Predicting Carbon Sequestered in an Even- Aged Sugi Forest Stand through Growth Pattern Classification

• Yanagihara H.

  筑波大学大学院システム情報工学研究科

• Yoshimoto A.

  東北大学大学院環境科学研究科

• Ninomiya Y.

  九州大学大学院数理学研究院

Bibliographic Information

Other Title

• 複数の成長パターンを持つスギ単純同齢林における炭素固定量予測

Abstract

In this paper, we presented a statistical procedure of estimating the clustered multivariate linear regression model for predicting the amount of carbon sequestered in a forest stand where there exist several growth patterns. The procedure is as follows: 1) By fitting a volume growth curve to the data of each sampled tree, parameters of the applied growth curve model are estimated. 2) By setting the estimated parameters as new observations, we classify growth patterns of sampled trees by k -means method based on the new observations. 3) We construct a multivariate normal linear regression model with dummy variables for k -clusters. 4) Among a set of the estimated regression models with the different number of clusters, the best model is selected by minimizing the resultant predictive Akaike’s information criterion (PAIC) for the remaining trees. 5) Finally, by using the best set of parameters of growth curves for the remaining trees, we predict the amount of carbon sequestered by remaining trees with its asymptotically 1−α confidence interval.

Journal

• FORMATH

  FORMATH 5 (0), 63-83, 2006

  FORMATH Research Society

Keywords

• evaluation of prediction in interpolation
• growth analysis
• information criterion
• k -means clustering
• model selection
• multivariate normal linear regression model

Details

• CRID: 1390285300162324480
• NII Article ID: 130007852338
• DOI: 10.15684/formath.05.004
• ISSN: 21885729
• Web Site: https://www.jstage.jst.go.jp/article/formath/5/0/5_05.004/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed