• Journal of the Korean Institute of Landscape Architecture
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• v.33 no.6 s.113
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• pp.51-77
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• 2006

Pond management is a critical part of overall golf course management, both during growth and maintenance modes of turf care. This study investigated 48 ponds in nine 18- or 27-hole golf courses to analyze the environmental characteristics of ponds. The research process had three phases: (1) inventory and analysis of grading plans and drainage plans, (2) field verification and interviews with greenskeepers, and (3) analyses of water quality and statistics. All data were collected from May to August in 2004. The results of this study can be summarized as follows: 1. It is desirable to site a golf course in a small watershed with high watershed eccentricity to control storm water runoff efficiently and to minimize soil erosion during construction. 2. The siting and size of a pond should be determined through a land-use analysis of the watershed for the purpose of ecological management. The bigger the forest-to-golf course ratio, the better the water quality will be. 3. The size and capacity of each individual ponds varied and there were many somewhat longish rather than round ponds. 4. There were many differences among golf courses in naturalness of the ponds, and the correlation between naturalness and area of aquatic plants was very high. 5. Analyses of pond water quality indicated that the degrees of Dissolved Oxygen, Chemical Oxygen Demanded and Suspended Solids were relatively low values but Total Phosphorus and Total Nitrogen were too high. Therefore a systematic approach is needed to solve e problem. Pesticide residues were not detected in all ponds. 6. Water depth and area of hydrophyte should be considered when designing an ecological pond. 7. All ponds used storm water as a main source of water supply and added underground water. Aquatic plants and physical methods such as water aeration and spray fountains were the main choices for maintaining a healthy aquatic environment.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.45 no.2
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• pp.46-55
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• 2013

Recently, there are growing interests on carbon emissions related in climate change which is worldwide emerging important issue. Some research works are now carrying out in order to reduce the carbon emission in pulp and paper industries by the synthesis of precipitated calcium carbonate using the exhaust carbon dioxide from combustion furnace or incinerator. However, for solving the original problems on carbon emission, we need to consider the analysis of basic methodology on $CO_2$ through the process efficiencies. There are two general tools for carbon emissions; one is the greenhouse gas inventory and the other is $LCCO_2$ method which is applied to particular items of raw materials and utilities in unit process. In this study, the carbon emissions in wood-containing printing paper production line were calculated by using $LCCO_2$ method. The general materials and utilities for paper production, such as fibrous materials, chemical additives, electric power, steam, and industrial water were analyzed. As the results, $Na_2SiO_3$ showed the highest loads in carbon emissions, and the total amount of carbon emissions was the highest in electricity. In the production line of printing paper using de-inked pulp and BTMP, as the mixing ratio of DIP was higher, the carbon emissions were decreased because of high use of electric power in TMP process.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.3
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• pp.259-268
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• 2012

This paper aims to estimate the carbon dioxide stocks in forests using airborne LiDAR data with a density of approximate 4.4 points per meter square. To achieve this goal, a processing chain consisting of bare earth Digital Terrain Model(DTM) extraction and individual tree top detection has been developed. As results of this experiment, the reliable DTM with type-II errors of 3.32% and tree positions with overall accuracy of 66.26% were extracted in the study area. The total estimated carbon dioxide stocks in the study area using extracted 3-D forests structures well suited with the traditional method by field measurements upto 7.2% error level. This results showed that LiDAR technology is highly valuable for replacing the existing forest resources inventory.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.817-819
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• 2003

