福建主要人工林生态系统碳贮量研究
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摘要
随着全球工业的不断进步发展,大气温室气体含量不断上升,全球气候变暖,越来越引起人们的关注。森林生态系统作为陆地生态系统的主要组成部分,其固碳和吸碳作用日趋显著。森林生态系统碳储量包括土壤有机碳储量、植被有机碳储量和凋落物层有机碳储量,其现有碳储量是评价森林生态系统吸收CO2功能的主要尺度。杉木(Cunninghamia lanceolata)、马尾松(Pinus massoniana)、毛竹(Phyllostachys hterocycla)和桉树(Eucalyptus grandis)都是中国南方重要的森林资源,经济用途广泛,是福建省分布最广的人工林生态系统。本文采用野外调研与室内试验相结合的方法,研究了福建杉木、马尾松、毛竹和桉树人工林生态系统碳密度及其空间分布特征,并用最小二乘—支持向量机法分别对各人工林土壤有机碳含量和有机碳密度进行了预测估计,分析了土壤有机碳密度与林龄、坡向、坡位的线性和非线性回归关系;运用多元线性回归模型预测不同人工林乔木层各器官生物量,在此基础上计算乔木层各器官的有机碳含量和有机碳密度;对杉木、马尾松和桉树人工林生态系统中,土壤各层有机碳密度与乔木层的干、枝、叶和根,林下植被的草本层和灌木层以及凋落物层进行了典型相关分析研究,深入分析土壤碳密度与地上部分碳密度的关系;最后,运用刀切法、投影寻踪等方法对乔木层碳密度与胸径、树高的关系进行模拟。主要研究结果如下:
1、土壤有机碳含量和有机碳密度的空间分布特征:有机碳含量和有机碳密度均垂直方向上表现为土壤表层有机碳含量随着土层深度的增加而逐渐降低,其中表层到第二层的降低速度最快;水平方向上,从大到小依次为毛竹>桉树>杉木>马尾松,下坡>中坡>上坡、阳坡>阴坡;杉木林和毛竹林土壤有机碳含量和有机碳密度表现出幼龄林>中龄林>成熟林的变化特征;而马尾松林地、桉树林地在不同发育阶段土壤有机碳密度趋势则相反,表现为幼龄林<中龄林<成熟林的变化特征。但因其受土壤碳含量、土壤容重、土层厚度的影响,又有一定变异性。
2、应用多元线性回归模型对杉木、马尾松、毛竹和桉树人工林生态系统中土壤有机碳密度与环境因子坡向和坡度以及林龄的关系,进行了多元回归分析。结果表明,林龄对杉木土壤剖面有机碳密度的影响比较显著,而对马尾松、毛竹和桉树3个林分土壤剖面有机碳密度的影响不显著,坡向和坡位对4个林分土壤剖面有机碳密度的影响均较小。但因多元线性回归的前提是假设土壤有机碳密度与各因子之间的线性关系。
3、进一步应用最小二乘支持向量机法对各人工林土壤碳密度进行预测,结果表明,对建阳和永安杉木人工林土壤表层有机碳密度预测的平均误差分别为0.077%和0.116%;对建阳和永安杉木人工林土壤剖面有机碳密度预测的平均误差为0.098%和0.082%;对建阳和永安马尾松人工林土壤表层有机碳密度预测的平均误差分别为0.082%和0.081%;对建阳和永安马尾松人工林土壤剖面有机碳密度预测的平均误差为2.559%和0.049%;对建阳和永安毛竹人工林土壤表层有机碳密度预测的平均误差分别为2.709%和3.928%;对建阳和永安毛竹人工林土壤剖面有机碳密度预测的平均误差为2.895%和2.474%;对桉树人工林土壤表层有机碳密度预测的平均误差为0.066%;对桉树人工林土壤剖面有机碳密度预测的平均误差为0.049%。说明LS-SVM模型对福建4种人工林土壤有机碳密度值的预测效果良好。
4、森林生态系统中各部分有机碳密度,特别是土壤有机碳密度受诸多因子影响,而且各因子之间可能存在线性或非线性的关系,且有主要因素和次要因素。本文采用尝试用建立扩展离散灰色序列模型,来预测土壤有机碳密度值,并确定影响土壤有机碳密度的主要变量。结果表明,杉木、马尾松和桉树人工林的林龄与土壤表层有机碳密度和土壤剖面有机碳密度之间的复相关系数均为最大值,因此确定林龄为杉木林、马尾松林和桉树林的主变量,其余因子为辅助变量。运用扩展灰色离散序列高阶动态预测模型对杉木、马尾松和桉树人工林表层土壤有机碳密度和土壤剖面有机碳密度进行模拟,结果表明相关系数R均大于0.7832,且预测值与实测值误差较小,说明扩展灰色离散序列高阶动态土壤有机碳密度预报模型对福建不同人工林土壤有机碳密度的预测效果良好。
5、选取福建建阳、永安地区的杉木、马尾松和桉树人工林表层土壤有机碳密度和土壤剖面有机碳密度为因变量,林龄(A)、坡位(P)、坡向(S)、林分密度(D)、坡度(G)、土壤容重(V)等为自变量,利用实数编码自适应约束优化遗传算法进行参数搜索,建立投影寻踪回归预测模型。结果表明,该模型在3个人工林土壤表层和剖面有机碳的预测中,其预测值平均误差区间为5.73%~10.56%,标准误差区间为7.25%~15.24%,相关系数介于0.886~0.940之间,说明改进的投影寻踪模型用于土壤有机碳密度预测,能达到理想效果,方法简单有效,模型适用性及实用性较强。
6、生物量模型的改进:乔木层各器官的生物量采用相对生长量法进行模拟,即利用各器官的生物量与D2H(其中D为胸径、H为树高)的关系进行模拟;本文在模拟W = a( D2H)b的基础上,对各器官生物量的改进模型W = a?DbHc进行优化模拟,结果表明模拟效果理想,其结果可以用来计算乔木层各器官有机碳密度。
7、不同森林类型乔木层有机碳密度存在差异,其中杉木林乔木层有机碳密度最高,为15.145 kg/m2,其次为马尾松林乔木层有机碳密度,为13.723 kg/m2,最小是桉树林乔木层有机碳密度,为5.662 kg/m2,表现出杉木林>马尾松林>毛竹林>桉树林的趋势。从各林分类型乔木层各器官有机碳密度来看,均表现出干>根>枝>叶的趋势。就树干而言,有机碳密度从大到小依次为:马尾松>杉木>桉树>毛竹;就树根而言,有机碳密度从大到小依次为:杉木>马尾松>毛竹>桉树;就树枝而言,有机碳密度从大到小依次为:杉木>马尾松>毛竹>桉树;就树叶而言,有机碳含量从大到小依次为:杉木>马尾松>桉树>毛竹。
8、不同林分的林下植被有机碳密度以马尾松最大,为0.263 kg/m2,其次为桉树和杉木,毛竹林下植被的碳密度最少,只有0.031 kg/m2。林下植被各层中,灌木层和凋落物层均以马尾松林的碳密度最大,分别为0.133 kg/m2和0.107 kg/m2,桉树和杉木其次,毛竹最少;草本层的有机碳含量则以杉木林最大,桉树和马尾松次之,毛竹最少。这进一步说明本研究中的几种人工林中,毛竹的林分结构比较单一,林分碳储量受人为干扰严重;马尾松和杉木人工林的林分结构相对比较丰富,林下植物种类多样,生物多样性较高。
9、4种人工林生态系统有机碳贮量各不相同,杉木人工林生态系统的有机碳储量最大,达到28.125 kg/m2;马尾松人工林生态系统的有机碳储量次之,为27.779 kg/m2;毛竹人工林生态系统的有机碳储量第三,为22.884 kg/m2;而桉树人工林生态系统的有机碳储量最小,为22.381 kg/m2。
10、森林生态系统是一个有机整体,各部分的有机碳含量除受环境因子的影响外又有一定得自相关性。本文将土壤亚层即h1、h2、h3和h4层的有机密度以及0-100cm的剖面有机碳密度一起设为第一组的x1、x2、x3、x4、x5典型变量,乔木层的干、根、枝、叶、草本层、灌木层和凋落层分别为第二组的y1、y2、y3、y4、y5、y6、y7典型变量,采用典型相关分析讨论地下部分碳密度对地上部分碳密度的影响,深入分析土壤碳密度与地上部分碳密度的关系。结果表明,土壤碳有机碳密度对u1的相对作用大小依土壤深度依次减小,即:第h1层( 0~20㎝)>第h2层(20~40㎝)>第h3层(40~60㎝)>第h4层(60~100㎝),其中第h1层是作用最大,第h4层的作用很小。v1与地上部分各层次有机碳密度(yi)的原始数据相关关系如下:v1与地上部分树干、树根有机碳密度(yi)的原始数据存在明显的正相关。
11、根据福建省永安和建阳杉木、马尾松、毛竹和桉树人工林64株调查材料,按刀切法原理对人工林乔木层有机碳密度的进行估算。经计算,人工林乔木层有机碳密度的估计量为12.740 kg/m2,且估计精度为93.239%。因此,利用刀切法估算人工林乔木层有机碳含量的结果可靠。
12、基于投影寻踪的乔木层有机碳密度回归模型:投影回归是用于分析和处理非正态、非线性数据的一种新方法。由于建立多元回归模型的前提是:影响因子与预报因子之间确切存在模型的假定相关关系,而各预测因子之间的相关关系并不是一致的线性或非线性,而是存在多种相关形式,因此采用一致线性或非线性形式建立的回归模型不能真实的反映回归关系。本文引入加权的思想,在改进单纯形算法的基础上建立了乔木层有机碳密度模型,取得了较满意的效果,12个训练样本拟合值的总体拟合精度较高,在建阳杉木人工林乔木层有机碳密度预测模型中,预测和实测值相比较,平均误差为3.163%,标准误差为2.096%,相关系数达0.923,精度较高,预测效果良好。
With the continuous progress of global industry, the content of greenhouse gases in atmosphere is rising continuously, and global warming is more and more cause the attention of people. Forest ecosystems are the main component of terrestrial ecosystems, their function of carbon fixation and carbon sinks become more significant. Carbon storage in forest ecosystems including organic carbon storage in soil, organic carbon storage in vegetation and organic carbon storage in litter layer, is the major source of evaluating forest ecosystem’s carbon dioxide absorption function. Cunninghamia lanceolata, Pinus massoniana, Phyllostachys edulis and Eucalyptus grandis are important forest resources in south of China with wide economic use, and they are the most widespread plantation ecosystems in Fujian province. This thesis use the method of combining field investigation and laboratory testing to study the plantation ecosystem’s carbon density and spatial distribution of Cunninghamia lanceolata, Pinus massoniana, Phyllostachys edulis and Eucalyptus grandis in Fujian province. Based on the data, the least squares -support vector machine method is used to separately predict and estimate the plantations’organic carbon content and organic carbon density in soil and analyze the linear and nonlinear regression between organic carbon density in soil, and stand age, slope exposure, slope position. Therefore this thesis apply the multivariable linear regression model to predict the biomass of different plantation’s various organs in tree layer and calculate the organic carbon content and organic carbon density of various organs in tree layer. In plantation ecosystems of Cunninghamia lanceolata, Pinus massoniana and Eucalyptus grandis, the canonical analysis research is carried out to analyze the relationships between each soil layer’s organic carbon density and tree layer’s trunk, branch, leaf and root, the herb layer, shrub layer and litter layer of undergrowth vegetation. Furthermore, the relationship between soil carbon density and aboveground carbon density is studied. Finally, the jackknife method, projection pursuit method, etc. are used to simulate the relationship between carbon density in the tree layer and DBH, tree height. The major research results are as follows:
1 The characteristic of soil organic carbon content and carbon density’s spatial distribution: organic carbon content and carbon density are all represented as the surface soil organic carbon content‘s decreasing along with soil depth’s increasing in the vertical direction, in which the fastest decline is from the surface layer to second layer. In the horizontal direction, the order is followed as Phyllostachys edulis>Eucalyptus grandis>Cunninghamia lanceolata>Pinus massoniana, downgrade>mesoslope>upslope, sunnyslope>dark-slope. Cunninghamia lanceolata forest and Phyllostachys edulis forest’s soil organic carbon content and organic carbon density represent the mutative characteristic of young forest>middle-aged forest>mature forest, but Pinus massoniana forest, Eucalyptus grandis forest at different developmental stages, the trend of soil organic carbon density is opposite, represent young forest 2 The multivariable linear regression model was used to carry out multiple regression analysis for the relationship between soil organic carbon density and environmental factors exposure, slope, and forest age in the plantation ecosystems of Cunninghamia lanceolata, Pinus massoniana, Phyllostachys edulis and Eucalyptus grandis. The results showed that forest age’s effect on organic carbon density of soil profile is more significant and emerges positive correlation, while the slope aspect and slope position’s effect on organic carbon density of soil profile is relatively small and negatively correlated. But the premise of multiple linear regressions is the hypothetical linear relationship between soil organic carbon density and each factor.
3 The least square support vector machine method is used to predict the plantation soil carbon density estimation. This thesis adopt (0,1) the normalization method for data processing and the Gaussian radial function as the kernel function to estimate the numerical value of surface soil organic carbon density and the profile organic carbon density. The prediction result is good, but the operation process is generally "black-box operation".
4 Among of various parts of the organic carbon density in forest ecosystems, especially in soil organic carbon density, which affected by many factors and each factor may exist between the linear or nonlinear relationship, and there were the main factors and secondary factors. In this paper, it used an extended sequence of discrete gray model to predict soil organic carbon density, and determined the impact of soil organic carbon density of the main variables. The results showed that, in fir, pine and eucalyptus forest plantation, the complex correlation coefficient value of age was the maximum, which related to soil organic carbon density and surface soil organic carbon density, thus the age of this forest fir, pine and eucalyptus forest was the main variables, the remaining factors were auxiliary variables. The correlation coefficient R were greater than 0.7832 when using gray high-end discrete sequence generalized dynamic model to simulated the fir, pine and eucalyptus plantations soil organic carbon density and soil organic carbon density. And the predicted values were similar with measured values, which indicated that the gray discrete series high order prediction model was fit to soil organic carbon density of soil organic carbon density in Fujian forests.
5 Surface soil organic carbon density and soil organic carbon density of Fir, pine and eucalyptus plantations in Jianyang, Yon’an area, Fujian province, were selected as dependent variable, age (A), slope position (P), slope (S), Lin Density (D), slope (G), soil bulk density (V) as the independent variables. Based on which, it established an projection pursuit regression model using adaptive real-coded genetic algorithm constrained optimization parameter search. The results showed that the model in three plantation soil surface and profile prediction of organic carbon, the average error range of the predicted value of 5.73% ~ 10.56%, standard error interval of 7.25% ~ 15.24%, the correlation coefficient between 0.886 ~ 0.940, which indicated that the improved projection pursuit model for prediction of soil organic carbon density, could achieve the desired results, and the model was simple and effective, applicable and practicable strongly.
