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Probability density functions and 95% confidence intervals of BEF values depending on the correlation of uncertainties with varying percentage of fixed intraplot variance, on x-axis the predicted BEF and on y-axis the frequency of observations based on Monte Carlo simulations (according to Tab. IV).
Source publication
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...
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Context 1
... uncertainty in the BEF was affected by the intraplot residual error dependency (i.e. the dependency between resid- ual errors of the biomass and volume models) (Fig. 3). The BEF showed 95% confidence intervals of 0.48 and 0.68 in the independent errors and 0.47 and 0.71 with full correlation be- tween the biomass and volume model residual errors at the plot level. The confidence intervals were 0.47 and 0.71 when the percentage of constant intraplot residual variance between the model errors was ...
Context 2
... of 0.48 and 0.68 in the independent errors and 0.47 and 0.71 with full correlation be- tween the biomass and volume model residual errors at the plot level. The confidence intervals were 0.47 and 0.71 when the percentage of constant intraplot residual variance between the model errors was reduced to 60%, while being 0.47 and 0.70 with 40% (Fig. 3 and Tab. ...
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Citations
... At present, monitoring grassland biomass mainly involves ground-based monitoring and remote sensing monitoring . Limited by labor and material resources, for groundbased monitoring, the large-scale monitoring, high-efficiency monitoring, and whole-process monitoring of grassland biomass are challenging (Catchpole and Wheeler, 1992;Lehtonen et al., 2007) while remote sensing monitoring is the most effective method for estimating grassland biomass in long time series and over large areas (Craine and Nippert, 2014;Eisfelder et al., 2012). ...
Grassland biomass monitoring is essential for assessing grassland health and carbon cycling. However, monitoring grassland biomass in drylands based on satellite remote sensing is challenging.Statistical regression models and machine learning have been used for the construction of grassland biomass models, but the predictive power for different grassland types is unclear. Additionally, the selection of the most appropriate variables to construct a biomass inversion model for different grassland types must be explored. Therefore,1201 ground-truthed data points collected from 2014-2021,including 15 Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices,geographic location and topographic data,and meteorological factors and vegetation biophysical indicators were screened for key variables using principal component analysis (PCA). The accuracy of multiple linear regression models, exponential regression models, power function models, support vector machine (SVM) models, random forest (RF) models, and neural network models was evaluated for the inversion of three types of grassland biomass. The results were as follows: (1) The biomass inversion accuracy of single vegetation indices was low, and the optimal vegetation indices were the soil-adjusted vegetation index (SAVI) (R2 = 0.255), normalized difference vegetation index (NDVI) (R2 = 0.372) and optimized soil-adjusted vegetation index (OSAVI) (R2 = 0.285). (2)Grassland above-ground biomass (AGB) was affected by various factors such as geographic location,topography, and meteorological factors, and the inverse models using a single environmental variable had large errors. (3) The main variables used to model biomass in the three types of grasslands were different. SAVI, aspect, slope, and precipitation (Prec.) were selected for desert grasslands; NDVI,shortwave infrared 2 (SWI2), longitude, mean temperature, and annual precipitation were selected for steppe;and OSAVI, phytochrome ratio (PPR), longitude, precipitation, and temperature were selected for meadows. (4) The non-parametric meadow biomass model was superior to the statistical regression model. (5) The RF model was the best model for the inversion of grassland biomass in Xinjiang, and this model had the highest accuracy for grassland biomass inversion (R2 = 0.656, root mean square error (RMSE) = 815.6 kg/ha),followed by meadow (R2 = 0.610, RMSE = 547.9 kg/ha) and desert grassland (R2 = 0.441, RMSE = 353.6 kg/ha).
... Moreover, site quality, stand density, and management measurements are incorporated into the growth models to improve their prediction accuracy or analyze the environmental effects. Among them, biomass equations only using the independent variable DBH or both independent variables DBH and H are widely used at the individual tree or stand scale [32][33][34]. Moreover, the individual tree models incorporating the aging variable are established to describe biomass growth [22,35]. ...
