Skip to content

Colonization of basaltic lava by lichens at Fagradalsfjall volcano (Iceland) imaged using drone-based hyperspectral remote sensing

Eva Wickert 1, Benjamin F. De Jarnatt2,3, Thomas R. Walter2,3, Samuel T. Thiele4, Bastian Siegmann5, Claudia Colesie6, Nicole Richter1

  • Affiliations:  1Institute of Natural Hazards and Georisks, RWTH Aachen University, Germany, 2German Research Centre for Geosciences (GFZ), Potsdam, Germany, 3Institute of Geosciences, University of Potsdam, 4Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Germany, 5Institute for Bio- and Geosciences, IBG-2: Plant Science, Forschungszentrum Jülich GmbH, Jülich, Germany, 6School of Geosciences, University of Edinburgh, United Kingdom 

  • Presentation type: Talk

  • Presentation time: Monday 09:00 - 09:15, Room S150

  • Programme No: 3.1.3

  • Theme 3 > Session 1

Abstract

Lichens are among the first organisms to colonize cooled lava flows, offering insights into volcanic landscapes, including, e.g., relative age and weathering intensity. Substrate properties such as porosity, permeability, texture, and mineral composition play an essential role in lichen community establishment. While lichens primarily obtain nutrients and minerals from the air, they are also considered to engage in a complex chemical exchange with the underlying rock surface. However, the relationship between lichen occurrence and the geochemical composition of lava remains underexplored. Investigating these rock-lichen interactions could provide valuable insights into the geochemical characteristics of volcanic materials. To deepen our understanding of interactions between lichens and volcanic rock, it is crucial to study the distribution patterns of various lichen species across different volcanic substrates. We acquired drone-based hyperspectral image data, together with field spectrometer point measurements of lichens on basaltic lava at the Fagradalsfjall volcano (Iceland) in August 2023 to characterize lichens' spectral signatures and assess their detectability for remote mapping. Using an open-source, Python-based (post-)processing workflow, we demonstrate the potential and challenges of our multi-sensor approach for mapping lichens on volcanic rocks. Although the results showed that different sensor characteristics caused offsets in the spectral signatures, we successfully derived meaningful band ratios that highlighted the presence of distinct lichen groups on the basaltic rock. These findings demonstrate the potential of drone-based hyperspectral imaging for lichen detection in volcanic environments, and thus pave the way for future studies that provide deeper insights into rock-lichen interactions.