Anatomical traits of Cryptomeria japonica tree rings studied by wavelet convolutional neural network

HANDLE Open Access
  • Nakajima, T
    Research Institute for Sustainable Humanosphere, Kyoto University
  • Kobayashi, K
    Graduate School of Agricultural and Life Sciences, The University of Tokyo
  • Sugiyama, J
    Research Institute for Sustainable Humanosphere, Kyoto University･College of Materials Science and Engineering, Nanjing Forestry University

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.

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

Journal

Details 詳細情報について

  • CRID
    1050007846242165632
  • NII Article ID
    120006799263
  • ISSN
    17551315
  • HANDLE
    2433/245858
  • Text Lang
    en
  • Article Type
    conference paper
  • Data Source
    • IRDB
    • CiNii Articles

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