• Journal of Korean Society of Forest Science
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• v.79 no.3
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• pp.231-244
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• 1990

As the complexity of society increases, the demand for the forest-based recreational site is also increased. This, in turn. makes congestion an ubiquitous type of externality in forest-based recreational site. Efficient resource allocation requires this congestion effect to be accounted and considered in the decision making process. In this content, this study was conducted to suggest a process which can be used to measure the congestion and determine the optimal number of users. Willigness to pay(WTP) function obtained from Book Han San National Park users suggested that every 20.000 users increased decrease the satisfaction of users obtained from the site visit be 27.3%. For the purpose of demonstration this WTP function is applied tit determine the optimal number of users which is estimated as about 73,000 persons per day.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.209-216
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• 2023

This study aimed to use national forest inventory data to develop a forest productivity index and yield prediction model of a Tilia amurensis stand. The site index displaying the forest productivity of the Tilia amurensis stand was developed as a Schumacher model, and the site index classification curve was generated from the model results; its distribution growth in Korea ranged from 8-16. The growth model using age as an independent variable for breast height and height diameter estimation was derived from the Chapman-Richards and Weibull model. The Fitness Indices of the estimation models were 0.32 and 0.11, respectively, which were generally low values, but the estimation-equation residuals were evenly distributed around 0, so we judged that there would be no issue in applying the equation. The stand basal area and site index of the Tilia amurensis stand had the greatest effect on the stand-volume change. These two factors were used to derive the Tilia amurensis stand yield model, and the model's determination coefficient was approximately 94%. After verifying the residual normality of the equation and autocorrelation of the growth factors in the yield model, no particular problems were observed. Finally, the growth and yield models of the Tilia amurensis stand were used to produce the makeshift stand yield table. According to this table, when the Tilia amurensis stand is 70 years old, the estimated stand-volume per hectare would be approximately 208 m³ . It is expected that these study results will be helpful for decision-making of Tilia amurensis stands management, which have high value as a forest resource for honey and timber.

• Journal of Korean Society of Forest Science
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• v.87 no.2
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• pp.300-307
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• 1998

It is not an easy task for forest managers to make sound decisions on forest management and operations planning because of huge sets of spatial and temporal data and complex decision-making processes involved. However, as an efficient tool, GIS techniques enable them to enhance broad understandings on forest inventory and management conditions. In this study, we developed a GIS model for field use in forest management. In building the model, we have chosen MapInfo version 4.0 as the basic engine of the model. The model also includes an interface module to help forest managers use MapInfo functions easily. It handles MapInfo functions required to manage inventory data and analyze spatial distributions of forest stands. For testing field applicability of the model, we have build field data sets for a district of Chunchun National Forest. Then, we tested functions through quarrying stand attributes and constructing thematic maps. In this paper, the structures and functions of the model as well as the results of field applications are discussed.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.55-64
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• 2017

In order to assess the spatial and dynamic changes in bamboo forests, this study used the national-level spatial data between 1980 and 2010 to extract spatial information of bamboo forests through GIS technology. The results showed that the distribution types were mainly expansion, normal, mixed, damage, and separation. In case of mixed bamboo forest in the Sacheon region, the expansion characteristics were: area 2.5 ha, velocity 0.08 ha/yr, and distance 1.1 m/yr. The Phyllostachys pubescens forest in the Geojae region showed the following characteristics: area 1.9 ha, velocity 0.06 ha/yr, and distance 0.9 m/yr with where along from valley to ridge. This approach could provide a valuable tool for decision-making and implementations such as the bamboo forest management plan, environmental impact assessment for a preventing the bamboo expansion, and sustainable managing the bamboo resources.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.459-471
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• 2023

This study identified the characteristics of changes in forest areas of North Korea's major regions (Gaesong, Goseong, Pyongyang, and Hyesan·Samsu) using data on degraded lands collected via monitoring by the National Institute of Forest Science. The data, spanning 1999 to 2018, were cross-analyzed to determine trends in land cover change, and hotspot analysis was conducted to confirm evident changes in the forest areas. The results showed that the areas of interest substantially transitioned to other land use types from 1999 to 2008. Contrastingly, the range of changes decreased from 2008 to 2018, with some areas regenerating into forests. Nevertheless, the hotspot analysis indicated that hotspots occurred more intensively in the outskirts of cities and forest edges from 2008 to 2018 than from 1999 to 2008. The analysis also showed that the aforementioned changes were caused by various aspects, depending on regional characteristics and social factors. This study can be used as a basic reference for decision-making on the selection of basic forest restoration targets and restoration methods in inter-Korean forest cooperation initiatives.

• Journal of Korean Society of Forest Science
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• v.104 no.2
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• pp.248-253
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• 2015

This study applied the portfolio approach as a means to provide decision-making information for the establishment of the optimal production plan for non-timber products. The target items of non-timber forest product were Chestnut, Jujube, Walnut and Astringent Persimmon. The data used in this study were the annual report of forestry production cost survey which contains the annual production, annual gross income, and annual product cost from 2008 to 2013. These data were used to calculate the expected return of non-timber forest product. The objective function in the portfolio models was to minimize the expected return volatility, called risk and the constrain was to achieve the minimum expected return rate. Results indicated that the production ratio of the nuts and fruits in 2013 was 7% for Chestnut, 20% for Jujube, 5% for Walnut and 68% for Astringent Persimmon. Furthermore, portfolio presented that the production ratio was 10% for Chestnut, 9% for Jujube, 3% for Walnut and 78% for Astringent Persimmon in the near future. The cause was analyzed due to maintain stable production and income of Astringent Persimmon and Chestnut. Meanwhile, the revenue of Walnuts and Jujube was in great variation with relatively higher revenues.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.615-625
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• 2011

As United Nation (UN) declared 2010 to be the International Year of Biodiversity, the biodiversity issue has gained much attention since the issue of climate changes. Also, related researches for protecting and conserving the biodiversity are accompanied in the world. In this study, National Ecology Information is obtained from Ministry of Environment and Korea Forest Service and is utilized to valuate biodiversity and ecosystem services in Pyeongchang, Kangwon-do in Korea. For this, they are categorized into direct- or indirect-use value and nonuse value. Research results show that the biodiversity and ecosystem services in Pyeongchang are assessed as 2 trillion and 460 billion won. From this research, we evaluate the economic value of biodiversity and ecosystem services, and also suggest the possibility to utilize them as basic information for a decision making to establish the biodiversity protection plan.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.135-143
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• 2020

Shortwave radiation and sunshine hours (SHOUR) are important variables having many applications, including crop growth. However, observational data for these variables have low horizontal resolution, rendering its application to related research and decision making on f arming practices challenging. In the present study, hourly solar radiation data were physically generated using the Land-Atmosphere Modeling Package (LAMP) at the National Center f or Agro-Meteorology, and then daily SHOUR fields were calculated through statistical downscaling. After data quality evaluation, including case studies, the SHOUR data were added to the existing publically accessible LAMP daily database. The LAMP daily dataset, newly updated with SHOUR, has been provided operationally as input data to the "Gyeonggi-do Agricultural Drought Prediction System," which predicts agricultural weather disasters and field crop growth status.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Journal of Korean Society of Rural Planning
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• v.28 no.4
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• pp.105-117
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• 2022

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R² of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.