6 The biomass of each organ in tree layer is simulated with relative growth method which is based on the relation between the biomass of each organ and D2H (D is diameter at breast height, H is the tree height). In this thesis, based on the simulation of W = a( D2H)b, optimized simulation is used for the improved model W = a?DbHc of various organs’biomass. The results showed that the simulation results are satisfactory and can be used to calculate the organic carbon density of tree layer in different organs.
7 The organic carbon density of different forest types in tree layer is different. Among them, tree layer of Cunninghamia lanceolata forest’s organic carbon density is the highest of 15.145 kg/m2, followed by the tree layer of Pinus massoniana forest’s organic carbon density of 13.723 kg/m2, the smallest is the tree layer of Phyllostachys edulis forest’s organic carbon density of 5.662 kg/m2. This showed the trend of Cunninghamia lanceolata forest> Pinus massoniana forest> Phyllostachys edulis forest. The organic carbon density of various organs in different kinds of forest’s tree layer all showed the trend of trunk> root> branch>leaf. In detail, on the part of trunks, the big to small order of organic carbon density is followed as: Pinus massoniana > Cunninghamia lanceolata > Eucalyptus grandis > Phyllostachys edulis. On the part of roots, the big to small order of organic carbon density is followed as: Cunninghamia lanceolata > Pinus massoniana > Phyllostachys edulis > Eucalyptus grandis. On the part of branches, the big to small order of organic carbon density is followed as: Cunninghamia lanceolata > Pinus massoniana > Phyllostachys edulis > Eucalyptus grandis. On the part of leaves, the big to small order of organic carbon density is followed as: Cunninghamia lanceolata > Pinus massoniana > Eucalyptus grandis > Phyllostachys edulis.
8 The largest organic carbon density of understory vegetation in different forest is Pinus massoniana of 0.263 kg/m2, followed by Eucalyptus grandis, Cunninghamia lanceolata, Phyllostachys edulis. The carbon density of the vegetation under Phyllostachys edulis forest is the least of only 0.031 kg/m2. Underground vegetation in each layer, the carbon density of pinus massoniana in shrub layer and litter layer is the largest of 0.133 kg/m2 and 0.107 kg/m2, followed by Eucalyptus grandis and Cunninghamia lanceolata, Phyllostachys edulis is the least. And, the largest organic carbon content in herb layer is Cunninghamia lanceolata forest, followed by Eucalyptus grandis, Pinus massoniana and Phyllostachys edulis. Hence, in this study, the forest structure of Phyllostachys edulis is simple and seriously disturbed by human. Pinus massoniana and Cunninghamia lanceolata plantations are relatively rich in forest structure, understory plant species is diversity and the biodiversity is high.
9 The carbon storage of four kinds of plantation’s ecosystem is different, The organic carbon storage of Cunninghamia lanceolata plantation’s ecosystem is the largest of 28.125 kg/m2, Pinus massoniana plantation is the second of 27.779 kg/m2. Phyllostachys edulis is the third of 22.884 kg/m2, and Eucalyptus grandis plantation is the smallest of 22.381 kg/m2.
10 Forest ecosystem is an organic whole, each part of the organic carbon content not only influenced by environmental factors, but also there must be self-relevant. This thesis makes the sub-layer organic density of the soil h1, h2, h3 and h4 and 0-100cm’s profile organic carbon density as the typical variable of first group x2, x3, x4, x5, and the tree layer of trunk, root, branch, leaf, herb layer, shrub layer and litter layer are the second group as y1, y2, y3, y4, y5, y6, y7. Canonical correlation analysis was carried out to discuss the effect of underground carbon density to aboveground carbon density and analyze the relationship between soil carbon density and aboveground carbon density. The results showed that the extent of relative effect of soil organic carbon density to u1 will successively reduce as the soil depth decline, and the order is the h1 layer (0 ~ 20 cm) >the h2 layer (20 ~ 40 cm) > the h3 layer (40 ~ 60 cm) >the h4 layer (60 ~ 100 cm) in which the role of the h1layer is the largest, the role of the h4 layer is little. The correlativity between v1 and the raw data of each layer’s organic carbon density on the aboveground part is as follows: a significant positive correlation is existed between v1 and aerial parts of tree trunks, roots organic carbon density (yi).
11 Based on the 64 survey materials of Cunninghamia lanceolata, Pinus massoniana, Phyllostachys edulis and Eucalyptus grandis in Yongan and Jianyang County, Fujian province the jackknife method was used to estimate the organic carbon density in tree layer of plantations. According to calculate results, the organic carbon density’s estimated amount is 12.740 kg/m2 in plantation tree layer, and the estimated accuracy is 93.239%. Therefore, the result of estimating the organic carbon content of plantation tree layer is reliable by using the method of jackknife.
12 Because the premise of establishing multiple regression models is that there is exactly exist the model’s assumed correlation between the impact factor and predicting factor and the correlation between predictors is not consistent linear or nonlinear and show several related forms, projection pursuit regression model of soil organic carbon density is adaptable for simulating. This thesis introduced the idea of weighting and established the soil organic carbon density model in the constrained based constringent and optimized genetic algorithm.
1、土壤有机碳含量和有机碳密度的空间分布特征:有机碳含量和有机碳密度均垂直方向上表现为土壤表层有机碳含量随着土层深度的增加而逐渐降低,其中表层到第二层的降低速度最快;水平方向上,从大到小依次为毛竹>桉树>杉木>马尾松,下坡>中坡>上坡、阳坡>阴坡;杉木林和毛竹林土壤有机碳含量和有机碳密度表现出幼龄林>中龄林>成熟林的变化特征;而马尾松林地、桉树林地在不同发育阶段土壤有机碳密度趋势则相反,表现为幼龄林<中龄林<成熟林的变化特征。但因其受土壤碳含量、土壤容重、土层厚度的影响,又有一定变异性。
2、应用多元线性回归模型对杉木、马尾松、毛竹和桉树人工林生态系统中土壤有机碳密度与环境因子坡向和坡度以及林龄的关系,进行了多元回归分析。结果表明,林龄对杉木土壤剖面有机碳密度的影响比较显著,而对马尾松、毛竹和桉树3个林分土壤剖面有机碳密度的影响不显著,坡向和坡位对4个林分土壤剖面有机碳密度的影响均较小。但因多元线性回归的前提是假设土壤有机碳密度与各因子之间的线性关系。
3、进一步应用最小二乘支持向量机法对各人工林土壤碳密度进行预测,结果表明,对建阳和永安杉木人工林土壤表层有机碳密度预测的平均误差分别为0.077%和0.116%;对建阳和永安杉木人工林土壤剖面有机碳密度预测的平均误差为0.098%和0.082%;对建阳和永安马尾松人工林土壤表层有机碳密度预测的平均误差分别为0.082%和0.081%;对建阳和永安马尾松人工林土壤剖面有机碳密度预测的平均误差为2.559%和0.049%;对建阳和永安毛竹人工林土壤表层有机碳密度预测的平均误差分别为2.709%和3.928%;对建阳和永安毛竹人工林土壤剖面有机碳密度预测的平均误差为2.895%和2.474%;对桉树人工林土壤表层有机碳密度预测的平均误差为0.066%;对桉树人工林土壤剖面有机碳密度预测的平均误差为0.049%。说明LS-SVM模型对福建4种人工林土壤有机碳密度值的预测效果良好。
4、森林生态系统中各部分有机碳密度,特别是土壤有机碳密度受诸多因子影响,而且各因子之间可能存在线性或非线性的关系,且有主要因素和次要因素。本文采用尝试用建立扩展离散灰色序列模型,来预测土壤有机碳密度值,并确定影响土壤有机碳密度的主要变量。结果表明,杉木、马尾松和桉树人工林的林龄与土壤表层有机碳密度和土壤剖面有机碳密度之间的复相关系数均为最大值,因此确定林龄为杉木林、马尾松林和桉树林的主变量,其余因子为辅助变量。运用扩展灰色离散序列高阶动态预测模型对杉木、马尾松和桉树人工林表层土壤有机碳密度和土壤剖面有机碳密度进行模拟,结果表明相关系数R均大于0.7832,且预测值与实测值误差较小,说明扩展灰色离散序列高阶动态土壤有机碳密度预报模型对福建不同人工林土壤有机碳密度的预测效果良好。
5、选取福建建阳、永安地区的杉木、马尾松和桉树人工林表层土壤有机碳密度和土壤剖面有机碳密度为因变量,林龄(A)、坡位(P)、坡向(S)、林分密度(D)、坡度(G)、土壤容重(V)等为自变量,利用实数编码自适应约束优化遗传算法进行参数搜索,建立投影寻踪回归预测模型。结果表明,该模型在3个人工林土壤表层和剖面有机碳的预测中,其预测值平均误差区间为5.73%~10.56%,标准误差区间为7.25%~15.24%,相关系数介于0.886~0.940之间,说明改进的投影寻踪模型用于土壤有机碳密度预测,能达到理想效果,方法简单有效,模型适用性及实用性较强。
6、生物量模型的改进:乔木层各器官的生物量采用相对生长量法进行模拟,即利用各器官的生物量与D2H(其中D为胸径、H为树高)的关系进行模拟;本文在模拟W = a( D2H)b的基础上,对各器官生物量的改进模型W = a?DbHc进行优化模拟,结果表明模拟效果理想,其结果可以用来计算乔木层各器官有机碳密度。
7、不同森林类型乔木层有机碳密度存在差异,其中杉木林乔木层有机碳密度最高,为15.145 kg/m2,其次为马尾松林乔木层有机碳密度,为13.723 kg/m2,最小是桉树林乔木层有机碳密度,为5.662 kg/m2,表现出杉木林>马尾松林>毛竹林>桉树林的趋势。从各林分类型乔木层各器官有机碳密度来看,均表现出干>根>枝>叶的趋势。就树干而言,有机碳密度从大到小依次为:马尾松>杉木>桉树>毛竹;就树根而言,有机碳密度从大到小依次为:杉木>马尾松>毛竹>桉树;就树枝而言,有机碳密度从大到小依次为:杉木>马尾松>毛竹>桉树;就树叶而言,有机碳含量从大到小依次为:杉木>马尾松>桉树>毛竹。
8、不同林分的林下植被有机碳密度以马尾松最大,为0.263 kg/m2,其次为桉树和杉木,毛竹林下植被的碳密度最少,只有0.031 kg/m2。林下植被各层中,灌木层和凋落物层均以马尾松林的碳密度最大,分别为0.133 kg/m2和0.107 kg/m2,桉树和杉木其次,毛竹最少;草本层的有机碳含量则以杉木林最大,桉树和马尾松次之,毛竹最少。这进一步说明本研究中的几种人工林中,毛竹的林分结构比较单一,林分碳储量受人为干扰严重;马尾松和杉木人工林的林分结构相对比较丰富,林下植物种类多样,生物多样性较高。
9、4种人工林生态系统有机碳贮量各不相同,杉木人工林生态系统的有机碳储量最大,达到28.125 kg/m2;马尾松人工林生态系统的有机碳储量次之,为27.779 kg/m2;毛竹人工林生态系统的有机碳储量第三,为22.884 kg/m2;而桉树人工林生态系统的有机碳储量最小,为22.381 kg/m2。
10、森林生态系统是一个有机整体,各部分的有机碳含量除受环境因子的影响外又有一定得自相关性。本文将土壤亚层即h1、h2、h3和h4层的有机密度以及0-100cm的剖面有机碳密度一起设为第一组的x1、x2、x3、x4、x5典型变量,乔木层的干、根、枝、叶、草本层、灌木层和凋落层分别为第二组的y1、y2、y3、y4、y5、y6、y7典型变量,采用典型相关分析讨论地下部分碳密度对地上部分碳密度的影响,深入分析土壤碳密度与地上部分碳密度的关系。结果表明,土壤碳有机碳密度对u1的相对作用大小依土壤深度依次减小,即:第h1层( 0~20㎝)>第h2层(20~40㎝)>第h3层(40~60㎝)>第h4层(60~100㎝),其中第h1层是作用最大,第h4层的作用很小。v1与地上部分各层次有机碳密度(yi)的原始数据相关关系如下:v1与地上部分树干、树根有机碳密度(yi)的原始数据存在明显的正相关。
11、根据福建省永安和建阳杉木、马尾松、毛竹和桉树人工林64株调查材料,按刀切法原理对人工林乔木层有机碳密度的进行估算。经计算,人工林乔木层有机碳密度的估计量为12.740 kg/m2,且估计精度为93.239%。因此,利用刀切法估算人工林乔木层有机碳含量的结果可靠。
12、基于投影寻踪的乔木层有机碳密度回归模型:投影回归是用于分析和处理非正态、非线性数据的一种新方法。由于建立多元回归模型的前提是:影响因子与预报因子之间确切存在模型的假定相关关系,而各预测因子之间的相关关系并不是一致的线性或非线性,而是存在多种相关形式,因此采用一致线性或非线性形式建立的回归模型不能真实的反映回归关系。本文引入加权的思想,在改进单纯形算法的基础上建立了乔木层有机碳密度模型,取得了较满意的效果,12个训练样本拟合值的总体拟合精度较高,在建阳杉木人工林乔木层有机碳密度预测模型中,预测和实测值相比较,平均误差为3.163%,标准误差为2.096%,相关系数达0.923,精度较高,预测效果良好。
With the continuous progress of global industry, the content of greenhouse gases in atmosphere is rising continuously, and global warming is more and more cause the attention of people. Forest ecosystems are the main component of terrestrial ecosystems, their function of carbon fixation and carbon sinks become more significant. Carbon storage in forest ecosystems including organic carbon storage in soil, organic carbon storage in vegetation and organic carbon storage in litter layer, is the major source of evaluating forest ecosystem’s carbon dioxide absorption function. Cunninghamia lanceolata, Pinus massoniana, Phyllostachys edulis and Eucalyptus grandis are important forest resources in south of China with wide economic use, and they are the most widespread plantation ecosystems in Fujian province. This thesis use the method of combining field investigation and laboratory testing to study the plantation ecosystem’s carbon density and spatial distribution of Cunninghamia lanceolata, Pinus massoniana, Phyllostachys edulis and Eucalyptus grandis in Fujian province. Based on the data, the least squares -support vector machine method is used to separately predict and estimate the plantations’organic carbon content and organic carbon density in soil and analyze the linear and nonlinear regression between organic carbon density in soil, and stand age, slope exposure, slope position. Therefore this thesis apply the multivariable linear regression model to predict the biomass of different plantation’s various organs in tree layer and calculate the organic carbon content and organic carbon density of various organs in tree layer. In plantation ecosystems of Cunninghamia lanceolata, Pinus massoniana and Eucalyptus grandis, the canonical analysis research is carried out to analyze the relationships between each soil layer’s organic carbon density and tree layer’s trunk, branch, leaf and root, the herb layer, shrub layer and litter layer of undergrowth vegetation. Furthermore, the relationship between soil carbon density and aboveground carbon density is studied. Finally, the jackknife method, projection pursuit method, etc. are used to simulate the relationship between carbon density in the tree layer and DBH, tree height. The major research results are as follows:
1 The characteristic of soil organic carbon content and carbon density’s spatial distribution: organic carbon content and carbon density are all represented as the surface soil organic carbon content‘s decreasing along with soil depth’s increasing in the vertical direction, in which the fastest decline is from the surface layer to second layer. In the horizontal direction, the order is followed as Phyllostachys edulis>Eucalyptus grandis>Cunninghamia lanceolata>Pinus massoniana, downgrade>mesoslope>upslope, sunnyslope>dark-slope. Cunninghamia lanceolata forest and Phyllostachys edulis forest’s soil organic carbon content and organic carbon density represent the mutative characteristic of young forest>middle-aged forest>mature forest, but Pinus massoniana forest, Eucalyptus grandis forest at different developmental stages, the trend of soil organic carbon density is opposite, represent young forest
3 The least square support vector machine method is used to predict the plantation soil carbon density estimation. This thesis adopt (0,1) the normalization method for data processing and the Gaussian radial function as the kernel function to estimate the numerical value of surface soil organic carbon density and the profile organic carbon density. The prediction result is good, but the operation process is generally "black-box operation".
