  Lichens are symbiotic organisms composed of fungi, algae, and/or
cyanobacteria that thrive in a variety of environments. They play important
roles in carbon and nitrogen cycling, and contribute directly and indirectly to
biodiversity. Ecologists typically monitor lichens by using them as indicators
to assess air quality and habitat conditions. In particular, epiphytic lichens,
which live on trees, are key markers of air quality and environmental health. A
new method of monitoring epiphytic lichens involves using time-lapse cameras to
gather images of lichen populations. These cameras are used by ecologists in
Newfoundland and Labrador to subsequently analyze and manually segment the
images to determine lichen thalli condition and change. These methods are
time-consuming and susceptible to observer bias. In this work, we aim to
automate the monitoring of lichens over extended periods and to estimate their
biomass and condition to facilitate the task of ecologists. To accomplish this,
our proposed framework uses semantic segmentation with an effective training
approach to automate monitoring and biomass estimation of epiphytic lichens on
time-lapse images. We show that our method has the potential to significantly
improve the accuracy and efficiency of lichen population monitoring, making it
a valuable tool for forest ecologists and environmental scientists to evaluate
the impact of climate change on Canada's forests. To the best of our knowledge,
this is the first time that such an approach has been used to assist ecologists
in monitoring and analyzing epiphytic lichens.
