• 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 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.370-377
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• 2018

Currently, Korea is ahead of the forest sector such as forest management, forest investigation and forest management, which is not insufficient compared with the forest advanced countries (Germany, Japan, Austria). However, there is a lack of systematic and advanced forest management plan and related research, and it is not enough to construct GIS for practical and complex analysis. Therefore, in order to perform forest analysis effectively, this study maps forest basic statistics (2010, 2015) based on GIS to map forest information. As a result, the forest area, growing stock, average growing stock, and forest rate could be produced with the maximized visual effect by detailed administrative districts, and systematic analysis of the time series changes was also possible. Forest area increased only in Goseong, Sejong, Cheolwon, Yeoncheon, Daejeon, and Seoul Guro-gu, and decreased in all other areas, while growing stock increased in most areas, Uljin, Ulleung, Seoul Nowon-gu, and Seoul Gangdong-gu. The average growing stock was found to increase in most areas excluding the four administrative districts and the forest rate was higher in 10 regions (Goseong, Yeoncheon, Gongju, Busan Dong-gu, Daegu Seo-gu, etc.) but it decreased in most regions excluding 10 regions. Based on this research, we plan to produce and analyze forest information maps for smaller administrative districts and more.

• Journal of agriculture & life science
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• v.45 no.3
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• pp.35-41
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• 2011

The objective of this study was to estimate domestic potential wood supply according to geographical and forest management conditions. In order to separate available wood supply area, analysis was conducted by separating natural, theoretical, geographical and technical supply area. Natural supply area was separated by extract stocked land from forest using digital stock map. Theoretical, geographical and technical supply area was separated by considering protection area, slope, streamside and road. Growing stock was calculated by using species and age-class of digital stock map. Potential wood supply was estimated by calculating growing stock of technical supply area. The results of growing stock of each supply area was shown that growing stock was from 244,150 to 596,248 thousand $m^3$. According to the results of this study, it was found that potential wood supply are likely to be over- or underestimated depending on the considered level of geographical and forest management conditions. Provincial potential wood supply was highly ranked in order of Gyeongbuk, jeonnam, Gyeongnam and Gangwon province.

• Atmosphere
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• v.33 no.1
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• pp.61-72
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• 2023

Accurate estimation of forest carbon stocks is important in establishing greenhouse gas reduction plans. In this study, we estimate the spatial distribution of forest carbon stocks using machine learning techniques based on high-resolution remote sensing data and detailed field survey data. The high-resolution remote sensing data used in this study are Landsat indices (EVI, NDVI, NDII) for monitoring vegetation vitality and Shuttle Radar Topography Mission (SRTM) data for describing topography. We also used the forest growing stock data from the National Forest Inventory (NFI) for estimating forest biomass. Based on these data, we built a model based on machine learning methods and optimized for Korean forest types to calculate the forest carbon stocks per grid unit. With the newly developed estimation model, we created forest carbon stocks maps and estimated the forest carbon stocks in South Korea. As a result, forest carbon stock in South Korea was estimated to be 432,214,520 tC in 2020. Furthermore, we estimated the loss of forest carbon stocks due to the Donghae-Uljin forest fire in 2022 using the forest carbon stock map in this study. The surrounding forest destroyed around the fire area was estimated to be about 24,835 ha and the loss of forest carbon stocks was estimated to be 1,396,457 tC. Our model serves as a tool to estimate spatially distributed local forest carbon stocks and facilitates accounting of real-time changes in the carbon balance as well as managing the LULUCF part of greenhouse gas inventories.

• Journal of Korean Society of Forest Science
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• v.99 no.6
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• pp.827-835
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• 2010

Reliable forest statistics provides important information to meet the UNFCCC. In this respect, the national forest inventory has played a crucial role to provide the reliable forest statistics for several decades. However, the previous forest statistics calculated by administrative district has not provided spatial information in a small scale. Thus, this study focused on developing models to estimate an explicit spatial distribution of forest growing stock. For this, first, stand volume model by stand types was developed using National Forest Inventory(NFI) data. Second, forest type map was integrated with this model. NFI data were used to calculate plot-level stand volume and basal area. The stand types of NFI plot including the species composition, age class, DBH class and crown density class are very crucial data to be connected with forest type map. Finally, polygonlevel stand volume map was developed with spatial uncertainty map. Average stand volume was estimated at 85.7 $m^3$/ha in the study area, and at 95% significance interval it was ranged from 79.7 $m^3$/ha to 91.8 $m^3$/ha.

