• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.4
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• pp.10-21
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• 2008

A forest functions classification map is an essential element for the management planning of national forests. This study was intended to make out the map at the stand level by utilizing the Forest Functions Evaluation Program(FFEP), developed by Korea Forest Research Institute. In this program, the potential of each function was evaluated in each grid cell, and then a forest functions estimation map was generated based on the optimum grid cell values in each sub-compartment unit. Finally, the program produced a forest functions classification map with consideration of the priority of the functions. The final forest functions classification map required for the national forest management planning made out overlapping those results which the rest of the forest classified referring priority functions classification map to national forest manager and classified according to the local administrative guidance and sustainable forest resources management guidance. The results indicated that the forest function classification using the FFEP program could be an efficient tool for providing the data required for national forest management planning. Also this study made a meaningful progress in the forest function classification by considering the local forest administrative guidance and sustainable forest resources management guidance.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.698-701
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• 2005

This paper examines that is extracted certain information in forest areas within high resolution imagery based on wavelet transformation. First of all, study areas are selected one more species distributed spots refer to forest type map. Next, study area is cut 256 x 256 pixels size because of image processing problem in large volume data. Prior to wavelet transformation, five texture parameters (contrast, dissimilarity, entropy, homogeneity, Angular Second Moment (ASM≫ calculated by using Gray Level Co-occurrence Matrix (GLCM). Five texture images are set that shifting window size is 3x3, distance .is 1 pixel, and angle is 45 degrees used. Wavelet function is selected Daubechies 4 wavelet basis functions. Result is summarized 3 points; First, Wavelet transformation images derived from contrast, dissimilarity (texture parameters) have on effect on edge elements detection and will have probability used forest road detection. Second, Wavelet fusion images derived from texture parameters and original image can apply to forest area classification because of clustering in Homogeneous forest type structure. Third, for grading evaluation in forest fire damaged area, if data fusion of established classification method, GLCM texture extraction concept and wavelet transformation technique effectively applied forest areas (also other areas), will obtain high accuracy result.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.13-19
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• 2006

In previous classification, forest recreation functions were evaluated by same factors and couldn't consider various characteristics of forest resources. The purpose of this study is to evaluate the recreational function of forest resources by applying different factors to each forest resources. We selected Daegu and Mt. Jiri as study area and divided forest resources into visitor-oriented and forest-oriented recreational resources. The level of recreational functions were evaluated with three grade(low, medium, high). In consequence, our study found out that it is more effective to evaluate forest recreational function by applying accessibility and attraction factors to each forest resources than previous work.

• Journal of Korean Society of Forest Science
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• v.104 no.2
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• pp.300-308
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• 2015

Landscape planning system established in 2007. It is necessary that forest landscape management will be established based on the long-term and wide scope plan for forest management. This study suggested the considering factor while the establishing forest landscape plan for forest characteristics. Forest landscape type was consisted of 4 medium classification(geographical resources, waterscape, forest resources, cultural resources) and 12 small classification(geographical resources: panorama of ridge, ridge of curious rock peaks, waterscape: waterfall, valley, lake, forest resources: crown layer scenery, royal azaleas of main ridge, autumnal tints of ridge slope, flowers in herbaceous plants, inside of forest, forest trail scenery, cultural resources: facilities). This study suggested that consideration on 6 functions of forest in landscape zone planning and forest landscape management plan each classification (main ridge, sense of season, waterscape, rock resources).

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1146-1148
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• 2003

This study purposes to present strategic forest planning that can support the identification of forest resources and the maximum uses of forest functions through FMIS combining GIS, RS and LP. Forest information such as compartment structure and forest road was entered into GIS to construct FMIS. Remotely sensed data were used to land cover classification, and then geographical attributes were identified. New grouping unit, so called 'arvest unit' was introduced as the forest management unit. Strategic forest planning based on the 'arvest unit' was proposed that considered together the economic, environmental aspect of forest.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.1
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• pp.57-71
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• 2007