4 Among of various parts of the organic carbon density in forest ecosystems, especially in soil organic carbon density, which affected by many factors and each factor may exist between the linear or nonlinear relationship, and there were the main factors and secondary factors. In this paper, it used an extended sequence of discrete gray model to predict soil organic carbon density, and determined the impact of soil organic carbon density of the main variables. The results showed that, in fir, pine and eucalyptus forest plantation, the complex correlation coefficient value of age was the maximum, which related to soil organic carbon density and surface soil organic carbon density, thus the age of this forest fir, pine and eucalyptus forest was the main variables, the remaining factors were auxiliary variables. The correlation coefficient R were greater than 0.7832 when using gray high-end discrete sequence generalized dynamic model to simulated the fir, pine and eucalyptus plantations soil organic carbon density and soil organic carbon density. And the predicted values were similar with measured values, which indicated that the gray discrete series high order prediction model was fit to soil organic carbon density of soil organic carbon density in Fujian forests.
5 Surface soil organic carbon density and soil organic carbon density of Fir, pine and eucalyptus plantations in Jianyang, Yon’an area, Fujian province, were selected as dependent variable, age (A), slope position (P), slope (S), Lin Density (D), slope (G), soil bulk density (V) as the independent variables. Based on which, it established an projection pursuit regression model using adaptive real-coded genetic algorithm constrained optimization parameter search. The results showed that the model in three plantation soil surface and profile prediction of organic carbon, the average error range of the predicted value of 5.73% ~ 10.56%, standard error interval of 7.25% ~ 15.24%, the correlation coefficient between 0.886 ~ 0.940, which indicated that the improved projection pursuit model for prediction of soil organic carbon density, could achieve the desired results, and the model was simple and effective, applicable and practicable strongly.
6 The biomass of each organ in tree layer is simulated with relative growth method which is based on the relation between the biomass of each organ and D2H (D is diameter at breast height, H is the tree height). In this thesis, based on the simulation of W = a( D2H)b, optimized simulation is used for the improved model W = a?DbHc of various organs’biomass. The results showed that the simulation results are satisfactory and can be used to calculate the organic carbon density of tree layer in different organs.
7 The organic carbon density of different forest types in tree layer is different. Among them, tree layer of Cunninghamia lanceolata forest’s organic carbon density is the highest of 15.145 kg/m2, followed by the tree layer of Pinus massoniana forest’s organic carbon density of 13.723 kg/m2, the smallest is the tree layer of Phyllostachys edulis forest’s organic carbon density of 5.662 kg/m2. This showed the trend of Cunninghamia lanceolata forest> Pinus massoniana forest> Phyllostachys edulis forest. The organic carbon density of various organs in different kinds of forest’s tree layer all showed the trend of trunk> root> branch>leaf. In detail, on the part of trunks, the big to small order of organic carbon density is followed as: Pinus massoniana > Cunninghamia lanceolata > Eucalyptus grandis > Phyllostachys edulis. On the part of roots, the big to small order of organic carbon density is followed as: Cunninghamia lanceolata > Pinus massoniana > Phyllostachys edulis > Eucalyptus grandis. On the part of branches, the big to small order of organic carbon density is followed as: Cunninghamia lanceolata > Pinus massoniana > Phyllostachys edulis > Eucalyptus grandis. On the part of leaves, the big to small order of organic carbon density is followed as: Cunninghamia lanceolata > Pinus massoniana > Eucalyptus grandis > Phyllostachys edulis.
8 The largest organic carbon density of understory vegetation in different forest is Pinus massoniana of 0.263 kg/m2, followed by Eucalyptus grandis, Cunninghamia lanceolata, Phyllostachys edulis. The carbon density of the vegetation under Phyllostachys edulis forest is the least of only 0.031 kg/m2. Underground vegetation in each layer, the carbon density of pinus massoniana in shrub layer and litter layer is the largest of 0.133 kg/m2 and 0.107 kg/m2, followed by Eucalyptus grandis and Cunninghamia lanceolata, Phyllostachys edulis is the least. And, the largest organic carbon content in herb layer is Cunninghamia lanceolata forest, followed by Eucalyptus grandis, Pinus massoniana and Phyllostachys edulis. Hence, in this study, the forest structure of Phyllostachys edulis is simple and seriously disturbed by human. Pinus massoniana and Cunninghamia lanceolata plantations are relatively rich in forest structure, understory plant species is diversity and the biodiversity is high.
9 The carbon storage of four kinds of plantation’s ecosystem is different, The organic carbon storage of Cunninghamia lanceolata plantation’s ecosystem is the largest of 28.125 kg/m2, Pinus massoniana plantation is the second of 27.779 kg/m2. Phyllostachys edulis is the third of 22.884 kg/m2, and Eucalyptus grandis plantation is the smallest of 22.381 kg/m2.
10 Forest ecosystem is an organic whole, each part of the organic carbon content not only influenced by environmental factors, but also there must be self-relevant. This thesis makes the sub-layer organic density of the soil h1, h2, h3 and h4 and 0-100cm’s profile organic carbon density as the typical variable of first group x2, x3, x4, x5, and the tree layer of trunk, root, branch, leaf, herb layer, shrub layer and litter layer are the second group as y1, y2, y3, y4, y5, y6, y7. Canonical correlation analysis was carried out to discuss the effect of underground carbon density to aboveground carbon density and analyze the relationship between soil carbon density and aboveground carbon density. The results showed that the extent of relative effect of soil organic carbon density to u1 will successively reduce as the soil depth decline, and the order is the h1 layer (0 ~ 20 cm) >the h2 layer (20 ~ 40 cm) > the h3 layer (40 ~ 60 cm) >the h4 layer (60 ~ 100 cm) in which the role of the h1layer is the largest, the role of the h4 layer is little. The correlativity between v1 and the raw data of each layer’s organic carbon density on the aboveground part is as follows: a significant positive correlation is existed between v1 and aerial parts of tree trunks, roots organic carbon density (yi).
11 Based on the 64 survey materials of Cunninghamia lanceolata, Pinus massoniana, Phyllostachys edulis and Eucalyptus grandis in Yongan and Jianyang County, Fujian province the jackknife method was used to estimate the organic carbon density in tree layer of plantations. According to calculate results, the organic carbon density’s estimated amount is 12.740 kg/m2 in plantation tree layer, and the estimated accuracy is 93.239%. Therefore, the result of estimating the organic carbon content of plantation tree layer is reliable by using the method of jackknife.
12 Because the premise of establishing multiple regression models is that there is exactly exist the model’s assumed correlation between the impact factor and predicting factor and the correlation between predictors is not consistent linear or nonlinear and show several related forms, projection pursuit regression model of soil organic carbon density is adaptable for simulating. This thesis introduced the idea of weighting and established the soil organic carbon density model in the constrained based constringent and optimized genetic algorithm.
引文
[1] Aber JD, McDowell W, Nadelhoffer KJ, et al. Nitrogen saturation in Northern forest ecosystems, hypotheses revisited[J] Bioscience, 1998, 48: 921-934.
[2] Alexeyev V, Birdsey R, Stakanov V et al. Carbon in vegetation of Russian forest: methods to estimate storage and geographical distribution[J]. Water, Air and Soil Poll, 1995,82:271-282.
[3] Anten N P R, Schieving F, Werger M J A. Patterns of light and nitrogen distribution in relation to whole canopy carbon gain in C3 and C4 mono and dicotyledonous species[J]. Oecologia, 1995,101: 504-513.
[4] Anten N P R, Werger MJA, Medina E. Nitrogen distribution and leaf area indices in relation to photosynthetic nitrogen use efficiency in savanna grasses[J]. Plant Ecology, 1998,138:63-75.
[5] Apps MJ and Kurz WA. The role of Canadian forest in the global carbon budget. In: Kanninen M ed. Carbon Balance of world’s forested ecosystems: Towards a Global Assessment. Finland: SILMU, 1994: 12-20.
[6] Beets PN, Whitehead D. Carbon partitioning in Pinus radiate stands in relation to foliage nitrogen status[J]. Tree Physiology, 1996,16:139-144.
[7] Botkin DB,Woodwell GM.Tempel N.Forest productivity estimated from carbon dioxide uptake[J]. Ecology,1970,51:1 057-1 060.
[8] Brown S,Lugo A E. A new estimate based on forest volumes[J]. Science, 1984,223:1290-1293.
[9] Brown S,Lugo A E. The storage and production of organic matter in tropical forests and their role in the global carbon cycle[J]. Biotropica,1982,14:161-187.
[10] Cannell MGR, Dewar RC. Carbon allocation in trees: a review of concepts for modeling[J]. Adwances in Ecological Research, 1994, 25:59-104.
[11] Cao MK, Woodward FI. Dynamic responses of terrestrial ecosystem carbon cycling to global climate change[J]. Nature, 1998,393:249-252.
[12] Chen W, Chen J M, Cihlar J. An integrated terrestrial ecosystem carbon-budget model based on changes indisturbance, climate, and atmospheric chemistry[J]. Ecological Modelling , 2000, 135:55-79.
[13] Chen Z. Interannual variability of carbon dioxide and water vapor fluxes above a boreal aspen forest[A]. In: Boston MA ed. Proceedings of the 23rd Conference on Agricultural and Forest Meteorology[C]. American Meteorological Society, 1998, 163-161.
[14] Ciais P. Large Northern Hemisphere terrestrial CO2 sink indicated by the C ratio of atmospheric CO2 [ J ]. Science, 1995,269: 1098-1102.
[15] Den Boer WMJ, Van den Tweel PA. The health condition of the Dutch forests in 1984[J]. Neth. J.Agric. Sci., 1985, 33:167-174.
[16] Dennis DB, Kell BW. Modeling CO2 and water vapor exchange of a temperate broadleaved forest across hourly to decadal time scales [J]. Ecological Modelling, 2001,142 :155-184.
[17] Detwiler R P, Charles A S. Tropical forests and the global carbon budget[J]. Science, 1988, 239: 42 - 47.
[18] Dickinson RE, Berry JA, Bonan GB, et al. Nitrogen controls on climate model evapotranspiration[J]. Journal of Climate, 2002, 15:278-295.
[19] Dixon PK, Brown S, Houghton RA et al. Carbon pools and flux of global forest ecosystems[J]. Science,1994, 262: 185-190.
[20] Dolman A J, Moors EJ, Elbers J A. The carbon uptake of a mid latitude pine forest growing on sandy soil[J]. Agric For Meteorol , 2002,111 :187-202.
[21] Ebermeyr, E. Die gesamte Lehre der Waldstreu mit Rucksicht auf die themische statik des Waldbaues [M]. Belln: J. Springer,1876, 116.
[22] Fang J Y,Wang G G,Liu G G,et al. Forest biomass of China:an estimate based on the biomass-volume relationship[J]. Ecological Applications,1998,8:1 084-1 091.
[23] Fang JY, Chen AP, Peng Ch H, Zhao Sh Q, Ci LJ. Changes in forest biomass carbon storage in China between 1949 and 1998[J]. Science, 2001, 292(5525):2320-2322(in Reports).
[24] Fowler D, Duyzer JH. Micrometeorological techniques for the measurement of trace gas exchange[A]. In: Andreae MO, Schimel DS eds. Exchange of Trace Gases Between Terrestrial Ecosystems and the Atmosphere[M], Wiley. Chichester, 1989:189-207.
[25] Friedlingstein P, Joel G, Field CB, et al. Toward an Alloction Scheme for Global Terrestrial Carbon Models[J]. Global Change Biology, 1999,5:755-770.
[26] Greco S, Baldocchi D D. Seasonal variations of CO2 and water vapor exchange rates over a temperate deciduous forest[J]. Global Change Biol, 1996,183-198.
[27] Houghton JT,Ding Y,Griggs DJ,et al. Climate Change 2001:The Scientific Basis. Contribution of Working GroupⅠto the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge,UK:Cambridge University Press,2001.
[28] Houghton R A. Temporal patterns of land-use change and carbon storage in China and tropical Asia[J]. Science in China(Series C),2002,45(Supp):10-17.
[29] Houghton RA, Davidson EA, Woodwell GM. Missing sinks, feedbacks, and understanding the role of terrestrial ecosystems in the global carbon balance[J]. Global Biogeochemical Cycles, 1998, 12:25-34.
[30] Houghton RA. Terrestrial carbon storage: Global lessons for Amazonian research[J]. Cienciae Cultura SaoPaulo, 1997, 49:58 - 72.
[31] Hungate BA, Dukes JS, Shaw MR, et al. Nitrogen and climate change. Science, 2003,302:1512-1513.
[32] IPCC. Land use, land-use change, and forestry-A special report of the IPCC [M].London: Cambridge University Press, 2000.
[33] Johnson WC,Sharpe DM.The ration of total to merchantable forest biomass and its application to the global carbon budget [J]. Canadian Journal of Forest Research,1983,13:372-383.
[34] Kimmins JP. Forest ecology[M]. New York: Macmillan Press, 1987.
