文献类型：期刊文献

中文题名：广东省常绿阔叶林生物量生长模型

英文题名：Biomass Growth Models for Evergreen Broad-leaved Forests in Guangdong

作者：曹磊[1] 刘晓彤[1] 李海奎[1] 雷渊才[1]

第一作者：曹磊

机构：[1]中国林业科学研究院资源信息研究所,北京100091

年份：2020

卷号：33

期号：5

起止页码：61-67

中文期刊名：林业科学研究

外文期刊名：Forest Research

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD2019_2020】；

基金：中央级公益性科研院所基本科研业务费专项资金(CAFYBB2018ZB006);国家自然科学基金项目(31770676)。

语种：中文

中文关键词：广东省;常绿阔叶林;生物量生长模型;哑变量;林分特征;参数分级

外文关键词：Guangdong province;evergreen broadleaved forest;biomass growth model;dummy variable;stand characteristics;parameter grading

分类号：S757

摘要：[目的]研究林分生物量生长模型,以期估算区域尺度上的森林生物量。[方法]利用广东省五期森林资源连续清查数据(1997,2002,2007,2012,2017),以30个固定样地为研究对象(每块样地均有30株以上五期保留木,共计1412株样木)。以Richards理论生长方程为基础,分别构建了基础生物量生长模型、含有林分特征的生物量生长模型、含有林分特征和立地条件的生长模型等不同形式的林分生物量生长模型,比较和评价了不同林分生物量生长模型的拟合效果。[结果]基础生物量生长模型拟合效果最差,调整决定系数Ra^2仅为0.475;将林分密度指标引入基础生物量生长模型后,拟合效果得到极大改善,R_a^2提高到了0.836;将哑变量引入含有林分特征的生长方程进行立地条件划分后,Ra^2达到0.961,拟合效果达到最优。[结论]含有林分特征的生物量生长方程一定程度反映了林分生物量生长与林分密度之间的关系,在此基础上划分样地类别进行参数分级,进一步提高了生物量生长模型的拟合精度,反映了不同立地条件下生产力之间的差别。
[Objective] To establish stand biomass growth models and estimate the forest biomass on the regional scale so as to provide supports and references for carbon measurement and accounting. [Method] The forest inventory data of five phases in Guangdong province(1997, 2002, 2007, 2012, and 2017) were used to select thirty fixed plots(with more than thirty reserved sample trees for five phases in each plot, totally 1 412 trees). Based on Richards theoretical growth function, the basic biomass growth model, stand-character-based biomass growth model, and stand-character-based and site-condition-based biomass growth model were established respectively using dummy variables. The biomass growth models established for stand level were evaluated and compared in this study. [Result] The basic biomass growth model performed the worst with the lowest R_a^2 of 0.475. When the stand density was added to the basic biomass growth model, the performance got improved greatly with the R^2 of 0.836. When the dummy variable were added to the stand-character-based biomass growth function, the performance got best with the highest R^2 of 0.961. [Conclusion] Based on the stand characters, the biomass growth function can reflect the relationship between biomass growth and stand density to some extents and got better goodness-of-fit. The introduction of the dummy variable to the stand-character-based biomass growth functions can improve the fitting accuracy further, indicating the difference of stand condition and production among different stand types.