Pyplis–A Python Software Toolbox for the Analysis of SO2 Camera Images for Emission Rate Retrievals from Point Sources
Ultraviolet (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). Over the past decade, 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 (e.g., cell vs. DOAS based camera calibration; optical flow vs. cross-correlation based gas-velocity retrieval). This inspired the
... inspired the development of Pyplis (Python plume imaging software), an open-source software toolbox written in Python 2.7, which unifies 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 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 (FOV) within the camera images (based on (1) Pearson correlation and (2) IFR inversion method). 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 (also referred to as light dilution). 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. Hence, Pyplis provides a state-of-the-art framework for more efficient and flexible analyses of UV SO 2 camera data and, therefore, marks an important step forward towards more transparency, reliability and inter-comparability of the results. 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. Geosciences 2017, 7, 134 2 of 24 scales (e.g., particle formation, radiation budget, e.g., [1, 2] ). Furthermore, the monitoring of SO 2 emissions from active volcanoes can provide insight into the magmatic degassing behaviour and hence plays an important role for the development of new risk assessment approaches (e.g.,     , and references therein). The gas composition of the emission plumes can, for instance, be studied using ground-based passive remote sensing techniques. The column-densities (CDs) of the gases in the plumes are quantified based on the absorption signature carried by scattered sunlight that has penetrated the plume. SO 2 -CDs, for instance, can be retrieved at ultraviolet (UV) wavelengths (i.e., around 310 nm) where it exhibits distinct absorption bands. Prominent examples for passive remote sensing instrumentation are the correlation spectrometer (COSPEC,  ), or instruments based on the technique of Differential Optical Absorption Spectroscopy (DOAS, , e.g., [9, 10] ). Over the past decade, the comparatively young technique of UV SO 2 cameras has gained in importance, since it enables the study of volcanic SO 2 emissions at unprecedented spatial and temporal resolution (e.g.,      ). This is particularly helpful to study multiple sources independently (e.g.,  ) or to investigate volcanic degassing characteristics by studying periodicities in the SO 2 emission rates (e.g.,    and references therein). The technique of UV SO 2 cameras has seen remarkable improvements over the past decade (e.g.,      ) and can now be considered one of the standard methods for ground-based remote sensing of SO 2 plumes. A drawback, however, is the low spectral resolution, restricting the technique to a single species and furthermore requiring external calibration. The retrieval of SO 2 emission rates from plume imagery comprises several analysis steps that are summarised in Table 1 and are illustrated in the flowchart shown in Appendix Figure A1 . Thanks to ongoing developments, today, researchers can choose between several methods for nearly all of the required steps (e.g., cell vs. DOAS calibration, velocity retrieval using optical flow vs. cross-correlation method). Available software solutions include Vulcamera , the IDL R source code provided by  and Plumetrack  . The first two programs include routines for cell calibration and cross-correlation based plume velocity retrievals. The IDL R program also includes a routine to perform a first order correction for the signal dilution effect (commonly referred to as light dilution, e.g.,  ). The software Plumetrack provides an interface to calculate gas velocities using an optical flow algorithm and can be applied to pre-calibrated SO 2 -CD images in order to retrieve SO 2 emission rates.