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.     

Integrated individual tree biomass simultaneous equations for two larch species in northeastern and northern China

Forest biomass estimation at large scale has become an important topic in the background of facing global climate change, and it is fundamental to develop individual tree biomass equations suitable for large-scale estimation. Based on the measured data of biomass components and stem volume from 100 sample trees of two larch species (Larix gmelinii and L. principis-rupprechtii) in northeastern and northern China, an integrated equation system including individual tree biomass equations, stem volume equation and height–diameter regression model were constructed using the dummy variable model and error-in-variable simultaneous equations. In the system, all the parameters of equations were estimated simultaneously, so that the aboveground biomass equation was compatible to stem volume equation and biomass conversion factor (B_CF) function; the belowground biomass equation was compatible to root-to-shoot ratio (R_SR) function; and stem wood, stem bark, branch and foliage biomass equations were additive to aboveground biomass equation. In addition, the system also ensured the compatibility between one- and two-variable models. The results showed that: (1) whether aboveground biomass equations or belowground biomass equations and stem volume equations, the estimates for larch in northeastern China were greater than those in northern China; (2) B_CF of a larch tree decreased with the growing diameter while R_SR increased with the growing diameter; (3) the proportion of stem wood biomass to aboveground biomass increased with the growing diameter while those of stem bark, branch, and foliage biomass decreased.     

Equations for estimating above- and belowground biomass of Norway spruce,Scots pine,birch spp. and European aspen in Latvia

This study aims to derive allometric functions to estimate the above- and belowground biomass components of the most important tree species in Latvia. The study material included a total of 81 Norway spruce (Picea abies [L.] Karst), 102 Scots pine (Pinus sylvestris L.), 105 birch spp. (mainly silver birch (Betula pendula Roth)) and 84 European aspen (Populus tremula L.) trees sampled in 124 forest stands. The suitability of three mathematical models for the prediction of total aboveground biomass, stem biomass, total live and dead branch biomass, belowground biomass and small root biomass was evaluated. Our analysis revealed that the use of the Intergovernmental Panel on Climate Change mean default values for the root-to-shoot ratio recommended for temperate and boreal ecological zones leads to the overestimation of root biomass of young trees, especially Scots pine and Norway spruce. Our findings indicate that biomass functions recommended for other Baltic Sea countries are not appropriate for the assessment of the biomass stock in Latvia’s forests because these lead to biased estimates. The biomass functions derived in our study are recommended for reporting the biomass stock in Latvia.     

中国湿地松立木生物量方程的研建

以我国湿地松154株样木的生物量实测数据为基础,综合利用分段建模方法和非线性误差变量联立方程组方法,建立了与立木材积方程相容的地上生物量方程和生物量转换因子模型,以及与地上生物量方程相容的地下生物量方程和根茎比模型。结果表明:所建地上生物量方程的平均预估误差小于5%,地下生物量方程的平均预估误差小于10%,全树生物量估计的平均预估误差小于3%,完全可满足森林生物量计量的精度要求,从而为我国湿地松林的生物量估计及碳汇能力评估提供了计量依据。     

An allometric equation for estimating stem biomass of Acacia auriculiformis in the north-eastern region of Bangladesh

Tree biomass plays an important role in sustainable management and in estimating forest carbon stocks. The objective of this study was to select the best model for measuring stem biomass of Acacia auriculiformis in the study area. Data from five hillocks and 120 individual trees from each hillock were used in this study. Twelve different forms of linear, power and exponential equations were compared in this study to select the best model. Two models (VI and XI) were selected based on R ², adjusted R ², the Akaike information criterion, F-statistics and the five assumptions of linear regression. Model VI was discarded based on the Durbin-Watson value of autocorrelation of the residuals, then the ARIMA (2, 0, 1) model was used to remove the autocorrelation from the model and the final bias-corrected model XI was derived. The model was validated with a test data set having the same range of DBH and stem height of the training data set on the basis of linear regression, Morisita's similarity index, and t-test for mean difference between predicted and expected biomass. A comparison between the best logarithmic and non-linear allometric model shows that the non-linear model produces systematic biases and overestimates stem biomass for larger trees. The overall results showed that the bias-corrected logarithmic model XI can be used efficiently for estimating stem biomass of A. auriculiformis in the northeastern region of Bangladesh.     

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.     

