• 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.

• Korean Journal of Environment and Ecology
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• v.33 no.2
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• pp.131-167
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• 2019

Securing reference information (inventory) is essential for proper conservation and management of protected areas. Moreover, the investigation results should be available for comparison and evaluation. For this study, we established seven investigational lattices and carried out the field examinations to obtain comparable data in the Donggang river basin ecological and scenery conservation area, We conducted a total of 16 field surveys and identified a total of 830 taxa that is composed of 127 families, 408 genera, 716 species, 10 subspecies, 88 varieties, and 16 forms. We identified a total of 487 taxa in the 6th investigational lattice and 457 taxa in the 4th. We observed 6 species (grade II) designated by the Ministry of Environment as the endangered plants, one "species to monitor" designated by the Ministry of Environment, and 23 taxa designated as endemic species. Regarding the floristic characteristics plants, 15 taxa of grade V, 27 taxa of grade IV, 38 taxa of grade III, 31 taxa of grade II, and 19 taxa of grade I were identified. The 6th investigational lattice showed the largest number of taxa at 70. A total of 58 taxa were identified as the naturalized plant, and the 6th investigational lattice showed the most at 35 taxa. The Donggang river basin ecological and scenery conservation area was assessed to be an extremely important area to conserve and maintain plants species diversity not only in the regional scale but also national scale.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.245-256
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• 2021

This study was conducted to identify the applicability of a LiDAR sensor to forest resources inventories by comparing data on a tree's position, height, and DBH obtained by the sensor with those by existing forest inventory methods, for the tree species of Criptomeria japonica in Jeolmul forest in Jeju, South Korea. To this end, a backpack personal LiDAR (Greenvalley International, Model D50) was employed. To facilitate the process of the data collection, patterns of collecting the data by the sensor were divided into seven ones, considering the density of sample plots and the work efficiency. Then, the accuracy of estimating the variables of each tree was assessed. The amount of time spent on acquiring and processing the data by each method was compared to evaluate the efficiency. The findings showed that the rate of detecting standing trees by the LiDAR was 100%. Also, the high statistical accuracy was observed in both Pattern 5 (DBH: RMSE 1.07 cm, Bias -0.79 cm, Height: RMSE 0.95 m, Bias -3.2 m), and Pattern 7 (DBH: RMSE 1.18 cm, Bias -0.82 cm, Height: RMSE 1.13 m, Bias -2.62 m), compared to the results drawn in the typical inventory manner. Concerning the time issue, 115 to 135 minutes per 1ha were taken to process the data by utilizing the LiDAR, while 375 to 1,115 spent in the existing way, proving the higher efficiency of the device. It can thus be concluded that using a backpack personal LiDAR helps increase efficiency in conducting a forest resources inventory in an planted coniferous forest with understory vegetation, implying a need for further research in a variety of forests.

• Journal of Forest and Environmental Science
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• v.33 no.2
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• pp.79-90
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• 2017

Improving the urban environmental quality relies mainly on the increasing of urban forests capacity to store carbon dioxide. This study assesses the floristic diversity of urban institutional lands in Bukavu and their potential to reduce atmospheric $CO_2$. An exhaustive inventory over three sites ($Coll{\grave{e}}ge$ Alfajiri, $Cath{\acute{e}}drale$ Notre-Dame de la Paix and Institut $Sup{\acute{e}}rieur$ $P{\acute{e}}dagogique$) of Bukavu led to the identification of 1,113 trees of which the diameter at breast height (1.30 m) ranged from 4.9 to 161 cm. Results reveal a floristic diversity made up of 4 families of conifers with 4 species and 14 of broadleaves with 21 species. Average densities were of $54trees\;ha^{-1}$ and $5.21m^2\;ha^{-1}$ of basal area. Urban-based allometric equations used yielded up to 312.8 tons of carbon stored in trees aboveground biomass equivalent to 1,147.9 tons of $CO_2$ reduced from the atmosphere over the three sites. The rate of carbon storage reaches $15.1tons\;ha^{-1}$. Thus, trees of the three institutional sites in Bukavu play an important role in reducing atmospheric $CO_2$ and contribute, thereby, to mitigate global climate change effects. Given the current environmental challenge associated with high population growth rate in cities, the urban forest ecosystem in DRC requires to be extended and further investigation.

