LiDAR-Derived Surface Roughness Signatures of Basaltic Lava Types at the Muliwai a Pele Lava Channel, Mauna Ulu, Hawai'i

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 (a a and pahoehoe) 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-pahoehoe 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 pahoehoe to slabby-pahoehoe is a meter-scale process, and the finer roughness characteristics of pahoehoe, such as ropes and toes, are not significantly affected. Furthermore, we introduce the ratio ENT/HOM (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