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Non-intersecting leaf insertion algorithm for tree structure models.

Markku Åkerblom1, Pasi Raumonen1, Eric Casella2

  • 1Laboratory of Mathematics, Tampere University of Technology, PO Box 553, 33101 Tampere, Finland.

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|March 6, 2018
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Summary
This summary is machine-generated.

We developed a new method to add realistic leaves to 3D tree models, improving simulations for applications in forestry and remote sensing. This tool efficiently generates leaf geometry and placement, enhancing the accuracy of ecological models.

Keywords:
laser scanningleaf distributionleaf insertionquantitative structure modeltree reconstruction

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Area of Science:

  • Computer Graphics
  • Forestry Science
  • Ecological Modeling

Background:

  • Quantitative structure models (QSMs) are crucial for representing tree geometry and branching.
  • Accurate leaf representation in QSMs is vital for realistic simulations, especially for models derived from terrestrial laser scanning (TLS).
  • Existing methods may lack flexibility in leaf distribution and detail.

Purpose of the Study:

  • To present an algorithm and data structure for inserting leaves into QSMs with user-defined distributions.
  • To enhance the realism of tree simulations, particularly those based on TLS data.
  • To provide a flexible tool for generating detailed leaf structures in computational models.

Main Methods:

  • Developed an algorithm for generating individual leaf geometry and placement based on user-defined distributions (location, size, angles).
  • Implemented a two-stage leaf generation process: geometry creation and intersection prevention through transformations.
  • Utilized cylinder-based structure models for demonstration, with software designed for compatibility with other model types.
  • Tested the method on English oak trees, measuring computational resources and comparing leaf area density with manual harvesting.

Main Results:

  • Leaf generation for individual trees took 6–18 minutes, depending on complexity.
  • The method allows for user-defined distributions of leaf shape, size, and orientation.
  • Initial comparisons with manual leaf harvesting showed potential, though results were not conclusive.
  • The approach demonstrated efficient data structure for storing leaf information.

Conclusions:

  • The developed algorithm and implementation offer a promising tool for creating more realistic tree simulations with leaves.
  • The method's flexibility in leaf distribution and compatibility with various QSM types are significant advantages.
  • Further validation across multiple tree species and TLS data is needed, but the approach holds potential for 3D structure and radiative transfer simulations in remote sensing, ecology, and forestry.