• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.359-362
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• 2007

Estimation of carbon in the natural forest regions is a pre-requisite for carbon management. In the light of increasing carbon dioxide concentration in the atmosphere, the amount of carbon present in the plants and soils are very much needed to estimate the sequestered carbons stock of any region. Carbon stock estimation studies are limited in India, especially in the natural forest regions of Eastern ghats of Tamil Nadu. Remote sensing, Geographical Information System (GIS) and global positioning system (GPS) were used along with extensive field and laboratory works to estimate the carbon stock in the living biomass and soil. About five forest types were identified and mapped using satellite data. The total biomass carbon including above and below ground were 2.74 Tg and the total soil organic carbon was 3.48 Tg. This study has yielded significant information about the carbon stock in a natural forest region and it could be used for future comparative studies.

• Journal of Forest and Environmental Science
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• v.37 no.2
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• pp.128-140
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• 2021

Tropical dry forests are one of the most threatened, widely distributed ecosystems in tropics and estimation of forest biomass is a crucial component of global carbon emission estimation. Therefore, the present study was aimed to quantify the biomass and carbon storage in trees on large scale (10, 1 ha plots) in the dry mixed evergreen forest of Javadhu forest of Eastern Ghats. Biomass of adult (≥10 cm DBH) trees was estimated by non-harvest methods. The total biomass of trees in this tropical dry mixed evergreen forest was ranged from 160.02 to 250.8 Mg/ha, with a mean of 202.04±24.64 Mg/ha. Among the 62 tree species enumerated, Memecylon umbellatum accumulated greater biomass and carbon stocks (24.29%) more than the other species in the 10 ha study plots. ANOVA revealed that there existed a significant variation in the total biomass and carbon stock among the three plant types (Evergreen, brevi-deciduous and deciduous (F (2, 17)=15.343, p<0.001). Basal area and density was significant positively correlated with aboveground biomass (R² 0.980; 0.680) while species richness exhibited negative correlation with above ground biomass (R² 0.167). Finding of present study may be interpreted as most of the trees in this forest are yet to be matured and there is a net addition to standing biomass leading to carbon storage.

• KIEAE Journal
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• v.8 no.4
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• pp.19-27
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• 2008

The carbon dioxide is brought from the energy consumption and regarded as a criteria material to estimate the Global Warming Potential. Building shares about 30% in national energy consumption and affects to environment as much as the energy consumption. But there is not enough data to forecast the amount of the carbon dioxide during the maintenance stage. Various factors are related with the energy consumption and carbon dioxide emission such as the physical area, the building exterior area, the maintenance type and location. Among these factors, the building carbon-dioxide emission can be estimated by the overall building characteristics such as the maintenance area, the number of household, the heating type, etc., The physical amount such as the thickness of the insulation and window infiltration could explained the limited scope and might not be use to estimate the total carbon-dioxide emission energy because the each value could not include or represent the overall building. In this paper, it provided the estimation model of the carbon-dioxide emission, explained by the overall building characteristics. These factors are shown as the maintenance area, no. of household, the heating type, the volume of the building, the ratio of the window to wall area etc., For providing the estimation model of th carbon-dioxide emission, it conducted the corelation analysis to filter the variables and suggested the estimation model with the power model and multiple regression model. Most of the model have a good statistics and fitted in the curve line.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.05a
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• pp.193-194
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• 2017

Effect of variable factors on carbon emissions in construction industry is hard to analysis. Therefore this study analyzies effect of variable factors on carbon emissions. This study shows importance of variable factors and emphasizes need of estimation of carbon emissions considering variable factors.

• Journal of Environmental Impact Assessment
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• v.20 no.4
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• pp.477-487
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• 2011

In this study, we deduced the best estimation methodology for amount of carbon sequestration by vegetation, through the case study using the data obtainable from the environmental assessment procedure. Our results showed that the estimation methodology using the national vegetation map was the best for the strategic environmental assessment, whileas those using the vegetation growth equation were applicable for environmental impact assessment procedure. Furthermore, we found that the amount of carbon sequestration by farmland and/or grassland, not by vegetation, was not negligible. Therefore, we concluded that the area of farmland and/or grassland need to be taken into account during the landuse planning.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.449-459
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• 2015

