  Natural forests are complex ecosystems whose tree species distribution and
their ecosystem functions are still not well understood. Sustainable management
of these forests is of high importance because of their significant role in
climate regulation, biodiversity, soil erosion and disaster prevention among
many other ecosystem services they provide. In Japan particularly, natural
forests are mainly located in steep mountains, hence the use of aerial imagery
in combination with computer vision are important modern tools that can be
applied to forest research. Thus, this study constitutes a preliminary research
in this field, aiming at classifying tree species in Japanese mixed forests
using UAV images and deep learning in two different mixed forest types: a black
pine (Pinus thunbergii)-black locust (Robinia pseudoacacia) and a larch (Larix
kaempferi)-oak (Quercus mongolica) mixed forest. Our results indicate that it
is possible to identify black locust trees with 62.6 % True Positives (TP) and
98.1% True Negatives (TN), while lower precision was reached for larch trees
(37.4% TP and 97.7% TN).
