• Asian Journal of Atmospheric Environment
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• v.7 no.1
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• pp.25-37
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• 2013

Open biomass burning (excluding biofuels) is an important contributor to air pollution in the Asian region. Estimation of emissions from fires, however, has been problematic, primarily because of uncertainty in the size and location of sources and in their temporal and spatial variability. Hence, more comprehensive tools to estimate wildfire emissions and that can characterize their temporal and spatial variability are needed. Furthermore, an emission processing system that can generate speciated, gridded, and temporally allocated emissions is needed to support air-quality modeling studies over Asia. For these reasons, a biomass-burning emissions modeling system based on satellite imagery was developed to better account for the spatial and temporal distributions of emissions. The BlueSky Framework, which was developed by the USDA Forest Service and US EPA, was used to develop the Asian biomass-burning emissions modeling system. The sub-models used for this study were the Fuel Characteristic Classification System (FCCS), CONSUME, and the Emissions Production Model (EPM). Our domain covers not only Asia but also Siberia and part of central Asia to assess the large boreal fires in the region. The MODIS fire products and vegetation map were used in this study. Using the developed modeling system, biomass-burning emissions were estimated during April and July 2008, and the results were compared with previous studies. Our results show good to fair agreement with those of GFEDv3 for most regions, ranging from 9.7 % in East Asia to 52% in Siberia. The SMOKE modeling system was combined with this system to generate three-dimensional model-ready emissions employing the fire-plume rise algorithm. This study suggests a practicable and maintainable methodology for supporting Asian air-quality modeling studies and to help understand the impact of air-pollutant emissions on Asian air quality.

• 한국태양에너지학회:학술대회논문집
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• 2008.11a
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• pp.306-309
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• 2008

Renewable energy information becomes one of the greatest issues, but it is difficult for a general user to manage and utilize new renewable energy information. Therefore we develop the utilization system of the resource map which aimed to provide the information for space analysis and vertification of the validity for development of each part of solar, wind, smallhydro, biomass, geothermal. But this system is needed to gather more supporting data and make resonable index to make various decisions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.215-231
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• 2020

Forest landscape models (FLMs) can be used to investigate the complex interactions of various ecological processes and patterns, which makes them useful tools to evaluate how environmental and anthropogenic variables can influence forest ecosystems. However, due to the large spatio-temporal scales in FLMs studies, parameterization and validation can be extremely challenging when applying to new study areas. To address this issue, we focused on the parameterization and application of a spatially explicit forest landscape model, LANDIS-II, to Mt. Gyebang, South Korea, with the use of the National Forest Inventory (NFI) and long-term ecological research (LTER) site data. In this study, we present the followings for the biomass succession extension of LANDIS-II: 1) species-specific and spatial parameters estimation for the biomass succession extension of LANDIS-II, 2) calibration, and 3) application and validation for Mt. Gyebang. For the biomass succession extension, we selected 14 tree species, and parameterized ecoregion map, initial community map, species growth characteristics. We produced ecoregion map using elevation, aspect, and topographic wetness index based on digital elevation model. Initial community map was produced based on NFI and sub-alpine survey data. Tree species growth parameters, such as aboveground net primary production and maximum aboveground biomass, were estimated from PnET-II model based on species physiological factors and environmental variables. Literature data were used to estimate species physiological factors, such as FolN, SLWmax, HalfSat, growing temperature, and shade tolerance. For calibration and validation purposes, we compared species-specific aboveground biomass of model outputs and NFI and sub-alpine survey data and calculated coefficient of determination (R²) and root mean square error (RMSE). The final model performed very well, with 0. 98 R² and 8. 9 RMSE. This study can serve as a foundation for the use of FLMs to other applications such as comparing alternative forest management scenarios and natural disturbance effects.

• Ecology and Resilient Infrastructure
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• v.5 no.2
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• pp.94-104
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• 2018

The Soyangho Reservoir in Korea has a large drawdown zone, with an annual maximum water level fluctuation of 37 m due to dam operations to maintain a stable water supply and control flooding, especially during the monsoon period. The floristic composition, distribution and biomass of the major plant communities in the drawdown zone of the Soyangho Reservoir were assessed in order to understand their responses to the wide water level fluctuation. Species richness of vascular plants was low, and species composition was dominated by herbaceous annuals. Principal coordinates analysis using both flora and environmental data identified slope angle and the distance from the dam as important factors determining floristic composition. The species richness was low in the steep drawdown zone close to the dam, where much of the soil surface was almost devoid of vegetation. In shallower slopes, distant from the dam plant communities composed of mainly annuals were found. The large fluctuation in water level exposed soil where these annuals could establish. An overall biomass of 122 t (metric tons) Dry Matter was estimated for the reservoir, containing ca 3.6 t N (nitrogen) and ca 0.3 t P (phosphorus); the role of the vegetation of the drawdown zone in carbon sequestration and water pollution were briefly discussed.

