Preprint Article Version 1 This version is not peer-reviewed

Pyplis - A Python Software Toolbox for the Analysis of SO2 Camera Data

Version 1 : Received: 12 October 2017 / Approved: 13 October 2017 / Online: 13 October 2017 (04:00:49 CEST)

A peer-reviewed article of this Preprint also exists.

Gliß, J.; Stebel, K.; Kylling, A.; Dinger, A.S.; Sihler, H.; Sudbø, A. Pyplis—A Python Software Toolbox for the Analysis of SO2 Camera Images for Emission Rate Retrievals from Point Sources. Geosciences 2017, 7, 134. Gliß, J.; Stebel, K.; Kylling, A.; Dinger, A.S.; Sihler, H.; Sudbø, A. Pyplis—A Python Software Toolbox for the Analysis of SO2 Camera Images for Emission Rate Retrievals from Point Sources. Geosciences 2017, 7, 134.

Journal reference: Geosciences 2017, 7, 134
DOI: 10.3390/geosciences7040134

Abstract

UV SO2 cameras have become a common tool to measure and monitor SO2-emission-rates, mostly from volcanoes but also from anthropogenic sources (e.g. power plants or ships). In the past years, the analysis of UV SO2 camera data has seen many improvements. As a result, for many of the required analysis steps, several alternatives exist today. This inspired the development of Pyplis, an open-source software toolbox written in Python 2.7, which aims to unify the most prevalent methods from literature within a single, cross-platform analysis framework. Pyplis comprises a vast collection of algorithms relevant for the analysis of UV SO2 camera data. These include several routines to retrieve plume background radiances as well as routines for cell and DOAS based camera calibration. The latter includes two independent methods to identify the DOAS field-of-view within the camera images. Plume velocities can be retrieved using an optical flow algorithm as well as signal cross-correlation. Furthermore, Pyplis includes a routine to perform a first order correction of the signal dilution effect. All required geometrical calculations are performed within a 3D model environment allowing for distance retrievals to plume and local terrain features on a pixel basis. SO2-emission-rates can be retrieved simultaneously for an arbitrary number of plume intersections. Pyplis has been extensively and successfully tested using data from several field campaigns. Here, the main features are introduced using a dataset obtained at Mt. Etna, Italy on 16 September 2015.

Subject Areas

volcanic gases; SO2; remote sensing; UV cameras; image processing; analysis software; Python 2.7

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