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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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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. |
format | Online Article Text |
id | pubmed-3693975 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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