An increase in atmospheric CO2 levels and global climate changes have led to an increased focus on CO2 capture mechanisms. The in situ quantification and spatial patterns of forest carbon stocks can provide a better picture of the carbon cycle and a deeper understanding of the functions and services of forest ecosystems. This study aimed to determine the aboveground (tree trunks) and belowground (soil and fine roots, at four depths) carbon stocks in a tropical forest in Brazil and to evaluate the spatial patterns of carbon in the three different compartments and in the total stock. Census data from a semideciduous seasonal forest were used to estimate the aboveground carbon stock. The carbon stocks of soil and fine roots were sampled in 52 plots at depths of 0-20, 20-40, 40-60, and 60-80 cm, combined with the measured bulk density. The total estimated carbon stock was 267.52 Mg ha-1, of which 35.23% was in aboveground biomass, 63.22% in soil, and 1.54% in roots. In the soil, a spatial pattern of the carbon stock was repeated at all depths analyzed, with a reduction in the amount of carbon as the depth increased. The carbon stock of the trees followed the same spatial pattern as the soil, indicating a relationship between these variables. In the fine roots, the carbon stock decreased with increasing depth, but the spatial gradient did not follow the same pattern as the soil and trees, which indicated that the root carbon stock was most likely influenced by other factors.
The semivariogram is used in geostatistics to predict the degree of spatial dependence, inferring about the relation of a spatialized variable. Nowadays there are methodologies that use with more efficiency the semivariogram parameters and the choice of method is important for selecting models for kriging. The aim of this study was to evaluate spatial dependence index to the selection of theoretical models to Kriging. The data were obtained from clonal stands of Eucalyptus sp. in three regions of Minas Gerais. Spherical and exponential models were fitted to volume, looking for obtain sets of parameters for the functions. To all adjustment were classified the structure of spatial continuity by using the methods GDE and IDE. Approximately 60% of the adjustments were classified as strong spatial dependence by the GDE method, while approaching 50% showed classification as strong by the method IDE. The GDE index ranked 117 adjustment as strong spatial dependece, being that by the new index (SDI) 40% would change the classification from strong to weak. When comparing the methods of least square adjustment and maximum likelihood, there were alterations in 28% of the analyzes. Although the kriging maps show high correlation, it was possible to observe the difference of area to the volumetric classes and consequently the mean volume. Using a robust database and more information from the semivariogram, the IDE showed to be more efficient to selection of models. Thus it is recommended to describe a spatial dependence because it includes all semivariogram parameters and correction factors for each model.
Reduction of sampling intensity in forest inventories to estimate the total height of eucalyptus treesReducción de la intensidad de muestreo en inventarios forestales para estimar la altura total de eucaliptos
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