Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea
Abstract Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lam, Tzeng Yih [verfasserIn] Li, Xiaodong [verfasserIn] Kim, Rae Hyun [verfasserIn] Lee, Kyeong Hak [verfasserIn] Son, Yeong Mo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of forestry research - Harbin : Univ., 1990, 26(2015), 4 vom: 21. Juni, Seite 875-885 |
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| Übergeordnetes Werk: |
volume:26 ; year:2015 ; number:4 ; day:21 ; month:06 ; pages:875-885 |
| Links: |
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| DOI / URN: |
10.1007/s11676-015-0089-x |
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SPR02220055X |
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| 520 | |a Abstract Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional biomass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF) and root-to-shoot ratio (RSR). Our objectives were to select probability density functions (PDFs) that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. From these publications we chose 52 study sites spread out across South Korea. The statistical model for the meta-analysis was a multilevel model with publication (data source) as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quantitative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publications were 1.016 (0.800–1.299), 1.414 (1.304–1.560) and 0.260 (0.200–0.335), respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation. | ||
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| 650 | 4 | |a Nesting structure |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Biomass estimation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Li, Xiaodong |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Rae Hyun |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Kyeong Hak |e verfasserin |4 aut | |
| 700 | 1 | |a Son, Yeong Mo |e verfasserin |4 aut | |
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10.1007/s11676-015-0089-x doi (DE-627)SPR02220055X (SPR)s11676-015-0089-x-e DE-627 ger DE-627 rakwb eng 630 640 ASE Lam, Tzeng Yih verfasserin aut Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional biomass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF) and root-to-shoot ratio (RSR). Our objectives were to select probability density functions (PDFs) that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. From these publications we chose 52 study sites spread out across South Korea. The statistical model for the meta-analysis was a multilevel model with publication (data source) as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quantitative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publications were 1.016 (0.800–1.299), 1.414 (1.304–1.560) and 0.260 (0.200–0.335), respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation. Uncertainty analysis (dpeaa)DE-He213 Monte Carlo simulation (dpeaa)DE-He213 Bayesian hierarchical model (dpeaa)DE-He213 Nesting structure (dpeaa)DE-He213 Biomass estimation (dpeaa)DE-He213 Li, Xiaodong verfasserin aut Kim, Rae Hyun verfasserin aut Lee, Kyeong Hak verfasserin aut Son, Yeong Mo verfasserin aut Enthalten in Journal of forestry research Harbin : Univ., 1990 26(2015), 4 vom: 21. Juni, Seite 875-885 (DE-627)529093545 (DE-600)2299615-1 1993-0607 nnns volume:26 year:2015 number:4 day:21 month:06 pages:875-885 https://dx.doi.org/10.1007/s11676-015-0089-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 26 2015 4 21 06 875-885 |
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10.1007/s11676-015-0089-x doi (DE-627)SPR02220055X (SPR)s11676-015-0089-x-e DE-627 ger DE-627 rakwb eng 630 640 ASE Lam, Tzeng Yih verfasserin aut Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional biomass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF) and root-to-shoot ratio (RSR). Our objectives were to select probability density functions (PDFs) that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. From these publications we chose 52 study sites spread out across South Korea. The statistical model for the meta-analysis was a multilevel model with publication (data source) as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quantitative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publications were 1.016 (0.800–1.299), 1.414 (1.304–1.560) and 0.260 (0.200–0.335), respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation. Uncertainty analysis (dpeaa)DE-He213 Monte Carlo simulation (dpeaa)DE-He213 Bayesian hierarchical model (dpeaa)DE-He213 Nesting structure (dpeaa)DE-He213 Biomass estimation (dpeaa)DE-He213 Li, Xiaodong verfasserin aut Kim, Rae Hyun verfasserin aut Lee, Kyeong Hak verfasserin aut Son, Yeong Mo verfasserin aut Enthalten in Journal of forestry research Harbin : Univ., 1990 26(2015), 4 vom: 21. Juni, Seite 875-885 (DE-627)529093545 (DE-600)2299615-1 1993-0607 nnns volume:26 year:2015 number:4 day:21 month:06 pages:875-885 https://dx.doi.org/10.1007/s11676-015-0089-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 26 2015 4 21 06 875-885 |
