生物量统一模型构建及非线性偏最小二乘辩识——以毛竹为例

树种生物量数据是研究许多林业与生态问题的基础,如何准确估算树种的生物量十分重要,建立生物量模型是生物量估测的主要手段.鉴于常用估算生物量模型具有多样性、经验性、在实际应用中有很大限制等特点,提出利用Chebyshev正交多项式系构建生物量估算统一模型的新思路,这种构建模型的方法有完备的理论基础、统一的模型结构,其实质是一种进行变量区间变换,缩小变量定义域的数值插值方法,模型阶数越高,插入的点越多,估算结果越符合实际,这与树木的树干解析与树木生长原理是相一致的;鉴于常用最小二乘具有的一些缺陷,用适应范围更广的偏最小二乘对统一模型进行参数辩识;指出毛竹生物量的研究与其它树种生物量的研究不同,根据毛竹自身的生长特征,把其生物量的研究分成2个阶段;最后估算了毛竹第1阶段的生物量,结果表明拟合精度很高:当提取55个主成分时,SPRESS,h达到最小,其值为31.4390,R2=0.9987,离差平方和为0.2518,比传统方法估计误差5.623提高了一个数量级.     

长白山林区森林生物量变化定量驱动分析

根据研究区单木生物量模型及森林资源清查资料计算样地生物量,采用试验精度较高的遥感模型进行4期遥感数据的森林生物量估算,获得区域单位面积生物量变化值,并利用bootstrap方法对引起这种变化的气象因素、森林经营活动因素和社会经济因素等驱动因子进行变量筛选,利用偏最小二乘算法建立不同时间段的森林生物量变化驱动模型,计算变量投影重要性指标（VIP）定量刻画各因素对森林生物量变化的影响重要程度.结果表明：目前人为因素对长白山林区森林生物量变化的影响程度（VIP值）已经小于自然因素,说明国家对林区的森林保护政策已经起到了明显的效果.本文拓宽了森林生物量变化驱动分析的内容,引入了VIP值对森林生物量的变化驱动因子进行定量刻画,为定量分析森林生物量的变化提供了一条新的途径.     

天山雪岭云杉林生物量估测及空间格局分析

雪岭云杉林是新疆天山山脉重要的水源涵养林,精确估算雪岭云杉林生物量及准确表征空间格局特征对其生态系统的生物生产力和生态服务功能的评估具有重要作用。结合Landsat 8 OLI遥感数据和66块天山雪岭云杉林样地调查数据,选择包括各波段灰度值、不同波段灰度值之间的线性和非线性组合(包括5种植被指数)以及环境因子在内的42个自变量,分别采用多元逐步回归分析法、偏最小二乘法和主成分分析法建立天山雪岭云杉林生物量估测模型。结果表明:多元逐步回归法采用3个自变量所建模型平均拟合精度为69.07%,绝对误差为64.50 t/hm~2,平均相对误差为10.89%,样地生物量实测值与预测值相关系数为0.465;偏最小二乘回归法采用11个自变量所建模型平均拟合精度为74.36%,绝对误差为144.94 t/hm~2,平均相对误差为28.78%,相关系数为0.717;主成分分析方法提取3个主成分,所建模型平均拟合精度为71.22%,相关系数为0.730;因此偏最小二乘法要优于主成分分析法和多元逐步回归法。天山雪岭云杉林生物量随经纬度的增加而降低,整体呈现西部高,中东部低的趋势;随海拔的升高呈"单峰"型变化;样地生物量主要分布在山脊位置,随坡度的增加呈先降低后升高,然后再下降的趋势;随着阴坡-阳坡的坡向变化,样地生物量逐渐降低。     

