Variations in the biomass of Eucalyptus plantations at a regional scale in Southern China

We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method(BEF) versus estimates obtained from a local biomass model,based on large-scale empirical field inventory sampling data.The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method.Relative to the local model,BEF overestimated accumulative biomass by 22.12%.The predominant sources of the total deviation (70.94%) were stand-structure variables.Stand age and diameter at breast height are the major factors.Compared with biotic variables,abiotic variables had a smaller overall contribution (29.06%),with elevation and soil depth being the most important among the examined abiotic factors.Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data.To minimize deviations,stand age and elevation should be included in regional forest-biomass estimation.     

Aboveground stand-level biomass estimation: a comparison of two methods for major forest species in northwest Spain

Context

The scaling-up approach (which requires the use of individual tree biomass equations and data) is one of the most commonly used methods for estimating stand biomass at a local scale. However, biomass prediction over large management areas requires more efficient methods.

Aims

Two methods of estimating aboveground stand biomass were developed and compared: stand biomass equations (SBE) including observed stand variables, and SBE including biomass expansion factors (BEF) and stand volume.

Methods

Two types of systems of additive equations were fitted simultaneously for components and total aboveground stand biomass, to ensure additivity. Inherent correlations among biomass components were also taken into account in the fitting process.

Results

The systems explained a high percentage of the observed variability. The SBE systems that included observed stand variables provided more accurate estimates than those that included BEF and stand volume. However, the latter were found to be more precise for stem wood and total aboveground biomass prediction.

Conclusions

Both approaches provide a direct link between forest inventory data, outputs from whole-stand growth models, and biomass estimates at stand level. Taking into account that the inventory effort is similar for both alternatives, the choice of which to use will depend on the data available and on the relative importance of the biomass components for the end-users.     

岷江上游亚高山暗针叶林的生物量碳密度

利用森林资源连续清查的样地数据,基于生物量与蓄积量之间的关系模型,估测岷江上游亚高山暗针叶林地上部分生物量碳密度、碳密度年增长率及其随林龄、海拔和坡向变化的分布规律.结果表明:岷江上游暗针叶林的成熟林、过熟林生物量碳密度较高,中龄林、幼龄林生物量碳密度较低,成熟林、过熟林生物量碳密度高于全国平均水平,而中龄林和近熟林低于全国平均水平,幼龄林与全国平均水平相近;中龄林生物量碳密度年增长率最大,为1.3%,其次为过熟林,生物量碳密度年增长率为0.8%,幼龄林生物量碳密度年增长率最小,为0.7%;海拔3600～3800m处生物量碳密度最大,明显高于其他海拔区段;海拔3000～3400m处生物量碳密度年增长率最高,为1.03%;半阴坡和半阳坡的生物量碳密度高且年增长率最大,其次是阴坡,阳坡生物量碳密度低,年增长率最小;过去20多年,岷江上游暗针叶林生物量碳密度呈现逐年增加的趋势,1997-2002年,生物量碳密度年平均增长率为1.15%,高于其他调查期间碳密度年增长率.     

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.     

基于印度遥感卫星IRS—P6的森林生物量估测模型研究

印度遥感卫星IRS—P6的LISS3数据由于其较高的空间分辨率和相对较低的数据价格而受到广泛关注,而利用LISS3数据估测森林生物量的研究报道较少。以高黎贡山自然保护区常绿阔叶林为研究对象,以2006年印度卫星IRS—P6的LISS3影像为主要数据源,利用地面样地胸径每木调查数据,结合生物量相对生长式,得出样地生物量。通过遥感数据提取4个波段的光谱值、6种植被指数,从DEM获取的海拔、坡度、坡向,共13个遥感及地学因子。在此基础上,提取13个因子的主成分,以前5个主成分值作自变量,建立主成分与地面生物量的回归模型,模型经方差分析及相关性检验,达到显著相关水平,相关系数R=0．7129。     

Estimating carbon stock in secondary forests: Decisions and uncertainties associated with allometric biomass models

