  Monitoring the distribution and size structure of long-living shrubs, such as
Juniperus communis, can be used to estimate the long-term effects of climate
change on high-mountain and high latitude ecosystems. Historical aerial
very-high resolution imagery offers a retrospective tool to monitor shrub
growth and distribution at high precision. Currently, deep learning models
provide impressive results for detecting and delineating the contour of objects
with defined shapes. However, adapting these models to detect natural objects
that express complex growth patterns, such as junipers, is still a challenging
task.
  This research presents a novel approach that leverages remotely sensed RGB
imagery in conjunction with Mask R-CNN-based instance segmentation models to
individually delineate Juniperus shrubs above the treeline in Sierra Nevada
(Spain). In this study, we propose a new data construction design that consists
in using photo interpreted (PI) and field work (FW) data to respectively
develop and externally validate the model. We also propose a new shrub-tailored
evaluation algorithm based on a new metric called Multiple Intersections over
Ground Truth Area (MIoGTA) to assess and optimize the model shrub delineation
performance. Finally, we deploy the developed model for the first time to
generate a wall-to-wall map of Juniperus individuals.
  The experimental results demonstrate the efficiency of our dual data
construction approach in overcoming the limitations associated with traditional
field survey methods. They also highlight the robustness of MIoGTA metric in
evaluating instance segmentation models on species with complex growth patterns
showing more resilience against data annotation uncertainty. Furthermore, they
show the effectiveness of employing Mask R-CNN with ResNet101-C4 backbone in
delineating PI and FW shrubs, achieving an F1-score of 87,87% and 76.86%,
respectively.
