Abstract
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.
Highlights
In the Earth Sciences, surface roughness is important for modeling natural phenomena and classifying features of interest [1,2]
The surface roughness analysis results are shown as maps and profiles for each lava flow features
The roughness assessment was acquired from four lava flow features: (1) spiny lava, (2) lava pond, (3) blocky surface, and (4) inflated channel
Summary
In the Earth Sciences, surface roughness is important for modeling natural phenomena and classifying features of interest [1,2]. Statistical descriptors of surface morphology, or roughness, are found in many applications, including volcanology, especially for analyzing lava flows. Field observations have long been used in the study of surface roughness of lava flow [2,3,4,5,6]. These analyses are mostly based on in situ measurements, which require extended time in the field [2,6,7,8]. A grid is laid out on the sample surface, and heights are measured manually or with a profiling instrument [2,3,4] and continuous Global
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