Data-based stochastic modeling of tree growth and structure formation

Abstract

Data-based stochastic modeling of tree growth and structure formation Potapov I., Järvenpää M., Åkerblom M., Raumonen P., Kaasalainen M. (2016). Data-based stochastic modeling of tree growth and structure formation. Silva Fennica vol. 50 no. 1 article id 1413. 11 p. 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 proofof-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. AbstractWe 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.

Bayes Forest: a data-intensive generator of morphological tree clones

“…Recently, we have reported a proof-of-concept study where we used reconstruction of a pine tree and the corresponding FSPM (named LIGNUM [ 13 , 29 ]) to demonstrate the practical feasibility of the approach [ 30 ]. Here, however, we develop a unifying interface (in the form of a programmable toolbox) for our procedure and use a general purpose fast procedural tree growth model from Palubicki et al [ 3 ].…”

“…In this work, we use the self-organizing tree model (SOT) with a shadow propagation algorithm [ 3 ] as SSM adapted for comparison with QSM. Note that more specialized tree growth models designed for the species in question would be better for the morphology optimization, and the usual choice is FSPMs (see e.g., [ 30 ]).…”

A generator of morphological clones for plant species

“…This makes the analysis and form fitting a more complex task as 190 compared with the tree shapes resulting from the growth with strong apical dominance (e.g. pine trees; 191 see (Potapov et al, 2016)). the higher order branches.…”

“…Strong dependence on data, where each 63 simulation would be calibrated automatically by data, is limited by both computation time and lack of 64 the fast measurement and processing systems allowing for a detailed 3D morphological reconstruction 65 of the real plant/tree. 66Recently, we have reported a proof-of-concept study where we used reconstruction of a pine tree and 100 the corresponding FSPM (named LIGNUM (Perttunen et al, 1996; Sievanen et al, 2008)) to 101 demonstrate the practical feasibility of the approach (Potapov et al, 2016). In this work, however, we 102 develop a unifying interface for our procedure and use general-purpose fast procedural tree growth 103 model from (Palubicki et al, 2009), since such a simple procedural model is easier to adapt (it is 104 simple, fast, and efficient) for technical experimentation with the whole algorithm.…”

“…Note that more specialized tree growth models 506 designed for the species in question would be easier subjects for the morphology optimization, but, 507 nevertheless, can be more valuable in biologically motivated studies (the usual choice is FSPM's, e.g. 508 (Potapov et al, 2016)). 509…”