrival were obtained. Due to the excellent phase tolerance of ICEBEAR new light was shed on the long standing low elevation angle problem. This led to the development of the proper phase reference vertical interferometry geometry, which allowed horizon pointing radar interferometers to unambiguously measure elevation angles near the horizon. Ultimately resulting in accurate elevation angles from zenith to horizon

    Interplanetary scintillation observation and space weather modelling

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    Interplanetary scintillation (IPS) refers to random fluctuations in radio intensity of distant small-diameter celestial object, over time periods of the order of 1 s. The scattering and scintillation of emergent radio waves are ascribed to turbulent density irregularities transported by the ubiquitous solar wind streams. The spatial correlation length of density irregularities and the Fresnel radius of radio diffraction are two key parameters in determining the scintillation pattern. Such a scintillation pattern can be measured and correlated between multi-station radio telescopes on the Earth. Using the “phase-changing screen” scenario based on the Born approximation, the bulk-flow speed and turbulent spectrum of the solar wind streams can be extracted from the single-station power spectra fitting and the multi-station cross-correlation analysis. Moreover, a numerical computer-assisted tomography (CAT) model, iteratively fit to a large number of IPS measurements over one Carrington rotation, can be used to reconstruct the global velocity and density structures in the inner heliosphere for the purpose of space weather modelling and prediction. In this review, we interpret the underlying physics governing the IPS phenomenon caused by the solar wind turbulence, describe the power spectrum and cross correlation of IPS signals, highlight the space weather application of IPS-CAT models, and emphasize the significant benefits from international cooperation within the Worldwide IPS Stations (WIPSS) network

    Dynamics Of Flood Flow In Red River Basin

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    In recent decades, flooding has become a major issue in many areas of the Upper Midwest. Many rivers and streams in the region had considerable increases in mean annual peak flows during this period, which was driven by a combination of natural factors including discharge synchrony with the spring thaw, ice jams, glacial lake plain, and a decrease in gradient downstream. The Red River of the North is a prominent river in the United States and Canada\u27s Upper Midwest. It flows from its headwaters in Minnesota and North Dakota to Lake Winnipeg in Manitoba. The river is well-known for its spring floods, which can cause havoc on communities along its banks. There is an increasing need to improve the characterization and identification of precursors in the Red River basin that affect the hydrological conditions that cause spring snowmelt floods and improve predictions to reduce Red River flood damage. This dissertation has developed different research that concerns the dynamics of floods in the Red River basin by integrating hydrological, hydraulic, and machine-learning models. The primary objectives were to improve flood prediction accuracy by deriving the parameters of the Muskingum Routing method using discharge measurements obtained by an Autonomous Surface Vehicle, to predict scour potential of the river through HEC-RAS modeling, and to provide an estimate of the flood progression downstream based on the flow characteristics. The study also compared the effectiveness of Seasonal Autoregressive Integrated Moving Average (SARIMA), Random Forest (RF), and Long Short-Term Memory (LSTM) algorithms for flood prediction. Additionally, the research investigated the surface water area variation and response to wet and dry seasons across the entire Red River basin, which can inform the development of effective flood mitigation strategies. The results of this study contributed to a better understanding of flood control strategies in the Red River Basin and helped to inform policy decisions related to flood mitigation in the region. Ultimately, this research aimed to understand the complex dynamics of the RRB and derive hydrological and hydraulic models that could help to improve flood prediction. The first research developed a linear and nonlinear Muskingum model with lateral inflows for flood routing in the Red River Basin using Salp Swarm Algorithm (SSA). The distributed Muskingum model is introduced to improve the accuracy and efficiency of the calculations. The study focuses on developing a linear and nonlinear Muskingum model for the Grand Forks and Drayton USGS stations deriving the parameters of the Muskingum Routing method using discharge measurements based on spatial variable exponent parameters. The suggested approach minimizes the Sum of Square Errors (SSE) between observed and routed outflows. The results show for an icy river like Red River, the Muskingum method proposed is a convenient way to predict outflow hydrographs caused by snowmelt. The second study improved flood inundation mapping accuracy in flood-prone rivers, such as the Red River of the North, by using simulation tools in HEC-RAS for flood modeling and determining Manning\u27s n coefficient. An Autonomous Surface Vehicle (ASV) was used to collect bathymetry and discharge data, including a flood event with a 16.5-year return period in 2022. The results showed that Manning\u27s n-coefficient of 0.07 and 0.15 for the channel and overbanks, respectively, agreed well with the observed and simulated water level values under steady flow conditions. The study also demonstrated the efficiency of using ASVs for flood mapping and examined the scour potential and any local scour development in the streambed near the bridge piers. The third study of this dissertation used hourly level records from three USGS stations to evaluate water level predictions using three methods: SARIMA, RF, and LSTM. The LSTM method outperformed the other methods, demonstrating high precision for flood water level prediction. The results showed that the LSTM method was a reliable choice for predicting flood water levels up to one week in advance. This study contributes to the development of data-driven forecasting systems that provide cost-effective solutions and improved performance in simulating the complex physical processes of floods using mathematical expressions. This last study focused on the spatiotemporal dynamics of surface water area in the Red River Basin (RRB) by using a high-resolution global surface water dataset to investigate the changes in surface water extent from 1990 to 2019. The results showed that there were four distinct phases of variation in surface water: wetting (1990-2001), dry (2002-2005), recent wetting (2006-2013), and recent drying (2014-2019). The transition from bare land to permanent and seasonal water area was observed during the wetting phase, while the other phases experienced relatively little fluctuation. Overall, this study contributes to a better understanding of the spatiotemporal variation of surface water area in the RRB and provides insights into the impact of recent wetting and drying periods on the lakes and wetlands of the RRB

