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Publication - Professor Lucy Berthoud

    3D Reconstruction of Volcanic Ash Plumes using Multi-Camera Computer Vision Techniques

    Citation

    Wood, K, Richardson, T, Berthoud, L, Watson, M, Thomas, H, Naismtih, A, Lucas, J & Calway, A, 2018, ‘3D Reconstruction of Volcanic Ash Plumes using Multi-Camera Computer Vision Techniques’., pp. 16492

    Abstract

    Volcanoes are natural emitters of gas and ash and transmit significant
    amounts of material into the atmosphere. These volcanic ash plumes can
    travel over great distances and have significant effects on local
    populations and users of the affected airspace e.g. civil air traffic.
    Currently, ash plumes are monitored using a combination of satellite
    imagery and dispersion modelling, however these models can be sensitive
    to source terms, leading to relatively large uncertainties in both the
    predicted region affected by the ash plume and its density. Historically
    there was a zero-tolerance approach to aircraft exposure to ash,
    however, since the 2010 eruption of Eyjafjallajökull, there has
    been a change to a dosage-based scheme. Therefore, airspace managers now
    require a more detailed knowledge of the ash in the atmosphere to
    optimise flight routes; carefully balancing the costs of increased
    maintenance against the costs of cancellations (or re-routing), all
    whilst ensuring safety standards are maintained. This study presents
    a method for the direct measurement of volcanic ash plume properties.
    The shape, drift direction, and dispersion of a plume is reconstructed
    in three dimensions using multi-view imagery collected from static
    ground-based cameras. A space carving method has been applied to the
    problem to estimate the total volume of the plume at each time step. By
    successively applying the method to sequential images, other properties
    such as the drift direction, ascent rate, and dispersion rate can be
    deduced. Due to the large distances involved in volcanic remote sensing,
    the method is particularly sensitive to the camera orientation, whereby
    misalignments on the order of one degree can lead to errors in the plume
    properties. This sensitivity has been analysed, and part of the
    presented algorithm includes a novel technique for accurately estimating
    the camera extrinsic orientation by comparing the real images to ones
    artificially created using high resolution DEM models. The DEM-based
    model world also serves as an excellent visualisation tool, allowing the
    user to interactively 'watch' the plume reconstruction from any view
    point. To increase computational efficiency and minimise the
    false-positives caused by meteorological clouds (which appear very
    similar to the plume), the algorithm also makes use of the
    time-connectivity between images and fits a probabilistic envelope to
    the plume, such that only the region of sky where the plume is expected
    to be located is processed. This could lead to the method being applied
    in real time on modest computing hardware. This study considers the
    overall physical dimensions of the plume, however future aims are to
    measure the 3D internal structure and add quantitative ash concentration
    retrieval methods, allowing a true ash dosage to be predicted. An
    example case-study data set was collected during an expedition to
    Volcán de Fuego in Guatemala and subsequently analysed using the
    method presented. Four multi-band IR cameras were positioned in local
    villages surrounding the volcano, such that they all had clear views of
    the summit and surrounding sky. Approximately 2000 images (500 per
    camera) were collected over 90 minutes, with at least three significant
    eruptions during that period.

    Full details in the University publications repository