In this study, the second-moment analysis of marked spatial point processes is applied to the characterization of the tree height distribution in two Scots pine (Pinus sylvestris L.) forests in the Central Mountain Range of Spain. The cumulative function Lm(d) weighted by the normalized mark variance is proposed to analyse the second-order properties of marked point patterns. The empirical Lm(d) was compared with two null models to assess the relationship between the spatial distribution of the trees and the tree height correlations: the first null model was used to characterize the spatial clustering of the trees and was derived from the complete spatial randomness model used with Ripley’s K(d) function. The second null model, which is derived from the random labelling model used with the intertype second-moment measure K12(d) (type 1 intensity conditioned to the type 2 intensity and vice versa), allows us to identify the mark correlations. The performance of the technique was assessed through simulated marked point patterns. The second-moment analysis showed that most of the analysed Scots pine stands have a uniform height distribution at small scale and greater heterogeneity at large scales, with the exception of an upper altitudinal stand, which exhibited heterogeneity at short distances. These results demonstrate the utility of second-moment analysis of marked point processes for characterizing height structure in forest stands and the interaction between the height and the spatial pattern of the trees