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10.1007/s11676-015-0089-x doi (DE-627)SPR02220055X (SPR)s11676-015-0089-x-e DE-627 ger DE-627 rakwb eng 630 640 ASE Lam, Tzeng Yih verfasserin aut Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional biomass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF) and root-to-shoot ratio (RSR). Our objectives were to select probability density functions (PDFs) that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. From these publications we chose 52 study sites spread out across South Korea. The statistical model for the meta-analysis was a multilevel model with publication (data source) as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quantitative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publications were 1.016 (0.800–1.299), 1.414 (1.304–1.560) and 0.260 (0.200–0.335), respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation. Uncertainty analysis (dpeaa)DE-He213 Monte Carlo simulation (dpeaa)DE-He213 Bayesian hierarchical model (dpeaa)DE-He213 Nesting structure (dpeaa)DE-He213 Biomass estimation (dpeaa)DE-He213 Li, Xiaodong verfasserin aut Kim, Rae Hyun verfasserin aut Lee, Kyeong Hak verfasserin aut Son, Yeong Mo verfasserin aut Enthalten in Journal of forestry research Harbin : Univ., 1990 26(2015), 4 vom: 21. Juni, Seite 875-885 (DE-627)529093545 (DE-600)2299615-1 1993-0607 nnns volume:26 year:2015 number:4 day:21 month:06 pages:875-885 https://dx.doi.org/10.1007/s11676-015-0089-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 26 2015 4 21 06 875-885 |
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10.1007/s11676-015-0089-x doi (DE-627)SPR02220055X (SPR)s11676-015-0089-x-e DE-627 ger DE-627 rakwb eng 630 640 ASE Lam, Tzeng Yih verfasserin aut Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional biomass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF) and root-to-shoot ratio (RSR). Our objectives were to select probability density functions (PDFs) that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. From these publications we chose 52 study sites spread out across South Korea. The statistical model for the meta-analysis was a multilevel model with publication (data source) as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quantitative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publications were 1.016 (0.800–1.299), 1.414 (1.304–1.560) and 0.260 (0.200–0.335), respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation. Uncertainty analysis (dpeaa)DE-He213 Monte Carlo simulation (dpeaa)DE-He213 Bayesian hierarchical model (dpeaa)DE-He213 Nesting structure (dpeaa)DE-He213 Biomass estimation (dpeaa)DE-He213 Li, Xiaodong verfasserin aut Kim, Rae Hyun verfasserin aut Lee, Kyeong Hak verfasserin aut Son, Yeong Mo verfasserin aut Enthalten in Journal of forestry research Harbin : Univ., 1990 26(2015), 4 vom: 21. Juni, Seite 875-885 (DE-627)529093545 (DE-600)2299615-1 1993-0607 nnns volume:26 year:2015 number:4 day:21 month:06 pages:875-885 https://dx.doi.org/10.1007/s11676-015-0089-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 26 2015 4 21 06 875-885 |
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10.1007/s11676-015-0089-x doi (DE-627)SPR02220055X (SPR)s11676-015-0089-x-e DE-627 ger DE-627 rakwb eng 630 640 ASE Lam, Tzeng Yih verfasserin aut Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional biomass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF) and root-to-shoot ratio (RSR). Our objectives were to select probability density functions (PDFs) that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. From these publications we chose 52 study sites spread out across South Korea. The statistical model for the meta-analysis was a multilevel model with publication (data source) as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quantitative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publications were 1.016 (0.800–1.299), 1.414 (1.304–1.560) and 0.260 (0.200–0.335), respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation. Uncertainty analysis (dpeaa)DE-He213 Monte Carlo simulation (dpeaa)DE-He213 Bayesian hierarchical model (dpeaa)DE-He213 Nesting structure (dpeaa)DE-He213 Biomass estimation (dpeaa)DE-He213 Li, Xiaodong verfasserin aut Kim, Rae Hyun verfasserin aut Lee, Kyeong Hak verfasserin aut Son, Yeong Mo verfasserin aut Enthalten in Journal of forestry research Harbin : Univ., 1990 26(2015), 4 vom: 21. Juni, Seite 875-885 (DE-627)529093545 (DE-600)2299615-1 1993-0607 nnns volume:26 year:2015 number:4 day:21 month:06 pages:875-885 https://dx.doi.org/10.1007/s11676-015-0089-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 26 2015 4 21 06 875-885 |
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Enthalten in Journal of forestry research 26(2015), 4 vom: 21. Juni, Seite 875-885 volume:26 year:2015 number:4 day:21 month:06 pages:875-885 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR02220055X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111091807.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11676-015-0089-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR02220055X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11676-015-0089-x-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">630</subfield><subfield code="a">640</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lam, Tzeng Yih</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional biomass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF) and root-to-shoot ratio (RSR). Our objectives were to select probability density functions (PDFs) that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. From these publications we chose 52 study sites spread out across South Korea. The statistical model for the meta-analysis was a multilevel model with publication (data source) as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quantitative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publications were 1.016 (0.800–1.299), 1.414 (1.304–1.560) and 0.260 (0.200–0.335), respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Uncertainty analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Monte Carlo simulation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian hierarchical model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nesting structure</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biomass estimation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Xiaodong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kim, Rae Hyun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lee, Kyeong Hak</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Son, Yeong Mo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of forestry research</subfield><subfield code="d">Harbin : Univ., 1990</subfield><subfield code="g">26(2015), 4 vom: 21. 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Lam, Tzeng Yih |
