Gentan Probability Analysis with a State-Dependent Discrete Forest Growth Model

概要

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The previous stochastic models applied for Gentan probability estimation utilized either a stationary or nonstationary Poisson process to describe the forest owners' harvesting behavior by means of the counting process. A nonstationary Poisson process has the advantage over a stationary Poisson process of capturing a time-dependent change of harvesting events. However, a nonstationary Poisson process can lack, one preferred characteristic of the probability theory when utilizing an average growth function with an asymptotic nature of growth. That is, the sum of the derived Gentan probabilities over time does not always become unity. In this paper, we introduce a state-dependent discrete forest growth model with an asymptotic nature of growth to overcome the problem, then propose a stochastic model applied for Gentan probability estimation. The Mitscherlich type growth functison is utilized. The derived probability law to capture the harvesting behavior is shown to be the binomial probability law. The derived probabilities prove to sum lap to unity over time.
一般社団法人日本森林学会の論文
2001-05-16