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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MDPI AG
2020-03-01
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| Series: | Geosciences |
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| Online Access: | https://www.mdpi.com/2076-3263/10/4/125 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T20:47:11Z |
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| id | doaj.art-bcee7997e2a24faaa904313490fae391 |
| institution | Directory Open Access Journal |
| issn | 2076-3263 |
| language | English |
| last_indexed | 2024-03-10T20:47:11Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geosciences |
| spelling | doaj.art-bcee7997e2a24faaa904313490fae3912023-11-19T20:13:46ZengMDPI AGGeosciences2076-32632020-03-0110412510.3390/geosciences10040125Lava Flow Roughness on the 2014–2015 Lava Flow-Field at Holuhraun, Iceland, Derived from Airborne LiDAR and PhotogrammetryMuhammad Aufaristama0Ármann Höskuldsson1Magnus Orn Ulfarsson2Ingibjörg Jónsdóttir3Thorvaldur Thordarson4Institute of Earth Sciences, University of Iceland, Sturlugata 7, 101 Reykjavík, IcelandInstitute of Earth Sciences, University of Iceland, Sturlugata 7, 101 Reykjavík, IcelandFaculty of Electrical and Computer Engineering, University of Iceland, Hjardarhagi 2-7, 107 Reykjavik, IcelandInstitute of Earth Sciences, University of Iceland, Sturlugata 7, 101 Reykjavík, IcelandInstitute of Earth Sciences, University of Iceland, Sturlugata 7, 101 Reykjavík, IcelandRoughness 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.https://www.mdpi.com/2076-3263/10/4/125lava roughnessTPIHurst exponentLiDARphotogrammetry |
| spellingShingle | 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 Geosciences lava roughness TPI Hurst exponent LiDAR photogrammetry |
| title | 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_short | 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 |
| topic | lava roughness TPI Hurst exponent LiDAR photogrammetry |
| url | https://www.mdpi.com/2076-3263/10/4/125 |
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