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Lam, Tzeng Yih ddc 630 misc Uncertainty analysis misc Monte Carlo simulation misc Bayesian hierarchical model misc Nesting structure misc Biomass estimation Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea |
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630 640 ASE Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea Uncertainty analysis (dpeaa)DE-He213 Monte Carlo simulation (dpeaa)DE-He213 Bayesian hierarchical model (dpeaa)DE-He213 Nesting structure (dpeaa)DE-He213 Biomass estimation (dpeaa)DE-He213 |
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ddc 630 misc Uncertainty analysis misc Monte Carlo simulation misc Bayesian hierarchical model misc Nesting structure misc Biomass estimation |
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ddc 630 misc Uncertainty analysis misc Monte Carlo simulation misc Bayesian hierarchical model misc Nesting structure misc Biomass estimation |
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Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea |
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Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea |
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Lam, Tzeng Yih Li, Xiaodong Kim, Rae Hyun Lee, Kyeong Hak Son, Yeong Mo |
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bayesian meta-analysis of regional biomass factors for quercus mongolica forests in south korea |
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Bayesian meta-analysis of regional biomass factors for Quercus mongolica forests in South Korea |
| abstract |
Abstract Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional biomass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF) and root-to-shoot ratio (RSR). Our objectives were to select probability density functions (PDFs) that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. From these publications we chose 52 study sites spread out across South Korea. The statistical model for the meta-analysis was a multilevel model with publication (data source) as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quantitative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publications were 1.016 (0.800–1.299), 1.414 (1.304–1.560) and 0.260 (0.200–0.335), respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation. |
| abstractGer |
Abstract Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional biomass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF) and root-to-shoot ratio (RSR). Our objectives were to select probability density functions (PDFs) that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. From these publications we chose 52 study sites spread out across South Korea. The statistical model for the meta-analysis was a multilevel model with publication (data source) as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quantitative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publications were 1.016 (0.800–1.299), 1.414 (1.304–1.560) and 0.260 (0.200–0.335), respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation. |
| abstract_unstemmed |
Abstract Indirect approaches to estimation of biomass factors are often applied to measure carbon flux in the forestry sector. An assumption underlying a country-level carbon stock estimate is the representativeness of these factors. Although intensive studies have been conducted to quantify biomass factors, each study typically covers a limited geographic area. The goal of this study was to employ a meta-analysis approach to develop regional biomass factors for Quercus mongolica forests in South Korea. The biomass factors of interest were biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF) and root-to-shoot ratio (RSR). Our objectives were to select probability density functions (PDFs) that best fitted the three biomass factors and to quantify their means and uncertainties. A total of 12 scientific publications were selected as data sources based on a set of criteria. From these publications we chose 52 study sites spread out across South Korea. The statistical model for the meta-analysis was a multilevel model with publication (data source) as the nesting factor specified under the Bayesian framework. Gamma, Log-normal and Weibull PDFs were evaluated. The Log-normal PDF yielded the best quantitative and qualitative fit for the three biomass factors. However, a poor fit of the PDF to the long right tail of observed BEF and RSR distributions was apparent. The median posterior estimates for means and 95 % credible intervals for BCEF, BEF and RSR across all 12 publications were 1.016 (0.800–1.299), 1.414 (1.304–1.560) and 0.260 (0.200–0.335), respectively. The Log-normal PDF proved useful for estimating carbon stock of Q. mongolica forests on a regional scale and for uncertainty analysis based on Monte Carlo simulation. |
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| score |
7.399762 |