青藏高原典型人工林幼树生物量模型构建

森林生物量计算是全球碳储量估算的基础,现已纳入全球国家森林清单项目。普遍的森林碳汇计量采用的材积源生物量法针对胸径5 cm以上的树木,幼树(胸径<6 cm,树高>0.3 m)的碳汇量并未被完整计入其中,导致生态系统碳汇能力被低估。基于青藏高原137株5种典型人工林幼树的实测生物量数据,以地径代替胸径作为预测变量,采用加权广义最小二乘法建立独立生物量模型,选择比例总量直接控制及代数和控制2种结构形式的相容性生物量模型,并通过加权非线性似乎不相关回归进行方程组估算,建立了整株及各组分的相容性生物量方程。结果表明: 二元相容性模型优于一元以及独立模型,对整株生物量来说,R²达到0.90～0.99,两种相容性模型对于不同树种来说各有优势但精度差距可以忽略,从林业生产实践角度考虑,比例总量直接控制生物量模型更有实践意义,从遥感技术的变量提取角度考虑,本研究构建了更适于遥感估算的幼树生物量模型,其整体上拟合精度高,可以准确地进行类似气候环境中的幼树整株和各组分生物量的估算。     

基于傅里叶变换近红外光谱实时分析1,3-丙二醇发酵过程生物量的在线监测方法

生物量是反映生物发酵过程进展的重要参数,对生物量进行实时监测可用于对发酵过程的调控优化。为克服目前主要采用的离线方法检测生物量时间滞后和人工测量误差较大等缺点,本研究针对1,3-丙二醇发酵过程设计了一个基于傅里叶变换近红外光谱实时分析技术的生物量在线监测实验平台,通过对实时采集光谱预处理以及敏感光谱段分析,应用偏最小二乘算法,建立了1,3-丙二醇发酵过程生物量变化的动态预测模型。以底物甘油浓度为60 g/L和40 g/L的发酵过程作为外部验证实验,分析得到模型的预测均方根误差分别为0.341 6和0.274 3,结果表明所建立的模型具有较好的实时预测能力,能够实现对1,3-丙二醇发酵过程中生物量的有效在线监测。     

大兴安岭植被生物量的ALOS PALSAR估算

利用野外实测调查数据,系统分析了ALOS PALSAR L波段HH(L-HH)极化数据与大兴安岭地区森林各成分参数的关系,并采用简单线性模型、指数模型和加入地理因子模型建立森林生物量的估算模型进行最优反演.结果表明:后向散射系数与树木地上部分总生物量相关性最大,其次是干生物量,L-HH数据可以用来反演正确的树木地上部分总生物量.3种模型中,加入地理因子模型降低了植被生物量估算的误差,精度达0.851,反演结果与实际相符.在41.5°入射角L-HH极化数据下,大兴安岭塔河林业局和阿木尔林业局的森林生物量饱和点在15.4 kg·m-2.     

基于FastICA盲源分离法去除土壤干扰的小麦生物量高光谱估算

高光谱技术是一种快速无损监测植被生物量的有效方法,但土壤背景的干扰一直是生物量监测的主要限制因素之一。本研究试图利用盲源分离(blind source separation,BSS)法分离出净植被光谱,达到消除土壤背景影响,提高小麦生物量估算精度的目的。本研究对110组小麦冠层光谱数据进行快速独立分量分析(fast independent component analysis,Fast ICA)处理,提取净植被光谱,并对比了Fast ICA处理前后所建的偏最小二乘回归(partial least squares regression,PLSR)模型估算精度。结果表明:Fast ICA算法可有效分离土壤光谱和植被光谱;且基于净植被光谱建立的小麦生物量估算模型精度得到明显提升,建模集RPDc(ratio of performance to deviation of the calibration)和交叉验证集RPDcv(ratio of performance to deviation of the cross calibration)分别由原始光谱的1.83和1.64提高至2.77和2.09;可见,Fast ICA可以作为有效的光谱数据预处理方法,显著提高小麦生物量的估算精度,为利用遥感技术进行大尺度、精准监测生物量提供了方法支持和理论依据。     

基于植被指数的北京军都山荆条灌丛生物量反演研究

基于环境卫星数据提取10种植被指数,辅以资源三号卫星数据提取的高精度数字高程模型(DEM)等数据,结合实地野外采样数据,以北京军都山为试验区采用最小二乘回归模型拟合植被指数与荆条灌丛冠层生物量的定量关系,并利用拟合结果对研究区灌从冠层生物量进行了反演估算,生成研究区荆条及其伴生灌丛生物量空间分布图。结果表明,文中所建立的多元线性回归模型在研究区具有较好的反演精度和预测能力。其模型显著性为显著(α0.01),相关系数为0.856,标准误差为58.5g/m~2;预测标准误差为98.1 g/m~2,决定系数为0.865。通过对研究区荆条灌丛的冠层生物量进行遥感估算,提出了一种利用遥感技术监测灌木群落生物量的新思路。     

