Remote sensing of lichens with drones for detecting dinosaur bones

Pickles, B. J. ORCID: https://orcid.org/0000-0002-9809-6455, Brown, C. M., Herridge-Berry, S., Martin, C. R., Dergousoff, M., Gilmar, T., Bell, P. R. and Peddle, D. R. (2025) Remote sensing of lichens with drones for detecting dinosaur bones. Current Biology, 35 (21). R1044-R1045. ISSN 1879-0445 doi: 10.1016/j.cub.2025.09.036

Abstract/Summary

Advances in palaeontology and evolutionary biology are often linked to the discovery of new fossil-bearing localities, yet these discoveries are typically serendipitous[1]. Here we report on modern organisms - lichens - that serve as biological indicators of vertebrate fossils in western North America and can be identified using remote sensing techniques. Lichens are symbioses between fungi and algae (and/or cyanobacteria) that play important ecological roles[2], and colonise many different substrates, including fossils[3]. Preferential colonisation of dinosaur bones by lichen (Figure 1A-B) has been anecdotally recognised for decades, with one palaeontologist speculating in 1980 that the vibrant orange pigmentation of lichens observed on densely colonised Centrosaurus fossils in Alberta, Canada, might be detectable using satellites (Darren H. Tanke, personal communication). Supporting the use of remote sensing for lichen detection, the spectral reflectance profiles of lichen pigments have previously been used as ecological indicator targets, for example to map caribou habitat[4]. Increasingly, remote sensing platforms are being used to study palaeontological sites[5], but our work is the first to document that a preferential association between modern lichens and ancient bones can be used to detect dinosaur fossils by remote sensing, for which we propose new spectral indices.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/124767
Identification Number/DOI	10.1016/j.cub.2025.09.036
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Ecology and Evolutionary Biology
Publisher	Elsevier
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