• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.255-261
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• 2009

A new polarimetric SAR image classification technique based on the degree of polarization (DoP) and the co-polarized phase-difference (CPD) is presented in this paper. Since the DoP and the CPD of a scattered wave provide information on the randomness of the scattering and the type of scattering mechanisms, at first, the statistics of the DoP and CPD are examined with measured polarimetric SAR image data. Then, a DoP-CPD diagram with appropriate boundaries between six different classes is developed based on the SAR image. The classification technique is verified using the JPL AirSAR and ALOS PALSAR polarimetric data. The technique may have capability to classify an SAR image into six major classes; a bare surface, a village, a crown-layer short vegetation canopy, a trunk-layer short vegetation canopy, a crown-layer forest, and a trunk-dominated forest.

• Korean Journal of Environment and Ecology
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• v.29 no.2
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• pp.236-249
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• 2015

The master plan of forest land management proposes forest watershed management that considers regional characteristics in order to overcome the problem of uniform forest land management. In order to manage the forest watersheds in Gyeonggi-do, this study classified 1,823 forest watersheds in Gyeonggi-do and attempted to understand their characteristics. It conducted a factor analysis and cluster analysis from the perspective of conservation value and development pressure using forest land indicators. In terms of conservation value, three factors were drawn: the topography factor, vegetation factor and public service factor, while in terms of development pressure, three factors were drawn: the easiness of development factor, economic benefits factor and development activity factor. Using these factors, forest watersheds were divided into three clusters in terms of conservation value while they were divided into three clusters in terms of development pressure. Using the results of the cluster analysis from a conservation-development perspective, the forest watersheds were classified into nine different types, and the characteristics were identified by each type. It is judged that the factors and clusters drawn as a result of the research accurately reflect the present conditions of Gyeonggi-do, and the nine types of forest watersheds have clear characteristics according to each type, which are judged to be utilized in forest management in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.2
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• pp.37-46
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• 2004

In order to decrease the area damaged by forest fires and to prevent the occurrence of forest fires, we are making an effort to improve prevention measures for forest fires. The objective of this study is to classify hazard regions where forest fires occur based on the factors that contribute to the occurrence of forest fires. Forest fire sites in the Uiseong-gun, Gyeongsangbuk-do were surveyed according to the factors of forest type and topographic characteristics where the forest fires occurred. We used a correlation analysis to determine the forest fire occurrence factors and a conditional probability analysis and GIS to determine a forest fire danger index. The resulting forest fire danger index was used in the classification of forest fire occurrence risk regions.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.4
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• pp.112-123
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• 2015

The purpose of this study is to analyze the biotope characteristics of Korean cities and set up biotope type structures for Korean cities based on biotope type classification, dominant biotope type, city's human and nature environmental characteristics and cluster analysis. The findings of the study are summarized as follows: First, regarding the analysis of biotope type classification, cities showed differences in terms of the standard of biotope classification and classification hierarchy. Next, the analysis of dominant biotope types showed the type of forest represents the largest area in most cities. Moreover, a city's characteristic analysis revealed large differences between cities. As a result of cluster analysis, cities were classified into five clusters overall. First, Cluster A showed a lower population level and urbanization level. Unlike other cities, Cluster A revealed that it has the largest percentage of agricultural areas. Cluster C showed very high levels in terms of population amount and urbanization conditions was named the 'Large-sized metropolitan cities-center of forest biotope area' based on it's characteristics. The findings of this study as summarized above are considered to play an important role in enabling detailed classification and preservation of biotope types fit for the characteristics of cities and minimizing the confusion caused by different biotope mapping methods when revising and complementing biotope maps.

• Journal of Forest and Environmental Science
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• v.37 no.4
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• pp.269-279
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• 2021

This study was carried out to introduce the forest cover types of the Baekdudaegan inhabiting the number of native tree species. In order to understand the vegetation distribution characteristics of the Baekdudaegan, a vegetation survey was conducted on the major 20 mountains of the Baekdudaegan. The vegetation data were collected from 3,959 sample points by the point-centered quarter method. Each mountain was classified into 4-7 forests by using various multivariate statistical methods such as cluster analysis, indicator species analysis, multiple discriminant analysis, and species composition analysis. The forests were classified mainly according to the relative abundance of Quercus mongolica. There was a total of 111 classified forests and these forests were integrated into the following nine forest cover types using the percentage similarity index and by clustering according to vegetation type: 1) Mongolian oak, 2) Mongolian oak and other deciduous, 3) Oaks (Mixed Quercus spp.), 4) Korean red pine, 5) Korean red pine and oaks, 6) ash, 7) mixed mesophytic, 8) subalpine zone coniferous, and 9) miscellaneous forest. Forests grouped within the subalpine zone coniferous and miscellaneous classifications were characterized by similar environmental conditions and those forests that did not fit in any other category, respectively.

