森林生物量研究的若干问题及完善途径

介绍了森林生物量研究的背景。侧重论述了森林样地生物量算法及其精度分析、森林生物量估计模型的精度分析、森林生物量监测的必要精度与不确定性、森林蓄积量与生物量的转换模型、大区域多树种生物量转换模型、利用广义3S技术建立森林生物量模型等理论、技术、方法问题。强调了森林生物量模型的稳健估计算法、模型的方差一协方差模型及其不确定度。     

林木生物量研究进展

森林是陆地生态系统的重要组成部分，林木生物量是衡量森林生产力的重要指标，生物量的分配格局不仅影响林木的生命活动，也能反映出林木对环境作出的适应性策略。该文介绍了林木生物量的分配规律，进而对目前林木生物量的测定方法进行了总结，旨在为后续林木生物量相关研究提供参考。     

基于GLAS数据的森林生物量估算研究进展

森林生物量是森林生态系统监测的一个重要指标。GLAS波形信息与森林冠层高度、生物量有较强的相关性,在森林冠层高度、生物量等参数估算中具有广阔的应用前景。本文简要介绍了GLAS激光雷达系统及其特点,重点总结归纳了应用GLAS进行森林冠层高度、生物量估算原理及方法,并对森林冠层高度、生物量估算模型作了介绍。     

八种亚热带森林类型乔木层地上生物量分配模型

以我国亚热带的8种森林类型的乔木层为研究对象,采用幂函数模型和多项 logit模型分别构建各森林类型的材积-生物量转换模型和林木器官生物量比例模型。结果表明：材积-生物量转换模型的拟合结果表现为除常绿阔叶林外,其他森林类型的材积与生物量均显著相关(R2＞0.5,P ＜0.001),预测残差随自变量的增大而升高,均方根误差为6.520～23.123 t·hm -2,抽样精度检验结果显示,材积-生物量转换模型的预测精度为31.14%～91.79%,除常绿阔叶林以外,其他森林类型的模型预测精度均达到70%以上;林木器官生物量比例模型的拟合结果表现为除常绿阔叶林外实测值与预测值的相关性均显著(P ＜0.05),预测残差随自变量的增大而减小;干、皮、枝和叶生物量比例模型的均方根误差均不超过0.1,分别为0.031～0.085,0.005～0.041,0.029～0.103和0.016～0.083,各林木器官比例的预测精度分别达到91.04%～96.14%,62.71%～94.48%,63.70%～94.47%和-8.86%～83.92%;各森林类型(除常绿阔叶林外)的乔木层地上材积-生物量模型和林木器官生物量比例模型的可行性较高,所得模型参数也可为亚热带森林生物量分配研究提供参考。     

生物量估测中的遥感技术

论述了森林生物量的概念和发展历史,介绍了常规森林生物量的估算方法,并进一步较为详尽地分析了广泛应用于生物量估算的遥感模型以及它们的影响因素。最后对应用遥感技术估测森林生物量的发展趋势作了论述。     

基于遥感信息的森林生物量、碳储量估测技术研究

利用遥感数据结合地面调查建立模型是定量评价森林生物量、碳储量的重要手段、以遥感数据及GIS易于获取的地学因子为主要信息源,应用逐步回归方法选取与森林生物量相关的7个遥感及地学因子为自变量,利用地面样地每木胸径调查数据结合生物量相对生长式获取样地生物量作为因变量,建立多元回归模型用于估测森林生物量．森林碳储量通过森林生物量结合碳转换系数获取、经方差分析检验,模型达到极显著相关水平．研究结果袁明,这一方法是获取较大尺度森林生物量、碳储量及其动态变化监测的有效方法．     

利用混合模型方法建立全国和区域相容性立木生物量方程

开展全国森林生物量监测和评估,建立适合较大区域范围的通用性立木生物量模型将成为必然趋势,而如何保证不同尺度范围森林生物量估计值的相容性,是必须面临的一个问题。以南方马尾松(Pinus massoniana)地上生物量数据为例,通过利用混合模型方法,同时建立全国和区域性立木生物量方程,为解决不同区域尺度范围内森林生物量估计的相容性问题提供有效途径。从模型反映的结果看,相同直径林木的地上生物量估计值随起源、地域的不同存在一定程度的差异,其差异大小可以通过混合模型中的随机效应来估计。该方法可推广应用于其它通用性模型(如材积方程)的建立。     