The conventional National Forest Inventory(NFI)-based forest carbon stock estimation method is suitable for national-scale estimation, but is not for regional-scale estimation due to the lack of NFI plots. In this study, for the purpose of regional-scale carbon stock estimation, we created grid-based forest carbon stock maps using spatial ancillary data and two types of up-scaling methods. Chungnam province was chosen to represent the study area and for which the $5^{th}$ NFI (2006~2009) data was collected. The first method (method 1) selects forest type map as ancillary data and uses regression model for forest carbon stock estimation, whereas the second method (method 2) uses satellite imagery and k-Nearest Neighbor(k-NN) algorithm. Additionally, in order to consider uncertainty effects, the final AGB carbon stock maps were generated by performing 200 iterative processes with Monte Carlo simulation. As a result, compared to the NFI-based estimation(21,136,911 tonC), the total carbon stock was over-estimated by method 1(22,948,151 tonC), but was under-estimated by method 2(19,750,315 tonC). In the paired T-test with 186 independent data, the average carbon stock estimation by the NFI-based method was statistically different from method2(p<0.01), but was not different from method1(p>0.01). In particular, by means of Monte Carlo simulation, it was found that the smoothing effect of k-NN algorithm and mis-registration error between NFI plots and satellite image can lead to large uncertainty in carbon stock estimation. Although method 1 was found suitable for carbon stock estimation of forest stands that feature heterogeneous trees in Korea, satellite-based method is still in demand to provide periodic estimates of un-investigated, large forest area. In these respects, future work will focus on spatial and temporal extent of study area and robust carbon stock estimation with various satellite images and estimation methods.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.651-664
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• 2014

Recently, the demands of accurate forest carbon stock estimation and mapping are increasing in Korea. This study investigates the feasibility of two methods, k-Nearest Neighbor (kNN) and Regression Tree Analysis (RTA), for carbon stock estimation of pilot areas, Gongju and Sejong cities. The 3rd and 5th ~ 6th NFI data were collected together with Landsat TM acquired in 1992, 2010 and Aster in 2009. Additionally, various vegetation indices and tasseled cap transformation were created for better estimation. Comparison between two methods was conducted by evaluating carbon statistics and visualizing carbon distributions on the map. The comparisons indicated clear strengths and weaknesses of two methods: kNN method has produced more consistent estimates regardless of types of satellite images, but its carbon maps were somewhat smooth to represent the dense carbon areas, particularly for Aster 2009 case. Meanwhile, RTA method has produced better performance on mean bias results and representation of dense carbon areas, but they were more subject to types of satellite images, representing high variability in spatial patterns of carbon maps. Finally, in order to identify the increases in carbon stock of study area, we created the difference maps by subtracting the 1992 carbon map from the 2009 and 2010 carbon maps. Consequently, it was found that the total carbon stock in Gongju and Sejong cities was drastically increased during that period.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.1
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• pp.129-134
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• 2010

Low-carbon Green Growth is highlighted as the main political issue from in and outof Korea. Recently Korean government announced the vision for low-carbon green growth. Considering this as a starting point the carbon emission estimation has become an important factor in the city planning. In order to realize the carbon reduction planning, this research was focused on the trend analyzes between the carbon exhaust estimation as well as the land use change for the past 40 years in Jinju. The image processing data of past aerial photography and the land suitability assessment databases were used to collect the useful information's for the land trend analysis for 40 years. As the results, the land use changes by new residential developments have led to increase the carbon emissions and population concentration rapidly. The urban management planning for low carbon and green growth should consider carbon emissions by population growth derived from land use change. Further research need to estimate the accurate carbon exhaust using relationship model with fuel consumption, carbon estimation, and land use.

• Journal of Climate Change Research
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• v.8 no.4
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• pp.369-375
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• 2017

In this study, it was intended to compare the two methodologies for forest management project through extension of rotation age: Korean Forest Carbon Offset Standard (KFOS) and Verified Carbon Standard (VCS). The amount of carbon removals and offset credits based on the two methodologies and their trends were analyzed in this study. The major difference between two methodologies were found at the process of estimation of baseline carbon removals. For instance, average carbon stock during the project period was used for estimation of baseline carbon removals in KFOS, while average carbon stock change during the 100 years was used in VCS. Due to the different approach for estimation of baseline carbon removal, the estimated offset credits were also different according to the two methodologies. In this study, 15 project scenarios were considered for comparison of two methodologies : 5 major coniferous stands in Korea (Pinus densiflora in Gangwon region, Pinus densiflora in Central region, Pinus koraiensis, Larix leptolepis, Chamaecyparis obtusa) with 3 project periods (30, 35, 40 years). The results showed that estimated carbon offset credits based on the KFOS methodology were higher for all 15 scenarios compared to those based on the VCS methodology. The KFOS showed a steep decline in the annual offset credit as project period gets longer, thus it is not desirable for projects with longer period. VCS is more acceptable for longer projects with a small difference according to the project periods. The results also indicated that Pinus densiflora in Gangwon, Pinus koraiensis, and Larix leptolepis are more desirable species for forest management project through the extension of ration age.

• 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.