• Journal of Forest and Environmental Science
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• v.24 no.3
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• pp.159-164
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• 2008

Forest inventory is a commercial term meaning the preparation of detailed descriptive list of articles with number, quantity and value of each item included. Forest inventory deals with the measurement of trees and stands, the estimation of their volume, growth prediction, biomass, carbon stocks and the description tree characteristics, as well as the land upon which they are growing. National Forest Inventory Center (NFIC) in Korea conducts national forest inventory every 5 years to obtain accurate baseline data for national forest policy. The permanent sample plot data used in were collected by NFI. The objective of this study was to develop methods for quantifying forest resources at national scale based on $5^{th}$ National Forest Inventory (NFI) data in Korea. Forest land area decreased from 6.44 to 6.38 million ha between 1997 and 2007, continuing a slight downward trend in area beginning in the late 1990s. However forest resources of the Korea have continued improving in general condition and quality, as measured by increased average size and volume of trees. Growing-stock volume of the Korea increased from 17 to 123.79 cubic meter per ha between 1976 and 2007. The biomass in Korea was estimated to be 153.81 tons per hectare and carbon stocks in Korea was estimated to be 84.36 tons per hectare by NFI data. This information is important for government officials, public administration, the private business sector, and the researcher. Forest Inventory should be implemented in a way to be able to monitor and assess the forests continuously.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.4
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• pp.257-267
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• 2023

The grassland section of the greenhouse gas inventory has limitations due to a lack of review and verification of biomass compared to organic carbon in soil while grassland is considered one of the carbon storages in terrestrial ecosystems. Considering the situation at internal and external where the calculation of greenhouse gas inventory is being upgraded to a method with higher scientific accuracy, research on standards and methods for calculating carbon accumulation of grassland biomass is required. The purpose of this study was to identify international trends in the calculation method of the grassland biomass sector that meets the Tier 2 method and to conduct a review of variables applicable to the Republic of Korea. Identify the estimation methods and access levels for grassland biomass through the National Inventory Report in the United Nations Framework Convention on Climate Change and type the main implications derived from overseas cases. And, a field survey was conducted on 28 grasslands in the Republic of Korea to analyse the applicability of major issues. Four major international issues regarding grassland biomass were identified. 1) country-specific coefficients by land use; 2) calculations on woody plants; 3) loss and recovery due to wildfire; 4) amount of change by human activities. As a result of field surveys and analysis of activity data available domestically, it was found that there was a significant difference in the amount of carbon in biomass according to use type classification and climate zone-soil type classification. Therefore, in order to create an inventory of grassland biomass at the Tier 2 level, a policy and institutional system for making activity data should develop country-specific coefficients for climate zones and soil types.

• Journal of Korean Society of Forest Science
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• v.106 no.2
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• pp.258-266
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• 2017

This study was conducted to develop a carbon storage distribution map of Pinus rigida stands in Muju-gun by using of the National Forest Inventory data and digital forest map. The relationships between the stand variables such as height, age, diameter at breast height (DBH), crown density and aboveground biomass of Pinus rigida were analyzed. The results showed that the crown density had the highest positive correlation with a value of 0.74 followed by the height variable with value of 0.61. The aboveground biomass regression models were developed to estimate biomass and carbon storage map. The results of this study showed that the average carbon storage was 58.2 ton C/ha while the total carbon stock of rigida pine forests in Muju area was estimated to be 430,963 C ton.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.311-320
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• 2009

This study was carried out to estimate forest biomass and to produce forest biomass thematic map for Muju county by combining field data from the 5$^{th}$ National Forest Inventory (2006-2007) and satellite data. For estimating forest biomass, two methods were examined using a Landsat TM-5(taken on April 28th, 2005) and field data: multi-variant regression modeling and t-Nearest Neighbor (k-NN) technique. Estimates of forest biomass by the two methods were compared by a cross-validation technique. The results showed that the two methods provide comparatively accurate estimation with similar RMSE (63.75$\sim$67.26ton/ha) and mean bias ($\pm$1ton/ha). However, it is concluded that the k-NN method for estimating forest biomass is superior in terms of estimation efficiency to the regression model. The total forest biomass of the study site is estimated 8.4 million ton, or 149 ton/ha by the k-NN technique.

• Journal of Forest and Environmental Science
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• v.36 no.1
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• pp.17-24
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• 2020

This study aimed to figure out and compare the increment of biomass by thinning intensity focused on the plantation of the two major coniferous species (Larix kaempferi and Pinus koraiensis) of South Korea. The inventory interval was three years under the effects of three types of thinning treatments; control (no thinning), light (20% thinning) and heavy (40% thinning). The results showed standing biomass increment of both species decreased as thinning intensity increased (heavylight>control). Meanwhile, the lowest of on-site biomass changes occurred in the control plot, and the greatest was in the heavy thinning plot because thinning was involved with leaving the felling residual biomass (leaves, branches and roots) on the site. According to the results from this short-term study, unthinned stands is preferable for maximizing standing biomass as well as carbon sequestration. However long-term investigation should be considered in order to see more clear results.

