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

Sarah Kentsch (), Savvas Karatsiolis (), Andreas Kamilaris (), Luca Tomhave () and Maximo Larry Lopez Caceres ()
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Sarah Kentsch: Yamagata University
Savvas Karatsiolis: Research Centre On Interactive Media, Smart Systems and Emerging Technologies (RISE)
Andreas Kamilaris: Research Centre On Interactive Media, Smart Systems and Emerging Technologies (RISE)
Luca Tomhave: Yamagata University
Maximo Larry Lopez Caceres: Yamagata University

A chapter in Advances and New Trends in Environmental Informatics, 2021, pp 255-270 from Springer

Abstract: 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).

Keywords: Aerial photography; Classification; Deep learning; Forestry; Mixed forests; Tree species (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-3-030-61969-5_18

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