Forest biomass measurement or estimation is critical for forest monitoring at the stand scale, but errors among different estimations in stand investigation are unclear. Thus, the Pinus densata natural forest in Shangri-La City, southwestern China, was selected as the research object to investigate the biomass of 84 plots and 100 samples of P. densata. The stand biomass was calculated using five methods: stand biomass growth with age (SBA), stem biomass combined with the biomass expansion factors (SB+BEF), stand volume combined with biomass conversion and expansion factors (SV+BCEF), individual tree biomass combined with stand diameter structure (IB+SDS), and individual tree biomass combined with stand density (IB+SD). The estimation errors of the five methods were then analyzed. The results showed that the suitable methods for estimating stand biomass are SB+BEF, M+BCEF, and IB+SDS. When using these three methods (SB+BEF, SV+BCEF, and IB+SDS) to estimate the biomass of different components, wood biomass estimation using SB+BEF is unsuitable, and root biomass estimation employing the IB+SDS method was not preferred. The SV+BCEF method was better for biomass estimation. Except for the branches, the mean relative error (MRE) of the other components presented minor errors in the estimation, while MRE was lower than other components in the range from −0.11%–28.93%. The SB+BEF was more appealing for branches biomass estimation, and its MRE is only 0.31% lower than SV+BCEF. The stand biomass strongly correlated with BEF, BCEF, stand structure, stand age, and other factors. Hence, the stand biomass growth model system established in this study effectively predicted the stand biomass dynamics and provided a theoretical basis and practical support for accurately estimating forest biomass growth.
... It is recommended to adopt the hyperbolic function with the inclusion of spatial error model and with D g as the predictor to estimate BCEF. 李 海 奎 等, 2012; Levy et al., 2004;Liski et al., 2006) , Shvidenko et al., 2002;张茂震等, 2009) ( Lehtonen et al., 2007;罗云建等, 2009) West et al., 1984;Gregorie, 1987) 。 综上 可 知, 关 于 BCEF 的 研 究 存 在 以 下 问 题: ...
Based on the national forest inventory sample plot data of larch plantation in northeast China, the best model form of biomass conversion and expansion factor(BCEF) were discussed, and the spatial autoregressive BCEF model was established for larch plantation in northeast China. The model is useful for accurate stand biomass estimations. Method: Selecting a variety of model forms to establish BCEF general regression model, from which the best fitting model is selected. The two spatial autoregressive methods, spatial error model(SEM) and spatial lag model(SLM), were used to renew the BCEF model. The determination coefficient(R2), root mean square error (RMSE) and relative root mean square error(rRMSE) were used to evaluate the model. Moran index(MI) was applied to test the spatial autocorrelation of all variables and BCEF model residuals. Result: 1) There is obvious spatial autocorrelation in BCEF data. When the spatial distance is small, the BCEF attributes of stands within a province are similar. The differences of BCEF attributes among provinces are gradually appeared with the increase of spatial distance, and tend to random distribution finally. 2) The fitting results of allometric model, logarithmic model and hyperbolic model are better than those of other regression models, and the optimal models varied with independent variables. Stand quadratic mean diameter (Dg) is the best variable for interpreting BCEF. The R2 of the effective model with Dg as an independent variable is between 0.945 and 0.958. Followed by stand mean height(H) and volume(V), the R2 of the effective model is more than 0.60. The explanatory ability of stand average age is slightly lower than that of Dg, H and V, and the R2 of its effective model is only about 0.50. Stand basal area(BA) and density(N) are poorly to explain the variance of BCEF with R2 less than 0.50. The residuals of the general regression model showed spatial autocorrelation. 3) The spatial autoregressive model of hyperbolic function with Dg as an independent variable is the best one with SEM better than SLM. Compared with the corresponding ordinary regression model, the R2 of SEM is increased by 3%, and the RMSE and rRMSE are reduced by 33% and 35%, respectively. The MI of the model residual is less than 0.02, which eliminates the spatial autocorrelation. Conclusion: The hyperbolic model is the most stable model for BCEF, and Dg is the best independent variable. It is recommended to adopt the hyperbolic function with the inclusion of spatial error model and with Dg as the predictor to estimate BCEF.