A review of the challenges and opportunities in estimating above ground forest biomass using tree-level models

Accurate biomass measurements and analyses are critical components in quantifying carbon stocks and sequestration rates, assessing potential impacts due to climate change, locating bio-energy processing plants, and mapping and planning fuel treatments. To this end, biomass equations will remain a key component of future carbon measurements and estimation. As researchers in biomass and carbon estimation, we review the present scenario of aboveground biomass estimation, focusing particularly on estimation using tree-level models and identify some cautionary points that we believe will improve the accuracy of biomass and carbon estimates to meet societal needs. In addition, we discuss the critical challenges in developing or calibrating tree biomass models and opportunities for improved biomass. Some of the opportunities to improve biomass estimate include integration of taper and other attributes and combining different data sources. Biomass estimation is a complex process, when possible, we should make use of already available resources such as wood density and forest inventory databases. Combining different data-sets for model development and using independent data-sets for model verification will offer opportunities to improve biomass estimation. Focus should also be made on belowground biomass estimation to accurately estimate the full forest contribution to carbon sequestration. In addition, we suggest developing comprehensive biomass estimation methods that account for differences in site and stand density and improve forest biomass modeling and validation at a range of spatial scales.     

Allometric models for estimation of aboveground biomass of Gmelina arborea Roxb. in pulpwood plantations of Bangladesh

Gmelina arborea Roxb. is a fast-growing, deciduous tree species native to Bangladesh. It has been widely planted since 1992 for commercial production of pulpwood in hilly areas of Bangladesh. The purpose of this study was to derive well-fitting allometric models for estimation of aboveground biomass, and carbon and nutrient (nitrogen, phosphorus and potassium) stocks in G. arborea to ensure sustainable production and management of this species. Eight linear models with 64 regression equations were tested for estimation of aboveground biomass, carbon and nutrient stocks in different parts (leaves, branches, bark and stem) of the plant. The best-fitting allometric models were selected in accordance with various relevant statistical criteria.     

Structure, allometry, and biomass of plantation Metasequoia glyptostroboides in Japan

We quantified structural features and the aboveground biomass of the deciduous conifer, Metasequoia glyptostroboides (Hu and Cheng) in six plantations in central Japan. In order to derive biomass estimates we dissected 14 M. glyptostroboides trees into three structural components (stem wood, branch wood and foliage) to develop allometric equations relating the mass of these components and of the whole tree to diameter at breast height (DBH). We found robust relationships at the branch and whole tree level that allow accurate prediction of component and whole tree biomass. Dominant tree height was similar within five older (>40 years) plantations (27–33 m) and shorter in a 20-year-old plantation (18 m). Average stem diameter varied from 12.8 cm in the youngest stand to greater than 35 cm in the oldest stand.

Metasequoia have relatively compact crowns distributed over the top 30% of the tree although the youngest stand had the deepest crown relative to tree height (up to 38%). At the individual tree level in older stands, 87% of the aboveground biomass was allocated to the stem, 9% to branch wood and 4% to foliage. We found little difference in the relative distribution of above ground biomass among the stands with the exception of lower foliage biomass in larger diameter trees. Total aboveground biomass of the older stands varied twofold, ranging from a maximum of 450 Mg ha⁻¹ in a 42-year-old stand to a minimum of 196 Mg ha⁻¹ in a 48-year-old stand. Total above ground biomass of the 20-year-old stand was 176 Mg ha⁻¹.     

我国杉木通用性立木生物量模型研究

以我国南方地区的最重要针叶树种杉木为研究对象,综合利用非线性混合模型、哑变量模型和误差变量联立方程组方法,建立了适合杉木不同生长区域（总体）应用的一体化一元和二元地上生物量方程及根茎比函数.结果表明：不同总体的地上生物量模型之间存在显著差异,总体（一）的估计值要大于总体（二）,而地下生物量则差异不明显;地上和地下生物量方程的平均预估误差分别在5％和10％以内,可应用于不同区域的杉木林生物量估计.     

