• Journal of Ecology and Environment
• /
• v.38 no.1
• /
• pp.75-84
• /
• 2015

Tropical forests play a critical role in mitigating climate change, and therefore, an accurate and precise estimation of tropical forest carbon (C) is needed. However, there are many uncertainties associated with C stock estimation in a tropical forest, mainly due to its large variations in biomass. Hence, we quantified C stocks in an intact lowland mixed dipterocarp forest (MDF) in Brunei, and investigated variations in biomass and topography. Tree, deadwood, and soil C stocks were estimated by using the allometric equation method, the line intersect method, and the sampling method, respectively. Understory vegetation and litter were also sampled. We then analyzed spatial variations in tree and deadwood biomass in relation to topography. The total C stock was 321.4 Mg C $ha^{-1}$, and living biomass, dead organic matter, and soil C stocks accounted for 67%, 11%, and 23%, respectively, of the total. The results reveal that there was a relatively high C stock, even compared to other tropical forests, and that there was no significant relationship between biomass and topography. Our results provide useful reference data and a greater understanding of biomass variations in lowland MDFs, which could be used for greenhouse gas emission-reduction projects.

• Journal of Advanced Navigation Technology
• /
• v.27 no.5
• /
• pp.649-657
• /
• 2023

ICAO adopted the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) at the 39th General Assembly in 2016, and 115 countries, including South Korea, expressed their intention to participate in CORSIA as of January 1, 2023. Since carbon generated in the aviation industry is mainly caused by greenhouse gases emitted from aircraft engines, fuel consumption must be reduced to reduce carbon emissions. Prior research, such as simulation, is essential to predict the effectiveness of each plan and to make decisions about its implementation. High-quality data is needed to derive accurate results, but it has been difficult to secure actual fuel consumption data, as they are considered to be classified airline data. Therefore, in this paper, after establishing a model that estimates fuel consumption based on actual fuel consumption data, the model is to be advanced to improve its accuracy.

• Journal of the Korean association of regional geographers
• /
• v.20 no.2
• /
• pp.206-216
• /
• 2014

In this study, forest types in Chungheongnam-do were analyzed using Landsat satellite images and digital forest type map as a means to estimate forest carbon stocks. NDVI and Tasseled Cap, ISODATA, and supervised classification among others were used to analyze the forest types. The forest carbon stocks of Chungcheongnam-do were estimated utilizing forest statistical data derived from the classified results. The results indicate that the analysis of forest types through supervised classification yielded the highest overall accuracy in analyzing forest types using satellite images. Coniferous forests(49.3%) accounted for the highest proportion in all the forest types of Chungcheongnam-do, followed by deciduous forests(28.0%) and mixed forests(22.7%). The results of a comparative analysis between forest carbon stocks estimates made using the modified digital forest type map and other estimation methods showed that the method using Tasseled Cap and unsupervised classification yielded the most similar forest carbon stock estimates. The most significant difference, though, was made when only the digital forest type map was used. It is expected that if carbon stocks are estimated by integrating satellite images and digital forest type maps in the future, more accurate results can be derived in estimating forest carbon stocks at a national level.

• Journal of Korean Society on Water Environment
• /
• v.33 no.3
• /
• pp.326-333
• /
• 2017

The lakes and reservoirs have been reported as important sources of carbon emissions to the atmosphere in many countries. Although field experiments and theoretical investigations based on the fundamental gas exchange theory have proposed the quantitative amounts of Net Atmospheric Flux (NAF) in various climate regions, there are still large uncertainties at the global scale estimation. Mechanistic models can be used for understanding and estimating the temporal and spatial variations of the NAFs considering complicated hydrodynamic and biogeochemical processes in a reservoir, but these models require extensive and expensive datasets and model parameters. On the other hand, data driven machine learning (ML) algorithms are likely to be alternative tools to estimate the NAFs in responding to independent environmental variables. The objective of this study was to develop random forest (RF) and multi-layer artificial neural network (ANN) models for the estimation of the daily $CO_2$ NAFs in Daecheong Reservoir located in Geum River of Korea, and compare the models performance against the multiple linear regression (MLR) model that proposed in the previous study (Chung et al., 2016). As a result, the RF and ANN models showed much enhanced performance in the estimation of the high NAF values, while MLR model significantly under estimated them. Across validation with 10-fold random samplings was applied to evaluate the performance of three models, and indicated that the ANN model is best, and followed by RF and MLR models.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.14 no.3
• /
• pp.236-256
• /
• 2011

