Comparison of point pattern analysis methods for classifying the spatial distributions of spruce-fir stands in the north-east USA

Abstract

The spatial distributions of tree locations in spruce-fir forest stands in the north-east USA were explored by various methods of spatial point pattern analysis. The results indicated that the 13 nearest neighbour statistics were not reliable because of the assumption violations for independent distance measures and large sample size requirements. Three other methods (i.e. refined nearest neighbour functions, Ripley's K-function and pair correlation function) seemed to capture the different aspects of the spatial patterns of these spruce-fir stands. The edge effect correction was very important to obtain unbiased results for the spatial point pattern analysis. The toroidal edge correction proved to be simple and satisfactory in this study compared with other edge correction methods. The results indicated that 24 plots (48 per cent) were classified as complete spatial random (CSR) point pattern, 17 (34 per cent) regular point pattern and 9 (18 per cent) clustered point pattern among the 50 plots. It was evident that the clustered plots were younger in age with much higher density and much smaller tree sizes than the CSR or regular plots. This classification scheme can be used as the basis for other spatial studies such as spatial point process modelling.