<em>Pyplis</em> - A Python Software Toolbox for the Analysis of SO₂ Camera Data

Abstract

UV SO 2 cameras have become a common tool to measure and monitor SO 2 -emission-rates, mostly from volcanoes but also from anthropogenic sources (e.g. power plants or ships). In the past years, the analysis of UV SO 2 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 5 methods from literature within a single, cross-platform analysis framework. Pyplis comprises a vast collection of algorithms relevant for the analysis of UV SO 2 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 10 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. SO 2 -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, 15 the main features are introduced using a dataset obtained at Mt. Etna, Italy on 16 September 2015.

Improved optical flow velocity analysis in SO&lt;sub&gt;2&lt;/sub&gt; camera images of volcanic plumes – implications for emission-rate retrievals investigated at Mt Etna, Italy and Guallatiri, Chile

“…The image analysis was performed using the Python software Pyplis (Gliß et al, 2017). In a first step, all images were corrected for electronic offset and dark current followed by a first-order correction for the signal dilution effect.…”

“…2) were determined using on/off sky reference images (SRI) recorded close in time to the plume image data. The background retrieval was done using the background modelling methods 6 (Etna) and 4 (Guallatiri) of the used analysis software Pyplis (Gliß et al, 2017; cf. Table 2 therein).…”

“…The position and extent of the DOAS-FOV (field of view) within the camera images are shown in Figs. 1 (Etna) and 2 (Guallatiri) and were identified using the Pearson correlation method described in Gliß et al (2017).…”

“…where m denotes interpolated image coordinates (i, j ) along , f is the camera focal length, d pl is the distance between the camera and the plume and s(m) is the integration step length (for details see Gliß et al, 2017). The effective velocity…”

“…Fig. 10 in Gliß et al, 2017). During this time the camera was pointed at a lower elevation angle and the images contained more suitable terrain features for the correction.…”