Poisson Voronoi tiling for finding clusters in spatial point patterns

In forest stand mapping a delineation of spatial compact clusters of trees with similar attributes can improve inventory accuracy and growth and yield predictions. To this end a Poisson Voronoi tiling (PVT) for identifying and delineating clusters ( features) in spatial point patterns is proposed. PVT operates on the assumption that the point density in clusters is higher than that outside the clusters. A spatial domain of an observed point pattern is tessellated repeatedly into k ( random) Poisson Voronoi cells. An average EM-based likelihood of feature based on observed cell point densities is computed for each point and location of interest. Points and locations of interest are then classified by maximizing a classification likelihood. PVT avoids the need to specify the number of clusters. In a direct comparison with a non-parametric maximum profile likelihood procedure, and a smoothed version of the same, PVT performed well on two artificial point patterns with known feature domain and points, and on two spatial point patterns of first returns from a forest lidar survey on Vancouver Island, British Columbia, Canada.