Editorial Feature

Remote Sensing for Volcanic Activity and Classifications

Volcano monitoring is the practice of observing volcanic activity and recording data to improve our understanding, advance our early warning and mitigation strategies. Remote sensing is a modern approach to volcano monitoring that has a high capacity for data recording.

remote sensing for volcanic activity

Image Credit: DanielFreyr/Shutterstock.com

Data can be remotely recorded on ground deformation, heat transfer, volcanic degassing, and ash dispersal, allowing for real-time observations and rapid mitigation responses to reduce economic costs and save lives. The use of traditional fieldwork and ground sensors can be challenging under certain conditions, such as those experiencing political unrest or being geographically isolated.

Remote sensing can now be used in places where researchers had previously relied on local communities for information.

Thermal Imaging for Volcano Monitoring

Many volcano observatories use a system that analyses satellite data from NASA’s MODIS (Moderate Resolution Imaging Spectroradiometer) sensors. This data is used to identify areas of high temperature that indicate volcanic activity. MODIS has a resolution of one kilometer, but it can detect features on a scale of tens of meters, such as lava flows and lakes. These satellites orbit the globe every forty-eight hours, which means that scientists analyzing MODIS data can detect eruptions anywhere in the world shortly after they occur.

The method that has been designed to analyze the MODIS data, MODVOLC (MODIS Thermal Alert System), uses algorithms that clean the data by rapidly deleting all the one-kilometer pixels that do not contain thermal hotspots. This cleaning process saves researchers time on data analysis and ensures that volcano modeling occurs almost in real-time.

Measuring Ground Deformation

Ground deformation occurs due to the movement of magma and other fluids in the subsurface before, throughout, and after an eruption (USGS). In the days preceding the eruption of Mount St Helens in 1980, the dome that appeared on its northern slope was growing by five feet every day. This relationship between ground deformation and volcanic activity means that monitoring this phenomenon is vital for prediction models.

Observing the early warnings of an eruption is crucial for delivering early warnings and recommending evacuation procedures to local authorities.

Ground-based monitoring approaches often use GPS sensors on the flanks of volcanoes (USGS), which give information on altitude and coordinates.

As a volcano swells, the GPS sensors record the change in position and track the movement of the slopes. However, ground-based monitoring techniques require intense fieldwork, which can be very expensive and sometimes be dangerous.

Remote sensing solves this problem by using Interferometric Synthetic Aperture Radar (InSAR) to track changes in altitude. InSAR techniques overlay images of an area taken at different times; if there has been subsidence or uplift, the light recorded by the satellite will have a different wavelength to the previous images (USGS).

The InSAR data is then used to produce interferograms that graphically show any changes in altitude. As well as its extensive spatial coverage, this method can be more cost-effective than ground-based techniques that require intense fieldwork to be conducted. 

Satellite Monitoring of Volcanic Ash 

Satellites are also used for more short-term monitoring, such as real-time tracking of volcanic ash clouds. Scientists at the Alaska Volcano Observatory (AVO) use MODIS and Advanced Very High-Resolution Radiometer (AVHRR) data, which they receive directly from the satellites and can analyze within minutes.

Volcanic ash is less harmful to human life when compared with other aspects of eruptions, carries a high economic risk. The 2010 eruption of Eyjafjallajökull is estimated to have reduced the global GDP by five billion USD. Using satellites for short-term monitoring of volcanic ash clouds can help air companies navigate them and reduce the economic loss to the industry.

Remote Classification of Volcanoes and Their Deposits 

A study used a multi-sensor unoccupied aerial system (UAS) to distinguish between the old lava and tephra deposits that were formed by the 2018 eruption of Sierra Negra in the Galapagos Islands.

Thermal Infrared (TIR) Images were used, and researchers found that the different deposits had different thermal heating rates associated with their density and size. Volcanic tephra, fragments of rock that are shot into the air by the volcano, were noted to have greater solar heating rates than lavas. The ‘a’a lava flows were found to have the lowest solar heating rates, and the pahoehoe flows were intermediate.

