Literature DB >> 17188419

Assessing the potential of native tree species for carbon sequestration forestry in Northeast China.

S C Thomas1, G Malczewski, M Saprunoff.   

Abstract

Although the native forests of China are exceptionally diverse, only a small number of tree species have been widely utilized in forest plantations and reforestation efforts. We used dendrochronological sampling methods to assess the potential growth and carbon sequestration of native tree species in Jilin Province, Northeast China. Trees were sampled in and near the Changbaishan Biosphere Reserve, with samples encompassing old-growth, disturbed forest, and plantations. To approximate conditions for planted trees, sampling focused on trees with exposed crowns (dominant and co-dominant individuals). A log-linear relationship was found between diameter increment and tree diameter, with a linear decrease in increment with increasing local basal area; no significant differences in these patterns between plantations and natural stands were detected for two commonly planted species (Pinus koraiensis and Larix olgensis). A growth model that incorporates observed feedbacks with individual tree size and local basal area (in conjunction with allometric models for tree biomass), was used to project stand-level biomass increment. Predicted growth trajectories were then linked to the carbon process model InTEC to provide estimates of carbon sequestration potential. Results indicate substantial differences among species, and suggest that certain native hardwoods (in particular Fraxinus mandshurica and Phellodendron amurense), have high potential for use in carbon forestry applications. Increased use of native hardwoods in carbon forestry in China is likely to have additional benefits in terms of economic diversification and enhanced provision of "ecosystem services", including biodiversity protection.

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Year:  2006        PMID: 17188419     DOI: 10.1016/j.jenvman.2006.04.027

Source DB:  PubMed          Journal:  J Environ Manage        ISSN: 0301-4797            Impact factor:   6.789


  3 in total

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Journal:  PLoS One       Date:  2017-05-11       Impact factor: 3.240

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Journal:  Int J Mol Sci       Date:  2022-07-07       Impact factor: 6.208

  3 in total

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