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

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Bayes Forest : A data-intensive generator of morphological tree clones. / Potapov, Ilya; Järvenpää, Marko; Åkerblom, Markku; Raumonen, Pasi; Kaasalainen, Mikko.

In: GigaScience, Vol. 6, No. 10, gix079, 2017, p. 1-13.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Potapov, I, Järvenpää, M, Åkerblom, M, Raumonen, P & Kaasalainen, M 2017, 'Bayes Forest: A data-intensive generator of morphological tree clones' GigaScience, vol. 6, no. 10, gix079, pp. 1-13. https://doi.org/10.1093/gigascience/gix079

APA

Potapov, I., Järvenpää, M., Åkerblom, M., Raumonen, P., & Kaasalainen, M. (2017). Bayes Forest: A data-intensive generator of morphological tree clones. GigaScience, 6(10), 1-13. [gix079]. https://doi.org/10.1093/gigascience/gix079

Vancouver

Author

Potapov, Ilya ; Järvenpää, Marko ; Åkerblom, Markku ; Raumonen, Pasi ; Kaasalainen, Mikko. / Bayes Forest : A data-intensive generator of morphological tree clones. In: GigaScience. 2017 ; Vol. 6, No. 10. pp. 1-13.

Bibtex - Download

@article{9116f9ca69574680976d177804957f28,
title = "Bayes Forest: A data-intensive generator of morphological tree clones",
abstract = "Detailed and realistic tree form generators have numerous applications in ecology and forestry. For example, the varying morphology of trees contributes differently to formation of landscapes, natural habitats of species, and eco-physiological characteristics of the biosphere. Here, we present an algorithm for generating morphological tree {"}clones{"} based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth model with simple stochastic rules. The algorithm is designed to produce tree forms, i.e., morphological clones, similar (and not identical) in respect to tree-level structure, but varying in fine-scale structural detail. Although we opted for certain choices in our algorithm, individual parts may vary depending on the application, making it a general adaptable pipeline. Namely, we showed that a specific multipurpose procedural stochastic growth model can be algorithmically adjusted to produce the morphological clones replicated from the target experimentally measured tree. For this, we developed a statistical measure of similarity (structural distance) between any given pair of trees, which allows for the comprehensive comparing of the tree morphologies by means of empirical distributions describing the geometrical and topological features of a tree. Finally, we developed a programmable interface to manipulate data required by the algorithm. Our algorithm can be used in a variety of applications for exploration of the morphological potential of the growth models (both theoretical and experimental), arising in all sectors of plant science research.",
keywords = "Empirical distributions, Large scale data, Morphological clone, Quantitative structure tree model, Stochastic data driven model, Terrestrial laser scanning",
author = "Ilya Potapov and Marko J{\"a}rvenp{\"a}{\"a} and Markku {\AA}kerblom and Pasi Raumonen and Mikko Kaasalainen",
year = "2017",
doi = "10.1093/gigascience/gix079",
language = "English",
volume = "6",
pages = "1--13",
journal = "GigaScience",
issn = "2047-217X",
number = "10",

}

RIS - Download

TY - JOUR

T1 - Bayes Forest

T2 - A data-intensive generator of morphological tree clones

AU - Potapov, Ilya

AU - Järvenpää, Marko

AU - Åkerblom, Markku

AU - Raumonen, Pasi

AU - Kaasalainen, Mikko

PY - 2017

Y1 - 2017

N2 - Detailed and realistic tree form generators have numerous applications in ecology and forestry. For example, the varying morphology of trees contributes differently to formation of landscapes, natural habitats of species, and eco-physiological characteristics of the biosphere. Here, we present an algorithm for generating morphological tree "clones" based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth model with simple stochastic rules. The algorithm is designed to produce tree forms, i.e., morphological clones, similar (and not identical) in respect to tree-level structure, but varying in fine-scale structural detail. Although we opted for certain choices in our algorithm, individual parts may vary depending on the application, making it a general adaptable pipeline. Namely, we showed that a specific multipurpose procedural stochastic growth model can be algorithmically adjusted to produce the morphological clones replicated from the target experimentally measured tree. For this, we developed a statistical measure of similarity (structural distance) between any given pair of trees, which allows for the comprehensive comparing of the tree morphologies by means of empirical distributions describing the geometrical and topological features of a tree. Finally, we developed a programmable interface to manipulate data required by the algorithm. Our algorithm can be used in a variety of applications for exploration of the morphological potential of the growth models (both theoretical and experimental), arising in all sectors of plant science research.

AB - Detailed and realistic tree form generators have numerous applications in ecology and forestry. For example, the varying morphology of trees contributes differently to formation of landscapes, natural habitats of species, and eco-physiological characteristics of the biosphere. Here, we present an algorithm for generating morphological tree "clones" based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth model with simple stochastic rules. The algorithm is designed to produce tree forms, i.e., morphological clones, similar (and not identical) in respect to tree-level structure, but varying in fine-scale structural detail. Although we opted for certain choices in our algorithm, individual parts may vary depending on the application, making it a general adaptable pipeline. Namely, we showed that a specific multipurpose procedural stochastic growth model can be algorithmically adjusted to produce the morphological clones replicated from the target experimentally measured tree. For this, we developed a statistical measure of similarity (structural distance) between any given pair of trees, which allows for the comprehensive comparing of the tree morphologies by means of empirical distributions describing the geometrical and topological features of a tree. Finally, we developed a programmable interface to manipulate data required by the algorithm. Our algorithm can be used in a variety of applications for exploration of the morphological potential of the growth models (both theoretical and experimental), arising in all sectors of plant science research.

KW - Empirical distributions

KW - Large scale data

KW - Morphological clone

KW - Quantitative structure tree model

KW - Stochastic data driven model

KW - Terrestrial laser scanning

UR - http://www.scopus.com/inward/record.url?scp=85032857287&partnerID=8YFLogxK

U2 - 10.1093/gigascience/gix079

DO - 10.1093/gigascience/gix079

M3 - Article

VL - 6

SP - 1

EP - 13

JO - GigaScience

JF - GigaScience

SN - 2047-217X

IS - 10

M1 - gix079

ER -

ID: 16209493