• Journal of Forest and Environmental Science
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• v.28 no.2
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• pp.68-74
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• 2012

A total of 45 Pinus sylvestnis var. mongolica trees from 9 plots in northeast China were destructively sampled to develop aboveground prediction equations for inventory application. Sampling plots covered a range of stand ages (12-47-years-old) and densities (450-3,840/ha). The distribution of aboveground biomass of whole-trees and tree component (stems, branches and leaves) of individual trees were studied and 4 equations were developed as functions of diameter at breast height (DBH), total height (HT). All the equations have good estimation effect with high prediction precision over 90%. Forest biomass was estimated based on the individual biomass prediction equations. It was found forest biomass of all organs increased with the increasing of stand age and density. And the period of 45-50 years was the suitable harvest time for Pinus sylvesris plantation.

• Journal of Korean Society of Forest Science
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• v.98 no.4
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• pp.409-416
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• 2009

Forest biomass estimation is essential for greenhouse gas inventories and terrestrial carbon accounting. Remote sensing allows for estimating forest biomass over a large area. This study was conducted to estimate forest biomass and to produce a forest biomass map for Muju county using forest biomass conversion table developed by field plot data from the 5th National Forest Inventory and Landsat TM-5. Correlation analysis was carried out to select suitable independent variables for developing regression models. It was resulted that the height class, crown closure density, and age class were highly correlated with forest biomass. Six regression models were used with the combination of these three stand variables and verified by validation statistics such as root mean square error (RMSE) and mean bias. It was found that a regression model with crown closure density and height class (Model V) was better than others for estimating forest biomass. A biomass conversion table by model V was produced and then used for estimating forest biomass in the study site. The total forest biomass of the Muju county was estimated about 8.8 million ton, or 128.3 ton/ha by the conversion table.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.455-465
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• 2011

In order to assess and mitigate climate change, the role of forest biomass as carbon sink has to be understood spatially and quantitatively. Since existing forest statistics can not provide spatial information about forest resources, it is needed to predict spatial distribution of forest biomass under an alternative scheme. This study focuses on developing an upscaling method that expands forest variables from plot to landscape scale to estimate spatially explicit aboveground biomass(AGB). For this, forest stand variables were extracted from National Forest Inventory(NFI) data and used to develop AGB regression models by tree species. Dominant/codominant height and crown density were used as explanatory variables of AGB regression models. Spatial distribution of AGB could be estimated using AGB models, forest type map and the stand height map that was developed by forest type map and height regression models. Finally, it was estimated that total amount of forest AGB in Danyang was 6,606,324 ton. This estimate was within standard error of AGB statistics calculated by sample-based estimator, which was 6,518,178 ton. This AGB upscaling method can provide the means that can easily estimate biomass in large area. But because forest type map used as base map was produced using categorical data, this method has limits to improve a precision of AGB map.

• Korean Journal of Environmental Biology
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• v.34 no.2
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• pp.73-78
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• 2016

Bhutanese forests have been well preserved and can sequester the atmospheric carbon (C). In spite of its importance, understanding Bhutanese forest C dynamics was very limited due to the lack of available data. However, forest C model can simulate forest C dynamics with comparatively limited data and references. In this study, we aimed to simulate Bhutanese forest C dynamics at 6 plots with the Forest Biomass and Dead organic matter Carbon (FBDC) model, which can simulate forest C cycles with small amount of input data. The total forest C stock ($Mg\;C\;ha^{-1}$) ranged from 118.35 to 200.04 with an average of 168.41. The C stocks ($Mg\;C\;ha^{-1}$) in biomass, litter, dead wood, and mineral soil were 3.40-88.13, 4.24-24.95, 1.99-20.31, 91.45-97.90, respectively. On average, the biomass, litter, dead wood, and mineral soil accounted for 36.0, 5.5, 2.5, and 56.0% of the total C stocks, respectively. Although our modeling approach was applied at a small pilot scale, it exhibited a potential to report Bhutanese forest C inventory with reliable methodology. In order to report the national forest C inventory, field work for major tree species and forest types in Bhutan are required.

• Journal of Forest and Environmental Science
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• v.38 no.3
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• pp.184-194
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• 2022

Triplochiton scleroxylon K. Schum is the plant species most affected by logging activities in the East Region of Cameroon due to its market value. This logging has impacted the ecological niche of the fern plant for which limited research has been done. The aim of this study is to contribute towards improving knowledge of fern richness and biomass on T. scleroxylon within the Central African sub-region. Fern data collection was done on 20 felled/harvested T. scleroxylon where, in addition to fern inventory, fern biomass was collected by the destructive method. The diameter and height of T. scleroxylon measured were used as explanatory variables in allometric equations for fern biomass estimation. Fern inventory was characterized using diversity index. Eight fern species were recorded on T. scleroxylon (≈5 species/T. scleroxylon). The minimum diameter where fern could be found is 59.4 cm. The average fern biomass found was 23.62 kg/T. scleroxylon. Pearson correlation coefficient showed a positive correlation (r>0.55) between fern biomass and T. scleroxylon diameter. For allometric equation, the logarithmic model improved better the adjustment than the non-logarithmic model. However, the quality of the adjustment is improved more when only the diameter is considered as an explanatory variable. Fern biomass is estimated to 90.08 kg/ha^-1 with 76.02 kg/ha^-1 being lost due to T. scleroxylon exploitation in the study area. This study is a contribution towards increasing knowledge of fern diversity specific to T. scleroxylon, and also fern biomass contribution to climate change mitigation and the potential carbon loss due to T. scleroxylon exploitation.