基于机载LiDAR数据的福建柏人工林林木参数提取

准确高效地提取人工林林木参数可为估算单木材积、林分蓄积量提供关键信息。本文提出基于机载LiDAR数据的高精度单木参数提取方法,其实现过程包括数据预处理、地面滤波、单木分割和参数提取。以福建省沙县官庄国有林场的福建柏大径材人工林为试验区,采集高密度机载点云数据,对点云进行去噪、重采样等预处理。使用布料滤波算法(CSF)分离出植被点云和地面点云,并采用Delaunay三角网法将植被点云数据插值生成数字表面模型(DSM),采用反距离加权插值法将地面点云数据插值生成数字高程模型(DEM),两者作差运算获得冠层高度模型(CHM)。利用分水岭分割算法分析不同分辨率的CHM对单木分割及参数提取精度的影响。采用点云距离聚类算法对归一化植被点云进行单木分割,分析不同的距离阈值对单木分割及参数提取精度的影响。结果表明：使用分水岭分割算法处理0.3 m分辨率CHM单木分割调和值最高,达到91.1%,提取的树高精度较优,决定系数(R²)达到0.967,均方根误差(RMSE)为0.890 m;使用间距阈值为平均冠幅的点云分割算法单木分割调和值最高,达到91.3%,提取的冠幅精度较优,R     

近似贝叶斯法在光合模型参数估计中的应用

长期以来,光合作用机理模型中参数的确定都是一个难点。该文提出一种参数反演的方法,称为近似贝叶斯法(APMC),用来确定Farquhar光合模型的生理参数。通过将整个冠层抽象为一片大叶的思维抽象,笔者进一步将APMC应用到冠层尺度的生理参数求解,使直接求算冠层尺度生理参数成为可能。该文详细介绍了使用APMC估算光合模型参数的具体算法,并用实测数据进行了验证。结果表明,APMC可以很好地应用于冠层光合模型参数的估计,估计所得的参数落在参数生理上下限值之间,应用1 948个实测数据进行检验,得到决定系数0.75。模拟值和实测值的线性回归曲线斜率为1.04,与理论上的1.0非常接近。这个方法对光合模型参数的获取或许有积极的意义。     

利用模拟退火算法优化Biome-BGC模型参数

生态过程模型建立在明确的机理之上,能够较好地模拟陆地生态系统的行为和特征,但模型众多的参数,成为模型具体应用的瓶颈。本文以Biome-BGC模型为例,采用模拟退火算法,对其生理、生态参数进行优化。在优化过程中,先对待优化参数进行了选择,然后采取逐步优化的方法进行优化。结果表明,使用优化后的参数,模型模拟结果与实际观测更为接近,参数优化能有效地降低模型模拟的不确定性。文中参数优化的过程和方法,可为生态模型的参数识别和优化提供一种实例和思路,有助于生态模型应用区域的扩展。     

R法遗传参数估值置信区间的计算方法和重复估计次数的研究

探讨R法遗传参数估值置信区间的计算方法和重复估计次数(NORE)对参数估值的影响,利用4种模型通过模拟产生数据集。基础群中公、母畜数分别为200和2000头,BLUP育种值选择5个世代。利用多变量乘法迭代(MMI)法,结合先决条件的共扼梯度(PCG)法求解混合模型方程组估计方差组分。用经典方法、Box-Cox变换后的经典方法和自助法计算参数估值的均数、标准误和置信区间。结果表明,重复估计次数较多时,3种方法均可;重复估计次数较少时,建议使用自助法。简单模型下需要较少的重复估计,但对于复杂模型则需要较多的重复估计。随模型中随机效应数的增加,直接遗传力高估。随着PCG和MMI轮次的增大,参数估值表现出低估的趋势。     

