Greyce Charllyne Benedet Maas

On the use of data mining for estimating carbon storage in the trees

Forests contribute to climate change mitigation by storing carbon in tree biomass. The amount of carbon stored in this carbon pool is estimated by using either allometric equations or biomass expansion factors. Both of the methods provide estimate of the carbon stock based on the biometric parameters of a model tree. This study calls attention to the potential advantages of the data mining technique known as instance-based classification, which is not used currently for this purpose. The analysis of the data on the carbon storage in 30 trees of Brazilian pine (Araucaria angustifolia) shows that the instance-based classification provides as relevant estimates as the conventional methods do. The coefficient of correlation between the estimated and measured values of carbon storage in tree biomass does not vary significantly with the choice of the method. The use of some other measures of method performance leads to the same result. In contrast to the convention methods the instance-based classification does not presume any specific form of the function relating carbon storage to the biometric parameters of the tree. Since the best form of such function is difficult to find, the instance-based classification could outperform the conventional methods in some cases, or simply get rid of the questions about the choice of the allometric equations.

Estimativa de carbono individual para Araucaria angustifolia

Forests are important carbon sinks that contribute to climate change mitigation. Quantifying the carbon stock is critical for measuring such mitigation potential. Araucaria angustifolia (Bert.) O. Ktze. Araucariaceae is a key forest species in southern Brazil, due to its ecological and economic importance. This study aimed at comparing two procedures for estimating individual carbon stock ( C ) of A. angustifolia , in pure stands established in the southern Parana State, Brazil. Individual carbon stocks were determined for thirty trees, as well as correlations between C , dendometric variables, Biomass Expansion Factor ( BEF ) and Root-to-Shoot Ratio ( R ). Regression equations of C , concerning Diameter Breast Height ( DBH ) and total height ( H ), were adjusted and compared to trunk volume ( V ) estimates combined with BEF and R . DBH and H showed high correlation to C , what did not happen to tree age, BEF and R . The regression equations of C , concerning DBH and H , adjusted well to the data set, providing reliable estimates. The same happened to the volume equations combined with expansion factors, which also provided statistically acceptable estimates. No difference was observed between the two procedures tested, being both reliable for estimating the individual carbon stock.

DINÂMICA SAZONAL DA PRODUÇÃO DE SERAPILHEIRA EM FRAGMENTO DE FLORESTA OMBRÓFILA MISTA MONTANA NO ESTADO DO PARANÁ

This study aimed to quantify the seasonal litterfall in a fragment of Araucaria Forest in São João do Triunfo, Parana State, during the year 2013 and assess the contribution of the most representative species in the production of leaf litter. Twenty seven suspended litter traps at 1.00 m above the ground and with a diameter of 1.00 m were systematically established in the area, in which the litter was collected every month for 12 months in the year in 2013. The material was separated into fractions: leaves, branches, reproductive material, needle-shaped branches and miscellaneous, and was subsequently dried in an oven and weighed. The leaves were identified and classified by species. The mass of the produced litter was 7.08 t ha, the spring being that of the greatest production season. The needle-shaped branches fraction of Araucaria angustifolia was the most representative in the