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Using the dummy variable model approach to construct compatible single-tree biomass equations at different scales - a case study for Masson pine (Pinus massoniana) in southern China  ( SCI-EXPANDED收录)   被引量:41

文献类型:期刊文献

英文题名:Using the dummy variable model approach to construct compatible single-tree biomass equations at different scales - a case study for Masson pine (Pinus massoniana) in southern China

作者:Zeng, Wei Sheng[1] Zhang, Hui Ru[1] Tang, Shou Zheng[1]

第一作者:Zeng, Wei Sheng

通信作者:Zeng, WS[1]

机构:[1]Chinese Acad Forestry, Inst Forest Resources Informat, Beijing 100091, Peoples R China

年份:2011

卷号:41

期号:7

起止页码:1547-1554

外文期刊名:CANADIAN JOURNAL OF FOREST RESEARCH

收录:;WOS:【SCI-EXPANDED(收录号:WOS:000295185600016)】;

基金:This work was initiated as part of the National Biomass Modeling Program for Continuous Forest Inventory (NBMP-CFI) funded by the State Forestry Administration of China. We acknowledge the NBMP-CFI team of the Central South Forest Inventory and Planning Institute of SFA for biomass data collection. Review comments made by Y.C. Lei and other two referees helped to improve the presentation of this manuscript. Also, the authors gratefully thank the editors for their careful work.

语种:英文

外文关键词:Biomass

摘要:It is fundamental for monitoring and assessment of national forest biomass to develop generalized single-tree biomass models suitable for large-scale forest biomass estimation. However, the compatibility of forest biomass estimates among different scales is a real problem. Based on the aboveground biomass data of Masson pine (Pinus massoniana Lamb.) in southern China, generalized single-tree biomass equations applying to national and regional forest biomass estimation were constructed using the dummy variable model method, which provided an effective approach to solving the compatibility of forest biomass estimates among different scales. The results show that aboveground biomass estimates of individual trees with the same diameter are different to some extent among the forest origins and geographic regions and that a dummy model with specific (local) parameters is better than a population-average model. The dummy variable model can be applied to develop other generalized compatible models such as tree volume equations.

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