[35] Kirschbauma MUF. To sink or burn?A discussion of the potential contributions of forests to greenhouse gas balances through storing carbon or providing biofuels [J]. Biomass and Bioenergy,2003,24:297-310.
[36] Kovel CGF de, Mierlo AJEM van, Wilms YJO, et al. Carbon and nitrogen in soil and vegetation at sites differing in sccessional age [J]. Plant Ecology, 2000, 149: 43-49.
[37] Levine JS, CoferⅢWR, Carbon Jr DR, etal. Biomass buring, adriver for global change. Environmental Science Technology, 1995, 120:120-125.
[38] Lieth H. Primary productivity of successional stages [C].Knapp R. Handbook of vegetation science,Ⅷ: vegetation dynamics. The Hague: Junk, 1974.
[39] Liu JX, Price DT, Chen J M. Nitrogen controls on ecosystem carbon sequestration: a model implementation and application to Saskatchewan,.Canada[J]. Ecological modeling, 2005, 186:178-195.
[40] Major J. Biomass accumulation in successions [C]. Knapp R. Handbook of vegetation science,Ⅷ: vegetation dynamics. The Hague: Junk,1974.
[41] McGuire AD, Melillo JM, Joyce L A. The role of nitrogen in the response of forest net primary production to elevated atmospheric carbon dioxide[J]. Annual Review of Ecology and Systematics, 1995,26:473-503.
[42] Meentemeyer V. An approach to the biometeorology of decomposer organisms[J]. International Journal of Biometerology, 1978, 22:94-102.
[43] Melillo JM,Gosz JR.Interactions of biogeochemical cycles in forest ecosystems[A]. In: The major biogeochemical cycles and their interactons[C]. Bolin B and Cook RB (Eds.).John Wiley and Sons, New York, 1983:177-222.
[44] Monteith JL , Unsworth MH. Principles of environmental physics , London ,1990:289.
[45] Moorhead DL, Reynolds JF. A general model of litter decomposition in the northern Chihuahuan desert[J]. Ecological Modelling , 1991,56:197-219.
[46] Nihlgard B. The ammonium hypothesis-an additional explanation to the forest dieback in Europe[J]. Ambio.,1985, 14:2-8.
[47] Norby RJ. Nitrogen depositon: a component of global change analyses[J]. NewPhytologist,1998,139(1): 189-200.
[48] Oliver L, Phillip s, Sandra B rown. Changes in the carbon balance of tropical forests: evidence from long term plots[J]. Science, 1998, 282: 439 - 442.
[49] Oren RR, Ellsworth DS, Johnsen KH, et al. Soil fertility limits carbon sequestration by forest ecosystems in a CO2-enriched atmosphere[J]. Nature, 2001:469-472.
[50] Paavolainen L. Nitrogen transformations in boreal forest soils in response to extreme manipulation treatments[C]. Academic Dissertation in Environmental Microbiology. Faculty of Agriculture and Forestry University of Helsinki, Helsinki,1999.
[51] Peter M A , Michael H U, Beverly E L, et al. Seasonal differences in carbon and water vapor exchange in young and old-growth ponderosa pine ecosystems[J]. Agric For Meteorol, 2002,111 :157-170.
[52] Poulton PR,Pye E,Hargreaves PR,et al . Accumulation of carbon and nitrogen by old arable land reverting to woodland[J]. Global Change Biology,2003,9:942-955.
[53] Raich JW,Schlesinger WH. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate [J]. Tellus,1992,44B:81.
[54] Rastetter EB, Ryan MG, Shaver GR, et al. A general biogeochemical model describing the responses of the C and N cycles in terrestrial ecosystems to changes in CO2, climate, and N deposition[J]. Tree Physiology, 1991, 9:101-126.
[55] Redfield AC. The biological control of chemical factors in the environment[J]. American Scientist, 1958, 46:205-221.
[56] Rodin L E, Bazilevich NI. Production and mineral cycling in terrestrial vegetation [M]. London: Oliver and Boyd Edinburgh, 1967.
[57] Roelofs JGM, Kempers AJ, Houdljk ALFM, et al. The effect of air-borne ammonium sulphate on Pinus nigra var. maritime in the Netherlands[J]. Plant Soil, 1985, 84:45-56.
[58] Rogers HH, Prior SA, Runion GB, et al. Root to shoot ratio of crops as influenced by CO2[J]. Plant and Soil, 1996, 187(2):229-248.
[59] Runyon J, Waring RH, Goward SN, et al. Environmental limits on net primary production and light-use efficiency across the Oregon transect[J]. Ecological Applicatons, 1994, 4:226-237.
[60] Saigusa N, Yamamoto S, Murayama S, et al. A long - term flux measurement over a cool- temperate deciduous forest by the eddy covariance method[M]. International workshop for Advanced Flux Network and Flux evaluation, Sapporo, 2000,74 :27-29.
[61] Schinozaki K, Yoda K, Hozumi K, et al. A quantitative analysis of plant form—the pipe model theory:Ⅰ.Basic Analyses[J]. Japanese Journal of Ecology, 1964, 14:97-103.
[62] Schlesinger WH, Lichter J. Limited carbon storage in soil and litter of experimental forest plots underelevated atmospheric CO2[J]. Nature, 2001,411:466-469.
[63] Shangguan ZP, Shao MA, Ren SJ, et al. Effect of Nitrogen on root and shoot relations and gas exchange in winter wheat[J]. Botanical Bulletin of Academia Sinica, 2004,45:49-54.
[64] Shif C H, Chen X W, Wang W J, Kentaro T T, Yukio A, Kaichiro S,Shigeru U. Vegetation characteristics of a larch-dominant site for CO2 flux monitoring study at the Laoshan Experimental Station in Northeast China [J].Eurasian Journal of Forest Research,2001,3:53- 60.
[65] Streets DG, Jiang KJ, Hu XL, Sinton JE, Zhang XQ, Xu DY, Jacobson MZ, Hansen JE. Recent reductions in China’s greenhouse gas emissions [J]. Science, 2001,294:1835-1836.
[66] Thompson WA, Wheeler AM. Photosynthesis by nature needles of field-grown Pinus radiate[J]. Forest Ecology and Management, 1992,52:225-242.
[67] Tian H, Mellilo J M, KichilghterD W , et al. Effects of inter annual climate variability on carbon storage in Amazonian ecosystems[J]. Nature, 1998, 396: 664 - 667.
[68] Tilman D. Plant strategies and the dynamics and structure of plant communities.[A] Princeton University Press, Princeton, NJ, 1988.
[69] Turner DP, Koepper GJ, Hamon ME et al. A carbon budget for forests of the conterminous United States[J]. Ecology Application, 1995,5:421-436.
[70] Valentini R. Respiration as the main determinant of carbon balance in European forests[J]. Nature, 2000, 404 :861-865.
[71] Van der Erden LJM. Toxicity of ammonia to plants. Agricult[J]. Environm., 1982, 7:223-235.
[72] Víctor J. Jaramillo, J. Boone Kauffman, Lyliana Rentería-Rodríguez, Dian L. Cummings, Lisa J. Ellingson. Biomass, carbon, and nitrogen pools in Mexican tropical dry forest landscape [J]. Ecosystems, 2003,6: 609-629.
[73] Vitousek PM, Aber JD, Howarth RW, Likens GE, Matson PA, Schindler DW, Schlesinger WH, Tilman DG. Human alteration of the global nitrogen cycle: sources and consequences[J]. Ecology Applicaton,1997, 7: 737-750.
[74] Vitousek PM, Howarth RW. Nitrogen limitation on land and in the sea: How can it occur? [J]. Biogeochemistry, 1991,13:87-115.
[75] Walker BH, Steffen WL. The nature of global change. In: Walker B, Steffen W, Canadell J, Eds. The terrestrial biosphere and global change[A]. Cambridge(UK): Cambridge University Press, 1999:1-18.
[76] WBGU. The accounting of biological sinks and sources under the Kyoto Protocol[R]. WBGU Special Report: 1998.
[77] Webber AN, Nie GY, Long SP. Acclimation of photosynthetic proteins to rising atmospheric CO2[J]. Photosynthesis Research, 1994,413-425.
[78] Wilson JB. A review of evidence in the control of root: shoot ratios in relation to models[J]. Annals of Botany, 1988, 61:433-449.
[79] Wofsy SC,Goullden ML,Munger JM,et al . Next exchange of CO2 in a mid-latitude forest[J]. Science,1993,260:1314-1317.
[80] Woodward FI, Smith TM, Emanuel WR. A global land primary productivity and phytogeography model[J]. Global Biogeochemical Cycles, 1995,9:471-490.
[81] Xue,L. Nutrient cycliug in a Chinese fir(Cunninghamia lanceloata)stand on a poor site in Yishan,Guangxi [J]. Forest Eco1ogy Management,1996,89:1I5-123.
[82] Yamamoto S, Murayama S, Saigusa N, et al. Seasonal and inter-annual variation of CO2 flux between a temperate forest and the atmosphere in Japan[J]. Tellus,1999, 50: 402-413.
[83]陈光水,杨玉盛,钱伟,等.格氏栲和杉木人工林地下碳分配[J].生态学报,2005,25,(11):2824-2829.
[84]陈灵芝,任继凯,鲍显诚.北京西山人工油松林群落学特征及生物量的研究[J].植物生态学与地植物学报,1984,8(3):173-181.
[85]陈文颖,吴宗鑫.未来中国的SO2和CO2排放控制对策[J].清华大学学报(自然科学版),2002,42(10): 1320-1323.
[86]陈宜瑜.中国全球变化的研究方向[J].地球科学进展, 1999, 14 (4) : 319-323.
[87]戴民汉,翟惟东,鲁中明,等.中国区域碳循环研究进展与展望[J].地球科学进展,2004,19(1):120-130.
[88]党承林,吴兆录.季风长绿阔叶林短刺栲群落的生物量研究[J].云南大学学报(自然科学版),1992,14(2):95-107.
[89]方精云,陈安平,赵淑清,等.中国森林生物量的估算:对Fang等Science一文(Science,2001,291:2320-2322)的若干说明[J].植物生态学报,2002,26(2):243-249.
[90]方精云,陈安平.中国森林植被的动态变化及其意义[J].植物学报,2001,43(9):967-973.
[91]方精云,刘国华,徐嵩龄.中国森林植被的生物量和净生产力[J].生态学报,1996,16:497-508.
[92]方精云,刘国华,朱彪,等.北京东灵山三种温带森林生态系统的碳循环[J].中国科学D辑:地球科学,2006,36(6):533-543.
[93]方精云.北半球中高纬度的森林碳库可能远小于目前的估算[J].植物生态学报,2000,24 (5) : 635-638.
[94]方运霆,莫江明.鼎湖山马尾松林生态系统碳素分配和贮量的研究[J].广西植物,2002,22(4):305-310.
[95]冯宗炜,陈楚莹,张家武.湖南会同地区马尾松林生物量的测定[J].林业科学,1982,18(2):127-134.
[96]冯宗炜,王效科,吴刚.中国森林生态系统的生物量和生产力[M].北京:科学出版社,1999,9.
[97]郭剑芬,杨玉盛,林鹏,等.木荷与杉木人工林枯枝落叶层溶解有机碳浓度及季节动态[J].厦门大学学报(自然科学版),2006,45(2):289-292.
[98]郭起荣,杨光耀,杜天真,等.中国竹林的碳素特征[J].世界竹藤通讯,2005,3(3):25-28.
[99]郭盛磊,阎秀峰,白冰等.落叶松幼苗光合特性对氮和磷缺乏的响应.应用生态学报,2005,16(4):589-594.
[100]何宗明,陈光水,刘剑斌,等.杉木林凋落物产量、分解率与储量的关系[J].应用与环境生物学报,2003,9(4):352-356.
[101]何宗明,李丽红,王义详,等. 33年生福建柏人工林碳库和碳吸存[J].山地学报,2003,21(3):296-303.
[102]胡建忠.黄河上游退耕地人工林的碳储量研究[J].北京林业大学学报,2005,27(6):1-8.
[103]黄昌勇主编.土壤学[M].北京:中国农业出版社,2000:1-311.
[104]姬惜珠,王红,张爱军.三北防护林中杨树的碳汇和放氧功能及其价值估算[J].河北林果研究,2005,20(3):217-219.
[105]蒋有绪.世界森林生态系统结构与功能的研究综述[J].林业科学研究,1995,8(3):314-321.
[106]焦燕,胡海清.黑龙江省森林植被碳储量及动态变化[J].应用生态学报,2005,16(12):2248-2252.
[107]李红梅,马友鑫,郭宗峰,等.西双版纳森林植被碳储量及影响因素分析[J].福建林学院学报,2005,25(4):368-372.
[108]李文华,邓坤枚,李飞.长白山主要生态系统生物量生产量的研究[J].森林生态系统研究(试刊),1981,34-50.
[109]李意德,吴仲民,曾庆波,等.尖峰岭热带山地雨林群落生产和二氧化碳同化净增量的初步研究[J].植物生态学报,1998,22(2):127.
[110]李正才,傅懋毅,徐德应.竹林生态系统与大气二氧化碳减量[J].竹子研究汇刊,2003,22(4):1-6.
[111]刘华,雷瑞德.我国森林生态系统碳储量和碳平衡的研究方法及进展[J].西北植物学报,2005,25(4):835-843.
[112]刘世荣.兴安落叶松人工林群落生物量及净初级生产力的研究[J].东北林业大学学报,1990,18(2):40-46.
[113]马钦彦,陈遐林,王娟,等.华北主要森林类型建群种的含碳率分析[J].北京林业大学学报,2002,24(5 /6)96- 100.
[114]毛子军.森林生态系统碳平衡估测方法及其研究进展[J].植物生态学报,2002,26(6): 731-738.
[115]潘维俦,李利村,高正衡. 2个不同地域类型杉木林的生物产量和营养元素分布[J].中南林业科技, 1979(4):1-l4.
[116]任海,彭少麟.恢复生态学[M].北京:科学出版社,2002.
[117]沈文清,马钦彦,刘允芬.森林生态系统碳收支状况研究进展[J].江西农业大学学报,2006,28(2):312-317.
[118]史军,刘纪远,高志强,等.造林对陆地碳汇的影响研究进展[J].地理科学进展,2004,23 (2) : 58 - 67.
[119]王秋凤,牛栋,于贵瑞,等.长白山森林生态系统CO2和水热通量的模拟研究[J].中国科学D辑(地球科学),2004, 34 (增刊Ⅱ): 131-140 .
[120]王绍强,刘纪远.土壤碳蓄积量变化的影响因素研究现状[J].地球科学进展,2002,17(4):528-534.