森林生物量估算中模型不确定性分析

单木生物量估算是区域森林生物量估算的基础。量化单木生物量模型中各种不确定性来源,分析各不确定性来源对森林生物量估算的影响,可为提高森林生物量估算精度提供理论依据。基于52株杉木地上部分生物量实测数据,建立杉木单木地上部分生物量一元与二元模型。在两种模型形式下,根据临安市2009年森林资源连续清查数据中杉木实测数据,分析单木生物量模型中所包含的2种不确定性,即模型参数不确定性和模型残差变异引起的不确定性。最后利用误差传播定律计算单木生物量模型总不确定性。结果表明,基于一元生物量模型的临安市杉木生物量估计均值为6.94 Mg/hm~2,由一元模型残差变异引起的生物量不确定性约为11.1%,模型参数误差引起的生物量不确定性约为14.4%,一元生物量模型估算合成不确定性为18.18%。基于二元生物量模型的临安市杉木生物量估计均值为7.71 Mg/hm~2,模型残差变异引起的不确定性约为7.0%,模型参数误差引起的不确定性约为8.53%,二元生物量模型估算合成不确定性为11.03%。研究表明模型参数不确定性随建模样本的增加逐渐降低,当建模样本由30增加到40再增加到52时,一元生物量模型模型参数不确定性分别为20.26%、16.19%、14.4%,二元生物量模型分别为13.09%、9.4%、8.53%。此外,建模样本的增加对残差变异不确定性也有一定影响,当建模样本由30增加到42再增加到48时,一元模型残差变异不确定性分别为15.2%,12.3%和11.7%;二元模型残差变异不确定性分别为13.3%,9.4%和8.3%。在2种不确定性来源中模型参数不确定性对估计结果影响最大,其次为模型残差变异。由于模型残差变异、参数不确定性与建模样本有关,因此可以通过增加建模样本来减小模型参数不确定性。二元生物量模型总的不确定性要低于一元生物量模型。     

南丰蜜桔可溶性固形物近红外检测的多元线性回归变量筛选

文章采用反向区间偏最小二乘法结合连续投影算法,筛选南丰蜜桔近红外检测的多元线性回归变量。对南丰蜜桔近红外光谱进行多元散射校正后,利用反向间隔偏最小二乘法,从500～1750 nm中初选出7个光谱区间,用于多元线性回归变量筛选。利用通过遗传算法和连续投影算法筛选出的变量建立了多元线性回归模型。经比较发现,利用反向区间偏最小二乘法结合连续投影算法筛选出的变量建立的多元线性回归模型,预测结果最优,模型预测相关系数为0.937,模型预测均方根误差为0.613 oBrix。结果表明,反向区间偏最小二乘法结合连续投影算法,可以有效地筛选近红外光谱的多元线性回归变量,提高南丰蜜桔可溶性固形物模型的预测精度。     

Relationships among the Stem, Aboveground and Total Biomass across Chinese Forests

Forest biomass plays a key role in the global carbon cycle. In the present study, a general allometric model was derived to predict the relationships among the stem biomass Ms, aboveground biomass MA and total biomass MT, based on previously developed scaling relationships for leaf, stem and root standing biomass. The model predicted complex scaling exponents for MT and/or MA with respect to Ms. Because annual biomass accumulation in the stem, root and branch far exceeded the annual increase in standing leaf biomass, we can predict that MT ∝MA ∝ Ms as a simple result of the model. Although slight variations existed in different phyletic affiliations （i.e. conifers versus angiosperms）, empirical results using Model Type Ⅱ （reduced major axis） regression supported the model＇s predictions. The predictive formulas among stem, aboveground and total biomass were obtained using Model Type I （ordinary least squares） regression to estimate forest biomass. Given the low mean percentage prediction errors for aboveground （and total biomass） based on the stem biomass, the results provided a reasonable method to estimate the biomass of forests at the individual level, which was insensitive to the variation in local environmental conditions （e.g. precipitation, temperature, etc.）.     

