广东省湿地松二元立木材积模型的编制

采用150株建模样本,以山本式固定参数模型和可变参数模型建立了广东省湿地松二元立木材积模型,模型自检及41株检验样木验证表明:固定参数模型和可变参数模型的精度均符合要求,能满足生产需要,但以可变参数模型具有更优的适用性能。     

广东省杉木树种二元立木材积可变参数模型研究

采用155株建模样本,利用现代建模理论和方法,以山本式固定参数模型和可变参数模型建立了广东省杉木树种的二元立木材积模型。模型自检及64株检验样木验证表明,固定参数模型和可变参数模型精度均符合建模要求,能满足生产需要,但后者具有更优的适用性能。     

利用可变参数和二阶回归方法建立海南省相思树立木材积模型

【目的】建立立木材积模型,为科学计量评价森林资源、完善森林资源监测体系提供重要依据。【方法】以海南省相思树为研究对象,以胸径和树高为自变量建立二元山本材积式模型、可变参数动态模型,采用以简单幂函数式和树高—胸径模型为基础的二阶回归估计方法建立一元胸径立木材积模型,分析对比建立的一元、二元立木材积模型拟合效果。【结果】1)相思树二元山本材积式模型和可变参数动态模型的确定系数均在0.98以上,平均预估精度均在98%以上。模型整体总体相对偏差(TRB)和平均系统偏差(MSB)均在±3%以内,山本材积式模型在8、20 cm径阶总相对偏差和平均系统偏差超出±3%范围;2)两种方法建立的一元立木材积模型的确定系数均在0.94以上,预估精度均在96%以上。采用分段建模和二阶回归总相对偏差和平均系统偏差趋近于0,而常规方法建模12、16、24 cm以上径阶的总相对偏差均超出5%范围。【结论】可变参数动态模型相较于山本材积式模型拟合精度更高,各径阶误差更小,明显优于山本材积式模型;采用二阶回归估计方法建立的模型能更好地反映材积随胸径大小的变化规律,既提高了模型的切合性能,又能较好地控制各径阶的偏差,明显提升了拟合效果,是建立一元立木材积模型的有效方法。     

马尾松人工林相对树高曲线模型构建

在广西马尾松Ⅰ类、Ⅱ类和Ⅲ类产区设置279块马尾松人工林标准样地,采集每木检尺数据,数据经预处理后选取理查德函数变形的可变参数式与固定参数式模型分别构建马尾松相对树高曲线模型,并以参数变动系数和综合修正决定系数(R²)、总相对误差(TRE)、平均系统误差(ASE)、平均百分标准误差(MPSE)及预估精度(P)等指标进行综合评价;同时,对该模型进行分区和分胸径、树高段及独立样本检验。结果显示:构建的最佳广西马尾松人工林理查德函数变形的固定参数相对树高曲线方程为■。其中c₁取值为2.254 6、变动系数为3.902%,c₂取值为1.198 2、变动系数为4.953%。该相对树高曲线模型的拟合效果佳,在通用性和适用性方面均表现良好,达到了编制林业数表模型的控制精度。     

南京地区马尾松多元混合效应材积模型研建

为了精确拟合南京地区马尾松林木胸径、树高与材积的相关关系,基于28块20 m×20 m的方形样地的531株马尾松样木数据,分别采用山本式材积模型和多元非线性混合效应模型拟合了马尾松胸径、树高与材积的相关关系。结果显示:山本式材积模型的相关指数为0.964 92,估计值的标准误为0.004 0,总相对偏差、平均相对误差、平均预估误差和平均相对误差绝对值均小于3%,多元非线性混合效应模型的相关指数为0.99997,估计值的标准误为0.000 049 85,总相对偏差、平均相对误差、平均预估误差和平均相对误差绝对值均小于0.3%,表明两个材积模型有很高的拟合优度和精度,适应性检验结果显示,山本式二元材积模型和多元非线性混合效应材积模型的平均相对误差、总相对偏差、平均相对误差绝对值、绝对残差和标准偏差均小于3%,统计量F值小于临界值,表明两个材积模型的适应性较强;与山本式二元材积模型相比,多元非线性混合效应材积模型的拟合优度和精度更高,适应性更强。     

