• Journal of Forest and Environmental Science
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• v.34 no.2
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• pp.145-152
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• 2018

Quantification of Carbon stock has become in the contest of changing climate and mitigation potential of forests. Two different forest types, Dry Shiwalik Sal Forest and Moist Shiwalik Sal Forest in Barkot and Lachchiwala of Doon Valley, Western Himalaya are selected for the study. Volume equations, destructive sampling and laboratory analysis are done to estimate the carbon stock in different carbon pools like trees, shrubs, herbs and soils. Considerable variations are observed in terms of carbon stocks in different forest types. In Dry Shiwalik Sal Forest, carbon stock density varied between 129.81 and $136.00MgCha^{-1}$ while in Moist Shiwalik Sal Forest, carbon stock density ranged from 222.29 to $271.67MgCha^{-1}$. Tree species like Shorea robusta, Syzigium cumini, Miliusa velutina, Acacia catechu, and Mallotus philippensis had significant role in carbon sequestration. Shorea robusta had contributed highest in carbon stock due to highest density. Total of 2,338,280.165 Mg carbon stock was estimated in all the forest types.

• Journal of Forest and Environmental Science
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• v.33 no.1
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• pp.39-48
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• 2017

With the growing global concern about climate change, relationship between carbon stock density and tree species has become important for international climate change mitigation programmes. In this study, 150 Quadrats were laid down to assess the diversity, biomass and carbon stocks in each of the forest ranges (Barkot Range, Lachchiwala Range and Thano Range) of Dehra Dun Forest Division in Doon Valley, Western Himalaya, India. Community level carbon stock density was analyzed using Two Way Indicator Species Analysis. Species Richness and Shannon Weiner index was correlated with the carbon stocks of Doon Valley. Positive and weak relationship was found between the carbon stock density and Shannon Weiner Index, and between carbon stock density and Species Richness.

• Journal of Forest and Environmental Science
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• v.36 no.1
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• pp.37-46
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• 2020

Vegetation carbon sequestration and regeneration are the two major parameters of forest research. In this study, we analyzed the vegetation carbon stock and regeneration of community-managed pine plantation of Kathmandu, central Nepal. Vegetation data were collected from 40 circular plots of 10 m radius (for the tree) and 1m radius (for seedling) applying a stratified random sampling and nested quadrat method. The carbon stock was estimated by Chave allometric model and estimated carbon stock was converted into CO₂ equivalents. Density-diameter (d-d) curve was also prepared to check the regeneration status and stability of the plantation. A d-d curve indicates the good regeneration status of the forest with a stable population in each size class. Diversity of trees was very low, only two tree species Pinus roxburghii and Eucalyptus citriodora occurred in the sample plots. Pine was the dominant tree in terms of density, basal area, biomass, carbon stock and CO₂ stock than the eucalyptus. The basal area, carbon stock and CO₂ stock of forest was 33±1.0 ㎡ ha^-1, 108±5.0 Mg ha^-1 and 394±18 Mg ha^-1, respectively. Seedling and tree density of the plantation was 4,965 ha^-1 and 339 ha^-1 respectively. The forest carbon stock showed a positive relationship with biomass, tree diameter, height and basal area but no relationship with tree density. Canopy cover and tree diameter have a negative effect on seedling density and regeneration. In conclusion, the community forest has a stable population in each size class, sequestering a significant amount of carbon and CO₂ emitted from densely populated Kathmandu metro city as the forest biomass hence have a potentiality to mitigate the global climate change.

• Journal of Forest and Environmental Science
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• v.32 no.2
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• pp.120-128
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• 2016

