Bayesian approach to tree detection with airborne laser scanning

In this paper, we propose a novel computational approach to tree detection. The aim is to reconstruct the positions, sizes and crown shapes of trees. This amounts to simultaneous tree localization and estimation of tree shapes by fitting multiple CHMs to ALS data. This estimation problem is written in Bayesian inversion framework. The benefit of the Bayesian approach is that it enables accounting for statistical prior information on the unknown parameters in the estimation. The prior information is explicitely written in the form of a probability distribution that models the unknowns. Here, the prior information is associated with the statistics of tree height, crown height and crown width. We hypothesize that utilizing such information in the estimation improves the detection of trees in dense forests, i.e. cases where the existing reconstruction methods usually fail. The feasibility of the proposed approach is tested with ALS data. The estimates are compared with field measurements. The preliminary results demonstrate that the positions and sizes of the trees can be tracked relatively well, even if tree crowns are interlaced.