Identification of Tree Species in Japanese Forests based on Aerial Photography and Deep Learning

TL;DR

This study explores using UAV imagery and deep learning to classify tree species in Japanese forests, demonstrating promising results for identifying certain species despite some limitations.

Contribution

It introduces a novel application of UAV-based imagery and deep learning for tree species classification in Japanese mixed forests.

Findings

Black locust trees identified with 62.6% TP and 98.1% TN.

Larch trees had lower identification accuracy, with 37.4% TP and 97.7% TN.

The approach shows potential for forest management and ecological research.

Abstract

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).