Forest sequesters large terrestrial carbon which is stored in the biomass of tree and plays a key role in reducing atmospheric carbon. Thus, the objectives of the present study were to assess the growing stock, above ground biomass and carbon in trees of Ponda watershed of Rajouri district (J&K). IRS-P6 LISS-III satellite data of October 2010 was used for preparation of land use/land cover map and forest density map of the study area by visual interpretation. The growing stock estimation was done for the study area as well as for the sample plots laid in forest and agriculture fields. The growing stock and biomass of trees were estimated using species specific volume equations and using specific gravity of wood, respectively. The total growing stock in the study area was estimated to be $0.25million\;m^3$ which varied between $85.94m^3/ha$ in open pine to $11.58m^3/ha$ in degraded pine forest. However in agriculture area, growing stock volume density of $14.85m^3/ha$ was recorded. Similarly, out of the total biomass (0.012 million tons) and carbon (0.056 million tons) in the study area, open pine forest accounted for the highest values of 43.74 t/ha and 19.68 t/ha and lowest values of 5.68 t/ha and 2.55 t/ha, respectively for the degraded pine forest. The biomass and carbon density in agriculture area obtained was 5.49 t/ha and 2.47 t/ha, respectively. In all the three forest classes Pinus roxburghii showed highest average values of growing stock volume density, biomass and carbon.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1097-1107
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• 2020

Assessment of soil carbon stock is essential for climate change mitigation and soil fertility. The digital soil mapping (DSM) is well known as a general technique to estimate the soil carbon stocks and upgrade previous soil maps. The aim of this study is to calculate the soil carbon stock in the top soil layer (0 to 30 cm) in Jeolla Province of South Korea using the DSM technique. To predict spatial carbon stock, we used Cubist, which a data-mining algorithm model base on tree regression. Soil samples (130 in total) were collected from three depths (0 to 10 cm, 10 to 20 cm, 20 to 30 cm) considering spatial distribution in Jeolla Province. These data were randomly divided into two sets for model calibration (70%) and validation (30%). The results showed that clay content, topographic wetness index (TWI), and digital elevation model (DEM) were the most important environmental covariate predictors of soil carbon stock. The predicted average soil carbon density was 3.88 kg·m-2. The R2 value representing the model's performance was 0.6, which was relatively high compared to a previous study. The total soil carbon stocks at a depth of 0 to 30 cm in Jeolla Province were estimated to be about 81 megatons.

• Journal of Forest and Environmental Science
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• v.29 no.2
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• pp.157-164
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• 2013

The campus of Chittagong University in Bangladesh is rich in forest ecosystem. The campus has large area with vast tract of land planted with valuable timber tree species. The present study identifies and discovers the potential growing stock of the plantations in the campus area. This Growing stock was measured in three parameters viz. volume, biomass and organic carbon stock. Study identified thirty three economically valuable forest tree species in the plantations of Chittagong University. Out of three growing stock parameters, volume of timber was found to be low in indigenous tree species in the plantation sites other than exotic species. This might be due to their slow growth rate and low density in the plantation sites. However, biomass and organic carbon stock of trees per hactre area showed that indigenous species gather and sequester more timber and carbon respectively than introduced species. Plantations of Chittagong University campus can acquire $25.51m^3/ha$ volume of economically important tree species, where biomass and organic carbon stock is 222.33 tonne/ha and 107.48 tonne/ha respectively. This result shows a positive impression on the plantation site to be considered as good forest reserve.

• Journal of Environmental Science International
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• v.31 no.9
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• pp.767-779
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• 2022

In this study, sediment cores were sampled from tidal flats (six sites) in the west and south coastal wetlands, the blue carbon stock in the tidal flat sediments was calculated, and the blue carbon stock characteristics and influencing factors were analyzed. The sediment particle size of the west coastal tidal flats was larger than that of the south coastal tidal flats, and the organic carbon content in the south coastal tidal flats was more than twice that of the west coastal tidal flats. Blue carbon stock per unit area was 28.4~36.8 Mg/ha on the west coastal tidal flats and 69.8~89.8 Mg/ha on the south coastal tidal flats, which was more than twice higher in the south coastal tidal flats than in the west coastal tidal flats. The total amount of blue carbon stock in the tidal flats was the highest in Suncheon Bay tidal flats at 153,626 Mg, and followed by Gomso Bay tidal flats at 141,750 Mg, Hampyeong Bay tidal flats at 58,420 Mg, Dongdae Bay tidal flats at 44,900 Mg, Cheonsu Bay tidal flats at 36,880 Mg, and Jinhae Bay tidal flats at 26,205 Mg. Blue carbon stock per unit area was higher in the south coastal tidal flats, but the total amount of blue carbon stock in the tidal flats was higher in the west coast. The slope of the regression function of blue carbon stock with respect to the organic carbon content in the tidal flat sediments was estimated to be about 0.05 to 0.07, and the slope of the regression function was higher in the west coastal tidal flats than in the south coastal tidal flats.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.167-167
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• 2023