[121]王绍强,周成虎,罗承文.中国陆地自然植被碳量空间分布特征探讨[J].地理科学进展,1999,18(3) :238-244.
[122]王文杰,石福臣,祖元刚,等.陆地生态系统CO2通量网的建立和发展[J].东北林业大学学报,2002,30(4):57- 61.
[123]王效科,冯宗炜,欧阳志云.中国森林生态系统的植物碳储量和碳密度研究[J].应用生态学报,2001,12(1) : 13-16.
[124]王效科,冯宗炜.森林生态系统生物量和碳储备的研究历史[A].王如松编.现代生态学的热点问题[C].北京:中国科学技术出版社,1995:335-347.
[125]王妍,张旭东,彭镇华,等.森林生态系统碳通量研究进展[J].世界林业研究,2006,19(3):12-17.
[126]王玉辉,周广胜,蒋延玲,等.基于森林资源清查资料的落叶松林生物量和净生长量估算模式[J].植物生态学报,2001,25(4):420-425.
[127]吴家兵,张玉书,关德新.森林生态系统CO2通量研究方法与进展[J].东北林业大学学报,2003,31(6):49- 51.
[128]项文化,田大伦,闫文德.森林生物量与生产力研究综述[J ].中南林业调查规划,2003,22(3):57- 60.
[129]杨洪晓,吴波,张金屯等.森林生态系统的固碳功能和碳储量研究进展[J].北京师范大学学报(自然科学版),2005,41(2):172-177.
[130]杨丽韫,李文华.长白山不同生态系统地下部分生物量及地下C贮量的调查[J].自然资源学报,2003,18(2):204-209.
[131]杨玉盛,郭剑芬,林鹏,等.格氏栲天然林与人工林粗木质残体碳库及养分库[J].林业科学,2005,41(3):7-11.
[132]杨玉盛,郭剑芬,林鹏,等.格氏栲天然林与人工林枯枝落叶层碳库及养分库[J].生态学报,2004,24(2):359-367.
[133]杨玉盛,刘艳丽,陈光水,等.格氏栲天然林与人工林土壤非保护性有机C含量及分配[J].生态学报,2004,24(1):1-8.
[134]杨玉盛,谢锦升,王义祥,等.杉木观光木混交林C库与C吸存[J].北京林业大学学报,2003,25(5):10-14.
[135]于占源,杨玉盛,陈光水.紫色土人工林生态系统碳库与碳吸存变化[J].应用生态学报,2004,15(10):1837-1841.
[136]张林,黄永,罗天祥,等.林分各器官生物量随林龄的变化规律——以杉木、马尾松人工林为例[J].中国科学院研究生院学报,2005,22(2):170-178.
[137]钟羡芳,陈光水,高人,等.杉木多代连栽地营造杉阔混交林碳库与碳吸存[J].福建师范大学学报(自然科学版),2006,22(2):99-103.
[138]周广胜,王玉辉.陆地生态系统类型转变与碳循环[J].植物生态学报,2002,26(2):250-254.
[139]周玉荣,于振良,赵士洞.我国主要森林生态系统碳贮量和碳平衡[J].植物生态学报,2000,24(5):518-522.
[140]佐藤大七郎,堤利夫.陆地植物群落的物质生产[M].北京:科学出版,1986,21-47.
[141]曲俊华,倪家明.多元回归模型分析与设计实现[J].中国电力教育,2007,(1):140~142.
[142]徐志敏,刘希玉.多元回归分析及其在辅助决策中的应用[J].计算机工程, 2004, 30 (21): 129 ~ 131.
[143]梁士楚,王伯荪.云贵鹅耳枥群落物种多样性及其刀切法估计[J].热带亚热带植物学报,2001,9(2):129～135.
[144] Vapnik V N. The nature of statistical learning[M]. New York: SpringerVerlag, 1995.
[145] SUYKENS J A K,VANDEWALLE J,De MOOR B.Optimal control by least squares support vector machines[J].Neural Networks.2001, 14(1):23–35.
[146]张学工.关于统计学习理论与支持向量[J].自动化学报,2000,26(1):32–42.
[147]朱家元,陈开陶,张恒喜.最小二乘支持向量机算法研究[J].计算机科学,2003,30(7):157–159.
[148] DENG Wen-hua, DONG He-quan, XU Hua-yong, et al. Support vector machine applied in study of ancient ceramics[A]//’05 Science and Technology of Ancient Ceramics 6 Proceedings of the International Symposium(ESAC’05)[C],Shanghai,China,2005:496–501.
[149] FLETCHER R. Practical Methods of Optimization[M].2nd ed. New York: John Wiley and Sons Inc,1987:31.
[150] NELLO Cristianini, JOHN Shawe-Taylor著.支持向量机导论[M].李国正,王猛,曾华军译.北京:电子工业出版社, 2003:30.
[151]刘涵,刘丁,郑岗,梁炎明,宋念龙.基于最小二乘支持向量机的天然气负荷预测[J].化工学报, 2004, 55(5):828-832.
[152]鲁法典,韩峻.刀切法在森林双重回归抽样调查中的应用初探[J].河北林果研究, 1999, 14(3):216-220.
[153]洪伟,吴承祯.马尾松人工林经营模式及其应用[M].北京:中国林业出版社,1999:1-16.
[154]康冰,刘世荣,张广军,等.广西大青山南亚热带马尾松、杉木混交林生态系统碳素积累和分配特征[J].生态学报,2006,26(5):1 320-1 329.
[155]张林,黄永,罗天祥,等.林分各器官生物量随林龄的变化规律——以杉木、马尾松人工林为例[J].中国科学院研究生院学报,2005,22(2):170-178.
[156]张国庆,黄从德,郭恒,等.不同密度马尾松人工林生态系统碳储量空间分布格局[J].2007,27(6):10-14.
[157]史大林,郑小贤.马尾松林碳储量成熟问题初探[J].林业资源管理,2007,(4):34-37.
[158]黄昌勇.土壤学[M].北京:中国农业出版社, 2000: 152-165.
[159] Woodwell G.M., Whittaker R.H., Reiners W.A. The biota and the world carbon budget[J]. Science, 1978, 199: 141-146
[160] Olson J.S., Watts J.A. & Allison L.J. Carbon in live vegetation of major world ecosystem. US Department of Energy DOE/NBGB-0037.1983, 15-25(Rep. Ornl-58260, Oak Ridge National Laboratory, Oak Ridge, TN)
[161] Post W.M., Emanuel W.R., Zinke P.J. et al. Soil carbon pools and world life zones[J]. Nature, 1982, 298: 156-159
[162] Kauppi P.E., Miehkainen K., Kuusela K. Biomass and carbon budget of European Forests, 1971 to 1990[J]. Science, 1992, 256: 70-74
[163] Kurz W.A. and Apps M.J. Contribution of northern forests to the global C cycle: Canada as a case study[J]. Water, Air, and Soil Pollution, 1993, 70: 163-176
[164]刘国华,傅伯杰,方精云.中国森林碳动态及其对全球碳平衡的贡献[J].生态学报,2000,20(3):733-740
[165]李意德,曾庆波,吴仲民,等.我国热带天然林植被C贮存量的估算[J].林业科学研究,1998,11(2):156-162
[166]李铭红,于明坚,陈启瑺,等.青冈常绿阔叶林的碳素动态[J].生态学报,1996,16(6):645-651
[167]方晰,田大伦,项文化,等.第二代杉木中幼林生态系统碳动态与平衡[J].中南林学院学报,2002,22(1):1-6
[168]江泽慧.论林业在可持续发展中的战略地位[J].国土经济,2000,1:4-7
[169]周国模,吴家森,姜培坤.不同管理模式对毛竹林碳贮量的影响[J].北京林业大学学报,2006,28(6):51-55
[170]余雪标,龙腾.我国桉树人工林经营及研究现状[J].热带农业科学,1999,(3):596-65.
[171]李宝福.福建省桉树引种选育研究动态及展望[J].当代生态农业, 2005,(4):45-48.
[172]陈秋波.桉树人工林土壤生物多样性问题研究[J].热带农业科学, 2002,(1):66.
[173]李宝福.闽南山地桉树人工林生物生产力与营养研究[J].江西农业大学学报,2000,22(5):145-147.
[174]郑郁善洪长福.沿海丘陵巨尾桉人工林水源涵养功能研究[J].江西农业大学学报,2000, 22(2):220-224.
[175] Schlesinger W H. Carbon storage in the calishe of arid soils: a case study from Arizona[J]. Soil Science, 1982,133: 247-255.
[176] Sombroek W G, Nachtergaele F O, Hebel A. Amount, dynamics and sequestering of carbon in tropical and subtropical soils[J]. AMBIO, 1993,22(7): 417-426.
[177]方精云,刘国华,徐嵩龄.中国陆地生态系统的碳库[A].见:王庚辰,温玉璞主编.温室气体浓度和排放监测及相关过程[C].北京:中国环境科学出版社, 1996. 109-128.
[178]王艳芬,陈佐忠, Larry T. Tieszen.人类活动对锡林郭勒地区主要草原土壤有机碳分布的影响[J].植物生态学报, 1998, 22(6): 545-551.
[179]李凌浩,刘先华,陈佐忠.内蒙古锡林河流域羊草草原生态系统碳素循环研究[J].植物学报, 1998,40(10): 955-961.
[180]吴仲民,曾庆波,李意德,等.尖峰岭热带森林土壤C储量和CO2排放量的初步研究[J].植物生态学报, 1997,21(5): 416-423.
[181]吴仲民,曾庆波,李意德,等.尖峰岭热带山地雨林C素库及皆伐影响的初步研究[J].应用生态学报, 1998,9(4): 341-344.
[182]李意德,吴仲民,曾庆波,等.尖峰岭热带山地雨林生态系统碳平衡的初步研究[J].生态学报, 1998,18(4): 371-378.
[183]李忠佩,王效举.红壤丘陵区土地利用方式变更后土壤有机碳动态变化的模拟[J].应用生态学报, 1998,9(4): 365-370.
[184]李凌浩.土地利用变化对草原生态系统土壤碳贮量的影响[J].植物生态学报, 1998,22(4): 300-302.
[185]徐德应.人类经营活动对森林土壤碳的影响[J].世界林业研究, 1994,5: 26-31.
[186]刘绍辉,方精云,清田信.北京山地温带森林的土壤呼吸[J].植物生态学报, 1998,22(2): 119-126.
[187]张林波,曹洪波,高吉喜,等.大气CO2浓度升高对土壤微生物的影响[J].生物学杂志, 1998,17(4): 33-38.
[188]王其兵,李凌浩,刘先华等.内蒙古锡林河流域草原土壤有机碳及氮素的空间异质性分析[J].植物生态学报, 1998,22(5): 409-414.
[189]李克让,王绍强,曹明奎.中国植被和土壤碳贮量[J].中国科学(D辑), 2003, 33(1): 72-80.
[190] Polglase P J , Paul K I , Khanna P K, et al . Change in soil carbon following Afforestation or reforestation[C]: Reviewof experimental evidence and development of a conceptual framework. National Carbon Accounting System Technical Report No. 20. Common Wealth of Australia. Printed in Australia for the Australian Greenhouse Office , 2000 , 1-119.
[191] Jenkinson D S , Rayner J H. The turnover of soil organic matter in some of rothamsted classical experiments[J]. Soil Sci , 1977 , 125 :298-305.
[192] Jobbagy E G, Jackson R B. The vertical distribution soil organic carbon and its relation to climate and vegetation[J]. Ecological Applications, 2000 , 10(2) :423-436.
[193]刘景双,杨继松.三江平原沼泽湿地土壤有机碳的垂直分布特征研究[J].水土保持学报, 2003, 17(3) :5-8.
[194]方华军,杨学明,等.东北黑土有机碳储量及其对大气CO2的贡献[J].水土保持学报, 2003, 17(3) :9-20.
[195]焦如珍,杨承栋,屠星南,等.杉木人工林不同发育阶段林下植被、土壤微生物、酶活性及养分的变化[J].林业科学研究, 1997, 10 (4) : 373 - 379
[196]杨玉盛,邱仁辉,俞新妥,等.不同栽杉代数林下植被营养元素的生物循环[J].东北林业大学学报, 1999, 27 (3) : 26 - 30
[197]范少辉,马祥庆,傅瑞树,等.不同栽植代数杉木林林下植被发育的比较研究[J].林业科学研究, 2001, 14 (1) : 8– 16
[198]何艺玲,傅懋毅.人工林林下植被的研究现状[J].林业科学研究, 2002, 15 (6) : 727 - 733
[199]余雪标,钟罗生,杨伟东,等.桉树人工林林下植被结构的研究[J].热带作物学报, 1999, 2 (1) : 66– 72
[200]杨承栋,焦如珍.发育林下植被是恢复杉木人工林地力的重要途径[J].林业科学, 1995a, 31 (3) : 316– 322
[201]杨承栋,焦如珍,屠星南,等.杉木林下植被对5-15 cm土壤性质的改良[J].林业科学研究, 1995b, 8 (5) : 514 - 519
[202]陈民,赵京岚,刘杰,等.人工林林下植被研究进展[J] .山东农业大学学报(自然科学版),2008,39(2):321-325
[203]梁宏温,温远光,温琳华,等.连栽对尾巨桉短周期人工林碳贮量的影响[J].生态学报, 2009, 29(8): 4242-4250.
[204]苏建芹,李文宝,刘春兰.刀切法在森林调查中的应用[J].林业机械与木工设备, 2002, 30(11): 33-34.
[205]魏文俊,王兵,李少宁,等.江西省森林植被乔木层碳储量与碳密度研究[J].江西农业大学学报(自然科学版), 2007, 29(5): 767-771.
[206]周玉荣,于振良,赵士洞.中国主要森林生态系统的碳贮量与碳平衡[J].植物生态学报, 2000, 24(5): 518-522.
[207] SEDJO R A. The carbon cycle and global forest ecosystem[J]. Water Air Soil Pollut , 1993 , 70 : 295 - 307.
[208]周国模,刘恩斌,佘光辉.森林土壤碳库研究方法进展[J].浙江林学院学报,2006,23(2):207-216.
[209] HOUGHTON R A , HOBBIE J E , MELILLO J M, et al . Changes in carbon content of terrestrial biota and soils between 1860 and 1980 : a net release of CO2 to the atmosphere [J ] . Ecol Monogr , 1983 , 53 : 235 - 262.
[210] HOUGHTON R A, SKOLE KL, LEFKOWITZ D S. Changes in the landscape of Latin America between 1850 and 1985( I )Net release of CO2 to the atmosphere [J ] . For Ecol Manage , 1991 , 38 : 173 - 199.
[211] DETWILER R P. Land use change and the global carbon cycle : the role of tropical soils[J ] . Biogeochem, 1986 , 2 : 67 - 93.
