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

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 hy...

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Published in:Remote Sensing
Main Authors: Muhammad Aufaristama, Armann Hoskuldsson, Magnus Orn Ulfarsson, Ingibjorg Jonsdottir, Thorvaldur Thordarson
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2019
Subjects:
Online Access:https://doi.org/10.3390/rs11050476
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spelling ftmdpi:oai:mdpi.com:/2072-4292/11/5/476/ 2023-08-20T04:07:24+02:00 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 agris 2019-02-26 application/pdf https://doi.org/10.3390/rs11050476 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs11050476 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 11; Issue 5; Pages: 476 hyperspectral FENIX lava field SMACC LSMA Text 2019 ftmdpi https://doi.org/10.3390/rs11050476 2023-07-31T22:04:22Z 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. Text Iceland MDPI Open Access Publishing Holuhraun ENVELOPE(-16.831,-16.831,64.852,64.852) Remote Sensing 11 5 476
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic hyperspectral
FENIX
lava field
SMACC
LSMA
spellingShingle hyperspectral
FENIX
lava field
SMACC
LSMA
Muhammad Aufaristama
Armann Hoskuldsson
Magnus Orn Ulfarsson
Ingibjorg Jonsdottir
Thorvaldur Thordarson
The 2014–2015 Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface
topic_facet hyperspectral
FENIX
lava field
SMACC
LSMA
description 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.
format Text
author Muhammad Aufaristama
Armann Hoskuldsson
Magnus Orn Ulfarsson
Ingibjorg Jonsdottir
Thorvaldur Thordarson
author_facet Muhammad Aufaristama
Armann Hoskuldsson
Magnus Orn Ulfarsson
Ingibjorg Jonsdottir
Thorvaldur Thordarson
author_sort Muhammad Aufaristama
title The 2014–2015 Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface
title_short The 2014–2015 Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface
title_full The 2014–2015 Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface
title_fullStr The 2014–2015 Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface
title_full_unstemmed The 2014–2015 Lava Flow Field at Holuhraun, Iceland: Using Airborne Hyperspectral Remote Sensing for Discriminating the Lava Surface
title_sort 2014–2015 lava flow field at holuhraun, iceland: using airborne hyperspectral remote sensing for discriminating the lava surface
publisher Multidisciplinary Digital Publishing Institute
publishDate 2019
url https://doi.org/10.3390/rs11050476
op_coverage agris
long_lat ENVELOPE(-16.831,-16.831,64.852,64.852)
geographic Holuhraun
geographic_facet Holuhraun
genre Iceland
genre_facet Iceland
op_source Remote Sensing; Volume 11; Issue 5; Pages: 476
op_relation Remote Sensing in Geology, Geomorphology and Hydrology
https://dx.doi.org/10.3390/rs11050476
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs11050476
container_title Remote Sensing
container_volume 11
container_issue 5
container_start_page 476
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