森林生长收获模型发展中存在的问题及相关建议

森林生长收获模型能比较准确地预估森林的生长速度并反映林木各个器官(枝、叶、根、干等)之间生物量的关系, 是掌握林分结构及生长动态变化规律的有效途径, 在林分生长量和收获量预估、生物量与碳储量计算、森林生态系统内部结构分析、森林多功能监测与评价等方面均具有广泛应用。文中简述森林生长收获模型的发展现状, 分析其发展中存在的主要问题, 并针对这些问题提出了发展森林生长收获模型的有关建议, 以期为相关研究提供借鉴与参考。     

国外沿海防护林生态及其效益研究进展

本文总结了最近１０年来国外沿海防护林生态和生态效益研究成果,主要包括森林生理生态特征、生物量及枯枝落叶研究;林木对环境的适应性,环境对森林生长的影响,自然灾害和环境污染对森林的危害;森林在涵养水源、保持水土、调节气候、改善土壤肥力方面的作用及其对其它各业的影响。此外,还讨论了森林经营对土壤和水质的影响。     

森林病虫害经济损失评估的一种新方法——曲线法

森林病虫害通过危害林木的根、干、枝、叶等组织，使林分蓄积量或生物量遭受损失，从而影响森林生态系统服务功能作用的发挥。本文采用林木生长曲线法估算不同林龄林分蓄积量，通过森林生物量转换因子连续函数法（BEF）实现林分蓄积量与生物量的转换。编制了我国主要森林病虫害危害程度分级表、林分蓄积量年生长损失率表和主要林木生物量转换因子基本常数表，并以马尾松为例拟合出林分蓄积量单木生长曲线。确定了我国森林单位重量生物量生态效益值（K）为1476．2元／t。应用林木生长曲线法和生物量转换因子连续函数法评估森林病虫害经济损失适用性强，准确度高，更能反映我国森林病虫害危害程度的实际状况。     

Species-specific biomass allometric models and expansion factors for indigenous and planted forests of the Mozambique highlands

Secondary Miombo woodlands and forest plantations occupy increasing areas in Mozambique, the former due to anthropogenic activities. Plantations, mainly species of Eucalyptus and Pinus, are being established on sites previously covered by secondary Miombo woodlands. This affects the evolution, cycle and spatiotemporal patterns of carbon(C) storage and stocks in forest ecosystems. The estimation of C storage, which is indispensable for formulating climate change policies on sequestrating CO_2, requires tools such as biomass models and biomass conversion and expansion factors(BCEF). In Mozambique, these tools are needed for both indigenous forests and plantations. The objective of this study is to fit species-specific allometric biomass models and BCEF for exotic and indigenous tree species. To incorporate efficient inter-species variability, biomass equations were fitted using nonlinear mixed-effects models. All tree component biomass models had good predictability; however, better predictive accuracy and ability was observed for the 2-predictors biomass model with tree height as a second predictor. The majority of the variability in BCEF was explained by the variation in tree species. Miombo species had larger crown biomass per unit of stem diameter and stored larger amounts of biomass per stem volume. However, due to relatively rapid growth, larger stem diameters, heights, and stand density, the plantations stored more biomass per tree and per unit area.     

