Abstract
Ultraviolet (UV) cameras are increasingly employed to map and measure SO2 abundances in volcanic emissions to the atmosphere. The main purpose of this is to estimate mass fluxes of SO2, which requires estimation of the transport velocity of the plume. In this paper, we present Plumetrack, open-source Python based software for computing SO2 fluxes from calibrated UV camera images. Designed to be the final component in UV camera processing toolchains, Plumetrack provides functionality for velocity estimation using optical-flow, flux calculation and error estimates. It can be used interactively via a graphical user interface or for batch processing via a commandline interface. We discuss the features and implementation details of Plumetrack, describe in detail a new flux calculation algorithm and demonstrate its performance on a set of synthetic UV camera images. The new algorithm is found to out perform the established flux calculation method, especially for highly spatiotemporally variable plumes. Furthermore, we show that the Plumetrack software may be successfully used with data from other imaging systems such as standard video cameras.
Highlights
SO2 flux is an important parameter in the characterisation of volcanic activity
Calibration of UV camera images is non-trivial (Kantzas et al, 2010; Kern et al, 2010, 2013; Lübcke et al, 2013) and is somewhat limited by the relatively wide bandpass of the filters used, their ability to image volcanic plumes in two dimensions is a great advantage compared with non-imaging ultraviolet spectroscopy
Oppenheimer computing SO2 fluxes from calibrated UV camera images, which uses a 2D optical flow algorithm to compute the plume motion between consecutive images
Summary
SO2 flux is an important parameter in the characterisation of volcanic activity. It is measured at numerous volcanoes worldwide as part of the operational monitoring campaigns of local observatories (e.g. Edmonds et al, 2003; Sweeney et al, 2008; Salerno et al, 2009) and is a major component of many research studies (e.g. Bani et al, 2012; Smekens et al, 2013; Pering et al, 2014; Carn et al, 2017; Moussallam et al, 2017). This is typically estimated from the UV camera images by cross-correlating integrated column amount values from two complete transects of the gas plume (i.e., hypothetical surfaces that bisect the plume approximately perpendicularly to its direction of motion, and projected into the image plane) at different distances (parallel to the dominant direction of motion) from the source (McGonigle et al, 2005; Williams-Jones et al, 2006; Mori and Burton, 2006) This technique assumes a single velocity for the entire plume, often resulting in a significant over-estimate of the flux (Peters et al, 2014), and requires a careful compromise between temporaland spatial-resolution when performing the correlation (Boichu et al, 2010). The benefits of 2D motion estimation algorithms for SO2 flux calculations are discussed in more detail by Peters et al (2014)
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