    Autonomisten metsÀkoneiden koneaistijÀrjestelmÀt

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    A prerequisite for increasing the autonomy of forest machinery is to provide robots with digital situational awareness, including a representation of the surrounding environment and the robot's own state in it. Therefore, this article-based dissertation proposes perception systems for autonomous or semi-autonomous forest machinery as a summary of seven publications. The work consists of several perception methods using machine vision, lidar, inertial sensors, and positioning sensors. The sensors are used together by means of probabilistic sensor fusion. Semi-autonomy is interpreted as a useful intermediary step, situated between current mechanized solutions and full autonomy, to assist the operator. In this work, the perception of the robot's self is achieved through estimation of its orientation and position in the world, the posture of its crane, and the pose of the attached tool. The view around the forest machine is produced with a rotating lidar, which provides approximately equal-density 3D measurements in all directions. Furthermore, a machine vision camera is used for detecting young trees among other vegetation, and sensor fusion of an actuated lidar and machine vision camera is utilized for detection and classification of tree species. In addition, in an operator-controlled semi-autonomous system, the operator requires a functional view of the data around the robot. To achieve this, the thesis proposes the use of an augmented reality interface, which requires measuring the pose of the operator's head-mounted display in the forest machine cabin. Here, this work adopts a sensor fusion solution for a head-mounted camera and inertial sensors. In order to increase the level of automation and productivity of forest machines, the work focuses on scientifically novel solutions that are also adaptable for industrial use in forest machinery. Therefore, all the proposed perception methods seek to address a real existing problem within current forest machinery. All the proposed solutions are implemented in a prototype forest machine and field tested in a forest. The proposed methods include posture measurement of a forestry crane, positioning of a freely hanging forestry crane attachment, attitude estimation of an all-terrain vehicle, positioning a head mounted camera in a forest machine cabin, detection of young trees for point cleaning, classification of tree species, and measurement of surrounding tree stems and the ground surface underneath.MetsÀkoneiden autonomia-asteen kasvattaminen edellyttÀÀ, ettÀ robotilla on digitaalinen tilannetieto sekÀ ympÀristöstÀ ettÀ robotin omasta toiminnasta. TÀmÀn saavuttamiseksi työssÀ on kehitetty autonomisen tai puoliautonomisen metsÀkoneen koneaistijÀrjestelmiÀ, jotka hyödyntÀvÀt konenÀkö-, laserkeilaus- ja inertia-antureita sekÀ paikannusantureita. Työ liittÀÀ yhteen seitsemÀssÀ artikkelissa toteutetut havainnointimenetelmÀt, joissa useiden anturien mittauksia yhdistetÀÀn sensorifuusiomenetelmillÀ. TyössÀ puoliautonomialla tarkoitetaan hyödyllisiÀ kuljettajaa avustavia vÀlivaiheita nykyisten mekanisoitujen ratkaisujen ja tÀyden autonomian vÀlillÀ. TyössÀ esitettÀvissÀ autonomisen metsÀkoneen koneaistijÀrjestelmissÀ koneen omaa toimintaa havainnoidaan estimoimalla koneen asentoa ja sijaintia, nosturin asentoa sekÀ siihen liitetyn työkalun asentoa suhteessa ympÀristöön. YleisnÀkymÀ metsÀkoneen ympÀrille toteutetaan pyörivÀllÀ laserkeilaimella, joka tuottaa lÀhes vakiotiheyksisiÀ 3D-mittauksia jokasuuntaisesti koneen ympÀristöstÀ. Nuoret puut tunnistetaan muun kasvillisuuden joukosta kÀyttÀen konenÀkökameraa. LisÀksi puiden tunnistamisessa ja puulajien luokittelussa kÀytetÀÀn konenÀkökameraa ja laserkeilainta yhdessÀ sensorifuusioratkaisun avulla. LisÀksi kuljettajan ohjaamassa puoliautonomisessa jÀrjestelmÀssÀ kuljettaja tarvitsee toimivan tavan ymmÀrtÀÀ koneen tuottaman mallin ympÀristöstÀ. TyössÀ tÀmÀ ehdotetaan toteutettavaksi lisÀtyn todellisuuden kÀyttöliittymÀn avulla, joka edellyttÀÀ metsÀkoneen ohjaamossa istuvan kuljettajan lisÀtyn todellisuuden lasien paikan ja asennon mittaamista. TyössÀ se toteutetaan kypÀrÀÀn asennetun kameran ja inertia-anturien sensorifuusiona. Jotta metsÀkoneiden automatisaatiotasoa ja tuottavuutta voidaan lisÀtÀ, työssÀ keskitytÀÀn uusiin tieteellisiin ratkaisuihin, jotka soveltuvat teolliseen kÀyttöön metsÀkoneissa. Kaikki esitetyt koneaistijÀrjestelmÀt pyrkivÀt vastaamaan todelliseen olemassa olevaan tarpeeseen nykyisten metsÀkoneiden kÀytössÀ. Siksi kaikki menetelmÀt on implementoitu prototyyppimetsÀkoneisiin ja tulokset on testattu metsÀympÀristössÀ. TyössÀ esitetyt menetelmÀt mahdollistavat metsÀkoneen nosturin, vapaasti riippuvan työkalun ja ajoneuvon asennon estimoinnin, lisÀtyn todellisuuden lasien asennon mittaamisen metsÀkoneen ohjaamossa, nuorten puiden havaitsemisen reikÀperkauksessa, ympÀröivien puiden puulajien tunnistuksen, sekÀ puun runkojen ja maanpinnan mittauksen

