ICESat-2 Signal Response and Roughness Analysis Combined with ASTER Derived Thermal Measurements to Interpret Lava Flow Morphologies

Abstract:

Lidar and thermal infrared data each provide critical information of specific surface characteristics such as particle size and elevation respectively. Using these data products in tandem can enhance surface interpretation over what can be derived from each individually. This combined approach using remote data is especially applicable to volcanic surfaces which are highly variable and difficult to access. ICESat-2 ATL08 (land and vegetation) and ASTER thermal infrared data were utilized to assess the validity of this method and the ability to separate flow morphologies. This study investigated how flow characteristics affect the ICESat-2 signal response and developed a multi-instrument method to advance terrain characterization using parameters/metrics from both thermal and lidar data. To study endmember morphologies and capture a large age range, pahoehoe and a’a flows, with smooth and rough morphology respectively, were examined in the Mauna Loa, Kilauea, and Mauna Ulu flow fields, Hawaii. Comparison of the total number of identified ATL08 labeled photons per meter along-track demonstrate a strong dependency on age and morphology, with a decrease of roughly 50% (~1.9 to 0.8 points/m along-track) from 10000 B.P. to 1945. This trend is caused by the lower reflectance of younger flows that will not reflect as much of the laser pulse. The overall signal response over pahoehoe (smooth) flows is higher because rougher a’a surfaces will scatter the pulse. Evaluation of roughness metrics derived from ATL08 labeled photons and ASTER derived temperature establish an inverse linear trend over a’a flows. These flows with higher roughness values display moderate to low temperatures due to the higher emissivity values and shadows created by the rough surface features. Alternatively, pahoehoe flows show a strong correlation between age and roughness, where older flows display a comparatively lower value caused by the exposure to weathering over time. These unique signatures in thermal and lidar data based on flow age and morphology demonstrate how a combined approach can improve the interpretation of flow morphology and age remotely.