Land degradation is a crucial issue in mountainous areas and is manifested in a variety of processes. For its assessment, application of existing models is not straightforward. In addition, data availability might be a problem. In this paper, a procedure for land degradation assessment is described, which follows a four-step approach: (1) detection, inventory and mapping of land degradation features, (2) assessing the magnitude of soil loss, (3) study of causal factors, and (4) hazard assessment by applying decision trees. This approach is applied to a case study in the Middle Mountain region of Nepal. The study shows that individual mass movement features such as debris slides and slumps can be easily mapped by photo interpretation techniques. Application of soil loss estimation models helps get insight on the magnitude of soil losses. In the study area soil losses are higher in rainfed crops on sloping terraces (highest soil loss is 32 tons/ha/yr) and minimal under dense forest and in irrigated rice fields (less than 1 ton/ha/yr). However there is high frequency of slope failures in the form of slumps in the rice fields. Debris slides are more common on south-facing slopes under rainfed agriculture or in degraded forest. Field evidences and analysis of causal factors for land degradation helps in building decision trees, the use of which for modelling land degradation has the advantage that attributes can be ranked and tested according to their importance. In addition, decision trees are simple to construct, easy to implement and very flexible in adaptations.

• Journal of The Geomorphological Association of Korea
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• v.27 no.3
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• pp.87-103
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• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.

• Journal of The Geomorphological Association of Korea
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• v.27 no.2
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• pp.65-80
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• 2020

This paper deals with risk assessment of life in a landslide-prone area by a GIS-based modeling method. Landslide susceptibility maps can provide a probability of landslide prone areas to mitigate or proper control this problems and to take any development plan and disaster management. A landslide inventory map of the study area was prepared based on past historical information and aerial photography analysis. A total of 550 landslides have been counted at the whole study area. The extracted landslides were randomly selected and divided into two different groups, 50% of the landslides were used for model calibration and the other were used for validation purpose. Eleven causative factors (continuous and thematic) such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in hazard analysis. The correlation between landslides and these factors, pixels were divided into several classes and frequency ratio was also extracted. Eventually, a landslide susceptibility map was constructed using a logistic regression model based on entire events. Moreover, the landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract a success rate curve. Based on the results, logistic regression produced an 85.18% accuracy, so we believed that the model was reliable and acceptable for the landslide susceptibility analysis on the study area. In addition, for risk assessment, vulnerability scale were added for social thematic data layer. The study area predictive landslide affected pixels 2,000 and 5,000 were also calculated for making a probability table. In final calculation, the 2,000 predictive landslide affected pixels were assumed to run. The total population causalities were estimated as 7.75 person that was relatively close to the actual number published in Korean Annual Disaster Report, 2006.

• Journal of Korean Society of Forest Science
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• v.98 no.6
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• pp.772-779
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• 2009

This study was conducted to estimate the carbon (C) contents in pure and mixed stands of pine (Pinus densiflora) and oak (Quercus spp.) trees for establishing the C inventory of forest ecosystems. A total of fifteen 20 m${\times}$20 m pure and mixed stands of pine and oak trees were chosen in natural forests in Hoengseong, Kangwon based on the basal area of all trees ${\geq}$ 5 cm DBH: three of 95% of pine and 5% oak trees [pine stand], three of 100% of oak trees [oak stand], and nine of 20 to 70% of pine and 80 to 30% of oak trees [mixed stand]. To estimate C contents in the study stands, biomass in vegetation, forest floor and coarse woody debris (CWD) were calculated and C concentrations in vegetation, forest floor, CWD and soil (0-30 cm) were analyzed. There was no significant difference in vegetation C contents among the stands; 147.6 Mg C/ha for the oak stand, 141.4 Mg C/ha for the pine stand and 115.8 Mg C/ha for the mixed stand. Forest floor C contents were significantly different among the stands (p<0.05); 12.7 Mg/ha for the pine stand, 9.9 Mg/ha for the oak stand, and 8.4 Mg/ha for the mixed stand. However, CWD C contents were not significantly different among the stands (p>0.05); 2.2 Mg/ha for the mixed stand, 1.7 Mg/ha for the oak stand, and 1.1 Mg/ha for the pine stand. Soil C contents up to 30 cm depth were not significantly different among the study stands; 44.4 Mg C/ha for the pine stand, 41.6 Mg C/ha for the mixed stand, and 33.3 Mg C/ha for the oak stand. Total ecosystem C contents were lower in the mixed stand than those in the pure stands, because vegetation C contents which occupied almost total ecosystem C contents were lower in the mixed stand than those in the pure stands; 199.6 Mg C/ha for the pine stand, 192.5 Mg C/ha for the oak stand and 169.1 Mg C/ha for the mixed stand. Lower vegetation C contents in the mixed stand might be influenced by interspecific competition between pine and oak trees and intraspecific competition among the oak trees resulted from high stand density. We suggest that forest management such as thinning to enhance C storage is indispensible for minimizing the competition in forest ecosystems.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.45-74
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• 2018

