Although generally given little attention in vegetation studies, ground-dwelling (terricolous) lichens are major
contributors to overall carbon and nitrogen cycling, albedo, biodiversity and biomass in many high-latitude
ecosystems. Changes in biomass of mat-forming pale lichens have the potential to affect vegetation, fauna,
climate and human activities including reindeer husbandry. Lichens have a complex spectral signature and
terricolous lichens have limited growth height, often growing in mixtures with taller vegetation. This has, so far,
prevented the development of remote sensing techniques to accurately assess lichen biomass, which would be a
powerful tool in ecosystem and ecological research and rangeland management. We present a Landsat based
remote sensing model developed using deep neural networks, trained with 8914 field records of lichen volume
collected for >20 years. In contrast to earlier proposed machine learning and regression methods for lichens, our
model exploited the ability of neural networks to handle mixed spatial resolution input. We trained candidate
models using input of 1 × 1 (30 × 30 m) and 3 × 3 Landsat pixels based on 7 reflective bands and 3 indices,
combined with a 10 m spatial resolution digital elevation model. We normalised elevation data locally for each
plot to remove the region-specific variation, while maintaining informative local variation in topography. The
final model predicted lichen volume in an evaluation set (n = 159) reaching an R2 of 0.57. NDVI and elevation
were the most important predictors, followed by the green band. Even with moderate tree cover density, the
model was efficient, offering a considerable improvement compared to earlier methods based on specific
reflectance. The model was in principle trained on data from Scandinavia, but when applied to sites in North
America and Russia, the predictions of the model corresponded well with our visual interpretations of lichen
abundance. We also accurately quantified a recent historic (35 years) change in lichen abundance in northern
Norway. This new method enables further spatial and temporal studies of variation and changes in lichen
biomass related to multiple research questions as well as rangeland management and economic and cultural
ecosystem services. Combined with information on changes in drivers such as climate, land use and management,
and air pollution, our model can be used to provide accurate estimates of ecosystem changes and to improve
vegetation-climate models by including pale lichens.
Remote sensing
Lichens
Terricolous lichens
Deep neural networks
Artificial intelligence
cladonia
Reindeer lichen
Light lichens
Light coloured lichens
Pale lichens
Landsat
