LiDAR-derived surface roughness signatures of basaltic lava types at the Muliwai a Pele Lava Channel, Mauna Ulu, Hawai‘i

Patrick L. Whelley, W. Brent Garry, Christopher W. Hamilton, Jacob E. Bleacher

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

We used light detection and ranging (LiDAR) data to calculate roughness patterns (homogeneity, mean-roughness, and entropy) for five lava types at two different resolutions (1.5 and 0.1 m/pixel). We found that end-member types (ʻaʻā and pāhoehoe) are separable (with 95% confidence) at both scales, indicating that roughness patterns are well suited for analyzing types of lava. Intermediate lavas were also explored, and we found that slabby-pāhoehoe is separable from the other end-members using 1.5 m/pixel data, but not in the 0.1 m/pixel analysis. This suggests that the conversion from pāhoehoe to slabby-pāhoehoe is a meter-scale process, and the finer roughness characteristics of pāhoehoe, such as ropes and toes, are not significantly affected. Furthermore, we introduce the ratio ENTHOM (derived from lava roughness) as a proxy for assessing local lava flow rate from topographic data. High entropy and low homogeneity regions correlate with high flow rate while low entropy and high homogeneity regions correlate with low flow rate. We suggest that this relationship is not directional, rather it is apparent through roughness differences of the associated lava type emplaced at the high and low rates, respectively.

Original languageEnglish (US)
Article number75
JournalBulletin of Volcanology
Volume79
Issue number11
DOIs
StatePublished - Nov 1 2017

Keywords

  • Lava
  • LiDAR
  • Roughness
  • Statistics

ASJC Scopus subject areas

  • Geochemistry and Petrology

Fingerprint

Dive into the research topics of 'LiDAR-derived surface roughness signatures of basaltic lava types at the Muliwai a Pele Lava Channel, Mauna Ulu, Hawai‘i'. Together they form a unique fingerprint.

Cite this