生态模型的灵敏度分析

灵敏度分析用于定性或定量地评价模型参数误差对模型结果产生的影响,是模型参数化过程和模型校正过程中的有用工具,具有重要的生态学意义．灵敏度分析包括局部灵敏度分析和全局灵敏度分析．局部灵敏度分析只检验单个参数的变化对模型结果的影响程度;全局灵敏度分析则检验多个参数的变化对模型运行结果总的影响,并分析每一个参数及其参数之间相互作用对模型结果的影响．目前,在对生态模型的灵敏度分析中,越来越倾向于使用全局灵敏度分析的方法．但国内仍多采用局部灵敏度分析方法,很少采用全局灵敏度分析方法．文中详细论述了局部灵敏分析和全局灵敏度分析的主要方法(一次变换法、多元回归法、Morris法、Sobol’法、傅里叶幅度灵敏度检验法和傅里叶幅度灵敏度检验扩展法),希望能为国内生态模型的发展提供一个比较完善的灵敏度分析方法库．结合国内外的灵敏度分析发展现状,指出联合灵敏度研究、灵敏度共性研究及空间直观景观模型的灵敏度分析将为生态模型灵敏度分析研究中的热点和难点．     

奎屯河流域春季融雪期SCS-CN模型参数取值方法

水资源是保障我国西北干旱半干旱地区生态环境安全的关键因素。以新疆奎屯河流域为例,通过修正SCS模型土壤持水量及初损率参数计算方法,寻找适用于干旱半干旱地区山区典型流域春季融雪期径流模拟模型,为流域掌握水资源量及生态用水提供决策依据。与以往研究不同之处在于:首先,引入度-日模型修正降水量参数,以满足流域降雨-融雪混合补给径流特征。其次,利用多期MODIS数据驱动的TS/VI特征空间理论结合土壤水分吸收平衡原理计算土壤持水量参数(S);再运用聚类分析法对初损率(λ)取值方法进行改进。通过参数算法改进后的SCS模型,参数率定期和验证期纳什效率系数和相对误差系数分别为0.92和0.64,0.7%和-1.5%。结果表明:1)参数算法改进后SCS模型能实现奎屯河流域春季融雪期日径流模拟。2)利用遥感大尺度地表信息参数化技术反演SCS模型参数,实现了遥感数据为SCS模型提供大尺度空间数据的同时,间接实现了模型参数由点状数据向面状数据转化的可能;3)初损率(λ)多组取值法可有效提高干旱半干旱地区大尺度流域径流模拟精度。     

非线性再生散度随机效应模型的极大似然估计及随机逼近算法

非线性再生散度随机效应模型包括了非线性随机效应模型和指数族非线性随机效应模型等．通过视模型中的随机效应为假想的缺失数据和应用Metropolis-Hastings(简称MH) 算法,提出了模型参数极大似然估计的随机逼近算法．模拟研究和实例分析表明了该算法的可行性．     

生态过程模型敏感参数最优取值的时空异质性分析——以BIOME-BGC模型为例

生态过程模型是当前研究陆地生态系统水循环、碳循环有力的工具,但此类模型参数众多,参数的合理取值对模型模拟结果有重要影响.以往研究对模型参数的敏感性以及参数的优化取值有诸多的分析和讨论,但有关参数最优取值的时空异质性关注较少.本文以BIOME-BGC模型为例,在常绿阔叶林、落叶阔叶林、C3草地3种植被类型下,通过构建敏感性判别指数,筛选出模型的敏感参数,并在每种植被类型下选取两个试验站点,使用模拟退火算法结合实测通量数据构建目标函数,获取各站点敏感参数逐月的最优取值,然后构建时间异质性判别指数、空间异质性判别指数和时空异质性判别指数对模型敏感参数最优取值的时空异质性进行定量分析.结果表明:BIOME-BGC模型在3种植被类型下遴选出的敏感参数大部分一致,少数有差异,但参数的敏感性强弱在不同植被类型下的表现不尽相同;BIOME-BGC模型敏感参数的最优取值,大都具有不同程度的时空异质性,但不同植被类型下,敏感参数最优取值的时空异质性表现各异;敏感参数中与植被生理、生态相关的参数,其时空异质性相对较小,而与环境、物候相关的参数,其时空异质性普遍较大;在3种植被类型下,模型敏感参数最优取值的时间异质性与空间异质性表现出显著的线性相关性;依据其最优取值的时空异质性,可对BIOME-BGC模型敏感参数进行类型划分,以便在实践应用中采取不同的参数率定策略.本研究结论有助于加深对生态过程模型参数特性及最优取值的理解,可为实践应用中模型参数的合理取值提供一种思路和参考.     

