First Advisor

Anderson, Steven W.

Degree Name

Master of Arts

Document Type

Thesis

Date Created

8-2019

Department

College of Natural and Health Sciences, Earth and Atmospheric Sciences, EAS Student Work

Abstract

If patterns of lava flow surface roughness at large and small scales can be tied features at similar scales using observations of active volcanoes, then roughness across a lava flow can be related to eruption characteristics such as rate of flow, viscosity, and underlying slope. This will further current understanding of emplacement rates and styles during the volcanically active period of mars’ history. Additionally, describing the effect of the Martian environment on volcanism is necessary to learn the full range of possible volcanic activity in the Solar System. This will also provide insight regarding volcanic hazards here on Earth. To investigate lava flow roughness on Earth and Mars, I acquired high resolution topography for lava flows from Mauna Ulu, Hawaii, Obsidian Dome and Amboy, California using Structure from Motion and/or LiDAR, as well as topography data of Tharsis from the HiRISE camera on the Mars Reconnaissance Orbiter. Mauna Ulu and Amboy were used as earthly analogues for the range of possible lava flow surfaces on mars. I applied two new approaches to determining roughness on lava flows – the Topographic Position Index and Roughness Doughnut. The approaches presented here may allow scientists to observe much finer features in flow fields than previously possible, thus providing new insights about the quantitative relationships between surface morphology and eruption characteristics. Finally, I used Principal Component Analysis to better understand the relationships between terrestrial and martian roughness. iv The goal of this project was to develop an efficient and cost-effective method of roughness comparison that can be applied to a variety of volcanic environments and scales. Mauna Ulu offers an opportunity to observe young flows, but the dominant weathering processes in this humid, tropical location are significantly different from processes active on Mars. Lava flows at Amboy are older than those produced by Mauna Ulu, and display varying levels of mantling by wind-blown sand, similar to expectations of Mars. Using datasets from both locations, I described how martian lava flows compare to the range of roughness measurements at both terrestrial sites. I also sought to investigate the effect of mantling of aeolian material on lava flow roughness, and if roughness is a useful tool to detect mantled lava flow features on Mars. Additionally, I aimed to relate roughness data from the terrestrial locations to lava flow features visible in Structure from Motion and LiDAR digital elevation models. Finally, I discuss the use of these methods to map volcanic features and environments in new locations on Earth and on Mars. Though Obsidian Dome was not a central part of this project, 1 meter per pixel LiDAR data was used to illustrate the roughness differences between silicic and mafic lava flows. Roughness values are higher at Obsidian Dome than values at the other locations, at every scale tested. This is consistent with observations by Plaut et al. (2004). Results show that suspected basaltic lava flows on Mars show similarities to the range of roughness values for basaltic flows at Amboy, California and Mauna Ulu, Hawaii. Roughness values for the basaltic environments are significantly different from those of Obsidian Dome. I was able to use roughness of lava flows within and outside of the main wind shadow at Amboy to describe the effect of mantling on the lava topography. Though a roughness trend was observed across mantled surfaces in v California, it is not robust enough to be used as the only method to detect mantled lava flows on Mars. Finally, both the RD and TPI methods can be used to map volcanic environments but would benefit from additional datasets.

Extent

84 pages

Local Identifiers

JamesThesis2019

Rights Statement

Copyright is held by the author.

Download

Share

COinS