Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

A conceptual model describing why laser height metrics derived from airborne discrete return laser scanner data are highly correlated with above ground biomass is proposed. Following from this conceptual model, the concept of canopy-based quantile estimators of above ground forest biomass is introduced and applied to an uneven-aged, mature to overmature, tolerant hardwood forest. Results from using the 0th, 25th, 50th, 75th and 100th percentiles of the distributions of laser canopy heights to estimate above ground biomass are reported. A comparison of the five models for each dependent variable group did not reveal any overt differences between models with respect to their predictive capabilities. The coefficient of determination (r ² ) for each model is greater than 0.80 and any two models may differ at most by up to 9%. Differences in root-mean-square error (RMSE) between models for above ground total, stem wood, stem bark, live branch and foliage biomass were 8.1, 5.1, 2.9, 2.1 and 1.1 Mg ha^?1, respectively.     

Functions for estimating belowground and whole tree biomass of birch in Norway

Obtaining accurate estimates of national belowground and whole tree biomass is important to better understand the global carbon cycle and to quantify biomass stocks and changes. However, the availability of individual tree belowground biomass functions is generally low due to the difficulty of extracting roots. Allometric birch (Betula pubescens Ehrh. and Betula pendula Roth) biomass functions were derived from 67 trees for belowground and whole tree biomass using diameter at breast height (dbh) and height as the independent variables. The sampled trees spanned a dbh range from 4.0 to 45.5?cm and the functions provided a good fit to the data (RMSE?=?14.2?kg for BG and 40.7?kg for whole tree with dbh as predictor). Belowground, total stem, live crown, and dead branch biomass comprised 29.2%, 52.2%, 18.1%, and 0.5% of the whole tree biomass, respectively. Observed root-to-shoot ratios were between 0.21 and 0.88 with a mean of 0.42. Comparisons with existing belowground birch biomass functions from Fennoscandia indicated considerable differences in estimates between existing functions. The derived data-set for belowground birch biomass is the largest in Fennoscandia and the developed functions are likely the best available for estimating national birch biomass stock and stock change in Norway.     

应用中分辨率遥感数据和地理信息系统改进森林材积和生物量评估策略(英文)

应用遥感技术、地理信息系统和野外观测数据,评估了热带森林环境下地上生物量和木材蓄积量。用于模拟森林属性的这些数据具有地理特异性和高度的不确定性,因此,这方面需要开展更多的研究工作。选取了16个试样地带1460个样地,测定树木胸径及其他用于评估生物量的其他森林属性。本实验在印尼加里曼丹东部的热带雨林开展。应用现有的胸径-生物量公式来评估地上生物量密度。估测值在研究区修正的GIS地图上重叠显示,计算各种地被物的生物量密度。用样品数据子集表达遥感方法来形成地上生物量和材积线性方程模型。皮尔森相关统计检验采用ETM条带反射率、植被指数、图像变化图层、主成分分析条带、缨帽变换、灰度共生矩阵纹理特征和DEM数据作为预报值。在显著的遥感数据中形成了两个线性模型。为了分析每块地被物总的生物量和材积量,对2000年到2003年卫星ETM图进行了预处理、最大似然估计法分类和主体分析过滤。遥感方法获得的结果表明:材积量为(158±16)m3·hm-2,地上生物量为(168±15)t·hm-2;而野外测定和地理信息系统估计的结果分别是材积量为(157±92)m3·hm-2、地上生物量为(167±94)t·hm-2。用多个瞬间ETM数据评估了从2000年到2003年间的生物量丰富度动态,结果发现这一时期总生物量呈略微的下降趋势。遥感技术评估的生物量丰富度低于地理信息系统和野外测定的结果。前一种测定方法估计2000年和2003年总生物量分别是10.47Gt和10.3Gt,而后一种则估计11.9Gt和11.6Gt。还发现,灰度共生矩阵纹理特征与材积量和生物量之间存在较强的相关性。图7表9参43。     

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。     

Aboveground biomass quantification and tree-level prediction models for the Brazilian subtropical Atlantic Forest