Evaluation of spectrofluorometry as a tool for estimation in fed-batch fermentations

Native culture fluorescence was investigated as an additional source of information for predicting biomass and glucose concentrations in a fed-batch fermentation of Alcaligenes eutrophus. Partial least squares (PLS) regression and a feed forward neural network (FFNN) coupled with principle component analysis (PCA) were each used to model the kinetics of the fermentation. Data from three fermentations was combined to form a training set for model calibration and data from a fourth fermentation was used as the testing set. The fluorescent soft-sensors were compared with a previously developed feed forward neural network soft-sensor model which used oxygen uptake rate (OUR), carbon dioxide evolution rate (CER), aeration rate, feed rate, and fermentor volume to estimate biomass and glucose concentrations. The best model performance for predicting both biomass and glucose concentrations was achieved using the native fluorescence-based models. Real data predictions of the biomass concentration in the testing set were obtained using both the PLS and FFNN PCA modeling utilizing fluorescence measurements plus the rate of change of the fluorescence measurements. Accurate predictions of the glucose concentration in the testing set were obtained using the FFNN PCA modeling technique utilizing the rate of change of the fluorescence measurements. Substrate exhaustion was indicated qualitatively by a first-order PLS model utilizing the rate of change of fluorescence measurements. These results indicate that native culture fluorescence shows promise for providing additional valuable information to enhance predictive modeling which cannot be extracted from other easily acquired measurements.     

人工神经网络与相容性生物量模型预测单木地上生物量的比较

森林生物量是林业生产经营和森林资源监测的重要指标,为探索高效低偏的单木生物量估测方法,引入人工神经网络.本研究采用黑龙江省东折棱河林场的101株长白落叶松地上生物量数据,基于不同变量(胸径、树高、冠幅)组合建立了4个聚合模型体系(AMS),采用加权回归消除模型的异方差.然后,基于最优的变量组合建立人工神经网络(ANN)...     

红壤丘陵区林下灌木生物量估算模型的建立及其应用

以中国科学院千烟洲生态试验站林下常见的16种物种作为研究对象,构建了单一物种以植冠面积(Ac)为变量的二次方程和以植冠投影体积(Vc)为变量的乘幂方程来估算物种生物量,以及16种物种的混合模型来估算其生物量,并将最佳生物量估算模型应用于不同森林内灌木层生物量的估计.不同森林的灌木层生物量组成存在较大差异.以物种单一模型计算的落叶阔叶林、次生林、人工针叶林灌木层的生物量分别为4773、3175和733kg.hm-2;以物种混合模型估算的结果略低于单一模型,分别为3946、2772和840kg.hm-2.混合模型在未能对所有物种建立单一模型的情况下估算灌木层生物量时,具有简便、实用性的特点.     

Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

Vegetation biomass is a key biophysical parameter for many ecological and environmental models. The accurate estimation of biomass is essential for improving the accuracy and applicability of these models. Light Detection and Ranging (LiDAR) data have been extensively used to estimate forest biomass. Recently, there has been an increasing interest in fusing LiDAR with other data sources for directly measuring or estimating vegetation characteristics. In this study, the potential of fused LiDAR and hyperspectral data for biomass estimation was tested in the middle Heihe River Basin, northwest China. A series of LiDAR and hyperspectral metrics were calculated to obtain the optimal biomass estimation model. To assess the prediction ability of the fused data, single and fused LiDAR and hyperspectral metrics were regressed against field-observed belowground biomass (BGB), aboveground biomass (AGB) and total forest biomass (TB). The partial least squares (PLS) regression method was used to reduce the multicollinearity problem associated with the input metrics. It was found that the estimation accuracy of forest biomass was affected by LiDAR plot size, and the optimal plot size in this study had a radius of 22 m. The results showed that LiDAR data alone could estimate biomass with a relative high accuracy, and hyperspectral data had lower prediction ability for forest biomass estimation than LiDAR data. The best estimation model was using a fusion of LiDAR and hyperspectral metrics (R² = 0.785, 0.893 and 0.882 for BGB, AGB and TB, respectively, with p < 0.0001). Compared with LiDAR metrics alone, the fused LiDAR and hyperspectral data improved R² by 5.8%, 2.2% and 2.6%, decreased AIC value by 1.9%, 1.1% and 1.2%, and reduced RMSE by 8.6%, 7.9% and 8.3% for BGB, AGB and TB, respectively. These results demonstrated that biomass accuracies could be improved by the use of fused LiDAR and hyperspectral data, although the improvement was slight when compared with LiDAR data alone. This slight improvement could be attributed to the complementary information contained in LiDAR and hyperspectral data. In conclusion, fusion of LiDAR and other remotely sensed data has great potential for improving biomass estimation accuracy.     