... When using regression model to predict forest biomass at large scale, the sources of uncertainty of estimated results included [87][88][89][90][91]: (1) measurement error of forest area [92,93]; ...
The accurate estimation of forest biomass is crucial for supporting climate change mitigation efforts such as sustainable forest management. Although traditional regression models have been widely used to link stand biomass with biotic and abiotic predictors, this approach has several disadvantages, including the difficulty in dealing with data autocorrelation, model selection, and convergence. While machine learning can overcome these challenges, the application remains limited, particularly at a large scale with consideration of climate variables. This study used the random forests (RF) algorithm to estimate stand aboveground biomass (AGB) and total biomass (TB) of larch (Larix spp.) plantations in north and northeast China and quantified the contributions of different predictors. The data for modelling biomass were collected from 445 sample plots of the National Forest Inventory (NFI). A total of 22 independent variables (6 stand and 16 climate variables) were used to develop and train climate-sensitive stand biomass models. Optimization of hyper parameters was implemented using grid search and 10-fold cross-validation. The coefficient of determination (R2) and root mean square error (RMSE) of the RF models were 0.9845 and 3.8008 t ha−1 for AGB, and 0.9836 and 5.1963 t ha−1 for TB. The cumulative contributions of stand and climate factors to stand biomass were >98% and <2%, respectively. The most crucial stand and climate variables were stand volume and annual heat-moisture index (AHM), with relative importance values of >60% and ~0.25%, respectively. The partial dependence plots illustrated the complicated relationships between climate factors and stand biomass. This study illustrated the power of RF for estimating stand biomass and understanding the effects of stand and climate factors on forest biomass. The application of RF can be useful for mapping of large-scale carbon stock.
... Ponce-Hernandez [64] described the principle of tree allometry in detail in connection with the measurement of carbon in biomass. Allometry, namely the biomass expansion factor (BEF) and biomass equations, is often one of main challenges in NFI use [62,65]. This is partly due to the expansion factors and equations being based on local studies [62], which may also be affected by the biomass growth in spruce forests that are recently significantly accelerating [66]. ...
Given the significance of national carbon inventories, the importance of large-scale estimates of carbon stocks is increasing. Accurate biomass estimates are essential for tracking changes in the carbon stock through repeated assessment of carbon stock, widely used for both vegetation and soil, to estimate carbon sequestration. Objectives: The aim of our study was to determine the variability of several aspects of the carbon stock value when the input matrix was (1) expressed either as a vector or as a raster; (2) expressed as in local (1:10,000) or regional (1:100,000) scale data; and (3) rasterized with different pixel sizes of 1, 10, 100, and 1000 m. Method: The look-up table method, where expert carbon content values are attached to the mapped landscape matrix. Results: Different formats of input matrix did not show fundamental differences with exceptions of the biggest raster of size 1000 m for the local level. At the regional level, no differences were notable. Conclusions: The results contribute to the specification of best practices for the evaluation of carbon storage as a mitigation measure, as well as the implementation of national carbon inventories.
... BEFs, including their generalization of stand biomass functions depending on stand volume, can be affected by environmental conditions and stand characteristics, such as the species composition (Lehtonen et al. 2004;Soares and Tomé 2004;Lehtonen et al. 2007;Petersson et al. 2012;Jagodziński et al. 2017). Some authors have also pointed to the dependence of the stand biomass-volume relationship on age or stand development stage (Jalkanen et al. 2005;Peichl and Arain 2007;Tobin and Nieuwenhuis 2007;Teobaldelli et al. 2009;Jagodziński et al. 2017). ...