[212]吴承祯,洪伟.杉木数量经营学引论[M].中国林业出版社,2000,1-7.
[213]田大伦,方晰,项文化.湖南会同杉木人工林生态系统碳素密度[J].生态学报, 2004, 4(11): 2382-2386.
[214]方晰,田大伦,项文化.速生阶段杉木人工林碳素密度、贮量和分布[J].林业科学, 2002, 38(3): 14-19
[215]方晰,田大伦,项文化,等.杉木人工林土壤有机碳的垂直分布特征[J].浙江林学院学报, 2004, 21(4): 418- 423.
[216]康冰,刘世荣,张广军,等.广西大青山南亚热带马尾松、杉木混交林生态系统碳素积累和分配特征[J].生态学报, 2006, 26(5): 1320-1329.
[217] Bolin B. Change of land biota and their importance for the carbon cycle [J].Science, 1977, 196(4290): 613-615.
[218] Bohn HL. Estimate of organic carbon in world soils [J].Soil Sci. Soc. Am. J.,1976, 40: 468-470.
[219] Bohn HL. Estimate of organic carbon in world soils [J].Soil Sci. Soc. Am. J.,1982, 46:1118-1119.
[220] Post W M, Emanuel W R, Zinke P J,et al.Soil carbon pools an world life zones [J].Nature, 1982, 298(8): 156-159.
[221] Schlesinger W H. Evidence from chronoseauence studies for a low carbon-storage potential of soil [J].Nature, 1990, 348(15): 23-234.
[222] Eswaran H, Van der Berg E, Reich P. Organic carbon in soils of the world [J].Soil Sci. Soc. Am.J.,1993, 57:192-194.
[223] Lacelle B. Canada s soil organic carbon database [A]. Lal R,etal.Soil Processes and the Carbon Cycle[C]. Boca Raton: CRC Press, 1997. 93-102.
[224] Titlyanove A A, Bulavko G I, Kudryashova S,et al.The reserves and losses of organic carbon in the soils of Siberia [J].Pochrovedenie, 1998, 1:51-59.
[225]李忠,孙波,赵其国.我国东部土壤有机碳的密度和储量.农业环境保护,2001,20(6):385-389.
[226] Jobbagy E G, Jackson R B. The vertical distribution of soil organic carbon and it’s relation to climate and vegetation [J ] . Ecol Appl , 2002 , 10(2) : 423 - 436.
[227]盛炜彤,范少辉.杉木人工林长期生产力保持机制研究[M].北京:科学出版社, 2005.
[228]罗云建,张小全.杉木连栽地力退化和杉阔混交林的土壤改良作用[J].生态学报, 2007, 27 (2):715-724.
[229]李鸿博,史锟.不同植物过程土壤剖面有机碳含量和含水量研究[J].大连铁道学院学报,2005,26(1):92-95.
[230]姜勇,张玉革,梁文举,等.潮棕壤不同利用方式有机碳剖面分布及碳储量[J].中国农业科学,2005,38(3):544-550.
[231]陶贞,沈承德,高全洲,等.高寒草甸土壤有机碳储量及其垂直分布特征[J].地理学报,2006,61(7):720-728.
[232]高惠漩.两个多重相关变量组的统计分析[J].数理统计与管理, 2002(1): 57-64.
[233]周鸿凯,郭建夫,黎华寿,等.光温因子与杂交水稻生态群体的产量和品质性状的典型相关分析[J].应用生态学报, 2006, 17(4): 663-667.
[234]李青,张传静,张强,等.利用计算机进行刀切法估算树干材积的实践[J].防护林科技, 2004, (5): 69-70.
[235]洪滔.千年桐人工林生态系统碳库研究[D] .福建农林大学硕士论文, 2009.
[236]梁煜峰.投影寻踪法和遗传算法在三峡入库洪水预报中的应用[D].四川大学硕士学位论文, 2006.
[237]付强.投影寻踪模型原理及应用[M].北京:科学出版社.2006: 131-139.
[238]张明罡.毛竹林水土保持体系下土壤侵蚀预测模型研究[D] .福建农林大学硕士论文, 2009.
[239]董志龙,何慧根,于涛,等.兰州大气环境质量影响因素相关分析[J].干旱区资源与环境, 2009, 23(12): 49-53.
[240]吴承祯,洪伟.基于改进的投影寻踪的森林生态系统生态价位分级模型研究[J].应用生态学报, 2006, 17 (3) : 357 - 361.
[2] Alexeyev V, Birdsey R, Stakanov V et al. Carbon in vegetation of Russian forest: methods to estimate storage and geographical distribution[J]. Water, Air and Soil Poll, 1995,82:271-282.
[3] Anten N P R, Schieving F, Werger M J A. Patterns of light and nitrogen distribution in relation to whole canopy carbon gain in C3 and C4 mono and dicotyledonous species[J]. Oecologia, 1995,101: 504-513.
[4] Anten N P R, Werger MJA, Medina E. Nitrogen distribution and leaf area indices in relation to photosynthetic nitrogen use efficiency in savanna grasses[J]. Plant Ecology, 1998,138:63-75.
[5] Apps MJ and Kurz WA. The role of Canadian forest in the global carbon budget. In: Kanninen M ed. Carbon Balance of world’s forested ecosystems: Towards a Global Assessment. Finland: SILMU, 1994: 12-20.
[6] Beets PN, Whitehead D. Carbon partitioning in Pinus radiate stands in relation to foliage nitrogen status[J]. Tree Physiology, 1996,16:139-144.
[7] Botkin DB,Woodwell GM.Tempel N.Forest productivity estimated from carbon dioxide uptake[J]. Ecology,1970,51:1 057-1 060.
[8] Brown S,Lugo A E. A new estimate based on forest volumes[J]. Science, 1984,223:1290-1293.
[9] Brown S,Lugo A E. The storage and production of organic matter in tropical forests and their role in the global carbon cycle[J]. Biotropica,1982,14:161-187.
[10] Cannell MGR, Dewar RC. Carbon allocation in trees: a review of concepts for modeling[J]. Adwances in Ecological Research, 1994, 25:59-104.
[11] Cao MK, Woodward FI. Dynamic responses of terrestrial ecosystem carbon cycling to global climate change[J]. Nature, 1998,393:249-252.
[12] Chen W, Chen J M, Cihlar J. An integrated terrestrial ecosystem carbon-budget model based on changes indisturbance, climate, and atmospheric chemistry[J]. Ecological Modelling , 2000, 135:55-79.
[13] Chen Z. Interannual variability of carbon dioxide and water vapor fluxes above a boreal aspen forest[A]. In: Boston MA ed. Proceedings of the 23rd Conference on Agricultural and Forest Meteorology[C]. American Meteorological Society, 1998, 163-161.
[14] Ciais P. Large Northern Hemisphere terrestrial CO2 sink indicated by the C ratio of atmospheric CO2 [ J ]. Science, 1995,269: 1098-1102.
[15] Den Boer WMJ, Van den Tweel PA. The health condition of the Dutch forests in 1984[J]. Neth. J.Agric. Sci., 1985, 33:167-174.
[16] Dennis DB, Kell BW. Modeling CO2 and water vapor exchange of a temperate broadleaved forest across hourly to decadal time scales [J]. Ecological Modelling, 2001,142 :155-184.
[17] Detwiler R P, Charles A S. Tropical forests and the global carbon budget[J]. Science, 1988, 239: 42 - 47.
[18] Dickinson RE, Berry JA, Bonan GB, et al. Nitrogen controls on climate model evapotranspiration[J]. Journal of Climate, 2002, 15:278-295.
[19] Dixon PK, Brown S, Houghton RA et al. Carbon pools and flux of global forest ecosystems[J]. Science,1994, 262: 185-190.
[20] Dolman A J, Moors EJ, Elbers J A. The carbon uptake of a mid latitude pine forest growing on sandy soil[J]. Agric For Meteorol , 2002,111 :187-202.
[21] Ebermeyr, E. Die gesamte Lehre der Waldstreu mit Rucksicht auf die themische statik des Waldbaues [M]. Belln: J. Springer,1876, 116.
[22] Fang J Y,Wang G G,Liu G G,et al. Forest biomass of China:an estimate based on the biomass-volume relationship[J]. Ecological Applications,1998,8:1 084-1 091.
[23] Fang JY, Chen AP, Peng Ch H, Zhao Sh Q, Ci LJ. Changes in forest biomass carbon storage in China between 1949 and 1998[J]. Science, 2001, 292(5525):2320-2322(in Reports).
[24] Fowler D, Duyzer JH. Micrometeorological techniques for the measurement of trace gas exchange[A]. In: Andreae MO, Schimel DS eds. Exchange of Trace Gases Between Terrestrial Ecosystems and the Atmosphere[M], Wiley. Chichester, 1989:189-207.
[25] Friedlingstein P, Joel G, Field CB, et al. Toward an Alloction Scheme for Global Terrestrial Carbon Models[J]. Global Change Biology, 1999,5:755-770.
[26] Greco S, Baldocchi D D. Seasonal variations of CO2 and water vapor exchange rates over a temperate deciduous forest[J]. Global Change Biol, 1996,183-198.
[27] Houghton JT,Ding Y,Griggs DJ,et al. Climate Change 2001:The Scientific Basis. Contribution of Working GroupⅠto the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge,UK:Cambridge University Press,2001.
[28] Houghton R A. Temporal patterns of land-use change and carbon storage in China and tropical Asia[J]. Science in China(Series C),2002,45(Supp):10-17.
[29] Houghton RA, Davidson EA, Woodwell GM. Missing sinks, feedbacks, and understanding the role of terrestrial ecosystems in the global carbon balance[J]. Global Biogeochemical Cycles, 1998, 12:25-34.
[30] Houghton RA. Terrestrial carbon storage: Global lessons for Amazonian research[J]. Cienciae Cultura SaoPaulo, 1997, 49:58 - 72.
[31] Hungate BA, Dukes JS, Shaw MR, et al. Nitrogen and climate change. Science, 2003,302:1512-1513.
[32] IPCC. Land use, land-use change, and forestry-A special report of the IPCC [M].London: Cambridge University Press, 2000.
[33] Johnson WC,Sharpe DM.The ration of total to merchantable forest biomass and its application to the global carbon budget [J]. Canadian Journal of Forest Research,1983,13:372-383.
[34] Kimmins JP. Forest ecology[M]. New York: Macmillan Press, 1987.
[35] Kirschbauma MUF. To sink or burn?A discussion of the potential contributions of forests to greenhouse gas balances through storing carbon or providing biofuels [J]. Biomass and Bioenergy,2003,24:297-310.
[36] Kovel CGF de, Mierlo AJEM van, Wilms YJO, et al. Carbon and nitrogen in soil and vegetation at sites differing in sccessional age [J]. Plant Ecology, 2000, 149: 43-49.
[37] Levine JS, CoferⅢWR, Carbon Jr DR, etal. Biomass buring, adriver for global change. Environmental Science Technology, 1995, 120:120-125.
[38] Lieth H. Primary productivity of successional stages [C].Knapp R. Handbook of vegetation science,Ⅷ: vegetation dynamics. The Hague: Junk, 1974.
[39] Liu JX, Price DT, Chen J M. Nitrogen controls on ecosystem carbon sequestration: a model implementation and application to Saskatchewan,.Canada[J]. Ecological modeling, 2005, 186:178-195.
[40] Major J. Biomass accumulation in successions [C]. Knapp R. Handbook of vegetation science,Ⅷ: vegetation dynamics. The Hague: Junk,1974.
[41] McGuire AD, Melillo JM, Joyce L A. The role of nitrogen in the response of forest net primary production to elevated atmospheric carbon dioxide[J]. Annual Review of Ecology and Systematics, 1995,26:473-503.
[42] Meentemeyer V. An approach to the biometeorology of decomposer organisms[J]. International Journal of Biometerology, 1978, 22:94-102.
[43] Melillo JM,Gosz JR.Interactions of biogeochemical cycles in forest ecosystems[A]. In: The major biogeochemical cycles and their interactons[C]. Bolin B and Cook RB (Eds.).John Wiley and Sons, New York, 1983:177-222.
[44] Monteith JL , Unsworth MH. Principles of environmental physics , London ,1990:289.
[45] Moorhead DL, Reynolds JF. A general model of litter decomposition in the northern Chihuahuan desert[J]. Ecological Modelling , 1991,56:197-219.
[46] Nihlgard B. The ammonium hypothesis-an additional explanation to the forest dieback in Europe[J]. Ambio.,1985, 14:2-8.
[47] Norby RJ. Nitrogen depositon: a component of global change analyses[J]. NewPhytologist,1998,139(1): 189-200.
[48] Oliver L, Phillip s, Sandra B rown. Changes in the carbon balance of tropical forests: evidence from long term plots[J]. Science, 1998, 282: 439 - 442.
[49] Oren RR, Ellsworth DS, Johnsen KH, et al. Soil fertility limits carbon sequestration by forest ecosystems in a CO2-enriched atmosphere[J]. Nature, 2001:469-472.
[50] Paavolainen L. Nitrogen transformations in boreal forest soils in response to extreme manipulation treatments[C]. Academic Dissertation in Environmental Microbiology. Faculty of Agriculture and Forestry University of Helsinki, Helsinki,1999.
[51] Peter M A , Michael H U, Beverly E L, et al. Seasonal differences in carbon and water vapor exchange in young and old-growth ponderosa pine ecosystems[J]. Agric For Meteorol, 2002,111 :157-170.
[52] Poulton PR,Pye E,Hargreaves PR,et al . Accumulation of carbon and nitrogen by old arable land reverting to woodland[J]. Global Change Biology,2003,9:942-955.
[53] Raich JW,Schlesinger WH. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate [J]. Tellus,1992,44B:81.
[54] Rastetter EB, Ryan MG, Shaver GR, et al. A general biogeochemical model describing the responses of the C and N cycles in terrestrial ecosystems to changes in CO2, climate, and N deposition[J]. Tree Physiology, 1991, 9:101-126.
[55] Redfield AC. The biological control of chemical factors in the environment[J]. American Scientist, 1958, 46:205-221.
[56] Rodin L E, Bazilevich NI. Production and mineral cycling in terrestrial vegetation [M]. London: Oliver and Boyd Edinburgh, 1967.
[57] Roelofs JGM, Kempers AJ, Houdljk ALFM, et al. The effect of air-borne ammonium sulphate on Pinus nigra var. maritime in the Netherlands[J]. Plant Soil, 1985, 84:45-56.
[58] Rogers HH, Prior SA, Runion GB, et al. Root to shoot ratio of crops as influenced by CO2[J]. Plant and Soil, 1996, 187(2):229-248.
