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The Further Development of Stem Taper and Volume Models Defined by Stochastic Differential Equations

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IAENG Transactions on Engineering Technologies

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 229))

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Abstract

Stem taper process measured repeatedly among a series of individual trees is standardly analyzed by fixed and mixed regression models. This stem taper process can be adequately modeled by parametric stochastic differential equations (SDEs). We focus on the segmented stem taper model defined by the Gompertz, geometric Brownian motion and Ornstein-Uhlenbeck stochastic processes. This class of models enables the representation of randomness in the taper dynamics. The parameter estimators are evaluated by maximum likelihood procedure. The SDEs stem taper models were fitted to a data set of Scots pine trees collected across the entire Lithuanian territory. Comparison of the predicted stem taper and stem volume with those obtained using regression based models showed a predictive power to the SDEs models.

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Authors and Affiliations

  1. Department of Forest Management, Aleksandras Stulginskis University, LT-53361, Kaunas, Lithuania

    Petras Rupšys

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  1. Petras Rupšys
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Correspondence to Petras Rupšys .

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Editors and Affiliations

  1. , Department of Multimedia Engineering, Mokpo National University, Dorim-ri, MuanGun 61, Chonnam, 534-729, Korea, Republic of (South Korea)

    Gi-Chul Yang

  2. IAENG Secretariat, Hung To Road, Unit 1, 1/F 37-39, Hong Kong, Hong Kong SAR

    Sio-long Ao

  3. School of Engineering, Applied Mathematics and Computing, Cranfield University, College Road, Cranfield, MK43 0AL, Bedfordshire, United Kingdom

    Len Gelman

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Rupšys, P. (2013). The Further Development of Stem Taper and Volume Models Defined by Stochastic Differential Equations. In: Yang, GC., Ao, Sl., Gelman, L. (eds) IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol 229. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6190-2_10

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  • DOI: https://doi.org/10.1007/978-94-007-6190-2_10

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-6189-6

  • Online ISBN: 978-94-007-6190-2

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