基于不同预测变量的天然椴树可加性地上生物量模型构建

森林生物量是森林生态系统的最基本数量特征,生物量数据是研究许多林业问题和生态问题的基础,因此,准确测定生物量对于计算碳储量以及研究气候变化、森林健康、森林生产力、养分循环等十分重要.目前,测算森林生物量常用的方法为生物量模型估算法.本研究基于小兴安岭地区和张广才岭地区97株实测生物量数据,建立了3个天然椴树立木可加性生物量模型系统(基于胸径的一元可加性生物量模型系统、基于胸径和树高的二元可加性生物量模型系统、基于最优变量的最优可加性生物量模型系统),采用非线性似乎不相关回归法进行参数估计,用加权方法解决模型的异方差问题,并采用“刀切法”进行模型检验.结果表明: 3种可加性生物量模型系统均能较好地对椴树各部分生物量进行拟合和预测(调整后确定系数R_a²>0.84,平均预测误差百分比MPE<8.5%,平均绝对误差MAE<16.3 kg,平均百分标准误差MPSE<28.5%),其中,树干和地上生物量的拟合效果优于树叶、树枝和树冠;在引入树高和树冠因子后,提高了模型的拟合效果和预测能力(R_a²提高0.01～0.04,MAE降低0.01～4.55 kg),缩小了预测值置信区间的范围,树干、树叶和地上生物量提高较多,树枝和树冠提高较少.总体来看,最优生物量模型系统效果最好,其次为二元生物量模型系统,再次是一元生物量模型系统,添加树高和树冠因子进行生物量模型的构建十分必要.     

Biomass estimation models and allocation patterns of 14 shrub species in Mountain Luya,Shanxi, China

Aims Shrub species have evolved specific strategies to regulate biomass allocation among various organs or between above- and belowground biomass and shrub biomass model is an important approach to estimate biomass allocation among different shrub species. This study was designed to establish the optimal estimation models for each organ (leaf, stem, and root), aboveground and total biomass of 14 common shrub species in Mountain Luya, Shanxi Province, China. Furthermore, we explored biomass allocation characteristics of these shrub species by using the index of leaf biomass fraction (leaf to total biomass), stem biomass fraction (stem to total biomass), root biomass fraction (root to total biomass), and root to shoot mass ratio (R/S) (belowground to aboveground biomass).
Methods We used plant height, basal diameter, canopy diameter and their combination as variables to establish the optimal biomass estimation models for each shrub species. In addition, we used the ratios of leaf, stem, root to total biomass, and belowground to aboveground biomass to explore the difference of biomass allocation patterns of 14 shrub species.
Important findings Most of biomass estimation models could be well expressed by the exponential and linear functions. Biomass for shorter shrub species with more stems could be better estimated by canopy area; biomass for taller shrub species with less stems could be better estimated by the sum of the square of total base diameter multiply stem height; and biomass for the rest shrub species could be better estimated by canopy volume. The averaged value for these shrub species was 0.61, 0.17, 0.48, and 0.35 for R/S, leaf biomass fraction, stem biomass fraction, and root biomass fraction, respectively. Except for leaf biomass fraction, R/S, stem biomass fraction, and root biomass fraction for shrubs with thorn was significantly greater than that for shrubs without thorn.     

