• Journal of Korean Society of Forest Science
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• v.98 no.4
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• pp.409-416
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• 2009

Forest biomass estimation is essential for greenhouse gas inventories and terrestrial carbon accounting. Remote sensing allows for estimating forest biomass over a large area. This study was conducted to estimate forest biomass and to produce a forest biomass map for Muju county using forest biomass conversion table developed by field plot data from the 5th National Forest Inventory and Landsat TM-5. Correlation analysis was carried out to select suitable independent variables for developing regression models. It was resulted that the height class, crown closure density, and age class were highly correlated with forest biomass. Six regression models were used with the combination of these three stand variables and verified by validation statistics such as root mean square error (RMSE) and mean bias. It was found that a regression model with crown closure density and height class (Model V) was better than others for estimating forest biomass. A biomass conversion table by model V was produced and then used for estimating forest biomass in the study site. The total forest biomass of the Muju county was estimated about 8.8 million ton, or 128.3 ton/ha by the conversion table.

• 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 Forest Science
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• v.96 no.4
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• pp.477-482
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• 2007

Forest biomass became a topic because we have growing interest in global environmental issues and environment-friendly energy resources. This study was carried out to estimate the forest biomass and develop a program for biomass information management in Korea. The total forest biomass (million ton) were 521 for gross forest, 403 for productive forest and 201 for commercial forest in 2005. Also, the annual biomass production in forest was 20 million ton which was equivalent to 94,290 Gkcal of heating value and about 9 billion won of paraffin oil. The biomass growing rate (every 10year) increased from 4.95% in 1985 to 5.30% in 1995 but turn down 4.46% in 2005. The factors that the forest stock could be converted to the forest biomass have developed according to forest type. Therefore, it is impossible to estimate the exact biomass by tree species. In this reason, the demands of the development of the factors by tree species was raised. In addition, it is on time to develop an equation for estimation of biomass by species using dbh and height as independent factors.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.13-20
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• 2013

Due to the global warming, international agreements have been propelled by industrialized countries. These days, there are various studies and projects to reduce the carbon emission quantity in South Korea, because South Korea is a strong candidate for a newly industrialized nation by Kyoto Protocol. Therefore, this study arranges plans to create various thematic map by producing database that can manage various datum based on grid spatial objects to manage quantity of forest biomass and carbon dioxide. Moreover, this study designs a system to create forest biomass by using the best method of calculation with LiDAR data and KOMPSAT-2 satellite images. In addition, this study designs a biomass monitoring system for public institutions to register biomass, suggesting actual plans to extract, manage, and utilized forest biomass.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.455-465
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• 2011

In order to assess and mitigate climate change, the role of forest biomass as carbon sink has to be understood spatially and quantitatively. Since existing forest statistics can not provide spatial information about forest resources, it is needed to predict spatial distribution of forest biomass under an alternative scheme. This study focuses on developing an upscaling method that expands forest variables from plot to landscape scale to estimate spatially explicit aboveground biomass(AGB). For this, forest stand variables were extracted from National Forest Inventory(NFI) data and used to develop AGB regression models by tree species. Dominant/codominant height and crown density were used as explanatory variables of AGB regression models. Spatial distribution of AGB could be estimated using AGB models, forest type map and the stand height map that was developed by forest type map and height regression models. Finally, it was estimated that total amount of forest AGB in Danyang was 6,606,324 ton. This estimate was within standard error of AGB statistics calculated by sample-based estimator, which was 6,518,178 ton. This AGB upscaling method can provide the means that can easily estimate biomass in large area. But because forest type map used as base map was produced using categorical data, this method has limits to improve a precision of AGB map.

