• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.325-327
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• 2003

Forest biomass is the basis of forest ecosystem. With the rapid development of remote sensing and computer technology, forest biomass estimation using remote sensing data is paid great attention and has acquired great achievements. This article focuses on discussion of methods of forest biomass estimation methods using Terra/MODIS data in Northeast China. The research include: combining the MODIS time series parameters with seasonal characteristics of forest species to identify major forest species; establishing a model to estimate forest biomass based on forest species; analyzing the effects of the existent forest biomass and increasing biomass on terrestrial carbon cycle. This research can help to make clear the mechanism of carbon cycle.

• 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.

• Journal of Korean Society of Forest Science
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• v.96 no.5
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• pp.591-601
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• 2007

Spatial information on forest biomass is an important factor to evaluate the capability of forest as a carbon sequestrator and is a core independent variable required to drive models which describe ecological processes such as carbon budget, hydrological budget, and energy flow. The objective of this study is to understand the relationship between satellite image and field data, and to quantitatively estimate and map the spatial distribution of forest biomass. Landsat Enhanced Thematic Mapper (ETM+) derived vegetation indices and field survey data were applied to estimate the biomass distribution of mountainous forest located in Gwangneung Experimental Forest (230 ha). Field survey data collected from the ground plots were used as the dependent variable, forest biomass, while satellite image reflectance data (Band 1~5 and Band 7), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and RVI (Ratio Vegetation Index) were used as the independent variables. The mean and total biomass of Gwangneung catchment area were estimated to be about 229.5 ton/ha and $52.8{\times}10^3$ tons respectively. Regression analysis revealed significant relationships between the measured biomass and Landsat derived variables in both of deciduous forest ($R^2=0.76$, P < 0.05) and coniferous forest ($R^2=0.75$, P < 0.05). However, there still exist many uncertainties in the estimation of forest ecosystem parameters based on vegetation remote sensing. Developing remote sensing techniques with adequate filed survey data over a long period are expected to increase the estimation accuracy of spatial information of the forest ecosystem.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.41 no.4
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• pp.15-24
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• 2009

This paper estimates the nation wide amount of forest biomass arising from management operation for domestic forest based on the simulations that are composed of five scenarios for selecting the target area of thinning. In 2009, the forest biomass arising from thinning is estimated to be 6,642,174 $m^3$. The estimates of forest biomass in 2015 and 2018 are 5,935,140 $m^3$ and 5,682,538 $m^3$, respectively. Since the target forest for thinning policy is estimated to be decreasing, the biomass generated by thinning will decline too. The estimates of forest biomass can be used to induce more effective application of woody biomass rather than one-sided use such as raw materials for solid fuels including pellets and charcoals.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.41 no.4
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• pp.25-32
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• 2009

Forest biomass can be used as various types of raw materials such as pulp, wood pellets, solid charcoals and so on. This paper estimates the nation wide amount of forest biomass based on the projection of forest areas for its effective and economic use. Several trend equations are used in projecting the forest areas. In 2009, the forest biomass arising from thinning is estimated be 6,591,575 $m^3$. The estimates of forest biomass in 2015 and 2018 are 6,375,627 $m^3$ and 6,284,779 $m^3$, respectively. Since the forest areas are projected to be declining, the biomass generated by thinning will decrease. This implies that the new alternatives for supplying raw materials for biofuels must be prepared before then.

• 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.98 no.5
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• pp.576-583
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• 2009

Based on the stem analysis and biomass measurement of 36 trees and 1,576 branches in Pinus sylvestris var. mongolica (Mongolian pine) plantations of Northeast China, this study was conducted to develop estimation model equation for leaf biomass of a single tree and branch, to examine the vertical distribution of leaf biomass in the crown, and to evaluate the proportional ratios of biomass by tree parts, stem, branch, and leaf. The results indicated that DBH and crown length were quite appropriate to estimate leaf biomass. The biomass of single branch was highly correlated with branch collar diameter and relative height of branch in the crown, but not much with stand density, site quality, and tree height. Weibull distribution function would have been appropriate to express vertical distribution of leaf biomass. The shape parameters from 29 sample trees out of 36 were less than 3.6, indicating that vertical distribution of leaf biomass in the crown was displayed by bell-shaped curve, a little inclined toward positive side. Apparent correlationship was obtained between leaf biomass and branch biomass having resulted in linear function equation. The stem biomass occupied around 80% and branch and leaf made up about 20% of total biomass in a single tree. As the level of tree class was increased from class I to class V, the proportion of the stem biomass to total biomass was gradually increased, but that of branch and leaf became decreased.

• Journal of Korean Society of Forest Science
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• v.96 no.5
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• pp.602-608
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• 2007

Forest plantations become important strategy not merely for the financial aspect, but for carbon sequestration and ecosystem stability. Forest plantations increase the density of the forest biomass, which reduce the increase in atmospheric carbon dioxide. Biomass density is also a useful variable for comparing structural and functional attributes of forest ecosystems across a wide range of environmental conditions. In this study, carbon sequestration of teak (Tectona grandis Linn. f.) in the individual tree and plantation levels estimation was carried out Site-specific allometric equation for the estimation of teak tree biomass was developed based on the direct measurement of fifteen (15) harvested trees in the Oak-twin Township of the Bago Yoma Region, Myanmar. A regression equation of the diameter at breast height (DBH) and the aboveground biomass (carbon content) was constructed to estimate the carbon storage level of plantations, which averaged 79 ton/ha. The average carbon accumulation in the soil (up to 30 cm in depth) was estimated 38.89 ton/ha, The highest mean annual increment (MAI) of total carbon was found in the 6-yr-old teak plantation (12.10 ton/ha/yr) whereas the lowest MAI was in the 26-yr-old teak plantation (4.31 ton/ha/yr).

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.389-401
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• 2011

Most nations around the world have expressed significant concern in the climate change due to a rapid increase in green-house gases and thus reach an international agreement to control total amount of these gases for the mitigation of global warming. As the most important absorber of carbon dioxide, one of major green-house gases, forest resources should be more tightly managed with a means to measure their total amount, forest biomass, efficiently and accurately. Forest biomass has close relations with forest areas and tree height. Airborne LiDAR data helps extract biophysical properties on forest resources such as tree height more efficiently by providing detailed spatial information about the wide-range ground surface. Many researchers have thus developed various methods to estimate tree height using LiDAR data, which retain different performance and characteristics depending on forest environment and data characteristics. In this study, we attempted to investigate such various techniques to estimate tree height, elaborate their advantages and limitations, and suggest future research directions. We first examined the characteristics of LiDAR data applied to forest studies and then analyzed methods on filtering, a precedent procedure for tree height estimation. Regarding the methods for tree height estimation, we classified them into two categories: individual tree-based and regression-based method and described the representative methods under each category with a summary of their analysis results. Finally, we reviewed techniques regarding data fusion between LiDAR and other remote sensing data for future work.