Forests are the largest biological reservoir of biomass and carbon on the planet. This fact turns them into the main tool to neutralise the CO₂ emitted by human activities. Despite such importance, the uncertainties associated with biomass estimates in forests, especially in (sub)tropical forests, are enormous. Facing this scenario, the objectives of this study were (1) to quantify through destructive sampling the aboveground biomass (AGB) of 105 trees of 47 species occurring in a secondary subtropical evergreen rainforest in Brazil; (2) to investigate the AGB distribution in different tree compartments; and (3) to fit tree-level models to improve biomass estimates for the referred forest type. The results revealed that most of the AGB was stored in the compartments stem and large branches (diameter ≥ 5 cm). There was an increase in the proportion of biomass – in relation to the total tree AGB (kg) – allocated in the large branches as tree diameter at breast height (DBH) increased; this pattern was not observed for the compartments stem, thin branches (diameter < 5 cm), and leaves. The compartments thin branches and leaves represented between 5.4% and 17.0%, and 1.3% and 2.9% of the total tree AGB, respectively. From the 10 fitted biomass models, the linearised power models yielded the smallest errors. The best performance model, which returned a mean bias of 1.7%, may be written as AGB = exp(?8.9807 + 2.1642·ln[DBH] + 0.5072·ln[h] + 0.9999·ln[ρ_bas]); Baskerville’s factor = 1.0175. If there are no (reliable) data on tree total height (h; m), the following model, which embedded the DBH and wood basic specific gravity (ρ_bas; kg m^?3), may be employed: AGB = exp(?9.0086 + 2.4606·ln[DBH] + 1.0895·ln[ρ_bas]); Baskerville’s factor = 1.0206.     

Functions for estimating aboveground biomass of birch in Norway

A suite of regional allometric aboveground biomass functions were derived for Betula pubescens and Betula pendula for Norwegian conditions. The data consisted of 67 trees sampled throughout Norway. A total of 14 component functions were developed for total aboveground, total stem, stemwood, stem bark, live crown, live branch, leaf, and dead branch biomass using combinations of diameter at breast height and height as predictor variables. Application of the derived functions to existing local southern Norwegian mountain birch and regional Swedish biomass datasets indicated an overall good predictive ability of the developed functions. However, the functions produced slight underestimates, suggesting that the respective birch populations had differing biomass allocation patterns. When the developed functions were applied to Norwegian National Forest Inventory data, they produced slightly higher biomass stock and stock change estimates than what is obtained using existing Swedish functions. The higher estimates were evident in the north, central, and western part of Norway, while estimates were similar in southeastern Norway where growing conditions are most similar to Swedish conditions. The analysis indicates that the derived functions are the best available for regional birch biomass stock and stock change estimation in Norway.     

Allometric relationship for estimating above-ground biomass of Aegialitis rotundifolia Roxb. of Sundarbans mangrove forest, in Bangladesh

Tree biomass plays a key role in sustainable management by providing different aspects of ecosystem. Estimation of above ground biomass by non-destructive means requires the development of allometric equations. Most researchers used DBH (diameter at breast height) and TH (total height) to develop allometric equation for a tree. Very few species-specific allometric equations are currently available for shrubs to estimate of biomass from measured plant attributes. Therefore, we used some of readily measurable variables to develop allometric equations such as girth at collar-height (GCH) and height of girth measuring point (GMH) with total height (TH) for A. rotundifolia, a mangrove species of Sundarbans of Bangladesh, as it is too dwarf to take DBH and too irregular in base to take Girth at a fixed height. Linear, non-linear and logarithmic regression techniques were tried to determine the best regression model to estimate the above-ground biomass of stem, branch and leaf. A total of 186 regression equations were generated from the combination of independent variables. Best fit regression equations were determined by examining co-efficient of determination (R2), co-efficient of variation (CV), mean-square of the error (MSerror), residual mean error (Rsme), and F-value. Multiple linear regression models showed more efficient over other types of regression equation. The performance of regression equations was increased by inclusion of GMH as an independent variable along with total height and GCH.     

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.     

A non-destructive method for estimating above-ground forest biomass in threatened woodlands

Current techniques for calculation of biomass in agroforestry require felling of many trees. Such methods are not well suited to the natural environment, especially if the environment is subject to anthropic degradation and if the wood supply to local populations is at stake. The method we describe here was used in a socioeconomic and ecological study of the biomass of a thuriferous juniper woodland (Juniperus thurifera L.) in the High Central Atlas mountains (Morocco). This computerized method reconstructs the different component volumes of a tree from two orthogonal-view photographs. Then, using the volume and the density of each component, it estimates the biomass of the tree. Regression curves were established between dendrometric parameters for 102 trees (tree height × crown projection area) and their estimated biomass by this computer method. A second-order polynomial equation gave the best regressions with a high coefficient of determination (R² = 0.96). To validate the method, the biomass of seven trees (cut in a previous study) was compared with those obtained from our regression equations, while in a second study, the biomass of three trees, estimated from photographs using the computer program, was compared with values obtained after felling and weighing the same trees.

The results show that the method is reliable with a mean error percentage varying between 2.5 and 7.5 per tree. For young trees, the accuracy of the biomass could be improved, for example, by using a tree morphology classification.     

