486 research outputs found

    Geodetic monitoring of complex shaped infrastructures using Ground-Based InSAR

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    In the context of climate change, alternatives to fossil energies need to be used as much as possible to produce electricity. Hydroelectric power generation through the utilisation of dams stands out as an exemplar of highly effective methodologies in this endeavour. Various monitoring sensors can be installed with different characteristics w.r.t. spatial resolution, temporal resolution and accuracy to assess their safe usage. Among the array of techniques available, it is noteworthy that ground-based synthetic aperture radar (GB-SAR) has not yet been widely adopted for this purpose. Despite its remarkable equilibrium between the aforementioned attributes, its sensitivity to atmospheric disruptions, specific acquisition geometry, and the requisite for phase unwrapping collectively contribute to constraining its usage. Several processing strategies are developed in this thesis to capitalise on all the opportunities of GB-SAR systems, such as continuous, flexible and autonomous observation combined with high resolutions and accuracy. The first challenge that needs to be solved is to accurately localise and estimate the azimuth of the GB-SAR to improve the geocoding of the image in the subsequent step. A ray tracing algorithm and tomographic techniques are used to recover these external parameters of the sensors. The introduction of corner reflectors for validation purposes confirms a significant error reduction. However, for the subsequent geocoding, challenges persist in scenarios involving vertical structures due to foreshortening and layover, which notably compromise the geocoding quality of the observed points. These issues arise when multiple points at varying elevations are encapsulated within a singular resolution cell, posing difficulties in pinpointing the precise location of the scattering point responsible for signal return. To surmount these hurdles, a Bayesian approach grounded in intensity models is formulated, offering a tool to enhance the accuracy of the geocoding process. The validation is assessed on a dam in the black forest in Germany, characterised by a very specific structure. The second part of this thesis is focused on the feasibility of using GB-SAR systems for long-term geodetic monitoring of large structures. A first assessment is made by testing large temporal baselines between acquisitions for epoch-wise monitoring. Due to large displacements, the phase unwrapping can not recover all the information. An improvement is made by adapting the geometry of the signal processing with the principal component analysis. The main case study consists of several campaigns from different stations at Enguri Dam in Georgia. The consistency of the estimated displacement map is assessed by comparing it to a numerical model calibrated on the plumblines data. It exhibits a strong agreement between the two results and comforts the usage of GB-SAR for epoch-wise monitoring, as it can measure several thousand points on the dam. It also exhibits the possibility of detecting local anomalies in the numerical model. Finally, the instrument has been installed for continuous monitoring for over two years at Enguri Dam. An adequate flowchart is developed to eliminate the drift happening with classical interferometric algorithms to achieve the accuracy required for geodetic monitoring. The analysis of the obtained time series confirms a very plausible result with classical parametric models of dam deformations. Moreover, the results of this processing strategy are also confronted with the numerical model and demonstrate a high consistency. The final comforting result is the comparison of the GB-SAR time series with the output from four GNSS stations installed on the dam crest. The developed algorithms and methods increase the capabilities of the GB-SAR for dam monitoring in different configurations. It can be a valuable and precious supplement to other classical sensors for long-term geodetic observation purposes as well as short-term monitoring in cases of particular dam operations

