Traditional methods for segmenting trees attempt to isolate prominent tree crowns from a lidar-derived canopy height model. This group of researchers have introduced a novel segmentation method, “layer stacking,” which slices the entire forest point cloud at 1-m height intervals and isolates trees in each layer. Merging the results from all layers produces representative tree profiles.
When compared to watershed delineation (a widely used segmentation algorithm), layer stacking correctly identified 15% more trees in unevenaged conifer stands, 7%–17% more in even-aged conifer stands, 26% more in mixedwood stands, and 26%–30% more (with 75% of trees correctly detected) in pure deciduous stands.
Overall, layer stacking’s commission error was mostly similar to or better than that of watershed delineation. Layer stacking performed particularly well in deciduous, leaf-off conditions, even those where tree crowns were less prominent. We conclude that in the tested forest types, layer stacking represents an improvement in segmentation when compared to existing algorithms.