TY - JOUR
T1 - The 2014-2015 lava flow field at Holuhraun, Iceland
T2 - Using airborne hyperspectral remote sensing for discriminating the lava surface
AU - Aufaristama, Muhammad
AU - Hoskuldsson, Armann
AU - Ulfarsson, Magnus Orn
AU - Jonsdottir, Ingibjorg
AU - Thordarson, Thorvaldur
PY - 2019/3/1
Y1 - 2019/3/1
N2 - 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.
AB - 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.
KW - FENIX
KW - Hyperspectral
KW - Lava field
KW - LSMA
KW - SMACC
KW - ITC-CV
U2 - 10.3390/rs11050476
DO - 10.3390/rs11050476
M3 - Article
AN - SCOPUS:85062949292
VL - 11
SP - 1
EP - 19
JO - Remote sensing
JF - Remote sensing
SN - 2072-4292
IS - 5
M1 - 476
ER -