article id 1413,
category
Research article
Highlights:
We propose a stochastic version of the tree growth model LIGNUM for producing tree structures consistent with detailed terrestrial laser scanning data, and we provide the proof-of-concept by using model-based simulations and real laser scanning data; Trees produced with the data-based model resemble the trees of the dataset, and are statistically similar but not copies of each other; the number of such synthetic trees is not limited.
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We introduce a general procedure to match a stochastic functional-structural tree model (here LIGNUM augmented with stochastic rules) with real tree structures depicted by quantitative structure models (QSMs) based on terrestrial laser scanning. The matching is done by iteratively finding the maximum correspondence between the measured tree structure and the stochastic choices of the algorithm. First, we analyze the match to synthetic data (generated by the model itself), where the target values of the parameters to be estimated are known in advance, and show that the algorithm converges properly. We then carry out the procedure on real data obtaining a realistic model. We thus conclude that the proposed stochastic structure model (SSM) approach is a viable solution for formulating realistic plant models based on data and accounting for the stochastic influences.
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Potapov,
Tampere University of Technology, Department of Mathematics, P.O. Box 553, FI-33101 Tampere, Finland
E-mail:
ilya.potapov@tut.fi
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Järvenpää,
Tampere University of Technology, Department of Mathematics, P.O. Box 553, FI-33101 Tampere, Finland
E-mail:
marko.jarvenpaa@tut.fi
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Åkerblom,
Tampere University of Technology, Department of Mathematics, P.O. Box 553, FI-33101 Tampere, Finland
E-mail:
markku.akerblom@tut.fi
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Raumonen,
Tampere University of Technology, Department of Mathematics, P.O. Box 553, FI-33101 Tampere, Finland
E-mail:
pasi.raumonen@tut.fi
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Kaasalainen,
Tampere University of Technology, Department of Mathematics, P.O. Box 553, FI-33101 Tampere, Finland
E-mail:
mikko.kaasalainen@tut.fi