Bayes Forest

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorPotapov, Ilya
dc.contributor.authorJärvenpää, Marko
dc.contributor.authorÅkerblom, Markku
dc.contributor.authorRaumonen, Pasi
dc.contributor.authorKaasalainen, Mikko
dc.contributor.departmentTampere University of Technology
dc.contributor.departmentDepartment of Computer Science
dc.date.accessioned2018-02-09T10:01:51Z
dc.date.available2018-02-09T10:01:51Z
dc.date.issued2017
dc.description.abstractDetailed 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.en
dc.description.versionPeer revieweden
dc.format.extent1-13
dc.format.mimetypeapplication/pdf
dc.identifier.citationPotapov , 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/gix079en
dc.identifier.doi10.1093/gigascience/gix079
dc.identifier.issn2047-217X
dc.identifier.otherPURE UUID: 9116f9ca-6957-4680-976d-177804957f28
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/9116f9ca-6957-4680-976d-177804957f28
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85032857287&partnerID=8YFLogxK
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/16209496/gix079.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/29905
dc.identifier.urnURN:NBN:fi:aalto-201802091402
dc.language.isoenen
dc.relation.ispartofseriesGigaScienceen
dc.relation.ispartofseriesVolume 6, issue 10en
dc.rightsopenAccessen
dc.subject.keywordEmpirical distributions
dc.subject.keywordLarge scale data
dc.subject.keywordMorphological clone
dc.subject.keywordQuantitative structure tree model
dc.subject.keywordStochastic data driven model
dc.subject.keywordTerrestrial laser scanning
dc.titleBayes Foresten
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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