    Insights into temperature controls on rockfall occurrence and cliff erosion

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    A variety of environmental triggers have been associated with the occurrence of rockfalls however their role and relative significance remains poorly constrained. This is in part due to the lack of concurrent data on rockfall occurrence and cliff face conditions at temporal resolutions that mirror the variability of environmental conditions, and over durations for large enough numbers of rockfall events to be captured. The aim of this thesis is to fill this data gap, and then to specifically focus on the role of temperature in triggering rockfall that this data illuminates. To achieve this, a long-term multiannual 3D rockfall dataset and contemporaneous Infrared Thermography (IRT) monitoring of cliff surface temperatures has been generated. The approaches used in this thesis are undertaken at East Cliff, Whitby, which is a coastal cliff located in North Yorkshire, UK. The monitored section is ~ 200 m wide and ~65 m high, with a total cliff face area of ~9,592 mÂČ. A method for the automated quantification of rockfall volumes is used to explore data collected between 2017–2019 and 2021, with the resulting inventory including > 8,300 rockfalls from 2017–2019 and > 4,100 rockfalls in 2021, totalling > 12,400 number of rockfalls. The analysis of the inventory demonstrates that during dry conditions, increases in rockfall frequency are coincident with diurnal surface temperature fluctuations, notably at sunrise, noon and sunset in all seasons, leading to a marked diurnal pattern of rockfall. Statistically significant relationships are observed to link cliff temperature and rockfall, highlighting the response of rock slopes to absolute temperatures and changes in temperature. This research also shows that inclement weather constitutes the dominant control over the annual production of rockfalls but also quantifies the period when temperature controls are dominant. Temperature-controlled rockfall activity is shown to have an important erosional role, particularly in periods of iterative erosion dominated by small size rockfalls. As such, this thesis provides for the first high-resolution evidence of temperature controls on rockfall activity, cliff erosion and landform development

    Realising Global Water Futures: a Summary of Progress in Delivering Solutions to Water Threats in an Era of Global Change

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    Canada First Research Excellence FundNon-Peer ReviewedOver the past six years the Global Water Futures program has produced a wide range of scientific findings and engagements with multiple types of potential users of the research. This briefing book provides a snapshot of some of the science advancements and user engagement that have taken place to date. Annual reports to the funding agency are the most up to date source of information: this compilation has been created from reports submitted by projects in 2022, representing both completed and current project work. The briefing book aims to provide quick access to information about GWF projects in a single place for GWF’s User Advisory Panel: we hope that knowing more about the research being produced will spark conversations about how to make the best use of the new knowledge in both policy and practice

    Selected Problems of High-Resolution Automotive Imaging Radar

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    This thesis aims at two selected problems in the development of high-resolution au- tomotive imaging radar: 1) The feasibility of using sub-THz for the next generation of automotive radar; 2) The development of the physics-based image segmentation approach on the automotive radar imagery. The wide range of feasibility studies on the use of sub-THz frequencies for auto- motive radar have been undertaken in the Microwave Integrated Systems Laboratory (MISL) at the University of Birmingham, and the candidate is in charge of the included study on the theoretical modelling and experimental verification of the attenuation through the vehicle infrastructures which is the first part of this thesis. The importance of this work is related to the fact that automotive radar is placed within the car infras- tructure. Therefore, it would be a potential show-stopper in the development of this innovation if attenuation within the car bumper or badge is prohibitively high. Both theoretical modelling and experimental measurement are conducted by considering the impact factors on the propagation properties of the sub-THz signal such as the incident angle, frequency, characteristic parameters of materials, and the thicknesses of infrastructure layers. The transmissivity of multilayered structure has been modelled and good agreement with the results of measurements was demonstrated, so that the developed approach can be used in further studies on propagation through car infrastruc- ture. The published results on transmissivity and complex permittivity of automotive paints are valuable for researchers in either field of THz technology or automotive radar. The image segmentation on automotive radar maps aims at identifying the passable and impassable areas for path planning in autonomous driving. Contrary to traditional radar, radar clutter is regarded as the physical meaningful information, which can deliver valuable feature information for surface characterization, and enable the full scene reconstruction of automotive radar maps. The proposed novel segmentation algorithm is a hybrid method composed of pre-segmentation based on image processing methods, and the region classification using the multivariate Gaussian distribution (MGD) classifier developed based on the statistical distribution feature parameters of radar returns of various areas. Moving target indication (MTI) is implemented for the first time based on frame-to-frame context association. The end-to-end segmentation framework is therefore achieved robustly with good segmentation performance, and automatically without human intervention

