Lava flow texture LiDAR signatures

Monday, 15 December 2014
Patrick Whelley1, William Brent Garry1, Stephen P Scheidt2, Rossman P Irwin III3, Jodi Fox4, Jacob E Bleacher1 and Christopher W Hamilton2, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)University of Arizona, Tucson, AZ, United States, (3)Smithsonian Institution, Washington, DC, United States, (4)University of Tasmania, Hobart, Australia
High-resolution point clouds and digital elevation models (DEMs) are used to investigate lava textures on the Big Island of Hawaii. An experienced geologist can distinguish fresh or degraded lava textures (e.g., blocky, a’a and pahoehoe) visually in the field. Lava texture depends significantly on eruption conditions, and it is therefore instructive, if accurately determined. In places where field investigations are prohibitive (e.g., Mercury, Venus, the Moon, Mars, Io and remote regions on Earth) lava texture must be assessed from remote sensing data. A reliable method for differentiating lava textures in remote sensing data remains elusive. We present preliminary results comparing properties of lava textures observed in airborne and terrestrial Light Detection and Ranging (LiDAR) data. Airborne data, in this study, were collected in 2011 by Airborne 1 Corporation and have a ~1m point spacing. The authors collected the terrestrial data during a May 2014 field season. The terrestrial scans have a heterogeneous point density. Points close to the scanner are 1 mm apart while 200 m in the distance points are 10 cm apart. Both platforms offer advantages and disadvantages beyond the differences in scale. Terrestrial scans are a quantitative representation of what a geologist sees “on the ground”. Airborne scans are a point of view routinely imaged by other remote sensing tools, and can therefore be quickly compared to complimentary data sets (e.g., spectral scans or image data). Preliminary results indicate that LiDAR-derived surface roughness, from both platforms, is useful for differentiating lava textures, but at different spatial scales. As all lava types are quite rough, it is not simply roughness that is the most advantageous parameter; rather patterns in surface roughness can be used to differentiate lava surfaces of varied textures. This work will lead to faster and more reliable volcanic mapping efforts for planetary exploration as well as terrestrial geohazards.