Background
National and international institutions periodically demand information on forest indicators that are used for global reporting. Among other aspects, the carbon accumulated in the biomass of forest species must be reported. For this purpose, one of the main sources of data is the National Forest Inventory (NFI), which together with statistical empirical approaches and updating procedures can even allow annual estimates of the requested indicators.
Methods
Stand level biomass models, relating the dry weight of the biomass with the stand volume were developed for the five main pine species in the Iberian Peninsula ( Pinus sylvestris , Pinus pinea , Pinus halepensis , Pinus nigra and Pinus pinaster ). The dependence of the model on aridity and/or mean tree size was explored, as well as the importance of including the stand form factor to correct model bias. Furthermore, the capability of the models to estimate forest carbon stocks, updated for a given year, was also analysed.
Results
The strong relationship between stand dry weight biomass and stand volume was modulated by the mean tree size, although the effect varied among the five pine species. Site humidity, measured using the Martonne aridity index, increased the biomass for a given volume in the cases of Pinus sylvestris , Pinus halepensis and Pinus nigra . Models that consider both mean tree size and stand form factor were more accurate and less biased than those that do not. The models developed allow carbon stocks in the main Iberian Peninsula pine forests to be estimated at stand level with biases of less than 0.2 Mg∙ha − 1 .
Conclusions
The results of this study reveal the importance of considering variables related with environmental conditions and stand structure when developing stand dry weight biomass models. The described methodology together with the models developed provide a precise tool that can be used for quantifying biomass and carbon stored in the Spanish pine forests in specific years when no field data are available.
... By using regression models in biomass estimation, uncertainties resulting from resolution, predictors, and specifications may exist (Sileshi and Gudeta, 2014;Fu et al., 2017a;Lehtonen et al., 2007;Zhou et al., 2019). For biomass models with multiple predictors, stand variables, such as density, average height, age, and volume, are examined, but environmental factors are always neglected. ...
Accurate forest carbon service accounting is essential for climate change mitigation. At present, the knowledge about whether and how climate-sensitive stand biomass models could reduce the uncertainty of forest biomass/carbon estimation is lacking. Hence, the aim of this study is to develop climate-sensitive stand biomass models and quantify their differences. Data containing 539 sample plots of larch plantations in northern and northeastern China were utilized to develop two basic and the corresponding climate-sensitive stand biomass models. Owing to the uncertainty from predictors, the forecast combination method was used to combine the two basic models (FCMs) and the two climate-sensitive models (CS-FCMs) and to quantify the difference in biomass estimations at the plot and regional scales. Results showed that the adjusted R² values of the stand biomass models were greater than 0.85 and the relative root mean square errors ranged from 5.51% to 22.62%. The CS-FCMs produced more accurate biomass estimates than the FCMs. The difference in biomass estimations derived from biomass models with and without climatic variables was 411,549 tons or 0.27% at the regional scale, but the carbon value difference amounted to about $8.3 million. This study underlined the importance of accurate carbon accounting based on climate-modified stand biomass models for forest carbon management.
... There are offered the BEFs in the form of mean values for woody species (Makarevskiy, 1991;Isaev et al., 1993;Van Camp et al., 2004;Durkaya et al., 2020), or dependent on stand age (Zamolodchikov et al., 1998;Lehtonen et al., 2007;Van Den Berge et al., 2021) or dependent on stem volume (Guo et al., 2010;Tang et al., 2016), or dependent on several of biometric indices obtained in the course of forest inventory Usoltsev et al., 2008;Teobaldelli et al., 2009;Usoltsev et al., 2011;González-García et al., 2013). ...
