Carbon Stock and Deposition in Phytomass of the Russian Forests

Isaev, A. S.; Korovin, G. N.; Zamolodchikov, D. G.; Utkin, A. I.; Pryaznikov, A.

doi:10.1007/978-94-017-0942-2_26

Cited by 27 publications

(25 citation statements)

References 9 publications

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“…The differences of our three independent estimates on phytomass of the Russian FA for 1990 (i.e., official inventory data, restored dynamics and GIS approach) are within the limits of ±2%. Our estimate differs by less than 1% from the arithmetic mean of three detailed assessments of forest phytomass reported by Alexeyev and Birdsey (1994), Isaev et al (1995) and Isaev and Korovin (1998). All of the estimates published before 1994 differ by 1.5-2 times (for a review, see Shvidenko and Nilsson, 2002).…”

Section: Discussioncontrasting

confidence: 73%

A synthesis of the impact of Russian forests on the global carbon budget for 1961–1998

Shvidenko

Nilsson

2003

Tellus B: Chemical and Physical Meteorology

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An attempt is made to synthesize the current understanding of the impact of Russian forests on the global carbon (C) budget for the period 1961-1998 (37 years), based on a detailed inventory of pools and fluxes in 1988-1992, and a historical reconstruction of a full forest carbon budget for 1961-1998. All major intermediate indicators of the budget (phytomass, net primary production, impact of disturbances, soil respiration, etc.) were independently estimated and compared with earlier reported results. During the entire period, the C pools of Russian forest land (FL, 882.0 × 10 6 ha in 1998) increased by 433 Tg C yr −1 , of which 153 Tg C yr −1 are accumulated in live biomass, 57 Tg C yr −1 in above-and belowground dead wood, and 223 Tg C yr −1 are sequestered in soil. A significant part of this increase deals with land-cover changes. The annual average C uptake by the FL from the atmosphere, defined by a flux-based method, is estimated to be −322 Tg C yr −1 for 1961-1998. The lateral transport to the lithosphere and hydrosphere comprised 47 Tg C yr −1 (including charcoal), which is considered part of the "missing C sink." The uncertainties (excluding unrecognized biases) of averages for the entire period are estimated to be in the range of ±5-8% and ±24% for major fluxes out/into the atmosphere and for net ecosystem exchange, respectively (a priori confidential probability of 0.9). If the impact of land-cover change is excluded, the average annual sink in 1961-1998, estimated by both pool-and flux-based methods, was 268 ± 94 and 272 ± 68 Tg C yr −1 , respectively. The reported results are in line with recent estimates for Northern Eurasia made by inverse modeling at the continental scale, if land classes other than forests contribute to the total sink of terrestrial vegetation.

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Section: Discussioncontrasting

confidence: 73%

A synthesis of the impact of Russian forests on the global carbon budget for 1961–1998

Shvidenko

Nilsson

2003

Tellus B: Chemical and Physical Meteorology

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“…However, the magnitude of the C sink is still controversial, partly due to uncertainties in the estimation of forest biomass carbon and its increment (Holland et al, 1999;Houghton et al, 2001;Jenkins et al, 2001;Houghton, 2003Houghton, , 2005Kauppi, 2003;Liski et al, 2003). For instance, biomass estimates of Russian forests varied from 28.0 Pg C to 35.1 Pg C by different authors (Alexeyev et al, 1995;Isaev et al, 1995) although the same forest data sources but different methods were used. In Brazil's Amazonian forests, estimates of forest biomass varied by more than a factor of two from 39 Pg C to 93 Pg C (Houghton et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…However, as a non-random sampling approach, the direct measurement of biomass on a local level does not accurately represent the average biomass within a region or country because it is often biased towards plots with large diameter trees, resulting in an overestimation of biomass (Brown et al, 1989;Botkin and Simpson, 1990;Dixon et al, 1994;Schroeder et al, 1997;Jenkins et al, 2001;Kauppi, 2003). Other approaches have used well-designed and statistically sound regional or national forest inventories available for many countries as a key data source to calculate forest biomass and account for the C budgets at regional scales (e.g., Brown et al, 1989;Kauppi et al, 1992;Birdsey, 1992;Birdsey andHeath, 1995, 2001;Alexeyev et al, 1995;Isaev et al, 1995;Turner et al, 1995;Brown and Schroeder, 1999;Fang et al, , 2005Jenkins et al, 2001;Goodale et al, 2002;Kauppi, 2003;Liski et al, 2003;Nabuurs et al, 2003;Smith et al, 2003Smith et al, , 2004.…”

