• Journal of Integrative Natural Science
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• v.13 no.1
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• pp.34-40
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• 2020

This study aims to create a model for predicting the number of extinguishment manpower to put out forest fires by taking into account the climate, the situation, and the extent of the damage at the time of the forest fires. Past research has been approached to determine the cause of the forest fire or to predict the occurrence of a forest fire. How to deal with forest fires is also a very important part of how to deal with them, so predicting the number of extinguishment manpower is important. Therefore predicting the number of extinguishment manpower that have been put into the forest fire is something that can be presented as a new perspective. This study presents a model for predicting the number of extinguishment manpower inputs considering the scale of the damage with forest fire on a scale bigger than 0.1 ha as data based on the forest fire annual report(Korea Forest Service; KFS) from 2015 to 2018 using the moderated multiple regression analysis. As a result, weather factors and extinguished time considering the damage show that affect forest fire extinguishment manpower.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.370-377
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• 2018

Currently, Korea is ahead of the forest sector such as forest management, forest investigation and forest management, which is not insufficient compared with the forest advanced countries (Germany, Japan, Austria). However, there is a lack of systematic and advanced forest management plan and related research, and it is not enough to construct GIS for practical and complex analysis. Therefore, in order to perform forest analysis effectively, this study maps forest basic statistics (2010, 2015) based on GIS to map forest information. As a result, the forest area, growing stock, average growing stock, and forest rate could be produced with the maximized visual effect by detailed administrative districts, and systematic analysis of the time series changes was also possible. Forest area increased only in Goseong, Sejong, Cheolwon, Yeoncheon, Daejeon, and Seoul Guro-gu, and decreased in all other areas, while growing stock increased in most areas, Uljin, Ulleung, Seoul Nowon-gu, and Seoul Gangdong-gu. The average growing stock was found to increase in most areas excluding the four administrative districts and the forest rate was higher in 10 regions (Goseong, Yeoncheon, Gongju, Busan Dong-gu, Daegu Seo-gu, etc.) but it decreased in most regions excluding 10 regions. Based on this research, we plan to produce and analyze forest information maps for smaller administrative districts and more.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.392-396
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• 2011

The purpose of this study was to develop the basic wood density (Abbreviated BWD) for Pinus densiflora and to evaluate the applicability of bootstrap simulation method. The data sets were divided into two groups based on eco-types in Korea, one from Gangwon type and the other from Jungbu type. The estimated BWDs derived from bootstrap simulation, which is one of the non-parametric statistics, were 0.418 ($g/cm^3$) in the Pinus densiflora in Gangwon while 0.464 ($g/cm^3$) in the Pinus densiflora in Jungbu. To evaluate the bootstrap simulation, the mean BWD, standard error and 95% confidence interval of probability density were estimated. The number of replication were 100, 500, 1,000, and 5,000 times that showed constant 95% confidence interval, while tended to decrease in terms of standard errors. The results of this study could be very useful to apply basic wood density values to calculate reliable carbon stocks for Pinus densiflora in Korea.

• Korean Journal of Agricultural Science
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• v.43 no.3
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• pp.299-319
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• 2016

The National Forest Inventory (NFI) is important for providing fundamental data for basic forest planning and the establishment of forest policies for the purpose of implementing sustainable forest management. The purpose of this study is to present the development of Korea's NFI including legal basis, sampling design, and measured variables and to review the usage of NFI data. The survey methods and forestry statistics among the Unites States, Canada, Japan, China, and European countries were briefly compared. Total 140 publications utilizing NFI data between 2008 and 2015 were categorized with 15 subjects. Korea has conducted the NFI 6 times since 1971, but only the $6^{th}$ NFI is comparable with the fifth, the previous NFI, because the permanent sampling plots have been shared between the periods. The Korean Forestry Statistics contains only half as many variables as that of advanced countries in Forestry. More researches were needed to improve consistent measurement of diverse variables through implementation of advanced technologies. Additional data for Forest Health Monitoring since the NFI $6^{th}$ must be under quality control which will be an essential part of the inventories for providing the chronological change of forest health.

• Journal of Korean Society of Forest Science
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• v.98 no.5
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• pp.548-557
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• 2009

For centuries, forests have been a key component of rural livelihood. They are important both socially and economically in Nepal. Firewood and fodder are the basic forest products that are extracted daily or weekly basis in most of the rural areas in Nepal. In this study, a field survey of 100 households was conducted to examine the degree of forest dependency and forest resource availability, households' livelihood strategy and their relationship with forest dependency in Chitwan, Nepal. A household' response indexes were constructed, Gini coefficient, Head Count Poverty Index (HCI) and Poverty Gap Index (PGI) were calculated and one way ANOVA test was also performed for data analysis. Data revealed that 82/81% of all households were constantly used forest for firewood and fodder collection respectively while 42% of households were used forest or forest fringe for grazing. The Forest Product Availability Indexes (FPAI) showed a sharp decline of forest resources from 0.781 to 0.308 for a 20-yr time horizon while timber wood was noticeably lowered than the other products. Yet, about 33% of households were below the poverty threshold line with 0.0945 PGI. Income distribution among the household showed a lower Gini coefficient 0.25 than 0.37 of landholdings size. However, mean income was significantly varies with F-statistics=246.348 at P=0.05 between income groups (rich, medium and poor). The extraction of firewood, fodder and other forest products were significantly different between the income group with F-statistics=16.480, 19.930, 29.956 at P=0.05 respectively. Similarly, landholdings size and education were also significantly different between the income groups with F-statistics=4.333, 5.981 at P=0.05 respectively. These findings suggested that income status of households was the major indicator of forest dependency while poor and medium groups were highly dependent on the forests for firewood, fodder and other products. Forest dependency still remains high and the availability of forest products that can be extracted from the remaining forestlands is decreasing. The high dependency of households on forest coupled with other socioeconomic attributes like education, poverty, small landholders and so on were possibly caused the forest degradation in Chitwan.Therefore, policy must be directed towards the poor livelihood supporting agenda that may enhance the financial conditions of rural households while it could reduce the degree of forest dependency inspired with other income generating activities in due course.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.391-404
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• 2019

