The 2014-2015 lava flow field at Holuhraun, Iceland: Using airborne hyperspectral remote sensing for discriminating the lava surface

Muhammad Aufaristama*, Armann Hoskuldsson, Magnus Orn Ulfarsson, Ingibjorg Jonsdottir, Thorvaldur Thordarson

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
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Abstract

The Holuhraun lava flow was the largest effusive eruption in Iceland for 230 years, with an estimated lava bulk volume of ~1.44 km3 and covering an area of ~84 km2. The six month long eruption at Holuhraun 2014-2015 generated a diverse surface environment. Therefore, the abundant data of airborne hyperspectral imagery above the lava field, calls for the use of time-efficient and accurate methods to unravel them. The hyperspectral data acquisition was acquired five months after the eruption finished, using an airborne FENIX-Hyperspectral sensor that was operated by the Natural Environment Research Council Airborne Research Facility (NERC-ARF). The datawere atmospherically corrected using the Quick Atmospheric Correction (QUAC) algorithm. Here we used the Sequential Maximum Angle Convex Cone (SMACC) method to find spectral endmembers and their abundances throughout the airborne hyperspectral image. In total we estimated 15 endmembers, and we grouped these endmembers into six groups; (1) basalt; (2) hot material; (3) oxidized surface; (4) sulfate mineral; (5) water; and (6) noise. These groups were based on the similar shape of the endmembers; however, the amplitude varies due to illumination conditions, spectral variability, and topography. We, thus, obtained the respective abundances fromeach endmember group using fully constrained linear spectral mixture analysis (LSMA). The methods offer an optimum and a fast selection for volcanic products segregation. However, ground truth spectra are needed for further analysis.

Original languageEnglish
Article number476
Pages (from-to)1-19
Number of pages19
JournalRemote sensing
Volume11
Issue number5
DOIs
Publication statusPublished - 1 Mar 2019
Externally publishedYes

Keywords

  • FENIX
  • Hyperspectral
  • Lava field
  • LSMA
  • SMACC
  • ITC-CV

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