• Environmental and Resource Economics Review
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• v.15 no.1
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• pp.1-26
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• 2006

This paper explores the relatilonship between state level environmental regulations and stocking and location decisions in the U.S livestock and poultry industry (beef, chicken, dairy and hogs), Rather than conduct this analysis on a species by species basis, the overall size of the livestock industry(expressed in animal units) and the size of Industry found on large, medium and small operations by state (48) and over time (29 years), which is panel data analysis, Generally, regulations seem to be induced by the structural change of industry; when industry creates externalities, regulators try to address them with policy tools to internalize them, Written regulatory stringency may not effect behavioral change; rather the state's willingness to enforce regulations seems to have a measurable influence. However, in the presence of rapid structural change, industry location is affected by written regulatory stringency. Policy enforcement activity was shown to influence inventory decisions in general and larger operations were found to be more sensitive to willingness to enforce than smaller operations.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.99-99
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• 2022

Jeju island has a humid subtropical climate and this climate zone is expected to migrate northward toward the main land, Korea Peninsula, as temperature increases are accelerated. Vegetation type has been inevitably shifted along with the climatic change, having more subtropical species native in southeast Asia or even in Africa. With the forest composition shift, it becomes more important than ever to analyze the water balance of the forest wihth the ongoing as well as upcoming climate change. Here, we implemented the Ecosystem Demography Biosphere Model (ED2) by initializing the key variables using forest inventory data (diameter at breast height in 2012). Out of 10,000 parameter sets randomly generated from prior distribution distributions of each parameter (i.e., Monte-Carlo Method), we selected four behavioral parameter sets using remote-sensing data (LAI-MOD15A2H, GPP-MOD17A2H, and ET-MOD16A2, 8-days at 500-m during 2001-2005), and evaluated the performances using eddy-covariance carbon flux data (2012 Mar.-Sep. 30-min) and remote sensing data between 2006-2020. We simulated each of the four RCP scenarios (2.6, 4.5, 6.0, and 8.5) from four climate forcings (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5 from ISIMIP2b). Based on those 64 simulation sets, we estimate the changes in water balance resulting from the forest composition shift, and also uncertainty in the estimates and the sensitivity of the estimates to the parameters, climate forcings, and RCP scenarios.

• Journal of Environmental Health Sciences
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• v.46 no.1
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• pp.1-10
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• 2020

Objectives: This study intends to evaluate the ecological risk of lead (Pb), arsenic (As), and their compounds according to the 2010 action plan on inventory and management for national priority chemicals and provide calculations of risks to the environment. By doing so, we aim to inform risk management measures for the target chemicals. Methods: We conducted species sensitivity distribution (SSD) analysis using the collected ecotoxicity data and obtained predicted no effect concentrations (PNECs) for the in-water environment using a hazardous concentration of 5% (HC5) protective of most species (95%) in the environment. Based on the calculated PNECs for aquatic organisms, PNEC values for soil and sediment were calculated using the partition coefficient. We also calculated predicted exposure concentration (PEC) from nation-wide environmental monitoring data and then the hazard quotient (HQ) was calculated using PNEC for environmental media. Results: Ecological toxicity data was categorized into five groups and five species for Pb and four groups and four species for As. Based on the HC5 values from SSD analysis, the PNEC value for aquatic organisms was calculated as 0.40 ㎍/L for Pb and 0.13 ㎍/L for As. PNEC values for soil and sediment calculated using a partition coefficient were 77.36 and 350.50 mg/kg for Pb and 24.20 and 112.75 mg/kg for As. The analysis of national environmental monitoring data showed that PEC values in water were 0.284 ㎍/L for Pb and 0.024 ㎍/L for As, while those in soil and sediment were respectively 45.9 and 44 mg/kg for Pb, and 11.40 and 19.80 mg/kg for As. Conclusions: HQs of Pb and As were 0.70 and 0.18 in water, while those in soil and sediment were 0.59 and 0.13 for Pb and 0.47 and 0.18 for As. With HQs <1 of lead and arsenic in the environment, their ecological risk levels are found to be low.

