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基于树木起源、立地分级和龄组的单木生物量模型
引用本文:李海奎,宁金魁. 基于树木起源、立地分级和龄组的单木生物量模型[J]. 生态学报, 2012, 32(3): 740-757
作者姓名:李海奎  宁金魁
作者单位:1. 中国林业科学研究院资源信息研究所,北京,100091
2. 江西农业大学园林与艺术学院,南昌,330045
基金项目:国家自然科学基金资助(31070485)
摘    要:以马尾松(Pinus massoniana)和落叶松(Larix)的大样本实测资料为建模样本,以独立抽取的样本为验证样本,把样本按起源、立地和龄组进行分级,采用与材积相容的两种相对生长方程,分普通最小二乘和两种加权最小二乘,对地上部分总生物量、地上各部分生物量和地下生物量进行模型拟合和验证,使用决定系数、均方根误差、总相对误差和估计精度等8项统计量对结果进行分析。结果表明:两个树种地上部分总生物量,立地分类方法,模型的拟合结果和适用性都最优;马尾松VAR模型较优,而落叶松CAR模型较好;两种加权最小二乘方法,在建模样本和验证样本中表现得不一致。在建模样本中,加权回归2(权重函数1/f0.5)略优于加权回归1(权重函数1/y0.5),但在验证样本中,加权回归1却明显优于加权回归2。而同时满足建模样本拟合结果最优和验证样本检验结果最优的组合中,只有加权回归1。两个树种地上部分各分量生物量,模型拟合结果和适用性,均为干材最优,树叶最差、树枝和树皮居中,样本分类、模型类型和加权最小二乘方法对干材生物量的影响,规律和地上部分总生物量相同;样本分类、模型类型和加权最小二乘方法的最优组合,用验证样本检验的结果,总相对误差树枝不超过±10.0%,树皮不超过±5.0%,树叶马尾松不超过±30.0%,落叶松不超过±20.0%。两个树种地下部分(根)生物量,样本按龄组分类方法,模型拟合结果最优,与材积相容的模型总体上优于与地上部分总生物量相容模型。

关 键 词:生物量模型  立地分级  龄组  加权最小二乘  地上部分生物量  地下部分生物量
收稿时间:2011-06-29
修稿时间:2011-11-14

Individual tree biomass model by tree origin, site classes and age groups
LI Haikui and NING Jinkui. Individual tree biomass model by tree origin, site classes and age groups[J]. Acta Ecologica Sinica, 2012, 32(3): 740-757
Authors:LI Haikui and NING Jinkui
Affiliation:Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China;College of Landscape and Art, Jiangxi Agricultural University, Nanchang 330045, China
Abstract:Individual tree biomass models were developed by tree origin, site classes and age groups of Pinus massoniana and Larix. Model validation was implemented using independent data. Two allometric equations, which were compatible to stem volume, were fitted by ordinary least square and two methods of weighted least square. Models for above-ground biomass, its component biomass and below-ground biomass were analyzed and evaluated through several important statistics such as coefficient of determination, root mean square error, total relative error and estimation accuracy. The results showed that above-ground biomass models of Pinus massoniana and Larix were best for fitted results and validated results by classified sites. Variable allometric ratio models is better for Pinus massoniana, while constant allometirc ratio model is better for Larix. Two methods of weighted least square showed different performance for model calibration and validation. Weighted regression No.2 model (weighted function 1/f0.5) was better for calibration data than Weighted regression No.1 model (weighted function 1/y0.5), however, the latter was better for validation sample than the former. The No.1 model presented best results for both construction and verification sample compared with ordinary least square.Models of components of above-ground biomass of Pinus massoniana and Larix gave same performance order on fitted and validated results, with best estimation for stem biomass, worst for leaf biomass and moderate for branch or bark biomass. Effects of sample classification method, two allometric equations and methods of weighted least square on stem biomass were same as that of above-ground biomass. With best combination of sample classification, allometric equations and methods of weighted least square, the total relative errors of biomass models were less than ±10.0% for branches, ±5.0% for barks and±30.0% and ±20.0% for leaves of Pinus massoniana and Larix, respectively.Comparing with base models, below-ground biomass models of Pinus massoniana and Larix by classified age groups, which were compatible to above-ground biomass and stem volume, respectively, had significantly improved on coefficient of determination and estimation accuracy, especially, the models for Larix raised more than 6% in coefficient of determination, more than 3% in estimation accuracy. In general, the model compatible to stem volume was better than the model compatible to above-ground biomass. The test results by above-ground biomass and stem volume of large sample data confirmed the same regular pattern.
Keywords:biomass model  site classification  age groups  weighted least square  above-ground biomass  below-ground biomass
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