881 research outputs found

    Middle Pleistocene paleoenvironmental reconstruction through phytolith analysis at the Manyara Beds, northern Tanzania

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    This project is aimed at developing a detailed habitat reconstruction for hominins living at the Manyara Beds of Northern Tanzania during the early Middle Pleistocene using phytolith remains. The dissertation comprises three interlinked, but independent studies. The first study examines the phytolith assemblages from modern surface soils and plants to create a referential baseline for studying phytoliths from the Acacia-Commiphora ecosystem surrounding the Manyara Beds, the same plant regions in which our ancestors reside. Phytoliths from 21 species of plants, including 11 unstudied taxa from this ecosystem, were characterized. Twenty-five composite surface soil samples from five sites were also analyzed. Using Stromberg's 2003 classification and interpretive scheme, this study has demonstrated that the dominant phytoliths for Commiphora are polyhedral epidermal cells, and Acacia is a rare producer of blocky-faceted rectangular plate morphotypes. The second study examines phytolith assemblages from archaeological and non-archaeological sites within the six-meter zone of the uppermost part of the lower Manyara Beds. In general, phytolith assemblage from archaeological and non-archaeological sites confirms the persistence of C4 grasslands. However, varied habitats were available for the Acheulean tool-making hominins at archaeological site MK 4, which featured palms, woody dicots, sedge, and grasslands taxa, including high proportions of warm arid and moist loving C3 and C4 PACMADs and dry adapted C4 chloridoids. There is also a small presence of wet-loving panicoids. The palms, sedges, Commelinaceae, and other aquatic monocots indicate that Manyara Beds were well-watered, at least with the occurrence of freshwater springs or rivers near the Lake shores. Therefore, inferences from phytolith assemblages from the Manyara Beds are consistent with the common predictions of many Plio-Pleistocene sites near the lake shores, pointing to hominin's dependence on water and food resources such as plants and game. The third study presents the analysis of 106 stone tool residue samples from the MK4 site to understand the function of the small flake assemblage found there. Ten tools yielded phytoliths, including two flaked and eight core tools. Phytoliths revealed the exploitation of plant resources, including grasses, palms, sedges, woody dicots, and other unknown taxa

    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 Erdoberfläche aus dem Weltraum. Sie basiert auf der Identifizierung und Analyse von stabilen Punktstreuern (sog. Persistent Scatterer, PS) durch die Anwendung von Ansätzen der Zeitreihenanalyse auf Stapel von SAR-Interferogrammen. PS Punkte dominieren die Rückstreuung der Auflösungszellen, in denen sie sich befinden, und werden durch geringfügige Dekorrelation charakterisiert. Verschiebungen solcher PS Punkte können mit einer potenziellen Submillimetergenauigkeit überwacht 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 für die Methode. Physische Veränderungen der Landoberfläche und Änderungen in der Aufnahmegeometrie können dazu führen, dass PS Punkte im Laufe der Zeit erscheinen oder verschwinden. Die Anzahl der kontinuierlich kohärenten PS Punkte nimmt mit zunehmender Länge der Zeitreihen ab, während die Anzahl der TPS Punkte zunimmt, die nur während eines oder mehrerer getrennter Segmente der analysierten Zeitreihe kohärent sind. Daher ist es wünschenswert, die Analyse solcher TPS Punkte in die PSI zu integrieren, um ein flexibles PSI-System zu entwickeln, das in der Lage ist mit dynamischen Veränderungen der Landoberfläche umzugehen und somit ein kontinuierliches Verschiebungsmonitoring ermöglicht. Eine weitere Herausforderung der PSI besteht darin, großflächiges Monitoring in Regionen mit komplexen atmosphärischen Bedingungen durchzuführen. Letztere führen zu hoher Unsicherheit in den Verschiebungszeitreihen bei großen Abständen zur räumlichen 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 präsentiert, die TPS Punkte vollständig in die PSI integriert. In Evaluierungsstudien mit echten SAR Daten wird gezeigt, dass die Integration von TPS Punkten tatsächlich die Bewältigung dynamischer Veränderungen der Landoberfläche ermöglicht und mit zunehmender Zeitreihenlänge zunehmende Relevanz für PSI-basierte Beobachtungsnetzwerke hat. Der zweite Hauptbeitrag ist die Vorstellung einer Methode zur kovarianzbasierten Referenzintegration in großflächige PSI-Anwendungen zur Schätzung von räumlich korreliertem Rauschen. Die Methode basiert auf der Abtastung des Rauschens an Referenzpixeln mit bekannten Verschiebungszeitreihen und anschließender Interpolation auf die restlichen PS Pixel unter Berücksichtigung der räumlichen Statistik des Rauschens. Es wird in einer Simulationsstudie sowie einer Studie mit realen Daten gezeigt, dass die Methode überlegene Leistung im Vergleich zu alternativen Methoden zur Reduktion von räumlich 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 geschätzten Landsenkungsraten zeigen eine hohe Variabilität auf kurzen sowie großen räumlichen Skalen. Die höchsten Senkungsraten von bis zu 6 cm pro Jahr treten hauptsächlich in städtischen Gebieten auf. Es kann gezeigt werden, dass der größte Teil der Landsenkung ihren Ursprung im oberflächennahen Untergrund hat. Die präsentierte Methode zur Reduzierung von räumlich korreliertem Rauschen verbessert die Ergebnisse signifikant, wenn eine angemessene räumliche Verteilung von Referenzgebieten verfügbar ist. In diesem Fall wird das Rauschen effektiv reduziert und unabhängige Ergebnisse von zwei Interferogrammstapeln, die aus unterschiedlichen Orbits aufgenommen wurden, zeigen große Übereinstimmung. Die Integration von TPS Punkten führt für die analysierte Zeitreihe von sechs Jahren zu einer deutlich größeren 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 anfängliche Bewegungszeitreihen zu analysieren