• Journal of agriculture & life science
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• v.43 no.1
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• pp.1-8
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• 2009

This study was conducted to examine the biomass, allometric equations, net primary production, above and total biomass expansion factors and stem density values for 27 years old Korean pine(Pinus koraiensis Siebold et Zuccarini) plantation at the Gangneung National Forest. After considering of the diameter distributions in the $20m{\times}20m$ plot measurement, a total of 5 representative sample trees were destructively sampled to measure green weights and dry weights of the four(root, stem, branch and foliage) protions of Korean pine trees. According to the results of this study, total dry weights were 117.6 kg/tree and 59.9 ton/ha. Aboveground biomass and total (above and belowground) biomass for this species were 59.9 and 82.4 ton/ha, respectively. Ratios of root to aboveground biomass were 0.38. Net primary production of aboveground biomass and belowground biomass were 9.4 and 11.3 ton/ha, respectively. Stem density was $0.49g/cm^{3}$. Above and total biomass expansion factors were 1.78 and 2.19, repectively. This information could be very useful to calculate carbon sequestrations by applying stem desity values and biomass expansion factors for Korean pine species.

• Journal of Korean Society of Forest Science
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• v.103 no.3
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• pp.431-438
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• 2014

This study was conducted to assess forest biomass resource which is a carbon sink and a renewable resource in Korea. The total forest biomass resource potential was 804 million tons, and conifers, broadleaved forest and mixed forest accounted for 265 million tons, 282 million tons, and 257 million tons, respectively. Proportionately to regional forest stocks, biomass potential of Gangwon-do had most biomass potential, followed by Gyeongsangbuk-do and Gyeongsangnam-do. The woody biomass from the byproduct of sawn timber in commercial harvesting was 707 thousand ton/year, and that from the byproduct of forest tending was 592 thousand ton/year. The amount resulted in about 1,300 thousand ton/year of potential supplies from forest biomass resource into the energy market. It's tonnage of oil equivalent(toe) was 585 thousand ton/year. In this study, we developed a program (BiomassMap V2.0) for forest biomass resource mapping. Used system to develop this program was Microsoft Office Excel, Microsoft Office Access ArcGIS and Microsoft Visual Basic 6.0. Additionally, This program made use of tool such as ESRI MapObjects2.1 in order to take advantage of spatial information. This program shows the map of total biomass stock, annual biomass growth at forest land in Korea, and biomass production from forest tending and commercial harvesting. The information can also be managed by the program. The biomass resource map can be identified by regional and forest type for the purpose of utilization. So, we expect the map and program to be very useful for forest managers in the near future.

• Proceedings of the Korea Forestry Energy Research Society Conference
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• 2011.02a
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• pp.37-40
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• 2011

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.3
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• pp.119-129
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• 2013

This study was conducted to predict the changes in forest biomass in different ecoprovinces and forest types under climate change scenario based on cumulative data (i.e., digital forest site and climate maps, National Forest Inventory data) and various prediction models. The results from this study showed that predicted changes over time in biomass varied according to ecoprovince and forest type in Korea. A reduction in biomass was predicted for all forest types associated with the mountain, southeastern hilly, and southwestern hilly ecoprovinces. On the other hand, the biomass was predicted to increase for the coniferous forest and mixed-forest types in the central hilly ecoprovince. Furthermore, increases in biomass are predicted for all forest types, except coniferous forests, in the coastal ecoprovince. The results from this study provide a basis for developing technology to predict forest impacts due to climate change by predicting changes in forest biomass based on the estimation of site index.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.3
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• pp.166-173
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• 2008

Recently, problem of earth environment being attended with international issue, people are concerned about the environmentally-friendly and renewable biomass energy. Especially, the forest biomass is more important because Korea have to control carbon footprint for Kyoto Protocol and Convention on Climate Change. In case of Korea, forest area covers the land about 2/3 of all country. It is needed that more economical and efficient method to estimate the biomass by remote sensing data which include wide coverage and is progressed by one-step. In this study, we estimate forest biomass with LiDAR data and aerial photograph. Three biomass equation is used and estimate mean biomass of single tree and entire biomass in plots. The results are compared with field data. $R^2$ of the mean biomass of single tree is greater than 0.8 and that of entire biomass in plots is greater than 0.65. In conclusion, the method using remote sensing data is verified more economical and efficient than previous field data method.