Indirect methods of large-scale forest biomass estimation

Forest biomass and its change over time have been measured at both local and large scales, an example for the latter being forest greenhouse gas inventories. Currently used methodologies to obtain stock change estimates for large forest areas are mostly based on forest inventory information as well as various factors, referred to as biomass factors, or biomass equations, which transform diameter, height or volume data into biomass estimates. However, while forest inventories usually apply statistically sound sampling and can provide representative estimates for large forest areas, the biomass factors or equations used are, in most cases, not representative, because they are based on local studies. Moreover, their application is controversial due to the inconsistent or inappropriate use of definitions involved. There is no standardized terminology of the various factors, and the use of terms and definitions is often confusing. The present contribution aims at systematically summarizing the main types of biomass factors (BF) and biomass equations (BE) and providing guidance on how to proceed when selecting, developing and applying proper factors or equations to be used in forest biomass estimation. The contribution builds on the guidance given by the IPCC (Good practice guidance for land use, land-use change and forestry, 2003) and suggests that proper application and reporting of biomass factors and equations and transparent and consistent reporting of forest carbon inventories are needed in both scientific literature and the greenhouse gas inventory reports of countries.

Comparison on Estimation of Wood Biomass Using Forest Inventory Data

Based on sixth and seventh national forestry inventory data of the six provinces,including Guangdong,Jiangxi,Guizhou,Shaanxi,Jilin and Beijing,the three methods(IPCC,continuous function for biomass expansion factor and weighted biomass regression model) were selected to estimate wood biomass in this paper.The estimation of the three methods were compared and analyzed from calculating process,method characters,repeatability and verifiability to stability of growth rate of biomass between two periods.The results showed the total biomass estimated by IPCC method with variable BEF2 was large,the total biomass estimated by IPCC method with constant BEF2 was small and the total biomasses estimated by continuous function for biomass expansion factor and weighted biomass regression model were middle.The biomass expansion factor derived from weighted regression model was most stable in the different provinces. Based on the seventh national forestry inventory data, the biomass expansion factors of various kinds of tree species derived from IPCC and the weighted regression model were more stable than the biomass expansion factors derived from continuous function method.The growth rate of biomass between two periods was the same regular pattern as the biomass expansion factors.     

湖南省2010年 LUCF 温室气体排放清单编制研究

利用湖南省森林资源清查数据,运用生物量法,从森林和其它木质生物质生物量碳储量变化及森林转化碳排放两方面对省级 LUCF 温室气体排放清单编制进行研究。结果表明：湖南省2010年 LUCF 净吸收温室气体1720．54万 t CO2当量,其中森林和其它木质生物质生物量碳储量变化净吸收温室气体1764．54万 t CO2当量,森林转化净排放温室气体44．0万 t CO2当量;通过增加乔木林碳储量,相当于吸收 CO24195．64万 t 。湖南省2008年的特大冰灾导致2010年 LUCF 活动吸收温室气体量比2005年减少了30．93％。     

Land use change and net C flux in Indian forests

This paper reports on the net carbon flux caused by deforestation and afforestation in India over the period from 1982 to 2002, separately for two time periods, 1982–1992 (PI) and 1992–2002 (PII), using the IPCC 2006 guidelines for greenhouse gas inventories. The approach accounts for forest and soil C pool changes for (a) forest areas remaining as forests, (b) afforested areas and (c) deforested areas. The data set used were remote sensing based forest cover for three time periods (1982, 1992, 2002), biomass increments, biomass expansion factors and wood density. In addition a number of required coefficients and parameters from published literature were adopted. In the 1982–2002 period, the forest cover changed from 64.20 Mha in 1982 to 63.96 and 67.83 Mha in 1992 and 2002 respectively. During the PI and PII periods, plantations were also established of 0.2 and 0.5 Mha yr⁻¹, while the annual deforestation rate was about 0.22 and 0.07 Mha in these periods, respectively.     