Secondary forests are a major terrestrial carbon sink and reliable estimates of their carbon stocks are pivotal for understanding the global carbon balance and initiatives to mitigate CO₂ emissions through forest management and reforestation. A common method to quantify carbon stocks in forests is the use of allometric regression models to convert forest inventory data to estimates of aboveground biomass (AGB). The use of allometric models implies decisions on the selection of extant models or the development of a local model, the predictor variables included in the selected model, and the number of trees and species for destructive biomass measurements. We assess uncertainties associated with these decisions using data from 94 secondary forest plots in central Panama and 244 harvested trees belonging to 26 locally abundant species. AGB estimates from species-specific models were used to assess relative errors of estimates from multispecies models. To reduce uncertainty in the estimation of plot AGB, including wood specific gravity (WSG) in the model was more important than the number of trees used for model fitting. However, decreasing the number of trees increased uncertainty of landscape-level AGB estimates substantially, while including WSG had limited effects on the accuracy of the landscape-level estimates. Predictions of stand and landscape AGB varied strongly among models, making model choice an important source of uncertainty. Local models provided more accurate AGB estimates than foreign models, but high variability in carbon stocks across the landscape implies that developing local models is only justified when landscape sampling is sufficiently intensive.     

云南省森林生物量与生产力研究

森林是陆地生态系统的主体,生物量是反映森林生态系统功能的重要指标.通过利用最新的(2002年)森林资源清查数据,以生物量与蓄积量之间的关系模型为基础,对云南省的森林生物量及生产力进行了估计,为碳汇研究及森林生态系统评价提供重要依据.     

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.     

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.     

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.     

Determinants of mortality in a mixed broad-leaved Korean pine forest in northeastern China

Empirical analyses of forest dynamics are important for understanding various ecological processes underlying particular forest communities, among which tree mortality is considered a key process driven by many local factors. To evaluate the effects of tree size, as well as biotic and abiotic factors on tree mortality, we compared species-specific mortality rates in a 21-ha temperate multi-species natural forest in northeastern China, where all trees had been mapped. Our study shows that the mortality rates are highly variable among the different tree species and the influence of habitat preference on the mortality rate of a species across habitats was insignificant. Using generalized linear mixed-effects models, we further found that among the drivers of tree survival, tree size had the strongest effect across different species. There are significant negative relationships between the basal area of conspecific neighbors and tree survival at the community level across almost all the guilds. Regarding abiotic factors, elevation had a greater effect on tree survival than other topographic variables did. Abiotic factors affected shrubs more than tall canopy species in terms of survival rate. Our study suggests that tree size, density-dependent effects and niche partitioning contribute to the regulation of survival pattern of temperate forest communities, but the relative importance of these factors varies greatly among guilds and species. This study has shown that it is essential to consider the relative importance of both, intrinsic (tree size) and extrinsic (biotic and abiotic) factors in analyzing tree mortality.     

Additive prediction of aboveground biomass for Pinus radiata (D. Don) plantations

A general and two country-specific systems of additive equations were developed to predict aboveground biomass of Pinus radiata plantations from stand variables that are routinely measured in inventory plots and predicted by conventional growth and yield models. The data for this work consisted of 319 plot-based biomass estimates that were derived from individual tree biomass equations developed in situ. These plot-based biomass estimates were compiled from studies reported in the forestry and ecological literature since 1960 and also from personal communications. They represent more than 60 sites worldwide with a majority in Australia and New Zealand. The systems of additive biomass equations developed from these data provide an alternative and addition to the current methods of estimating the aboveground biomass of P. radiata plantations. They also provide a direct linkage between forest inventory measures, outputs from conventional growth and yield models and biomass and carbon stock estimates at the same spatial scale. This direct linkage provides a new basis for scaling to a remote sensing image from which biomass and carbon stocks across the landscape can be mapped. Comparisons of prediction accuracies between this approach and other methods such as scaling up from individual tree biomass estimates and biomass expansion factors highlighted considerable methodological differences in the estimates of aboveground biomass and associated uncertainties over a range of stand age and conditions. These differences should be carefully evaluated before adopting a particular method to estimate aboveground biomass and carbon stocks of P. radiata plantations at a local, regional or national scale.     

Variation in biomass expansion factors for China’s forests in relation to forest type, climate, and stand development

? Context

Biomass expansion factors (BEFs, defined as the ratios of tree component biomass (branch, leaf, aboveground section, root, and whole) to stem biomass) are important parameters for quantifying forest biomass and carbon stock. However, little information is available about possible causes of the variability in BEFs at large scales.

? Aims

We examined whether and how BEFs vary with forest types, climate (mean annual temperature, MAT; mean annual precipitation, MAP), and stand development (stand age and size) at the national scale for China.

? Method

Using our compiled biomass dataset, we calculated values for BEFs and explored their relationships to forest types, climate, and stand development.

