Investigating the use of the Saharan dust index as a tool for the detection of volcanic ash in SEVIRI imagery

• Published in: Journal of Volcanology and Geothermal Research
• Main Authors: Taylor, Isabelle, Mackie, Shona, Watson, Matthew
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2015
• Subjects: Ash detection; Saharan dust index; Satellite remote sensing; SEVIRI; Thermal infrared; Volcanic ash; Etna; Eyjafjallajökull; Iceland
• Online Access: https://hdl.handle.net/1983/ca5a208d-7ac7-4ce1-981e-c7b8ed272097; https://research-information.bris.ac.uk/en/publications/ca5a208d-7ac7-4ce1-981e-c7b8ed272097; https://doi.org/10.1016/j.jvolgeores.2015.08.014; http://www.scopus.com/inward/record.url?scp=84941283932&partnerID=8YFLogxK

Despite the similar spectral signatures of ash and desert dust, relatively little has been done to explore the application of dust detection techniques to the problem of volcanic ash detection. The Saharan dust index (SDI) is routinely implemented for dust monitoring at some centres and could be utilised for volcanic ash detection with little computational expense, thereby providing a product that forecasters already have some familiarity with to complement the suite of existing ash detection tools. We illustrate one way in which the index could be implemented for the purpose of ash detection by applying it to three scenes containing volcanic ash from the 2010 Eyjafjallajökull eruption, Iceland and the 2011 eruption of Puyehue, Chile. It was also applied to an image acquired over Etna in January 2011, where a volcanic plume is clearly visible but is unlikely to contain any ash. These examples demonstrate the potential of the SDI as a tool for ash monitoring under different environmental and atmospheric conditions. In addition to presenting a valuable qualitative product to aid monitoring, this work includes a quantitative assessment of the detection skill using a manually constructed expert ash mask. The optimum implementation of any technique is likely to be dependent on both atmospheric conditions and on the properties of the imaged ash (which is often unknown in a real-time situation). Here we take advantage of access to a 'truth' rarely available in a real-time situation and calculate an ash mask based on the optimum threshold for the specific scene, which is then used to demonstrate the potential of the SDI. The SDI mask is compared to masks calculated from a simplistic implementation of the more traditional split window method, again exploiting our access to the 'truth' to set the most appropriate threshold for each scene, and to a probabilistic method that is implemented without reference to the 'truth' and which provides useful insights into the likely cloud-/ash-contamination of each pixel. Since the ...