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。     

科尔沁沙质草地生物量对水、氮、磷添加的响应(英文)

为探寻科尔沁沙地东南部草地生产力自然恢复的主要限制因素,该文采用3因素2水平析因设计研究了水、氮、磷添加对沙质草地的影响。8种处理分别为添加水(W)、加氮肥(N)、加磷肥(P)、加水+氮肥(WN)、加水+磷肥(WP)、加氮肥+磷肥(NP)、加水+氮肥+磷肥(WNP)和对照(CK),每种处理6次重复,随机分配在48个4m×4m的样方中,样方之间留2m缓冲带。研究结果表明:科尔沁沙质草地生产力的恢复受氮素的限制,与水和磷无关;2005年生长季所有添加氮肥的样方,生物量和地上净初级生产力均较对照明显增加(P<0.05);禾本科根量在地下生物量中占优势。受限于地下生物量测定,目前的研究可能低估了我国北方草地的净初级生产力。     

Basal area growth models for individual trees of Norway spruce, Scots pine, birch and other broadleaves in Norway

Distance-independent individual tree growth models based on about 30,000 observations from the National Forest Inventory and the Norwegian Forest Research Institute have been developed for the main tree species in Norway. The models predict 5-year basal area increment over bark for trees larger than 5 cm at breast height. Potential input variables were of four types: size of the tree, competition indices, site conditions, and stand variables including species, mixtures and layers. The squared correlation coefficient (R²) varied from 0.26 to 0.55. The accuracy of the models was tested by comparing the individual tree models with Norwegian diameter increment models. The accuracy is similar, but individual tree models forecast diameter distributions directly. The inclusion of species mixture and layer as variables increases the reliability of the models in mixed and in uneven-aged stands.     

Stem biomass and volume models of selected tropical tree species in West Africa

Estimating tree volume and biomass constitutes an essential part of the forest resources assessment and the evaluation of the climate change mitigation potential of forests through biomass accumulation and carbon sequestration. This research article provides stem volume and biomass equations applicable to five tree species, namely Afzelia africana Sm. (Caesalpiniaceae), Anogeissus leiocarpa (DC.) Guill. and Perr. (Combretaceae), Ceiba pentandra (L.) Gaertn. (Bombacaceae), Dialium guineense Willd. (Caesalpiniaceae), Diospyros mespiliformis Hochst. ex A.DC. (Ebenaceae) in natural protected tropical forests and, in addition, Tectona grandis L.f. (Verbenaceae) in plantations. In addition to the tree species specific equations, basic wood density, as well as carbon, nitrogen, organic matter and ash content were determined for these tree species in tropical conditions in West Africa. One hundred and sixty-two sample trees were measured through non-destructive sampling and analysed for volume and biomass. Stem biomass and stem volume were modelled as a function of diameter (at breast height; Dbh) and stem height (height to the crown base). Logarithmic models are presented that utilise Dbh and height data to predict tree component biomass and stem volumes. Alternative models are given that afford prediction based on Dbh data alone, assuming height data to be unavailable. Models that include height are preferred, having better predictive capabilities. Ranges in carbon, nitrogen and ash contents are given as well. The successful development of predictive models through the use of non-destructive methods in this study provide valuable data and tools for use in determining the contribution of these major African rainforest tree species to global carbon stocks, while ensuring the preservation of this valued African resource. This study needs to be expanded to further regions and tree species to complete a full inventory of all tree species, emphasising the relevance of African trees to carbon stocks at a global scale.     

The inventory of carbon stock in New Zealand’s post-1989 planted forest for reporting under the Kyoto protocol

The United Nations Framework Convention on Climate Change (UNFCCC) requires reporting net carbon stock changes and anthropogenic greenhouse gas emissions, including those related to forests. This paper describes the design and implementation of a nation-wide forest inventory of New Zealand’s planted post-1989 forests that arose from Land Use, Land-Use Change and Forestry activities (LULUCF) under Article 3.3 of the Kyoto Protocol. The majority of these forests are planted with Pinus radiata, with the remainder made up of other species exotic to New Zealand. At the start of the project there was no on-going national forest inventory that could be used as a basis for calculating carbon stocks and meet Good Practice Guidelines.A network of ground-based permanent sample plots was installed with airborne LiDAR (Light Detection and Ranging) for double sampling using regression estimators to predict carbon in each of the four carbon pools of above- and below-ground live biomass, dead wood and litter. Measurement, data acquisition and quality assurance/control protocols were developed specifically for the inventory, carried out in 2007 and 2008. Plots were located at the intersection of a forest with a 4 km square grid, coincident with an equivalent 8 km square grid established over the indigenous forest and “grassland with woody biomass” (Other Wooded Land). Planted tree carbon within a ground plot was calculated by an integrated system of growth, wood density and compartment allocation models utilising the data from measurements of trees and shrubs on the plots. This system, called the Forest Carbon Predictor, predicts past and future carbon in a stand and is conditioned so that the calculated basal area and mean top height equals that obtained by conventional mensuration methods at the time of the plot measurement. Mean per hectare carbon stocks were then multiplied by an estimate of the total area of post 1989 forests obtained from wall to wall mapping using a combination of satellite imagery and ortho-photography.The network of permanent samples plots and LiDAR double sampling methodology was designed to be simple and robust to change over time. In the future, using LiDAR should achieve sampling efficiencies over using ground plots alone and reduces any problems regarding restricted access on the ground. The network is to be remeasured at the end of commitment period 1, 2012, and the carbon stocks re-estimated in order to calculate change.     

