



Volcanic ash clouds are widely recognized as a significant hazard to aviation and to populations. As a result, there is a considerable effort being expended by scientists to mitigate this hazard, involving remote sensing of volcanic clouds by satellite sensors.
In this lesson SEVIRI (Spinning Enhanced Visible and InfraRed Imager) and MODIS (MODerate-resolution Imaging Spectroradiometer) are used to look at some techniques for using Earth Observation data to detect volcanic ash clouds and monitor the dynamics of a volcanic eruption, focussing on the eruption of Eyjafjallajökull in Iceland on May 2010.
Suitable for university students or continued professional development training (intermediate level).
NOTE: This lesson requires Bilko 3.4 from July 2014 or later, earlier versions of the software can not carry out the polyline extraction described in the lesson.
On this page: Introduction Lesson Overview Data and Tools Lesson Downloads

Introduction
The early detection of volcanic ash clouds is both a scientific and practical issue which can have significant impacts on human activities. Volcanic eruptions can represent a serious socio-economic and a severe environmental hazard. Plume height, reaching typical altitudes of modern aerial routes, can affect flight safety and have huge knock-on effects on air traffic control, making necessary the re-routing of airways. The volcanic eruptions may have both short-term effects, regarding health threats to people living in the area near the volcano, and long-term effects, since airborne ash clouds may affect both surface ocean biogeochemical cycles and control atmospheric feedbacks of climate trend.
Eyjafjallajökull is a smaller ice cap located in Iceland and, despite its small extensions, covers a stratovolcano whose eruptions often cause problems to the people living and working in the surrounding areas due to potentially lethal encounters with the released glacier burst (or jökulhlaup, in Icelandic) and the large-scale discharge of melt water reaching the sand on the lowland plains (or sandur) to the north of the volcano. Eyjafjallajökull stratovolcano is located under the ice cap, placed in the Icelandic East Volcanic Zone, the most active of the four Icelandic volcanic zones due to its position over the Mid-Atlantic Ridge, the divergent tectonic plate boundary between the Eurasian Plate and the North American Plate.
The 2010 eruptions of the Eyjafjallajökull lasted several weeks, sustaining an average magma discharge of several hundred tonnes per second and producing large quantities of lapilli, coarse, fine and very fine ash particles which were advected towards south and south-east along the major European air traffic routes, causing an unprecedented flight crisis (even worse than the one after the 11 September attacks).
Lesson Overview
Aim and objectives
This lesson provides a general overview of the use of Earth Observation data to study such phenomena. Data from SEVIRI (Spinning Enhanced Visible and InfraRed Imager) and MODIS (MODerate-resolution Imaging Spectroradiometer) radiometers are used to analyze the features of volcanic plumes from Eyjafjallajökull, which the prevailing northerly winds stretched across the Atlantic Ocean toward mainland Europe. Both single bands and combined products are used as examples of basic volcanic ash detection algorithms to identify volcanic plumes and separate them from precipitating hydrometeors and rain clouds.
At the end of the lesson you should be able to:
- Access and display SEVIRI multichannel raw radiances to understand how the instrument "sees" the environment at different spectral bands
- Access and display MODIS level 1B calibrated and geolocated radiances with 500 m resolution to understand how the instrument "sees" the environment at different spectral bands;
- Compare data at different spectral bands to focus on those which better suit with ash cloud analysis;
- Combine data from different spectral bands to build a volcanic ash detection algorithm;
- Combine data from different spectral bands to create RGB composite images;
- Use Bilko's tools (formulas, palettes and stretches) to process the data.
Lesson content
The lesson is divided into the following sections:
- Opening and examining SEVIRI data from the region of interest;
- Opening and examining MODIS data from the region of interest;
- Processing the data to create visible composite images;
- Processing the data to build up an ash detection algorithm.
Data and tools for this lesson
Data sets
Seviri data are available for download to registered users from ESA on-line archives such as GPOD (http://gpod.es.esa.int/).
- MSG_201005081200.zip: SEVIRI data acquired at 12:00 UTC on 8th May 2010
- MSG_201101122300.zip: Seviri data acquired at 23:00 UTC on 12th Jan 2011
MODIS data are available from NASA on-line archives such as LAADS Web (http://ladsweb.nascom.nasa.gov/data/search.html).
- MOD02HKM.A2010129.1225.005.7z: MODIS image acquired by Aqua spacecraft at 12:25 on 9th May 2010
Bilko tools
Bilko formula documents
- SEVIRI_BTD.frm
- SEVIRI_Variant_VASD.frm
- SEVIRI_VASD.frm
Bilko palette documents
- B_palette.pal
- G_palette.pal
- JET_palette.pal
- R_palette.pal
Bilko stretch document
- MODIS_RGB_stretch.str
Downloading the lesson
The lesson downloads contain everything you need to complete the lesson. This includes the data and tools listed above, and three PDF documents:
- Lesson Activities - step by step instructions to carry out hands-on data analysis, brief explanations and questions to test understanding.
- Background - more detailed information on topics included in the lesson.
- Model Answers Answers to the questions posed in the 'Activities' with explanations to enable students to follow the reasoning that led to the model answer.
To download the lesson and data you must be a registered user. If you have not yet done so you can register here. Once your registration is confirmed you can download the lesson after submitting your registered e-mail address below. This will take you to the download page where you can select the documents, tools and data you wish to download.