EU-wide maps of growing stock and above-ground biomass in forests based on remote sensing and field measurements

The overall objective of this study was to combine national forest inventory data and remotely sensed data to produce pan-European maps on growing stock and above-ground woody biomass for the two species groups “broadleaves” and “conifers”. An automatic up-scaling approach making use of satellite remote sensing data and field measurement data was applied for EU-wide mapping of growing stock and above-ground biomass in forests. The approach is based on sampling and allows the direct combination of data with different measurement units such as forest inventory plot data and satellite remote sensing data. For the classification, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used. Comprehensive field measurement data from national forest inventories for 98,979 locations from 16 countries were used for which tree species and growing stock estimates were available. The classification results were evaluated by comparison with regional estimates derived independently from the classification from national forest inventories. The validation at the regional level shows a high correlation between the classification results and the field based estimates with correlation coefficient r = 0.96 for coniferous, r = 0.94 for broadleaved and r = 0.97 for total growing stock per hectare. The mean absolute error of the estimations is 25 m³/ha for coniferous, 20 m³/ha for broadleaved and 25 m³/ha for total growing stock per hectare. Biomass conversion and expansion factors were applied to convert the growing stock classification results to carbon stock in above-ground biomass. As results of the classification, coniferous and broadleaved growing stock as well as carbon stock of the above-ground biomass is mapped on a wall-to-wall basis with a spatial resolution of 500 m × 500 m per grid cell. The mapped area is 5 million km², of which 2 million km² are forests, and covers the whole European Union, the EFTA countries, the Balkans, Belarus, the Ukraine, Moldova, Armenia, Azerbaijan, Georgia and Turkey.     

河南省杨树人工林生物量研究

利用河南省第7次森林资源连续清查资料,分别采用生物量转换因子法、生物量回归模型法、生物量转换因子连续函数法对河南省杨树人工林生物量进行了估算。经比较模型的适用范围,最终采用生物量转换因子法估算值作为河南省杨树人工林生物量;中、幼林生物量占整个杨树人工林生物量总量的80.47%,表明杨树人工林生物量在各龄组中分布不均衡;河南省杨树人工林生物量总体质量不高,但具有较大发展空间,通过加强杨树人工林的抚育管理措施,对提升整个区域的杨树生物量水平具有重要意义。     

Estimating above-ground biomass of trees: comparing Bayesian calibration with regression technique

The commitment to report greenhouse gas emissions requires an estimation of biomass stocks and their changes in forests. When this was first done, representative biomass functions for most common tree species were very often not available. In Germany, an estimation method based on solid volume was developed (expansion procedure). It is easy to apply because the required information is available for nearly all relevant tree species. However, the distributions of neither parameters nor prediction intervals are available. In this study, two different methods to estimate above-ground biomass for Norway spruce (Picea abies), European beech (Fagus sylvatica), and Scots pine (Pinus sylvestris) are compared. First, an approach based on information from the literature was used to predict above-ground biomass. It is basically the same method used in greenhouse gas reporting in Germany and was applied with prior and posterior parameters. Second, equations for direct estimation of biomass with standard regression techniques were developed. A sample of above-ground biomass of trees was measured in campaigns conducted previously to the third National Forest Inventory in Germany (2012). The data permitted the application of Bayesian calibration (BC) to estimate posterior distribution of the parameters for the expansion procedure. Moreover, BC enables the calculation of prediction intervals which are necessary for error estimations required for reporting. The two methods are compared with regard to predictive accuracy via cross-validation, under varying sample sizes. Our findings show that BC of the expansion procedure performs better, especially when sample size is small. We therefore encourage the use of existing knowledge together with small samples of observed biomass (e.g., for rare tree species) to gain predictive accuracy in biomass estimation.     

Estimating single-tree branch biomass of Norway spruce with terrestrial laser scanning using voxel-based and crown dimension features

Abstract

Many remote sensing-based methods estimating forest biomass rely on allometric biomass models for field reference data. Terrestrial laser scanning (TLS) has emerged as a tool for detailed data collection in forestry applications, and the methods have been proposed to derive, e.g. tree position, diameter-at-breast-height, and stem volume from TLS data. In this study, TLS-derived features were related to destructively sampled branch biomass of Norway spruce at the single-tree level, and the results were compared to conventional allometric models with field measured diameter and height. TLS features were derived following two approaches: one voxel-based approach with a detailed analysis of the interaction between individual voxels and each laser beam. The features were derived using voxels of size 0.1, 0.2, and 0.4 m, and the effect of the voxel size was assessed. The voxel-derived features were compared to features derived from crown dimension measurements in the unified TLS point cloud data. TLS-derived variables were used in regression models, and prediction accuracies were assessed through a Monte Carlo cross-validation procedure. The model based on 0.4 m voxel data yielded the best prediction accuracy, with a root mean square error (RMSE) of 32%. The accuracy was found to decrease with an increase in voxel size, i.e. the model based on the 0.1 m voxel yielded the lowest accuracy. The model based on crown measurements had an RMSE of 34%. The accuracies of the predictions from the TLS-based models were found to be higher than from conventional allometric models, but the improvement was relatively small.     