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Forest planning utilizing high spatial resolution data

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    This thesis presents planning approaches adapted for high spatial resolution data from remote sensing and evaluate whether such approaches can enhance the provision of ecosystem services from forests. The presented methods are compared with conventional, stand-level methods. The main focus lies on the planning concept of dynamic treatment units (DTU), where treatments in small units for modelling ecosystem processes and forest management are clustered spatiotemporally to form treatment units realistic in practical forestry. The methodological foundation of the thesis is mainly airborne laser scanning data (raster cells 12.5x12.5 m2), different optimization methods and the forest decision support system Heureka. Paper I demonstrates a mixed-integer programming model for DTU planning, and the results highlight the economic advances of clustering harvests. Paper II and III presents an addition to a DTU heuristic from the literature and further evaluates its performance. Results show that direct modelling of fixed costs for harvest operations can improve plans and that DTU planning enhances the economic outcome of forestry. The higher spatial resolution of data in the DTU approach enables the planning model to assign management with higher precision than if stand-based planning is applied. Paper IV evaluates whether this phenomenon is also valid for ecological values. Here, an approach adapted for cell-level data is compared to a schematic approach, dealing with stand-level data, for the purpose of allocating retention patches. The evaluation of economic and ecological values indicate that high spatial resolution data and an adapted planning approach increased the ecological values, while differences in economy were small. In conclusion, the studies in this thesis demonstrate how forest planning can utilize high spatial resolution data from remote sensing, and the results suggest that there is a potential to increase the overall provision of ecosystem services if such methods are applied

    A kinetic Fokker-Planck algorithm for simulating multiscale gas flows

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    Numerical, aerodynamic analysis of spacecraft requires the modeling of rarefied hypersonic flows. Such flow regimes are usually dominated by broad shock waves and strong expansion flows. In such areas of the flow the gas is far from its equilibrium state and therefore conventional modeling approaches such as the Euler or Navier-Stokes equations cannot be used. Instead, non-equilibrium modeling approaches must be applied. While most non-equilibrium flow solvers are computationally expensive, a recently introduced kinetic Fokker-Planck (FP) method shows the potential of describing non-equilibrium flows with satisfactory accuracy and, at the same time, significantly reducing computational costs. However, the application of kinetic FP solvers was so far still limited to simple, single species gases. The aim of this study is to extend the capabilities of the kinetic FP approach for describing complex gas flows. Particular attention is paid to the modeling of non-equilibrium aerodynamics, as it is relevant for describing spacecraft related gas flows. Methods for describing polyatomic species as well as gas mixtures within the kinetic FP framework are constructed. All models are intensively validated by comparison to already established numerical methods, as well as in comparison to experimental studies. Excited energy states are modeled by a stochastic jump process described by a master equation. This approach allows the description of both continuous and discrete energy levels. Gas mixtures are modeled based on the hard-sphere and variable hard-sphere collision potentials. For both cases, FP models are constructed for an arbitrary number of species. The efficiency of the described models is investigated and different strategies are proposed to use kinetic FP methods efficiently. The expansion of synthetic air from an axially symmetric orifice is numerically reproduced using the developed models and results are compared with experimental measurements. Although the numerical simulations capture several magnitudes of Knudsen numbers, from the continuum flow in the reservoir up to the free-molecular far field, good agreement between simulation and experiment is seen

    The Potential of New LiDAR Datasets for Archaeology in Switzerland

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    LiDAR and its derived elevation models have revolutionized archaeological research in forested areas around the globe. Almost a third of Switzerland is covered in forests. The number of archaeological sites recorded in forests in Switzerland is, however, limited. Given these circumstances, it is surprising how underutilized LiDAR data are in archaeological research in the country. As the Federal Office of Topography swisstopo is finalizing the acquisition of new LiDAR datasets, increasing the covered area and allowing for limited time series analyses, these data should be used to the fullest extent. This article describes the open access datasets and elaborates on their potential for archaeological research and cultural heritage management. By employing LiDAR data on a large scale, Swiss archaeological research would likely substantially increase the number of recorded heritage sites. Additionally, this will have the effect of complementing the palimpsests of past anthropogenic activity throughout the landscape while reducing survey biases in the archaeological record

