Lava Flow Roughness on the 2014–2015 Lava Flow-Field at Holuhraun, Iceland, Derived from Airborne LiDAR and Photogrammetry
Roughness can be used to characterize the morphologies of a lava flow. It can be used to identify lava flow features, provide insight into eruption conditions, and link roughness pattern across a lava flow to emplacement conditions. In this study, we use both the topographic position index (TPI) and...
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ftmdpi:oai:mdpi.com:/2076-3263/10/4/125/ 2023-08-20T04:07:30+02:00 Lava Flow Roughness on the 2014–2015 Lava Flow-Field at Holuhraun, Iceland, Derived from Airborne LiDAR and Photogrammetry Muhammad Aufaristama Ármann Höskuldsson Magnus Orn Ulfarsson Ingibjörg Jónsdóttir Thorvaldur Thordarson agris 2020-03-31 application/pdf https://doi.org/10.3390/geosciences10040125 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/geosciences10040125 https://creativecommons.org/licenses/by/4.0/ Geosciences; Volume 10; Issue 4; Pages: 125 lava roughness TPI Hurst exponent LiDAR photogrammetry Text 2020 ftmdpi https://doi.org/10.3390/geosciences10040125 2023-07-31T23:18:38Z Roughness can be used to characterize the morphologies of a lava flow. It can be used to identify lava flow features, provide insight into eruption conditions, and link roughness pattern across a lava flow to emplacement conditions. In this study, we use both the topographic position index (TPI) and the one-dimensional Hurst exponent (H) to derive lava flow unit roughness on the 2014–2015 lava field at Holuhraun using both airborne LiDAR and photogrammetric datasets. The roughness assessment was acquired from four lava flow features: (1) spiny lava, (2) lava pond, (3) blocky surface, and (4) inflated channel. The TPI patterns on spiny lava and inflated channels show that the intermediate TPI values correspond to a small surficial slope indicating a flat and smooth surface. Lava pond is characterized by low to high TPI values and forms a wave-like pattern. Meanwhile, irregular transitions patterns from low to high TPI values indicate a rough surface that is found in blocky surface and flow margins. The surface roughness of these lava features falls within the H range of 0.30 ± 0.05 to 0.76 ± 0.04. The roughest surface is the blocky, and inflated lava flows appear to be the smoothest surface among these four lava units. In general, the Hurst exponent values in the 2014–2015 lava field at Holuhraun has a strong tendency in 0.5, both TPI and Hurst exponent successfully derive quantitative flow roughness. Text Iceland MDPI Open Access Publishing Holuhraun ENVELOPE(-16.831,-16.831,64.852,64.852) Geosciences 10 4 125 |
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Open Polar |
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MDPI Open Access Publishing |
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language |
English |
topic |
lava roughness TPI Hurst exponent LiDAR photogrammetry |
spellingShingle |
lava roughness TPI Hurst exponent LiDAR photogrammetry Muhammad Aufaristama Ármann Höskuldsson Magnus Orn Ulfarsson Ingibjörg Jónsdóttir Thorvaldur Thordarson Lava Flow Roughness on the 2014–2015 Lava Flow-Field at Holuhraun, Iceland, Derived from Airborne LiDAR and Photogrammetry |
topic_facet |
lava roughness TPI Hurst exponent LiDAR photogrammetry |
description |
Roughness can be used to characterize the morphologies of a lava flow. It can be used to identify lava flow features, provide insight into eruption conditions, and link roughness pattern across a lava flow to emplacement conditions. In this study, we use both the topographic position index (TPI) and the one-dimensional Hurst exponent (H) to derive lava flow unit roughness on the 2014–2015 lava field at Holuhraun using both airborne LiDAR and photogrammetric datasets. The roughness assessment was acquired from four lava flow features: (1) spiny lava, (2) lava pond, (3) blocky surface, and (4) inflated channel. The TPI patterns on spiny lava and inflated channels show that the intermediate TPI values correspond to a small surficial slope indicating a flat and smooth surface. Lava pond is characterized by low to high TPI values and forms a wave-like pattern. Meanwhile, irregular transitions patterns from low to high TPI values indicate a rough surface that is found in blocky surface and flow margins. The surface roughness of these lava features falls within the H range of 0.30 ± 0.05 to 0.76 ± 0.04. The roughest surface is the blocky, and inflated lava flows appear to be the smoothest surface among these four lava units. In general, the Hurst exponent values in the 2014–2015 lava field at Holuhraun has a strong tendency in 0.5, both TPI and Hurst exponent successfully derive quantitative flow roughness. |
format |
Text |
author |
Muhammad Aufaristama Ármann Höskuldsson Magnus Orn Ulfarsson Ingibjörg Jónsdóttir Thorvaldur Thordarson |
author_facet |
Muhammad Aufaristama Ármann Höskuldsson Magnus Orn Ulfarsson Ingibjörg Jónsdóttir Thorvaldur Thordarson |
author_sort |
Muhammad Aufaristama |
title |
Lava Flow Roughness on the 2014–2015 Lava Flow-Field at Holuhraun, Iceland, Derived from Airborne LiDAR and Photogrammetry |
title_short |
Lava Flow Roughness on the 2014–2015 Lava Flow-Field at Holuhraun, Iceland, Derived from Airborne LiDAR and Photogrammetry |
title_full |
Lava Flow Roughness on the 2014–2015 Lava Flow-Field at Holuhraun, Iceland, Derived from Airborne LiDAR and Photogrammetry |
title_fullStr |
Lava Flow Roughness on the 2014–2015 Lava Flow-Field at Holuhraun, Iceland, Derived from Airborne LiDAR and Photogrammetry |
title_full_unstemmed |
Lava Flow Roughness on the 2014–2015 Lava Flow-Field at Holuhraun, Iceland, Derived from Airborne LiDAR and Photogrammetry |
title_sort |
lava flow roughness on the 2014–2015 lava flow-field at holuhraun, iceland, derived from airborne lidar and photogrammetry |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/geosciences10040125 |
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 |
Geosciences; Volume 10; Issue 4; Pages: 125 |
op_relation |
https://dx.doi.org/10.3390/geosciences10040125 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/geosciences10040125 |
container_title |
Geosciences |
container_volume |
10 |
container_issue |
4 |
container_start_page |
125 |
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1774719166265163776 |