Improved Estimates of Biomass Expansion Factors for Russian Forests

Schepaschenko, Dmitry; Moltchanova, Elena; Shvidenko, А.; Blyshchyk, Volodymyr; Дмитриев, Е. В.; Martynenko, Olga; See, Linda; Kraxner, Florian

doi:10.3390/f9060312

Cited by 57 publications

(61 citation statements)

References 16 publications

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“…This wide range of uncertainty highlights the need for providing locally-fit models. This tendency was also reflected in the study of Schepaschenko et al [41], who found high-level geographic variability of BCEFs values across climatic gradients in Russia. The high variability of BCEFs provided by IPCC is most probably connected with its wide range of geographic origin: among eight source studies one comes from China, one form Japan, one from Australia, four from USA and one from Germany.…”

Section: Accuracy Of Stand-level Biomass Modelssupporting

confidence: 61%

“…Analysis of accuracy of our and published models for biomass estimation at the stand level revealed that height-based models and estimation using BCEF models provided the lowest biases ( Figure 6, NSE of 0.81 and 0.99, respectively). Approaches recommended by IPCC and Schepaschenko et al [41] provided overestimated biomass, which deviated the most in cases of the biggest forest stands (NSE of −0.86, −0.23 and 0.40, respectively). Models provided by Teobaldelli et al [24] provided lower overestimation of L. decidua biomass (NSE of 0.77 and 0.99, for age-and volume-based models, respectively).…”

Section: Comparison Of Stand Level Biomass Modelsmentioning

confidence: 92%

“…Applying BCEF values for boreal Larix from IPCC guidelines resulted in an average of 71.5% higher biomass and for temperate coniferous excluding Pinus-an average of 91.8% higher biomass. Some studies provide BCEF values lower than those provided by IPCC [21,41]. However, applying the lower value of the range provided by IPCC to temperate coniferous species resulted in mean underestimation of biomass by an average of 19.9%.…”

Section: Accuracy Of Stand-level Biomass Modelsmentioning

confidence: 94%

“…Castedo-Dorado et al [20] presented stand-level biomass estimation for six tree species (including three conifers: Pinus pinaster Aiton, P. radiata D. Don and P. sylvestris), and found that forest stand density was the weakest predictor of forest stand biomass. It may be connected with the trade-off in usage of resources between trees in dense and sparse forest stands-the same amount of light allows for growth of more trees with thinner trunks or fewer with thicker trunks, and also influences belowground biomass [41]. Thus, it is also possible that trees growing in lower density stands invest more resources to belowground biomass (e.g., to coarse roots) for mechanical stability than to aboveground biomass for better capture of light.…”

Section: Influence Of Forest Stand Parameters On Biomass Estimationmentioning

confidence: 99%

“…Literature review of stand-level biomass estimation for L. decidua also revealed relatively small data sets. Orzeł et al [40] showed mean BCEF for aboveground biomass of mixed L. decidua forest stands of 0.5828 t m −3 and Schepaschenko et al [41] for Larix spp. stands of 0.640 ± 0.008.…”

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Tree- and Stand-Level Biomass Estimation in a Larix decidua Mill. Chronosequence

Jagodziński

Dyderski

Gęsikiewicz

et al. 2018

Forests

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Carbon pool assessments in forests is one of the most important tasks of forest ecology. Despite the wide cultivation range, and economical and traditional importance, the aboveground biomass of European larch (Larix decidua Mill.) stands is poorly characterized. To increase knowledge about forest biomass accumulation and to provide a set of tools for aboveground biomass estimation, we studied a chronosequence of 12 larch forest stands (7-120 years old). From these stands, we measured the biomass of 96 sample trees ranging from 1.9 to 57.9 cm in diameter at breast height. We provided age-specific and generalized allometric equations, biomass conversion and expansion factors (BCEFs) and biomass models based on forest stand characteristics. Aboveground biomass of stands ranged from 4.46 (7-year-old forest stand) to 445.76 Mg ha −1 (106-year-old). Stand biomass increased with increasing stand age, basal area, mean diameter, height and total stem volume and decreased with increasing density. BCEFs of the aboveground biomass and stem were almost constant (mean BCEFs of 0.4688 and 0.3833 Mg m −3 , respectively). Our generalized models at the tree and stand level had lower bias in predicting the biomass of the forest stands studied, than other published models. The set of tools provided fills the gap in biomass estimation caused by the low number of studies on larch biomass, which allows for better estimation of forest carbon pools.Forest stand biomass can be estimated at two levels of detail: tree level and stand level. For tree level the most frequent approach is usage of allometric equations (AEs)-regression models allowing for tree biomass estimation using simple measurements, i.e., diameter at breast height or height [17][18][19]. AEs based on individual tree measurements require time-and money-consuming inventories, but yield higher accuracy of biomass estimation. Stand level estimation is usually conducted using forest stand models (e.g., [20,21]) or biomass expansion and conversion factors (BCEFs; e.g., [12,[22][23][24]). BCEFs are literally proportions of forest stand biomass and tree stand volume, allowing for easy conversion of forest inventory data (growing stock) into biomass. However, this approach is double-biased by errors of statistical models and errors of forest inventory. For example, BCEFs are not useful in cases of young forest stands where volume of merchantable wood is underestimated [21].The need for elaboration of more accurate biomass models is connected with the variability of biomass allocation patterns. The proportions of carbohydrates invested in development of particular organs vary climatically [25], but also with increasing age (e.g., [26][27][28]). For that reason, younger trees need separate models both at the tree (e.g., [23,29,30]) and stand (e.g., [21,22,24]) levels. Moreover, according to metabolic scaling theory [31] and its expansion [32], biomass allocation is connected with tree dimensions. In cases of managed forests, initial spacing of forest stands also influence both bioma...

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Section: Accuracy Of Stand-level Biomass Modelssupporting

confidence: 61%

Section: Comparison Of Stand Level Biomass Modelsmentioning

confidence: 92%

Section: Accuracy Of Stand-level Biomass Modelsmentioning

confidence: 94%

Section: Influence Of Forest Stand Parameters On Biomass Estimationmentioning

confidence: 99%

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confidence: 99%

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