[59] Runyon J, Waring RH, Goward SN, et al. Environmental limits on net primary production and light-use efficiency across the Oregon transect[J]. Ecological Applicatons, 1994, 4:226-237.
[60] Saigusa N, Yamamoto S, Murayama S, et al. A long - term flux measurement over a cool- temperate deciduous forest by the eddy covariance method[M]. International workshop for Advanced Flux Network and Flux evaluation, Sapporo, 2000,74 :27-29.
[61] Schinozaki K, Yoda K, Hozumi K, et al. A quantitative analysis of plant form—the pipe model theory:Ⅰ.Basic Analyses[J]. Japanese Journal of Ecology, 1964, 14:97-103.
[62] Schlesinger WH, Lichter J. Limited carbon storage in soil and litter of experimental forest plots underelevated atmospheric CO2[J]. Nature, 2001,411:466-469.
[63] Shangguan ZP, Shao MA, Ren SJ, et al. Effect of Nitrogen on root and shoot relations and gas exchange in winter wheat[J]. Botanical Bulletin of Academia Sinica, 2004,45:49-54.
[64] Shif C H, Chen X W, Wang W J, Kentaro T T, Yukio A, Kaichiro S,Shigeru U. Vegetation characteristics of a larch-dominant site for CO2 flux monitoring study at the Laoshan Experimental Station in Northeast China [J].Eurasian Journal of Forest Research,2001,3:53- 60.
[65] Streets DG, Jiang KJ, Hu XL, Sinton JE, Zhang XQ, Xu DY, Jacobson MZ, Hansen JE. Recent reductions in China’s greenhouse gas emissions [J]. Science, 2001,294:1835-1836.
[66] Thompson WA, Wheeler AM. Photosynthesis by nature needles of field-grown Pinus radiate[J]. Forest Ecology and Management, 1992,52:225-242.
[67] Tian H, Mellilo J M, KichilghterD W , et al. Effects of inter annual climate variability on carbon storage in Amazonian ecosystems[J]. Nature, 1998, 396: 664 - 667.
[68] Tilman D. Plant strategies and the dynamics and structure of plant communities.[A] Princeton University Press, Princeton, NJ, 1988.
[69] Turner DP, Koepper GJ, Hamon ME et al. A carbon budget for forests of the conterminous United States[J]. Ecology Application, 1995,5:421-436.
[70] Valentini R. Respiration as the main determinant of carbon balance in European forests[J]. Nature, 2000, 404 :861-865.
[71] Van der Erden LJM. Toxicity of ammonia to plants. Agricult[J]. Environm., 1982, 7:223-235.
[72] Víctor J. Jaramillo, J. Boone Kauffman, Lyliana Rentería-Rodríguez, Dian L. Cummings, Lisa J. Ellingson. Biomass, carbon, and nitrogen pools in Mexican tropical dry forest landscape [J]. Ecosystems, 2003,6: 609-629.
[73] Vitousek PM, Aber JD, Howarth RW, Likens GE, Matson PA, Schindler DW, Schlesinger WH, Tilman DG. Human alteration of the global nitrogen cycle: sources and consequences[J]. Ecology Applicaton,1997, 7: 737-750.
[74] Vitousek PM, Howarth RW. Nitrogen limitation on land and in the sea: How can it occur? [J]. Biogeochemistry, 1991,13:87-115.
[75] Walker BH, Steffen WL. The nature of global change. In: Walker B, Steffen W, Canadell J, Eds. The terrestrial biosphere and global change[A]. Cambridge(UK): Cambridge University Press, 1999:1-18.
[76] WBGU. The accounting of biological sinks and sources under the Kyoto Protocol[R]. WBGU Special Report: 1998.
[77] Webber AN, Nie GY, Long SP. Acclimation of photosynthetic proteins to rising atmospheric CO2[J]. Photosynthesis Research, 1994,413-425.
[78] Wilson JB. A review of evidence in the control of root: shoot ratios in relation to models[J]. Annals of Botany, 1988, 61:433-449.
[79] Wofsy SC,Goullden ML,Munger JM,et al . Next exchange of CO2 in a mid-latitude forest[J]. Science,1993,260:1314-1317.
[80] Woodward FI, Smith TM, Emanuel WR. A global land primary productivity and phytogeography model[J]. Global Biogeochemical Cycles, 1995,9:471-490.
[81] Xue,L. Nutrient cycliug in a Chinese fir(Cunninghamia lanceloata)stand on a poor site in Yishan,Guangxi [J]. Forest Eco1ogy Management,1996,89:1I5-123.
[82] Yamamoto S, Murayama S, Saigusa N, et al. Seasonal and inter-annual variation of CO2 flux between a temperate forest and the atmosphere in Japan[J]. Tellus,1999, 50: 402-413.
[83]陈光水,杨玉盛,钱伟,等.格氏栲和杉木人工林地下碳分配[J].生态学报,2005,25,(11):2824-2829.
[84]陈灵芝,任继凯,鲍显诚.北京西山人工油松林群落学特征及生物量的研究[J].植物生态学与地植物学报,1984,8(3):173-181.
[85]陈文颖,吴宗鑫.未来中国的SO2和CO2排放控制对策[J].清华大学学报(自然科学版),2002,42(10): 1320-1323.
[86]陈宜瑜.中国全球变化的研究方向[J].地球科学进展, 1999, 14 (4) : 319-323.
[87]戴民汉,翟惟东,鲁中明,等.中国区域碳循环研究进展与展望[J].地球科学进展,2004,19(1):120-130.
[88]党承林,吴兆录.季风长绿阔叶林短刺栲群落的生物量研究[J].云南大学学报(自然科学版),1992,14(2):95-107.
[89]方精云,陈安平,赵淑清,等.中国森林生物量的估算:对Fang等Science一文(Science,2001,291:2320-2322)的若干说明[J].植物生态学报,2002,26(2):243-249.
[90]方精云,陈安平.中国森林植被的动态变化及其意义[J].植物学报,2001,43(9):967-973.
[91]方精云,刘国华,徐嵩龄.中国森林植被的生物量和净生产力[J].生态学报,1996,16:497-508.
[92]方精云,刘国华,朱彪,等.北京东灵山三种温带森林生态系统的碳循环[J].中国科学D辑:地球科学,2006,36(6):533-543.
[93]方精云.北半球中高纬度的森林碳库可能远小于目前的估算[J].植物生态学报,2000,24 (5) : 635-638.
[94]方运霆,莫江明.鼎湖山马尾松林生态系统碳素分配和贮量的研究[J].广西植物,2002,22(4):305-310.
[95]冯宗炜,陈楚莹,张家武.湖南会同地区马尾松林生物量的测定[J].林业科学,1982,18(2):127-134.
[96]冯宗炜,王效科,吴刚.中国森林生态系统的生物量和生产力[M].北京:科学出版社,1999,9.
[97]郭剑芬,杨玉盛,林鹏,等.木荷与杉木人工林枯枝落叶层溶解有机碳浓度及季节动态[J].厦门大学学报(自然科学版),2006,45(2):289-292.
[98]郭起荣,杨光耀,杜天真,等.中国竹林的碳素特征[J].世界竹藤通讯,2005,3(3):25-28.
[99]郭盛磊,阎秀峰,白冰等.落叶松幼苗光合特性对氮和磷缺乏的响应.应用生态学报,2005,16(4):589-594.
[100]何宗明,陈光水,刘剑斌,等.杉木林凋落物产量、分解率与储量的关系[J].应用与环境生物学报,2003,9(4):352-356.
[101]何宗明,李丽红,王义详,等. 33年生福建柏人工林碳库和碳吸存[J].山地学报,2003,21(3):296-303.
[102]胡建忠.黄河上游退耕地人工林的碳储量研究[J].北京林业大学学报,2005,27(6):1-8.
[103]黄昌勇主编.土壤学[M].北京:中国农业出版社,2000:1-311.
[104]姬惜珠,王红,张爱军.三北防护林中杨树的碳汇和放氧功能及其价值估算[J].河北林果研究,2005,20(3):217-219.
[105]蒋有绪.世界森林生态系统结构与功能的研究综述[J].林业科学研究,1995,8(3):314-321.
[106]焦燕,胡海清.黑龙江省森林植被碳储量及动态变化[J].应用生态学报,2005,16(12):2248-2252.
[107]李红梅,马友鑫,郭宗峰,等.西双版纳森林植被碳储量及影响因素分析[J].福建林学院学报,2005,25(4):368-372.
[108]李文华,邓坤枚,李飞.长白山主要生态系统生物量生产量的研究[J].森林生态系统研究(试刊),1981,34-50.
[109]李意德,吴仲民,曾庆波,等.尖峰岭热带山地雨林群落生产和二氧化碳同化净增量的初步研究[J].植物生态学报,1998,22(2):127.
[110]李正才,傅懋毅,徐德应.竹林生态系统与大气二氧化碳减量[J].竹子研究汇刊,2003,22(4):1-6.
[111]刘华,雷瑞德.我国森林生态系统碳储量和碳平衡的研究方法及进展[J].西北植物学报,2005,25(4):835-843.
[112]刘世荣.兴安落叶松人工林群落生物量及净初级生产力的研究[J].东北林业大学学报,1990,18(2):40-46.
[113]马钦彦,陈遐林,王娟,等.华北主要森林类型建群种的含碳率分析[J].北京林业大学学报,2002,24(5 /6)96- 100.
[114]毛子军.森林生态系统碳平衡估测方法及其研究进展[J].植物生态学报,2002,26(6): 731-738.
[115]潘维俦,李利村,高正衡. 2个不同地域类型杉木林的生物产量和营养元素分布[J].中南林业科技, 1979(4):1-l4.
[116]任海,彭少麟.恢复生态学[M].北京:科学出版社,2002.
[117]沈文清,马钦彦,刘允芬.森林生态系统碳收支状况研究进展[J].江西农业大学学报,2006,28(2):312-317.
[118]史军,刘纪远,高志强,等.造林对陆地碳汇的影响研究进展[J].地理科学进展,2004,23 (2) : 58 - 67.
[119]王秋凤,牛栋,于贵瑞,等.长白山森林生态系统CO2和水热通量的模拟研究[J].中国科学D辑(地球科学),2004, 34 (增刊Ⅱ): 131-140 .
[120]王绍强,刘纪远.土壤碳蓄积量变化的影响因素研究现状[J].地球科学进展,2002,17(4):528-534.
[121]王绍强,周成虎,罗承文.中国陆地自然植被碳量空间分布特征探讨[J].地理科学进展,1999,18(3) :238-244.
[122]王文杰,石福臣,祖元刚,等.陆地生态系统CO2通量网的建立和发展[J].东北林业大学学报,2002,30(4):57- 61.
[123]王效科,冯宗炜,欧阳志云.中国森林生态系统的植物碳储量和碳密度研究[J].应用生态学报,2001,12(1) : 13-16.
[124]王效科,冯宗炜.森林生态系统生物量和碳储备的研究历史[A].王如松编.现代生态学的热点问题[C].北京:中国科学技术出版社,1995:335-347.
[125]王妍,张旭东,彭镇华,等.森林生态系统碳通量研究进展[J].世界林业研究,2006,19(3):12-17.
[126]王玉辉,周广胜,蒋延玲,等.基于森林资源清查资料的落叶松林生物量和净生长量估算模式[J].植物生态学报,2001,25(4):420-425.
[127]吴家兵,张玉书,关德新.森林生态系统CO2通量研究方法与进展[J].东北林业大学学报,2003,31(6):49- 51.
[128]项文化,田大伦,闫文德.森林生物量与生产力研究综述[J ].中南林业调查规划,2003,22(3):57- 60.
[129]杨洪晓,吴波,张金屯等.森林生态系统的固碳功能和碳储量研究进展[J].北京师范大学学报(自然科学版),2005,41(2):172-177.
[130]杨丽韫,李文华.长白山不同生态系统地下部分生物量及地下C贮量的调查[J].自然资源学报,2003,18(2):204-209.
[131]杨玉盛,郭剑芬,林鹏,等.格氏栲天然林与人工林粗木质残体碳库及养分库[J].林业科学,2005,41(3):7-11.
[132]杨玉盛,郭剑芬,林鹏,等.格氏栲天然林与人工林枯枝落叶层碳库及养分库[J].生态学报,2004,24(2):359-367.
[133]杨玉盛,刘艳丽,陈光水,等.格氏栲天然林与人工林土壤非保护性有机C含量及分配[J].生态学报,2004,24(1):1-8.
[134]杨玉盛,谢锦升,王义祥,等.杉木观光木混交林C库与C吸存[J].北京林业大学学报,2003,25(5):10-14.
[135]于占源,杨玉盛,陈光水.紫色土人工林生态系统碳库与碳吸存变化[J].应用生态学报,2004,15(10):1837-1841.
[136]张林,黄永,罗天祥,等.林分各器官生物量随林龄的变化规律——以杉木、马尾松人工林为例[J].中国科学院研究生院学报,2005,22(2):170-178.
[137]钟羡芳,陈光水,高人,等.杉木多代连栽地营造杉阔混交林碳库与碳吸存[J].福建师范大学学报(自然科学版),2006,22(2):99-103.
[138]周广胜,王玉辉.陆地生态系统类型转变与碳循环[J].植物生态学报,2002,26(2):250-254.
[139]周玉荣,于振良,赵士洞.我国主要森林生态系统碳贮量和碳平衡[J].植物生态学报,2000,24(5):518-522.
[140]佐藤大七郎,堤利夫.陆地植物群落的物质生产[M].北京:科学出版,1986,21-47.
[141]曲俊华,倪家明.多元回归模型分析与设计实现[J].中国电力教育,2007,(1):140~142.
[142]徐志敏,刘希玉.多元回归分析及其在辅助决策中的应用[J].计算机工程, 2004, 30 (21): 129 ~ 131.
[143]梁士楚,王伯荪.云贵鹅耳枥群落物种多样性及其刀切法估计[J].热带亚热带植物学报,2001,9(2):129～135.
[144] Vapnik V N. The nature of statistical learning[M]. New York: SpringerVerlag, 1995.
[145] SUYKENS J A K,VANDEWALLE J,De MOOR B.Optimal control by least squares support vector machines[J].Neural Networks.2001, 14(1):23–35.
[146]张学工.关于统计学习理论与支持向量[J].自动化学报,2000,26(1):32–42.
[147]朱家元,陈开陶,张恒喜.最小二乘支持向量机算法研究[J].计算机科学,2003,30(7):157–159.
[148] DENG Wen-hua, DONG He-quan, XU Hua-yong, et al. Support vector machine applied in study of ancient ceramics[A]//’05 Science and Technology of Ancient Ceramics 6 Proceedings of the International Symposium(ESAC’05)[C],Shanghai,China,2005:496–501.
