Abstract
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.
Highlights
Forests play an important role in the global carbon balance, fixing carbon from the atmosphere, but can become a source of emissions [1,2,3]
The Biomass Expansion Factor (BEF) ranged from 1.05 to 2.06, indicating some trees have low canopy biomass while others have much more since this fraction is larger than that of the bole itself
R in turn ranged from 0.03 to 0.12, indicating that the expression of underground biomass varies among individuals
Summary
Forests play an important role in the global carbon balance, fixing carbon from the atmosphere, but can become a source of emissions [1,2,3] Their function is critical to global efforts to combat climate change. Traditional indirect methods imply the use of some type of biomass and Regression equations whose dependent variable is individual tree carbon (C) and whose independent variables are tree measurement parameters, are often called allometric equations [10]. In most instances they are appropriate because they offer direct estimates of C as functions of tree measurements, which are easy to measure and apply. They are not always satisfactory in terms of accuracy, and result in error levels beyond tolerance thresholds in forestry measurements
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