Predicting tree growth using light availability as an indicator of competition

Tree growth modelling and predictions are essential components of forest planning and ecological research. Airborne laser scanning data can provide detailed information about tree crowns, where primary photosynthesis happens. The aim of this study was to investigate, whether intercepted solar radiation received by tree crowns, modelled using drone-based Light Detection and Ranging (LiDAR) data, can be used as an indicator of competition in individual tree growth models instead of traditional competition variables such as basal area or basal area of larger trees. The analysis was based on repeated field measurements and a single acquisition of drone-based laser scanning data at the time of first field measurement. Nonlinear mixed-effects models were developed to predict individual tree stem volume growth. While crown metrics, such as crown volume and crown surface area, did not improve model performance compared to model based solely on field-measured variables, information about direct solar radiation based on ray tracing and tree crown information led to the best-fitting model. These results suggest that drone-based LiDAR measurements can provide such measurements of crown metrics and light availability that are both practically feasible and physiologically justified, and lead to better model fit than the traditional field measurement-based approaches.