Uso de descritores homogêneos para caracterização de padrões espaciais de pontos: uma aplicação em dados florestais

Abstract

A point processis a collection of points generated by a stochastic mechanism that can be used to represent various random phenomena identified by occurrences or points in space. The methods of punctual processes of analysis of first and second order effects allow the exploration and characterization of spatial patterns of phenomena under study in several areas of knowledge. In forest management of native species, it is extremely important to characterize the spatial configuration of native tree species. Such interests boil down to characterizing how individuals relate within the Forest. In this work, the objective is to use homogeneous descriptors to analyze the spatial pattern in forest location data of trees of the native tree species Siparuna guianensisin a forest fragment of Atlantic Forest located in Lavras -MG. The kernel estimator will be used to analyze the variation of intensities and homogeneous descriptors based on Ripley's K function and J to describe the second order effects (spatial dependence). 1000 Monte Carlo simulations were used under the assumption of complete spatial randomness to construct the confidence intervals of the descriptor functions. Based on the results, it is concluded that the species under study is distributed in aggregate form in the study region. It was also possible to observe that such methods have great potential for identifying spatial patterns.