Using TIR images and a digital elevation model produced by photogrammetry, the study successfully identified the different deposits and lava forms that covered the volcano. 

These findings are important because different volcanic deposits carry different risks. A’a’ lava flows move more slowly than the pahoehoe and do not travel as far. Aerial projectiles such as tephra travel rapidly and can cause damage to property and injury without warning. It is essential to be able to classify volcanoes and their deposits to produce effective mitigation strategies.

The Future of Remote Sensing in Volcano Modelling

Remote sensing is a powerful tool that can fill in the gaps left by ground-based techniques and give information in a broader context concerning the interactions between volcanoes and their environments. However, the dense spatio-temporal information recorded takes a long time to analyze.

This density of information requires computer algorithms that can rapidly sort the data, meaning that the future of remote sensing may depend on machine learning as our capacity to record data increases.

Volcano monitoring groups must use the full spectrum of remote sensing methods to effectively monitor eruptions, as each method described in this article can only provide a part of the solution.

One project taking on this challenge is MOUNTS, which aims to merge all remote sensing methods with AI algorithms to develop a complete global volcano monitoring program.

Continue reading: Preventative Measures for Natural Disasters: the Role of Sensors and IoT Technologies.

References and Further Reading:

Schmidt, L.J., (2004) Sensing Remote Volcanoes. Supporting Earth Observing Science 2004, p.12. Available at: https://earthdata.nasa.gov/learn/sensing-our-planet/sensing-remote-volcanoes

Carr, B.B., Lev, E., Sawi, T., Bennett, K.A., Edwards, C.S., Soule, S.A., Vargas, S.V. and Marliyani, G.I., (2021) Mapping and classification of volcanic deposits using multi-sensor unoccupied aerial systems. Remote Sensing of Environment264, p.112581. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0034425721003011?via%3Dihub

Coppola, D., et al. (2020) Thermal remote sensing for global volcano monitoring: experiences from the MIROVA system. Frontiers in Earth Science7, p.362. Available at: https://www.frontiersin.org/articles/10.3389/feart.2019.00362/full

Valade, S., Ley, A., Massimetti, F., D’Hondt, O., Laiolo, M., Coppola, D., Loibl, D., Hellwich, O. and Walter, T.R., (2019) Towards global volcano monitoring using multi-sensor sentinel missions and artificial intelligence: The MOUNTS monitoring system. Remote Sensing11(13), p.1528. Available at: https://www.mdpi.com/2072-4292/11/13/1528

USGS. Movement on the Surface Provides Information About the Subsurface. [Online] Volcano Hazards Program. Available at: https://www.usgs.gov/natural-hazards/volcano-hazards/movement-surface-provides-information-about-subsurface.

Wright, T.J., Parsons, B.E. and Lu, Z., (2004) Toward mapping surface deformation in three dimensions using InSAR. Geophysical Research Letters31(1). Available at: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2003GL018827

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.


Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Orsi, Aarran. (2021, December 15). Remote Sensing for Volcanic Activity and Classifications. AZoSensors. Retrieved on December 10, 2022 from https://www.azosensors.com/article.aspx?ArticleID=2390.

  • MLA

    Orsi, Aarran. "Remote Sensing for Volcanic Activity and Classifications". AZoSensors. 10 December 2022. <https://www.azosensors.com/article.aspx?ArticleID=2390>.

  • Chicago

    Orsi, Aarran. "Remote Sensing for Volcanic Activity and Classifications". AZoSensors. https://www.azosensors.com/article.aspx?ArticleID=2390. (accessed December 10, 2022).

  • Harvard

    Orsi, Aarran. 2021. Remote Sensing for Volcanic Activity and Classifications. AZoSensors, viewed 10 December 2022, https://www.azosensors.com/article.aspx?ArticleID=2390.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this article?

Leave your feedback
Your comment type