海南省松树二元立木材积模型研建

以海南松树为研究对象,利用162株松树样本数据,以山本材积式为模型基础,建立固定参数和可变参数二元立木材积模型。结果表明:无论固定参数材积模型,还是可变参数材积模型,模型预估精度高,达到了98%以上;模型在整体上和各径阶上,TRE和MSE基本上在±3%以内,具有较好的全面切合性能;可变参数材积模型明显要优于固定参数模型,应该作为生产上首选二元材积模型。     

马尾松人工纯林全林整体生物量模型的构建

以湖南省会同县马尾松纯林为研究对象,采用两阶段度量误差模型法对马尾松林分蓄积量与各组分生物量回归模型和全林整体生长模型进行融合,并对模型的参数进行重新估计,得到马尾松纯林全林整体生物量模型,经检验模型精度较高,适用性强。     

基于形态结构特征的马尾松几何建模研究

以马尾松生长发育的观测数据为依据,提出基于马尾松形态结构特征的交互式几何建模方法,模拟马尾松不同生长发育时期的几何模型.试验证明:这种基于形态结构特征的几何建模技术对模拟和表现马尾松取得了良好的效果.     

湖南省马尾松相对树高曲线模型研建

基于湖南省主要树种模型研建项目中50块马尾松样地的每木胸径和树高实测数据，经数据预处理后，构建了马尾松固定参数相对树高曲线模型和可变参数相对树高曲线模型。基于构建的树高曲线模型，计算林木材积和样地蓄积，并以二元材积公式计算蓄积作为真值，将基于树高曲线模型计算的蓄积、二元材积公式计算蓄积和一元材积公式计算蓄积进行对比。结果表明：固定参数和可变参数相对树高曲线模型的决定系数分别为0.844和0.857,体现了较好的拟合精度；两种方法统计的总体蓄积相对误差均在5%以内，满足林业行业标准要求的允许误差范围；分径级组或分起源统计样地蓄积相对误差，相对树高曲线模型均较一元材积公式表现出更高的精度，能有效避免产生较大偏差。构建的马尾松相对树高曲线模型能应用于生产实践。     

利用分段建模方法建立海南省加勒比松一元立木材积模型

以海南省加勒比松为研究对象,以山本材积式为模型基础,分别采用常规建模方法 (直接建立一元材积模型)和分段建模方法建立加勒比松一元立木材积模型,并对两种方法进行比较。结果表明:两种方法建立的一元材积模型精度较高,均到达95%以上,且整体上差异不大;相对于常规方法,利用分段建模方法建立的一元材积模型在各个径阶上均能保证总相对误差和平均系统误差)在±5%以内,因此型的全面切合性能更好,是生产上的首选模型。     

基于叶重的桉树单叶面积估计模型

叶面积是树木生长模型和树木的生理过程的一个重要参数。根据叶重量估计叶面积。本文以我国南方主要树种桉树为对象,实测了从25株桉树上摘取的455片桉树叶子的重量和面积,分别建立了基于叶鲜重的嫩叶与成熟叶单叶面积预测模型。其中80％的数据用于建模,20％的数据用于模型验证。基于模型拟合效果、误差和残差分布,确定了成熟叶和嫩叶的叶面积模型分别为幂函数和线性方程。经检验,模型精度均达95%以上。建立的模型为估计单叶面积提供了一个简单可靠的方法,并且在桉树的结构-功能模型研究中有应用潜力。     

