Volcanic ash clouds can present an aviation hazard over distances of thousands of kilometres and, to help to mitigate this hazard, advanced numerical models are used to forecast ash dispersion in the atmosphere. However, forecast accuracy is usually limited by uncertainties in initial conditions such as the eruption rate and the vertical distribution of ash injected above the volcano. Here, we demonstrate the potential of the Telematics Earth Observation Mission (TOM) picosatellite formation, due for launch in 2020, to provide valuable information for constraining ash cloud dispersion models through simultaneous image acquisition from three satellites. TOM will carry commercial frame cameras. Using photogrammetric simulations, we show that such data should enable ash cloud heights to be determined with a precision (~30–140 m depending on configuration) comparable to that of lidar observations (30–180 m depending on the cloud height). To support these estimates, we processed photographs taken from the International Space Station of the 2009 Sarychev Peak eruption, as a proxy for TOM imagery. Structure-from-motion photogrammetric software successfully reconstructed the 3-D form of the ascending ash cloud, as well as surrounding cloud layers. Direct estimates of the precision of the ash cloud height measurements, as well as comparisons between independently processed image sets, indicate that a vertical measurement precision of ~200 m was achieved. Image sets acquired at different times captured the plume dynamics and enabled a mean ascent velocity of 14 m s-1 to be estimated for regions above 7 km. In contrast, the uppermost regions of the column (at a measured cloud top height of ~11 km) were not ascending significantly, enabling us to constrain a 1-D plume ascent model, from which estimates for the vent size (50 m) and eruption mass flux (2.6×106 kg s-1) could be made. Thus, we demonstrate that nanosatellite imagery has the potential for substantially reducing uncertainties in ash dispersion models by providing valuable information on eruptive conditions.
This is the author’s version of a work that was accepted for publication in
Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in
Remote Sensing of Environment, 210, 2018 DOI: 10.1016/j.rse.2018.02.061
