Predicting the spatial pattern of trees by airborne laser scanning

The spatial pattern of trees can be defined as a property of their location in relation to each other. In this study, the spatial pattern was summarized into three categories, regular, random, and clustered, using Ripley's L-function. The study was carried out at 79 sample plots located in a managed forest in Finland. The goal was to study how well the spatial pattern of trees can be predicted by airborne laser scanning (ALS) data. ALS-derived predictions were based upon individual tree detection (ITD), semi-individual tree detection (semi-ITD), and plot-level metrics calculated from the canopy height model, AREA. The kappa value for ITD was almost zero, which indicates no agreement. The semi-ITD and AREA methods performed better, although kappa values were only 0.34 and 0.24, respectively. It appears difficult to detect a particularly clustered spatial pattern.