• Journal of Korean Society of Forest Science
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• v.76 no.4
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• pp.361-369
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• 1987

The idea of multiple-use of forest land is tile one field of economics to improve the efficiency of forest land, and is the famous management technique widely used in the developed forestry country. This paper introduces the STEM and the constraint method, which is one kind of mathematical programming techniques used for multiple forest Land use, and discusses the differences between these two methods by using the hypothetical data.

• Journal of Korean Society of Forest Science
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• v.88 no.2
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• pp.273-281
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• 1999

In this study, linear programming (LP) was applied to solving for optimal harvesting schedules of multiple-use forest management in Mt. Kari area managed by Chunchun National Forest Station. Associated with the geographic characteristics, the study area was classified into 4 large management units or watersheds and simultaneously applied were the site-specific levels of management constraints : nondeclining yield, initial cut for existing stands, % cut area, the volume of soil erosion, timber production and carbon storage, ending inventory condition and % area species selection for regeneration. The problem was formulated using both Model I and Model II techniques. In this paper, the formulations are presented and the results of the optimal solutions are discussed for comparison purposes.

• Journal of Korean Society of Forest Science
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• v.85 no.2
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• pp.172-179
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• 1996

This paper described the application of fuzzy multiobjective linear programming to solving a multiple-use problem of forest resources management. At first the concepts of linear programming, fuzzy linear programming and fuzzy multiobjective linear programming were introduced briefly. In order to illustrate a role of fuzzy multiobjective linear programming in the process of multiple-use forest planning, the natural recreation forest in Mt. Yoomyung was selected for this study. A fuzzy multiobjective linear programming model is formulated with data obtained from this Mt. Yoomyumg natural recreation forest to solve the multiple-use management planning problem of forest resources. Finally, the results, which were obtained from the calculation of this model, were discussed. The maximal value of the membership function(${\lambda}$) was 0.29, when the timber production and the forest recreation function were optimized at the same time through the fuzzy multiobjective linear programming. The cutting area in each period was 102.7ha, while total cutting area was 410.8ha for 4 periods. During 4 periods $57,904m^3$ will be harvested from this natural recreation forest and at the same time total visitors were estimated to be about 8.6 millions persons.

• Journal of Korean Society of Forest Science
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• v.76 no.2
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• pp.169-177
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• 1987

The concept of multiple-use forestry can be considered as a simple application of the economic theory which commands the efficient utilization of resources. This paper reviews two important branches of the economic theory-theory of the firm and the capital theory-and discusses various methodologies of measuring the non-timber benefits from the forest. In addition, an empirical analysis with an example of the Deogyu National Park is presented. For this purpose, the Clawson's Travel Cost Method was employed.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.302-305
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• 2004

The aim of this study is to apply and crossvalidate a spatial logistic multiple-regression model at Boun, Korea, using a Geographic Information System (GIS). Landslide locations in the Boun area were identified by interpretation of aerial photographs and field surveys. Maps of the topography, soil type, forest cover, geology, and land-use were constructed from a spatial database. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, diameter, and density of forest were extracted from the forest database. Lithology was extracted from the geological database and land-use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using landslide-occurrence factors by logistic multiple-regression methods. For validation and cross-validation, the result of the analysis was applied both to the study area, Boun, and another area, Youngin, Korea. The validation and cross-validation results showed satisfactory agreement between the susceptibility map and the existing data with respect to landslide locations. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy.

• Current Research on Agriculture and Life Sciences
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• v.23
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• pp.53-60
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• 2005

This study was carried out in order to produce useful material for the forest multiple use and forest protection management by soil physio-chemical analysis of studied area in Mt. Namsan. The result of soil physio-chemical analysis and statical analysis represented as following. In side of physical properties of forest soil in Mt. Namsan, Soil depth was average 31.4cm and available soil depth was average 20.0cm. Soil type was Brown forest soil that representative soil type in Korea. Soil texture was sandy loom(SL) except valley area. In side of chemical properties, the range of soil acidity was 4.29 ~ 5.19 (average 4.76), organic matter content was 3.17% that compared the lowest value to organic matter content of Korea forest soil. Available phosphorus was 3.64ppm that was lower than others forest soil. Exchangeable cation content was similar to the Korea brown forest soil. Cation exchange capacity(CEC) was $8.22cmol_c/kg$ in Mt. Narnsan.