• Journal of Korean Society of Rural Planning
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• v.16 no.4
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• pp.109-116
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• 2010

Climate change is known to affect both natural and managed ecosystems, and will likely impact on the terrestrail carbon balance. This paper reports the effects of climate change on spatial-temporal changes in carbon reductions in South Korea's during 2000-2100. Future carbon (C) stock distributions are simulated for the same period using various spatial data sets including land cover, net primary production(NPP) and leaf area index (LAI) obtained from MODIS(Moderate Resolution Imaging Spectroradiometer), and climate data from Data Assimilation Office(DAO) and Korea Meteorological Administration(KMA). This study attempts to predict future NPP using multiple linear regression and to model dependence of soil respiration on soil temperature. Plants store large amounts of carbon during the growing periods. During 2030-2100, Carbon accumulation in vegetation was increased to $566{\sim}610gC/m^2$/year owing to climate change. On the other hand, soil respiration is a key ecosystem process that releases carbon from the soil in the form of carbon dioxide. The estimated soil respiration spatially ranged from $49gC/m^2$/year to $231gC/m^2$/year in the year of 2010, and correlating well with the reference value. This results include Spatial-Temporal C reduction variation caused by climate change. Therefore this results is more comprehensive than previous results. The uncertainty in this study is still large, but it can be reduced if a detailed map becomes available.

• Journal of Korean Society of Forest Science
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• v.96 no.3
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• pp.259-268
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• 2007

This study was conducted to produce forest thematic maps and estimate forest statistics for Pyeongchang Gun using the kNN technique, which has been applied to produce thematic maps of variables of interest including unobserved plots by combining field plot data, remotely sensed data and other digital map data in forest inventories. The estimation errors for three horizontal reference areas (HRAs), whose radii are 20, 40 and 60 km respectively, were compared. Although the precision for the 40 km radius was lower compared to that for the 60 km radius, the 40 km radius was found to be an efficient HRA because their difference in precision was modest. At a value of k=5 nearest neighbors for the selected HRA, the overall accuracy was high. As a result, using the k=5 neighbors within the HRA of 40 km radius, thematic maps of number of trees, basal area, and growing stock per hectare were generated. As compared to the forest statistics based on field sample plots, the estimated means of each parameter from the produced maps were underestimated.

• Journal of Environmental Impact Assessment
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• v.27 no.2
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• pp.124-138
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• 2018

This research was conducted to examine the availability of spatial data for assessing absorption rates of $CO_2$ in the forest of Ansan-si and evaluate the validity of methods that analyze $CO_2$ absorption. To statistically assess the $CO_2$ absorption rates per year, the 1:5,000 Digital Forest-Map (Lim5000) and Standard Carbon Removal of Major Forest Species (SCRMF) methods were employed. Furthermore, Land Cover Map (LCM) was also used to verify $CO_2$ absorption rate availability per year. Great variations in $CO_2$ absorption rates occurred before and after the year 2010. This was due to improvement in precision and accuracy of the Forest Basic Statistics (FBS) in 2010, which resulted in rapid increase in growing stock. Thus, calibration of data prior to 2010 is necessary, based on recent FBS standards. Previous studies that employed Lim5000 and FBS (2015, 2010) did not take into account the $CO_2$ absorption rates of different tree species, and the combination of SCRMF and Lim5000 resulted in $CO_2$ absorption of 42,369 ton. In contrast to the combination of SCRMF and Lim5000, LCM and SCRMF resulted in $CO_2$ absorption of 40,696 ton. Homoscedasticity tests for Lim5000 and LCM resulted in p-value <0.01, with a difference in $CO_2$ absorption of 1,673 ton. Given that $CO_2$ absorption in forests is an important factor that reduces greenhouse gas emissions, the findings of this study should provide fundamental information for supporting a wide range of decision-making processes for land use and management.