Landslide is a natural hazard that threats lives and properties in many areas around the world. Landslides are difficult to recognize, particularly in rainforest regions. Thus, an accurate, detailed, and updated inventory map is required for landslide susceptibility, hazard, and risk analyses. The inconsistency in the results obtained using different features selection techniques in the literature has highlighted the importance of evaluating these techniques. Thus, in this study, six techniques of features selection were evaluated. Very-high-resolution LiDAR point clouds and orthophotos were acquired simultaneously in a rainforest area of Cameron Highlands, Malaysia by airborne laser scanning (LiDAR). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Training samples were evaluated using a stratified random sampling method and set to 70% training samples. Two machine-learning algorithms, namely, Support Vector Machine (SVM) and Random Forest (RF), were used to evaluate the performance of each features selection algorithm. The overall accuracies of the SVM and RF models revealed that three of the six algorithms exhibited higher ranks in landslide detection. Results indicated that the classification accuracies of the RF classifier were higher than the SVM classifier using either all features or only the optimal features. The proposed techniques performed well in detecting the landslides in a rainforest area of Malaysia, and these techniques can be easily extended to similar regions.

• Journal of The Geomorphological Association of Korea
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• v.23 no.2
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• pp.109-125
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• 2016

This study aims to identify the landslide susceptible zones of Boeun area and provide reliable landslide susceptibility maps by applying different modeling methods. Aerial photographs and field survey on the Boeun area identified landslide inventory map that consists of 388 landslide locations. A total ofseven landslide causative factors (elevation, slope angle, slope aspect, geology, soil, forest and land-use) were extracted from the database and then converted into raster. Landslide causative factors were provided to investigate about the spatial relationship between each factor and landslide occurrence by using fuzzy set and logistic regression model. Fuzzy membership value and logistic regression coefficient were employed to determine each factor's rating for landslide susceptibility mapping. Then, the landslide susceptibility maps were compared and validated by cross validation technique. In the cross validation process, 50% of observed landslides were selected randomly by Excel and two success rate curves (SRC) were generated for each landslide susceptibility map. The result demonstrates the 84.34% and 83.29% accuracy ratio for logistic regression model and fuzzy set model respectively. It means that both models were very reliable and reasonable methods for landslide susceptibility analysis.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.1
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• pp.29-39
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• 2016

The purpose of this study was to suggest the improvement of the Mountainous Landscape Impact Review(MLIR) system for the conservation and eco-friendly use of mountain scenery. In order to understand the status of the MLIR system, a comparative analysis was conducted of the MLIR official guidelines and the 100 MLIR reports submitted to the Korea Forest Service from 2011 to 2013. In addition, an Importance-Performance Analysis(IPA) was conducted to take into account stakeholder opinions and to determine the first priorities to be improved upon in operation and functions of the MLIR system. The results of the IPA in evaluating the MLIR system showed that the components in the MLIR system that should be primarily improved are mountainous landscape resource inventory, objective and quantitative selection of viewpoints, and a checklist for examining the damage expected in mountainous lands. To Revitalize the Mountainous Landscape Impact Review system, the professional knowledge and experience of the stakeholders should be enhanced by education and training in the MLIR system over the short-term, while the effective functioning of the MLIR system should be reinforced by differentiation and connectivity of the MLIR system with similar institutions, and by emphasizing the uniqueness and properties of mountainous landscapes over the long-term.