ROUGHNESS ANALYSIS OF THE HOLUHRAUN LAVA FLOW-FIELD FOR LUNAR AND MARTIAN VOLCANIC ANALOGUES

We used radar and LiDAR remote sensing data to understand the roughness, morphology and emplacement processes of the 2014‒2015 Holuhraun lava flow field in Iceland, as an analogue for lunar and Martian lava flows. Remote sensing observations alone can provide general distinctions between smooth and rough lava flows. However, more information is required for us to quantitatively distinguish lava flow types on other worlds, especially those that exhibit similar roughness and morphological appearances but different emplacement processes and volcanic histories (e.g., transitional rubbly pāhoehoe vs `a`ā). On Earth, we use ground-truth measurements acquired in the field to confirm remote sensing interpretations, but on the Moon and Mars in-situ data is limited or non-existent. By studying and comparing analogous terrestrial lava flows at multiple roughness scales using remote sensing data, we can provide further insight into the emplacement processes of lava flows on the Moon and Mars. In this study, we conduct a detailed analysis of the surface roughness of the Holuhraun lava flow-field at the cm and dm-scale. Up to five lava flow types are present at Holuhraun, each with a unique surface roughness characteristic: spiny, rubbly, pāhoehoe-like, `a`ā-like and shelly.

We quantified dm and cm-scale roughness by calculating the circular polarization ratio (CPR) from UAVSAR L-Band data and roughness statistics (RMS slope (Cs) and Hurst exponent (H)) from LiDAR (2.5‒5 cm/pixel). Unlike dm-scale roughness, which can be inferred from radar observations of other worlds, cm-scale roughness is not widely available in planetary data systems. By seeking a correlation between the dm and cm-scale roughness, we can infer the cm-scale roughness properties of lava flows on the Moon and Mars with analogous dm-scale roughness. Our preliminary results show no quantifiable method to distinguish the lava flow types using CPR, despite there being contrasting backscatter signatures in the data.