Customers' fixed characteristics have often been used to predict customer behavior. It has recently become possible to track customer web logs as customer activities move from offline to online. It has become possible to collect large amounts of web log data; however, the researchers only focused on organizing the log data or describing the technical characteristics. In this study, we predict the decision-making time until each customer makes the first reservation, using Airbnb customer data provided by the Kaggle website. This data set includes basic customer information such as gender, age, and web logs. We use various methodologies to find the optimal model and compare prediction errors for cases with web log data and without it. We consider six models such as Lasso, SVM, Random Forest, and XGBoost to explore the effectiveness of the web log data. As a result, we choose Random Forest as our optimal model with a misclassification rate of about 20%. In addition, we confirm that using web log data in our study doubles the prediction accuracy in predicting customer behavior compared to not using it.

• Journal of Korean Society of Forest Science
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• v.85 no.3
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• pp.487-495
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• 1996

The objective of this study was to investigate the actual state of the national project to promote the management of Forest Management Cooperatives(FMC). To fulfil the objective, we have reviewed recent statistics, regulations, and publications related with FMC and interviewed officials and professionals engaged in FMC-related organizations. Also cognition of members on the actual state of the management of FMC's was studied by a questionnaire survey. The questionnaire was designed to get understandings of the general cognition of FMC's members associated with forest management and FMC's management activities. According to the results of the survey, more than 50% of members are not interested in forest management and most of them are not satisfied with the activity of FMC. In this paper, the results of analyses for the survey are discussed in detail. On the other hand, statistics indicate that the basic policy for FMC contributes to rapid growth in the number of local FMC's. However, the increase of FMC's has negative effect on management conditions of existing FMC's because of reduced budget allocation from the government. In addition, we concluded that some parts of current regulations for FMC are unfavorable in promoting the spontaneous management activities of local FMC's.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.504-511
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• 2020

This study estimated planned felling volumes to set targets for management planning of nationwide country-owned forests. Estimates were made using timber harvest prediction methods that use probability density functions, including area weighting (AW), area ratio weighting (ARW), and sample area change ratio weighting (SCRW). Country-owned forest areas in 2010 and 2015 were used to estimate planned felling volumes, as shown in basic forest statistics, and calculations were made assuming that the felling areas were the changes in the forest area over the 5-year period. For the age classes of V-VI, the average felling ages for AW, ARW, and SCRW were 5.41, 5.56, and 5.37, respectively, and the felling areas were 594,462, 586,704, and 580,852 ha, respectively, with ARW reaching closest to the actual changes. The actual changes in the areas and chi-squared test results were most stable with the SCRW method. This study showed that SCRW was more adequate than AW and ARW as a method to predict timber harvests for forest management planning.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1119-1126
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• 2002

In this paper, we propose two predictive spatial data mining based on spatial statistics and apply for predicting the forest fire hazardous area. These are conditional probability and likelihood ratio methods. In these approaches, the prediction models and estimation procedures are depending un the basic quantitative relationships of spatial data sets relevant forest fire with respect to selected the past forest fire ignition areas. To make forest fire hazardous area prediction map using the two proposed methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. In comparison of the prediction power of the two proposed prediction model, the likelihood ratio method is mort powerful than conditional probability method. The proposed model for prediction of forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrence and effective placement of forest fire monitoring equipment and manpower.

• Journal of Environmental Impact Assessment
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• v.20 no.4
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• pp.523-538
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• 2011

This study analyzes the changes in the surface area of each forest cover, based on temperature data analysis and satellite imagery as the basic methods for the impact assessment of climate change on regional units. Furthermore, future changes in the forest cover are predicted using the double exponential smoothing method. The results of the study have shown an overall increase in annual mean temperature in the studied region since 1990, and an especially increased rate in winter and autumn compared to other seasons. The multi-temporal analysis of the changes in the forest cover using satellite images showed a large decrease of coniferous forests, and a continual increase in deciduous forests and mixed forests. Such changes are attributed to the increase in annual mean temperature of the studied regions. The analysis of changes in the surface area of each forest cover using the statistical data displayed similar tendencies as that of the forest cover categorizing results from the satellite images. Accordingly, rapid changes in forest cover following the increase of temperature in the studied regions could be expected. The results of the study of the forest cover surface using the double exponential smoothing method predict a continual decrease in coniferous forests until 2050. On the contrary, deciduous forests and mixed forests are predicted to show continually increasing tendencies. Deciduous forests have been predicted to increase the most in the future. With these results, the data on forest cover can be usefully applied as the main index for climate change. Further qualitative results are expected to be deduced from these data in the future, compared to the analyses of the relationship between tree species of forest and climate factors.