基于环境卫星数据的黄河湿地植被生物量反演研究

回归模型拟合植被指数与生物量的定量关系是植被生物量反演的重要研究方法之一.研究在此基础上,基于环境卫星遥感数据和同步野外实地采样数据,以郑州黄河湿地自然保护区为试验区,比较MLRM(多元线性回归模型)与SCRM(一元曲线回归模型)反演植被生物量的能力,并估算研究区植被生物量,生成研究区生物量分布图.结果表明,文中所建立的MLRM在研究区具有较好的反演精度和预测能力.其模型显著性检验为极显著,相关系数为0.9791,模型拟合精度达到29.8 g/m2,其模型预测结果系统误差为49.9g/m2,均方根误差为67.2 g/m2,预测决定系数为0.8742,比传统的一元回归模型具有更高的精度和可靠性.估算研究区域2010年8月湿生植被生物量约为6.849199 t/hm2,相对误差为4.73％.     

Woody biomass estimation of Tarchonanthus camphoratus in three woodland stands at Naivasha, Kenya

Individual tree biomass equations were developed for a Tarchonanthus woodland and tested for their applicability in three woodland stands of Tarchonathus at Naivasha Kenya based on the felled tree measurements. Three homogenous woodland stands identified via the use of cluster analysis formed the basic sampling units. Forty-five Tarchonanthus camphoratus trees of varying diameter classes from 2 cm to 28 cm were selected and felled from the three stand types. Alternative relationships were analysed and the four best models are presented ( Tables 1 and 2 ). To test the accuracy of the developed models, a further fifteen trees per stand were felled and their dry weights calculated and compared with their estimated dry weights. For each model, validation was performed per stand with the aim of determining whether a particular model is applicable to a specific stand or to all stand types. The best model based on the adjusted R ², standard error of estimate and distribution of residuals is presented and compared with previously existing equations. The model uses the square root transformed form of biomass. It was concluded that the model presented here could be used to estimate tree biomass in all stands of the woodland dominated by T. camphoratus , which in many places produces nearly pure stands, excluding other woody species . The fact that the accuracy of estimation tended to vary slightly from stand to stand suggests that the model may only apply to this woodland and to any other whose structure does not differ significantly from it.  

  Table 1  Equations for estimation of standing biomass of Tarchonanthus camphoratus trees     

20.

基于机载激光雷达的小兴安岭温带森林组分生物量反演   总被引：6，自引：0，他引：6       下载免费PDF全文

庞勇  李增元 《植物生态学报》2012,36(10):1095-1105

使用小兴安岭温带森林机载遥感-地面观测同步试验获取的机载激光雷达(light detection and ranging, Lidar)点云数据和地面实测样地数据, 估测了典型森林类型的树叶、树枝、树干、地上、树根和总生物量等组分的生物量。从激光雷达数据中提取了两组变量(树冠高度变量组和植被密度变量组)作为自变量, 并采用逐步回归方法进行自变量选择。结果表明: 激光雷达数据得到的变量与森林各组分生物量有很强的相关性; 对于针叶林、阔叶林和针阔叶混交林三种不同森林类型生物量的估测结果是: 针叶林优于阔叶林, 阔叶林优于针阔叶混交林; 不区分森林类型的各组分生物量估测与地面实测值显著相关, 模型决定系数在0.6以上; 区分森林类型进行建模可以进一步提高生物量的估测精度。     

基于机载激光雷达的小兴安岭温带森林组分生物量反演

使用小兴安岭温带森林机载遥感-地面观测同步试验获取的机载激光雷达(light detection and ranging, Lidar)点云数据和地面实测样地数据, 估测了典型森林类型的树叶、树枝、树干、地上、树根和总生物量等组分的生物量。从激光雷达数据中提取了两组变量(树冠高度变量组和植被密度变量组)作为自变量, 并采用逐步回归方法进行自变量选择。结果表明: 激光雷达数据得到的变量与森林各组分生物量有很强的相关性; 对于针叶林、阔叶林和针阔叶混交林三种不同森林类型生物量的估测结果是: 针叶林优于阔叶林, 阔叶林优于针阔叶混交林; 不区分森林类型的各组分生物量估测与地面实测值显著相关, 模型决定系数在0.6以上; 区分森林类型进行建模可以进一步提高生物量的估测精度。