Forest carbon stocks change due to land use change is an important data required by UNFCCC(United Nations framework convention on climate change). Spatially explicit estimation of forest carbon stocks based on IPCC GPG(intergovernmental panel on climate change good practice guidance) tier 3 gives high reliability. But a current estimation which was aggregated from NFI data doesn't have detail forest carbon stocks by polygon or cell. In order to improve an estimation remote sensing and GIS have been used especially in Europe and North America. We divided research trends in main countries into 4 categories such as remote sensing, GIS, geostatistics and environmental modeling considering spatial heterogeneity. The easiest way to apply is combination NFI data with forest type map based on GIS. Considering especially complicated forest structure of Korea, geostatistics is useful to estimate local variation of forest carbon. In addition, fine scale image is good for verification of forest carbon stocks and determination of CDM site. Related domestic researches are still on initial status and forest carbon stocks are mainly estimated using k-nearest neighbor(k-NN). In order to select suitable method for forest in Korea, an applicability of diverse spatial data and algorithm must be considered. Also the comparison between methods is required.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.17 no.4
• /
• pp.69-85
• /
• 2014

Carbon stocks of NFI plots can be accurately estimated using field survey information. However, an accurate estimation of carbon stocks in other unsurveyed sites is very difficult. In order to fill this gap, various spatial information can be used as an ancillary data. In South Korea, there is the 1:5,000 forest type map that was produced by digital air-photo interpretation and field survey. Because this map contains very detailed forest information, it can be used as the high-quality spatial data for estimating carbon stocks. In this study, we compared three upscaling methods based on the 1:5,000 forest type map and 5th national forest inventory data. Map algebra(method 1), RK(Regression Kriging)(method 2), and GWR(Geographically Weighted Regression)(method 3) were applied to estimate forest carbon stock in Chungcheong-nam Do and Daejeon metropolitan city. The range of carbon stocks from method 2(1.39~138.80 tonC/ha) and method 3(1.28~149.98 tonC/ha) were more similar to that of previous method(1.56~156.40 tonC/ha) than that of method 1(0.00~93.37 tonC/ha). This result shows that RK and GWR considering spatial autocorrelation can show spatial heterogeneity of carbon stocks. We carried out paired t-test for carbon stock data using 186 sample points to assess estimation accuracy. As a result, the average carbon stocks of method 2 and field survey method were not significantly different at p=0.05 using paired t-test. And the result of method 2 showed the lowest RMSE. Therefore regression kriging method is useful to consider spatial variations of carbon stocks distribution in rugged terrain and complex forest stand.

• Journal of Energy Engineering
• /
• v.11 no.1
• /
• pp.34-46
• /
• 2002

Carbon accounting is a key issue in the discussions on global warming/CO$_2$mitigation. This paper applies both the IPCC Approach and the NEAT (Non-Energy use Emission Accounting Tables) model, a bottom-up approach, to estimate the potential CO$_2$ emissions (carbon storage) originating from the non-energy use as to assess the actual CO$_2$ emissions (carbon release) from the use of fossil fuels in Korea. The current Korean carbon accounting seems to overestimate the potential CO$_2$ emissions and with it to underestimate the actual CO$_2$ emissions. The estimation shows that the potential CO$_2$ emissions calculated according to the IPCC Approach are lower than those calculated using the NEAT model. This is because the IPCC default storage fraction for naphtha seems to be low for the Korean petrochemical production structure, on the one hand and because the IPCC Approach does not consider the trade with short life petrochemical products, on the other hand. This paper shows that a bottom-up approach like the NEAT model can contribute to overcome some of limitations of the IPCC guidelines, especially by considering the international trade with short life petrochemical products and by estimating the storage fractions of fossil fuels used as feedstocks for the country in consideration. This paper emphasizes the importance of accurate energy statistics for carbon accounting.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.11 no.1 s.53
• /
• pp.29-35
• /
• 2007

Flexural capacity estimation test of concrete filled carbon tube (CFCT) column under the cyclic lateral load was carried out in this study. Thickness of carbon tube and winding angles of carbon fiber were chosen as test parameters and two types of column with square and circular sections were manufactured. To act axial and lateral load, three dynamic actuators were used and all specimens were made with actual size. Flexural stiffness, ability of deformation, energy dissipation capacity and ductility behavior. of CFCT column were analyzed with test data.

• Korean Journal of Environmental Biology
• /
• v.22 no.1
• /
• pp.227-232
• /
• 2004

Hydrological and biological studies on ecosystems of the lakes 'etang de Berre' and 'etang de Vaime', the four rivers flowing into these lakes, and the Mediterranean Sea are carried out during the whole two-year period. The phytoplankton population of the lakes 'etang de Berre' and 'etang de Vaine' is larger than that of the seawater or freshwater populations of four neighbouring rivers. This is due to the increasing nutriments such as phosphate, nitrate, and silicate flowing into the lakes from the four rivers. The superfluous phytoplanktons in the lakes flow into the Mediterranean Sea via the Caronte Canal. Phytoplanktons multiplicated by phosphate of lake 'etang de Berre' can produce 10,160 tons of assimilated carbon per year, and those multiplicated by nitrate produce 18,450 tons of assimilated carbon per year. According to Steeman Nielsen's primary production estimation, phytoplanktons produce about 45,000 tons of carbon per year through assimilation in lake'4tang de Berre' and 10,000 tons of carbon per year in lake 'etang de Vaime'. The amount of carbon produced by phytoplanktons and the amount of phosphate, and nitrate are different according to the sea, river, and estuary.

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