Enhancing the value of multiple use plantations: a case study from southeast Queensland, Australia

In the 1990s, an expansion of small-scale (farm) forestry in medium to low rainfall areas was considered to be an important part of increasing the national forest estate but it remains a very minor source of timber, largely confined to the higher rainfall areas. In most areas, returns from timber are much less than for alternative land uses, even with low discount rates. If however, there are additional returns from plantation grazing and carbon sequestration and there are other potential management gains, multiple use plantations may be more attractive. The goal of this study is to estimate the net present values of multiple use spotted gum plantations in a medium rainfall area of southeast Queensland. For the case study, production, carbon sequestration and emissions data were supplemented by formal and informal interviews with landholders, sawmill staff and government extension personnel. Forest inventory, biomass and soil sampling, and stakeholder interviews were used as sources of primary data. The costs and benefits data were converted into monetary terms and discounted to produce net present values. Evaluations in this study identify the optimal rotation age of plantations to be 33–34?years. This is the case if including carbon and stock values, and using either farm- or factory-gate timber prices. The net present value increases significantly however if farmers harvest the trees themselves. In addition, at harvesting age, it was found that carbon and stock had the potential to account for 19.2 and 11.4?% respectively of the total returns from spotted gum plantations. Policy initiatives to support the farm forestry sub-sector should include pricing greenhouse gas emissions and developing and strengthening farmers co-operatives and marketing institutions to enhance farmers’ bargaining power.     

Assessing 3D point clouds from aerial photographs for species-specific forest inventories

In this study we assessed the potential of using photogrammetric data for species-specific forest inventories. The method is based on a combination of Dirichlet and ordinary linear regression models. This approach was used to predict species proportions, main tree species, total, and species-specific volume. Structural and spectral variables were used as predictors. The models were validated using 63 independent validation stands. The results from airborne laser scanning (ALS) data combined with spectral data and photogrammetric data obtained using aerial imagery with different forward overlaps of 80% and 60% were compared. The best photogrammetry-based models predicted species proportions with a relative root mean square error (RMSE) of 21.4%, classified dominant species with 79% accuracy, predicted total volume with relative RMSE of 13.4%, and predicted species-specific volume with relative RMSE of 36.6%, 46.5%, and 84.9% for spruce, pine, and deciduous species, respectively. The results were similar for the three point cloud datasets obtained from aerial imagery and ALS and the accuracies of the predictions were comparable to methods used in operational FMI. The study highlights the effectiveness of forest inventories carried out using photogrammetric data, which – differently from ALS, can include species-specific information without relying on multiple data sources.     

国家森林资源连续清查中的材积估计问题探讨

从分析我国森林资源连续清查中的材积估计问题入手,提出为满足新世纪林业发展的需要,必须对目前用一元材积表估计材积的方法加以改进,并进一步补充完善材积表和生物量表等基础林业数表。     

Soil detritivore macro-invertebrate assemblages throughout a managed beech rotation

This work addresses the driving factors responsible for patterns in the detritivore macrofaunal communities of a managed beechwood chronosequence (28 to 197 years old, Normandy, France). We investigated the variation patterns of density, biomass and diversities of detritivore macrofauna throughout this rotation. Multivariate analyses were carried out to identify the main covariation patterns between species and some properties of their physical environment, and to describe the main ecological gradients constraining the macro-invertebrate community assembly. A total of 6 earthworm, 6 woodlouse and 7 millipede species were found in the whole data set. Density, biomass and diversity were profoundly influenced by forest ageing, mainly because of variation in humic epipedon spatial variability. Three groups of species were identified according to their environmental requirements. Some hypotheses regarding the external (related to management practices) or internal (related to inter-specific interactions) assembly rules behind species assemblages are proposed, an approach which has rarely been used in soil ecology. Finally, the impact of forestry practices on soil functioning through their impact on detritivore macro-invertebrate communities is discussed.     