[149] FLETCHER R. Practical Methods of Optimization[M].2nd ed. New York: John Wiley and Sons Inc,1987:31.
[150] NELLO Cristianini, JOHN Shawe-Taylor著.支持向量机导论[M].李国正,王猛,曾华军译.北京:电子工业出版社, 2003:30.
[151]刘涵,刘丁,郑岗,梁炎明,宋念龙.基于最小二乘支持向量机的天然气负荷预测[J].化工学报, 2004, 55(5):828-832.
[152]鲁法典,韩峻.刀切法在森林双重回归抽样调查中的应用初探[J].河北林果研究, 1999, 14(3):216-220.
[153]洪伟,吴承祯.马尾松人工林经营模式及其应用[M].北京:中国林业出版社,1999:1-16.
[154]康冰,刘世荣,张广军,等.广西大青山南亚热带马尾松、杉木混交林生态系统碳素积累和分配特征[J].生态学报,2006,26(5):1 320-1 329.
[155]张林,黄永,罗天祥,等.林分各器官生物量随林龄的变化规律——以杉木、马尾松人工林为例[J].中国科学院研究生院学报,2005,22(2):170-178.
[156]张国庆,黄从德,郭恒,等.不同密度马尾松人工林生态系统碳储量空间分布格局[J].2007,27(6):10-14.
[157]史大林,郑小贤.马尾松林碳储量成熟问题初探[J].林业资源管理,2007,(4):34-37.
[158]黄昌勇.土壤学[M].北京:中国农业出版社, 2000: 152-165.
[159] Woodwell G.M., Whittaker R.H., Reiners W.A. The biota and the world carbon budget[J]. Science, 1978, 199: 141-146
[160] Olson J.S., Watts J.A. & Allison L.J. Carbon in live vegetation of major world ecosystem. US Department of Energy DOE/NBGB-0037.1983, 15-25(Rep. Ornl-58260, Oak Ridge National Laboratory, Oak Ridge, TN)
[161] Post W.M., Emanuel W.R., Zinke P.J. et al. Soil carbon pools and world life zones[J]. Nature, 1982, 298: 156-159
[162] Kauppi P.E., Miehkainen K., Kuusela K. Biomass and carbon budget of European Forests, 1971 to 1990[J]. Science, 1992, 256: 70-74
[163] Kurz W.A. and Apps M.J. Contribution of northern forests to the global C cycle: Canada as a case study[J]. Water, Air, and Soil Pollution, 1993, 70: 163-176
[164]刘国华,傅伯杰,方精云.中国森林碳动态及其对全球碳平衡的贡献[J].生态学报,2000,20(3):733-740
[165]李意德,曾庆波,吴仲民,等.我国热带天然林植被C贮存量的估算[J].林业科学研究,1998,11(2):156-162
[166]李铭红,于明坚,陈启瑺,等.青冈常绿阔叶林的碳素动态[J].生态学报,1996,16(6):645-651
[167]方晰,田大伦,项文化,等.第二代杉木中幼林生态系统碳动态与平衡[J].中南林学院学报,2002,22(1):1-6
[168]江泽慧.论林业在可持续发展中的战略地位[J].国土经济,2000,1:4-7
[169]周国模,吴家森,姜培坤.不同管理模式对毛竹林碳贮量的影响[J].北京林业大学学报,2006,28(6):51-55
[170]余雪标,龙腾.我国桉树人工林经营及研究现状[J].热带农业科学,1999,(3):596-65.
[171]李宝福.福建省桉树引种选育研究动态及展望[J].当代生态农业, 2005,(4):45-48.
[172]陈秋波.桉树人工林土壤生物多样性问题研究[J].热带农业科学, 2002,(1):66.
[173]李宝福.闽南山地桉树人工林生物生产力与营养研究[J].江西农业大学学报,2000,22(5):145-147.
[174]郑郁善洪长福.沿海丘陵巨尾桉人工林水源涵养功能研究[J].江西农业大学学报,2000, 22(2):220-224.
[175] Schlesinger W H. Carbon storage in the calishe of arid soils: a case study from Arizona[J]. Soil Science, 1982,133: 247-255.
[176] Sombroek W G, Nachtergaele F O, Hebel A. Amount, dynamics and sequestering of carbon in tropical and subtropical soils[J]. AMBIO, 1993,22(7): 417-426.
[177]方精云,刘国华,徐嵩龄.中国陆地生态系统的碳库[A].见:王庚辰,温玉璞主编.温室气体浓度和排放监测及相关过程[C].北京:中国环境科学出版社, 1996. 109-128.
[178]王艳芬,陈佐忠, Larry T. Tieszen.人类活动对锡林郭勒地区主要草原土壤有机碳分布的影响[J].植物生态学报, 1998, 22(6): 545-551.
[179]李凌浩,刘先华,陈佐忠.内蒙古锡林河流域羊草草原生态系统碳素循环研究[J].植物学报, 1998,40(10): 955-961.
[180]吴仲民,曾庆波,李意德,等.尖峰岭热带森林土壤C储量和CO2排放量的初步研究[J].植物生态学报, 1997,21(5): 416-423.
[181]吴仲民,曾庆波,李意德,等.尖峰岭热带山地雨林C素库及皆伐影响的初步研究[J].应用生态学报, 1998,9(4): 341-344.
[182]李意德,吴仲民,曾庆波,等.尖峰岭热带山地雨林生态系统碳平衡的初步研究[J].生态学报, 1998,18(4): 371-378.
[183]李忠佩,王效举.红壤丘陵区土地利用方式变更后土壤有机碳动态变化的模拟[J].应用生态学报, 1998,9(4): 365-370.
[184]李凌浩.土地利用变化对草原生态系统土壤碳贮量的影响[J].植物生态学报, 1998,22(4): 300-302.
[185]徐德应.人类经营活动对森林土壤碳的影响[J].世界林业研究, 1994,5: 26-31.
[186]刘绍辉,方精云,清田信.北京山地温带森林的土壤呼吸[J].植物生态学报, 1998,22(2): 119-126.
[187]张林波,曹洪波,高吉喜,等.大气CO2浓度升高对土壤微生物的影响[J].生物学杂志, 1998,17(4): 33-38.
[188]王其兵,李凌浩,刘先华等.内蒙古锡林河流域草原土壤有机碳及氮素的空间异质性分析[J].植物生态学报, 1998,22(5): 409-414.
[189]李克让,王绍强,曹明奎.中国植被和土壤碳贮量[J].中国科学(D辑), 2003, 33(1): 72-80.
[190] Polglase P J , Paul K I , Khanna P K, et al . Change in soil carbon following Afforestation or reforestation[C]: Reviewof experimental evidence and development of a conceptual framework. National Carbon Accounting System Technical Report No. 20. Common Wealth of Australia. Printed in Australia for the Australian Greenhouse Office , 2000 , 1-119.
[191] Jenkinson D S , Rayner J H. The turnover of soil organic matter in some of rothamsted classical experiments[J]. Soil Sci , 1977 , 125 :298-305.
[192] Jobbagy E G, Jackson R B. The vertical distribution soil organic carbon and its relation to climate and vegetation[J]. Ecological Applications, 2000 , 10(2) :423-436.
[193]刘景双,杨继松.三江平原沼泽湿地土壤有机碳的垂直分布特征研究[J].水土保持学报, 2003, 17(3) :5-8.
[194]方华军,杨学明,等.东北黑土有机碳储量及其对大气CO2的贡献[J].水土保持学报, 2003, 17(3) :9-20.
[195]焦如珍,杨承栋,屠星南,等.杉木人工林不同发育阶段林下植被、土壤微生物、酶活性及养分的变化[J].林业科学研究, 1997, 10 (4) : 373 - 379
[196]杨玉盛,邱仁辉,俞新妥,等.不同栽杉代数林下植被营养元素的生物循环[J].东北林业大学学报, 1999, 27 (3) : 26 - 30
[197]范少辉,马祥庆,傅瑞树,等.不同栽植代数杉木林林下植被发育的比较研究[J].林业科学研究, 2001, 14 (1) : 8– 16
[198]何艺玲,傅懋毅.人工林林下植被的研究现状[J].林业科学研究, 2002, 15 (6) : 727 - 733
[199]余雪标,钟罗生,杨伟东,等.桉树人工林林下植被结构的研究[J].热带作物学报, 1999, 2 (1) : 66– 72
[200]杨承栋,焦如珍.发育林下植被是恢复杉木人工林地力的重要途径[J].林业科学, 1995a, 31 (3) : 316– 322
[201]杨承栋,焦如珍,屠星南,等.杉木林下植被对5-15 cm土壤性质的改良[J].林业科学研究, 1995b, 8 (5) : 514 - 519
[202]陈民,赵京岚,刘杰,等.人工林林下植被研究进展[J] .山东农业大学学报(自然科学版),2008,39(2):321-325
[203]梁宏温,温远光,温琳华,等.连栽对尾巨桉短周期人工林碳贮量的影响[J].生态学报, 2009, 29(8): 4242-4250.
[204]苏建芹,李文宝,刘春兰.刀切法在森林调查中的应用[J].林业机械与木工设备, 2002, 30(11): 33-34.
[205]魏文俊,王兵,李少宁,等.江西省森林植被乔木层碳储量与碳密度研究[J].江西农业大学学报(自然科学版), 2007, 29(5): 767-771.
[206]周玉荣,于振良,赵士洞.中国主要森林生态系统的碳贮量与碳平衡[J].植物生态学报, 2000, 24(5): 518-522.
[207] SEDJO R A. The carbon cycle and global forest ecosystem[J]. Water Air Soil Pollut , 1993 , 70 : 295 - 307.
[208]周国模,刘恩斌,佘光辉.森林土壤碳库研究方法进展[J].浙江林学院学报,2006,23(2):207-216.
[209] HOUGHTON R A , HOBBIE J E , MELILLO J M, et al . Changes in carbon content of terrestrial biota and soils between 1860 and 1980 : a net release of CO2 to the atmosphere [J ] . Ecol Monogr , 1983 , 53 : 235 - 262.
[210] HOUGHTON R A, SKOLE KL, LEFKOWITZ D S. Changes in the landscape of Latin America between 1850 and 1985( I )Net release of CO2 to the atmosphere [J ] . For Ecol Manage , 1991 , 38 : 173 - 199.
[211] DETWILER R P. Land use change and the global carbon cycle : the role of tropical soils[J ] . Biogeochem, 1986 , 2 : 67 - 93.
[212]吴承祯,洪伟.杉木数量经营学引论[M].中国林业出版社,2000,1-7.
[213]田大伦,方晰,项文化.湖南会同杉木人工林生态系统碳素密度[J].生态学报, 2004, 4(11): 2382-2386.
[214]方晰,田大伦,项文化.速生阶段杉木人工林碳素密度、贮量和分布[J].林业科学, 2002, 38(3): 14-19
[215]方晰,田大伦,项文化,等.杉木人工林土壤有机碳的垂直分布特征[J].浙江林学院学报, 2004, 21(4): 418- 423.
[216]康冰,刘世荣,张广军,等.广西大青山南亚热带马尾松、杉木混交林生态系统碳素积累和分配特征[J].生态学报, 2006, 26(5): 1320-1329.
[217] Bolin B. Change of land biota and their importance for the carbon cycle [J].Science, 1977, 196(4290): 613-615.
[218] Bohn HL. Estimate of organic carbon in world soils [J].Soil Sci. Soc. Am. J.,1976, 40: 468-470.
[219] Bohn HL. Estimate of organic carbon in world soils [J].Soil Sci. Soc. Am. J.,1982, 46:1118-1119.
[220] Post W M, Emanuel W R, Zinke P J,et al.Soil carbon pools an world life zones [J].Nature, 1982, 298(8): 156-159.
[221] Schlesinger W H. Evidence from chronoseauence studies for a low carbon-storage potential of soil [J].Nature, 1990, 348(15): 23-234.
[222] Eswaran H, Van der Berg E, Reich P. Organic carbon in soils of the world [J].Soil Sci. Soc. Am.J.,1993, 57:192-194.
[223] Lacelle B. Canada s soil organic carbon database [A]. Lal R,etal.Soil Processes and the Carbon Cycle[C]. Boca Raton: CRC Press, 1997. 93-102.
[224] Titlyanove A A, Bulavko G I, Kudryashova S,et al.The reserves and losses of organic carbon in the soils of Siberia [J].Pochrovedenie, 1998, 1:51-59.
[225]李忠,孙波,赵其国.我国东部土壤有机碳的密度和储量.农业环境保护,2001,20(6):385-389.
[226] Jobbagy E G, Jackson R B. The vertical distribution of soil organic carbon and it’s relation to climate and vegetation [J ] . Ecol Appl , 2002 , 10(2) : 423 - 436.
[227]盛炜彤,范少辉.杉木人工林长期生产力保持机制研究[M].北京:科学出版社, 2005.
[228]罗云建,张小全.杉木连栽地力退化和杉阔混交林的土壤改良作用[J].生态学报, 2007, 27 (2):715-724.
[229]李鸿博,史锟.不同植物过程土壤剖面有机碳含量和含水量研究[J].大连铁道学院学报,2005,26(1):92-95.
[230]姜勇,张玉革,梁文举,等.潮棕壤不同利用方式有机碳剖面分布及碳储量[J].中国农业科学,2005,38(3):544-550.
[231]陶贞,沈承德,高全洲,等.高寒草甸土壤有机碳储量及其垂直分布特征[J].地理学报,2006,61(7):720-728.
[232]高惠漩.两个多重相关变量组的统计分析[J].数理统计与管理, 2002(1): 57-64.
[233]周鸿凯,郭建夫,黎华寿,等.光温因子与杂交水稻生态群体的产量和品质性状的典型相关分析[J].应用生态学报, 2006, 17(4): 663-667.
[234]李青,张传静,张强,等.利用计算机进行刀切法估算树干材积的实践[J].防护林科技, 2004, (5): 69-70.
[235]洪滔.千年桐人工林生态系统碳库研究[D] .福建农林大学硕士论文, 2009.
[236]梁煜峰.投影寻踪法和遗传算法在三峡入库洪水预报中的应用[D].四川大学硕士学位论文, 2006.
[237]付强.投影寻踪模型原理及应用[M].北京:科学出版社.2006: 131-139.
[238]张明罡.毛竹林水土保持体系下土壤侵蚀预测模型研究[D] .福建农林大学硕士论文, 2009.
[239]董志龙,何慧根,于涛,等.兰州大气环境质量影响因素相关分析[J].干旱区资源与环境, 2009, 23(12): 49-53.
[240]吴承祯,洪伟.基于改进的投影寻踪的森林生态系统生态价位分级模型研究[J].应用生态学报, 2006, 17 (3) : 357 - 361.