• 한국공간정보시스템학회:학술대회논문집
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• 한국공간정보시스템학회 2007년도 GIS 공동춘계학술대회 논문집
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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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• 제37권2호
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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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• 제8권4호
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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.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2017년도 춘계 학술논문 발표대회
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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.

• 환경영향평가
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• 제20권4호
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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.

• 대한원격탐사학회지
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• 제31권5호
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• pp.449-459
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• 2015

기존의 국가산림자원조사(National Forest Inventory, NFI)에 의한 산림탄소저장량 추정 방법은 국가 규모의 평균 탄소저장량 추정에는 충분하지만 표본점 개수가 부족한 시 군 단위의 세밀한 추정은 어렵다. 본 연구에서는 시 군별 산림탄소저장량 추정을 위해 공간 자료를 보조 자료로 이용하고 2가지 업스케일링 방법을 적용하여 격자별 산림탄소저장량 정보를 가진 산림탄소지도를 제작하였다. 대상지역은 충청남도로 2가지 방법 모두 제 5차 NFI(2006~2009) 자료를 활용하였다. 방법 1은 임상도를 보조 자료로 선택하고 NFI 기반 산림탄소저장량 회귀모델을 이용하였다. 방법 2는 위성영상을 보조 자료로 선택하고 k-NN을 이용하여 산림탄소저장량을 추정하였다. 불확실성을 고려하기 위해 200회 몬테카를로 시뮬레이션을 수행하여 최종 AGB 탄소지도를 산출하였다. 방법 1에서는 충청남도의 총 산림탄소저장량이 22,948,151 tonC으로 기존의 현지조사표본 기반 추정치(21,136,911 tonC)에 비해 과대추정을, 방법 2에서는 19,750,315 tonC로 과소추정되는 경향을 나타내었다. 독립검증 지점(n=186)의 탄소저장량에 대한 대응표본 T-검정 결과, 방법 2의 평균 추정치와 NFI 표본 기반 평균 추정치는 통계적으로 유의한 차이가 있는 반면(p<0.01), 방법 1의 평균 추정치는 NFI 표본 기반 평균 추정치와 통계적으로 유의한 차이가 없는 것으로 평가되었다(p>0.01). 특히, 방법 2의 경우 k-NN의 스무딩 효과 및 몬테카를로 시뮬레이션을 통해 위성영상과 표본점의 mis-registration 오차가 추정오차에 큰 영향을 미칠 수 있음이 발견되었다. 임상도를 활용한 방법 1이 임분 구조가 복잡한 우리나라 산림의 탄소량 추정에 효과적일 수 있지만, 미조사 지점의 주기적인 갱신 및 대면적 추정에 유리한 위성영상의 활용은 여전히 필수적이다, 따라서 시공간적인 확장과 함께 보다 신뢰할 수 있는 산림탄소저장량 추정을 위해 다양한 위성영상 자료 및 활용 기법에 관한 연구가 필요할 것으로 사료된다.

• 대한원격탐사학회지
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• 제30권5호
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• pp.651-664
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• 2014

최근 주기적이고 정확한 산림바이오매스 탄소저장량 추정에 대한 필요성이 한국에서도 점차 증가하고 있다. 본 연구에서는 k-Nearest Neighbor (kNN) 및 Regression Tree Analysis (RTA) 알고리즘을 대상으로 공주 및 세종시를 대상으로 한 탄소량 변화 탐지를 통해 그 효용성을 비교 분석 하고자 하였다. 현장 자료로는 제 3차 및 제 5, 6차 국가산림자원조사 자료를 이용하였으며, 위성영상자료는 1992년, 2010년에 취득된 Landsat TM과 2009년에 취득된 Aster 영상을 이용하였다. 또한, 추정정확도를 향상시키기 위해 각 영상으로부터 다양한 식생지수를 생성하였다. 두 방법론의 비교를 위해 RMSE 및 평균편의(mean bias)를 포함한 각종 탄소통계량을 계산하였으며, 대상지역에 대한 탄소분포지도를 생성하고 비교를 수행하였다. 그 결과, kNN 알고리즘은 영상에 상관없이 보다 안정적인 추정결과를 나타낸 반면, 스무딩 효과로 인해 탄소의 공간분포가 뚜렷하지 않은 단점이 발견되었다. RTA의 경우 평균편의 결과 및 탄소의 공간분포가 명확히 나타나는 장점이 있으나, 위성영상에 따라 탄소추정량에서 큰 차이를 나타내었다. 최종적으로 2009년 및 2010년 탄소지도에서 1992년 탄소지도를 차분한 탄소차분지도를 생성을 통해 공주시 및 세종시 지역의 산림 탄소저장량이 급격히 증가했음을 확인하였다.

• 대한공간정보학회지
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• 제18권1호
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• pp.129-134
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• 2010

저탄소 녹색성장은 국내외적으로 중요한 정치적 이슈가 되고 있으며, 한국정부는 최근 저탄소 녹색성장을 위한 비젼을 발표하였다. 이런 관점에서 탄소배출 추정은 도시계획에 있어서 중요한 요소가 되고 있다. 탄소저감 계획을 수립하기 위하여 본 연구에서는 과거 40년 동안 진주시의 탄소배출 추정과 토지이용변화의 상호추이변화를 분석하였다. 토지적성평가 데이터베이스와 항공영상의 영상처리자료는 과거 40년간의 토지이용변화를 분석하는데 유효한 정보를 주었으며, 신주거지 개발에 의한 토지이용변화는 급격한 인구집중과 탄소배출증가를 가져왔다. 앞으로 저탄소 녹색성장을 위한 도시관리계획에 있어서 토지이용변화에 따른 탄소배출 증가를 계획수립 시 반드시 고려해야하며, 향 후 토지이용과 연료소비추정이 포함된 정확한 탄소배출 추정모델개발에 대한 추가적인 연구가 필요하다고 사료된다.

• 한국기후변화학회지
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• 제8권4호
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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.

• 대기
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• 제33권1호
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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.