While a concept on biotop or the urgency of its classification systems have been under discussion recently, this study aims to examine outdoor biotop classification systems for environment-friendly community. To this end, the feasibility of creating a biotop in the community and application elements were generated and biotops were classified and categorized. Then, elements that can be applied in consideration of traditional Korean techniques were generated and biotop classification systems and specific components in residential areas were reviewed. As for the result of this study, based on a preliminary draft prepared through literature review, considerations for biotop classification systems were taken into account. Then, based on classification criteria for biotop formats, biotop functions and biotop types, a second-tier classification system was developed. Criteria for biotop formats included surfaces, lines and points while criteria for biotop functions were large cores, small bases, corridors, stepping stones and ecological islands. Criteria for habitat types were divided to include natural forest, developed green areas, lacustrine wetland, palustrine wetland, shrubs, grasslands, linear habitats, vacant plots and practical green areas, which were sub-categorized. As for the biotop classification system, macro-classification divided biotops into three types-space, line and point-based on biotop formats. Meso-classification had five groups and micro-classification had 21 groups based on habitat types. Future studies should focus on the ecological features of each biotop categories generated in this study and their creation and management techniques to find many practical methods to create, protect and manage outdoor biotop for environment-friendly community.

• KIISE Transactions on Computing Practices
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• v.22 no.9
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• pp.455-460
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• 2016

Recently, with more severe types felonies such as robbery and sexual violence, the importance of crime prediction and prevention is emphasized. For accurate and prompt crime prediction and prevention, both a classification model of crime with high accuracy based on past criminal records and well-designed system interface are required. However previous studies on the analysis of crime factors have limitations in terms of accuracy due to the difficulty of data preprocessing. In addition, existing crime monitoring systems merely offer a vast amount of crime analysis results, thereby they fail to provide users with functions for more effective monitoring. In this paper, we propose a classification model for types of crime based on random-forest algorithms and system design factors for real-time crime prediction. From our experiments, we proved that our proposed classification model is superior to others that only use criminal records in terms of accuracy. Through the analysis of existing crime monitoring systems, we also designed and developed a system for real-time crime monitoring.

• Journal of People, Plants, and Environment
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• v.24 no.6
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• pp.651-662
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• 2021

Background and objective: This study identifies whether children's planning-organizing executive function can be significantly classified and predicted by home environment quality and wealth factors. Methods: For empirical analysis, we used the data collected from the 10th Panel Study on Korean Children in 2017. Using machine learning tools such as support vector machine (SVM) and random forest (RF), we evaluated the accuracy of the model in which home environment factors classify and predict children's planning-organizing executive functions, and extract the relative importance of variables that determine these executive functions by income group. Results: First, SVM analysis shows that home environment quality and wealth factors show high accuracy in classification and prediction in all three groups. Second, RF analysis shows that estate had the highest predictive power in the high-income group, followed by income, asset, learning, reinforcement, and emotional environment. In the middle-income group, emotional environment showed the highest score, followed by estate, asset, reinforcement, and income. In the low-income group, estate showed the highest score, followed by income, asset, learning, reinforcement, and emotional environment. Conclusion: This study confirmed that home environment quality and wealth factors are significant factors in predicting children's planning-organizing executive functions.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.317-330
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• 2022

Recently, the policy regarding climate change in Korea and overseas has been to promote the utilization of forest biomass to achieve net zero emissions. In addition, with the implementation of the unused forest biomass system in 2018, the size of the Korean market for manufacturing wood pellets and wood chips using unused forest biomass is rapidly expanding. Therefore, it is necessary to estimate the total amount of unused forest biomass that can be used as an energy source and to identify the capacity that can be continuously produced annually. In this study, we estimated the actual forest area that can be produced of logging residue and the potential amount of unused forest biomass resources based on GT (green ton). Using a forest functions classification map (1 : 25,000), 5th digital forest type map (1 : 25,000), and digital elevation model (DEM), the forest area with a slope of 30° or less and mountain ridges of 70% or less was estimated based on production forest and IV age class or more. The total forest area where unused forest biomass can be produced was estimated to be 1,453,047 ha. Based on GT, the total amount of unused forest biomass potential resources in Korea was estimated to be 117,741,436 tons. By forest type, coniferous forests were estimated to be 48,513,580 tons (41.2%), broad-leaved forests 27,419,391 tons (23.3%), and mixed forests 41,808,465 tons (35.5%). Data from this research analysis can be used as basic data to estimate commercial use of unused forest biomass.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.364-380
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• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.