Generalized allometric volume and biomass equations for some tree species in Europe

Since biomass is one of the key variables in ecosystem studies, widespread effort has aimed to facilitating its estimation. Numerous stand-specific volume and biomass equations are available, but these cannot be used for scaling up biomass to the regional level where several age-classes and structural types of stands coexist. Therefore simplified generalized volume and biomass equations are needed. In the present study, generalized biomass and volume regression equations were developed for the main tree species in Europe. These equations were based on data compiled from several published studies and are syntheses of the published equations. The results show that these generalized equations explain 64–99% of the variation in values predicted by the original published equations, with higher values for stem than for crown components.

Generalized functions of biomass expansion factors for conifers and broadleaved by stand age,growing stock and site index

Parties to the Kyoto Protocol and/or the United Nations Framework Convention on Climate Change (UNFCCC) are required to account for their direct human-induced carbon emissions and removals including those from forestry and other land use related activities. In most European countries, the forestry related greenhouse gas inventories are largely or exclusively based on converting tree volume data from national forest inventories to biomass using biomass conversion and expansion factors (BCEFs). However, country specific data for many species are often lacking, which considerably increases the uncertainties of the greenhouse gas inventories. The focus of this research was to develop, using internationally published datasets that cover a large geographical area, an extended set of generalized curves of such biomass expansion factors for several species or species groups by age, growing stock and site index.     

Comparison between TanDEM-X- and ALS-based estimation of aboveground biomass and tree height in boreal forests

Interferometric Synthetic Aperture Radar (InSAR) data from TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) were used to estimate aboveground biomass (AGB) and tree height with linear regression models. These were compared to models based on airborne laser scanning (ALS) data at two Swedish boreal forest test sites, Krycklan (64°N19°E) and Remningstorp (58°N13°E). The predictions were validated using field data at the stand-level (0.5–26.1 ha) and at the plot-level (10 m radius). Additionally, the ALS metrics percentile 99 (p99) and vegetation ratio, commonly used to estimate AGB and tree height, were estimated in order to investigate the feasibility of replacing ALS data with TanDEM-X InSAR data. Both AGB and tree height could be estimated with about the same accuracy at the stand-level from both TanDEM-X- and ALS-based data. The AGB was estimated with 17.2% and 14.6% root mean square error (RMSE) and the tree height with 7.6% and 4.1% RMSE from TanDEM-X data at the stand-level at the two test sites Krycklan and Remningstorp. The Pearson correlation coefficients between the TanDEM-X height and the ALS height p99 were r?=?.98 and r?=?.95 at the two test sites. The TanDEM-X height contains information related to both tree height and forest density, which was validated from several estimation models.     

海南岛马占相思生物量模型构建方法研究

基于84株马占相思伐倒木实测生物量数据,构建了马占相思总量及各组分生物量估测模型,然后利用非线性度量误差方法,建立总量生物量与各组分(地上、树干、树根、树枝、树叶、树冠)的相容性生物量模型,拟采用可加性总量直接控制和分级联合控制2种方案对上述模型进行拟合和评价。结果表明:1)由胸径、树高变量构建的总量及各组分二元生物量独立模型预估效果较好,其确定系数均在0.85以上,最高达0.97;2)就可加性总量直接控制法而言,拟合的对数模型进一步提高了生物量模型的稳定性;3)这2种方案所建的二元模型拟合效果以分级联合控制方案略优,这2种模型在准确性上没有明显的差别;4)可认为利用度量误差法构建的马占相思生物量模型拟合精度高,实用性好。