Comportamento espacial de características biométricas para Eucalyptus spp ao longo do ciclo da floresta

Abstract

To assist in the practice of forest inventory, it is important to consider and associate geospatial data collection and analysis. It is essential to understand the spatial variations of biometric characteristics to support management with greater accuracy and lower cost. In this context, the objective was to evaluate the spatial continuity structure of different biometric characteristics in eucalyptus clonal plantations throughout the forest cycle, as well as to verify the behavior of these characteristics through kriging maps. Data were collected in 107 plots, obtained through continuous forest inventories (IFC) carried out in the years 2016 to 2019 with Eucalyptus spp clones. In each plot, the volume (m3

), basal area (m2

), total height (m) and average height of the dominant trees (m) were analyzed. For all biometric characteristics, exploratory data analysis, experimental semivariogram adjustments, adjustment statistics, Spatial Dependence Index (IDE), Degree of Spatial Dependence (ED) and staggered semivariograms over the years were performed. The spatial, spherical, exponential and Gaussian models were adjusted to the experimental semivariograms by the Weighted Minimum Squares Method. The best fit model was used by ordinary kriging in the spatialization of the variables analyzed. The correlation between the values estimated by kriging for each variable in its respective measurement age was performed by means of the matrix of correlation between maps. The results by the staggered semivariogram showed that all biometric characteristics presented spatial dependence over the years. The model that best represented the database was exponential. The use of kriging provided the perception of changes in the different biometric characteristics in their respective years of measurement, allowing us to infer about the development of Eucalyptus spp. The correlation between the maps generated through ordinary kriging found that the initial ages are less correlated with older ages for all biometric variables analyzed. However, the correlation allowed us to infer that the kriging maps had a spatial pattern of distribution of the variable, similar between the ages evaluated. Verifying the most productive areas and less productive areas remained spatially located in the same place. Allowing the possibility of generating strata at younger ages.