? Results

BEFs varied greatly across forest types and functional groups. They were significantly related to climate and stand development (especially tree height). However, the relationships between BEFs and MAT and MAP were generally different in deciduous forests and evergreen forests, and BEF–climate relationships were weaker in deciduous forests than in evergreen forests and pine forests.

? Conclusion

To reduce uncertainties induced by BEFs in estimates of forest biomass and carbon stock, values for BEFs should be applied for a specified forest, and BEF functions with influencing factors (e.g., tree height and climate) should be developed as predictor variables for the specified forest.     

Individual tree biomass equations and growth models sensitive to climate variables for <Emphasis Type="Italic">Larix</Emphasis> spp. in China

Climate change has increased the need of information on amount of forest biomass. The biomass and carbon storage for larch (Larix spp.) in large geographic regions in China were failed to be accurately estimated from current biomass equations, because they were usually based on a few sample trees on local sites, generally incompatible to volume estimation, and not additive between components and total biomass. China needs reliable biomass estimation of the important species in the whole country. This study was based on the mensuration data of above- and belowground biomass from 600 and 198 destructive sample trees of larch from four regions in China, respectively. The main purpose was to develop compatible individual tree equations on both national and regional levels for above- and belowground biomass, biomass conversion factor and root-to-shoot ratio, using the nonlinear error-in-variable simultaneous equation approach. In addition, diameter at breast height (D) and tree height (H) growth models were also developed, and effects of key climate variables on biomass variation and growth process were analyzed. The results showed that mean prediction errors (MPEs) of regional aboveground biomass models were from 3.86 to 7.52%, and total relative errors (TREs) are within ±3%; and for regional belowground biomass equations, the MPEs are from 9.91 to 28.85%, and the TREs are within ±4%. The above- and belowground biomass and D- and H-growth were significantly related to mean annual temperature and mean annual precipitation. The biomass equations and growth models developed in this paper will provide good basis for estimating and predicting biomass of larch forests in China.     

A comparison of carbon assessment methods for optimizing timber production and carbon sequestration in Scots pine stands

Projected changes in forest carbon stocks and carbon balance differ according to the choice of estimation methods and the carbon pools considered. Here, we compared three carbon assessment methods for optimizing timber production and carbon sequestration in six example Scots pine (Pinus sylvestris L.) stands in Finland. The forest carbon stock was assessed, with three methods: stem carbon, biomass expansion factors (BEFs), and a process-based model. Given a carbon price of 40 € t⁻¹ (equivalent to 10.9 € t⁻¹ CO₂) and a 3% discount rate, the highest average carbon stock and mean annual increment (MAI) were obtained with the BEF method. Increasing the carbon price from 0 to 200 € t⁻¹ resulted in longer optimal rotations and higher MAI, and increased the average carbon stock, especially when carbon was assessed by the BEF method. Comparison of these carbon assessment methods, using economic sensitivity analyses, indicated that optimal thinning regimes and average carbon stocks are strongly dependent on the assessment method. The process-based method led to less frequent thinnings and shorter rotations than the BEF method, due to different predictions of biomass production. As a cost-effective option, optimal thinning regimes play a very important role in timber production and carbon sequestration.     

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。     

Potential knowledge gain in large-scale simulations of forest carbon fluxes from remotely sensed biomass and height

Global vegetation models (GVMs) simulate CO₂, water and energy fluxes at large scales, typically no smaller than 10 × 10 km. GVM simulations are thus expected to simulate the average functioning, but not the local variability. The two main limiting factors in refining this scale are (1) the scale at which the pedo-climatic inputs - temperature, precipitation, soil water reserve, etc. - are available to drive models and (2) the lack of geospatial information on the vegetation type and the age of forest stands. This study assesses how remotely sensed biomass or stand height could help the new generation of GVMs, which explicitly represent forest age structure and management, to better simulate this local variability. For the ORCHIDEE-FM model, we find that a simple assimilation of biomass or height brings down the root mean square error (RMSE) of some simulated carbon fluxes by 30-50%. Current error levels of remote sensing estimates do not impact this improvement for large gross fluxes (e.g. terrestrial ecosystem respiration), but they reduce the improvement of simulated net ecosystem productivity, adding 13.5-21% of RMSE to assimilations using the in situ estimates. The data assimilation under study is more effective to improve the simulation of respiration than the simulation of photosynthesis. The assimilation of height or biomass in ORCHIDEE-FM enables the correct retrieval of variables that are more difficult to measure over large areas, such as stand age. A combined assimilation of biomass and net ecosystem productivity could possibly enable the new generation of GVMs to retrieve other variables that are seldom measured, such as soil carbon content.     

