The Canopy Semi-analytic Pgap And Radiative Transfer (CanSPART) model: Validation using ground based lidar

A canopy structure model based on simple geometric forms and parameterised with plot-scale statistical biometric data is used to predict gap probability (Pgap) profiles. The Semi-analytic Pgap And Radiative Transfer (CanSPART) model is flexibly formulated using distributions of tree height, crown and stem dimensions and can be used for ‘bottom-up’ or ‘top-down’ modelling of gap profiles. Gap probabilities derived from a ground-based scanning lidar are used to validate the output of the model. Direct comparisons of Pgap are made at a range of zenith angles as well as mean foliage profiles calculated from a combination of profiles at multiple angles. Test sites include one pine plantation and several eucalypt woodland sites with varying density, structural complexity, layering and clumping. It is shown that this model performs better in clumped canopies than a simple exponential model and there is agreement with the lidar data except in the near-vertical profiles where spatial variance is not fully accounted for in the measured data. Model-derived canopy gap probabilities compared with observations using a one-sample t-test are significant for 11 out of 12 test sites. Simplification of some of the model inputs was successful at some test sites, but at the most complex site the full model was needed to satisfactorily replicate the shape of the measured gap probability profiles.

► A canopy structure model uses biometric data to predict gap probability profiles.
► Structural complexity, layering and clumping are modelled.
► Model results are compared with gap and foliage profiles from ground-based lidar.
► Complex model replicates the shape of profiles better than simplified models.