Comparison of estimators and feature selection procedures in forest inventory based on airborne laser scanning and digital aerial imagery

Digital maps of forest resources are a crucial factor in successful forestry applications. Since manual measurement of this data on large areas is infeasible, maps must be constructed using a sample field data set and a prediction model constructed from remote sensing materials, of which airborne laser scanning (ALS) data and aerial images are currently widely used in management planning inventories. ALS data is suitable for the prediction of variables related to the size and volume of trees, whereas optical imagery helps in improving distinction between tree species. We studied the prediction of forest attributes using field data from National Forest Inventory complemented with ad hoc field plots in combination with ALS and aerial imagery data in Aland province, Finland. We applied feature selection with genetic algorithm and greedy forward selection and compared multiple linear and nonlinear estimators. Maximally around 40 features from a total of 154 were required to achieve the best prediction performances. Tree height was predicted with normalized root mean squared error value of 0.1 and tree volume with a value around 0.25. Predicting the volumes of spruce and broadleaved trees was the most challenging due to small proportions of these tree species in the study area.     

森林、林业活动与温室气体的减排增汇

大气中CO_2等温室气体浓度上升引起的全球变暖,威胁着人类生存和社会经济的可持续发展。在减少温室气体排放、稳定大气CO_2浓度的措施中,森林和林业活动扮演着重要的角色。森林可吸收并固定大气CO_2,是大气CO_2的吸收汇和贮存库;而毁林是大气CO_2的重要排放源。通过适当的林业活动可增强碳吸收汇、保护现有的碳贮存,通过替代措施可减少化石燃料引起的温室气体排放。因此,林业活动在未来减缓大气温室气体上升方面将发挥重要作用。阐明了全球和中国森林生态系统在减缓大气CO_2浓度上升中的作用以及与土地利用变化和林业有关的减排增汇措施和潜力,以期对我国制定CO_2减排增汇政策提供参考依据。     

Harvested wood products accounting in the post Kyoto commitment period

An increase in the amount of harvested wood products (HWP) from sustainable forestry would help to reduce levels of atmospheric carbon. In the first commitment period of the Kyoto Protocol (2008–2012), this carbon stock effect of HWP is ignored, and forest harvesting is treated as an instantaneous emission of carbon dioxide. However, in the next commitment period of the United Nations Framework Convention on Climate Change from 2013, the carbon stock changes resulting from HWP will be taken into account in the national greenhouse gas inventories. The Japan Wood Research Society called for a roundtable conference of eight research societies, industrial associations, and nongovernmental organizations that are involved with wood utilization. At the conference, accounting approaches for HWP were discussed and a consensus was reached that the stock change approach should be adopted in the next commitment period.     

Evaluating changes in switchgrass physiology,biomass, and light-use efficiency under artificial shade to estimate yields if intercropped with Pinus taeda L.

There is growing interest in using switchgrass (Panicum virgatum L.) as a biofuel intercrop in forestry systems. However, there are limited data on the longevity of intercropped bioenergy crops, particularly with respect to light availability as the overstory tree canopy matures. Therefore, we conducted a greenhouse study to determine the effects of shading on switchgrass growth. Four treatments, each with different photosynthetically active radiation (PAR) levels, were investigated inside the greenhouse: control (no shade cloth, 49 % of full sunlight), low (under 36 % shade cloth), medium (under 52 % shade cloth), and heavy shade (under 78 % shade cloth). We determined the effect of shading from March to October 2011 on individually potted, multi-tillered switchgrass transplants cut to a stubble height of 10 cm. In the greenhouse, there was a reduction in tiller number, tiller height, gas exchange rates (photosynthesis and stomatal conductance), leaf area, above- and belowground biomass and light-use efficiency with increasing shade. Total (above- and belowground) biomass in the control measured 374 ± 22 compared to 9 ± 2 g pot^?1 under heavy shade (11 % of full sunlight). Corresponding light-use efficiencies were 3.7 ± 0.2 and 1.4 ± 0.2 g MJ^?1, respectively. We also compared PAR levels and associated aboveground switchgrass biomass from inside the greenhouse to PAR levels in the inter-row regions of a range of loblolly pine (Pinus taeda L.) stands from across the southeastern United States (U.S.) to estimate when light may limit the growth of intercropped species under field conditions. Results from the light environment of loblolly pine plantations in the field suggest that switchgrass biomass will be significantly reduced at a loblolly pine leaf area index between 1.95 and 2.25, which occurs on average between ages 6 and 8 years across the U.S. Southeast in intensively managed pine plantations. These leaf area indices correspond to a 60–65 % reduction in PAR from open sky.     