Satellite-based radar mapping of British forest age and Net Ecosystem Exchange using ERS tandem coherence

A method of mapping forest age structure using satellite remote sensing data in combination with ground data is used to form an age structure map of mainland Britain’s forests. Age structure is then used to demonstrate a method of calculating Net Ecosystem Exchange (NEE) for a region of forest. Synthetic Aperture Radar (SAR) coherence data provides an indication of forest biomass, which is related to forest age using forest management data at five independent locations. Coherence data is sensitive to time varying environmental effects and hence requires extensive calibration of the function relating coherence to forest age. The calibration approach appropriate in Britain makes use of the extensive ground GIS data available. Age structure information for nearly 3 million hectares of forest is generated, of which 70% is privately owned and age information is otherwise unavailable. The resulting map has a spatial resolution that can differentiate individual forest stands and provides detailed regional age and biomass estimates. Comparison of stand age estimates with ground data is has potential to provide growth rate and felling information. The age structure map is used in combination with an exemplary function relating forest age to NEE to estimate atmospheric carbon exchange for England, Scotland and Wales. This method predicts threefold higher (10.87 M t a⁻¹) forest carbon uptake than national inventory figures. The remote sensing data also indicates age estimates that conflict with the ground data in Wales, which is explainable by the introduction of partial felling practices in this region during the time period of the coherence acquisitions.     

Above- and belowground biomass measurements in an unthinned stand of Sitka spruce (<Emphasis Type="Italic">Picea sitchensis</Emphasis> (Bong) Carr.)

Reporting carbon (C) stocks in tree biomass (above- and belowground) to the United Nations Framework Convention on Climate Change (UNFCCC) should be transparent and verifiable. The development of nationally specific data is considered ‘good practice’ to assist in meeting these reporting requirements. From this study, biomass functions were developed for estimating above- and belowground C stock in a 19-year-old stand of Sitka spruce (Picea sitchensis (Bong) Carr.). Our estimates were then tested against current default values used for reporting in Ireland and literature equations. Ten trees were destructively sampled to develop aboveground and tree component biomass equations. The roots were excavated and a root:shoot (R) ratio developed to estimate belowground biomass. Application of the total aboveground biomass function yielded a C stock estimate for the stand of 74 tonnes C ha⁻¹, with an uncertainty of 7%. The R ratio was determined to be 0.23, with an uncertainty of 10%. The C stock estimate of the belowground biomass component was then calculated to be 17 tonnes C ha⁻¹, with an uncertainty of 12%. The equivalent C stock estimate from the biomass expansion factor (BEF) method, applying Ireland’s currently reported default values for BEF (inclusive of belowground biomass), wood density and C concentration and methods for estimating volume, was found to be 60 tonnes C ha⁻¹, with an uncertainty of 26%. We found that volume tables, currently used for determining merchantable timber volume in Irish forestry conditions, underestimated volume since they did not extend to the yield of the forest under investigation. Mean stock values for belowground biomass compared well with that generated using published models.     

Mapping tree aboveground biomass and carbon in Omo Forest Reserve Nigeria using Landsat 8 OLI data

Protected areas in Nigeria are important ecosystems for carbon storage. The aim of this study was to estimate and map tree aboveground biomass (TAGB) and carbon (TAGC) within a tropical forest in Nigeria. Stepwise regression analysis was implemented to develop models for predicting TAGB in the forest stand, by integrating field TAGB data with Landsat 8 OLI data. Spectral variables used in the analysis include spectral bands, vegetation indices, tasseled cap indices and principal components. Model validation was performed using independent sample plots. The results showed that incorporating more than one category of spectral variables improved the prediction of TAGB. The best-fit model was applied to map the spatial distribution of TAGB and TAGC. The TAGC was estimated as 52.3% of TAGB, based on the average carbon content of tree species derived in this study. Average TAGB and TAGC estimates for the forest stand were 373.1 ± 165.4 t ha^?1 and 194 ± 82.7 t ha^?1, respectively. Reliable estimates of TAGB and TAGC for the forest reserve were obtained. This study provides important information required to manage the forest stand for optimal carbon sequestration.