非线性再生散度随机效应模型的极大似然估计及EM算法

非线性再生散度随机效应模型是指数族非线性随机效应模型和非线性再生散度模型的推广和发展．通过视模型中的随机效应为假想的缺失数据和应用Metropolis-Hastings（MH）算法,提出了模型参数极大似然估计的Monte-Carlo EM（MCEM）算法,并用模拟研究和实例分析说明了该算法的可行性．     

FvCB生物化学光合模型及A-C_i曲线测定

由Farquhar、von Caemmerer和Berry提出的生物化学光合模型(以下简称FvCB模型)是一个基于光合碳反应过程的CO_2响应模型。此模型认为C3植物叶片光合速率(A)由3个生物化学过程速率中的最低者——核酮糖-1,5-双磷酸羧化酶/加氧酶(Rubisco)所能支持的羧化速率、电子传递所能支持的核酮糖-1,5-双磷酸(Ru BP)再生速率和磷酸丙糖(TP)利用速率决定。利用改进的FvCB模型对光合速率-胞间CO_2浓度(A-C_i)曲线进行拟合,能有效地估计最大羧化速率、最大电子传递速率、TP利用速率、明呼吸速率、叶肉细胞导度等生化参数,促进我们对植物光合生理及其响应环境变化的理解和预测。该文首先详细地描述了FvCB模型,并分析了此模型分段性和过参数化的特点。然后介绍利用FvCB模型对A-C_i曲线进行拟合,从而估计叶片光合生化参数的研究进展。光合生化参数估计经历了主观分段、分段拟合到客观分段、整体拟合几个阶段,目标函数的最小化方法也从传统的最小二乘法为主转向基于现代计算机技术的迭代算法(如遗传算法、模拟退火算法)。然而,如要进一步提高参数估计的可靠性和精确性,还需加强Rubisco动力学属性和温度依赖性方面的研究。最后,为了获取能更有效地进行参数估计的光合数据,根据目前对FvCB模型拟合的认知,整合并改进了A-C_i曲线的测定方法。     

基于心脏模型推断心肌病理状态的仿真研究

采用有限元方法建立了人体心脏力学模型, 并基于该仿真模型提出了一种无创推断心肌病理状态的新方案,并通过心肌梗塞定位试验加以验证。仿真研究表明这种基于模型参数优化解的方法能将心肌梗塞定位到模型基本单元的空间范围,可用于提取心肌疾病信息。     