基于国家森林资源清查数据的不同生物量和碳储量估计方法的对比分析

[目的]通过对不同生物量和碳储量的估计方法进行对比分析,为确定在国家森林资源清查中生物量和碳储量的具体估计方法提供依据。[方法]以广东省2012年森林资源清查的100个杉木林和80个马尾松林的实测样地资料为基础,利用近年来我国建立的主要树种立木生物量模型,对改进IPCC法、生物量模型法和转换因子连续函数法(即方精云法)3种方法按一元和二元模型共6种方案进行了对比;同时,基于改进IPCC法一元和二元模型的生物量估计值,用平均含碳系数法、组分含碳系数法和固定含碳系数(0.5或0.47)法分别对碳储量进行估计。[结果]用二元生物量模型法得到的杉木林和马尾松林样地的总生物量分别为320 Mg和331 Mg,一元生物量模型法的结果分别相差0.9%和6.2%;改进IPCC法的估计结果,采用二元和一元模型时杉木林分别相差-3.6%和-11.9%,马尾松林分别相差-8.5%和-19.6%;而方精云法的估计结果,采用二元和一元模型时杉木林分别相差6.65倍和6.60倍,马尾松林分别相差-14.3%和-18.0%。平均含碳系数法和组分含碳系数法的碳储量估计结果,杉木林仅相差0.2%,马尾松林相差约0.4%;固定含碳系数法的估计结果因树种而异,对杉木林要低估0.6%5.4%,对马尾松林要低估3.3%9.1%。[结论]对生物量的估计,采用生物量模型法准确性最高,而林木水平的生物量模型其预估精度要高于林分水平的模型;IPCC法是基于材积源的通用方法,将其中的缺省参数改进为可变参数模型,可大大提高方法的适应性;方精云法只是基于IPCC法所建立的林分水平模型在大尺度上的一种具体应用方法,其精度要低于林木水平的生物量模型法,不适于中小尺度应用。对碳储量的估计,采用平均含碳系数法与组分含碳系数法差异很小,但采用固定含碳系数法则误差较大。     

基于树高和树冠因子的立木材积与地上生物量相容模型研究

【目的】无人机机载激光雷达能够准确地测定单木、林分乃至大尺度森林结构参数(树高和树冠因子)。为应用无人机激光雷达技术准确估测森林蓄积量、生物量和碳储量提供计量依据和技术支撑。【方法】以150株实测马尾松生物量样本数据为研究对象,采用非线性回归估计方法和度量误差联立方程组方法,分析立木材积和地上生物量与树高、树冠因子的相关性,并在此基础上研究建立基于树高和树冠因子的立木材积与地上生物量相容模型。【结果】单株材积和地上生物量与树高因子的相关性最为紧密,其次才是树冠因子;基于树高和冠幅因子的二元材积和地上生物量模型预估精度较高,达到92%以上,再考虑冠长因子的三元模型预估精度改进不大;基于树高和冠幅因子的二元立木材积与地上生物量相容模型估计效果更好,相对于一元相容模型系统而言,二元相容模型拟合效果有较大幅度提高,预估精度达到92%以上。【结论】采用度量误差联立方程组方法可以有效解决基于树高和树冠因子的立木材积与地上生物量相容问题,并且预估精度达到92%以上,所建二元立木材积与地上生物量相容模型可为应用激光雷达技术反演森林蓄积量和生物量提供计量依据。     

Spatial variation and prediction of forest biomass in a heterogeneous landscape

Large areas assessments of forest biomass distribution are a challenge in heterogeneous landscapes, where variations in tree growth and species composition occur over short distances. In this study, we use statistical and geospatial modeling on densely sampled forest biomass data to analyze the relative importance of ecological and physiographic variables as determinants of spatial variation of forest biomass in the environmentally heterogeneous region of the Big Sur, California. We estimated biomass in 280 forest plots (one plot per 2.85 km2) and measured an array of ecological (vegetation community type, distance to edge, amount of surrounding non-forest vegetation, soil properties, fire history) and physiographic drivers (elevation, potential soil moisture and solar radiation, proximity to the coast) of tree growth at each plot location. Our geostatistical analyses revealed that biomass distribution is spatially structured and autocorrelated up to 3.1 km. Regression tree (RT) models showed that both physiographic and ecological factors influenced biomass distribution. Across randomly selected sample densities (sample size 112 to 280), ecological effects of vegetation community type and distance to forest edge, and physiographic effects of elevation, potential soil moisture and solar radiation were the most consistent predictors of biomass. Topographic moisture index and potential solar radiation had a positive effect on biomass, indicating the importance of topographically-mediated energy and moisture on plant growth and biomass accumulation. RT model explained 35% of the variation in biomass and spatially autocorrelated variation were retained in regession residuals. Regression kriging model, developed from RT combined with kriging of regression residuals, was used to map biomass across the Big Sur. This study demonstrates how statistical and geospatial modeling can be used to discriminate the relative importance of physiographic and ecologic effects on forest biomass and develop spatial models to predict and map biomass distribution across a heterogeneous landscape.     

