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Open AccessArticle

The 2014–2015 Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface

1
Institute of Earth Sciences, University of Iceland, Sturlugata 7, 101 Reykjavík, Iceland
2
Faculty of Electrical and Computer Engineering, University of Iceland, Hjardarhagi 2-7, 107 Reykjavik, Iceland
3
Faculty of Earth Sciences, University of Iceland, Sturlugata 7, 101 Reykjavík, Iceland
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(5), 476; https://doi.org/10.3390/rs11050476
Received: 31 January 2019 / Revised: 18 February 2019 / Accepted: 18 February 2019 / Published: 26 February 2019
(This article belongs to the Special Issue Remote Sensing of Volcanic Processes and Risk)
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 data were 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 from each 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. View Full-Text
Keywords: hyperspectral; FENIX; lava field; SMACC; LSMA hyperspectral; FENIX; lava field; SMACC; LSMA
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Aufaristama, M.; Hoskuldsson, A.; Ulfarsson, M.O.; Jonsdottir, I.; Thordarson, T. The 2014–2015 Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface. Remote Sens. 2019, 11, 476.

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