• Journal of Korean Society of Forest Science
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• v.95 no.1
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• pp.50-54
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• 2006

Biomass expansion factors and stem density values were commonly used in converting stand volumes into total carbon stocks for the purpose of national inventories of greenhouse gas emissions and carbon sequestration. The objective of this study was to examine the influence of stand age classes on aboveground and total biomass expansion factors, and stem density values in Chamaecyparis obtusa species. A total of 25 representative sample trees based on the three different stand age classes were destructively sampled to measure green weights and dry weights of the major four(root, stem, branch and foliage) portions of C. obtusa species grown in Jangseung-gun of southern Korea. According to the results of this study, as stand age classes increase, total biomass expansion factors tended to be decreased with the ranges from 3.64 to 1.44, while the stem density values tended to be slightly increased with the ranges from $0.35(g/cm^3)$ to $0.44(g/cm^3)$. There were statistically significant differences in biomass expansion factors and stem density values between stand age classes, but became nearly constant after 30 years old for C. obtusa species. This information could be very useful to improve a national-scaled inventory of greenhouse gas emissions and carbon sequestration for the C. obtusa species by applying different biomass expansion factors and stem density values.

• Journal of agriculture & life science
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• v.43 no.6
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• pp.1-6
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• 2009

The purpose of this study was to analyze annual tree ring growth characteristics for major tree species distributed in Chungbuk province. A total of 800 sample trees from 56 permanent sampling plots measured by the 5th Korean National Forest Inventory Program in 2007 was used for the calculation of annual growth rates. According to the results of this study, the species of Robinia pseudoacacia(2.30mm/yr) showed the best annual tree ring growth rates and the others are Quercus serrata(2.27mm/yr)>Prunus sargentii(1.98mm/yr)> and Larix leptolepis(1.98mm/yr) in order. Most of the major tree species in Chungbuk province, as tree age and stand density increased, annual tree ring growth rates tended to decreased. This information could be very useful for forest managers to understand annual tree ring growth characteristics in Chungbuk province.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.291-304
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• 2021

Updating a forest type map is essential for sustainable forest resource management and monitoring to cope with climate change and various environmental problems. According to the necessity of efficient and wide-area forestry remote sensing, CAS500-4 (Compact Advanced Satellite 500-4; The agriculture and forestry satellite) project has been confirmed and scheduled for launch in 2023. Before launching and utilizing CAS500-4, this study aimed to pre-evaluation the possibility of satellite-based tree species classification using RapidEye, which has similar specifications to the CAS500-4. In this study, the study area was the Chuncheon forest management complex, Gangwon-do. The spectral information was extracted from the growing season image. And the GLCM texture information was derived from the growing and non-growing seasons NIR bands. Both information were used to classification with random forest machine learning method. In this study, tree species were classified into nine classes to the coniferous tree (Korean red pine, Korean pine, Japanese larch), broad-leaved trees (Mongolian oak, Oriental cork oak, East Asian white birch, Korean Castanea, and other broad-leaved trees), and mixed forest. Finally, the classification accuracy was calculated by comparing the forest type map and classification results. As a result, the accuracy was 39.41% when only spectral information was used and 69.29% when both spectral information and texture information was used. For future study, the applicability of the CAS500-4 will be improved by substituting additional variables that more effectively reflect vegetation's ecological characteristics.