    Advancing the Monitoring Capabilities of Mountain Snowpack Fluctuations at Various Spatial and Temporal Scales

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    Snow is a critical water resource for the western US and many regions across the globe. However, our ability to accurately monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge in mountain regions. No single sensor currently has the ability to directly measure snow water equivalent (SWE) from space at a spatial scale suitable for water supply forecasting in mountain environments. This knowledge gap calls for the innovative use of remote sensing techniques, computational tools, and data science methods to advance our ability to estimate mountain snowpacks across a range of spatial and temporal scales. The goal of this dissertation is to advance our capabilities for understanding snowpack across watershed-relevant spatial and temporal scales. Two research approaches were used to accomplish this goal: quantifying the physiographic controls and sensitivities of hydrologically important snow metrics and progressing our ability to use L-band interferometric synthetic aperture radar (InSAR) to measure SWE changes. First, we quantify the physiographic controls and various snowpack metrics in the Sierra Nevada using a novel gridded SWE reanalysis dataset. Such work demonstrates the complexity of snowpack processes and the need for fine-resolution snowpack information. Next, using L-band Interferometric Synthetic Aperture Radar (InSAR) from the NASA SnowEx campaign, both snow ablation and accumulation are estimated in the Jemez Mountains, NM. The radar-derived retrievals are evaluated utilizing a combination of optical snow-cover data, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). Lastly, we compare multisensor optical-radar approaches for SWE retrievals and find that moderate-resolution legacy satellite products provide sufficient results. The results of this work show that L-band InSAR is a suitable technique for global SWE monitoring when used synergistically with optical SCA data and snowpack modeling. While two distinctive methods are present in this research, they both work towards advancing our ability to understand the dynamics of mountain snowpack
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