黄土丘陵区沙棘地上部生物量估测模型

根据沙棘生长因子与其地上部生物量间的相关关系,以生长因子为自变量,沙棘地上部生物量为因变量,应用主成分分析和多元回归分析方法,依据最优子集和平均残差平方和的优选原则,从单生长因子、双生长因子、多生长因子估测模型中,筛选出具有最优性质的沙棘地上部生物量估测模型,复相关系数为０．９４２１～０．９９５９,经检验均达十分显著水平。这一研究结果改进了非破坏性调查沙棘地上部生物量的方法。     

Improving the estimate of forest biomass carbon storage by combining two forest inventory systems

Quantifying forest carbon storage and its spatial distribution at regional scales is critical for the creation of greenhouse gases inventories, the evaluation of forest services and carbon-oriented forest management. The plot-based forest inventory (PBFI) and stand-based forest inventory (SBFI) collect extensive information on trees and stands respectively, and together, provide an opportunity to improve the regional estimates of forest carbon. In this study, we applied the SBFI to overcome the spatial extent limits of the PBFI in neighboring plots and improve the regional carbon estimation. We found that the forests in Sichuan Province reserved a total of 624.2?Tg?C in biomass and featured a large spatial heterogeneity, with high values in natural forests and low values in plantations. We found that the solo use of PBFI derived a slightly higher (46.63?Mg?C/ha) estimation on average compared with the integrated method (43.6?Mg?C/ha). However, when considering the spatial distribution, the PBFI generated an overestimation of young forests located between 3000and 4000?m in elevation, and an underestimation in mature forests. The spatially explicit biomass carbon estimation could be helpful in guiding regional forest management and biodiversity conservation.     

北京地区栓皮栎地上部分生物量的研究

以北京地区栓皮栎人工林为研究对象,应用典型样地调查法和相对生长法对乔木生物量展开研究,结果显示：各器官中生物量大小顺序为树干〉枝条〉叶片,其中树干约占65%,树冠约占35%。通过回归分析探讨了各变量的相关性,并对其各器官的生物量与胸径和树高进行模型拟合。其中树干和树冠的生物量与胸径、树高的拟合方程分别是：W=0.018（D2H）1.09、W=0.9（D）2.128。     

基于C5.0与Apriori算法的森林生物量等级评价与因子关联分析

针对生物量影响因子量化研究较少、方法单一及区域生物量评价不足且基于单个树种生物量模型进行评价时工作量过大的问题,以孟家岗林场的三类小班清查数据为基础,选取与生物量水平相关的11个因子,利用C5.0算法进行生物量决策树建模,并进一步利用Apriror算法进行生物量强影响因子的关联规则挖掘。结果表明:生物量决策树模型的分类预测精度为88.78%,生物量影响因子的量化结果分别为树高(0.348)、胸径(0.225)、林分类型(0.196)、龄级(0.162)、郁闭度(0.134)、坡度(0.096)、海拔(0.074)、坡向(0.065)、立地类型(0.052)和坡位(0.037);得到707条置信度在80%以上、支持度在10%以上的因子关联规则,揭示了生物量影响因子间的隐含关联关系。建立的生物量决策树模型能为快速的区域生物量预测和评价提供模型参考,建立的关联规则评估模型能够为以碳汇为目标的森林生产与经营提供客观评价指标。     

Testing the durability of timber above ground: A review on methodology

The majority of timber products in outdoor use are exposed above ground, e.g. façades, terrace decking, playground equipment, garden furniture, windows, balconies or carports. In contrast, the durability of wood and wood products is most often determined in laboratory against Basidiomycete monocultures or in-ground field tests, where wood samples are submitted to permanent wetting. Worldwide, only a few above ground field test methods evaluating durability against fungal decay have been standardized. Wood used in above ground situations can be exposed to a wide range of moisture loads reflecting different design details such as varying shelter, distance to ground, ventilation and water trapping, whereas temperature and rainfall variations are overall influences on service life performance. The aim of this review was to gather information about standardized and non-standardized above ground field test methods used to determine the durability of wood and wood-based products. In total, more than 60 methods have been evaluated according to different criteria, such as principle set-up and design, severity of exposure and distance to ground. Their suitability to reflect a certain exposure under real-life conditions is discussed as well as practical aspects regarding acceleration measures, decay assessment and practicability, costs and time efforts.     