• Journal of Korean Society of Forest Science
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• v.101 no.4
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• pp.547-557
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• 2012

This study purposed to estimate the forest biomass using k-Nearest Neighbor (k-NN) algorithm. Multiple data sources were used for the analysis such as forest type map, field survey data and Landsat TM data. The accuracy of forest biomass was evaluated with the forest stratification, horizontal reference area (HRA) and spatial filtering. Forests were divided into 3 types such as conifers, broadleaved, and Korean pine (Pinus koriansis) forests. The applied radii of HRA were 4 km, 5 km and 10 km, respectively. The estimated biomass and mean bias for conifers forest was 222 t/ha and 1.8 t/ha when the value of k=8, the radius of HRA was 4 km, and $5{\times}5$ modal was filtered. The estimated forest biomass of Korean pine was 245 t/ha when the value of k=8, the radius of HRA was 4km. The estimated mean biomass and mean bias for broadleaved forests were 251 t/ha and -1.6 t/ha, respectively, when the value of k=6, the radius of HRA was 10 km. The estimated total forest biomass by k-NN method was 799,000t and 237 t/ha. The estimated mean biomass by ${\kappa}NN$method was about 1t/ha more than that of filed survey data.

• Journal of the Korean Solar Energy Society
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• v.36 no.1
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• pp.39-47
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• 2016

Geographic information system (GIS) based renewable energy resource map including potential analysis can play a crucial role not only to develop the national plan for renewable energy deployment but also to make strategic investment decision in the private sector. Korea Institute of Energy Research (KIER) has been developing domestic maps about several resources such as solar, wind, hydro, biomass, and geothermal, as well as conducting research on methodologies for potential analysis. Furthermore, the institute is trying to transfer related technologies and know-how to foreign countries, recently. In this context, the main purpose of this study is to introduce the business model of renewable energy resource map. From the value chain analysis, we focus on the government-side market in foreign countries, such as the development of the national level renewable energy resource map and the support of the national renewable energy plan. For about 180 countries, we segment the customers according to the consideration of economic capacity, renewable energy resource capacity, existence of renewable resource map, current portion of renewable energy facility capacity, and renewable energy policies, and we conclude that the target customers are non-Organization for Economic Co-operation and Development (non-OECD) countries or some OECD countries, their per capita GDP are under the average among OECD countries, that do not have renewable resource map yet. We segment the target customers into four groups, and suggest different strategies for market positioning and financing strategy based on Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis. This study can help to develop the business strategy about the development of renewable energy resource map in foreign countries.

• 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 Korean Society of Rural Planning
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• v.19 no.2
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• pp.121-128
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• 2013

The greenhouse gas emission according to the energy consumption is the cause of global warming. With various climates, it is occurs the direct problems to ecosystem. The various studies are being to reduce the carbon dioxide, which accounts for more than 80% of the total greenhouse gas emissions. In this study, estimate the carbon usage using potential biomass extracted from forest type map according to land-use by satellite image, and estimate the amount of carbon dioxide, according to the energy consumption of urban area. The $CO_2$ adsorption is extracted by the amount of forest based on the direct absorption of tree, the other used investigated value. The $CO_2$ emission in Jecheon was 3,985,900 $TCO_2$ by energy consumption. At the land cover classification, the forest is analyzed as 624,085ha and the farmland is 148,700ha. The carbon dioxide absorption was estimated at 1,834,850 Tons from analyzed forest. In case of farmland, it was also estimated at 706,658 Tons.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.5
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• pp.29-37
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• 2018

Biodiversity refers to the diversity of organisms originating from all sources, including terrestrial ecosystems, aquatic ecosystems, and complex ecosystems and it is considered to be the standard of the area to be preserved and protected. So The Importance of environmental assessment for biodiversity conservation is increasing and International efforts to quantify biodiversity and to develop indices have been made, but there are insufficient researches on the use of biomass databases and their quantification in Korea. In this study, the biodiversity map was constructed using the 3rd National Ecosystem Survey Mammal Data with three spatial units(Administrative Area, 1:5,000 index map, hexagonal lattice). and the difference of map constructed by spatial unit was suggested to help research on quantification and evaluation of biodiversity in the future. As a result of the study, biodiversity index for the same area varied according to the spatial unit and overall average and standard deviation were different too. Therefore it is necessary to utilize appropriate spatial unit considering the suitability and purpose of quantification rather than using specific unit. It also showed the necessity of establishing a standard for biodiversity index as a result of comparative analysis with ecosystem and nature map. Based on this research, comprehensive efforts should be made for the sustainable development of the country through further research and institutional improvement for quantification and evaluation of biodiversity, set standards.

• Korean Journal of Remote Sensing
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• v.23 no.1
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• pp.21-32
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• 2007

Fire fuel map is one of the most critical factors for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential to reduce the uncertainty in mapping fuels and offers the best approach for improving our abilities. Especially, Hyperspectral sensor have a great potential for mapping vegetation properties because of their high spectral resolution. The objective of this paper is to evaluate the potential of mapping fuel properties using Hyperion hyperspectral remote sensing data acquired in April, 2002. Fuel properties are divided into four broad categories: 1) fuel moisture, 2) fuel green live biomass, 3) fuel condition and 4) fuel types. Fuel moisture and fuel green biomass were assessed using canopy moisture, derived from the expression of liquid water in the reflectance spectrum of plants. Fuel condition was assessed using endmember fractions from spectral mixture analysis (SMA). Fuel types were classified by fuel models based on the results of SMA. Although Hyperion imagery included a lot of sensor noise and poor performance in liquid water band, the overall results showed that Hyperion imagery have good potential for wildfire fuel mapping.