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

2020/07/17 by Sarah Kentsch, Savvas Karatsiolis, Kentsch, Sarah +7
Chemistry · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Remote Sensing and LiDAR Applications #Remote Sensing in Agriculture #Wood and Agarwood Research #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2007.08907

openalex publication_date 2020/07/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

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).

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