Mapping and classification of volcanic deposits using multi-sensor unoccupied aerial systems

Brett B. Carr, Einat Lev, Theresa Sawi, Kristen A. Bennett, Christopher S. Edwards, S. Adam Soule, Silvia Vallejo Vargas, Gayatri Indah Marliyani

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The deposits from volcanic eruptions represent the record of activity at a volcano. Identification, classification, and interpretation of these deposits are crucial to the understanding of volcanic processes and assessing hazards. However, deposits often cover large areas and can be difficult or dangerous to access, making field mapping hazardous and time-consuming. Remote sensing techniques are often used to map and identify the deposits of volcanic eruptions, though these techniques present their own trade-offs in terms of image resolution, wavelength, and observation frequency. Here, we present a new approach for mapping and classifying volcanic deposits using a multi-sensor unoccupied aerial system (UAS) and demonstrate its application on lava and tephra deposits associated with the 2018 eruption of Sierra Negra volcano (Galápagos Archipelago, Ecuador). We surveyed the study area and collected visible and thermal infrared (TIR) images. We used structure-from-motion photogrammetry to create a digital elevation model (DEM) from the visual images and calculated the solar heating rate of the surface from temperature maps based on the TIR images. We find that the solar heating rate is highest for tephra deposits and lowest for ʻaʻā lava, with pāhoehoe lava having intermediate values. This is consistent with the solar heating rate correlating to the density and particle size of the surface. The solar heating rate for the lava flow also decreases with increasing distance from the vent, consistent with an increase in density as the lava degasses. We combined the surface roughness (calculated from the DEM) and the solar heating rate of the surface to remotely classify tephra deposits and different lava morphologies. We applied both supervised and unsupervised machine learning algorithms. A supervised classification method can replicate the manual classification while the unsupervised method can identify major surface units with no ground truth information. These methods allow for remote mapping and classification at high spatial resolution (< 1 m) of a variety of volcanic deposits, with potential for application to deposits from other processes (e.g., fluvial, glacial) and deposits on other planetary bodies.

Original languageEnglish (US)
Article number112581
JournalRemote Sensing of Environment
Volume264
DOIs
StatePublished - Oct 2021

Keywords

  • Land surface classification
  • Lava flows
  • Mapping of volcanic deposits
  • Thermal remote sensing
  • Unoccupied aerial systems (UAS)

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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