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Stochastic Structural Plant Models

Stochastic Structural Plant Models. a platform for morphological diversity Ilya Potapov Tampere University of Technology. Closer Data-Model Interaction. Usually Functional-Structural Plant Models (FSPM) utilize fixed parameters obtained from “average” experiment.

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Stochastic Structural Plant Models

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  1. Stochastic Structural Plant Models a platform for morphological diversity Ilya Potapov Tampere University of Technology

  2. Closer Data-Model Interaction • Usually Functional-Structural Plant Models (FSPM) utilize fixed parameters obtained from “average” experiment. • Experimentally defined parameters imply certain conditions that are not universal. • Different objects and experimentations are referred to for the same model. • Simple functions assumed. • Data are not used every time the model runs.

  3. Statistical clone generator • Stochastic Structural Model (SSM) is a statistical “clone” generator… • That is, SSM produces trees with statistically similar structural characteristics, but not exact copies of each other.

  4. Forest applications • Single-species defined forest • Two-species forests

  5. Data = QSM Quantitative Structural tree Model (QSM) Laser scanning 3D point cloud • QSM possesses all geometrical and topological information • Cylinder-based model (or any other geometrical primitive) PasiRaumonen et al., Remote Sens.5(2), 491-520, 2013.

  6. SSM = a structure model with stochasticity • Full description compatibility with QSM (i.e. the same geometrical and topological information). • Stochastic rules of growth to account for variability.

  7. SSM compatible with Data DATA MODEL Goal: Find the maximum correspondence between DATA and MODEL data sets OR: Find maximum overlapping of the distributions

  8. Intraspecific diversity What makes two trees different? How much are they different? Particularly: How much do the (genetic) clones differ?

  9. Bio-diversity: quantitative aspect Ds(cypress_1,cypress_2) = ? Ds(frog, cypress_1) = ? What is the mutual (structural) distanceDs between objects?

  10. Construction of a (structural) distance • Characteristics to measure. • Algorithm to quantitatively compare the measured characteristics from two sources. • Ds(DATA,MODEL) = 0 Full correspondence between DATA and MODEL.

  11. Overall algorithm (“Bayes Forest”)

  12. Open questions • Faster/simpler SSM candidates to facilitate the calibration of the “Bayes Forest” algorithm. • Quantitative relation between real (genetic) clones and SSM-generated “clones”.

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