THE DIRICHLET-MULTINOMIAL MODEL: ACCOUNTING FOR INTER-TRIAL VARIATION IN REPLICATED RATINGS

Differences in sensory acuity and hedonic reactions to products lead to latent groups in pooled ratings data. Manufacturing locations and time differences also are sources of rating heterogeneity. Intensity and hedonic ratings are ordered categorical data. Categorical responses follow a multinomial distribution and this distribution can be applied to pooled data over trials if the multinomial probabilities are constant from trial to trial. The common test statistic used for comparing vectors of proportions or frequencies is the Pearson chi-square statistic. When ratings data are obtained from repeated ratings experiments or from a cluster sampling procedure, the covariance matrix for the vector of category proportions can differ dramatically from the one assumed for the multinomial model because of inter-trial. This effect is referred to as overdispersion. The standard multinomial model does not fit overdispersed multinomial data. The practical implication of this is that an inflated Type I error can result in a seriously erroneous conclusion. Another implication is that overdispersion is a measurable quantity that may be of interest because it can be used to signal the presence of latent segments. The Dirichlet-Multinomial (DM) model is introduced in this paper to fit overdispersed intensity and hedonic ratings data. Methods for estimating the parameters of the DM model and the test statistics based on them to test against a specified vector or compare vectors of proportions are given. A novel theoretical contribution of this paper is a method for calculating the power of the tests. This method is useful both in evaluating the tests and determining sample size and the number of trials. A test for goodness of fit of the multinomial model against the DM model is also given. The DM model can be extended further to the Generalized Dirichlet-Multinomial (GDM) model, in which multiple sources of variation are considered. The GDM model and its applications are discussed in this paper. Applications of the DM and GDM models in sensory and consumer research are illustrated using numerical examples.     

Determination of material parameters of the two-dimensional Holzapfel–Weizsäcker type model based on uniaxial extension data of arterial walls

Soft tissues are anisotropic materials yet a majority of mechanical property tests have been uniaxial, which often failed to recapitulate the tensile response in other directions. This paper aims to study the feasibility of determining material parameters of anisotropic tissues by uniaxial extension with a minimal loss of anisotropic information. We assumed that by preselecting a certain constitutive model, we could give the constitutive parameters based on uniaxial extension data from orthogonal strip samples. In our study, the Holzapfel–Weizsäcker type strain energy density function (H–W model) was used to determine the material parameters of arterial walls from two fresh donation bodies. The key points we applied were the relationships between strain components in uniaxial tensile tests and the methods of stochastic optimisation. Further numerical experiments were taken. The estimate–effect ratio, defined by the number of data with the precision of estimation less than 0.5% over whole size of data, was calculated to demonstrate the feasibility of our method. The material parameters for Chinese aorta and pulmonary artery were given with the maximum root mean square (RMS) errors 0.042, and the minimal estimate–effect ratio in numerical experiments was 90.79%. Our results suggest that the constitutive parameters of arterial walls can be determined from uniaxial extension data, given the passive mechanical behaviour governed by H–W model. This method may apply to other tissues using different constitutive models.     

用PFU法研究微型生物群集过程中数据的处理

根据MacArthur-Wilson的岛屿区系平衡模型S_t=S_(eq)(1-e~(GT)),可以从野外生态效应试验和室内毒性试验中,提出3个功能参数(S_(eq)、G、t_(90％))进行比较。本文提出两种计算方法:复合梯形法和最小二乘法,后者已在计算机上实现了BASIC计算程序。从数学理论上论证,最小二乘法误差较小,但如果实验布局合理,两种计算方法能得到十分一致的结果。实验模型是否符合理论模型,可以用统计学上的拟合差异度检验法来检验。     

THE MIXED-MODEL ANALYSIS OF VARIANCE APPLIED TO QUANTITATIVE GENETICS: BIOLOGICAL MEANING OF THE PARAMETERS

The mixed-model factorial analysis of variance has been used in many recent studies in evolutionary quantitative genetics. Two competing formulations of the mixed-model ANOVA are commonly used, the “Scheffe” model and the “SAS” model; these models differ in both their assumptions and in the way in which variance components due to the main effect of random factors are defined. The biological meanings of the two variance component definitions have often been unappreciated, however. A full understanding of these meanings leads to the conclusion that the mixed-model ANOVA could have been used to much greater effect by many recent authors. The variance component due to the random main effect under the two-way SAS model is the covariance in true means associated with a level of the random factor (e.g., families) across levels of the fixed factor (e.g., environments). Therefore the SAS model has a natural application for estimating the genetic correlation between a character expressed in different environments and testing whether it differs from zero. The variance component due to the random main effect under the two-way Scheffe model is the variance in marginal means (i.e., means over levels of the fixed factor) among levels of the random factor. Therefore the Scheffe model has a natural application for estimating genetic variances and heritabilities in populations using a defined mixture of environments. Procedures and assumptions necessary for these applications of the models are discussed. While exact significance tests under the SAS model require balanced data and the assumptions that family effects are normally distributed with equal variances in the different environments, the model can be useful even when these conditions are not met (e.g., for providing an unbiased estimate of the across-environment genetic covariance). Contrary to statements in a recent paper, exact significance tests regarding the variance in marginal means as well as unbiased estimates can be readily obtained from unbalanced designs with no restrictive assumptions about the distributions or variance-covariance structure of family effects.     