浙江天童国家森林公园常绿阔叶林主要组成树种材积与生物量相关关系探讨

以天童国家森林公园常绿阔叶林主要组成树种为研究对象,通过测定其各组分生物量,建立了实测生物量与材积之间的回归模型,结果表明:主要组成树种各组分生物量与其材积之间存在着密切的相关关系,各主要树种材积与生物量的回归模型存在差异;回归方程精度较高,用于根据地区常绿阔叶林主要树种材积推算其生物量是可行的。     

Allometric equations from a non-destructive approach for biomass prediction in natural forest and plantation in West Africa

Allometric equations are required for a rapid estimation of commercial timber volume and forest biomass stocks. In order to preserve the forest ecosystem, this study applied a non-destructive sampling approach to measure biophysical properties of living trees. From these measurements, volume and biomass models were developed for 11 dominant tree species in a semi-deciduous natural forest and for Acacia auriculiformis in a plantation located in southern Benin. The observations were combined to develop also generic models applicable to non-dominant tree species. Wood samples of the tree species were collected with an increment borer and analysed in the laboratory to determine species-specific wood densities. The sample size was composed of 243 trees in natural forest and 21 trees in plantation. The measurements were conducted in 30 plots of 50 m × 50 m. The graphical assessment of correlation between model outputs (biomass and volume) and variables (diameter and height) and the statistical analysis confirmed that the logarithmic model with two variables had the best predictions. The assessment also confirmed that the model using diameter only as a variable had good predictions when observations on height were unavailable. The comparative analysis of model predictions showed that the generic model in this study over-estimated biomass by up to 74.80% for certain species and under-estimated biomass by 21.18% for other species. The study shows that there are no statistically significant differences between the wood densities in this research and that published in previous studies.     

贺兰山灰榆疏林单株生物量回归模型的研究

对贺兰山东麓天然灰榆疏林林分进行了调查研究。实测灰榆单株的地上和地下生物量,应用相关分析方法,探讨灰榆单株各器官生物量与树高(H)、胸径(D)、1/2树高处直径(D1/2)和胸径平方乘树高(D2H)的相关关系,结果表明:1)贺兰山东麓天然灰榆疏林单株各器官生物量分配比率为树干>树根>树枝>树皮>树叶。2)各器官生物量拟合的预测模型中,树干、树枝和树叶的生物量预测模型拟合效果较好,而且具有一定的实用价值;树枝和树皮的生物量预测模型拟合效果一般;任一自变量与单株生物量拟合的预测方程适用性均较好。     

Allometric equations for accurate estimation of above-ground biomass in logged-over tropical rainforests in Sarawak, Malaysia

Although allometric equations can be used to accurately estimate biomass and/or carbon stock in forest ecosystems, few have been developed for logged-over tropical rainforests in Southeast Asia. We developed allometric relationships between tree size variables (stem diameter at breast height (dbh) and tree height) and leaf, branch, stem and total above-ground biomass in two logged-over tropical rainforests with different soil conditions in Sarawak, Malaysia. The study sites were originally classified as mainly lowland dipterocarp forest and have been selectively logged in the past 20 years. In total, 30 individuals from 27 species were harvested to measure above-ground parts. The correlation coefficients for the allometric relationships obtained for total above-ground biomass as a function of dbh had high values (0.99), although the relationships for leaf biomass had a relatively low coefficient (0.83). We also found relatively high coefficients for allometric relationships between tree height and plant-part biomass, ranging from 0.82 to 0.97. Moreover, there were no differences for allometric equations of total above-ground biomass between study sites. A comparison of equations of above-ground biomass in various previously reported tropical rainforests and pan-tropic general equations imply that our allometric equations differ largely from the equations for tropical primary forests, early successional secondary forest, and even for the general models. Therefore, choosing the biomass estimation models for above-ground biomass in the logged-over forests of Southeast Asia requires careful consideration of their suitability.     

Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data

Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.