华北落叶松林生物量与生物多样性关系研究

以河北省木兰林管局境内华北落叶松人工林为研究对象,在采用标准地调查方法取得主要林分因子调查数据的基础上,利用ForStat统计软件,建立了华北落叶松人工林生物多样性与生物量关系模型。结果表明:未成林造林地,灌木与草本生物量与生物多样性呈正相关关系;幼龄林林木分化尚不明显,生物量与林分密度呈线性关系,符合y=0.005 1x+4.334 3,R2=0.842 7;幼龄林物种丰富度在25左右时生物量达到最大;近熟林生物量与生物多样性呈正相关。     

Analysis of spatio-temporal changes in forest biomass in China

Forests play a central role in the global carbon cycle.China's forests have a high carbon sequestration potential owing to their wide distribution,young age and relatively low carbon density.Forest biomass is an essential variable for assessing carbon sequestration capacity,thus determining the spatio-temporal changes of forest biomass is critical to the national carbon budget and to contribute to sustainable forest management.Based on Chinese for-est inventory data (1999-2013),this study explored spatial patterns of forest biomass at a grid resolution of 1 km by applying a downscaling method and further analyzed spatio-temporal changes of biomass at different spatial scales.The main findings are:(1) the regression relationship between forest biomass and the associated influencing factors at a provincial scale can be applied to estimate biomass at a pixel scale by employing a downscaling method;(2) for-est biomass had a distinct spatial pattern with the greatest biomass occurring in the major mountain ranges;(3) forest biomass changes had a notable spatial distribution pattern;increase (i.e.,carbon sinks) occurred in east and southeast China,decreases (i.e.,carbon sources) were observed in the northeast to southwest,with the largest biomass losses in the Hengduan Mountains,Southern Hainan and Northern Da Hinggan Mountains;and,(4) forest vegetation functioned as a carbon sink during 1999-2013 with a net increase in biomass of 3.71 Pg.     

Biometric relationships for non-destructive above ground biomass estimations in young plantations of Acacia salicina Lindl. and Eucalyptus occidentalis Endl.

Above ground oven dried biomass (BM) of individual trees in young stands of Acacia salicina Lindl. and Eucalyptus occidentalis Endl. were correlated linearly and logarithmically to the square of the basal diameter at 0.2 m (DB) and to the latter multiplied by the height of the tree. Number of main stems at basal height per tree were included as well. DB measurements in young Eucalypt stands provided better BM estimates than the commonly used diameter at breast height (1.3 m) (DBH).The following biometric relationships, after cross-validation against an independent data set, showed the best fit: BM=0.1282* (DB²) and BM=0.1700* (DB²) for A. salicina and E. occidentalis, respectively, with (DB²) ranging from 0–400 cm² and BM in kg tree^–1. For acceptable relative errors in biomass estimations (DB²) should be larger than 100 cm².Foliage to wood ratios at (DB²)<100 cm² for both tree species generally exceeded 1, but rapidly leveled off at 0.81±0.28 and 0.92±0.19 for the Acacia and Eucalypt spp., respectively, at higher (DB²) values.     

Inventory-based estimates of forest biomass carbon stocks in China: A comparison of three methods

Several studies have reported different estimates for forest biomass carbon (C) stocks in China. The discrepancy among these estimates may be largely attributed to the methods used. In this study, we used three methods [mean biomass density method (MBM), mean ratio method (MRM), and continuous biomass expansion factor (BEF) method (abbreviated as CBM)] applied to forest inventory data to estimate China's forest biomass C stocks and their changes from 1984 to 2003. The three methods generated various estimates of the biomass C stocks: the lowest (4.0–5.9 Pg C) from CBM and the highest (5.7–7.7 Pg C) from MBM, with an intermediate estimate (4.2–6.2 Pg C) from MRM. Forest age class is a major factor responsible for these method-induced differences. MBM overestimates biomass for young-aged forests, but underestimates biomass for old-aged forests; while the reverse is true for MRM. Further, the three methods resulted in different estimates of biomass C stocks for different forest types. For temperate/subtropical mixed forests, MBM generated a 92% higher estimate than CBM and MRM generated a 14% lower than CBM. The degree of the overestimates is closely related with the proportion of young-aged forest within total area of each forest type.     

Allometric equations for estimating the foliage biomass of Scots pine

The research described in this paper was performed in the Niepolomice Forest (Southern Poland) in 2001 as part of the Forest Environmental Monitoring and Management System (FOREMMS; 5FP IST) project. The material for the present study consisted of the measurement results of the biomass of Scots pine shoots with needles and needles alone carried out on 113 felled sample trees. The purpose of this study was to construct empirical equations for estimating the foliage biomass of Scots pine from easy to measure parameters. To achieve this aim, the dependence of the foliage biomass of Scots pine on stem diameter, height, age, crown length, basal area increment of the trees was analyzed. Using the biometric characteristics such as: tree diameter at breast height (dbh), basal area increment, age, height, and crown length empirical equations for estimating the foliage biomass of Scots pine reasonably precisely have been established. The created empirical equation gives accurate foliage biomass estimates. The explained variability varies between 65 and 85%, it depends on the number of variables applied in the equation. The equations presented in this paper were created with a view to their possible use in ecological studies where biomass quantity may be used, for example, in modeling carbon circulation in the forest ecosystem. From the point of view of forestry practice, these equations may help to assess biomass production in Scots pine stands.