Comparison of allometric equations and biomass expansion factors for six evergreen broad-leaved trees in subtropical forests in southern Korea

Kim, Choonsig; Baek, Gyeongwon; Yoo, Byung Oh; Jung, Su Young; Lee, Kwang Soo; An, Ki Wan

doi:10.1080/10549811.2018.1528157

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…Currently, a substantial amount of research has been conducted on the calculation of forest biomass carbon reserves, and many relevant research strategies have been reported. However, owing to differences in research site, forest type, research scale, and biomass measurement methods, there are substantial differences in the methods of estimation and determination of carbon reserves [50,51]. Among these methods, the biomass, biomass-volume relationship, biomass conversion factor, and chamber methods are used in large-scale estimations.…”

Section: Effects Of the Methods Of Estimationmentioning

confidence: 99%

Forest biomass carbon pool dynamics in Tibet Autonomous Region of China: Inventory data 1999-2019

Bian

Xia

et al. 2021

PLoS ONE

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According to the forest resources inventory data for different periods and the latest estimation parameters of forest carbon reserves in China, the carbon reserves and carbon density of forest biomass in the Tibet Autonomous Region from 1999 to 2019 were estimated using the IPCC international carbon reserves estimation model. The results showed that, during the past 20 years, the forest area, forest stock, and biomass carbon storage in Tibet have been steadily increasing, with an average annual increase of 1.85×104 hm2, 0.033×107 m3, and 0.22×107 t, respectively. Influenced by geographical conditions and the natural environment, the forest area and biomass carbon storage gradually increased from the northwest to the southeast, particularly in Linzhi and Changdu, where there are many primitive forests, which serve as important carbon sinks in Tibet. In terms of the composition of tree species, coniferous forests are dominant in Tibet, particularly those containing Abies fabri, Picea asperata, and Pinus densata, which comprise approximately 45% of the total forest area in Tibet. The ecological location of Tibet has resulted in the area being dominated by shelter forest, comprising 68.76% of the total area, 64.72% of the total forest stock, and 66.34% of the total biomass carbon reserves. The biomass carbon storage was observed to first increase and then decrease with increasing forest age, which is primarily caused by tree growth characteristics. In over-mature forests, trees’ photosynthesis decreases along with their accumulation of organic matter, and the trees can die. In addition, this study also observed that the proportion of mature and over-mature forest in Tibet is excessively large, which is not conducive to the sustainable development of forestry in the region. This problem should be addressed in future management and utilization activities.

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Section: Effects Of the Methods Of Estimationmentioning

confidence: 99%

Forest biomass carbon pool dynamics in Tibet Autonomous Region of China: Inventory data 1999-2019

Bian

Xia

et al. 2021

PLoS ONE

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“…Many studies reported the allometric equation and BEF widely utilized to estimate individual tree biomass in forest ecosystems, both in tropics and subtropics (Krejza et al 2017;Lisboa et al 2018;Kim et al 2019;Mahmood et al 2020). However, comparing both techniques for supporting tree biomass estimation in community forests is rarely documented, especially in Indonesia.…”

Section: Introductionmentioning

confidence: 99%

Comparison of individual tree aboveground biomass estimation in community forests using allometric equation and expansion factor in Magetan, East Java, Indonesia

Setiahadi

2021

Biodiversitas

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Abstract. Setiahadi R. 2021. Comparison of individual tree aboveground biomass estimation in community forests using allometric equation and expansion factor in Magetan, East Java, Indonesia. Biodiversitas 22: 3899-3909. The use of allometric equation and biomass expansion factor can facilitate more efficient tree biomass estimation. This study evaluates the accuracy of the allometric equation and expansion factor for quantifying the individual tree aboveground biomass in community forest tree species. Destructive sampling n on 120 trees from four different species: Falcataria moluccana, Melia azedarach, Swietenia macrophylla, and Tectona grandis. For each tree sample, aboveground biomass measured at every tree component, i.e., stem, branches, and leaves. The allometric equation developed using regression analysis with several predictor variables, such as diameter at breast height (D), squared diameter at breast height combined with tree height (D2H), and D and H separately. On another side, the biomass expansion factor was calculated based on the total aboveground biomass and stem biomass ratio. The results found the highest mean aboveground biomass for all species are M. azedarach (326.36±88.40 kg tree-1), S. macrophylla (244.47±98.73 kg tree-1), T. grandis (173.31±80.97 kg tree-1), and F. moluccana (56.56±23.10 kg tree-1). The most significant average biomass expansion factor observed in M. azedarach (1.78±0.03), adhered by T. grandis (1.66±0.09), S. macrophylla (1.61±0.04), and F. moluccana (1.59±0.06). The equation ln? = lna + b x ln (D) was best for estimating aboveground biomass in each tree component and a total of four species with an accuracy of more than 90%.

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“…For example, Brown [21] and Fang [22] et al calculated the biomass of different forest types and forests in different countries by biomass expansion factor. Kim [23] compared of allometric equations and biomass expansion factor to calculate the subtropical broadleaf species in South Korea. Park [24] used biomass expansion factor, allometric equation and stand biomass to calculate the biomass of Pinus thunbergii (Pinus thunbergii Parl.)…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Carbon Storage and Its Drivers in Guangdong Province from 1979 to 2012

Tian

2021

Forests

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Accurately estimating the carbon storage of forest ecosystems and deriving the driving factors affecting forest carbon storage are the prerequisites and foundations for promoting the development of global carbon sinks. In order to explore an effective approach for monitoring the carbon sink of forests in developed coastal areas on a large scale, in this paper, Guangdong Province was taken as the case study region; eight periods plots of data of national forest resources continuous inventory were used to estimate the forest carbon storage and carbon density in Guangdong Province from 1979 to 2012; unary linear regression and standard deviation ellipse were used to analyze the dynamic change of carbon storage; and the structural equation model was used to study the driving factors of forest carbon storage. The results showed that: (1) From 1979 to 2012, the forest carbon storage in Guangdong Province increased by 15,087.93 × 104 t, and the forest carbon density increased by 17.66 t/ha. (2) After 2007, the main body of forest carbon storage changed from coniferous species to broadleaf species. (3) From 1979 to 2012, the proportion of young and middle-aged forest carbon storage continued to decline, but it still occupied the dominant component. (4) The forest carbon storage and carbon density in the northern region of Guangdong Province are higher than those in the southern region. (5) Stand factors and environmental factors have a positive effect on forest carbon storage, and understory factors have a negative effect. In conclusion, although forest carbon storage has fluctuated under the influence of forestry policies and human activities, the overall carbon storage and carbon density of Guangdong Province have been increasing. Tree species have become more abundant and the proportion of coniferous forest to broadleaf forest became more rationalized. The forest age group structure is continuously optimized. We also compared our results with that of other provinces in China and other countries with approximate latitude and climatic conditions. The carbon sink potential of Guangdong Province is huge in the future.

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Comparison of allometric equations and biomass expansion factors for six evergreen broad-leaved trees in subtropical forests in southern Korea

Cited by 4 publications

References 21 publications

Forest biomass carbon pool dynamics in Tibet Autonomous Region of China: Inventory data 1999-2019

Forest biomass carbon pool dynamics in Tibet Autonomous Region of China: Inventory data 1999-2019

Comparison of individual tree aboveground biomass estimation in community forests using allometric equation and expansion factor in Magetan, East Java, Indonesia

Dynamics of Carbon Storage and Its Drivers in Guangdong Province from 1979 to 2012

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