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BayesForest is an algorithm for realistic modeling of morphological clones. Here the morphological clones are defined as trees similar (and not the same) at the tree-level structure with varying fine-scale structural detail.
The BayesForest algorithm is composed of several sub-algorithms ultimately connected to each other and constituting an algorithmic pipeline. The pipeline is highly flexible and each of the choices of the pipeline can be substituted when needed depending on efficiency or application.
BayesForest Toolbox is a Matlab-based programmable wrapper for running the pipeline, handling necessary data, and facilitating tree morphology exploration.
BayesForest produces clonal tree morphologies by iteratively minimizing a "distance" between empirical distributions of structural features of DATA and MODEL trees by varying parameters of the MODEL. The resulting best-fit MODEL tree has "optimal" parameter values that generate trees statistically similar to the DATA tree, but not exact copies of each other.
BayesForest is based upon five distinct parts:
- Quantitative Structural Model (QSM): the DATA tree. We propose using Terrestrial Laser Scanning for generating QSM --- faster obtaining of the raw data and easier extraction of the model with geometry and topology defined (see the reconstruction algorithm in 1-5).
that is to be obtained from the Terrestrial Laser Scanning (TLS) data (see [Raumonen et al., Remote Sensing 2013]).
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Stochastic Structural Model (SSM), that is an analytical tree growth model. For example, one of Functional-Structural Plant Models (FSPM) or structural models with heuristic rules for growth (SHM or procedural models).
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Structural data sets, that is data sets U relating different physical dimensions as well as spatial location of various parts and segments of a tree with optional sorting by the topological characteristics.
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Measure of structural distance, that is a measure of proximity between any two data sets, in other words, a value quantifying how similar the two data sets are.
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Optimization routine, that is an iterative procedure capable of finding a minimum of any given function (Newton algorithm, genetic algorithm etc.).
[1] Raumonen P, Kaasalainen M, Åkerblom M, Kaasalainen S, Kaartinen H, Vastaranta M, et al. Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data. Remote Sensing. 2013;5:491-520.
[2] Calders K, Newnham G, Burt A, Murphy S, Raumonen P, Herold M, et al. Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods in Ecol Evol. 2015;6:198-208.
[3] Hackenberg J, Spiecker H, Calders K, Disney M, Raumonen P. SimpleTree - an efficient open source tool to build tree models from TLS clouds. Forests. 2015;6:4245-4294.
[4] Kaasalainen S, Krooks A, Liski J, Raumonen P, Kaartinen H, Kaasalainen M, et al. Change Detection of Tree Biomass with Terrestrial Laser Scanning and Quantitative Structure Modeling. Remote Sensing. 2014;6:3906-3922.
[5] Raumonen P, Casella E, Calders K, Murphy S, Åkerblom M, Kaasalainen M. Massive-scale Tree Modelling from TLS Data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2015;II-3/W4:189-196.