基于两层次线性混合效应模型的杉木林单木胸径生长量模型

基于两层次线性混合效应模型方法,建立江西省杉木人工林单木胸径生长量模型.研究所用数据来自于长期观测的固定样地数据,数据库包括82个区域、365个样地、5416株树木共计16248条记录.为了解决不同区域及不同样地之间的差异,本文构建的混合模型分别考虑样地层次、区域层次及两层次的随机参数效应.针对数据存在的重复测量及嵌套结构特性,在模拟时选择合适的异方差和自相关模型矩阵来解决此类问题.最后利用独立的抽样验证数据对模拟结果进行验证.结果表明:林分断面积、对象木胸径、林分内大于对象木的断面积之和与对象木胸径的比值以及海拔对单木胸径生长量有显著影响.与林业中常用的传统最小二乘方法相比,采用混合效应模型方法后模型的模拟精度和验证精度均有提高.选择适合的异方差和自相关函数后,模型比只考虑参数的随机效应有更好的适应性,并体现出了混合效应模型的灵活性和准确性.     

基于特征的参数化齿轮系统的二次开发

分析了Solid Edge软件在齿轮设计中存在的不足，以VBA作为开发工具，根据特征参数化造型思想，开发出了基于特征的参数化齿轮系统生成模块，提高了绘图效率。     

时滞和平衡含水率直接估计法的有效性分析

通过对不同直径落叶松枯枝含水率和环境条件的室内连续观测,分别使用Nelson模型和Simard模型作为平衡含水率响应模型,估计可燃物时滞和平衡含水率响应函数,然后以参数估计值预测可燃物含水率,分析比较建模样本数和平衡含水率模型不同对参数估计和含水率预测误差的影响。结果表明:1)基于Nelson模型的直接估计法在建模样本数较大时(84个),结果稳健,预测误差小,方法是有效的。2)采用Simard模型直接估计可燃物时滞和平衡含水率时,在建模样本数较少时,其预测效果不如Nelson模型,但当建模样本数较多(超过84)时,2个模型预测效果没有显著差别。     

广西速丰桉林分二元材积生长率表研制

速丰桉一年四季生长,没有明显年轮的特征,难以对单株木进行树干解析确定林木生长量。在没有足够数量的固定标准地连续定期监测数据的情况下,采取临时标准地年龄序列的调查方法,选用适当模型结构,再通过引进可变参数改进生长模型,建立以年龄、胸径为基础的林分材积生长率模型,并编制广西速丰桉林分二元材积生长率表。经检验,方法可行,预估精度满足林业数表建模要求,可应用于速丰桉林材积生长量的预估。     

A variance-covariance structure to take into account repeated measurements and heteroscedasticity in growth modeling

This study proposes a within-subject variance-covariance (VC) structure to take into account repeated measurements and heteroscedasticity in a context of growth modeling. The VC structure integrates a variance function and a continuous autoregressive covariance structure. It was tested on a nonlinear growth model parameterized with data from permanent sample plots. Using a stand-level approach, basal area growth was independently modeled for red spruce (Picea rubens Sarg.) and balsam fir [Abies balsamea (L.) Mill.] in mixed stands. For both species, the implementation of the VC structure significantly improved the maximum likelihood of the model. In both cases, it efficiently accounted for heteroscedasticity and autocorrelation, since the normalized residuals no longer exhibited departures from the assumptions of independent error terms with homogeneous variances. Moreover, compared with traditional nonlinear least squares (NLS) models, models parameterized with this VC structure may generate more accurate predictions when prior information is available. This case study demonstrates that the implementation of a VC structure may provide parameter estimates that are consistent with asymptotically unbiased variances in a context of nonlinear growth modeling using a stand-level approach. Since the variances are no longer biased, the hypothesis tests performed on the estimates are valid when the number of observations is large.     