Uncertainty estimation of biomass expansion factors for Norway spruce in the Czech Republic

Nation wide estimates of the changes in forest biomass are needed for the greenhouse gas (GHG) reporting under the Climate Convention. The bases for national GHG reporting concerning forest sector are the national forest inventory (NFI) programmes. Since these programmes were mostly established for monitoring of timber resources, one of the current challenges for the NFIs is the development of methodology, such as biomass expansion factors (BEFs). The methodology for carbon stock change estimation should be transparent and verifiable, but this demand is not currently met due to the fact that the source data and uncertainty in the applied BEFs are not known. Here we developed BEFs with uncertainty estimation applicable to stand wise inventory of Norway spruce forests in the Czech Republic. BEFs were constructed, based on tree wise data from permanent research plots, by applying biomass and volume models to tree-level data. These BEFs were age-dependent and their uncertainty was sensitive to the dependencies among errors. Most of the uncertainty in the BEFs was due to uncertainty in the biomass and volume models applied.     

Combining harvest sample data with inventory data to estimate forest biomass

Forest biomass databases which go beyond stem volume are needed for carbon balance calculations and also for forest monitoring, forest damage inventories and the solution of other related problems. In this article, it is shown how recursive multiple regression analysis can combine forest inventory data with biomass harvest data. The stand‐level conversion factor derived for Pinus sylvestris L. branch biomass for the “Severka”; Forest Farm shows a reasonable goodness of fit, with a coefficient of determination of R² = 0.87. Statistically significant coefficients were achieved by including stand density and the quadratic mean diameter in the regression. Equations developed for Switzerland, using Burger's Picea abies (L.) Karst. branch and needle conversion factors for individual trees, show R² = 0.87 and R ² = 0.91, achieved by taking h _dom.50, elevation, diameter at breast height and age into account.     

Integration of remote sensing-based bioenergy inventory data and optimal bucking for stand-level decision making

This paper tests the reliability of a biomass prediction procedure which combines aerial data collection, biometric models and optimisation for forest management planning. Tree stock information is obtained by predicting species-specific diameter and height distributions by a combination of field sampling, ALS data and aerial photographs. The subsequent steps in the chain are (1) assignment of the plots to forestry operation classes by means of remote sensing-based tree stock estimates, (2) estimation of the biomass components removed by simulating forestry operations, and (3) estimation of forest owners’ income flow from optimised bucking of the species-specific diameter distributions. The error effects caused by these steps are analysed, and the applicability of remote sensing–based data collection for biomass inventories and planning is assessed. The approach used for assigning the plots to operation classes resulted in moderate accuracies (75%). The reliability estimates indicated quite poor performance when predicting the biomass components removed in forest treatments, with RMSEs of 33.0–69.4% in the case of final cutting and 76.9–228.0% in the case of thinning. The relative RMSEs of the above-ground biomass estimates of the standing stock were about 19%. The relative bias for the biomasses removed was 10.0–88.6% and that for the standing stock biomasses 0.0%. When optimising bucking, the bucked assortments were larger and the incomes enhanced with this estimation method relative to the reference. This explains why the estimation of forest owner’s incomes in the energy wood thinning simulations led to suboptimal decisions and income losses.     

Greenhouse gas balance of harvesting stumps and logging residues for energy in Sweden

Abstract

In this case study, forest fuel procurement chains of stumps and logging residues were evaluated using a Life Cycle Assessment perspective. Direct emissions from combustion were not included, but soil organic carbon change was included as changes in carbon stocks in litter and soil. The results showed that primary forest biomass for energy has a climate impact which is time dependent. However, in long-time perspectives, there are large greenhouse gas (GHG) savings. In a short-term (20 years), there were no GHG savings when natural gas or coal was replaced. This is due to the fact that the harvest lead to decreased input of organic matter to the soil which is compared to a reference where biomass are left to decompose. The reduction in soil organic carbon may have been underestimated in the stump harvest systems studied, as the effect of soil disturbance per se was not included. Important factors when assessing GHG balance of forest fuels, besides the time horizon used, were site productivity, geographical position and forest fuel resource (stumps or logging residues). When assessing the greenhouse gas savings, efficiency of the end-use, allocation method between heat and power and type of fossil fuel replaced were also important.