• Journal of Forest and Environmental Science
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• v.29 no.2
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• pp.147-156
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• 2013

Multiple use forestry is capable of generating income for forest based communities through Non-Timber forest products (NTFPs) which provide food, medicine, materials for domestic use and cash income for communities adjoining forest areas in developing countries. This study evaluates the economics of producing ginger rhizomes under teak canopy in a multiple land use system during 2007 and 2008 in even aged teak plantations in Ibadan and Ife, Nigeria. Twelve $6m^2$ sample plots were randomly selected in Completely Randomized Block Design within and outside the plantation. Average ginger rhizome of (50-60 g) were planted on the slightly tilled soil. NPK 15:15:15 was applied at 180 kg/ha on a split unit dose. ANOVA, Profitability, Benefit-Cost (B/C) ratio were used to analyze data. Results showed no significant differences between sites in ginger rhizome yield, (0.089 and 0.718, ${\rho}{\leq}0.05$) in 2007 and 2008 respectively. Average yield were higher outside teak canopy in both sites and treatments, (Ibadan -40.05 g>32.9 g, Ife -67.6 g>25.2 g and Ibadan -41.3 g>31.5 g, Ife -66.8 g>25.0 g) with and without NPK respectively. NPK had no effect on yields within teak plantation, (Ibadan -31.5<32.9 g, Ife -25 g<25.2 g). Ginger rhizome production was viable financially without inorganic fertilizer during second cropping season within and outside plantation (B/C=1.02, 1.09) respectively. Ginger could be raised profitably under teak canopy, however, studies on insolation requirement of ginger under teak canopy and other tree plantations are recommended.

• Journal of Advanced Engineering and Technology
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• v.11 no.4
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• pp.273-277
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• 2018

In the field of forest disaster prevention, drones are expected to save higher human resources than the existing manpower has, and produce high-efficiency results over time. However, operational limitations brought by short flight times have brought the environment of limited use of the various capabilities of the drone, and the existing development systems operating the multi-drone are mainly for performance purpose, so it is a difficult to use for forest disaster prevention. The purpose of this paper is to design the concept based on multi-drone operation procedure through analysis of mission of ground control system for forest disaster prevention.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.386-389
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• 1999

In this study, visible and near infrared channels of NOAA/AVHRR data were used to classify land use and vegetation types over Korean peninsula. Analyzing forest stand structures and prediction of forest productivity using satellite data were also reviewed. Land use and land cover classification was made by unsupervised clustering methods. After monthly Normalized Difference Vegetation Index (NDVI) composite images were derived from April to November 1998, the derived composite images were used as temporal feature vector's in this clustering analysis. Visually interpreted, the classification result was satisfactory in overall for it matched well with the general land cover patterns. But subclassification of forests into coniferous, deciduous, and mixed forests were much confused due to the effects of low ground resolution of AVHRR data and without defined classification scheme. To investigate into the forest stand structures, digital forest type maps were used as an ancillary data. Forest type maps, which were compiled and digitalized by Forestry Research Institute, were registered to AVHRR image coordinates. Two data sets were compared and percent forest cover over whole region was estimated by multiple regression analysis. Using this method, other forest stand structure characteristics within the primary data pixels are expected to be extracted and estimated.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.331-341
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• 2023

Handling missing values in data analysis is essential in constructing a good prediction model. The easiest way to handle missing values is to use complete case data, but this can lead to information loss within the data and invalid conclusions in data analysis. Imputation is a technique that replaces missing data with alternative values obtained from information in a dataset. Conventional imputation methods include K-nearest-neighbor imputation and multiple imputations. Recent methods include missForest, missRanger, and mixgb ,all which use machine learning algorithms. This paper compares the imputation techniques for datasets with mixed datatypes in various situations, such as data size, missing ratios, and missing mechanisms. To evaluate the performance of each method in mixed datasets, we propose a new imputation performance measure (IPM) that is a unified measurement applicable to numerical and categorical variables. We believe this metric can help find the best imputation method. Finally, we summarize the comparison results with imputation performances and computational times.