A THURSTONIAN VARIANT OF THE BETA-BINOMIAL MODEL FOR REPLICATED DIFFERENCE TESTS

The beta-binomial model is combined with a Thurstonian psychometric function to obtain estimates of the parameters of a distribution applicable to replicated difference tests. A method of estimating the variance of d'obtained from these tests is provided. A formula for determining sample size, which is composed of the number of trials (or panelists) and the number of replications, to determine d'is also given.     

Inference of haplotype effects in case-control studies using unphased genotype and environmental data

A retrospective likelihood-based approach was proposed to test and estimate the effect of haplotype on disease risk using unphased genotype data with adjustment for environmental covariates. The proposed method was also extended to handle the data in which the haplotype and environmental covariates are not independent. Likelihood ratio tests were constructed to test the effects of haplotype and gene-environment interaction. The model parameters such as haplotype effect size was estimated using an Expectation Conditional-Maximization (ECM) algorithm developed by Meng and Rubin (1993). Model-based variance estimates were derived using the observed information matrix. Simulation studies were conducted for three different genetic effect models, including dominant effect, recessive effect, and additive effect. The results showed that the proposed method generated unbiased parameter estimates, proper type I error, and true beta coverage probabilities. The model performed well with small or large sample sizes, as well as short or long haplotypes.     

Improving profile HMM discrimination by adapting transition probabilities

Profile hidden Markov models (HMMs) are used to model protein families and for detecting evolutionary relationships between proteins. Such a profile HMM is typically constructed from a multiple alignment of a set of related sequences. Transition probability parameters in an HMM are used to model insertions and deletions in the alignment. We show here that taking into account unrelated sequences when estimating the transition probability parameters helps to construct more discriminative models for the global/local alignment mode. After normal HMM training, a simple heuristic is employed that adjusts the transition probabilities between match and delete states according to observed transitions in the training set relative to the unrelated (noise) set. The method is called adaptive transition probabilities (ATP) and is based on the HMMER package implementation. It was benchmarked in two remote homology tests based on the Pfam and the SCOP classifications. Compared to the HMMER default procedure, the rate of misclassification was reduced significantly in both tests and across all levels of error rate.     

单纯形加速法拟合生态学中的非线性模型

本文以Logistic模型,Taylor幂法则模型,Holling功能反应模型,以及种群内禀增长力Rm等模型的拟合和参数估计为例,探讨单纯形加速法在生态模型优化拟合和参数估计中的应用.结果表明,单纯形加速法拟合生态学中的非线性模型不仅适用广泛,而且拟合过程是直接求原来非线性模型的最优拟合,因而优于生态学中通常使用的将原模型“线性化后再拟合”的方法,而与其它一些最优化方法,如:麦夸方法、枚举选优法等比较,由于单纯形法不需计算目标函数的偏导数,因而计算不受目标函数及其偏导函数复杂程度的限制,而且对于各种模型其求优计算过程十分相似,可以编制统一的计算程序.本研究所编制的计算机程序对于本文未提到的其它一些模型也是完全适用的,在应用时仅需修改定义目标函数的自定义函数语句即可.研究也发现,在求优过程中,只要搜索系数选择适当和实际数据合理,是可以保证寻优成功的.