Soil organic carbon (SOC) is a critical component of soil health and is crucial in mitigating climate change by sequestering carbon from the atmosphere. Accurate estimation of SOC storage is essential for understanding SOC dynamics and developing effective soil management strategies. This study aimed to investigate the factors influencing the spatial distribution of SOC storage in South Korea, using bulk density (BD) prediction to estimate SOC stock. The study utilized data from 393 soil series collected from various land uses across South Korea established by Korea Rural Development Administration from 1968-1999. The samples were analyzed for soil properties such as soil texture, pH, and BD, and SOC stock was estimated using a predictive model based on BD. The average SOC stock in South Korea at 30 cm topsoil was 49.1 Mg/ha. The study results revealed that soil texture and land use were the most significant factors influencing the spatial distribution of SOC storage in South Korea. Forested areas had significantly higher SOC storage than other land use types. Climate variables such as temperature and precipitation had a relative influence on SOC storage. The findings of this study provide valuable insights into the factors influencing the spatial distribution of SOC storage in South Korea.

• Journal of Korean Society of Forest Science
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• v.103 no.3
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• pp.446-452
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• 2014

This research aimed to provide a method to estimate growing stock and carbon stock using the characteristics of forest type map such as the age-class, DBH class and crown density class. We transformed the growing stock data of national forest inventory (mainly Kangwon-do province) onto those of time when the forest type map was established. We developed a simulation model for the growing stock using the transformed data and the characteristics of forest type map by the quantification method I. By comparing partial correlation coefficient, we found that quantification of growing stock was largely affected by age-class followed by crown density class, forest type and DBH class. The growing stock, was estimated as minimum in the broadleaved forest with age-class II, DBH class 'Small', and crown density class 'Low' as $20.0m^3/ha$, whereas showed maximum value in the coniferous forest with age-class VI, DBH class 'Large', and crown density class 'High' as $305.0m^3/ha$. The growing stock for coniferous, broadleaved, and mixed forest were estimated as $30.5{\sim}305.0m^3/ha$, $20.0{\sim}200.4m^3/ha$, and $23.8{\sim}238.1m^3/ha$, respectively. When we compared the carbon stock by forest type, the carbon stock by age class based on growing stock was maximum when DBH class was 'Large' and crown density class was 'High' regardless of forest type. This estimation of growing stock by using characteristic of forest type can be used to estimate the changes in growing stock and carbon stock resulting from deforestation or natural disaster. In addition, we hope it provide a useful advice when forest officials and policy makers have to make decisions in regard to forest management.

• Journal of Korean Society of Rural Planning
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• v.21 no.4
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• pp.27-33
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• 2015

Forest ecosystem is considered as an important stepping stone to minimize the impact of climate change. However, the rapid urbanization has caused fragmentation of forest ecosystem. The fragmentation of forest patch results in edge effect which brings about adverse impacts on forest function and structure. Degradation of forest ecosystem decreases carbon sequestration because edge effect reduces productivity. Therefore, we analyzed the impact of forest edge effect on forest ecosystem carbon stock change in Seongnam-si, Gyeonggi-do. We used connectivity analysis to determine forest edge and core area. The field study sites were selected with considering forest age, density, class and soil type. Secondly, forest carbon stock was calculated with allometric equation. The soil carbon stock was derived from Walkely-Black method. Lastly, Mann-Whitney test was conducted to validate differences between carbon stock in edge and core area. As a result of study, the connectivity analysis was effective to determine forest edge and core. The core and edge of forest patch showed different composition of tree species and soil properties. Carbon stock per tree in the edge area was lower than that in the core area. However, the difference of soil organic carbon content between the edge and core were relatively small. This assessment can be applied for the conservation of forest patch as well as quantitative assessment on the forest carbon stock change caused by fragmentation.