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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 parameter...

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Autores principales: Sanquetta, Carlos Roberto, Wojciechowski, Jaime, Corte, Ana Paula Dalla, Rodrigues, Aurélio Lourenço, Maas, Greyce Charllyne Benedet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3693975/
https://www.ncbi.nlm.nih.gov/pubmed/23758745
http://dx.doi.org/10.1186/1750-0680-8-6
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author Sanquetta, Carlos Roberto
Wojciechowski, Jaime
Corte, Ana Paula Dalla
Rodrigues, Aurélio Lourenço
Maas, Greyce Charllyne Benedet
author_facet Sanquetta, Carlos Roberto
Wojciechowski, Jaime
Corte, Ana Paula Dalla
Rodrigues, Aurélio Lourenço
Maas, Greyce Charllyne Benedet
author_sort Sanquetta, Carlos Roberto
collection PubMed
description 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.
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spelling pubmed-36939752013-06-28 On the use of data mining for estimating carbon storage in the trees Sanquetta, Carlos Roberto Wojciechowski, Jaime Corte, Ana Paula Dalla Rodrigues, Aurélio Lourenço Maas, Greyce Charllyne Benedet Carbon Balance Manag Methodology 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. BioMed Central 2013-06-10 /pmc/articles/PMC3693975/ /pubmed/23758745 http://dx.doi.org/10.1186/1750-0680-8-6 Text en Copyright © 2013 Sanquetta et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology
Sanquetta, Carlos Roberto
Wojciechowski, Jaime
Corte, Ana Paula Dalla
Rodrigues, Aurélio Lourenço
Maas, Greyce Charllyne Benedet
On the use of data mining for estimating carbon storage in the trees
title On the use of data mining for estimating carbon storage in the trees
title_full On the use of data mining for estimating carbon storage in the trees
title_fullStr On the use of data mining for estimating carbon storage in the trees
title_full_unstemmed On the use of data mining for estimating carbon storage in the trees
title_short On the use of data mining for estimating carbon storage in the trees
title_sort on the use of data mining for estimating carbon storage in the trees
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3693975/
https://www.ncbi.nlm.nih.gov/pubmed/23758745
http://dx.doi.org/10.1186/1750-0680-8-6
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