... Wirth et al. [26] pointed out the need to find BEFs for young trees, as those established for older trees are not readily applicable due to different biomass allocation patterns at different ages. Also, Pajtík et al. [18], Petersson et al. [19], Lehtonen et al. [23,105] and Tobin and Nieuwenhuis [106] showed that BEFs vary with tree size and age. ...
Converting data from national forest inventories to carbon stocks for greenhouse gas reporting generally relies on biomass expansion factors (BEFs) that expand stem volumes to whole tree volumes. However, BEFs for trees outside forests like trees in hedgerows are not yet included in the IPCC reports. These are expected to be different from forest trees as hedgerow trees are exposed to more solar radiation and have more growing space. We present age-dependent BEF curves for hedgerow-grown pedunculate oak (Quercus robur L.), common alder (Alnus glutinosa (L.) Gaertn.) and silver birch (Betula pendula Roth). We scanned 73 trees in northern Belgium using terrestrial LiDAR (Light Detection and Ranging). Via quantitative structure models, we estimated total volume and stem volume (diameter greater than 7 cm); we then calculated BEF as the ratio of total volume to stem volume. BEFs decreased exponentially with tree age, converging at 1.18, 1.9 and 1.92 for alder, birch and oak, respectively. For alder, this value is comparable to values of forest-grown alder; for birch and oak, these values are substantially higher, indicating a bigger part of the total volume is branch wood instead of stem wood. Total wood volume in hedgerows varied from 131.2 to 751.8 m³ per running kilometre, accounting for 30.0 to 222.8 Mg carbon stored, respectively. Only half of the produced wood in hedgerows was classified as stem wood, the other half as branch wood. Our findings show that hedgerow-specific BEFs should be used when applications for biobased economies are drafted. Also, hedgerows should be included in national carbon budgets as they represent non-negligible stocks.
... Wirth et al. (2004) pointed out the need to find BEFs for young trees, as those established for older trees are not readily applicable due to different biomass allocation patterns at different ages. Also Lehtonen et al. (2004Lehtonen et al. ( , 2007, Tobin and Nieuwenhuis (2007), Pajtík et al. (2011) and Petersson et al. (2012) showed that BEFs vary with tree size and age. ...
Climate change and loss of biodiversity are causing important economic and societal problems that are among the greatest global challenges of our time. Despite being a significant contributor to climate change and biodiversity loss, agriculture also has the capacity to provide solutions in this challenge. Agricultural lands can be designed and managed to be multifunctional, i.e. providing not only food, but also supporting biodiversity and delivering a broad set of ecosystem services. Hedgerow systems can play an important role in the realisation of such multifunctional landscapes. In this study, we address some of the pressing knowledge gaps on the plant biodiversity supported by hedgerow systems, the level of wood production they provide, and the degree to which they contribute to carbon sequestration.
The high proportion of plant species present in the hedgerow systems clearly emphasises their role as (surrogate) habitat in the open landscape. The observed increase in plant species richness over a period of 40 years in hedgerow systems – opposite to the trend in nearby forests – indicates their importance as refugia and source habitats for plant species of both the open and closed habitats. Hedgerow trees are exposed to substantial solar radiation and consequently develop heavy crowns, resulting in higher proportions of branch wood (logs with diameter < 7 cm) compared to forest trees. Tree densities in hedgerow systems are high and hedgerow trees show a continuous diameter growth with aging, resulting in high yearly wood increments and carbon sequestration rates in their above-ground biomass. In addition, also in the hedgerow soil, carbon is sequestered through decomposition processes resulting in significantly higher soil carbon stocks compared to grass margins.
Our findings help to underpin the multifunctional value of hedgerow systems in agricultural lands. We argue that hedgerow conservation will benefit biodiversity at the landscape level. Also, it could be very interesting to include hedgerow systems in landscape biomass budgets, using hedgerow-specific allometries (wood increments, proportion branch wood). Moreover, hedgerow systems represent non-negligible carbon stocks in biomass and soil and should be included in national carbon budgets.
