Radiative transfer modeling in structurally complex stands: towards a better understanding of parametrization
Key message
The best options to parametrize a radiative transfer model change according to the response variable used for fitting. To predict transmitted radiation, the turbid medium approach performs much better than the porous envelop, especially when accounting for the intra-specific variations in leaf area density but crown shape has limited effects. When fitting with tree growth data, the porous envelop approach combined with the more complex crown shape provides better results. When using a joint optimization with both variables, the better options are the turbid medium and the more detailed approach for describing crown shape and leaf area density.
Abstract
Context Solar radiation transfer is a key process of tree growth dynamics in forest.
Aims Determining the best options to parametrize a forest radiative transfer model in heterogeneous oak and beech stands from Belgium.
Methods Calibration and evaluation of a forest radiative transfer module coupled to a spatially explicit tree growth model were repeated for different configuration options (i.e., turbid medium vs porous envelope to calculate light interception by trees, crown shapes of contrasting complexity to account for their asymmetry) and response variables used for fitting (transmitted radiation and/or tree growth data).
Results The turbid medium outperformed the porous envelope approach. The more complex crown shapes enabling to account for crown asymmetry improved performances when including growth data in the calibration.
Conclusion Our results provide insights on the options to select when parametrizing a forest radiative 3D-crown transfer model depending on the research or application objectives.
Keywords
Light interception; Heterogeneous forests; Crown asymmetry; Beer-Lambert; Porous envelope; Bayesian optimization
Publication
André, F., de Wergifosse, L., de Coligny, F. et al. Radiative transfer modeling in structurally complex stands: towards a better understanding of parametrization. Annals of Forest Science 78, 92 (2021). https://doi.org/10.1007/s13595-021-01106-8
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Data availability
The datasets used during the current study are available from the Zenodo repository https://doi.org/10.5281/zenodo.5500144
Handling Editor
Erwin Dreyer