    Land Use and Land Cover Mapping in a Changing World

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    It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classiïŹcation systems

    Télédétection spatiale de dépÎts d'avalanche pour le suivi de zones à risque avalancheux

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    La surveillance du manteau neigeux, la prĂ©vision de son Ă©volution et l'estimation du risque d'avalanche qui en dĂ©coule font partie des missions de MĂ©tĂ©o-France. L'hĂ©tĂ©rogĂ©nĂ©itĂ© des montagnes induite par le relief rend la prĂ©vision nivo-mĂ©tĂ©orologique de ces territoires particuliĂšrement difficile. A cela s'ajoute une forte demande sociĂ©tale de disposer de prĂ©visions fiables de l'enneigement et du risque d'avalanche avec un niveau d'exigence sans cesse en accroissement tant sur la qualitĂ© des prĂ©visions que sur leur forme et leur Ă©tendue gĂ©ographique. La mise Ă  disposition aux prĂ©visionnistes nivologues et aux autres acteurs de montagne d'observation systĂ©matique sur l'activitĂ© avalancheuse permettrait une meilleure qualification des situations prĂ©vues par rapport aux Ă©vĂ©nements passĂ©s et contribuerait Ă  l'amĂ©lioration de la gestion du risque avalancheux. La localisation et l'estimation de taille des dĂ©pĂŽts d'avalanches sont d'une grande importance pour les Ă©tudes de stabilitĂ© du manteau neigeux. Les avancĂ©es en matiĂšre de tĂ©lĂ©dĂ©tection spatiale offrent la possibilitĂ© aux chercheurs et acteurs de montagne de suivre au plus prĂšs l'Ă©volution du manteau neigeux. Dans cette thĂšse, nous exploitons les donnĂ©es d'observations satellitaires de Sentinel-1 couvrant l'intĂ©gralitĂ© des Alpes pour dĂ©tecter les zones de dĂ©bris avalancheux et pour suivre l'Ă©volution de l'activitĂ© avalancheuse Ă  l'Ă©chelle d'une rĂ©gion d'intĂ©rĂȘt. Une mĂ©thode est dĂ©veloppĂ©e permettant de dĂ©tecter automatiquement les zones de dĂ©bris d'avalanche de neige en utilisant une technique de segmentation novatrices des images Sentinel-1. Plusieurs autres dĂ©veloppements algorithmiques sont menĂ©s pour amĂ©liorer les dĂ©tections impliquant des mĂ©thodes de filtrage de fausses dĂ©tection et de classification pour passer d'un ensemble de pixels avalancheux Ă  un Ă©vĂ©nement avalancheux. Les estimations de dĂ©bris sont Ă©tudiĂ©es et leurs dĂ©pendances selon la direction de l'orbite, les caractĂ©ristiques du terrain (pente, altitude, orientation) examinĂ©es. La mĂ©thode de dĂ©tection est Ă©galement Ă©valuĂ©e avec succĂšs Ă  l'aide d'une base de donnĂ©es indĂ©pendante issues d’identifications de dĂ©bris d’avalanches sur une image optique de haute rĂ©solution. Par la suite, nous dĂ©rivons des indicateurs spatialisĂ©s Ă  l'Ă©chelle des massifs de l'activitĂ© avalancheuses. Il s'agit de lignes de dĂ©bris d'avalanches estimĂ©es par pente et par bandes d'altitudes. Ces indicateurs sont comparĂ©s Ă  des donnĂ©es d'observations in-situ. Ce travail de thĂšse permet le dĂ©veloppement de nouveaux produits Ă  valeur ajoutĂ©e et est l'occasion d'initier une rĂ©flexion sur les besoins pour la prĂ©vision du risque d'avalanche et notamment le besoin de disposer d'indicateurs « synthĂ©tisables » sur des fenĂȘtres temporelles d'intĂ©rĂȘts (hebdomadaire, mensuelle, saisonniĂšre) Ă  des Ă©chelles allant de l'Ă©chelle locale (de l'Ă©vĂ©nement) Ă  l'Ă©chelle du massif

