  Lichens, organisms resulting from a symbiosis between a fungus and an algae,
are frequently used as age estimators, especially in recent geological deposits
and archaeological structures, using the correlation between lichen size and
age. Current non-automated manual lichen and measurement (with ruler, calipers
or using digital image processing tools) is a time-consuming and laborious
process, especially when the number of samples is high.
  This work presents a workflow and set of image acquisition and processing
tools developed to efficiently identify lichen thalli in flat rocky surfaces,
and to produce relevant lichen size statistics (percentage cover, number of
thalli, their area and perimeter).
  The developed workflow uses a regular digital camera for image capture along
with specially designed targets to allow for automatic image correction and
scale assignment. After this step, lichen identification is done in a flow
comprising assisted image segmentation and classification based on interactive
foreground extraction tool (GrabCut) and automatic classification of images
using Simple Linear Iterative Clustering (SLIC) for image segmentation and
Support Vector Machines (SV) and Random Forest classifiers.
  Initial evaluation shows promising results. The manual classification of
images (for training) using GrabCut show an average speedup of 4 if compared
with currently used techniques and presents an average precision of 95\%. The
automatic classification using SLIC and SVM with default parameters produces
results with average precision higher than 70\%. The developed system is
flexible and allows a considerable reduction of processing time, the workflow
allows it applicability to data sets of new lichen populations.
