A hybrid method for delineating homogeneous forest segments from LiDAR data in Catalonia (Spain)

Laser scanning data are increasingly used to segment forests into homogeneous units, calledsegments or stand compartments. Region growing and region merging have been traditionallyemployed for this purpose. Recently, alternative methods such as cellular automata (CA), self-organizing maps (SOM), and combinatorial optimization have emerged, promising morehomogeneous stand delineations. However, these newer methods often require fine-tuningdue to rugged stand boundaries. To address this, we propose and assess a novel hybridapproach that combines two segmentation algorithms, resulting in smoother boundariesand homogeneous stands. Our hybrid method outperforms traditional fine-tuning techniques,particularly using CA or SOM for initial segmentation and then refining it with another cellularautomaton (CA’), adjusted to produce smooth boundaries. The new two-step hybrid methodresulted in better segmentation results than the one-step algorithm combined with previouslysuggested fine-tuning methods. SOM-CA’ showed the highest degree of explained variance(R2) of the LiDAR metrics used in the segmentation. CA-CA’ reached almost the same R2 valueswith a larger average segment area. When the R2 of the LiDAR metrics was used as the criterion,the segments produced by the hybrid method outperformed the stand delineations of thecurrent Spanish forest map with a clear margin.