Data-based stochastic modeling of tree growth and structure formation
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Data-based stochastic modeling of tree growth and structure formation. / Potapov, Ilya; Järvenpää, Marko; Åkerblom, Markku; Raumonen, Pasi; Kaasalainen, Mikko.
In: Silva Fennica, Vol. 50, No. 1, 1413, 2016.Research output: Contribution to journal › Article › Scientific › peer-review
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TY - JOUR
T1 - Data-based stochastic modeling of tree growth and structure formation
AU - Potapov, Ilya
AU - Järvenpää, Marko
AU - Åkerblom, Markku
AU - Raumonen, Pasi
AU - Kaasalainen, Mikko
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Data fitting
KW - Form diversity
KW - Morphological plasticity
KW - Plant model
KW - Quantitative structure models
KW - Stochastic functional-structural
KW - Terrestrial lidar
U2 - 10.14214/sf.1413
DO - 10.14214/sf.1413
M3 - Article
VL - 50
JO - Silva Fennica
JF - Silva Fennica
SN - 0037-5330
IS - 1
M1 - 1413
ER -