[1]朱光玉,胡 松,符利勇*.基于哑变量的湖南栎类天然林林分断面积生长模型[J].南京林业大学学报(自然科学版),2018,42(02):155-162.[doi:10.3969/j.issn.1000-2006.201704059]
 ZHU Guangyu,HU Song,FU Liyong*.Basal area growth model for oak natural forest in Hunan Province based on dummy variable[J].Journal of Nanjing Forestry University(Natural Science Edition),2018,42(02):155-162.[doi:10.3969/j.issn.1000-2006.201704059]
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基于哑变量的湖南栎类天然林林分断面积生长模型
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《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

卷:
42
期数:
2018年02期
页码:
155-162
栏目:
专题报道
出版日期:
2018-03-20

文章信息/Info

Title:
Basal area growth model for oak natural forest in Hunan Province based on dummy variable
文章编号:
1000-2006(2018)02-0155-08
作者:
朱光玉12胡 松1符利勇2*
1.中南林业科技大学林学院,湖南 长沙 410004; 2. 中国林业科学研究院资源信息研究所,北京 100091
Author(s):
ZHU Guangyu12 HU Song1 FU Liyong2*
1. Forestry College,Central South University of Forest & Technology, Changsha 410004,China; 2. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
关键词:
栎类天然林 林分断面积 林分类型 立地类型 哑变量
Keywords:
Keywords:oak natural forest stand basal area forest type site type dummy variable
分类号:
S758
DOI:
10.3969/j.issn.1000-2006.201704059
文献标志码:
A
摘要:
【目的】建立含林分类型或立地类型哑变量的栎类林分断面积生长模型,为湖南栎类天然林林分断面积生长收获和预估提供理论支持。【方法】以湖南省5个区域51块栎类天然混交林样地为研究对象,选取6个具有生物学意义的理论生长方程,构建含年龄、平均优势高及林分密度指标的林分断面积生长模型,比较不同理论生长方程与密度指标对栎类天然林断面积模型拟合效果的影响,从中筛选出拟合优度较高的模型作为构建哑变量模型的基础模型; 考虑混交林立地类型的差异与优势树种的聚集分布,划分林分类型与立地类型,并分别作为哑变量加入基础模型参数及其组合中,比较林分类型哑变量模型、立地类型哑变量模型与基础模型模拟效果的差异。【结果】以株树密度作为密度指标的断面积生长模型决定系数(R2)在0.47~0.51之间,P值均小于93%,以林分密度指数作为密度指标的断面积生长模型决定系数(R2)在0.85~0.92之间,P值均大于95%,说明密度指数模拟效果优于株树密度模拟效果,其中含年龄、平均优势高与林分密度指数的Schumacher模型决定系数最大(R2=0.924 2),模拟效果最优。以Schumacher模型作为基础模型,构建含林分类型或立地类型的哑变量的模型,基础模型、林分类型模型、立地类型模型的决定系数分别为0.924 2、0.979 8、0.997 6,以立地类型作哑变量的模型要优于基础模型与林分类型模型。【结论】含哑变量模型可以有效解决天然混交林优势树种分布与立地类型差异对断面积预估的影响,提高建模的精度与模型的适用性。
Abstract:
【Objective】This paper established a basal area growth model containing a forest type or site type dummy variable for oak populations in Hunan Province. The purpose of this study was to provide a theoretical reference for basal area-based harvest and growth forecasts.【Method】In total, 51 plots of natural oak mixed forest in five locations in Hunan Province were used to fit six basal area growth models with biological significance, which included the independent variables of stand age, mean dominant height, and stand density index, by applying the Forstat package. The effects and performances of different equations and density indices on the model simulation were then compared.Then, the model with the best fit was chosen as the basis for building dummy variable models.In terms of forest types and site types partitioned by considering the differences in site types and dominant tree species, the dummy variable models were constructed and their simulation performances were accordingly evaluated.【Result】 The basal area growth models of the stand density index had determinant coefficients ranging from 0.85 to 0.92 and a prediction accuracy greater than 95%,which were much better than the tree density models with the determinant coefficients ranging from 0.47 to 0.51 and a prediction accuracy less than 93%. Schumacher model had the best simulation result, with the highest R2(0.924 2). Schumacher model was used as the basic model to establish the dummy variable model. The result showed that the site type model(R2=0.997 6)was better than the forest type(R2=0.979 8)and basic models(R2=0.924 2).【Conclusion】The models with dummy variables can effectively solve the influence of dominant tree species distribution and site type differences,improve the modeling accuracy, and provide a reference and basis for oak natural forest growth and harvest management.

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备注/Memo

备注/Memo:
基金项目:国家自然科学基金项目(31570631); 国家林业局项目(1692016-06); 中国博士后基金项目(2014M550103) 第一作者:朱光玉(zgy1111999@163.com),副教授。*通信作者:符利勇(fuly@caf.ac.cn),副研究员。
更新日期/Last Update: 2018-06-12