    River landform dynamics detection and responses to morphology change in the rivers of North Luzon, the Philippines

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    River morphology detection has been improved considerably with the application of remote sensing and developments in computer science. However, applications that extract landforms within the active river channel remain limited, and there is a lack of studies from tropical regions. This thesis developed and then applied a workflow employing Sentinel-2 imagery for seasonal and annual river landform classification. Image downscaling approaches were investigated, and the performance of object-based image segmentation was assessed. The area to point regression kriging (ATPRK) approach was chosen to downscale coarser 20 m resolution Sentinel-2 bands to finer 10 m resolution bands. All features were set or processed at 10 m resolution before applying support vector machine (SVM) classification. To improve machine learning classification accuracy, Sentinel-2 acquisitions across one year, which incorporates multiple seasons, should be used. For rivers with different hydrological or geology settings, the thesis considered collecting river specific ground truth data to build a training model to avoid underfitting of models from other hydrological/geological settings. Applying the workflow, three landforms (water, unvegetated bars and vegetated bars) were classified within the active channel of the Bislak, Laoag, Abra and Cagayan Rivers, north Luzon, the Philippines, between 2016 to 2021, respectively. The spatial-temporal river landform datasets enabled the quantitative analysis of the river morphology changes. Water and unvegetated bars showed clear seasonal dynamics in all four rivers, whilst vegetated bars only showed seasonality in the rivers located in the northwest Luzon (the Bislak, Laoag and Abra Rivers). This thesis employed correlated coefficients to investigate the longitudinal correlation between river landforms and active width. It was found that vegetated bar areas always have strong significant correlations (≄0.67) with the active widths in all four rivers, whilst correlation coefficients between vegetated bar areas and active widths in the wet season are higher than that in the dry season. Ensemble empirical mode decomposition (EEMD) was applied to detect landform periodicity; this method indicated that water and vegetated bars commonly showed synchronised fluctuations with precipitation, while unvegetated bars had an anti-phase oscillation with precipitation. In the case of EEMD, deviations from periodic consistency in river pattern may reflect the influence of extreme events and/or human disturbance. Coefficient of variation (COV) was then used to evaluate the stability of the landforms; results suggested that the interplay of faults, elevation, confinement and tributary locations impacted landform stability. Finally, tributary inflow impacts on the mainstem river were investigated for eight tributaries of the lowland Cagayan River, also on Luzon Island. Longitudinal variations in channel morphology and stability, and temporal changes in landform frequency, using Simpson’s diversity index and COV, showed downstream widening associated with tributaries that was controlled by water discharge, with a secondary sediment flux effect. Overall, this thesis provided a novel example of combining remote sensing and GIS science, computing science, statistical science, and river morphology science to study the earth surface processes synthetically and quantitatively within river active channels in the tropical north Luzon, the Philippines. This work demonstrated how the fusion of techniques from these disciplines can be used to detect and analyse river landform changes, with potential applications for river management and restoration

    Progress and prospects for research on Martian topographic features and typical landform identification

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    The study of Martian surface topography is important for understanding the geological evolution of Mars and revealing the spatial differentiation of the Martian landscape. Identifying typical landform units is a fundamental task when studying the origin and evolution of Mars and provides important information for landing on and exploring Mars, as well as estimating the age of the Martian surface and inferring the evolution of the Earth’s environment. In this paper, we first investigate Mars exploration, data acquisition and mapping, and the classification methods of Martian landforms. Then, the identification of several typical Martian landform types, such as aeolian landforms, fluvial landforms, and impact landforms, is shown in detail. Finally, the prospects of Mars data acquisition, landform mapping, and the construction and identification of the Martian landform classification system are presented. The construction of the Martian landform classification system and the identification of typical Martian landforms using deep learning are important development directions in planetary science

    Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

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    With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work
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