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Wykorzystanie danych termalnych pozyskanych z pułapu lotniczego do określania stanu zdrowotnego wybranych gatunków drzew

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    Celem pracy było sprawdzenie, czy dane termalne z zakresu średniej podczerwieni (3,6–4,9 μm) pozyskane z pułapu lotniczego mogą być wykorzystane do badań kondycji zdrowotnej drzew. W tym celu przeprowadzono trzy analizy na niezależnych zbiorach danych w różnych środowiskach. Badania wykonane na danych termalnych pozyskanych w ciągu dnia wykazały, że temperatura korony jest cechą specyficzną dla gatunku i zależy od położenia drzewa w terenie. Drzewa znajdujące się wewnątrz lasu miały niższą temperaturę koron do 0,70oC niż te rosnące poza lasem. Gatunkiem o najwyższej temperaturze, niezależnie od godziny pozyskania danych lotniczych, był Pinus sylvestris. Niskimi temperaturami charakteryzowały się Alnus glutinosa, Quercus rubra i Quercus petraea. Badania nad identyfikacją miejsc żerowania kornika drukarza wykazały, że fuzja danych termalnych i skanowania laserowego umożliwiły wyznaczenie temperatury koron pojedynczych drzew Picea abies i sklasyfikowanie ich do trzech klas zdrowotnych (drzewa 'zdrowe' o średniej temperaturze 27,70oC; 'o osłabionej kondycji' 28,57oC i 'martwe' 30,17oC). Opracowany został schemat postępowania wykorzystujący automatyczną segmentację i uczenie maszynowe do identyfikacji drzew 'o osłabionej kondycji' i 'martwych'. Badania przeprowadzone w środowisku miejskim wykazały statystycznie istotne różnice między klasami kondycji zdrowotnej drzew zarówno na danych pozyskanych w dzień jak i w nocy. Korony drzew zdrowych były chłodniejsze w porównaniu do koron drzew zamierających. Średnia wartość różnicy wynosiła 3,28oC w ciągu dnia oraz 1,06oC w nocy. Podsumowując, lotnicze dane termalne z zakresu średniej podczerwieni mogą być wykorzystane do badań kondycji zdrowotnej wybranych gatunków drzew. Zmienność temperatur koron jest cechą zależną od gatunku i może być wskaźnikiem stanu zdrowotnego w środowisku naturalnym i miejskim."InterDOC-STARt – Interdyscyplinarne Studia Doktoranckie na Wydziale BiOŚ UŁ” – Program Operacyjny Wiedza Edukacja Rozwój 2014-2020, Oś priorytetowa III. Szkolnictwo wyższe dla gospodarki i rozwoju, Działanie 3.2 Studia doktoranckie. Nr projektu: POWR.03.02.00-IP.08-00-DOK/16. Realizowany w latach 2018-2022

    Evaluation of Multi-frequency Synthetic Aperture Radar for Subsurface Archaeological Prospection in Arid Environments

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    The discovery of the subsurface paleochannels in the Saharan Desert with the 1981 Shuttle Imaging Radar (SIR-A) sensor was hugely significant in the field of synthetic aperture radar (SAR) remote sensing. Although previous studies had indicated the ability of microwaves to penetrate the earth’s surface in arid environments, this was the first applicable instance of subsurface imaging using a spaceborne sensor. And the discovery of the ‘radar rivers’ with associated archaeological evidence in this inhospitable environment proved the existence of an earlier less arid paleoclimate that supported past populations. Since the 1980’s SAR subsurface prospection in arid environments has progressed, albeit primarily in the fields of hydrology and geology, with archaeology being investigated to a lesser extent. Currently there is a lack of standardised methods for data acquisition and processing regarding subsurface imaging, difficulties in image interpretation and insufficient supporting quantitative verification. These barriers keep SAR technology from becoming as integral as other remote sensing techniques in archaeological practice The main objective of this thesis is to undertake a multi-frequency SAR analysis across different site types in arid landscapes to evaluate and enhance techniques for analysing SAR within the context of archaeological subsurface prospection. The analysis and associated fieldwork aim to address the gap in the literature regarding field verification of SAR image interpretation and contribute to the understanding of SAR microwave penetration in arid environments. The results presented in this thesis demonstrate successful subsurface imaging of subtle feature(s) at the site of ‘Uqdat al-Bakrah, Oman with X-band data. Because shorter wavelengths are often ignored due to their limited penetration depths as compared to the C-band or L-band data, the effectiveness of X-band sensors in archaeological prospection at this site is significant. In addition, the associated ground penetrating radar and excavation fieldwork undertaken at ‘Uqdat al-Bakrah confirm the image interpretation and support the quantitative information regarding microwave penetration

    A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

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    Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure
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