Forest inventory inference with spatial model strata

ABSTRACT

In design-based model assisted inference from data gathered in a large area forest inventory under a probability sampling design, one should anticipate spatial heterogeneity in the regression coefficients of an assisting model. The consequence of such heterogeneity is that a global estimate of a root mean squared error (RMSE) becomes unsuited for local predictions. With data from the Danish National Forest Inventory, we demonstrate how to: obtain an assisting model with the lasso method; test for spatial stationarity in regression coefficients of the assisting model; and identify spatial model strata for a post-stratification with either a finite mixture modeling or a lasso spatial clustered coefficients method. Spatial model strata apply to any domain and small area estimation problem without the need for complex modeling when domains or small area changes with shifting user needs. One should not à priori expect a spatial model stratification to improve design-based population and strata estimates of precision, but the reliability of domain and small area RMSEs will improve in presence of statistically significant spatial model strata.     

用二类调查样地建立落叶松单木直径生长模型

根据来自吉林省汪清林业局二类调查的１４个落叶松固定样地的３４６株复测林木数据,建立了与我关的落叶松木平方直径生长最模型。它包含了单木直径生长模型所需考虑的林木大小,立地条件和林木竞争这３个因素,并具有与年龄、地位指数无关的特点,便于实践中应用,逐步回归的结果表明,落叶松单木平方直径生长最模型需要考虑的主要因子是林木直径值、林分断面积和坡度。     

A review of stand basal area growth models

Growth and yield modeling has a long history in forestry. The methods of measuring the growth of stand basal area have evolved from those developed in the U.S.A. and Germany during the last century. Stand basal area modeling has progressed rapidly since the first widely used model was published by the U.S. Forest Service. Over the years, a variety of models have been developed for predicting the growth and yield of uneven/even-aged stands using stand-level approaches. The modeling methodology has not only moved from an empirical approach to a more ecological process-based approach but also accommodated a variety of techniques such as: 1) simultaneous equation methods, 2) difference models, 3) artificial neural network techniques, 4) linear/nonlinear regres-sion models, and 5) matrix models. Empirical models using statistical methods were developed to reproduce accurately and precisely field observations. In contrast, process models have a shorter history, developed originally as research and education tools with the aim of increasing the understanding of cause and effect relationships. Empirical and process models can be married into hybrid mod-els in which the shortcomings of both component approaches can, to some extent, be overcome. Algebraic difference forms of stand basal area models which consist of stand age, stand density and site quality can fully describe stand growth dynamics. This paper reviews the current literature regarding stand basal area models, discusses the basic types of models and their merits and outlines recent progress in modeling growth and dynamics of stand basal area. Future trends involving algebraic difference forms, good fitting variables and model types into stand basal area modeling strategies are discussed.     

Surface area processing in GIS for different mountain regions

Surface area is a fundamental parameter derived from terrain analysis in geographical information systems (GIS) for modeling the real world. Therefore, an evaluation of the techniques implemented by GIS models is important in order to obtain reliable area measurements. The study compares two raster-based methods with a TIN-based (triangulated irregular network) method for surface area approximation using a digital elevation model (DEM) with 25 m × 25 m resolution in six mountain regions of Shandong Province, China. Raster-based method Ⅰ calculates surface-area grids from DEMs directly. Eight 3-dimensional triangles connecting the center point of each cell with the center points of the eight surrounding cells are generated and then the areas of the portions of each triangle that lay within the cell boundary are calculated and summed. Raster-based method Ⅱ produces the surface area of each cell directly from the pixel size and the slope value for the pixel. The results demonstrate that the surface areas calculated by raster-based method Ⅰ are equal to those of the TIN-based method. The results of raster-based method Ⅱ are the lowest but the difference in areas between the two raster-based methods decreases with the decrease in terrain complexity. Compared to the method with TINs, raster-based method Ⅱ can do better because it has some advantages, such as neighborhood analysis, more consistent output and faster processing speed. The results also demonstrate that the difference between surface and horizontal areas should be considered if more than 30% of the area of a region has slopes steeper than 18.2 degrees.