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

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 5 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 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. 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, 15 the main features are introduced using a dataset obtained at Mt. Etna, Italy on 16 September 2015.

[1]  A. Kylling,et al.  Optical flow gas velocity analysis in plumes using UV cameras – Implications for SO 2 -emission-rate retrievals investigated at Mt. Etna, Italy, and Guallatiri, Chile , 2017 .

[2]  Yang Wang,et al.  In-operation field-of-view retrieval (IFR) for satellite and ground-based DOAS-type instruments applying coincident high-resolution imager data , 2016 .

[3]  G Tamburello,et al.  Spatially resolved SO2 flux emissions from Mt Etna , 2016, Geophysical research letters.

[4]  Hugo Delgado-Granados,et al.  Image-based correction of the light dilution effect for SO2 camera measurements , 2015 .

[5]  Christoph Kern,et al.  Intercomparison of SO2 camera systems for imaging volcanic gas plumes , 2015 .

[6]  Lopaka Lee,et al.  An automated SO 2 camera system for continuous, real-time monitoring of gas emissions from Kīlauea Volcano's summit Overlook Crater , 2015 .

[7]  A. Prata,et al.  First estimates of fumarolic SO2 fluxes from Putana volcano, Chile, using an ultraviolet imaging camera , 2015 .

[8]  C. Oppenheimer,et al.  Use of Motion Estimation Algorithms for Improved Flux Measurements Using SO 2 Cameras , 2015 .

[9]  N. Bobrowski,et al.  Gas emission strength and evolution of the molar ratio of BrO/SO2 in the plume of Nyiragongo in comparison to Etna , 2015 .

[10]  Clive Oppenheimer,et al.  Volcanic Degassing: Process and Impact , 2014 .

[11]  U. Platt,et al.  BrO/SO 2 molar ratios from scanning DOAS measurements in the NOVAC network , 2013 .

[12]  Andrea Cannata,et al.  Periodic volcanic degassing behavior: The Mount Etna example , 2013 .

[13]  Christoph Kern,et al.  Improving the accuracy of SO2column densities and emission rates obtained from upward‐looking UV‐spectroscopic measurements of volcanic plumes by taking realistic radiative transfer into account , 2012 .

[14]  Christoph Kern,et al.  On the absolute calibration of SO 2 cameras , 2012 .

[15]  Euripides P. Kantzas,et al.  Vulcamera: a program for measuring volcanic SO2 using UV cameras , 2011 .

[16]  Michael A. Saunders,et al.  LSMR: An Iterative Algorithm for Sparse Least-Squares Problems , 2010, SIAM J. Sci. Comput..

[17]  U. Platt,et al.  Early in-flight detection of SO 2 via Differential Optical Absorption Spectroscopy: a feasible aviation safety measure to prevent potential encounters with volcanic plumes , 2010 .

[18]  Robert G. Bryant,et al.  Protocols for UV camera volcanic SO2 measurements , 2010 .

[19]  Christoph Kern,et al.  Theoretical description of functionality, applications, and limitations of SO 2 cameras for the remote sensing of volcanic plumes , 2010 .

[20]  Christoph Kern,et al.  Network for Observation of Volcanic and Atmospheric Change (NOVAC)—A global network for volcanic gas monitoring: Network layout and instrument description , 2010 .

[21]  Christoph Kern,et al.  Radiative transfer corrections for accurate spectroscopic measurements of volcanic gas emissions , 2010 .

[22]  C. Amante,et al.  ETOPO1 Global Relief Model converted to PanMap layer format , 2009 .

[23]  Ulrich Platt,et al.  Differential optical absorption spectroscopy , 2008 .

[24]  Vincent J. Realmuto,et al.  Development of an ultra-violet digital camera for volcanic SO2 imaging , 2007 .

[25]  Mike Burton,et al.  The SO2 camera: A simple, fast and cheap method for ground‐based imaging of SO2 in volcanic plumes , 2006 .

[26]  Stefan Kraus,et al.  DOASIS: a framework design for DOAS , 2006 .

[27]  Clive Oppenheimer,et al.  Plume velocity determination for volcanic SO2 flux measurements , 2005 .

[28]  Gunnar Farnebäck,et al.  Two-Frame Motion Estimation Based on Polynomial Expansion , 2003, SCIA.

[29]  A. Robock.  Volcanic eruptions and climate , 2000 .

[30]  R. Sparks,et al.  VOLATILES IN MAGMAS , 1994 .

[31]  Ulrich Platt,et al.  Direct measurements of atmospheric CH2O, HNO2, O3, NO2, and SO2 by differential optical absorption in the near UV , 1980 .

[32]  M M Millan,et al.  The applications of optical correlation techniques to the remote sensing of SO 2 plumes using sky light. , 1971, Atmospheric environment.