Section: Introductionmentioning

confidence: 99%

Overestimated Biomass Carbon Pools of the Northern mid- and High Latitude Forests

et al. 2006

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Abstract. The biomass carbon (C) stock of forests is one of key parameters for the study of regional and global carbon cycles. Literature reviews shows that inventory-based forest C stocks documented for major countries in the middle and high northern latitudes fall within a narrow range of 36-56 Mg C ha −1 with an overall area-weighted mean of 43.6 Mg C ha −1 . These estimates are 0.40 to 0.71 times smaller than those (61-108 Mg C ha −1 ) used in previous analysis of balancing the global carbon budget. A statistical analysis, using the global forest biomass database, implies that aboveground biomass per hectare is proportional to forest mean height [biomass in Mg/ha = 10.63 (height in m)] in closed-canopy forests in the study regions, indicating that forest height can be a proxy of regional biomass C stocks. The narrow range of C stocks is likely a result of similar forest height across the northern regions. The lower biomass C stock obtained in this study strongly suggests that the role of the northern forests in the global carbon cycle needs to be re-evaluated. Our findings also suggest that regional estimates of biomass could be readily made from the use of satellite methods such as lidar that can measure forest canopy height over large regions.

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“…Although most research has been conducted for tropical forests, Houghton et al (2001;; Jenkins et al (2003);and Pacala et al (2001) pointed out that forest biomass for the mid-and high latitudes in the Northern Hemisphere is also uncertain due to error in the estimation of tree and plot biomass and its increment. For instance, biomass estimates of Russian forests varied from 112.0 Pg to 140.4 Pg by different authors (Alexeyev et al, 1995;Isaev et al, 1995) although the same forest data sources but different methods were used. Brown (1997) and Achard et al (2004);and IPCC (2006) integrated a contrast analysis.…”

Section: Uncertainties Of Agb Evaluationsmentioning

confidence: 99%

Methods od Assessment of Aboveground Tree Biomass

Návar¹

2010

Biomass

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Source: Biomass, Book edited by: Maggie Momba and Faizal Bux, ISBN 978-953-307-113-8, pp. 202, September 2010, Sciyo, Croatia, downloaded from SCIYO.COM www.intechopen.com Biomass 28 understand weakness and robustness of available methods to compute tree and eventually plot and regional aboveground biomass. For places deprived of tree allometry, a combination of a wide range of allometric equations developed off site appears to improve tree M evaluations according to the Central Limit Theorem. Biomass stocks and their spatial distribution remain poorly evaluated at the plot scale regardless of the wealth of information on tree biomass allometry (Chavé et al., 2003; Houghton et al., 2001, 20015;Návar et al., 2010). The conventional methodology that expands tree M to sample inventory stands is: a) a grid of sampling plots and b) allometric equations fit tree data recorded in the forest inventory, since there is scarce information on allometric equations that straightforward calculate plot or stand M. New approaches that employ timber volume are named BEF and at the present they require calibration to appraise local plot M (Brown, 2002). Uncertainties of more than two orders of magnitude are identified when calculating plot M by applying different off site allometric models to forest inventory datasets and main sources of variation are: a) the error due to tree measurements, b) ground sampling uncertainty, and above all, c) the error due to the choice of an allometric model relating M to other tree dimensions (Chavé et al., 2003;Návar et al., 2010). Tree or plot M interpolates at larger spatial scales, AGB, by a variety of field measurements, environmental gradients and remote sensing techniques (Houghton, 2005a,b). A diversity of remote sensing techniques, spatial resolutions, tree and forest attributes, and interpolation methodologies make AGB assessment highly variable, with uncertainties as large as three orders of magnitude. Main sources of variation are attributable to: a) the precision of estimated tree or stand M, b) the interpolation method applied, c) the lack of a good correlation between ground and remote sensing data, d) the correct location of ground data, e) the representativeness of plots across the landscape, f) temporal variations in the satellite image, g) the correct area of each forest class, and h) others. Combining remote field data collection techniques (LIDAR) with locally-derived tree allometry and the semi-empirical shape-dimensional non-destructive model of tree M assessment would eventually improve AGB at the spatial scale of interest. Given this brief literature review, the reliable M estimation of trees, plots, stands or tree communities remains a key challenge for the successful implementation of sustainable forest management plans. This paper deals with the description of available tree allometry, how they contrast to provide tree, plot and regional M assessments and what are the future challenges ahead. Preliminarily observations point towards the combination of available conv...

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Carbon Stock and Deposition in Phytomass of the Russian Forests

Cited by 27 publications

References 9 publications

A synthesis of the impact of Russian forests on the global carbon budget for 1961–1998

A synthesis of the impact of Russian forests on the global carbon budget for 1961–1998

Overestimated Biomass Carbon Pools of the Northern mid- and High Latitude Forests

Methods od Assessment of Aboveground Tree Biomass

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