The analysis of the consequences of land use (in particular forest use) may be considered as a partial step towards an integrated modelling of a land system. Let us consider a forest territory, where a gap-cut is made, and after a given time period the eventual change in the spatial distribution of undergrowth plants and tree seedlings is to be detected (see Mihók et al., 2005 and Gálhidy et al., 2006). If floristic data are collected along a line transect, we can try to detect the change in the plant distributions along the transect, the so-called change-point, and see whether this occurs at the geometric frontier of the human intervention. The problem, on a theoretical level, can be addressed using the methodology of change-point analysis which is a technically involved branch of mathematical statistics (see e.g. Brodsky and Darkhovsky, 1993; Csörgö and Horváth, 1997), widely used to explore the possible temporal or spatial structure of local homogeneity from collected data. (The main application fields of change-point analysis include meteorology, hydrology, or environmental studies, economy, quality control in industry, biology and medicine.) In this paper we propose a practical, operative approach, using only technique of classical statistics. In our case, given a plant species, along a line transect quadrats have been located and in each quadrat the individuals have been counted. We consider these data as samples of two distributions of the same type but with different parameters, separated by a change-point K. Based on the maximum likelihood approach, an algorithm is given to estimate K. Since the distribution of the change-point estimate is not known, as a substitute of its confidence interval, the so-called change-interval will be calculated, using an adaptation of the bootstrap method. (For this widely applied simulation method see e.g. Efron and Tibshirani, 1993, a justification of the use of bootstrap in this case can be found in Ferger, 1993.) The implementation of the above algorithms was realized with the application of the statistical software "R". As an illustration, for a concrete plant species, the maximum likelihood estimation of the change-point and the calculation of the above mentioned change-interval will be presented.