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Title: Anatomical traits of Cryptomeria japonica tree rings studied by wavelet convolutional neural network
Authors: Nakajima, T
Kobayashi, K  kyouindb  KAKEN_id  orcid (unconfirmed)
Sugiyama, J  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 杉山, 淳司
Issue Date: Feb-2020
Publisher: IOP publishing
Journal title: IOP Conference Series: Earth and Environmental Science
Volume: 415
Thesis number: 012027
Abstract: Tree ring analysis is an important field of science, and is vital in modeling the environmental response system of tree growth. In most cases, analyses have been conducted using one parameter from one tree ring, e.g., ring-width, density, or ratio of stable isotopes. The information within a ring, however, has been less studied, although it offers many more possibilities for investigation, such as seasonal responses over shorter time scales. Therefore, to elucidate the sub-seasonal climatic response of softwood (Cryptomeria japonica), we investigate the use of a wavelet–convolutional neural network (CNN) model, which incorporates spectral information that is normally lost in conventional CNN models. This paper highlights the usefulness of the wavelet-CNN for classifying cross-sectional optical micrographs and extracting structural information specific to a calendar year. Class activation maps indicate that the dimension and position of cells in a radial file are likely to be discriminative features for the wavelet-CNN. This study shows that wavelet-CNNs have the potential to be highly effective methods for dendrochronology.
Description: INAFOR EXPO 2019 - International Conference on Forest Products (ICFP) 2019: Adopting the Renewable Bioenergy and Waste Utilization to Support Circular Economy and Sustainable Environment 28 August 2019, Bogor, West Java, Indonesia
Rights: Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
DOI(Published Version): 10.1088/1755-1315/415/1/012027
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