Evidence map›Paper›PMID 29503724›Full record

ArticleInterface focus2018

Non-intersecting leaf insertion algorithm for tree structure models.

Markku Åkerblom, Pasi Raumonen, Eric Casella, Mathias I Disney, F Mark Danson, Rachel Gaulton, Lucy A Schofield, Mikko Kaasalainen

Abstract read
In one paragraph

Article in Interface focus, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Markku ÅkerblomLaboratory of Mathematics, Tampere University of Technology, PO Box 553, 33101 Tampere, Finland.ORCID 0000-0002-6512-232X
Pasi RaumonenLaboratory of Mathematics, Tampere University of Technology, PO Box 553, 33101 Tampere, Finland.ORCID 0000-0001-5471-0970
Eric CasellaCentre for Sustainable Forestry and Climate Change, Forest Research, Farnham GU10 4LH, UK.ORCID 0000-0002-5429-7159
Mathias I DisneyDepartment of Geography, University College London, Gower Street, London WC1E 6BT, UK.ORCID 0000-0002-2407-4026
F Mark DansonSchool of Environment and Life Sciences, University of Salford, Salford M5 4WT, UK.ORCID 0000-0002-3984-0432
Rachel GaultonSchool of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.ORCID 0000-0002-0706-0298
Lucy A SchofieldSchool of Humanities, Religion and Philosophy, York St John University, York YO31 7EX, UK.
Mikko KaasalainenLaboratory of Mathematics, Tampere University of Technology, PO Box 553, 33101 Tampere, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present an algorithm and an implementation to insert broadleaves or needleleaves into a quantitative structure model according to an arbitrary distribution, and a data structure to store the required information efficiently. A structure model contains the geometry and branching structure of a tree. The purpose of this work is to offer a tool for making more realistic simulations of tree models with leaves, particularly for tree models developed from terrestrial laser scanning (TLS) measurements. We demonstrate leaf insertion using cylinder-based structure models, but the associated software implementation is written in a way that enables the easy use of other types of structure models. Distributions controlling leaf location, size and angles as well as the shape of individual leaves are user definable, allowing any type of distribution. The leaf generation process consist of two stages, the first of which generates individual leaf geometry following the input distributions, while in the other stage intersections are prevented by carrying out transformations when required. Initial testing was carried out on English oak trees to demonstrate the approach and to assess the required computational resources. Depending on the size and complexity of the tree, leaf generation takes between 6 and 18 min. Various leaf area density distributions were defined, and the resulting leaf covers were compared with manual leaf harvesting measurements. The results are not conclusive, but they show great potential for the method. In the future, if our method is demonstrated to work well for TLS data from multiple tree types, the approach is likely to be very useful for three-dimensional structure and radiative transfer simulation applications, including remote sensing, ecology and forestry, among others.

Indexed as

laser scanningleaf distributionleaf insertionquantitative structure modeltree reconstruction

Identifiers

PMID29503724
PMCPMC5829186

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.