• Journal of the Korean Wood Science and Technology
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• v.47 no.2
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• pp.229-238
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• 2019

Various wood defects occur during tree growing or wood processing. Thus, to use wood practically, it is necessary to objectively assess their quality based on the usage requirement by accurately classifying their defects. However, manual visual grading and species classification may result in differences due to subjective decisions; therefore, computer-vision-based image analysis is required for the objective evaluation of wood quality and the speeding up of wood production. In this study, the SIFT+k-NN and CNN models were used to implement a model that automatically classifies knots and analyze its accuracy. Toward this end, a total of 1,172 knot images in various shapes from five domestic conifers were used for learning and validation. For the SIFT+k-NN model, SIFT technology was used to extract properties from the knot images and k-NN was used for the classification, resulting in the classification with an accuracy of up to 60.53% when k-index was 17. The CNN model comprised 8 convolution layers and 3 hidden layers, and its maximum accuracy was 88.09% after 1205 epoch, which was higher than that of the SIFT+k-NN model. Moreover, if there is a large difference in the number of images by knot types, the SIFT+k-NN tended to show a learning biased toward the knot type with a higher number of images, whereas the CNN model did not show a drastic bias regardless of the difference in the number of images. Therefore, the CNN model showed better performance in knot classification. It is determined that the wood knot classification by the CNN model will show a sufficient accuracy in its practical applicability.

• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.2 s.115
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• pp.1-17
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• 2006

The purpose of this study was to analyze biotope types of urban land-use patterns. Forest areas were considered according to vegetation type and potential for succession. Urban ecosystem structure was analyzed according to land use, land coverage, vegetation structure (actual vegetation, diameter at breast height, layer structure, and revetment). As a results of the classification, the biotopes were divided into 71 types according to the urban ecosystem structure. In the case of the Hanam province, the biotopes were divided into 51 types: 26 forest types; 5 swampy and grass land types; 3 farm land types; 3 types of planted land, and 8 types of urbanization.

• Journal of Korean Society of Forest Science
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• v.34 no.1
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• pp.47-56
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• 1977

I have investigated and compared the morphology of vascular bundle shown in the section of culm wall of bamboo trees growing on Mt. Jiri which were classified by Grosser and Liese with their methods of morphological classification. The results obtained were as follows: 1. It was shown that there are no b.g.i. types of bamboo classified by Grosser and Liese among the bamboo trees on Mt. Jiri (Phyllostachys and Sasa). 2. As for the thickness of the culm wall in the culm, it was shown that the culm wall of the Phyllostachys becomes thinner in proportion to its nearness to the upper part of the tree, but no distinctive difference appeared in the Sasa. 3. The c, d, and e types of Sasa were the same as those of the Phyllostachys, but there was a vascular bundle type of the a' type, which was quite different from that of the Phyllostachys. 4. It was shown that the a', d, and e types of Sasa were distributed in a zone less than 500m above sea level, but no a' type was distributed in the high mountain area except for the c, d and e types which ranged from 600m to 1000m above sea level. Such facts mean that the vascular bundle sheath has changed in quantity because of the height of mountain. 5. In general, as compared with the Phyllostachys, the Sasa (types a, c, d and e which included a new type a) have fewer vascular bundles. 6. Considering the above results, it is thought that not by the current Sasa classification method based on observation of the the study of Sasa form the outside, but by a new method of classification based on the aspect of the physiological construction as seen from the inside wall is advanced. I believe this new method of classification to be a first step towards an epoch-making methodological advance and encourage the further study of it.

• Journal of Korean Society of Forest Science
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• v.103 no.4
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• pp.564-573
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• 2014

The objective of this study was to select a representative coastal disaster prevention forest type for each coastal area. In this study, we used cluster analysis with the results obtained from investigation for density of growing stock, tree height, DBH, and forest width and length of major coastal disaster prevention forests distributed in the west, the south, and the east coasts. The results showed that the coastal disaster prevention forests for each coast were classified into two types: a forest type with small DBH and high growing stock density (W1) or with high tree height (W2) in the west coast, a forest type with small tree height (S1) or with large DBH (S2) in the south coast, and a forest type with small growing stock density (E1) or with small tree height and low DBH (E2) in the east coast. The coastal disaster prevention forests located in Gurye beach (Hwangchon-ri, Wonbuk-myeon, Taean-gun, Chungcheongnam-do) and in Gohsapo beach (Unsna-ri, Byeonsan-myeon, Buan-gun, Jeollabuk-do) were selected as the representative forests of W1 and W2, respectively. In addition, the coastal disaster prevention forests located in Namyang beach (Namyang-ri, Seolcheon-myeon, Namhae-gun, Gyeongsangnam-do) and in Donggo beach (Donggo-ri, Sinji-myeon, Wando-gun, Jeollanam-do) were selected as the representative forests of S1 and S2, respectively. Last, the coastal disaster prevention forests located in Bonggil beach (Bonggil-ri, Yangbuk-myeon, Gyeongju-si, Gyeongsangbuk-do) and in Anmeok beach (Gyeonso-dong, Gangneung-si, Gangwon-do) were selected as the representative forests of E1 and E2, respectively. Our finding is expected to be used as baseline data in establishing the most appropriate coastal disaster prevention forest for each coast.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_1
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• pp.257-268
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• 2024

Soccer is type of sport that carries a high risk of injury. Injury is not only cause in the unlucky soccer carrier and also team performance as well as financial effects can be worse since soccer is a team-based game. The duration of recovery from a soccer injury typically relies on its type and severity. Therefore, we conduct this research in order to predict the probability of players injury type using machine learning technologies in this paper. Furthermore, we compare different machine learning models to find the best fit model. This paper utilizes various supervised classification machine learning models, including Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes. Moreover, based on our finding the KNN and Decision models achieved the highest accuracy rates at 70%, surpassing other models. The Random Forest model followed closely with an accuracy score of 62%. Among the evaluated models, the Naive Bayes model demonstrated the lowest accuracy at 56%. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history.