| 基于似乎不相关回归和哑变量的日本落叶松单木生物量模型构建 |
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| 引用本文： | 申家朋,陈东升,孙晓梅,张守攻. 基于似乎不相关回归和哑变量的日本落叶松单木生物量模型构建[J]. 浙江农林大学学报, 2019, 36(5): 877-885. DOI: 10.11833/j.issn.2095-0756.2019.05.005 |
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| 作者姓名： | 申家朋 陈东升 孙晓梅 张守攻 |
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| 作者单位： | 中国林业科学研究院 林业研究所 国家林业和草原局林木培育重点实验室, 北京 100091 |
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| 基金项目： | 国家自然科学基金重点资助项目31430017 |
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| 摘 要： | 精确地估算林木生物量对于了解大尺度森林生物量、碳储量及其动态变化具有重要意义。以甘肃省、湖北省、辽宁省3个区域共计161株日本落叶松Larix kaempferi单木各器官组分（树干、树皮、树叶、树枝、树根）生物量数据为例，基于似乎不相关回归和哑变量的方法，建立了适合不同区域、不同器官组分的日本落叶松单木通用性生物量方程。结果表明:与普通模型相比，构建的3个哑变量生物量通用模型不仅解决了不同器官组分的相容性，还提高了生物量估测精度，复相关系数增加了0.28%~0.44%，均方根误差减少了0.40%~6.61%，绝对偏差减少了1.63%~6.61%。单独引入1个哑变量时，区域哑变量构建的生物量通用模型预估精度高于发育阶段作为哑变量构建的生物量通用模型；而同时引入2个哑变量时，预估精度分别高于单独引入1个哑变量的生物量通用模型，表明同时考虑区域和发育阶段构建的日本落叶松生物量模型为最佳模型。因此，考虑将区域和发育阶段同时作为哑变量并应用似乎不相关法来构建单木生物量模型，可以解决大尺度生物量模型的通用性和不同组分的相容性问题。
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| 关 键 词： | 森林测计学 日本落叶松 似乎不相关回归方法 哑变量 通用方程 |
| 收稿时间： | 2018-09-10 |
Modeling a single-tree biomass equation by seemingly unrelated regression and dummy variables with Larix kaempferi |
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| SHEN Jiapeng,CHEN Dongsheng,SUN Xiaomei,ZHANG Shougong. Modeling a single-tree biomass equation by seemingly unrelated regression and dummy variables with Larix kaempferi[J]. Journal of Zhejiang A&F University, 2019, 36(5): 877-885. DOI: 10.11833/j.issn.2095-0756.2019.05.005 |
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| Authors: | SHEN Jiapeng CHEN Dongsheng SUN Xiaomei ZHANG Shougong |
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| Affiliation: | Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China |
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| Abstract: | Developing generalized single-tree biomass models suitable for forest biomass estimation is an effective way to provide scientific approaches. To simplify biomass modeling and improve the accuracy of model estimation for better understanding of biomass, carbon stocks, and dynamics in large-scale forests and to precisely estimate tree biomass, this study used stem, bark, needle, branch, and root biomass of Larix kaempferi of 161 sample trees in Gansu, Hubei, and Liaoning Provinces to generalize single-tree biomass equations suitable for different organs and regions using seemingly unrelated regression and dummy variable modeling methods. Results showed that the generalized biomass equations not only solved compatibility problems with different components but also increased accuracy with an average increase of 0.28%-0.44% in R2, decreased 0.40%-6.61% in the root mean square error (ERMS), and decreased 1.63%-6.61% in the mean abosolute bias (BMA). Effects due to region increased accuracy more than effects due to developmental stages. When both region and developmental stages were added to the dummy variable model, it was more accurate and produced the best equation. Therefore, we suggest that both regional and developmental stages be considered as dummy variables to establish generalized biomass equations in order to solve the compatibility problem with different components as well as for overcoming problems of generalizing with different regions. |
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