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A wood density and aboveground biomass variability assessment using pre-felling inventory data in Costa Rica
BACKGROUND: The high spatio-temporal variability of aboveground biomass (AGB) in tropical forests is a large source of uncertainty in forest carbon stock estimation. Due to their spatial distribution and sampling intensity, pre-felling inventories are a potential source of ground level data that cou...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4165877/ https://www.ncbi.nlm.nih.gov/pubmed/25243018 http://dx.doi.org/10.1186/s13021-014-0009-y |
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author | Svob, Sienna Arroyo-Mora, J Pablo Kalacska, Margaret |
author_facet | Svob, Sienna Arroyo-Mora, J Pablo Kalacska, Margaret |
author_sort | Svob, Sienna |
collection | PubMed |
description | BACKGROUND: The high spatio-temporal variability of aboveground biomass (AGB) in tropical forests is a large source of uncertainty in forest carbon stock estimation. Due to their spatial distribution and sampling intensity, pre-felling inventories are a potential source of ground level data that could help reduce this uncertainty at larger spatial scales. Further, exploring the factors known to influence tropical forest biomass, such as wood density and large tree density, will improve our knowledge of biomass distribution across tropical regions. Here, we evaluate (1) the variability of wood density and (2) the variability of AGB across five ecosystems of Costa Rica. RESULTS: Using forest management (pre-felling) inventories we found that, of the regions studied, Huetar Norte had the highest mean wood density of trees with a diameter at breast height (DBH) greater than or equal to 30 cm, 0.623 ± 0.182 g cm(-3) (mean ± standard deviation). Although the greatest wood density was observed in Huetar Norte, the highest mean estimated AGB (EAGB) of trees with a DBH greater than or equal to 30 cm was observed in Osa peninsula (173.47 ± 60.23 Mg ha(-1)). The density of large trees explained approximately 50% of EAGB variability across the five ecosystems studied. Comparing our study's EAGB to published estimates reveals that, in the regions of Costa Rica where AGB has been previously sampled, our forest management data produced similar values. CONCLUSIONS: This study presents the most spatially rich analysis of ground level AGB data in Costa Rica to date. Using forest management data, we found that EAGB within and among five Costa Rican ecosystems is highly variable. Combining commercial logging inventories with ecological plots will provide a more representative ground level dataset for the calibration of the models and remotely sensed data used to EAGB at regional and national scales. Additionally, because the non-protected areas of the tropics offer the greatest opportunity to reduce rates of deforestation and forest degradation, logging inventories offer a promising source of data to support mechanisms such as the United Nations REDD + (Reducing Emissions from Tropical Deforestation and Degradation) program. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13021-014-0009-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4165877 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-41658772014-09-18 A wood density and aboveground biomass variability assessment using pre-felling inventory data in Costa Rica Svob, Sienna Arroyo-Mora, J Pablo Kalacska, Margaret Carbon Balance Manag Research BACKGROUND: The high spatio-temporal variability of aboveground biomass (AGB) in tropical forests is a large source of uncertainty in forest carbon stock estimation. Due to their spatial distribution and sampling intensity, pre-felling inventories are a potential source of ground level data that could help reduce this uncertainty at larger spatial scales. Further, exploring the factors known to influence tropical forest biomass, such as wood density and large tree density, will improve our knowledge of biomass distribution across tropical regions. Here, we evaluate (1) the variability of wood density and (2) the variability of AGB across five ecosystems of Costa Rica. RESULTS: Using forest management (pre-felling) inventories we found that, of the regions studied, Huetar Norte had the highest mean wood density of trees with a diameter at breast height (DBH) greater than or equal to 30 cm, 0.623 ± 0.182 g cm(-3) (mean ± standard deviation). Although the greatest wood density was observed in Huetar Norte, the highest mean estimated AGB (EAGB) of trees with a DBH greater than or equal to 30 cm was observed in Osa peninsula (173.47 ± 60.23 Mg ha(-1)). The density of large trees explained approximately 50% of EAGB variability across the five ecosystems studied. Comparing our study's EAGB to published estimates reveals that, in the regions of Costa Rica where AGB has been previously sampled, our forest management data produced similar values. CONCLUSIONS: This study presents the most spatially rich analysis of ground level AGB data in Costa Rica to date. Using forest management data, we found that EAGB within and among five Costa Rican ecosystems is highly variable. Combining commercial logging inventories with ecological plots will provide a more representative ground level dataset for the calibration of the models and remotely sensed data used to EAGB at regional and national scales. Additionally, because the non-protected areas of the tropics offer the greatest opportunity to reduce rates of deforestation and forest degradation, logging inventories offer a promising source of data to support mechanisms such as the United Nations REDD + (Reducing Emissions from Tropical Deforestation and Degradation) program. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13021-014-0009-y) contains supplementary material, which is available to authorized users. Springer International Publishing 2014-09-17 /pmc/articles/PMC4165877/ /pubmed/25243018 http://dx.doi.org/10.1186/s13021-014-0009-y Text en © Svob et al.; licensee Springer. 2014 This article is published under license to BioMed Central Ltd. 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 credited. |
spellingShingle | Research Svob, Sienna Arroyo-Mora, J Pablo Kalacska, Margaret A wood density and aboveground biomass variability assessment using pre-felling inventory data in Costa Rica |
title | A wood density and aboveground biomass variability assessment using pre-felling inventory data in Costa Rica |
title_full | A wood density and aboveground biomass variability assessment using pre-felling inventory data in Costa Rica |
title_fullStr | A wood density and aboveground biomass variability assessment using pre-felling inventory data in Costa Rica |
title_full_unstemmed | A wood density and aboveground biomass variability assessment using pre-felling inventory data in Costa Rica |
title_short | A wood density and aboveground biomass variability assessment using pre-felling inventory data in Costa Rica |
title_sort | wood density and aboveground biomass variability assessment using pre-felling inventory data in costa rica |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4165877/ https://www.ncbi.nlm.nih.gov/pubmed/25243018 http://dx.doi.org/10.1186/s13021-014-0009-y |
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